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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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 |
doi_str_mv | 10.1016/j.fuel.2020.119049 |
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fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2479481894</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0016236120320457</els_id><sourcerecordid>2479481894</sourcerecordid><originalsourceid>FETCH-LOGICAL-c328t-3838ba1f610da83a2e9c002b5e224c6bd7bb573bde2f931407e3f62712238ed53</originalsourceid><addsrcrecordid>eNp9kEtLxDAUhYMoOI7-AVcB1x3zaJsU3MjgCwZ0oeuQNDeS0jZj0ir-ezNUt64uHL5z7r0HoUtKNpTQ-rrbuBn6DSMsC7QhZXOEVlQKXgha8WO0IpkqGK_pKTpLqSOECFmVK9S9RD3aqcfjPBiIODgc9pMf5gEbH6yHBD12MQzYhWCxH-2cpuh1j790mgAH3-McgP_IfAU2PWTFhfinwvjuRzhHJ073CS5-5xq93d-9bh-L3fPD0_Z2V7ScyangkkujqaspsVpyzaBpCWGmAsbKtjZWGFMJbiww13BaEgHc1UxQxrgEW_E1ulpy9zF8zJAm1YU5jnmlYqVoSkllU2aKLVQbQ0oRnNpHP-j4rShRh05Vpw7fqEOnauk0m24WE-T7Pz1ElVoPYwvWR2gnZYP_z_4DSf-ARQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2479481894</pqid></control><display><type>article</type><title>Prandtl number of optimum biodiesel from food industrial waste oil and diesel fuel blend for diesel engine</title><source>Elsevier ScienceDirect Journals Complete</source><creator>David Samuel, Olusegun ; Adekojo Waheed, M. ; Taheri-Garavand, A. ; Verma, Tikendra Nath ; Dairo, Olawale U. ; Bolaji, Bukola O. ; Afzal, Asif</creator><creatorcontrib>David Samuel, Olusegun ; Adekojo Waheed, M. ; Taheri-Garavand, A. ; Verma, Tikendra Nath ; Dairo, Olawale U. ; Bolaji, Bukola O. ; Afzal, Asif</creatorcontrib><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. 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. 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><subject>Artificial Neural Network</subject><subject>Artificial neural networks</subject><subject>Biodiesel</subject><subject>Biodiesel fuels</subject><subject>Biofuels</subject><subject>Brakes</subject><subject>Combustion</subject><subject>Diesel</subject><subject>Diesel engines</subject><subject>Diesel fuels</subject><subject>Food industry</subject><subject>Fuel consumption</subject><subject>Heat transfer</subject><subject>Industrial wastes</subject><subject>Modelling</subject><subject>Neural networks</subject><subject>Oil wastes</subject><subject>Optimization</subject><subject>Power consumption</subject><subject>Prandtl number</subject><subject>Prediction</subject><subject>Reaction time</subject><subject>Renewable energy</subject><subject>Response surface methodology</subject><subject>Root-mean-square errors</subject><subject>Specific heat</subject><subject>Standard error</subject><subject>Thermal conductivity</subject><subject>Thermal diffusivity</subject><subject>Thermodynamic efficiency</subject><subject>Thermophysical properties</subject><subject>Torque</subject><subject>Transport properties</subject><issn>0016-2361</issn><issn>1873-7153</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp9kEtLxDAUhYMoOI7-AVcB1x3zaJsU3MjgCwZ0oeuQNDeS0jZj0ir-ezNUt64uHL5z7r0HoUtKNpTQ-rrbuBn6DSMsC7QhZXOEVlQKXgha8WO0IpkqGK_pKTpLqSOECFmVK9S9RD3aqcfjPBiIODgc9pMf5gEbH6yHBD12MQzYhWCxH-2cpuh1j790mgAH3-McgP_IfAU2PWTFhfinwvjuRzhHJ073CS5-5xq93d-9bh-L3fPD0_Z2V7ScyangkkujqaspsVpyzaBpCWGmAsbKtjZWGFMJbiww13BaEgHc1UxQxrgEW_E1ulpy9zF8zJAm1YU5jnmlYqVoSkllU2aKLVQbQ0oRnNpHP-j4rShRh05Vpw7fqEOnauk0m24WE-T7Pz1ElVoPYwvWR2gnZYP_z_4DSf-ARQ</recordid><startdate>20210201</startdate><enddate>20210201</enddate><creator>David Samuel, Olusegun</creator><creator>Adekojo Waheed, M.</creator><creator>Taheri-Garavand, A.</creator><creator>Verma, Tikendra Nath</creator><creator>Dairo, Olawale U.</creator><creator>Bolaji, Bukola O.</creator><creator>Afzal, Asif</creator><general>Elsevier Ltd</general><general>Elsevier BV</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QO</scope><scope>7QQ</scope><scope>7SC</scope><scope>7SE</scope><scope>7SP</scope><scope>7SR</scope><scope>7T7</scope><scope>7TA</scope><scope>7TB</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>C1K</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>H8G</scope><scope>JG9</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P64</scope><orcidid>https://orcid.org/0000-0003-2961-6186</orcidid></search><sort><creationdate>20210201</creationdate><title>Prandtl number of optimum biodiesel from food industrial waste oil and diesel fuel blend for diesel engine</title><author>David Samuel, Olusegun ; Adekojo Waheed, M. ; Taheri-Garavand, A. ; Verma, Tikendra Nath ; Dairo, Olawale U. ; Bolaji, Bukola O. ; Afzal, Asif</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c328t-3838ba1f610da83a2e9c002b5e224c6bd7bb573bde2f931407e3f62712238ed53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Artificial Neural Network</topic><topic>Artificial neural networks</topic><topic>Biodiesel</topic><topic>Biodiesel fuels</topic><topic>Biofuels</topic><topic>Brakes</topic><topic>Combustion</topic><topic>Diesel</topic><topic>Diesel engines</topic><topic>Diesel fuels</topic><topic>Food industry</topic><topic>Fuel consumption</topic><topic>Heat transfer</topic><topic>Industrial wastes</topic><topic>Modelling</topic><topic>Neural networks</topic><topic>Oil wastes</topic><topic>Optimization</topic><topic>Power consumption</topic><topic>Prandtl number</topic><topic>Prediction</topic><topic>Reaction time</topic><topic>Renewable energy</topic><topic>Response surface methodology</topic><topic>Root-mean-square errors</topic><topic>Specific heat</topic><topic>Standard error</topic><topic>Thermal conductivity</topic><topic>Thermal diffusivity</topic><topic>Thermodynamic efficiency</topic><topic>Thermophysical properties</topic><topic>Torque</topic><topic>Transport properties</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><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><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Copper Technical Reference Library</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Biotechnology and BioEngineering Abstracts</collection><jtitle>Fuel (Guildford)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>David Samuel, Olusegun</au><au>Adekojo Waheed, M.</au><au>Taheri-Garavand, A.</au><au>Verma, Tikendra Nath</au><au>Dairo, Olawale U.</au><au>Bolaji, Bukola O.</au><au>Afzal, Asif</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Prandtl number of optimum biodiesel from food industrial waste oil and diesel fuel blend for diesel engine</atitle><jtitle>Fuel (Guildford)</jtitle><date>2021-02-01</date><risdate>2021</risdate><volume>285</volume><spage>119049</spage><pages>119049-</pages><artnum>119049</artnum><issn>0016-2361</issn><eissn>1873-7153</eissn><abstract>[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. 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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