Predictive Modeling of Biogas Production Using Machine Learning

Pournima Gaikwad, Archit Chavan, Sunny Ghodekar, Darshan Marale and Dr. Hemlata Karne
Additional contact information
Pournima Gaikwad: Dept. Chemical Engineering Vishwakarma Institute of Technology
Archit Chavan: Dept. Chemical Engineering Vishwakarma Institute of Technology
Sunny Ghodekar: Dept. Chemical Engineering Vishwakarma Institute of Technology
Darshan Marale: Dept. Chemical Engineering Vishwakarma Institute of Technology
Dr. Hemlata Karne: Dept. Chemical Engineering Vishwakarma Institute of Technology

International Journal of Latest Technology in Engineering, Management & Applied Science, 2025, vol. 14, issue 5, 54-61

Abstract: This project focuses on predictive modeling of biogas production using machine learning to improve efficiency, reliability, and scalability. The dataset, sourced from Professor Jackson Milano’s research at Universidade Positivo, spans 14 months of continuous monitoring on a ranch in southern Brazil, using cow manure in four biodigester configurations. The data was cleaned and preprocessed, and machine learning algorithms such as Linear Regression, XGBoost, LightGBM, Random Forest, and TensorFlow were used to develop models for estimating biogas yield. The iterative training process ensured high predictive accuracy. A user-friendly web interface was developed to allow real-time interaction with the model, enabling users to input parameters and receive biogas output predictions. This project showcases the potential of machine learning in optimizing renewable energy systems, promoting sustainability, and smarter energy management.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.ijltemas.in/DigitalLibrary/Vol.14Issue5/54-61.pdf (application/pdf)
https://www.ijltemas.in/papers/volume-14-issue-5/54-61.html (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bjb:journl:v:14:y:2025:i:5:p:54-61

Access Statistics for this article

International Journal of Latest Technology in Engineering, Management & Applied Science is currently edited by Dr. Pawan Verma

More articles in International Journal of Latest Technology in Engineering, Management & Applied Science from International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS)
Bibliographic data for series maintained by Dr. Pawan Verma ().