ANN and Multilayer-ELM based prediction of combustion, performance and emission characteristics of a diesel engine fuelled with Diesel-DTBP blends
Most of the earlier studies have used cetane improver (CI) with diesel engine fuels in order to compensate for the reduction in cetane number (CN) when biodiesel or alcohol fuels are added into neat diesel (ND) fuel. The studies which blended CI with ND are much limited in available literature. The...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-08-01
|
| Series: | Case Studies in Thermal Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214157X25005830 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849730073186271232 |
|---|---|
| author | Hiren Dave Vinay Vakharia Hitesh Panchal Md Irfanul Haque Siddiqui Dan Dobrotă |
| author_facet | Hiren Dave Vinay Vakharia Hitesh Panchal Md Irfanul Haque Siddiqui Dan Dobrotă |
| author_sort | Hiren Dave |
| collection | DOAJ |
| description | Most of the earlier studies have used cetane improver (CI) with diesel engine fuels in order to compensate for the reduction in cetane number (CN) when biodiesel or alcohol fuels are added into neat diesel (ND) fuel. The studies which blended CI with ND are much limited in available literature. The presented work investigates diesel engine combustion, performance and emission characteristics using ND blended with CI. Di-tert-butyl peroxide (DTBP) was used as CI and blended with ND in proportions of 0.7 % and 1.5 % by volume and blends were designated as A1 and A2 respectively. The experiments were carried out at a fixed engine load of 80 % and three different engine speeds of 1600 rpm, 2000 rpm and 2400 rpm respectively. Two machine learning (ML) models: Artificial Neural Network (ANN) and Multilayer Extreme Learning Machine (MELM) were developed to predict the key engine characteristics such as brake specific fuel consumption (BSFC), maximum cylinder pressure (Pmax), smoke emissions and nitrogen oxides (NOx) emissions. The experimental results showed that increasing engine speed deteriorates the overall combustion process and impacts fuel consumption as well as smoke emissions negatively. BSFC and smoke emissions were increased by 14.83 % and 28.95 % when speed increased from 1600 rpm to 2400 rpm. Moreover, ML models achieved least error metrics for all three key engine performance metrics. The prediction results validate the robustness of ANN and MELM in terms of capturing dynamics in predicting engine characteristics and emphasize its potential for estimation of engine performance and emissions. |
| format | Article |
| id | doaj-art-edab95ec58a84a9785fc69d48641afd6 |
| institution | DOAJ |
| issn | 2214-157X |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Thermal Engineering |
| spelling | doaj-art-edab95ec58a84a9785fc69d48641afd62025-08-20T03:08:59ZengElsevierCase Studies in Thermal Engineering2214-157X2025-08-017210632310.1016/j.csite.2025.106323ANN and Multilayer-ELM based prediction of combustion, performance and emission characteristics of a diesel engine fuelled with Diesel-DTBP blendsHiren Dave0Vinay Vakharia1Hitesh Panchal2Md Irfanul Haque Siddiqui3Dan Dobrotă4Department of Mechanical Engineering, School of Technology, Pandit Deendayal Energy University, Gandhinagar, Gujarat, 382007, IndiaDepartment of Mechanical Engineering, School of Technology, Pandit Deendayal Energy University, Gandhinagar, Gujarat, 382007, India; Corresponding author.Department of Mechanical Engineering, Government Engineering College Patan, Gujarat, IndiaDepartment of Mechanical Engineering, College of Engineering, King Saud University, Riyadh, 12372, Saudi ArabiaFaculty of Engineering, Department of Industrial Engineering and Management, Lucian Blaga University of Sibiu, Sibiu, 550024, RomaniaMost of the earlier studies have used cetane improver (CI) with diesel engine fuels in order to compensate for the reduction in cetane number (CN) when biodiesel or alcohol fuels are added into neat diesel (ND) fuel. The studies which blended CI with ND are much limited in available literature. The presented work investigates diesel engine combustion, performance and emission characteristics using ND blended with CI. Di-tert-butyl peroxide (DTBP) was used as CI and blended with ND in proportions of 0.7 % and 1.5 % by volume and blends were designated as A1 and A2 respectively. The experiments were carried out at a fixed engine load of 80 % and three different engine speeds of 1600 rpm, 2000 rpm and 2400 rpm respectively. Two machine learning (ML) models: Artificial Neural Network (ANN) and Multilayer Extreme Learning Machine (MELM) were developed to predict the key engine characteristics such as brake specific fuel consumption (BSFC), maximum cylinder pressure (Pmax), smoke emissions and nitrogen oxides (NOx) emissions. The experimental results showed that increasing engine speed deteriorates the overall combustion process and impacts fuel consumption as well as smoke emissions negatively. BSFC and smoke emissions were increased by 14.83 % and 28.95 % when speed increased from 1600 rpm to 2400 rpm. Moreover, ML models achieved least error metrics for all three key engine performance metrics. The prediction results validate the robustness of ANN and MELM in terms of capturing dynamics in predicting engine characteristics and emphasize its potential for estimation of engine performance and emissions.http://www.sciencedirect.com/science/article/pii/S2214157X25005830Diesel engine combustionCetane improverPredictionArtificial neural networkMultilayer ELM |
| spellingShingle | Hiren Dave Vinay Vakharia Hitesh Panchal Md Irfanul Haque Siddiqui Dan Dobrotă ANN and Multilayer-ELM based prediction of combustion, performance and emission characteristics of a diesel engine fuelled with Diesel-DTBP blends Case Studies in Thermal Engineering Diesel engine combustion Cetane improver Prediction Artificial neural network Multilayer ELM |
| title | ANN and Multilayer-ELM based prediction of combustion, performance and emission characteristics of a diesel engine fuelled with Diesel-DTBP blends |
| title_full | ANN and Multilayer-ELM based prediction of combustion, performance and emission characteristics of a diesel engine fuelled with Diesel-DTBP blends |
| title_fullStr | ANN and Multilayer-ELM based prediction of combustion, performance and emission characteristics of a diesel engine fuelled with Diesel-DTBP blends |
| title_full_unstemmed | ANN and Multilayer-ELM based prediction of combustion, performance and emission characteristics of a diesel engine fuelled with Diesel-DTBP blends |
| title_short | ANN and Multilayer-ELM based prediction of combustion, performance and emission characteristics of a diesel engine fuelled with Diesel-DTBP blends |
| title_sort | ann and multilayer elm based prediction of combustion performance and emission characteristics of a diesel engine fuelled with diesel dtbp blends |
| topic | Diesel engine combustion Cetane improver Prediction Artificial neural network Multilayer ELM |
| url | http://www.sciencedirect.com/science/article/pii/S2214157X25005830 |
| work_keys_str_mv | AT hirendave annandmultilayerelmbasedpredictionofcombustionperformanceandemissioncharacteristicsofadieselenginefuelledwithdieseldtbpblends AT vinayvakharia annandmultilayerelmbasedpredictionofcombustionperformanceandemissioncharacteristicsofadieselenginefuelledwithdieseldtbpblends AT hiteshpanchal annandmultilayerelmbasedpredictionofcombustionperformanceandemissioncharacteristicsofadieselenginefuelledwithdieseldtbpblends AT mdirfanulhaquesiddiqui annandmultilayerelmbasedpredictionofcombustionperformanceandemissioncharacteristicsofadieselenginefuelledwithdieseldtbpblends AT dandobrota annandmultilayerelmbasedpredictionofcombustionperformanceandemissioncharacteristicsofadieselenginefuelledwithdieseldtbpblends |