A hybrid statistical-machine learning approach for experimental analysis of biogas production in a waste to energy plant using a vacuum evaporator systems
Anaerobic digestion offers a promising avenue for sustainable energy generation, waste management while producing biogas. Nonetheless, it yields a liquid by product known as digestate, necessitating treatment to prevent environmental harm and optimize resource utilization. However, improper operatio...
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Elsevier
2025-09-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025025204 |
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| author | Vakkar Ali Praveen Pachauri Azhar Equbal Osama Khan Mohd Parvez Haidar Howari Taufique Ahamad Ashok Kumar Yadav Brahmdeo Yadav |
| author_facet | Vakkar Ali Praveen Pachauri Azhar Equbal Osama Khan Mohd Parvez Haidar Howari Taufique Ahamad Ashok Kumar Yadav Brahmdeo Yadav |
| author_sort | Vakkar Ali |
| collection | DOAJ |
| description | Anaerobic digestion offers a promising avenue for sustainable energy generation, waste management while producing biogas. Nonetheless, it yields a liquid by product known as digestate, necessitating treatment to prevent environmental harm and optimize resource utilization. However, improper operation of these systems can lead to cavitation, which can cause significant equipment damage and lower production of biogas and bio-hydrogen. This study addresses a critical research gap by investigating the influence of key operating parameters as pressure (P), temperature (T), and flow rate (FR) on cavitation phenomena within vacuum evaporators, which significantly impact system durability during large-scale digestate treatment. Optimizing these parameters while mitigating cavitation effects improves energy efficiency, prolonged equipment lifespan, and reliable operation of vacuum evaporators in large-scale biomass digestate treatment systems. k-means machine learning clustering validated by statistical modelling combined methodology is used for this analysis. From the clustering analysis, the optimal setting conditions for cavitation free system came out to be T = 70 ⁰C, P = 85 bar and FR = 10 kg/s. Also, the percentage errors came out to be minimum for machine learning model of around 5.3 %, 5 % and 4.7 % for energy efficiency, equipment durability and process throughput respectively. The model achieved dominance over others with a desirability score of 0.96. Dataset number 8 performed the best as compared to other datasets, falling into the optimal (best) cluster, with energy efficiency of 80 %, equipment durability of 18 months, and process throughput of 200 L/hr. |
| format | Article |
| id | doaj-art-8a4bd1f676b840d5910eb2231b8341d2 |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-8a4bd1f676b840d5910eb2231b8341d22025-08-20T03:32:04ZengElsevierResults in Engineering2590-12302025-09-012710645110.1016/j.rineng.2025.106451A hybrid statistical-machine learning approach for experimental analysis of biogas production in a waste to energy plant using a vacuum evaporator systemsVakkar Ali0Praveen Pachauri1Azhar Equbal2Osama Khan3Mohd Parvez4Haidar Howari5Taufique Ahamad6Ashok Kumar Yadav7Brahmdeo Yadav8Department of Mechanical and Industrial Engineering, College of Engineering, Majmaah University, Al-Majmaah 11952, Saudi ArabiaGovernment Polytechnic Siwan, Department of Science, Technology and Technical Education, Government of Bihar, Patna, Bihar, IndiaDepartment of Mechanical Engineering, Jamia Millia Islamia, New Delhi 110025, IndiaDepartment of Mechanical Engineering, Jamia Millia Islamia, New Delhi 110025, India; Corresponding author.Department of Mechanical Engineering, Al-Falah University, Faridabad, Haryana 121004, IndiaDepartment of Physics, College of Science, Qassim University, Buraidah 51452, Al-Qassim, Saudi ArabiaDepartment of Mechanical Engineering, Al-Falah University, Faridabad, Haryana 121004, IndiaSymbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, IndiaDepartment of Civil Engineering, BIT Sindri, Dhanbad, Jharkhand, IndiaAnaerobic digestion offers a promising avenue for sustainable energy generation, waste management while producing biogas. Nonetheless, it yields a liquid by product known as digestate, necessitating treatment to prevent environmental harm and optimize resource utilization. However, improper operation of these systems can lead to cavitation, which can cause significant equipment damage and lower production of biogas and bio-hydrogen. This study addresses a critical research gap by investigating the influence of key operating parameters as pressure (P), temperature (T), and flow rate (FR) on cavitation phenomena within vacuum evaporators, which significantly impact system durability during large-scale digestate treatment. Optimizing these parameters while mitigating cavitation effects improves energy efficiency, prolonged equipment lifespan, and reliable operation of vacuum evaporators in large-scale biomass digestate treatment systems. k-means machine learning clustering validated by statistical modelling combined methodology is used for this analysis. From the clustering analysis, the optimal setting conditions for cavitation free system came out to be T = 70 ⁰C, P = 85 bar and FR = 10 kg/s. Also, the percentage errors came out to be minimum for machine learning model of around 5.3 %, 5 % and 4.7 % for energy efficiency, equipment durability and process throughput respectively. The model achieved dominance over others with a desirability score of 0.96. Dataset number 8 performed the best as compared to other datasets, falling into the optimal (best) cluster, with energy efficiency of 80 %, equipment durability of 18 months, and process throughput of 200 L/hr.http://www.sciencedirect.com/science/article/pii/S2590123025025204Hydrogen storageEnergy efficiencyMachine learningVacuum evaporatorStatistical analysis |
| spellingShingle | Vakkar Ali Praveen Pachauri Azhar Equbal Osama Khan Mohd Parvez Haidar Howari Taufique Ahamad Ashok Kumar Yadav Brahmdeo Yadav A hybrid statistical-machine learning approach for experimental analysis of biogas production in a waste to energy plant using a vacuum evaporator systems Results in Engineering Hydrogen storage Energy efficiency Machine learning Vacuum evaporator Statistical analysis |
| title | A hybrid statistical-machine learning approach for experimental analysis of biogas production in a waste to energy plant using a vacuum evaporator systems |
| title_full | A hybrid statistical-machine learning approach for experimental analysis of biogas production in a waste to energy plant using a vacuum evaporator systems |
| title_fullStr | A hybrid statistical-machine learning approach for experimental analysis of biogas production in a waste to energy plant using a vacuum evaporator systems |
| title_full_unstemmed | A hybrid statistical-machine learning approach for experimental analysis of biogas production in a waste to energy plant using a vacuum evaporator systems |
| title_short | A hybrid statistical-machine learning approach for experimental analysis of biogas production in a waste to energy plant using a vacuum evaporator systems |
| title_sort | hybrid statistical machine learning approach for experimental analysis of biogas production in a waste to energy plant using a vacuum evaporator systems |
| topic | Hydrogen storage Energy efficiency Machine learning Vacuum evaporator Statistical analysis |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025025204 |
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