Prandtl number of optimum biodiesel from food industrial waste oil and diesel fuel blend for diesel engine

[Display omitted] •ANN provided a higher predicted yield for food industrial waste oil methyl ester (FIWOME) compared to RSM.•Correlations for the specific heat capacity (Cp), thermal diffusivity (TD) and thermal conductivity (TC) of FIWOME established for the first time.•The Cp and TD are correlate...

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Veröffentlicht in:Fuel (Guildford) 2021-02, Vol.285, p.119049, Article 119049
Hauptverfasser: David Samuel, Olusegun, Adekojo Waheed, M., Taheri-Garavand, A., Verma, Tikendra Nath, Dairo, Olawale U., Bolaji, Bukola O., Afzal, Asif
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container_issue
container_start_page 119049
container_title Fuel (Guildford)
container_volume 285
creator David Samuel, Olusegun
Adekojo Waheed, M.
Taheri-Garavand, A.
Verma, Tikendra Nath
Dairo, Olawale U.
Bolaji, Bukola O.
Afzal, Asif
description [Display omitted] •ANN provided a higher predicted yield for food industrial waste oil methyl ester (FIWOME) compared to RSM.•Correlations for the specific heat capacity (Cp), thermal diffusivity (TD) and thermal conductivity (TC) of FIWOME established for the first time.•The Cp and TD are correlated with FIWOME percent through the least square regression.•The TC was correlated with the FIWOME fraction through linear regressions.•The major properties of FIWOME concurred agreed with previous studies and compiled with biodiesel standards.•The performance of B22.5 gave a good improvement in term of BTE. Unconventional biodiesel characterization techniques using thermophysical and transport properties have been receiving increasing attention due to its advantages over fundamental combustion and simulation of heat transfer in solving heat transfer, chemical, and bioenergy characteristics of biodiesel combustion. In this study, the optimum production yield of Food Industrial Waste Oil Methyl Ester (FIWOME, B100, FIWOB) was modelled using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) techniques. The basic properties of the fuel types were determined using ASTM test methods, while specific heat capacity (Cp), thermal diffusivity (TD), thermal conductivity (TC) and Prandtl number (Pr) were determined using standard methods. Diesel engine performance indicators such as Engine Torque (ET), Brake Power (BP), Brake Specific Fuel Consumption (BSFC) and Brake Thermal Efficiency (BTE) were determined for different fuel types using a Perkins diesel engine. The estimated Coefficient of Determination (R2) of 0.9820, Root Mean-Square-Error (RMSE) of 1.7403, Standard Error of Prediction (SEP) of 0.0215, Mean Average Error (MAE) of 1.3790, and Average Absolute Deviation (AAD) of 1.6389 for RSM compared to those of R2 (0.9847), RMSE (1.6071), SEP (0.0199), MAE (1.1425), and AAD (1.2583) for ANN exhibited the robustness of the ANN tool over the RSM technique. Optimal biodiesel-- diesel fuel (B0) blend was 22.5% volume ratio called as B22.5. The optimum yield of FIWOME of 92.5% was achieved at the methanol/oil molar ratio of 5.99, KOH of 1.1 wt.%, and a reaction time of 77.6 min. The basic properties of FIWOME determined complied with both ASTM D6751 and EN 14214 specifications. The values of Cp and TD increased and decreased respectively with biodiesel percent in fuel in a quadratic manner. The TC and Pr were correlated with the biodiesel fraction through
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Unconventional biodiesel characterization techniques using thermophysical and transport properties have been receiving increasing attention due to its advantages over fundamental combustion and simulation of heat transfer in solving heat transfer, chemical, and bioenergy characteristics of biodiesel combustion. In this study, the optimum production yield of Food Industrial Waste Oil Methyl Ester (FIWOME, B100, FIWOB) was modelled using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) techniques. The basic properties of the fuel types were determined using ASTM test methods, while specific heat capacity (Cp), thermal diffusivity (TD), thermal conductivity (TC) and Prandtl number (Pr) were determined using standard methods. Diesel engine performance indicators such as Engine Torque (ET), Brake Power (BP), Brake Specific Fuel Consumption (BSFC) and Brake Thermal Efficiency (BTE) were determined for different fuel types using a Perkins diesel engine. The estimated Coefficient of Determination (R2) of 0.9820, Root Mean-Square-Error (RMSE) of 1.7403, Standard Error of Prediction (SEP) of 0.0215, Mean Average Error (MAE) of 1.3790, and Average Absolute Deviation (AAD) of 1.6389 for RSM compared to those of R2 (0.9847), RMSE (1.6071), SEP (0.0199), MAE (1.1425), and AAD (1.2583) for ANN exhibited the robustness of the ANN tool over the RSM technique. Optimal biodiesel-- diesel fuel (B0) blend was 22.5% volume ratio called as B22.5. The optimum yield of FIWOME of 92.5% was achieved at the methanol/oil molar ratio of 5.99, KOH of 1.1 wt.%, and a reaction time of 77.6 min. The basic properties of FIWOME determined complied with both ASTM D6751 and EN 14214 specifications. The values of Cp and TD increased and decreased respectively with biodiesel percent in fuel in a quadratic manner. The TC and Pr were correlated with the biodiesel fraction through linear regressions. The brake power, brake torque, and brake specific fuel consumption were observed to increase with an increase in biodiesel percentage level. The determined performance characteristics of B22.5, which is above B20 as recommended for powered diesel engine gave a good improvement in terms of BTE and Pr making FIWOME based biodiesel a viable substitute for fossil diesel.</description><identifier>ISSN: 0016-2361</identifier><identifier>EISSN: 1873-7153</identifier><identifier>DOI: 10.1016/j.fuel.2020.119049</identifier><language>eng</language><publisher>Kidlington: Elsevier Ltd</publisher><subject>Artificial Neural Network ; Artificial neural networks ; Biodiesel ; Biodiesel fuels ; Biofuels ; Brakes ; Combustion ; Diesel ; Diesel engines ; Diesel fuels ; Food industry ; Fuel consumption ; Heat transfer ; Industrial wastes ; Modelling ; Neural networks ; Oil wastes ; Optimization ; Power consumption ; Prandtl number ; Prediction ; Reaction time ; Renewable energy ; Response surface methodology ; Root-mean-square errors ; Specific heat ; Standard error ; Thermal conductivity ; Thermal diffusivity ; Thermodynamic efficiency ; Thermophysical properties ; Torque ; Transport properties</subject><ispartof>Fuel (Guildford), 2021-02, Vol.285, p.119049, Article 119049</ispartof><rights>2020 Elsevier Ltd</rights><rights>Copyright Elsevier BV Feb 1, 2021</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c328t-3838ba1f610da83a2e9c002b5e224c6bd7bb573bde2f931407e3f62712238ed53</citedby><cites>FETCH-LOGICAL-c328t-3838ba1f610da83a2e9c002b5e224c6bd7bb573bde2f931407e3f62712238ed53</cites><orcidid>0000-0003-2961-6186</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.fuel.2020.119049$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids></links><search><creatorcontrib>David Samuel, Olusegun</creatorcontrib><creatorcontrib>Adekojo Waheed, M.</creatorcontrib><creatorcontrib>Taheri-Garavand, A.</creatorcontrib><creatorcontrib>Verma, Tikendra Nath</creatorcontrib><creatorcontrib>Dairo, Olawale U.</creatorcontrib><creatorcontrib>Bolaji, Bukola O.</creatorcontrib><creatorcontrib>Afzal, Asif</creatorcontrib><title>Prandtl number of optimum biodiesel from food industrial waste oil and diesel fuel blend for diesel engine</title><title>Fuel (Guildford)</title><description>[Display omitted] •ANN provided a higher predicted yield for food industrial waste oil methyl ester (FIWOME) compared to RSM.•Correlations for the specific heat capacity (Cp), thermal diffusivity (TD) and thermal conductivity (TC) of FIWOME established for the first time.•The Cp and TD are correlated with FIWOME percent through the least square regression.•The TC was correlated with the FIWOME fraction through linear regressions.•The major properties of FIWOME concurred agreed with previous studies and compiled with biodiesel standards.•The performance of B22.5 gave a good improvement in term of BTE. Unconventional biodiesel characterization techniques using thermophysical and transport properties have been receiving increasing attention due to its advantages over fundamental combustion and simulation of heat transfer in solving heat transfer, chemical, and bioenergy characteristics of biodiesel combustion. In this study, the optimum production yield of Food Industrial Waste Oil Methyl Ester (FIWOME, B100, FIWOB) was modelled using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) techniques. The basic properties of the fuel types were determined using ASTM test methods, while specific heat capacity (Cp), thermal diffusivity (TD), thermal conductivity (TC) and Prandtl number (Pr) were determined using standard methods. Diesel engine performance indicators such as Engine Torque (ET), Brake Power (BP), Brake Specific Fuel Consumption (BSFC) and Brake Thermal Efficiency (BTE) were determined for different fuel types using a Perkins diesel engine. The estimated Coefficient of Determination (R2) of 0.9820, Root Mean-Square-Error (RMSE) of 1.7403, Standard Error of Prediction (SEP) of 0.0215, Mean Average Error (MAE) of 1.3790, and Average Absolute Deviation (AAD) of 1.6389 for RSM compared to those of R2 (0.9847), RMSE (1.6071), SEP (0.0199), MAE (1.1425), and AAD (1.2583) for ANN exhibited the robustness of the ANN tool over the RSM technique. Optimal biodiesel-- diesel fuel (B0) blend was 22.5% volume ratio called as B22.5. The optimum yield of FIWOME of 92.5% was achieved at the methanol/oil molar ratio of 5.99, KOH of 1.1 wt.%, and a reaction time of 77.6 min. The basic properties of FIWOME determined complied with both ASTM D6751 and EN 14214 specifications. The values of Cp and TD increased and decreased respectively with biodiesel percent in fuel in a quadratic manner. The TC and Pr were correlated with the biodiesel fraction through linear regressions. The brake power, brake torque, and brake specific fuel consumption were observed to increase with an increase in biodiesel percentage level. 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Unconventional biodiesel characterization techniques using thermophysical and transport properties have been receiving increasing attention due to its advantages over fundamental combustion and simulation of heat transfer in solving heat transfer, chemical, and bioenergy characteristics of biodiesel combustion. In this study, the optimum production yield of Food Industrial Waste Oil Methyl Ester (FIWOME, B100, FIWOB) was modelled using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) techniques. The basic properties of the fuel types were determined using ASTM test methods, while specific heat capacity (Cp), thermal diffusivity (TD), thermal conductivity (TC) and Prandtl number (Pr) were determined using standard methods. Diesel engine performance indicators such as Engine Torque (ET), Brake Power (BP), Brake Specific Fuel Consumption (BSFC) and Brake Thermal Efficiency (BTE) were determined for different fuel types using a Perkins diesel engine. The estimated Coefficient of Determination (R2) of 0.9820, Root Mean-Square-Error (RMSE) of 1.7403, Standard Error of Prediction (SEP) of 0.0215, Mean Average Error (MAE) of 1.3790, and Average Absolute Deviation (AAD) of 1.6389 for RSM compared to those of R2 (0.9847), RMSE (1.6071), SEP (0.0199), MAE (1.1425), and AAD (1.2583) for ANN exhibited the robustness of the ANN tool over the RSM technique. Optimal biodiesel-- diesel fuel (B0) blend was 22.5% volume ratio called as B22.5. The optimum yield of FIWOME of 92.5% was achieved at the methanol/oil molar ratio of 5.99, KOH of 1.1 wt.%, and a reaction time of 77.6 min. The basic properties of FIWOME determined complied with both ASTM D6751 and EN 14214 specifications. The values of Cp and TD increased and decreased respectively with biodiesel percent in fuel in a quadratic manner. The TC and Pr were correlated with the biodiesel fraction through linear regressions. The brake power, brake torque, and brake specific fuel consumption were observed to increase with an increase in biodiesel percentage level. The determined performance characteristics of B22.5, which is above B20 as recommended for powered diesel engine gave a good improvement in terms of BTE and Pr making FIWOME based biodiesel a viable substitute for fossil diesel.</abstract><cop>Kidlington</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.fuel.2020.119049</doi><orcidid>https://orcid.org/0000-0003-2961-6186</orcidid></addata></record>
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subjects Artificial Neural Network
Artificial neural networks
Biodiesel
Biodiesel fuels
Biofuels
Brakes
Combustion
Diesel
Diesel engines
Diesel fuels
Food industry
Fuel consumption
Heat transfer
Industrial wastes
Modelling
Neural networks
Oil wastes
Optimization
Power consumption
Prandtl number
Prediction
Reaction time
Renewable energy
Response surface methodology
Root-mean-square errors
Specific heat
Standard error
Thermal conductivity
Thermal diffusivity
Thermodynamic efficiency
Thermophysical properties
Torque
Transport properties
title Prandtl number of optimum biodiesel from food industrial waste oil and diesel fuel blend for diesel engine
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