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description Publicationkeyboard_double_arrow_right Article , Journal 2021Publisher:Elsevier BV Authors: Nidret Ibrić; Elvis Ahmetović; Zdravko Kravanja; Ignacio E. Grossmann;Abstract This work presents a simultaneous mixed-integer nonlinear programming (MINLP) optimisation model and an efficient iterative solution strategy that can be successfully applied to various heat-integrated water networks (HIWNs), including large-scale problems with a large number of water streams. These problems are highly nonlinear, non-convex and combinatorial. To circumvent such difficulties, including network complexity as well as identifying the roles of water streams in the heat exchanger network (HEN) whether they are hot or cold, a modified convex hull formulation proposed by Ahmetovic and Kravanja [1] is applied. The overall model combines the water network (WN), wastewater treatment network (WTN), heat integration (HI), and heat exchanger network synthesis (HENS) models. This model is iteratively solved in three steps including targeting and design steps. The proposed model and solution strategy are tested on large-scale problems. To the best of our knowledge, the results obtained for all the problems in this paper are better than those reported in the current literature.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2021.121354&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu18 citations 18 popularity Top 10% influence Average impulse Top 10% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2021.121354&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2021Publisher:Elsevier BV Authors: Nidret Ibrić; Elvis Ahmetović; Zdravko Kravanja; Ignacio E. Grossmann;Abstract This work presents a simultaneous mixed-integer nonlinear programming (MINLP) optimisation model and an efficient iterative solution strategy that can be successfully applied to various heat-integrated water networks (HIWNs), including large-scale problems with a large number of water streams. These problems are highly nonlinear, non-convex and combinatorial. To circumvent such difficulties, including network complexity as well as identifying the roles of water streams in the heat exchanger network (HEN) whether they are hot or cold, a modified convex hull formulation proposed by Ahmetovic and Kravanja [1] is applied. The overall model combines the water network (WN), wastewater treatment network (WTN), heat integration (HI), and heat exchanger network synthesis (HENS) models. This model is iteratively solved in three steps including targeting and design steps. The proposed model and solution strategy are tested on large-scale problems. To the best of our knowledge, the results obtained for all the problems in this paper are better than those reported in the current literature.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2021.121354&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu18 citations 18 popularity Top 10% influence Average impulse Top 10% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2021.121354&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2007 United StatesPublisher:Elsevier BV Authors: Sezgen, Osman; Goldman, Charles; Krishnarao, P.;Abstract As electricity markets deregulate and energy tariffs increasingly expose customers to commodity price volatility, it is difficult for energy consumers to assess the economic value of investments in technologies that manage electricity demand in response to changing energy prices. The key uncertainties in evaluating the economics of demand–response technologies are the level and volatility of future wholesale energy prices. In this paper, we demonstrate that financial engineering methodologies originally developed for pricing equity and commodity derivatives (e.g., futures, swaps, options) can be used to estimate the value of demand-response technologies. We adapt models used to value energy options and assets to value three common demand–response strategies: load curtailment, load shifting or displacement, and short-term fuel substitution—specifically, distributed generation. These option models represent an improvement to traditional discounted cash flow methods for assessing the relative merits of demand-side technology investments in restructured electricity markets.
Energy arrow_drop_down eScholarship - University of CaliforniaArticle . 2005Data sources: eScholarship - University of CaliforniaeScholarship - University of CaliforniaArticle . 2005Data sources: eScholarship - University of Californiaadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2006.03.024&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen bronze 103 citations 103 popularity Top 1% influence Top 1% impulse Top 10% Powered by BIP!
more_vert Energy arrow_drop_down eScholarship - University of CaliforniaArticle . 2005Data sources: eScholarship - University of CaliforniaeScholarship - University of CaliforniaArticle . 2005Data sources: eScholarship - University of Californiaadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2006.03.024&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2007 United StatesPublisher:Elsevier BV Authors: Sezgen, Osman; Goldman, Charles; Krishnarao, P.;Abstract As electricity markets deregulate and energy tariffs increasingly expose customers to commodity price volatility, it is difficult for energy consumers to assess the economic value of investments in technologies that manage electricity demand in response to changing energy prices. The key uncertainties in evaluating the economics of demand–response technologies are the level and volatility of future wholesale energy prices. In this paper, we demonstrate that financial engineering methodologies originally developed for pricing equity and commodity derivatives (e.g., futures, swaps, options) can be used to estimate the value of demand-response technologies. We adapt models used to value energy options and assets to value three common demand–response strategies: load curtailment, load shifting or displacement, and short-term fuel substitution—specifically, distributed generation. These option models represent an improvement to traditional discounted cash flow methods for assessing the relative merits of demand-side technology investments in restructured electricity markets.
Energy arrow_drop_down eScholarship - University of CaliforniaArticle . 2005Data sources: eScholarship - University of CaliforniaeScholarship - University of CaliforniaArticle . 2005Data sources: eScholarship - University of Californiaadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2006.03.024&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen bronze 103 citations 103 popularity Top 1% influence Top 1% impulse Top 10% Powered by BIP!
more_vert Energy arrow_drop_down eScholarship - University of CaliforniaArticle . 2005Data sources: eScholarship - University of CaliforniaeScholarship - University of CaliforniaArticle . 2005Data sources: eScholarship - University of Californiaadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2006.03.024&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2022Publisher:Elsevier BV Authors: Daniel J. Pastor; Bradley T. Ewing;add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2022.124298&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu2 citations 2 popularity Top 10% influence Average impulse Average Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2022.124298&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2022Publisher:Elsevier BV Authors: Daniel J. Pastor; Bradley T. Ewing;add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2022.124298&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu2 citations 2 popularity Top 10% influence Average impulse Average Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2022.124298&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2013Publisher:Elsevier BV Authors: Dariush Zare; Mohsen Ranjbaran;Abstract The performance of microwave-assisted fluidized bed drying of soybeans was simulated (using a previously validated mathematical model) and analyzed based on the first- and second law of thermodynamics. The energy and exergy analysis were carried out for several drying conditions. The effects of inlet air temperature, microwave power density, bed thickness and inlet air velocity on the efficiencies and inefficiencies of drying process have been simulated and discussed. Generally, application of microwave energy during fluidized bed drying enhanced the exergy efficiency of drying process. However, the results showed that it was more efficient not to apply microwave energy at the first stage of fluidized bed drying process. The application of higher levels of drying air temperature led in higher exergy efficiencies. The values of mean relative deviations for the predictions of efficiencies and inefficiencies of drying process were less than 14%, compared with those calculated using experimental data.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2013.06.057&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu89 citations 89 popularity Top 1% influence Top 10% impulse Top 10% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2013.06.057&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2013Publisher:Elsevier BV Authors: Dariush Zare; Mohsen Ranjbaran;Abstract The performance of microwave-assisted fluidized bed drying of soybeans was simulated (using a previously validated mathematical model) and analyzed based on the first- and second law of thermodynamics. The energy and exergy analysis were carried out for several drying conditions. The effects of inlet air temperature, microwave power density, bed thickness and inlet air velocity on the efficiencies and inefficiencies of drying process have been simulated and discussed. Generally, application of microwave energy during fluidized bed drying enhanced the exergy efficiency of drying process. However, the results showed that it was more efficient not to apply microwave energy at the first stage of fluidized bed drying process. The application of higher levels of drying air temperature led in higher exergy efficiencies. The values of mean relative deviations for the predictions of efficiencies and inefficiencies of drying process were less than 14%, compared with those calculated using experimental data.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2013.06.057&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu89 citations 89 popularity Top 1% influence Top 10% impulse Top 10% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2013.06.057&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2020Publisher:Elsevier BV Yuxi Wang; Jingxin Wang; Mark H. Eisenbies; Jamie L. Schuler; Timothy A. Volk; Damon Hartley;Abstract A mixed-integer linear programming model was developed to optimize the multiple biomass feedstock supply chains, including feedstock establishment, harvest, storage, transportation, and preprocessing. The model was applied for analyses of multiple biomass feedstocks at county level for 13 states in the northeastern United States. In the base case with a demand of 180,000 dry Mg/year of biomass, the delivered costs ranged from $67.90 to $86.97 per dry Mg with an average of $79.58/dry Mg. The biomass delivered costs by county were from $67.90 to 150.81 per dry Mg across the northeastern U.S. Considered the entire study area, the delivered cost averaged $85.30/dry Mg for forest residues, $84.47/dry Mg for hybrid willow, $99.68 for switchgrass and $97.87 per dry Mg for Miscanthus. Seventy seven out of 387 counties could be able to deliver biomass at $84 per dry Mg or less a target set by US DOE by 2022. A sensitivity analysis was also conducted to evaluate the effects of feedstock availability, feedstock price, moisture content, procurement radius, and facility demand on the delivered cost. Our results showed that procurement radius, facility capacity, and forest residue availability were the most sensitive factors affecting the biomass delivered costs.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2020.117260&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesbronze 40 citations 40 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2020.117260&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2020Publisher:Elsevier BV Yuxi Wang; Jingxin Wang; Mark H. Eisenbies; Jamie L. Schuler; Timothy A. Volk; Damon Hartley;Abstract A mixed-integer linear programming model was developed to optimize the multiple biomass feedstock supply chains, including feedstock establishment, harvest, storage, transportation, and preprocessing. The model was applied for analyses of multiple biomass feedstocks at county level for 13 states in the northeastern United States. In the base case with a demand of 180,000 dry Mg/year of biomass, the delivered costs ranged from $67.90 to $86.97 per dry Mg with an average of $79.58/dry Mg. The biomass delivered costs by county were from $67.90 to 150.81 per dry Mg across the northeastern U.S. Considered the entire study area, the delivered cost averaged $85.30/dry Mg for forest residues, $84.47/dry Mg for hybrid willow, $99.68 for switchgrass and $97.87 per dry Mg for Miscanthus. Seventy seven out of 387 counties could be able to deliver biomass at $84 per dry Mg or less a target set by US DOE by 2022. A sensitivity analysis was also conducted to evaluate the effects of feedstock availability, feedstock price, moisture content, procurement radius, and facility demand on the delivered cost. Our results showed that procurement radius, facility capacity, and forest residue availability were the most sensitive factors affecting the biomass delivered costs.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2020.117260&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesbronze 40 citations 40 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2020.117260&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 1986Publisher:Elsevier BV Authors: Robert H. Gray;Abstract Chemists, biologists, ecologists, and engineers have been developing a data-base to identify and evaluate risks to humans and the environment and strategies to minimize potential risks from large-scale coal liquefaction. Coal-liquids produced by various processes and under various stages of design and operating conditions have been screened for potential health and environmental effects. Toxicologically active materials have been fractionated and chemical constituents of biologically active fractions have been identified, and the environmental fate of problematic agents is being determined. Results indicate that coal-derived liquids are generally more toxicologically active than shale oil and petroleum crudes. Bioactive agents include primary aromatic amines (PAA), polynuclear aromatic hydrocarbons (PAH), phenolics, and others. Some components of coal-derived materials are taken up by biota and metabolized. Hydrotreating reduces PAA, PAH, and phenol content, as well as toxicological response to coal-liquids. Selective distillation restricts PAA and PAH content, and mutagenicity and carcinogenicity to high-boiling-range materials. Other process conditions and environmental factors also influence chemical characteristics and toxicological activity of coal-derived liquids. Recent findings indicate that biological responses to a given coal-derived liquid component vary, depending on whether that material is presented to the organism or environment as a pure compound or in a complex mixture. The data-base described provides input for assessment and has been used by developers when selecting process modifications and product slates that minimize risk to humans and the environment. These data have also been used in developing occupational health and industrial hygiene practices and may aid in selection of control technologies, mitigative strategies, special handling and accident prevention procedures, or spill-clean-up options to enhance the environmental acceptability of a coal liquefaction industry.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/0360-5442(86)90070-8&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu2 citations 2 popularity Average influence Average impulse Average Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/0360-5442(86)90070-8&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 1986Publisher:Elsevier BV Authors: Robert H. Gray;Abstract Chemists, biologists, ecologists, and engineers have been developing a data-base to identify and evaluate risks to humans and the environment and strategies to minimize potential risks from large-scale coal liquefaction. Coal-liquids produced by various processes and under various stages of design and operating conditions have been screened for potential health and environmental effects. Toxicologically active materials have been fractionated and chemical constituents of biologically active fractions have been identified, and the environmental fate of problematic agents is being determined. Results indicate that coal-derived liquids are generally more toxicologically active than shale oil and petroleum crudes. Bioactive agents include primary aromatic amines (PAA), polynuclear aromatic hydrocarbons (PAH), phenolics, and others. Some components of coal-derived materials are taken up by biota and metabolized. Hydrotreating reduces PAA, PAH, and phenol content, as well as toxicological response to coal-liquids. Selective distillation restricts PAA and PAH content, and mutagenicity and carcinogenicity to high-boiling-range materials. Other process conditions and environmental factors also influence chemical characteristics and toxicological activity of coal-derived liquids. Recent findings indicate that biological responses to a given coal-derived liquid component vary, depending on whether that material is presented to the organism or environment as a pure compound or in a complex mixture. The data-base described provides input for assessment and has been used by developers when selecting process modifications and product slates that minimize risk to humans and the environment. These data have also been used in developing occupational health and industrial hygiene practices and may aid in selection of control technologies, mitigative strategies, special handling and accident prevention procedures, or spill-clean-up options to enhance the environmental acceptability of a coal liquefaction industry.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/0360-5442(86)90070-8&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu2 citations 2 popularity Average influence Average impulse Average Powered by BIP!
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/0360-5442(86)90070-8&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2017Publisher:Elsevier BV Zi-Feng Ma; Yi-Jun He; Qian-Kun Wang; Jia-Ni Shen; Guo-Bin Zhong;Abstract The thermal coupled equivalent circuit model provides a vital role not only in accurate and reliable state monitoring, but also in effective thermal management of lithium-ion batteries. However, it lacks appropriate modeling strategies for including both the temperature and state of charge effects into the thermal coupled equivalent circuit model. In this study, a unified artificial neural network based thermal coupled equivalent circuit model approach is proposed to accurately and reliably capture the electrical and thermal dynamics of lithium-ion batteries. Both reversible and irreversible heat generation mechanisms are introduced in the thermal model. The quantitative relationship between circuit parameters and temperature/state of charge in equivalent circuit model is modeled by artificial neural network. Both electrical and thermal related parameters are simultaneously identified by means of least square strategy with l 1 -norm penalty on output weights in artificial neural network and positive constraints on circuit parameters. The effectiveness of the proposed artificial neural network based thermal coupled equivalent circuit model approach is validated by the experimental constant current discharge, pulse current discharge test and hybrid pulse power characterization test of a commercial large-format pouch-type lithium-ion battery. It implies that the proposed hybrid modeling strategy can provide a general framework for the inclusion of other effects such as health state and current into battery models and can be easily extended to more complicated models such as first-principle electrochemical-thermal model.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2017.07.035&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu110 citations 110 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2017.07.035&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2017Publisher:Elsevier BV Zi-Feng Ma; Yi-Jun He; Qian-Kun Wang; Jia-Ni Shen; Guo-Bin Zhong;Abstract The thermal coupled equivalent circuit model provides a vital role not only in accurate and reliable state monitoring, but also in effective thermal management of lithium-ion batteries. However, it lacks appropriate modeling strategies for including both the temperature and state of charge effects into the thermal coupled equivalent circuit model. In this study, a unified artificial neural network based thermal coupled equivalent circuit model approach is proposed to accurately and reliably capture the electrical and thermal dynamics of lithium-ion batteries. Both reversible and irreversible heat generation mechanisms are introduced in the thermal model. The quantitative relationship between circuit parameters and temperature/state of charge in equivalent circuit model is modeled by artificial neural network. Both electrical and thermal related parameters are simultaneously identified by means of least square strategy with l 1 -norm penalty on output weights in artificial neural network and positive constraints on circuit parameters. The effectiveness of the proposed artificial neural network based thermal coupled equivalent circuit model approach is validated by the experimental constant current discharge, pulse current discharge test and hybrid pulse power characterization test of a commercial large-format pouch-type lithium-ion battery. It implies that the proposed hybrid modeling strategy can provide a general framework for the inclusion of other effects such as health state and current into battery models and can be easily extended to more complicated models such as first-principle electrochemical-thermal model.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2017.07.035&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu110 citations 110 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2017.07.035&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2021Publisher:Elsevier BV Authors: Yiming Li; Saeed Solaymani;Abstract Industry and agriculture in Malaysia are the main contributors to economic growth and employment. These sectors also play an important role in Malaysia's total exports. The question then is whether technological innovation, sectoral output, and exports growth have had a real impact on these two sectors, which are very important for policy-making. This paper attempts to empirically identify such relations using econometric methods, including an autoregressive distributed lag (ARDL) bounds testing method and a dynamic ordinary least squares (DOLS) during 1978–2018. The results confirmed that overall long-run economic growth is the main contributor to the increase in energy consumption with a greater magnitude than in the short-run. In the long-run, an increase of 1% in economic growth leads to an increase of 4.6% and 1.1% in energy demand in agriculture and industrial sectors, respectively. Exports are the second largest contributor to energy demand in the overall economy and the agriculture sector. Finally, the technological innovation that enhances energy efficiency is only effective in reducing energy consumption in the industrial sector, which ultimately reduces emissions.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2021.121040&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu45 citations 45 popularity Top 10% influence Top 10% impulse Top 1% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2021.121040&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2021Publisher:Elsevier BV Authors: Yiming Li; Saeed Solaymani;Abstract Industry and agriculture in Malaysia are the main contributors to economic growth and employment. These sectors also play an important role in Malaysia's total exports. The question then is whether technological innovation, sectoral output, and exports growth have had a real impact on these two sectors, which are very important for policy-making. This paper attempts to empirically identify such relations using econometric methods, including an autoregressive distributed lag (ARDL) bounds testing method and a dynamic ordinary least squares (DOLS) during 1978–2018. The results confirmed that overall long-run economic growth is the main contributor to the increase in energy consumption with a greater magnitude than in the short-run. In the long-run, an increase of 1% in economic growth leads to an increase of 4.6% and 1.1% in energy demand in agriculture and industrial sectors, respectively. Exports are the second largest contributor to energy demand in the overall economy and the agriculture sector. Finally, the technological innovation that enhances energy efficiency is only effective in reducing energy consumption in the industrial sector, which ultimately reduces emissions.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2021.121040&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu45 citations 45 popularity Top 10% influence Top 10% impulse Top 1% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2021.121040&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2019 MalaysiaPublisher:Elsevier BV Authors: Sakiru Adebola Solarin; Muhammad Shahbaz; Shawkat Hammoudeh;Abstract This paper examines the relationship between hydroelectricity consumption and economic growth in China, while controlling for fossil fuel consumption, financial development, capital, institutional quality and globalization and its components for the period, 1970–2014. We have employed the Bayer and Hanck, (2013) combined cointegration test to examine the long-run relationships between those variables as well as the autoregressive distributed lag method with structural breaks as a robustness check. The empirical findings demonstrate a long-run relationship between those variables. Hydroelectricity consumption, fossil fuel consumption, capital, financial development and globalization and its components have a positive influence on GDP in China. The findings also provide predominant evidence on the long-run feedback hypothesis between the variables. The findings suggest that policies should be implemented to increase the role hydropower in the energy mix for sustainable economic growth in the country.
Energy arrow_drop_down Multimedia University, Malaysia: SHDL@MMU Digital RepositoryArticle . 2019Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2018.11.061&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu28 citations 28 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!
more_vert Energy arrow_drop_down Multimedia University, Malaysia: SHDL@MMU Digital RepositoryArticle . 2019Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2018.11.061&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2019 MalaysiaPublisher:Elsevier BV Authors: Sakiru Adebola Solarin; Muhammad Shahbaz; Shawkat Hammoudeh;Abstract This paper examines the relationship between hydroelectricity consumption and economic growth in China, while controlling for fossil fuel consumption, financial development, capital, institutional quality and globalization and its components for the period, 1970–2014. We have employed the Bayer and Hanck, (2013) combined cointegration test to examine the long-run relationships between those variables as well as the autoregressive distributed lag method with structural breaks as a robustness check. The empirical findings demonstrate a long-run relationship between those variables. Hydroelectricity consumption, fossil fuel consumption, capital, financial development and globalization and its components have a positive influence on GDP in China. The findings also provide predominant evidence on the long-run feedback hypothesis between the variables. The findings suggest that policies should be implemented to increase the role hydropower in the energy mix for sustainable economic growth in the country.
Energy arrow_drop_down Multimedia University, Malaysia: SHDL@MMU Digital RepositoryArticle . 2019Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2018.11.061&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu28 citations 28 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!
more_vert Energy arrow_drop_down Multimedia University, Malaysia: SHDL@MMU Digital RepositoryArticle . 2019Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2018.11.061&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2020Publisher:Elsevier BV Zhongwen Li; Bing Lu; Yixiang Gao; Weizhen Dong; Shuhui Li; Xingang Fu;Abstract This paper presents an optimal building energy management strategy for the demand response of multi-thermal-zone buildings in the smart electricity grid environment. The proposed method includes a machine learning model, based on a neural network, for a building heating ventilation and air conditioning system. The learned model is then applied to an optimization problem to determine the optimal management scheduling of building loads. The goal of the optimization problem is to minimize building electricity costs and reduce the overall building energy consumption during peak load hours while satisfying human comfort demand. To overcome the coupling issue between the building internal-heat-gain loads and the building heating ventilation and air conditioning system, an iterative algorithm is proposed to solve the optimization problem. In each iteration, a mixed-integer linear programming technique is used to solve a sub-optimization problem for the building internal-heat-gain loads and its results are then applied to another sub-optimization problem, solved by using a particle swarm technique, for the building heating ventilation and air conditioning system. The iterative optimization algorithm stops when convergence between the optimization for the building heating ventilation and air conditioning system and the optimization for the building internal-heat-gain loads is properly reached. EnergyPlus is used to build and simulate complex buildings with multiple-thermal zones according to real-life conditions. The simulation model is also used to test and evaluate the effectiveness of the proposed machine-learning model and the iterative optimization algorithm and the improvement of building energy management in terms of energy consumption efficiency, cost saving, and satisfaction of human comfort.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2020.118411&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu37 citations 37 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2020.118411&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2020Publisher:Elsevier BV Zhongwen Li; Bing Lu; Yixiang Gao; Weizhen Dong; Shuhui Li; Xingang Fu;Abstract This paper presents an optimal building energy management strategy for the demand response of multi-thermal-zone buildings in the smart electricity grid environment. The proposed method includes a machine learning model, based on a neural network, for a building heating ventilation and air conditioning system. The learned model is then applied to an optimization problem to determine the optimal management scheduling of building loads. The goal of the optimization problem is to minimize building electricity costs and reduce the overall building energy consumption during peak load hours while satisfying human comfort demand. To overcome the coupling issue between the building internal-heat-gain loads and the building heating ventilation and air conditioning system, an iterative algorithm is proposed to solve the optimization problem. In each iteration, a mixed-integer linear programming technique is used to solve a sub-optimization problem for the building internal-heat-gain loads and its results are then applied to another sub-optimization problem, solved by using a particle swarm technique, for the building heating ventilation and air conditioning system. The iterative optimization algorithm stops when convergence between the optimization for the building heating ventilation and air conditioning system and the optimization for the building internal-heat-gain loads is properly reached. EnergyPlus is used to build and simulate complex buildings with multiple-thermal zones according to real-life conditions. The simulation model is also used to test and evaluate the effectiveness of the proposed machine-learning model and the iterative optimization algorithm and the improvement of building energy management in terms of energy consumption efficiency, cost saving, and satisfaction of human comfort.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2020.118411&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu37 citations 37 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2020.118411&type=result"></script>'); --> </script>
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description Publicationkeyboard_double_arrow_right Article , Journal 2021Publisher:Elsevier BV Authors: Nidret Ibrić; Elvis Ahmetović; Zdravko Kravanja; Ignacio E. Grossmann;Abstract This work presents a simultaneous mixed-integer nonlinear programming (MINLP) optimisation model and an efficient iterative solution strategy that can be successfully applied to various heat-integrated water networks (HIWNs), including large-scale problems with a large number of water streams. These problems are highly nonlinear, non-convex and combinatorial. To circumvent such difficulties, including network complexity as well as identifying the roles of water streams in the heat exchanger network (HEN) whether they are hot or cold, a modified convex hull formulation proposed by Ahmetovic and Kravanja [1] is applied. The overall model combines the water network (WN), wastewater treatment network (WTN), heat integration (HI), and heat exchanger network synthesis (HENS) models. This model is iteratively solved in three steps including targeting and design steps. The proposed model and solution strategy are tested on large-scale problems. To the best of our knowledge, the results obtained for all the problems in this paper are better than those reported in the current literature.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2021.121354&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu18 citations 18 popularity Top 10% influence Average impulse Top 10% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2021.121354&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2021Publisher:Elsevier BV Authors: Nidret Ibrić; Elvis Ahmetović; Zdravko Kravanja; Ignacio E. Grossmann;Abstract This work presents a simultaneous mixed-integer nonlinear programming (MINLP) optimisation model and an efficient iterative solution strategy that can be successfully applied to various heat-integrated water networks (HIWNs), including large-scale problems with a large number of water streams. These problems are highly nonlinear, non-convex and combinatorial. To circumvent such difficulties, including network complexity as well as identifying the roles of water streams in the heat exchanger network (HEN) whether they are hot or cold, a modified convex hull formulation proposed by Ahmetovic and Kravanja [1] is applied. The overall model combines the water network (WN), wastewater treatment network (WTN), heat integration (HI), and heat exchanger network synthesis (HENS) models. This model is iteratively solved in three steps including targeting and design steps. The proposed model and solution strategy are tested on large-scale problems. To the best of our knowledge, the results obtained for all the problems in this paper are better than those reported in the current literature.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2021.121354&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu18 citations 18 popularity Top 10% influence Average impulse Top 10% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2021.121354&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2007 United StatesPublisher:Elsevier BV Authors: Sezgen, Osman; Goldman, Charles; Krishnarao, P.;Abstract As electricity markets deregulate and energy tariffs increasingly expose customers to commodity price volatility, it is difficult for energy consumers to assess the economic value of investments in technologies that manage electricity demand in response to changing energy prices. The key uncertainties in evaluating the economics of demand–response technologies are the level and volatility of future wholesale energy prices. In this paper, we demonstrate that financial engineering methodologies originally developed for pricing equity and commodity derivatives (e.g., futures, swaps, options) can be used to estimate the value of demand-response technologies. We adapt models used to value energy options and assets to value three common demand–response strategies: load curtailment, load shifting or displacement, and short-term fuel substitution—specifically, distributed generation. These option models represent an improvement to traditional discounted cash flow methods for assessing the relative merits of demand-side technology investments in restructured electricity markets.
Energy arrow_drop_down eScholarship - University of CaliforniaArticle . 2005Data sources: eScholarship - University of CaliforniaeScholarship - University of CaliforniaArticle . 2005Data sources: eScholarship - University of Californiaadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2006.03.024&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen bronze 103 citations 103 popularity Top 1% influence Top 1% impulse Top 10% Powered by BIP!
more_vert Energy arrow_drop_down eScholarship - University of CaliforniaArticle . 2005Data sources: eScholarship - University of CaliforniaeScholarship - University of CaliforniaArticle . 2005Data sources: eScholarship - University of Californiaadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2006.03.024&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2007 United StatesPublisher:Elsevier BV Authors: Sezgen, Osman; Goldman, Charles; Krishnarao, P.;Abstract As electricity markets deregulate and energy tariffs increasingly expose customers to commodity price volatility, it is difficult for energy consumers to assess the economic value of investments in technologies that manage electricity demand in response to changing energy prices. The key uncertainties in evaluating the economics of demand–response technologies are the level and volatility of future wholesale energy prices. In this paper, we demonstrate that financial engineering methodologies originally developed for pricing equity and commodity derivatives (e.g., futures, swaps, options) can be used to estimate the value of demand-response technologies. We adapt models used to value energy options and assets to value three common demand–response strategies: load curtailment, load shifting or displacement, and short-term fuel substitution—specifically, distributed generation. These option models represent an improvement to traditional discounted cash flow methods for assessing the relative merits of demand-side technology investments in restructured electricity markets.
Energy arrow_drop_down eScholarship - University of CaliforniaArticle . 2005Data sources: eScholarship - University of CaliforniaeScholarship - University of CaliforniaArticle . 2005Data sources: eScholarship - University of Californiaadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2006.03.024&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen bronze 103 citations 103 popularity Top 1% influence Top 1% impulse Top 10% Powered by BIP!
more_vert Energy arrow_drop_down eScholarship - University of CaliforniaArticle . 2005Data sources: eScholarship - University of CaliforniaeScholarship - University of CaliforniaArticle . 2005Data sources: eScholarship - University of Californiaadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2006.03.024&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2022Publisher:Elsevier BV Authors: Daniel J. Pastor; Bradley T. Ewing;add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2022.124298&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu2 citations 2 popularity Top 10% influence Average impulse Average Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2022.124298&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2022Publisher:Elsevier BV Authors: Daniel J. Pastor; Bradley T. Ewing;add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2022.124298&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu2 citations 2 popularity Top 10% influence Average impulse Average Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2022.124298&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2013Publisher:Elsevier BV Authors: Dariush Zare; Mohsen Ranjbaran;Abstract The performance of microwave-assisted fluidized bed drying of soybeans was simulated (using a previously validated mathematical model) and analyzed based on the first- and second law of thermodynamics. The energy and exergy analysis were carried out for several drying conditions. The effects of inlet air temperature, microwave power density, bed thickness and inlet air velocity on the efficiencies and inefficiencies of drying process have been simulated and discussed. Generally, application of microwave energy during fluidized bed drying enhanced the exergy efficiency of drying process. However, the results showed that it was more efficient not to apply microwave energy at the first stage of fluidized bed drying process. The application of higher levels of drying air temperature led in higher exergy efficiencies. The values of mean relative deviations for the predictions of efficiencies and inefficiencies of drying process were less than 14%, compared with those calculated using experimental data.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2013.06.057&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu89 citations 89 popularity Top 1% influence Top 10% impulse Top 10% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2013.06.057&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2013Publisher:Elsevier BV Authors: Dariush Zare; Mohsen Ranjbaran;Abstract The performance of microwave-assisted fluidized bed drying of soybeans was simulated (using a previously validated mathematical model) and analyzed based on the first- and second law of thermodynamics. The energy and exergy analysis were carried out for several drying conditions. The effects of inlet air temperature, microwave power density, bed thickness and inlet air velocity on the efficiencies and inefficiencies of drying process have been simulated and discussed. Generally, application of microwave energy during fluidized bed drying enhanced the exergy efficiency of drying process. However, the results showed that it was more efficient not to apply microwave energy at the first stage of fluidized bed drying process. The application of higher levels of drying air temperature led in higher exergy efficiencies. The values of mean relative deviations for the predictions of efficiencies and inefficiencies of drying process were less than 14%, compared with those calculated using experimental data.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2013.06.057&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu89 citations 89 popularity Top 1% influence Top 10% impulse Top 10% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2013.06.057&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2020Publisher:Elsevier BV Yuxi Wang; Jingxin Wang; Mark H. Eisenbies; Jamie L. Schuler; Timothy A. Volk; Damon Hartley;Abstract A mixed-integer linear programming model was developed to optimize the multiple biomass feedstock supply chains, including feedstock establishment, harvest, storage, transportation, and preprocessing. The model was applied for analyses of multiple biomass feedstocks at county level for 13 states in the northeastern United States. In the base case with a demand of 180,000 dry Mg/year of biomass, the delivered costs ranged from $67.90 to $86.97 per dry Mg with an average of $79.58/dry Mg. The biomass delivered costs by county were from $67.90 to 150.81 per dry Mg across the northeastern U.S. Considered the entire study area, the delivered cost averaged $85.30/dry Mg for forest residues, $84.47/dry Mg for hybrid willow, $99.68 for switchgrass and $97.87 per dry Mg for Miscanthus. Seventy seven out of 387 counties could be able to deliver biomass at $84 per dry Mg or less a target set by US DOE by 2022. A sensitivity analysis was also conducted to evaluate the effects of feedstock availability, feedstock price, moisture content, procurement radius, and facility demand on the delivered cost. Our results showed that procurement radius, facility capacity, and forest residue availability were the most sensitive factors affecting the biomass delivered costs.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2020.117260&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesbronze 40 citations 40 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2020.117260&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2020Publisher:Elsevier BV Yuxi Wang; Jingxin Wang; Mark H. Eisenbies; Jamie L. Schuler; Timothy A. Volk; Damon Hartley;Abstract A mixed-integer linear programming model was developed to optimize the multiple biomass feedstock supply chains, including feedstock establishment, harvest, storage, transportation, and preprocessing. The model was applied for analyses of multiple biomass feedstocks at county level for 13 states in the northeastern United States. In the base case with a demand of 180,000 dry Mg/year of biomass, the delivered costs ranged from $67.90 to $86.97 per dry Mg with an average of $79.58/dry Mg. The biomass delivered costs by county were from $67.90 to 150.81 per dry Mg across the northeastern U.S. Considered the entire study area, the delivered cost averaged $85.30/dry Mg for forest residues, $84.47/dry Mg for hybrid willow, $99.68 for switchgrass and $97.87 per dry Mg for Miscanthus. Seventy seven out of 387 counties could be able to deliver biomass at $84 per dry Mg or less a target set by US DOE by 2022. A sensitivity analysis was also conducted to evaluate the effects of feedstock availability, feedstock price, moisture content, procurement radius, and facility demand on the delivered cost. Our results showed that procurement radius, facility capacity, and forest residue availability were the most sensitive factors affecting the biomass delivered costs.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2020.117260&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesbronze 40 citations 40 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2020.117260&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 1986Publisher:Elsevier BV Authors: Robert H. Gray;Abstract Chemists, biologists, ecologists, and engineers have been developing a data-base to identify and evaluate risks to humans and the environment and strategies to minimize potential risks from large-scale coal liquefaction. Coal-liquids produced by various processes and under various stages of design and operating conditions have been screened for potential health and environmental effects. Toxicologically active materials have been fractionated and chemical constituents of biologically active fractions have been identified, and the environmental fate of problematic agents is being determined. Results indicate that coal-derived liquids are generally more toxicologically active than shale oil and petroleum crudes. Bioactive agents include primary aromatic amines (PAA), polynuclear aromatic hydrocarbons (PAH), phenolics, and others. Some components of coal-derived materials are taken up by biota and metabolized. Hydrotreating reduces PAA, PAH, and phenol content, as well as toxicological response to coal-liquids. Selective distillation restricts PAA and PAH content, and mutagenicity and carcinogenicity to high-boiling-range materials. Other process conditions and environmental factors also influence chemical characteristics and toxicological activity of coal-derived liquids. Recent findings indicate that biological responses to a given coal-derived liquid component vary, depending on whether that material is presented to the organism or environment as a pure compound or in a complex mixture. The data-base described provides input for assessment and has been used by developers when selecting process modifications and product slates that minimize risk to humans and the environment. These data have also been used in developing occupational health and industrial hygiene practices and may aid in selection of control technologies, mitigative strategies, special handling and accident prevention procedures, or spill-clean-up options to enhance the environmental acceptability of a coal liquefaction industry.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/0360-5442(86)90070-8&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu2 citations 2 popularity Average influence Average impulse Average Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/0360-5442(86)90070-8&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 1986Publisher:Elsevier BV Authors: Robert H. Gray;Abstract Chemists, biologists, ecologists, and engineers have been developing a data-base to identify and evaluate risks to humans and the environment and strategies to minimize potential risks from large-scale coal liquefaction. Coal-liquids produced by various processes and under various stages of design and operating conditions have been screened for potential health and environmental effects. Toxicologically active materials have been fractionated and chemical constituents of biologically active fractions have been identified, and the environmental fate of problematic agents is being determined. Results indicate that coal-derived liquids are generally more toxicologically active than shale oil and petroleum crudes. Bioactive agents include primary aromatic amines (PAA), polynuclear aromatic hydrocarbons (PAH), phenolics, and others. Some components of coal-derived materials are taken up by biota and metabolized. Hydrotreating reduces PAA, PAH, and phenol content, as well as toxicological response to coal-liquids. Selective distillation restricts PAA and PAH content, and mutagenicity and carcinogenicity to high-boiling-range materials. Other process conditions and environmental factors also influence chemical characteristics and toxicological activity of coal-derived liquids. Recent findings indicate that biological responses to a given coal-derived liquid component vary, depending on whether that material is presented to the organism or environment as a pure compound or in a complex mixture. The data-base described provides input for assessment and has been used by developers when selecting process modifications and product slates that minimize risk to humans and the environment. These data have also been used in developing occupational health and industrial hygiene practices and may aid in selection of control technologies, mitigative strategies, special handling and accident prevention procedures, or spill-clean-up options to enhance the environmental acceptability of a coal liquefaction industry.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/0360-5442(86)90070-8&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu2 citations 2 popularity Average influence Average impulse Average Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/0360-5442(86)90070-8&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2017Publisher:Elsevier BV Zi-Feng Ma; Yi-Jun He; Qian-Kun Wang; Jia-Ni Shen; Guo-Bin Zhong;Abstract The thermal coupled equivalent circuit model provides a vital role not only in accurate and reliable state monitoring, but also in effective thermal management of lithium-ion batteries. However, it lacks appropriate modeling strategies for including both the temperature and state of charge effects into the thermal coupled equivalent circuit model. In this study, a unified artificial neural network based thermal coupled equivalent circuit model approach is proposed to accurately and reliably capture the electrical and thermal dynamics of lithium-ion batteries. Both reversible and irreversible heat generation mechanisms are introduced in the thermal model. The quantitative relationship between circuit parameters and temperature/state of charge in equivalent circuit model is modeled by artificial neural network. Both electrical and thermal related parameters are simultaneously identified by means of least square strategy with l 1 -norm penalty on output weights in artificial neural network and positive constraints on circuit parameters. The effectiveness of the proposed artificial neural network based thermal coupled equivalent circuit model approach is validated by the experimental constant current discharge, pulse current discharge test and hybrid pulse power characterization test of a commercial large-format pouch-type lithium-ion battery. It implies that the proposed hybrid modeling strategy can provide a general framework for the inclusion of other effects such as health state and current into battery models and can be easily extended to more complicated models such as first-principle electrochemical-thermal model.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2017.07.035&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu110 citations 110 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2017.07.035&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2017Publisher:Elsevier BV Zi-Feng Ma; Yi-Jun He; Qian-Kun Wang; Jia-Ni Shen; Guo-Bin Zhong;Abstract The thermal coupled equivalent circuit model provides a vital role not only in accurate and reliable state monitoring, but also in effective thermal management of lithium-ion batteries. However, it lacks appropriate modeling strategies for including both the temperature and state of charge effects into the thermal coupled equivalent circuit model. In this study, a unified artificial neural network based thermal coupled equivalent circuit model approach is proposed to accurately and reliably capture the electrical and thermal dynamics of lithium-ion batteries. Both reversible and irreversible heat generation mechanisms are introduced in the thermal model. The quantitative relationship between circuit parameters and temperature/state of charge in equivalent circuit model is modeled by artificial neural network. Both electrical and thermal related parameters are simultaneously identified by means of least square strategy with l 1 -norm penalty on output weights in artificial neural network and positive constraints on circuit parameters. The effectiveness of the proposed artificial neural network based thermal coupled equivalent circuit model approach is validated by the experimental constant current discharge, pulse current discharge test and hybrid pulse power characterization test of a commercial large-format pouch-type lithium-ion battery. It implies that the proposed hybrid modeling strategy can provide a general framework for the inclusion of other effects such as health state and current into battery models and can be easily extended to more complicated models such as first-principle electrochemical-thermal model.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2017.07.035&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu110 citations 110 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2017.07.035&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2021Publisher:Elsevier BV Authors: Yiming Li; Saeed Solaymani;Abstract Industry and agriculture in Malaysia are the main contributors to economic growth and employment. These sectors also play an important role in Malaysia's total exports. The question then is whether technological innovation, sectoral output, and exports growth have had a real impact on these two sectors, which are very important for policy-making. This paper attempts to empirically identify such relations using econometric methods, including an autoregressive distributed lag (ARDL) bounds testing method and a dynamic ordinary least squares (DOLS) during 1978–2018. The results confirmed that overall long-run economic growth is the main contributor to the increase in energy consumption with a greater magnitude than in the short-run. In the long-run, an increase of 1% in economic growth leads to an increase of 4.6% and 1.1% in energy demand in agriculture and industrial sectors, respectively. Exports are the second largest contributor to energy demand in the overall economy and the agriculture sector. Finally, the technological innovation that enhances energy efficiency is only effective in reducing energy consumption in the industrial sector, which ultimately reduces emissions.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2021.121040&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu45 citations 45 popularity Top 10% influence Top 10% impulse Top 1% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2021.121040&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2021Publisher:Elsevier BV Authors: Yiming Li; Saeed Solaymani;Abstract Industry and agriculture in Malaysia are the main contributors to economic growth and employment. These sectors also play an important role in Malaysia's total exports. The question then is whether technological innovation, sectoral output, and exports growth have had a real impact on these two sectors, which are very important for policy-making. This paper attempts to empirically identify such relations using econometric methods, including an autoregressive distributed lag (ARDL) bounds testing method and a dynamic ordinary least squares (DOLS) during 1978–2018. The results confirmed that overall long-run economic growth is the main contributor to the increase in energy consumption with a greater magnitude than in the short-run. In the long-run, an increase of 1% in economic growth leads to an increase of 4.6% and 1.1% in energy demand in agriculture and industrial sectors, respectively. Exports are the second largest contributor to energy demand in the overall economy and the agriculture sector. Finally, the technological innovation that enhances energy efficiency is only effective in reducing energy consumption in the industrial sector, which ultimately reduces emissions.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2021.121040&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu45 citations 45 popularity Top 10% influence Top 10% impulse Top 1% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2021.121040&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2019 MalaysiaPublisher:Elsevier BV Authors: Sakiru Adebola Solarin; Muhammad Shahbaz; Shawkat Hammoudeh;Abstract This paper examines the relationship between hydroelectricity consumption and economic growth in China, while controlling for fossil fuel consumption, financial development, capital, institutional quality and globalization and its components for the period, 1970–2014. We have employed the Bayer and Hanck, (2013) combined cointegration test to examine the long-run relationships between those variables as well as the autoregressive distributed lag method with structural breaks as a robustness check. The empirical findings demonstrate a long-run relationship between those variables. Hydroelectricity consumption, fossil fuel consumption, capital, financial development and globalization and its components have a positive influence on GDP in China. The findings also provide predominant evidence on the long-run feedback hypothesis between the variables. The findings suggest that policies should be implemented to increase the role hydropower in the energy mix for sustainable economic growth in the country.
Energy arrow_drop_down Multimedia University, Malaysia: SHDL@MMU Digital RepositoryArticle . 2019Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2018.11.061&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu28 citations 28 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!
more_vert Energy arrow_drop_down Multimedia University, Malaysia: SHDL@MMU Digital RepositoryArticle . 2019Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2018.11.061&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2019 MalaysiaPublisher:Elsevier BV Authors: Sakiru Adebola Solarin; Muhammad Shahbaz; Shawkat Hammoudeh;Abstract This paper examines the relationship between hydroelectricity consumption and economic growth in China, while controlling for fossil fuel consumption, financial development, capital, institutional quality and globalization and its components for the period, 1970–2014. We have employed the Bayer and Hanck, (2013) combined cointegration test to examine the long-run relationships between those variables as well as the autoregressive distributed lag method with structural breaks as a robustness check. The empirical findings demonstrate a long-run relationship between those variables. Hydroelectricity consumption, fossil fuel consumption, capital, financial development and globalization and its components have a positive influence on GDP in China. The findings also provide predominant evidence on the long-run feedback hypothesis between the variables. The findings suggest that policies should be implemented to increase the role hydropower in the energy mix for sustainable economic growth in the country.
Energy arrow_drop_down Multimedia University, Malaysia: SHDL@MMU Digital RepositoryArticle . 2019Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2018.11.061&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu28 citations 28 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!
more_vert Energy arrow_drop_down Multimedia University, Malaysia: SHDL@MMU Digital RepositoryArticle . 2019Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2018.11.061&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2020Publisher:Elsevier BV Zhongwen Li; Bing Lu; Yixiang Gao; Weizhen Dong; Shuhui Li; Xingang Fu;Abstract This paper presents an optimal building energy management strategy for the demand response of multi-thermal-zone buildings in the smart electricity grid environment. The proposed method includes a machine learning model, based on a neural network, for a building heating ventilation and air conditioning system. The learned model is then applied to an optimization problem to determine the optimal management scheduling of building loads. The goal of the optimization problem is to minimize building electricity costs and reduce the overall building energy consumption during peak load hours while satisfying human comfort demand. To overcome the coupling issue between the building internal-heat-gain loads and the building heating ventilation and air conditioning system, an iterative algorithm is proposed to solve the optimization problem. In each iteration, a mixed-integer linear programming technique is used to solve a sub-optimization problem for the building internal-heat-gain loads and its results are then applied to another sub-optimization problem, solved by using a particle swarm technique, for the building heating ventilation and air conditioning system. The iterative optimization algorithm stops when convergence between the optimization for the building heating ventilation and air conditioning system and the optimization for the building internal-heat-gain loads is properly reached. EnergyPlus is used to build and simulate complex buildings with multiple-thermal zones according to real-life conditions. The simulation model is also used to test and evaluate the effectiveness of the proposed machine-learning model and the iterative optimization algorithm and the improvement of building energy management in terms of energy consumption efficiency, cost saving, and satisfaction of human comfort.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2020.118411&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu37 citations 37 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2020.118411&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2020Publisher:Elsevier BV Zhongwen Li; Bing Lu; Yixiang Gao; Weizhen Dong; Shuhui Li; Xingang Fu;Abstract This paper presents an optimal building energy management strategy for the demand response of multi-thermal-zone buildings in the smart electricity grid environment. The proposed method includes a machine learning model, based on a neural network, for a building heating ventilation and air conditioning system. The learned model is then applied to an optimization problem to determine the optimal management scheduling of building loads. The goal of the optimization problem is to minimize building electricity costs and reduce the overall building energy consumption during peak load hours while satisfying human comfort demand. To overcome the coupling issue between the building internal-heat-gain loads and the building heating ventilation and air conditioning system, an iterative algorithm is proposed to solve the optimization problem. In each iteration, a mixed-integer linear programming technique is used to solve a sub-optimization problem for the building internal-heat-gain loads and its results are then applied to another sub-optimization problem, solved by using a particle swarm technique, for the building heating ventilation and air conditioning system. The iterative optimization algorithm stops when convergence between the optimization for the building heating ventilation and air conditioning system and the optimization for the building internal-heat-gain loads is properly reached. EnergyPlus is used to build and simulate complex buildings with multiple-thermal zones according to real-life conditions. The simulation model is also used to test and evaluate the effectiveness of the proposed machine-learning model and the iterative optimization algorithm and the improvement of building energy management in terms of energy consumption efficiency, cost saving, and satisfaction of human comfort.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2020.118411&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu37 citations 37 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2020.118411&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu