Loading…

Prediction and optimization of diesel engine characteristics for various fuel injection timing: Operated by third generation green fuel with alumina nano additive

•Azolla pinnata algae were utilized to produce green biodiesel.•ANN & RSM models were used for predicting the CI Engine characteristics.•The desirability approach was applied for optimizing the CI Engine characteristics.•The effects of Alumina nano additive dosed green biodiesel blend for variou...

Full description

Saved in:
Bibliographic Details
Published in:Sustainable energy technologies and assessments 2022-10, Vol.53, p.102751, Article 102751
Main Authors: Sankar, Prabakaran, Thangavelu, Mohanraj, Moorthy, Venkatesan, Mahaboob Subhani, Shaik, Manimaran, Rajayokkiam
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c300t-1015e7e5036c7c7c7be046695ce38b8b6dd15a4bb3cff3ae717948dc91f7b35c3
cites cdi_FETCH-LOGICAL-c300t-1015e7e5036c7c7c7be046695ce38b8b6dd15a4bb3cff3ae717948dc91f7b35c3
container_end_page
container_issue
container_start_page 102751
container_title Sustainable energy technologies and assessments
container_volume 53
creator Sankar, Prabakaran
Thangavelu, Mohanraj
Moorthy, Venkatesan
Mahaboob Subhani, Shaik
Manimaran, Rajayokkiam
description •Azolla pinnata algae were utilized to produce green biodiesel.•ANN & RSM models were used for predicting the CI Engine characteristics.•The desirability approach was applied for optimizing the CI Engine characteristics.•The effects of Alumina nano additive dosed green biodiesel blend for various fuel injection timing were analysed. Azolla pinnata is a macroalgae commonly known as mosquito weed that is widely utilized for biomass production and biodiesel because of its capacity to thrive in low nitrogen environments. In this study, the various concentration (25 ppm, 50 ppm and 75 ppm) of alumina nanoparticles dosed Azolla pinnata biodiesel blend performance and emission parameters were analyzed on a single-cylinder compression ignition engine for different load conditions and fuel injection timings. Using the experimental results, the artificial neural network and response surface methodology regression models were developed and compared its prediction capabilities of output parameters as brake thermal efficiency, brake specific fuel consumption, hydrocarbon, carbon monoxide, nitrogen oxide and smoke. The identified best model was applied to optimize the input parameters of nano additive, engine load and fuel injection timing. The attained R2 (0.9992) and root mean square error 2.437 values exposed that the developed response surface methodology model was more accurate than artificial neural network. The best possible responses provided through the desirability function approach were brake thermal efficiency (19.05 %), brake specific fuel consumption (785.66 g/kWh), hydrocarbon (25.18 ppm), carbon monoxide (0.0576 %), nitrogen oxide (443.79 ppm), smoke (7.48 %) respectively for optimized working factors of nano additive (44 ppm), engine load (1.368 kW) and fuel injection timing (26° before top dead centre). The overall error percentage calculated through the validation study was observed to be under 5 %. The developed response surface methodology model produced good results, which are helpful in predicting and optimizing engine emissions and performance parameters.
doi_str_mv 10.1016/j.seta.2022.102751
format article
fullrecord <record><control><sourceid>elsevier_cross</sourceid><recordid>TN_cdi_crossref_primary_10_1016_j_seta_2022_102751</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S2213138822007998</els_id><sourcerecordid>S2213138822007998</sourcerecordid><originalsourceid>FETCH-LOGICAL-c300t-1015e7e5036c7c7c7be046695ce38b8b6dd15a4bb3cff3ae717948dc91f7b35c3</originalsourceid><addsrcrecordid>eNp9kMtOAyEUhlloYlP7Aq54gakwzK3GjWm8JU3qQteEgTPT07TQAK2pj-OTyjiuDQvIn_P9OXyE3HA254xXt9t5gKjmOcvzFOR1yS_IJM-5yLhomisyC2HLGOOi4gVnE_L95sGgjugsVdZQd4i4xy_1G7iOGoQAOwq2RwtUb5RXOoLHEFEH2jlPT8qjO6b3Mc2h3cJYNtTY_o6uD-BVBEPbM40b9Ib2YIdoGOo9gB3JT4wbqnbHRClqlXVUGYMRT3BNLju1CzD7u6fk4-nxffmSrdbPr8uHVaYFYzFL3y-hhpKJStfDaYEVVbUoNYimbdrKGF6qom2F7jqhoOb1omiMXvCubkWpxZTkY6_2LgQPnTx43Ct_lpzJQa7cykGuHOTKUW6C7kcI0mYnBC-DRrA6SfXJhDQO_8N_AELUicQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Prediction and optimization of diesel engine characteristics for various fuel injection timing: Operated by third generation green fuel with alumina nano additive</title><source>ScienceDirect Journals</source><creator>Sankar, Prabakaran ; Thangavelu, Mohanraj ; Moorthy, Venkatesan ; Mahaboob Subhani, Shaik ; Manimaran, Rajayokkiam</creator><creatorcontrib>Sankar, Prabakaran ; Thangavelu, Mohanraj ; Moorthy, Venkatesan ; Mahaboob Subhani, Shaik ; Manimaran, Rajayokkiam</creatorcontrib><description>•Azolla pinnata algae were utilized to produce green biodiesel.•ANN &amp; RSM models were used for predicting the CI Engine characteristics.•The desirability approach was applied for optimizing the CI Engine characteristics.•The effects of Alumina nano additive dosed green biodiesel blend for various fuel injection timing were analysed. Azolla pinnata is a macroalgae commonly known as mosquito weed that is widely utilized for biomass production and biodiesel because of its capacity to thrive in low nitrogen environments. In this study, the various concentration (25 ppm, 50 ppm and 75 ppm) of alumina nanoparticles dosed Azolla pinnata biodiesel blend performance and emission parameters were analyzed on a single-cylinder compression ignition engine for different load conditions and fuel injection timings. Using the experimental results, the artificial neural network and response surface methodology regression models were developed and compared its prediction capabilities of output parameters as brake thermal efficiency, brake specific fuel consumption, hydrocarbon, carbon monoxide, nitrogen oxide and smoke. The identified best model was applied to optimize the input parameters of nano additive, engine load and fuel injection timing. The attained R2 (0.9992) and root mean square error 2.437 values exposed that the developed response surface methodology model was more accurate than artificial neural network. The best possible responses provided through the desirability function approach were brake thermal efficiency (19.05 %), brake specific fuel consumption (785.66 g/kWh), hydrocarbon (25.18 ppm), carbon monoxide (0.0576 %), nitrogen oxide (443.79 ppm), smoke (7.48 %) respectively for optimized working factors of nano additive (44 ppm), engine load (1.368 kW) and fuel injection timing (26° before top dead centre). The overall error percentage calculated through the validation study was observed to be under 5 %. The developed response surface methodology model produced good results, which are helpful in predicting and optimizing engine emissions and performance parameters.</description><identifier>ISSN: 2213-1388</identifier><identifier>DOI: 10.1016/j.seta.2022.102751</identifier><language>eng</language><publisher>Elsevier Ltd</publisher><subject>Artificial neural network ; Azolla pinnata algae ; Desirability approach ; Nano additive ; Response surface methodology ; Variable fuel injection timing</subject><ispartof>Sustainable energy technologies and assessments, 2022-10, Vol.53, p.102751, Article 102751</ispartof><rights>2022 Elsevier Ltd</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c300t-1015e7e5036c7c7c7be046695ce38b8b6dd15a4bb3cff3ae717948dc91f7b35c3</citedby><cites>FETCH-LOGICAL-c300t-1015e7e5036c7c7c7be046695ce38b8b6dd15a4bb3cff3ae717948dc91f7b35c3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27903,27904</link.rule.ids></links><search><creatorcontrib>Sankar, Prabakaran</creatorcontrib><creatorcontrib>Thangavelu, Mohanraj</creatorcontrib><creatorcontrib>Moorthy, Venkatesan</creatorcontrib><creatorcontrib>Mahaboob Subhani, Shaik</creatorcontrib><creatorcontrib>Manimaran, Rajayokkiam</creatorcontrib><title>Prediction and optimization of diesel engine characteristics for various fuel injection timing: Operated by third generation green fuel with alumina nano additive</title><title>Sustainable energy technologies and assessments</title><description>•Azolla pinnata algae were utilized to produce green biodiesel.•ANN &amp; RSM models were used for predicting the CI Engine characteristics.•The desirability approach was applied for optimizing the CI Engine characteristics.•The effects of Alumina nano additive dosed green biodiesel blend for various fuel injection timing were analysed. Azolla pinnata is a macroalgae commonly known as mosquito weed that is widely utilized for biomass production and biodiesel because of its capacity to thrive in low nitrogen environments. In this study, the various concentration (25 ppm, 50 ppm and 75 ppm) of alumina nanoparticles dosed Azolla pinnata biodiesel blend performance and emission parameters were analyzed on a single-cylinder compression ignition engine for different load conditions and fuel injection timings. Using the experimental results, the artificial neural network and response surface methodology regression models were developed and compared its prediction capabilities of output parameters as brake thermal efficiency, brake specific fuel consumption, hydrocarbon, carbon monoxide, nitrogen oxide and smoke. The identified best model was applied to optimize the input parameters of nano additive, engine load and fuel injection timing. The attained R2 (0.9992) and root mean square error 2.437 values exposed that the developed response surface methodology model was more accurate than artificial neural network. The best possible responses provided through the desirability function approach were brake thermal efficiency (19.05 %), brake specific fuel consumption (785.66 g/kWh), hydrocarbon (25.18 ppm), carbon monoxide (0.0576 %), nitrogen oxide (443.79 ppm), smoke (7.48 %) respectively for optimized working factors of nano additive (44 ppm), engine load (1.368 kW) and fuel injection timing (26° before top dead centre). The overall error percentage calculated through the validation study was observed to be under 5 %. The developed response surface methodology model produced good results, which are helpful in predicting and optimizing engine emissions and performance parameters.</description><subject>Artificial neural network</subject><subject>Azolla pinnata algae</subject><subject>Desirability approach</subject><subject>Nano additive</subject><subject>Response surface methodology</subject><subject>Variable fuel injection timing</subject><issn>2213-1388</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNp9kMtOAyEUhlloYlP7Aq54gakwzK3GjWm8JU3qQteEgTPT07TQAK2pj-OTyjiuDQvIn_P9OXyE3HA254xXt9t5gKjmOcvzFOR1yS_IJM-5yLhomisyC2HLGOOi4gVnE_L95sGgjugsVdZQd4i4xy_1G7iOGoQAOwq2RwtUb5RXOoLHEFEH2jlPT8qjO6b3Mc2h3cJYNtTY_o6uD-BVBEPbM40b9Ib2YIdoGOo9gB3JT4wbqnbHRClqlXVUGYMRT3BNLju1CzD7u6fk4-nxffmSrdbPr8uHVaYFYzFL3y-hhpKJStfDaYEVVbUoNYimbdrKGF6qom2F7jqhoOb1omiMXvCubkWpxZTkY6_2LgQPnTx43Ct_lpzJQa7cykGuHOTKUW6C7kcI0mYnBC-DRrA6SfXJhDQO_8N_AELUicQ</recordid><startdate>202210</startdate><enddate>202210</enddate><creator>Sankar, Prabakaran</creator><creator>Thangavelu, Mohanraj</creator><creator>Moorthy, Venkatesan</creator><creator>Mahaboob Subhani, Shaik</creator><creator>Manimaran, Rajayokkiam</creator><general>Elsevier Ltd</general><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>202210</creationdate><title>Prediction and optimization of diesel engine characteristics for various fuel injection timing: Operated by third generation green fuel with alumina nano additive</title><author>Sankar, Prabakaran ; Thangavelu, Mohanraj ; Moorthy, Venkatesan ; Mahaboob Subhani, Shaik ; Manimaran, Rajayokkiam</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c300t-1015e7e5036c7c7c7be046695ce38b8b6dd15a4bb3cff3ae717948dc91f7b35c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Artificial neural network</topic><topic>Azolla pinnata algae</topic><topic>Desirability approach</topic><topic>Nano additive</topic><topic>Response surface methodology</topic><topic>Variable fuel injection timing</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Sankar, Prabakaran</creatorcontrib><creatorcontrib>Thangavelu, Mohanraj</creatorcontrib><creatorcontrib>Moorthy, Venkatesan</creatorcontrib><creatorcontrib>Mahaboob Subhani, Shaik</creatorcontrib><creatorcontrib>Manimaran, Rajayokkiam</creatorcontrib><collection>CrossRef</collection><jtitle>Sustainable energy technologies and assessments</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Sankar, Prabakaran</au><au>Thangavelu, Mohanraj</au><au>Moorthy, Venkatesan</au><au>Mahaboob Subhani, Shaik</au><au>Manimaran, Rajayokkiam</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Prediction and optimization of diesel engine characteristics for various fuel injection timing: Operated by third generation green fuel with alumina nano additive</atitle><jtitle>Sustainable energy technologies and assessments</jtitle><date>2022-10</date><risdate>2022</risdate><volume>53</volume><spage>102751</spage><pages>102751-</pages><artnum>102751</artnum><issn>2213-1388</issn><abstract>•Azolla pinnata algae were utilized to produce green biodiesel.•ANN &amp; RSM models were used for predicting the CI Engine characteristics.•The desirability approach was applied for optimizing the CI Engine characteristics.•The effects of Alumina nano additive dosed green biodiesel blend for various fuel injection timing were analysed. Azolla pinnata is a macroalgae commonly known as mosquito weed that is widely utilized for biomass production and biodiesel because of its capacity to thrive in low nitrogen environments. In this study, the various concentration (25 ppm, 50 ppm and 75 ppm) of alumina nanoparticles dosed Azolla pinnata biodiesel blend performance and emission parameters were analyzed on a single-cylinder compression ignition engine for different load conditions and fuel injection timings. Using the experimental results, the artificial neural network and response surface methodology regression models were developed and compared its prediction capabilities of output parameters as brake thermal efficiency, brake specific fuel consumption, hydrocarbon, carbon monoxide, nitrogen oxide and smoke. The identified best model was applied to optimize the input parameters of nano additive, engine load and fuel injection timing. The attained R2 (0.9992) and root mean square error 2.437 values exposed that the developed response surface methodology model was more accurate than artificial neural network. The best possible responses provided through the desirability function approach were brake thermal efficiency (19.05 %), brake specific fuel consumption (785.66 g/kWh), hydrocarbon (25.18 ppm), carbon monoxide (0.0576 %), nitrogen oxide (443.79 ppm), smoke (7.48 %) respectively for optimized working factors of nano additive (44 ppm), engine load (1.368 kW) and fuel injection timing (26° before top dead centre). The overall error percentage calculated through the validation study was observed to be under 5 %. The developed response surface methodology model produced good results, which are helpful in predicting and optimizing engine emissions and performance parameters.</abstract><pub>Elsevier Ltd</pub><doi>10.1016/j.seta.2022.102751</doi></addata></record>
fulltext fulltext
identifier ISSN: 2213-1388
ispartof Sustainable energy technologies and assessments, 2022-10, Vol.53, p.102751, Article 102751
issn 2213-1388
language eng
recordid cdi_crossref_primary_10_1016_j_seta_2022_102751
source ScienceDirect Journals
subjects Artificial neural network
Azolla pinnata algae
Desirability approach
Nano additive
Response surface methodology
Variable fuel injection timing
title Prediction and optimization of diesel engine characteristics for various fuel injection timing: Operated by third generation green fuel with alumina nano additive
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-21T13%3A13%3A10IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-elsevier_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Prediction%20and%20optimization%20of%20diesel%20engine%20characteristics%20for%20various%20fuel%20injection%20timing:%20Operated%20by%20third%20generation%20green%20fuel%20with%20alumina%20nano%20additive&rft.jtitle=Sustainable%20energy%20technologies%20and%20assessments&rft.au=Sankar,%20Prabakaran&rft.date=2022-10&rft.volume=53&rft.spage=102751&rft.pages=102751-&rft.artnum=102751&rft.issn=2213-1388&rft_id=info:doi/10.1016/j.seta.2022.102751&rft_dat=%3Celsevier_cross%3ES2213138822007998%3C/elsevier_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c300t-1015e7e5036c7c7c7be046695ce38b8b6dd15a4bb3cff3ae717948dc91f7b35c3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true