Artificial intelligence-driven modeling of biodiesel production from fats, oils, and grease (FOG) with process optimization via particle swarm optimization
This study presents the design and optimization of a biodiesel production process, emphasizing the integration of machine learning (ML) models and process optimization techniques. The biodiesel production process involves multiple stages, including feedstock preparation, esterification, and transest...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-04-01
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| Series: | Energy Conversion and Management: X |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590174525001321 |
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| author | Badril Azhar Muhammad Ikhsan Taipabu Cries Avian Karthickeyan Viswanathan Wei Wu Raymond Lau |
| author_facet | Badril Azhar Muhammad Ikhsan Taipabu Cries Avian Karthickeyan Viswanathan Wei Wu Raymond Lau |
| author_sort | Badril Azhar |
| collection | DOAJ |
| description | This study presents the design and optimization of a biodiesel production process, emphasizing the integration of machine learning (ML) models and process optimization techniques. The biodiesel production process involves multiple stages, including feedstock preparation, esterification, and transesterification, with catalysts Amberlyst-15 and KOH used in continuous stirred-tank reactors (CSTRs). Sensitivity analysis reveals that high conversions of free fatty acids (94 %) and triglycerides (97 %) are achievable under optimized operating conditions. To enhance process efficiency, adjustments to reaction temperature, time, and methanol-to-oil ratios are proposed, resulting in lower energy consumption and material costs. A ML model evaluation, using various algorithms, identify XGBoost, Extra Trees, Gradient Boosting, LGBM, and Random Forest demonstrate the best performer for predicting process parameters, achieving an R2 value of nearly to 1. Particle Swarm Optimization (PSO) is then employed to optimize the selected ML model (XGBoost), leading to the identification of optimal input parameters for biodiesel production. The optimized process, combined with heat integration, reduces pre-heating energy requirements by 80.9 % and total heat duties by 19.9 %. The findings demonstrate the effectiveness of combining ML and optimization techniques to enhance biodiesel production efficiency while reducing costs and energy consumption. |
| format | Article |
| id | doaj-art-e93332f749904a9b82ac90c8a3b120e7 |
| institution | DOAJ |
| issn | 2590-1745 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Conversion and Management: X |
| spelling | doaj-art-e93332f749904a9b82ac90c8a3b120e72025-08-20T03:10:27ZengElsevierEnergy Conversion and Management: X2590-17452025-04-012610100010.1016/j.ecmx.2025.101000Artificial intelligence-driven modeling of biodiesel production from fats, oils, and grease (FOG) with process optimization via particle swarm optimizationBadril Azhar0Muhammad Ikhsan Taipabu1Cries Avian2Karthickeyan Viswanathan3Wei Wu4Raymond Lau5Department of Chemical Engineering, Institut Teknologi Sepuluh Nopember, Surabaya 60111, IndonesiaDepartment of Chemical Engineering, Pattimura University, Ambon 97134, IndonesiaDepartement of Electrical Engineering, Brawijaya University, IndonesiaDepartment of Mechanical Engineering, Dr. N.G.P. Institute of Technology, Coimbatore 641048, IndiaDepartment of Chemical Engineering, National Cheng Kung University, Tainan 70101, Taiwan; Academy of Innovative Semiconductor and Sustainable Manufacturing, National Cheng Kung University, Tainan 70101, Taiwan; Corresponding author.School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, 62 Nanyang Drive, Singapore 637459 SingaporeThis study presents the design and optimization of a biodiesel production process, emphasizing the integration of machine learning (ML) models and process optimization techniques. The biodiesel production process involves multiple stages, including feedstock preparation, esterification, and transesterification, with catalysts Amberlyst-15 and KOH used in continuous stirred-tank reactors (CSTRs). Sensitivity analysis reveals that high conversions of free fatty acids (94 %) and triglycerides (97 %) are achievable under optimized operating conditions. To enhance process efficiency, adjustments to reaction temperature, time, and methanol-to-oil ratios are proposed, resulting in lower energy consumption and material costs. A ML model evaluation, using various algorithms, identify XGBoost, Extra Trees, Gradient Boosting, LGBM, and Random Forest demonstrate the best performer for predicting process parameters, achieving an R2 value of nearly to 1. Particle Swarm Optimization (PSO) is then employed to optimize the selected ML model (XGBoost), leading to the identification of optimal input parameters for biodiesel production. The optimized process, combined with heat integration, reduces pre-heating energy requirements by 80.9 % and total heat duties by 19.9 %. The findings demonstrate the effectiveness of combining ML and optimization techniques to enhance biodiesel production efficiency while reducing costs and energy consumption.http://www.sciencedirect.com/science/article/pii/S2590174525001321Artificial intelligenceBiodiesel ProductionMachine learningParticle Swarm Optimization |
| spellingShingle | Badril Azhar Muhammad Ikhsan Taipabu Cries Avian Karthickeyan Viswanathan Wei Wu Raymond Lau Artificial intelligence-driven modeling of biodiesel production from fats, oils, and grease (FOG) with process optimization via particle swarm optimization Energy Conversion and Management: X Artificial intelligence Biodiesel Production Machine learning Particle Swarm Optimization |
| title | Artificial intelligence-driven modeling of biodiesel production from fats, oils, and grease (FOG) with process optimization via particle swarm optimization |
| title_full | Artificial intelligence-driven modeling of biodiesel production from fats, oils, and grease (FOG) with process optimization via particle swarm optimization |
| title_fullStr | Artificial intelligence-driven modeling of biodiesel production from fats, oils, and grease (FOG) with process optimization via particle swarm optimization |
| title_full_unstemmed | Artificial intelligence-driven modeling of biodiesel production from fats, oils, and grease (FOG) with process optimization via particle swarm optimization |
| title_short | Artificial intelligence-driven modeling of biodiesel production from fats, oils, and grease (FOG) with process optimization via particle swarm optimization |
| title_sort | artificial intelligence driven modeling of biodiesel production from fats oils and grease fog with process optimization via particle swarm optimization |
| topic | Artificial intelligence Biodiesel Production Machine learning Particle Swarm Optimization |
| url | http://www.sciencedirect.com/science/article/pii/S2590174525001321 |
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