14 citations 14 popularity Average influence Top 10% impulse Average 
Powered by BIP!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.<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=10.1016/0196-8904(87)90109-9&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu14 citations 14 popularity Average influence Top 10% impulse Average Powered by BIP!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.<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=10.1016/0196-8904(87)90109-9&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu- Suhani Agarwal;
Pranav V. Kherdekar; Pranav V. Kherdekar
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesPranav V. Kherdekar in OpenAIRE Divesh Bhatia; Divesh Bhatia
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesDivesh Bhatia in OpenAIRE4 citations 4 popularity Average influence Average impulse Average Powered by BIP!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.<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=10.1021/acs.energyfuels.2c00411&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - Suhani Agarwal; Pranav V. Kherdekar;
Pranav V. Kherdekar
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesPranav V. Kherdekar in OpenAIRE Divesh Bhatia; Divesh Bhatia
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesDivesh Bhatia in OpenAIRE4 citations 4 popularity Average influence Average impulse Average Powered by BIP!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.<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=10.1021/acs.energyfuels.2c00411&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu 
Senthilkumar Pachamuthu; Senthilkumar Pachamuthu
Harvested from ORCID Public Data FileSenthilkumar Pachamuthu in OpenAIRE Terese Løvås; Johan E. Hustad; Dhandapani Kannan; +1 AuthorsTerese Løvås
Harvested from ORCID Public Data FileTerese Løvås in OpenAIRE Senthilkumar Pachamuthu; Senthilkumar Pachamuthu
Harvested from ORCID Public Data FileSenthilkumar Pachamuthu in OpenAIRE Terese Løvås; Johan E. Hustad; Dhandapani Kannan;Terese Løvås
Harvested from ORCID Public Data FileTerese Løvås in OpenAIRE Md. Nurun Nabi; Md. Nurun Nabi
Harvested from ORCID Public Data FileMd. Nurun Nabi in OpenAIREAbstract In this work addition of ethanol to high viscosity jatropha methyl ester (JME) through port injection is investigated in order to determine its effect fuel viscosity reduction on diesel engine performance. In addition to viscosity alteration, the impact of ethanol addition on combustion characteristics such as combustion duration, ignition delay and emissions levels from diesel engines fuelled with blends of ethanol, diesel and JME is studied in particular. It is found that blending of oxygenated fuels with diesel modifies the chemical structure and physical properties which again alter the engine operating conditions, combustion parameters and emissions levels. However, the injection of only 5% ethanol through port injection allows for a total of 25% blending of biofuels into diesel yet keeping the fuel characteristics close to that of conventional diesel. However, both experimental and numerical results show that ethanol addition in JME blended diesel results in a slight increase in fuel consumption and thermal efficiency for the same power outputs as that of conventional diesel fuel. Also, the combustion characteristics with ethanol addition include improved maximum in-cylinder peak pressure, cumulative heat release (CHR) rate of heat release (ROHR), in-cylinder peak temperature and combustion duration. Regarding emission characteristics the experimental results show significant reduction in smoke, carbon monoxide (CO) and total hydrocarbon (THC) emissions with extended oxygen mass percentage in the fuel at higher engine loads. However, oxides of nitrogen (NOx) emissions are found to increase at high loads although the common tradeoff between smoke and NOx is found to be more prominent for the oxygenated fuels.
116 citations 116 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!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.<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.enconman.2011.09.010&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu- Senthilkumar Pachamuthu;
Senthilkumar Pachamuthu
Harvested from ORCID Public Data FileSenthilkumar Pachamuthu in OpenAIRE Terese Løvås; Johan E. Hustad; Dhandapani Kannan; +1 AuthorsTerese Løvås
Harvested from ORCID Public Data FileTerese Løvås in OpenAIRE Senthilkumar Pachamuthu; Senthilkumar Pachamuthu
Harvested from ORCID Public Data FileSenthilkumar Pachamuthu in OpenAIRE Terese Løvås; Johan E. Hustad; Dhandapani Kannan;Terese Løvås
Harvested from ORCID Public Data FileTerese Løvås in OpenAIRE Md. Nurun Nabi; Md. Nurun Nabi
Harvested from ORCID Public Data FileMd. Nurun Nabi in OpenAIREAbstract In this work addition of ethanol to high viscosity jatropha methyl ester (JME) through port injection is investigated in order to determine its effect fuel viscosity reduction on diesel engine performance. In addition to viscosity alteration, the impact of ethanol addition on combustion characteristics such as combustion duration, ignition delay and emissions levels from diesel engines fuelled with blends of ethanol, diesel and JME is studied in particular. It is found that blending of oxygenated fuels with diesel modifies the chemical structure and physical properties which again alter the engine operating conditions, combustion parameters and emissions levels. However, the injection of only 5% ethanol through port injection allows for a total of 25% blending of biofuels into diesel yet keeping the fuel characteristics close to that of conventional diesel. However, both experimental and numerical results show that ethanol addition in JME blended diesel results in a slight increase in fuel consumption and thermal efficiency for the same power outputs as that of conventional diesel fuel. Also, the combustion characteristics with ethanol addition include improved maximum in-cylinder peak pressure, cumulative heat release (CHR) rate of heat release (ROHR), in-cylinder peak temperature and combustion duration. Regarding emission characteristics the experimental results show significant reduction in smoke, carbon monoxide (CO) and total hydrocarbon (THC) emissions with extended oxygen mass percentage in the fuel at higher engine loads. However, oxides of nitrogen (NOx) emissions are found to increase at high loads although the common tradeoff between smoke and NOx is found to be more prominent for the oxygenated fuels.
116 citations 116 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!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.<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.enconman.2011.09.010&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - Rajender S. Sangwan;
Rajender S. Sangwan
Harvested from ORCID Public Data FileRajender S. Sangwan in OpenAIRE Sushil Kumar Kansal; Sandeep Kumar; Pranati Kundu; +3 AuthorsSushil Kumar Kansal
Harvested from ORCID Public Data FileSushil Kumar Kansal in OpenAIRE Rajender S. Sangwan; Rajender S. Sangwan
Harvested from ORCID Public Data FileRajender S. Sangwan in OpenAIRE Sushil Kumar Kansal; Sandeep Kumar; Pranati Kundu; Troy Runge; Vivek Ahluwalia;Sushil Kumar Kansal
Harvested from ORCID Public Data FileSushil Kumar Kansal in OpenAIRE Sasikumar Elumalai; Sasikumar Elumalai
Harvested from ORCID Public Data FileSasikumar Elumalai in OpenAIREIn this study, levulinic acid (LA) was produced from rice straw biomass in co-solvent biphasic reactor system consisting of hydrochloric acid and dichloromethane organic solvent. The modified protocol achieved a 15% wt LA yield through the synergistic effect of acid and acidic products (auto-catalysis) and the designed system allowed facile recovery of LA to the organic phase. Further purification of the resulting extractant was achieved through traditional column chromatography, which yielded a high purity LA product while recovering ∼85% wt. Upon charcoal treatment of the resultant fraction generated an industrial grade target molecule of ∼99% purity with ∼95% wt recovery. The system allows the solvent to be easily recovered, in excess of 90%, which was shown to be able to be recycled up to 5 runs without significant loss of final product concentrations. Overall, this system points to a method to significantly reduce manufacturing cost during large-scale LA preparation.
48 citations 48 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!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.<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.biortech.2017.12.033&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - Rajender S. Sangwan;
Rajender S. Sangwan
Harvested from ORCID Public Data FileRajender S. Sangwan in OpenAIRE Sushil Kumar Kansal; Sandeep Kumar; Pranati Kundu; +3 AuthorsSushil Kumar Kansal
Harvested from ORCID Public Data FileSushil Kumar Kansal in OpenAIRE Rajender S. Sangwan; Rajender S. Sangwan
Harvested from ORCID Public Data FileRajender S. Sangwan in OpenAIRE Sushil Kumar Kansal; Sandeep Kumar; Pranati Kundu; Troy Runge; Vivek Ahluwalia;Sushil Kumar Kansal
Harvested from ORCID Public Data FileSushil Kumar Kansal in OpenAIRE Sasikumar Elumalai; Sasikumar Elumalai
Harvested from ORCID Public Data FileSasikumar Elumalai in OpenAIREIn this study, levulinic acid (LA) was produced from rice straw biomass in co-solvent biphasic reactor system consisting of hydrochloric acid and dichloromethane organic solvent. The modified protocol achieved a 15% wt LA yield through the synergistic effect of acid and acidic products (auto-catalysis) and the designed system allowed facile recovery of LA to the organic phase. Further purification of the resulting extractant was achieved through traditional column chromatography, which yielded a high purity LA product while recovering ∼85% wt. Upon charcoal treatment of the resultant fraction generated an industrial grade target molecule of ∼99% purity with ∼95% wt recovery. The system allows the solvent to be easily recovered, in excess of 90%, which was shown to be able to be recycled up to 5 runs without significant loss of final product concentrations. Overall, this system points to a method to significantly reduce manufacturing cost during large-scale LA preparation.
48 citations 48 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!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.<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.biortech.2017.12.033&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - Rachna Vaish;
Rachna Vaish
Harvested from ORCID Public Data FileRachna Vaish in OpenAIRE U.D. Dwivedi; U.D. Dwivedi
Harvested from ORCID Public Data FileU.D. Dwivedi in OpenAIRE Saurabh Tewari; Saurabh Tewari
Harvested from ORCID Public Data FileSaurabh Tewari in OpenAIRE S.M. Tripathi; S.M. Tripathi
Harvested from ORCID Public Data FileS.M. Tripathi in OpenAIREAbstract Newer generation sources and loads are posing new challenges to the conventional power system protection schemes. Adaptive and intelligent protection methodology, based on advanced measurement techniques and intelligent fault diagnosis such as machine learning (ML), is found to be useful to meet these challenges. A large number of research works are reported on ML-based power system fault diagnosis. However, ML techniques are evolving at a very fast pace, and an inclusive, as well as state-of-the-art review on ML-based power system fault diagnosis, is not available in the literature. Given this need and growing trend towards ML, the study presented in this paper aims to provide a comprehensive review of ML-based power system fault diagnosis. At first, efforts have been made to enlist the issues present in conventional fault diagnosis which led to the popularity of ML techniques. Also, a baseline framework and workflow for ML-based fault diagnosis are presented. Next, various unsupervised and supervised learning techniques have been discussed separately which have been used by several researchers for fault diagnosis. The discussion throughout is supported with tabulated facts for fault detection, classification and localization works with techniques used, different simulation tools used, and their application system. The advantages and disadvantages of all the techniques of fault diagnosis have also been discussed which will help the readers in the selection of techniques for their research. A brief review of reinforcement learning and transfer learning is also given as they are gaining popularity in power system-related studies and have the potential to be used for fault diagnosis. Finally, the research trends, some key issues, and directions for future research have been highlighted.
106 citations 106 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!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.<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.engappai.2021.104504&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - Rachna Vaish;
Rachna Vaish
Harvested from ORCID Public Data FileRachna Vaish in OpenAIRE U.D. Dwivedi; U.D. Dwivedi
Harvested from ORCID Public Data FileU.D. Dwivedi in OpenAIRE Saurabh Tewari; Saurabh Tewari
Harvested from ORCID Public Data FileSaurabh Tewari in OpenAIRE S.M. Tripathi; S.M. Tripathi
Harvested from ORCID Public Data FileS.M. Tripathi in OpenAIREAbstract Newer generation sources and loads are posing new challenges to the conventional power system protection schemes. Adaptive and intelligent protection methodology, based on advanced measurement techniques and intelligent fault diagnosis such as machine learning (ML), is found to be useful to meet these challenges. A large number of research works are reported on ML-based power system fault diagnosis. However, ML techniques are evolving at a very fast pace, and an inclusive, as well as state-of-the-art review on ML-based power system fault diagnosis, is not available in the literature. Given this need and growing trend towards ML, the study presented in this paper aims to provide a comprehensive review of ML-based power system fault diagnosis. At first, efforts have been made to enlist the issues present in conventional fault diagnosis which led to the popularity of ML techniques. Also, a baseline framework and workflow for ML-based fault diagnosis are presented. Next, various unsupervised and supervised learning techniques have been discussed separately which have been used by several researchers for fault diagnosis. The discussion throughout is supported with tabulated facts for fault detection, classification and localization works with techniques used, different simulation tools used, and their application system. The advantages and disadvantages of all the techniques of fault diagnosis have also been discussed which will help the readers in the selection of techniques for their research. A brief review of reinforcement learning and transfer learning is also given as they are gaining popularity in power system-related studies and have the potential to be used for fault diagnosis. Finally, the research trends, some key issues, and directions for future research have been highlighted.
106 citations 106 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!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.<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.engappai.2021.104504&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu 0 citations 0 popularity Average influence Average impulse Average Powered by BIP!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.<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=10.1109/tpwrd.2007.893940&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!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.<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=10.1109/tpwrd.2007.893940&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu- Yamini Sumathi; Cheng-Di Dong;
Yamini Sumathi
Harvested from ORCID Public Data FileYamini Sumathi in OpenAIRE Reeta Rani Singhania; Chiu-Wen Chen; +2 AuthorsReeta Rani Singhania
Harvested from ORCID Public Data FileReeta Rani Singhania in OpenAIRE Yamini Sumathi; Cheng-Di Dong;Yamini Sumathi
Harvested from ORCID Public Data FileYamini Sumathi in OpenAIRE Reeta Rani Singhania; Chiu-Wen Chen;Reeta Rani Singhania
Harvested from ORCID Public Data FileReeta Rani Singhania in OpenAIRE Baskar Gurunathan; Baskar Gurunathan
Harvested from ORCID Public Data FileBaskar Gurunathan in OpenAIRE Anil Kumar Patel; Anil Kumar Patel
Harvested from ORCID Public Data FileAnil Kumar Patel in OpenAIREMicroalgae are promising sources of valuable compounds: carotenoids, polyunsaturated fatty acids, lipids, etc. To overcome the feasibility challenge due to low yield and attain commercial potential, researchers merge technologies to enhance algal bioprocess. In this context, nanomaterials are attractive for enhancing microalgal bioprocessing, from cultivation to downstream extraction. Nanomaterials enhance biomass and product yields (mainly lipid and carotenoids) through improved nutrient uptake and stress tolerance during cultivation. They also provide mechanistic insights from recent studies. They also revolutionize harvesting via nano-induced sedimentation, flocculation, and flotation. Downstream processing benefits from nanomaterials, improving extraction and purification. Special attention is given to cost-effective extraction, showcasing nanomaterial integration, and providing a comparative account. The review also profiles nanomaterial types, including metallic nanoparticles, magnetic nanomaterials, carbon-based nanomaterials, silica nanoparticles, polymers, and functionalized nanomaterials. Challenges and future trends are discussed, emphasizing nanomaterials' role in advancing sustainable and efficient microalgal bioprocessing, unlocking their potential for bio-based industries.
4 citations 4 popularity Average influence Average impulse Average Powered by BIP!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.<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.biortech.2024.130749&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - Yamini Sumathi; Cheng-Di Dong;
Yamini Sumathi
Harvested from ORCID Public Data FileYamini Sumathi in OpenAIRE Reeta Rani Singhania; Chiu-Wen Chen; +2 AuthorsReeta Rani Singhania
Harvested from ORCID Public Data FileReeta Rani Singhania in OpenAIRE Yamini Sumathi; Cheng-Di Dong;Yamini Sumathi
Harvested from ORCID Public Data FileYamini Sumathi in OpenAIRE Reeta Rani Singhania; Chiu-Wen Chen;Reeta Rani Singhania
Harvested from ORCID Public Data FileReeta Rani Singhania in OpenAIRE Baskar Gurunathan; Baskar Gurunathan
Harvested from ORCID Public Data FileBaskar Gurunathan in OpenAIRE Anil Kumar Patel; Anil Kumar Patel
Harvested from ORCID Public Data FileAnil Kumar Patel in OpenAIREMicroalgae are promising sources of valuable compounds: carotenoids, polyunsaturated fatty acids, lipids, etc. To overcome the feasibility challenge due to low yield and attain commercial potential, researchers merge technologies to enhance algal bioprocess. In this context, nanomaterials are attractive for enhancing microalgal bioprocessing, from cultivation to downstream extraction. Nanomaterials enhance biomass and product yields (mainly lipid and carotenoids) through improved nutrient uptake and stress tolerance during cultivation. They also provide mechanistic insights from recent studies. They also revolutionize harvesting via nano-induced sedimentation, flocculation, and flotation. Downstream processing benefits from nanomaterials, improving extraction and purification. Special attention is given to cost-effective extraction, showcasing nanomaterial integration, and providing a comparative account. The review also profiles nanomaterial types, including metallic nanoparticles, magnetic nanomaterials, carbon-based nanomaterials, silica nanoparticles, polymers, and functionalized nanomaterials. Challenges and future trends are discussed, emphasizing nanomaterials' role in advancing sustainable and efficient microalgal bioprocessing, unlocking their potential for bio-based industries.
4 citations 4 popularity Average influence Average impulse Average Powered by BIP!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.<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.biortech.2024.130749&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu 6 citations 6 popularity Top 10% influence Average impulse Top 10% Powered by BIP!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.<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.matpr.2022.01.168&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu6 citations 6 popularity Top 10% influence Average impulse Top 10% Powered by BIP!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.<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.matpr.2022.01.168&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu- Mohammad Sarmadivaleh;
Mohammad Sarmadivaleh
Harvested from ORCID Public Data FileMohammad Sarmadivaleh in OpenAIRE Ahmed Barifcani; Ahmed Barifcani
Harvested from ORCID Public Data FileAhmed Barifcani in OpenAIRE Nilesh Kumar Jha; Nilesh Kumar Jha; +2 AuthorsNilesh Kumar Jha
Harvested from ORCID Public Data FileNilesh Kumar Jha in OpenAIRE Mohammad Sarmadivaleh; Mohammad Sarmadivaleh
Harvested from ORCID Public Data FileMohammad Sarmadivaleh in OpenAIRE Ahmed Barifcani; Ahmed Barifcani
Harvested from ORCID Public Data FileAhmed Barifcani in OpenAIRE Nilesh Kumar Jha; Nilesh Kumar Jha; Stefan Iglauer;Nilesh Kumar Jha
Harvested from ORCID Public Data FileNilesh Kumar Jha in OpenAIRE Jitendra S. Sangwai; Jitendra S. Sangwai
Harvested from ORCID Public Data FileJitendra S. Sangwai in OpenAIRELow-salinity water injection emerges to be a cost-effective and environmentally friendly enhanced oil recovery technique. Furthermore, additives, such as the surfactant and nanoparticles in combina...
56 citations 56 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!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.<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=10.1021/acs.energyfuels.8b03406&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - Mohammad Sarmadivaleh;
Mohammad Sarmadivaleh
Harvested from ORCID Public Data FileMohammad Sarmadivaleh in OpenAIRE Ahmed Barifcani; Ahmed Barifcani
Harvested from ORCID Public Data FileAhmed Barifcani in OpenAIRE Nilesh Kumar Jha; Nilesh Kumar Jha; +2 AuthorsNilesh Kumar Jha
Harvested from ORCID Public Data FileNilesh Kumar Jha in OpenAIRE Mohammad Sarmadivaleh; Mohammad Sarmadivaleh
Harvested from ORCID Public Data FileMohammad Sarmadivaleh in OpenAIRE Ahmed Barifcani; Ahmed Barifcani
Harvested from ORCID Public Data FileAhmed Barifcani in OpenAIRE Nilesh Kumar Jha; Nilesh Kumar Jha; Stefan Iglauer;Nilesh Kumar Jha
Harvested from ORCID Public Data FileNilesh Kumar Jha in OpenAIRE Jitendra S. Sangwai; Jitendra S. Sangwai
Harvested from ORCID Public Data FileJitendra S. Sangwai in OpenAIRELow-salinity water injection emerges to be a cost-effective and environmentally friendly enhanced oil recovery technique. Furthermore, additives, such as the surfactant and nanoparticles in combina...
56 citations 56 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!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.<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=10.1021/acs.energyfuels.8b03406&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - EC| MefCO2 Bellotti, D.;
Bellotti, D.
Harvested from ORCID Public Data FileBellotti, D. in OpenAIRE Rivarolo, M.; Rivarolo, M.
Harvested from ORCID Public Data FileRivarolo, M. in OpenAIRE Magistri, L.; Magistri, L.
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesMagistri, L. in OpenAIRE Massardo, A. F.; Massardo, A. F.
Harvested from ORCID Public Data FileMassardo, A. F. in OpenAIREAbstract This paper aims to present a feasibility study of the innovative plant for methanol synthesis from carbon dioxide-sequestered by fossil fuel power plant and hydrogen, which is produced by water electrolyzer employing the over-production on the electrical grid. The thermo-economic analysis is performed in the framework of the MefCO2 H2020 EU project and it is referred to the German economic scenario, properly taking into account the real market costs and cost functions for different components of the plant. Three different plant capacities for methanol production (4000 10,000 and 50,000 ton/year) have been investigated, assuming an average cost for electrical energy to feed electrolysers and analyzing the influence of the most significant parameters (oxygen selling option, methanol selling price and electrolysers’ capital cost) on the profitability of the plant. The analysis has been performed in W-ECoMP, software for the thermo-economic analysis and plant optimization developed by the University of Genoa.
181 citations 181 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!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.<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.jcou.2017.07.001&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - EC| MefCO2 Bellotti, D.;
Bellotti, D.
Harvested from ORCID Public Data FileBellotti, D. in OpenAIRE Rivarolo, M.; Rivarolo, M.
Harvested from ORCID Public Data FileRivarolo, M. in OpenAIRE Magistri, L.; Magistri, L.
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesMagistri, L. in OpenAIRE Massardo, A. F.; Massardo, A. F.
Harvested from ORCID Public Data FileMassardo, A. F. in OpenAIREAbstract This paper aims to present a feasibility study of the innovative plant for methanol synthesis from carbon dioxide-sequestered by fossil fuel power plant and hydrogen, which is produced by water electrolyzer employing the over-production on the electrical grid. The thermo-economic analysis is performed in the framework of the MefCO2 H2020 EU project and it is referred to the German economic scenario, properly taking into account the real market costs and cost functions for different components of the plant. Three different plant capacities for methanol production (4000 10,000 and 50,000 ton/year) have been investigated, assuming an average cost for electrical energy to feed electrolysers and analyzing the influence of the most significant parameters (oxygen selling option, methanol selling price and electrolysers’ capital cost) on the profitability of the plant. The analysis has been performed in W-ECoMP, software for the thermo-economic analysis and plant optimization developed by the University of Genoa.
181 citations 181 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!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.<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.jcou.2017.07.001&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu
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