Modeling current and future role of agricultural waste in the production of bioethanol for gasoline vehicles
This study addressed the urgent need for biofuels even in countries such as Iran, which has fairly good fossil fuel resources. The problems, necessities of the present, and future demand for biofuels are discussed. As the transportation sector is one of the largest sources of air pollution, this stu...
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Veröffentlicht in: | Bioresources 2021-08, Vol.16 (3), p.4798-4813 |
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creator | Badamchizadeh, Shahram Latibari, Ahmad Jahan Tajdini, Ajang Pourmousa, Shademan Lashgari, Amir |
description | This study addressed the urgent need for biofuels even in countries such as Iran, which has fairly good fossil fuel resources. The problems, necessities of the present, and future demand for biofuels are discussed. As the transportation sector is one of the largest sources of air pollution, this study has focused on this sector. This issue was examined from a global perspective, and then within the context of domestic bioethanol production using agricultural residues and proposing different scenarios. The first step in implementing this policy is the accurate forecast of the demand for second-generation bioethanol in the coming years. A nonlinear auto regressive neural network was applied to predict gasoline demand based on Mackey-Glass chaotic time series. Gasoline demand is forecasted by 2030, based on projected volumes of gasoline in different bioethanol mixture scenarios. Results revealed that using scenarios E10, E15, E25, the volume of bioethanol needed by 2030 will amount to 10.12, 15.16 and 25.31 million L per day which can be produced using agricultural products wastes. |
doi_str_mv | 10.15376/biores.16.3.4798-4813 |
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The problems, necessities of the present, and future demand for biofuels are discussed. As the transportation sector is one of the largest sources of air pollution, this study has focused on this sector. This issue was examined from a global perspective, and then within the context of domestic bioethanol production using agricultural residues and proposing different scenarios. The first step in implementing this policy is the accurate forecast of the demand for second-generation bioethanol in the coming years. A nonlinear auto regressive neural network was applied to predict gasoline demand based on Mackey-Glass chaotic time series. Gasoline demand is forecasted by 2030, based on projected volumes of gasoline in different bioethanol mixture scenarios. 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subjects | Agricultural products Agricultural wastes Air pollution Biodiesel fuels Biofuels Climate change Crop residues Demand Economic forecasting Emissions Ethanol Fossil fuels Gasoline Greenhouse effect Neural networks Pollution sources Transportation industry |
title | Modeling current and future role of agricultural waste in the production of bioethanol for gasoline vehicles |
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