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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.

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.

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.

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.

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.

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.

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.