Renewable fuels like hydrogen and biodiesels can very well suit to diesel engine applications as they address problems of energy scarcity, foreign exchange savings and emission norms. Production of hydrogen and biodie...Renewable fuels like hydrogen and biodiesels can very well suit to diesel engine applications as they address problems of energy scarcity, foreign exchange savings and emission norms. Production of hydrogen and biodiesel to industrial scale with low cost techniques can pave way for their efficient use in engine applications. In view of this, an attempt has been made to operate a modified diesel engine on these high potential renewable fuel combinations. An experimental study was carried out to evaluate the performance, combustion and emission characteristics of diesel engine operated in dual fuel (DF) mode fuelled with esters of honne (EHNO), honge (EHO) oils and hydrogen induction. The study revealed that the brake thermal efficiency increased up to 20% hydrogen energy ratio (HER) and then it decreased. The emissions such as hydrocarbon (HC), Carbon monoxide (CO) and smoke decreased with HER while oxides of nitrogen (NOx) increased. The combustion parameters like peak pressure, ignition delay and heat release rate (HRR) increased with HER.展开更多
De nos jours,la crise de confiance dans notre société est de plus en plus grave.Ces derniers temps,elle touche particulièrement les entreprises agroalimentaires:l’huile recyclée ou le lait
The present work examines the use of a non-edible vegetable oil namely honne oil,a new possible source of alternative fuel for diesel engine.A Direct Injection(DI)diesel engine typically used in agricultural sector wa...The present work examines the use of a non-edible vegetable oil namely honne oil,a new possible source of alternative fuel for diesel engine.A Direct Injection(DI)diesel engine typically used in agricultural sector was operated on Neat Diesel(ND)and neat honne oil(H100).At maximum load,with H100,brake thermal efficiency and NOx emission decreased where as emissions like CO,HC,smoke opacity increased.With H100,peak cylinder pressure and maximum rate of pressure rise decreased compared to ND.With H100,occurrence of peak pressure is away from top dead center compared to ND.With H100,ignition delay and combustion duration increased compared to ND.展开更多
This study attempts to accelerate the learning ability of an artificial electric field algorithm(AEFA)by attributing it with two mechanisms:elitism and opposition-based learning.Elitism advances the convergence of the...This study attempts to accelerate the learning ability of an artificial electric field algorithm(AEFA)by attributing it with two mechanisms:elitism and opposition-based learning.Elitism advances the convergence of the AEFA towards global optima by retaining the fine-tuned solutions obtained thus far,and opposition-based learning helps enhance its exploration ability.The new version of the AEFA,called elitist opposition leaning-based AEFA(EOAEFA),retains the properties of the basic AEFA while taking advantage of both elitism and opposition-based learning.Hence,the improved version attempts to reach optimum solutions by enabling the diversification of solutions with guaranteed convergence.Higher-order neural networks(HONNs)have single-layer adjustable parameters,fast learning,a robust fault tolerance,and good approximation ability compared with multilayer neural networks.They consider a higher order of input signals,increased the dimensionality of inputs through functional expansion and could thus discriminate between them.However,determining the number of expansion units in HONNs along with their associated parameters(i.e.,weight and threshold)is a bottleneck in the design of such networks.Here,we used EOAEFA to design two HONNs,namely,a pi-sigma neural network and a functional link artificial neural network,called EOAEFA-PSNN and EOAEFA-FLN,respectively,in a fully automated manner.The proposed models were evaluated on financial time-series datasets,focusing on predicting four closing prices,four exchange rates,and three energy prices.Experiments,comparative studies,and statistical tests were conducted to establish the efficacy of the proposed approach.展开更多
文摘Renewable fuels like hydrogen and biodiesels can very well suit to diesel engine applications as they address problems of energy scarcity, foreign exchange savings and emission norms. Production of hydrogen and biodiesel to industrial scale with low cost techniques can pave way for their efficient use in engine applications. In view of this, an attempt has been made to operate a modified diesel engine on these high potential renewable fuel combinations. An experimental study was carried out to evaluate the performance, combustion and emission characteristics of diesel engine operated in dual fuel (DF) mode fuelled with esters of honne (EHNO), honge (EHO) oils and hydrogen induction. The study revealed that the brake thermal efficiency increased up to 20% hydrogen energy ratio (HER) and then it decreased. The emissions such as hydrocarbon (HC), Carbon monoxide (CO) and smoke decreased with HER while oxides of nitrogen (NOx) increased. The combustion parameters like peak pressure, ignition delay and heat release rate (HRR) increased with HER.
文摘De nos jours,la crise de confiance dans notre société est de plus en plus grave.Ces derniers temps,elle touche particulièrement les entreprises agroalimentaires:l’huile recyclée ou le lait
文摘The present work examines the use of a non-edible vegetable oil namely honne oil,a new possible source of alternative fuel for diesel engine.A Direct Injection(DI)diesel engine typically used in agricultural sector was operated on Neat Diesel(ND)and neat honne oil(H100).At maximum load,with H100,brake thermal efficiency and NOx emission decreased where as emissions like CO,HC,smoke opacity increased.With H100,peak cylinder pressure and maximum rate of pressure rise decreased compared to ND.With H100,occurrence of peak pressure is away from top dead center compared to ND.With H100,ignition delay and combustion duration increased compared to ND.
基金supported by the Yonsei Fellow Program funded by Lee Youn Jae,Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korean government,Ministry of Science and ICT(MSIT)(No.2020-0-01361,Artificial Intelligence Graduate School Program(Yonsei University)No.2022-0-00113,Developing a Sustainable Collaborative Multi-modal Lifelong Learning Framework)the support of Teachers Associateship for Research Excellence(TARE)Fellowship(No.TAR/2021/00006)of the Science and Engineering Research Board(SERB),Government of India.
文摘This study attempts to accelerate the learning ability of an artificial electric field algorithm(AEFA)by attributing it with two mechanisms:elitism and opposition-based learning.Elitism advances the convergence of the AEFA towards global optima by retaining the fine-tuned solutions obtained thus far,and opposition-based learning helps enhance its exploration ability.The new version of the AEFA,called elitist opposition leaning-based AEFA(EOAEFA),retains the properties of the basic AEFA while taking advantage of both elitism and opposition-based learning.Hence,the improved version attempts to reach optimum solutions by enabling the diversification of solutions with guaranteed convergence.Higher-order neural networks(HONNs)have single-layer adjustable parameters,fast learning,a robust fault tolerance,and good approximation ability compared with multilayer neural networks.They consider a higher order of input signals,increased the dimensionality of inputs through functional expansion and could thus discriminate between them.However,determining the number of expansion units in HONNs along with their associated parameters(i.e.,weight and threshold)is a bottleneck in the design of such networks.Here,we used EOAEFA to design two HONNs,namely,a pi-sigma neural network and a functional link artificial neural network,called EOAEFA-PSNN and EOAEFA-FLN,respectively,in a fully automated manner.The proposed models were evaluated on financial time-series datasets,focusing on predicting four closing prices,four exchange rates,and three energy prices.Experiments,comparative studies,and statistical tests were conducted to establish the efficacy of the proposed approach.
