Applied Chemical Engineering

Machine Learning Strategies for Enhancing Syngas Quality, Biofuel Stability, and Air Pollution Control in Bio-Energy Plants

Sonali Shrikant Patil

Department of Mechatronics Engineering, Marathwada MitraMandal's Institute of Technology, Pune – 411047, SPPU, Pune, Maharashtra, India.

Snehal Mayur Banarase

Department of Civil Engineering, Dr. D. Y. Patil Institute of Technology, Sant Tukaram Nagar, Pimpri, Pune – 411018, Maharashtra, India; Dnyaan Prasad Global University (DPGU), School of Technology and Research — Dr. D. Y. Patil Unitech Society, Sant Tukaram Nagar, Pimpri, Pune – 411018, Maharashtra, India

Dinesh Keloth kaithari

Department of Mechanical and Industrial Engineering, College of Engineering, National University of Science and Technology, Muscat, Oman.

N. Bharathiraja

Biomedical Engineering Department, Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College, Chennai – 600055, Tamil Nadu, India.

Santosh Bhauso Takale

Department of Computer Applications, Vishwakarma University, Laxminagar, Kondhwa (Bk.), Pune – 411048, Maharashtra, India.

Pratik V. Lepse

Ramdeobaba University, Ramdeo Tekdi, Katol Road, Nagpur – 440013, Maharashtra, India.

Pushparaj Sunil Warke

Department of Mechanical Engineering, Dr. D. Y. Patil Institute of Technology, Sant Tukaram Nagar, Pimpri, Pune – 411018, Maharashtra, India ;Dnyaan Prasad Global University (DPGU), School of Technology and Research — Dr. D. Y. Patil Unitech Society, Sant Tukaram Nagar, Pimpri, Pune – 411018, Maharashtra, India

Muralidhar Ingale

Department of Mechanical Engineering, Dr. D. Y. Patil Institute of Technology, Sant Tukaram Nagar, Pimpri, Pune – 411018, Maharashtra, India ;Dnyaan Prasad Global University (DPGU), School of Technology and Research — Dr. D. Y. Patil Unitech Society, Sant Tukaram Nagar, Pimpri, Pune – 411018, Maharashtra, India

Anant Sidhappa Kurhade

Department of Mechanical Engineering, Dr. D. Y. Patil Institute of Technology, Sant Tukaram Nagar, Pimpri, Pune – 411018, Maharashtra, India ;Dnyaan Prasad Global University (DPGU), School of Technology and Research — Dr. D. Y. Patil Unitech Society, Sant Tukaram Nagar, Pimpri, Pune – 411018, Maharashtra, India

DOI: https://doi.org/10.59429/ace.v9i1.5880

Keywords: Air Pollution Control, Bio-Energy Plants, Biofuel Stability, Machine Learning, Syngas Quality

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Abstract

Bio-energy plants play a key role in the transition toward low-carbon energy systems, yet their large-scale deployment is constrained by variability in syngas quality, biofuel instability during storage, and fluctuating air pollutant emissions. This review examines how machine learning (ML) methods support improved decision-making across bio-energy value chains by linking multi-source data with predictive and adaptive control strategies. The study synthesizes recent advances in ML-based modelling for syngas composition prediction, biofuel stability assessment, and real-time emission monitoring and mitigation. Emphasis is placed on uncertainty-aware models and hybrid approaches that combine data-driven learning with process knowledge to address feedstock heterogeneity and dynamic operating conditions. The findings show that ML enhances operational efficiency, supports cleaner production, and improves system reliability by enabling proactive control rather than reactive adjustments. From a sustainability perspective, these outcomes directly contribute to SDG 7 (Affordable and Clean Energy) through higher efficiency and reliability of bio-energy systems, SDG 9 (Industry, Innovation, and Infrastructure) by promoting intelligent and resilient industrial processes, SDG 12 (Responsible Consumption and Production) via optimized resource use and reduced waste, and SDG 13 (Climate Action) through lower emissions and improved carbon performance. Overall, the review highlights ML as a practical decision-support tool for industry and policy stakeholders seeking resilient, data-driven pathways toward sustainable bio-energy deployment.

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[102]. Kurhade AS, Waware SY, Bhambare PS, Biradar R, Yadav RS, Patil VN. A comprehensive study on Calophyllum inophyllum biodiesel and dimethyl carbonate blends: performance optimization and emission control in diesel engines. J Mines Met Fuels. 2024;72(5):499-507. https://doi.org/10.18311/jmmf/2024/45188

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[109]. Rami Reddy S, Murali G, Dhana Raju V. Assessment of diethyl ether as a fuel additive on diesel engine characteristics powered with waste mango seed biodiesel blend. Int J Ambient Energy. 2022 Dec 31;43(1):3365-76. https://doi.org/10.1080/01430750.2020.1824944

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