Applied Chemical Engineering

Smart Biorefineries: Machine Learning for Process Control, Resource Utilization, and Emission Monitoring

Sonali Shrikant Patil

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

Madhuri Karad

Department of Electronics and Telecommunication 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

S. Manjula Gandhi

Department of Computing (Software Systems), Coimbatore Institute of Technology, Civil Aerodrome Post, Avinashi Road, Peelamedu, Coimbatore – 641014, Tamil Nadu, India

Mahesh Ganpat Bhong

Department of Mechanical Engineering, Indira College of Engineering and Management, Indira Chanakya Campus (ICC), Parandwadi, Pune – 410506, Maharashtra, India

Kiran Dattatray Devade

Department of Mechanical Engineering, Indira College of Engineering and Management, Indira Chanakya Campus (ICC), Parandwadi, Pune – 410506, Maharashtra, India

Vishakha Avinash Mahajan

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

Ghanasham Chandrakant Sarode

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

Ganesh Patil

Department of Electrical 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.v9i1.5874

Keywords: Bio-refinery; emission monitoring; machine learning; process control; resource optimization; sustainability

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Abstract

Bio-refineries are central to achieving a sustainable bioeconomy by converting renewable biomass into fuels, chemicals, and energy, directly supporting global climate and resource-efficiency targets. Their operation remains challenging due to nonlinear process behavior, feedstock variability, and the need to simultaneously address productivity and environmental compliance. While machine learning (ML) has been increasingly applied to individual bio-refinery operations, existing studies lack an integrated perspective that links process control, resource use optimization, and emission monitoring with sustainability objectives defined under the Sustainable Development Goals (SDGs). The objective of this review is to analyze and synthesize recent advances in ML applications for smart bio-refineries, with a focus on improving process efficiency (SDG 9: Industry, Innovation and Infrastructure), reducing energy and water consumption (SDG 7: Affordable and Clean Energy; SDG 6: Clean Water and Sanitation), and minimizing environmental emissions (SDG 12: Responsible Consumption and Production; SDG 13: Climate Action). A structured review methodology was adopted, covering peer-reviewed studies on supervised, unsupervised, and reinforcement learning methods applied to fermentation, thermochemical conversion, heat integration, and emission monitoring. The review shows that ML models, including artificial neural networks, ensemble methods, and hybrid ML–physics frameworks, enhance predictive accuracy, stabilize process operation, and enable proactive emission control. Reported case studies demonstrate measurable reductions in energy demand, improved resource utilization, and better compliance with emission limits. These findings underline the role of ML as a practical enabler of sustainable, low-emission bio-refineries. By linking operational performance with SDG-oriented outcomes, this review provides a clear framework for deploying ML technologies to support environmentally responsible and economically viable bio-refinery systems.

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[83]. Chougule SM, Murali G, Kurhade AS. Dynamic simulation and performance evaluation of vibratory bowl feeders integrated with paddle shaft mechanisms. Adv Sci Technol Res J. 2025;19(7). https://doi.org/10.12913/22998624/203873

[84]. Chougule SM, Murali G, Kurhade AS. Design and analysis of industrial material handling systems using FEA and dynamic simulation techniques. J Sci Ind Res. 2025 Jun 18;84(6):645-53. https://doi.org/10.56042/jsir.v84i6.17512

[85]. Kurhade AS, Siraskar GD, Raut PN, Dolas AS, Murali G, Dalvi SA, Waware SY, Yadav RS. Investigating the impact of oxygenated additives on exhaust emissions from unleaded gasoline vehicles. J Mines Met Fuels. 2025 Feb 1;73(2). https://doi.org/10.18311/jmmf/2025/47410

[86]. Siraskar GD, Kurhade AS, Murali G, Prakash MA, Bharathiraja N, Dharmadhikari DD, Waware SY. Turbulence model comparison and optimal geometry identification in trapped vortex combustors: a RANS-based study. Int J Mod Phys C. 2025 Sep 24:2650020. https://doi.org/10.1142/S0129183126500208

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[89]. Keloth Kaithari D, Ayyappadas MT, Goel S, Shahin A, Patil SK, Chaudhari SS, et al. A review on GA-NN based control strategies for floating solar-ocean hybrid energy platforms. Appl Chem Eng. 2025 Sep 15;8(3):ACE-5745. https://doi.org/10.59429/ace.v8i3.5745

[90]. Bhambare PS, Kaulage A, Darade MM, Murali G, Dixit SM, Vali PSNM, et al. Artificial intelligence for sustainable environmental management in the mining sector: a review. Appl Chem Eng. 2025 Sep 18;8(3):ACE-5756. https://doi.org/10.59429/ace.v8i3.5756

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[92]. Tatiya M, Darade MM, Shinde BA, Kumbhare MP, Taware RD, Chougule SM, et al. AI applications in tailings and waste management: improving safety, recycling, and water utilization. Appl Chem Eng. 2025 Nov 5;8(4):ACE-5789. https://doi.org/10.59429/ace.v8i4.5789

[93]. Upadhe SN, Mhamane SC, Kurhade AS, Bapat PV, Dhavale DB, Kore LJ. Water saving and hygienic faucet for public places in developing countries. In: Techno-Societal 2018: Proceedings of the 2nd International Conference on Advanced Technologies for Societal Applications. Vol 1. Cham: Springer; 2019. p. 617-24. https://doi.org/10.1007/978-3-030-16848-3_56

[94]. Kurhade AS, Siraskar GD, Darade MM, Dhumal JR, Kardile CS, Biradar R, Patil SP, Waware SY. Predicting heat transfer enhancement with twisted tape inserts using fuzzy logic techniques in heat exchangers. J Mines Met Fuels. 2024;72(7):743-50. https://doi.org/10.18311/jmmf/2024/45348

[95]. Kurhade AS, Siraskar GD, Kondhalkar GE, Darade MM, Yadav RS, Biradar R, Waware SY, Charwad GA. Optimizing aerofoil design: a comprehensive analysis of aerodynamic efficiency through CFD simulations and wind tunnel experiments. J Mines Met Fuels. 2024;72(7):713-24. https://doi.org/10.18311/jmmf/2024/45361

[96]. Kurhade AS, Kadam AA, Biradar R, Bhambare PS, Gadekar T, Patil P, Yadav RS, Waware SY. Experimental investigation of heat transfer from symmetric and asymmetric IC chips mounted on the SMPS board with and without PCM. J Adv Res Fluid Mech Therm Sci. 2024;121(1):137-47. https://doi.org/10.37934/arfmts.121.1.137147

[97]. Kurhade AS, Siraskar GD, Bhambare PS, Dixit SM, Waware SY. Numerical investigation on the influence of substrate board thermal conductivity on electronic component temperature regulation. J Adv Res Numer Heat Trans. 2024;23(1):28-37. https://doi.org/10.37934/arnht.23.1.2837

[98]. Kurhade AS, Waware SY, Munde KH, Biradar R, Yadav RS, Patil P, Patil VN, Dalvi SA. Performance of solar collector using recycled aluminum cans for drying. J Mines Met Fuels. 2024 May 1;72(5). https://doi.org/10.18311/jmmf/2024/44643

[99]. Kurhade AS, Kardekar NB, Bhambare PS, Waware SY, Yadav RS, Pawar P, Kirpekar S. A comprehensive review of electronic cooling technologies in harsh field environments: obstacles, progress, and prospects. J Mines Met Fuels. 2024;72(6):557-79. https://doi.org/10.18311/jmmf/2024/45212

[100]. 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

[101]. Kurhade AS, Biradar R, Yadav RS, Patil P, Kardekar NB, Waware SY, Munde KH, Nimbalkar AG, Murali G. Predictive placement of IC chips using ANN-GA approach for efficient thermal cooling. J Adv Res Fluid Mech Therm Sci. 2024;118(2):137-47. https://doi.org/10.37934/arfmts.118.2.137147

[102]. Waware SY, Chougule SM, Yadav RS, Biradar R, Patil P, Munde KH, Kardekar NB, Nimbalkar AG, Kurhade AS, Murali G, Kore SS. A comprehensive evaluation of recent studies investigating nanofluids utilization in heat exchangers. J Adv Res Fluid Mech Therm Sci. 2024;119(2):160-72. https://doi.org/10.37934/arfmts.119.2.160172

[103]. Kurhade AS, Murali G, Rao TV. CFD approach for thermal management to enhance the reliability of IC chips. Int J Eng Trends Technol. 2022;71(3):65-72. https://doi.org/10.14445/22315381/IJETT-V71I3P208

[104]. Kurhade AS, Rao TV, Mathew VK, Patil NG. Effect of thermal conductivity of substrate board for temperature control of electronic components: a numerical study. Int J Mod Phys C. 2021 Oct 26;32(10):2150132. https://doi.org/10.1142/S0129183121501321

[105]. Waware SY, Kore SS, Kurhade AS, Patil SP. Innovative heat transfer enhancement in tubular heat exchanger: an experimental investigation with minijet impingement. J Adv Res Fluid Mech Therm Sci. 2024;116(2):51-8. https://doi.org/10.37934/arfmts.116.2.5158

[106]. Kurhade AS, Murali G. Thermal control of IC chips using phase change material: a CFD investigation. Int J Mod Phys C. 2022 Dec 28;33(12):2250159. https://doi.org/10.1142/S0129183122501595

[107]. 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

[108]. Emeema J, Murali G, Reddi BV, Mangesh VL. Investigations on paraffin wax/CQD composite phase change material: improved latent heat and thermal stability. J Energy Storage. 2024 Apr 30;85:111056. https://doi.org/10.1016/j.est.2024.111056

[109]. Rami Reddy S, Murali G, Dhana Raju V. Influence of decanol as fuel additive on characteristics of diesel engine powered with mango seed biodiesel blend. Int J Ambient Energy. 2022 Dec 31;43(1):2875-88. https://doi.org/10.1080/01430750.2020.1783356

[110]. Tamiloli N, Venkatesan J, Murali G, Kodali SP, Sampath Kumar T, Arunkumar MP. Optimization of end milling on Al-SiC-fly ash metal matrix composite using TOPSIS and fuzzy logic. SN Appl Sci. 2019;1(10):1204. https://doi.org/10.1007/s42452-019-1191-z

[111]. S. Manjula Gandhi, S. Sugumaran, N. Alangudi Balaji, Govindarajan Murali, Amruta Kundalik Mule, Harish Velingkar, et al. Artificial Intelligence in Predictive Toxicology: Modelling Xenobiotic Interactions and Human Risk Assessment. Appl. Chem. Eng. 2025 Dec. 17; 8(4):ACE-5826. https://doi.org/10.59429/ace.v8i4.5826.

[112]. Manjusha Tatiya, Babaso A. Shinde, Navnath B. Pokale, Mahesh Sarada, Mahesh M. Bulhe, Govindrajan Murali, et al. AI-Driven Process Control for Enhancing Safety and Efficiency in Oil Refining. Appl. Chem. Eng. 2025 Nov. 24;8(4):ACE-5792. https://doi.org/10.59429/ace.v8i4.5792

[113]. Ramdas Biradar, Babaso A. Shinde, Milind Manikrao Darade, Tushar Gaikwad, Seeram Srinivasa Rao, Aarti Puri, et al. AI Applications in Smart Mineral Processing: Ore Characterization, Sorting, and Efficiency. Appl. Chem. Eng. 2025 Nov. 24 ;8(4):ACE-5791. https://doi.org/10.59429/ace.v8i4.5791

[114]. Pallavi Vishnu Kharat, Beena Nawghare, N. Alangudi Balaji, Vishvas V. Kalunge, Charu P. Kumbhare, Tejasvini Rahul Katkar, et al. Data-Driven Prediction of Biofuel Yield and Combustion Emissions Using AI Techniques. Appl. Chem. Eng. 2025 Dec. 25;8(4): ACE-5841. https://doi.org/10.59429/ace.v8i4.5841

[115]. Swapnil S. Chaudhari, Kundan Kale, Manisha Raghuvanshi, Torana Kamble, Ramsing Thakur, Sagar Arjun Dalvi, et al. Machine learning for waste-to-energy processes: Resource evaluation, conversion efficiency, and environmental effects. Appl. Chem. Eng. 2026 Jan. 6 ;9(1): ACE-5850. https://doi.org/10.59429/ace.v9i1.5850

[116]. Madhuri Karad, Nidhi Sharma, Khaja Gulam Hussain, Vakiti Sreelatha Reddy, Madhuri Ghuge, Sagar Arjun Dalvi, et al. AI-Based Pollution Monitoring in Bio-Energy Production Chains: Methods, Applications, and Gaps. Appl. Chem. Eng. 2026 Jan. 6 ;9(1): ACE-5848. https://doi.org/10.59429/ace.v9i1.5848

[117]. Smita Desai, Sushama Shirke, Vishvas V. Kalunge, Sireesha Koneru, Gaurav Raju Khobragade, Vidhi Rajendra Kadam, et al. Machine Learning Approaches for Biomass Resource Mapping and Sustainable Energy Planning. Appl. Chem. Eng. 2025 Dec. 29; 9(1): ACE-5843. https://doi.org/10.59429/ace.v9i1.5843