Applied Chemical Engineering

AI-Based Pollution Monitoring in Bio-Energy Production Chains: Methods, Applications, and Gaps

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 - 411018, Pune, Maharashtra, India

Nidhi Sharma

Department of Applied Sciences and Engineering, AISSM’s Institute of Information Technology, Pune - 411001, Maharashtra, India.

Khaja Gulam Hussain

Department of Mechanical Engineering, RGM College of Engineering and Technology, Nandyal - 518501, Andhra Pradesh, India.

Vakiti Sreelatha Reddy

Department of Electronics and Instrumentation Engineering, CVR College of Engineering, Ibrahimpatnam, Hyderabad - 500074, Telangana, India.

Madhuri Ghuge

Department of Computer Engineering, Bharati Vidyapeeth College of Engineering, Navi Mumbai – 400614, Maharashtra, India.

Sagar Arjun Dalvi

Department of Mechanical Engineering, Dr. Bapuji Salunkhe Institute of Engineering & Technology (BSIET), Tarabai Park, Kolhapur - 416003, Maharashtra, India.

Rupesh Gangadhar Mahajan

Department of Computer 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 - 411018, Pune, Maharashtra, India

Shital Yashwant Waware

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 - 411018, Pune, 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 - 411018, Pune, Maharashtra, India

DOI: https://doi.org/10.59429/ace.v9i1.5848

Keywords: AI Monitoring, Bio-Energy, Emissions, Hybrid Modelling, Sensor Networks, Affordable and Clean Energy; Climate Action

---

Abstract

The rapid growth of bio-energy production is closely aligned with global sustainability agendas, particularly the Sustainable Development Goals (SDGs) related to Affordable and Clean Energy (SDG 7), Industry, Innovation and Infrastructure (SDG 9), and Climate Action (SDG 13). Effective pollution monitoring across the bio-energy production chain is essential to ensure that renewable energy expansion does not lead to unintended environmental burdens. Current research largely treats artificial intelligence (AI) applications in environmental monitoring and bio-energy systems as separate domains, creating a research gap in integrated, process-wide frameworks that connect emission sources, sensor networks, data pipelines, and AI models across all production stages. The objective of this study is to critically review AI-based pollution monitoring approaches for bio-energy systems and to assess their capability to support sustainable and responsible energy production in line with SDG targets. The methodology involves a structured synthesis of recent literature on sensing technologies, data acquisition and preprocessing, machine learning and deep learning models, and hybrid physics-informed approaches applied from biomass handling to biofuel refining. The key findings show that AI-enabled monitoring improves real-time emission estimation, early detection of abnormal events, and short-term forecasting, supporting cleaner production pathways. At the same time, challenges related to sensor drift, data scarcity, model transferability, and interpretability limit large-scale adoption. The implications of this review highlight the need for open benchmark datasets, robust calibration strategies, and explainable AI models to strengthen regulatory trust, promote sustainable industrial practices, and contribute directly to achieving SDG-linked environmental and energy objectives.

---

References

[1]. Popescu SM, Mansoor S, Wani OA, Kumar SS, Sharma V, Sharma A, Arya VM, Kirkham MB, Hou D, Bolan N, Chung YS. Artificial intelligence and IoT driven technologies for environmental pollution monitoring and management. Front Environ Sci. 2024;12. https://doi.org/10.3389/fenvs.2024.1336088

[2]. Olawade DB, Wada OZ, Ige AO, Egbewole BI, Olojo AS, Oladapo BI. Artificial intelligence in environmental monitoring: Advancements, challenges, and future directions. Hygiene Environ Health Adv. 2024;12:100114. https://doi.org/10.1016/j.heha.2024.100114

[3]. Afoma UM, Singh S, Mishra AK, Sharma CK, Gupta K, Mishra MK, Roy B, Verma VV, Sharma VK. Integrating Artificial Intelligence in Environmental Monitoring: A Paradigm Shift in Data-Driven Sustainability. EcoHealth. 2025. https://doi.org/10.1007/s10393-025-01752-8

[4]. Alotaibi E, Nassif N. Artificial intelligence in environmental monitoring: in-depth analysis. Discover Artif Intell. 2024;4(1). https://doi.org/10.1007/s44163-024-00198-1

[5]. Dahake R, Metre KV, Kale NR, Dange BJ, Mahankale NR. AI-Powered Environmental Monitoring and Conservation Strategies. Int J Eng Trends Technol. 2024;72(8):1. https://doi.org/10.14445/22315381/IJETT-V72I8P101

[6]. Chisom ON, Biu PW, Umoh AA, Obaedo BO, Adegbite AO, Abatan A. Reviewing the role of AI in environmental monitoring and conservation: A data-driven revolution for our planet. World J Adv Res Rev. 2024;21(1):161. https://doi.org/10.30574/wjarr.2024.21.1.2720

[7]. Anbarasu K, Sundaram T, Sathishkumar K, Alam MM, Al-Sehemi AG, Devarajan Y. Harnessing artificial intelligence for sustainable bioenergy: optimization, waste reduction, and environmental sustainability. Bioresour Technol. 2024;131893. https://doi.org/10.1016/j.biortech.2024.131893

[8]. Singh S, Shahi S. AI For Earth: Enhancing Environmental Sustainability With Technology. Res J Med Sci. 2024;18(11):219–225. https://doi.org/10.36478/makrjms.2024.11.219.225

[9]. Srivastava A, Sharma H. AI-driven environmental monitoring using Google Earth Engine. In: Smart Sensors, Measurement and Instrumentation. Springer Nature; 2024. p. 375. https://doi.org/10.1007/978-3-031-68602-3_19

[10]. Magazzino C. The impact of deep learning on environmental science. Deleted J. 2024;1(1). https://doi.org/10.1186/s44329-024-00003-5

[11]. Arowolo M, Aaron WC, Kugbiyi AO, Eteng US, Iloh D, Aguma CP, Olagunju AO. Integrating AI-enhanced remote sensing technologies with IoT networks for precision environmental monitoring and predictive ecosystem management. World J Adv Res Rev. 2024;23(2):2156. https://doi.org/10.30574/wjarr.2024.23.2.2573

[12]. Zhu J, Li L. Advancements in image classification for environmental monitoring using AI. Front Environ Sci. 2025;13. https://doi.org/10.3389/fenvs.2025.1562287

[13]. Ahmad I, Sana A, Kano M, Cheema II, Menezes BC, Shahzad J, Ullah Z, Khan M, Habib A. Machine learning applications in biofuels’ life cycle: soil, feedstock, production, consumption, and emissions. Energies. 2021;14(16):5072. https://doi.org/10.3390/en14165072

[14]. Sugumaran D, Udakandage MD, Kodippili SP, Alwis MMD, Attigala DL, Ranasinghe NN, Manatunga DC, Dassanayake RS, Zhou Y, Liu Y. Artificial intelligence in sustainable organic waste treatment: a review. Waste Dispos Sustain Energy. 2025;7(3):539. https://doi.org/10.1007/s42768-025-00246-1

[15]. Wang Z, Peng X, Xia A, Shah AA, Huang Y, Zhu X, Zhu X, Liao Q. The role of machine learning to boost the bioenergy and biofuels conversion. Bioresour Technol. 2021;343:126099. https://doi.org/10.1016/j.biortech.2021.126099

[16]. Patil M. Environmental sustainability in the age of deep learning: balancing technological advancement with ecological responsibility. Deleted J. 2024;20:2377. https://doi.org/10.52783/jes.3221

[17]. Causevic A, Causevic S, Fielding M, Barrott J. Artificial intelligence for sustainability: opportunities and risks of utilizing Earth observation technologies to protect forests. Discover Conserv. 2024;1(1). https://doi.org/10.1007/s44353-024-00002-2

[18]. Yang J, Li C, Lo LSH, Zhang X, Chen Z, Gao J, Ulrich C, Dai Z, Nakaoka M, Yang H, Cheng J. AI-assisted environmental DNA metabarcoding and underwater optical imaging for noninvasive marine environmental monitoring. J Mar Sci Eng. 2024;12(10):1729. https://doi.org/10.3390/jmse12101729

[19]. Ali G, Mijwil MM, Adamopoulos I, Ayad J. Leveraging IoT, remote sensing, and AI for sustainable forest management. Babylon J Internet Things. 2025;1. https://doi.org/10.58496/BJIoT/2025/001

[20]. Liao M, Yao Y. Applications of AI-based modeling for bioenergy systems: a review. GCB Bioenergy. 2021;13(5):774. https://doi.org/10.1111/gcbb.12816

[21]. Shah M, Wever M, Espig M. A framework for assessing the potential of AI in the circular bioeconomy. Sustainability. 2025;17(8):3535. https://doi.org/10.3390/su17083535

[22]. Wang TJ, Zuo Y, Manda T, Hwarari D, Yang L. AI, ML and deep learning for sustainable forestry management and conservation. Plants. 2025;14(7):998. https://doi.org/10.3390/plants14070998

[23]. Adefemi A, Ukpoju EA, Adekoya OO, Abatan A, Adegbite AO. Artificial intelligence in environmental health and public safety: a comprehensive review of USA strategies. World J Adv Res Rev. 2023;20(3):1420. https://doi.org/10.30574/wjarr.2023.20.3.2591

[24]. Hua J, Wang R, Hu YC, Chen Z, Chen L, Osman AI, Farghali M, Huang L, Feng J, Wang H, Zhang X, Zhou X, Yap P. AI for calculating and predicting building carbon emissions: a review. Environ Chem Lett. 2025. https://doi.org/10.1007/s10311-024-01799-z

[25]. Pellegrino J, Fanney AH, Bushby ST, Domanski PA, Healy WM, Persily AK. Measurement science roadmap for net-zero energy buildings workshop summary report. 2010. https://doi.org/10.6028/NIST.TN.1660

[26]. Wang Y, Wang B, Wang R. Detection technologies for indoor hazardous air pollutants and particulate matter. Aerosol Air Qual Res. 2023;23(12):230193. https://doi.org/10.4209/aaqr.230193

[27]. Nagahage ISP, Nagahage EAAD, Fujino T. Applicability of a low-cost sensor–based methane monitoring system for continuous multi-channel sampling. Environ Monit Assess. 2021;193(8). https://doi.org/10.1007/s10661-021-09290-w

[28]. Cai Q, Zeng N, Yang X, Xu C, Wang Z, Han P. Field evaluation of low-cost CO₂ sensors using a reference instrument. Atmos Meas Tech. 2025;18(18):4871. https://doi.org/10.5194/amt-18-4871-2025

[29]. Achinas S, Quintero MdJD, Euverink GJW. Embodiment of machine learning in biogas power industry: an opinion. Front Energy Res. 2025;13. https://doi.org/10.3389/fenrg.2025.1589782

[30]. Yang C, Daigger GT, Belia E, Kerkez B. Extracting useful signals from flawed sensor data: hybrid data-driven approaches with physical factors. Water Res. 2020;185:116282. https://doi.org/10.1016/j.watres.2020.116282

[31]. Sserujongi R, Ogenrwot D, Niwamanya N, et al. Design and evaluation of a scalable data pipeline for AI-driven air quality monitoring. arXiv. 2025.

[32]. Wang C, He T, Zhou H, Zhang Z, Lee C. AI-enhanced sensors for next-generation healthcare and biomedical platforms. Bioelectron Med. 2023;9(1). https://doi.org/10.1186/s42234-023-00118-1

[33]. Winarto R, Purnomo MH, Anggraeni W. Enhancing predictive emissions monitoring: data preprocessing for XGBoost-based models. 2025;278. https://doi.org/10.1109/KST65016.2025.11003353

[34]. Sen S, Husom EJ, Göknil A, et al. Virtual sensors for erroneous data repair in manufacturing. Comput Ind. 2023;149:103917. https://doi.org/10.1016/j.compind.2023.103917

[35]. Le TT, Paramasivam P, Adril E, et al. Harnessing machine learning for renewable energy potential. Int J Renew Energy Dev. 2024;13(4):783. https://doi.org/10.61435/ijred.2024.60387

[36]. Proceedings of the 20th Conference on Computer Science and Intelligence Systems (FedCSIS). Ann Comput Sci Inf Syst. 2025;43. https://doi.org/10.15439/978-83-973291-6-4

[37]. Ojadi JO, Owulade OA, Odionu CS, Onukwulu EC. Deep learning models for predicting and mitigating industrial environmental impact. Int J Sci Res Sci Eng Technol. 2025;12(2):119. https://doi.org/10.32628/IJSRSET25122109

[38]. Si M, Du K. Predictive emissions model using gradient boosting. Environ Technol Innov. 2020;20:101028. https://doi.org/10.1016/j.eti.2020.101028

[39]. Si M, Wiens BM, Du K. Long-term evaluation of ML methods for air emission monitoring. Res Sq. 2023.

[40]. Nguyen TH, Paramasivam P, Dong VH, Le HC, Nguyen DC. AI and ML applications in renewable energy. JOIV Int J Informatics Vis. 2024;8(1):55. https://doi.org/10.62527/joiv.8.1.2637

[41]. Cihan P. Bayesian hyperparameter optimization of ML models for biomass gasification gases. Appl Sci. 2025;15(3):1018. https://doi.org/10.3390/app15031018

[42]. Poudel L, Tincher D, Phan DN, Bhowmik R. Deep learning for amine emission monitoring in carbon capture plants. arXiv. 2025.

[43]. Mosavi A, Salimi M, Ardabili S, et al. Machine learning models in energy systems: a systematic review. Energies. 2019;12(7):1301. https://doi.org/10.3390/en12071301

[44]. Shi Y, Han L, Zhang X, et al. Hybrid deep learning and process-based models for agriculture. arXiv. 2025.

[45]. Oliveira P, Marcondes FS, Duarte MS, et al. Incdualpathnet: hybrid model for predicting energy production in wastewater plants. Neural Comput Appl. 2025. https://doi.org/10.1007/s00521-025-11545-3

[46]. Nair LG, Verma P. Carbon potential of lignocellulosic biomass and the role of ML in biorefineries. Bioresour Bioprocess. 2025;12(1). https://doi.org/10.1186/s40643-025-00935-z

[47]. Clauser NM, Felissia FE, Área MC, Vallejos ME. Integrating bioeconomy and Industry 4.0 into biorefinery process design. BioResources. 2022;17(3):5510. https://doi.org/10.15376/biores.17.3.Clauser

[48]. Fang B, Yu J, Chen Z, Osman AI, Farghali M, Ihara I, Hamza E, Rooney DW, Yap P. AI for waste management in smart cities: a review. Environ Chem Lett. 2023;21(4):1959. https://doi.org/10.1007/s10311-023-01604-3

[49]. Ali MI, Shabbir K, Ali S, et al. AI and its impact on biotechnology. Nepal J Biotechnol. 2024;12(1). https://doi.org/10.54796/njb.v12i1.312

[50]. Abdullah M, Malik HA, Ali AM, et al. AI-driven algae biorefineries for sustainable bioeconomy. Curr Pollut Rep. 2025;11(1). https://doi.org/10.1007/s40726-025-00352-y

[51]. Marzban N, Psarianos M, Herrmann C, et al. Smart integrated biorefineries in bioeconomy. Biofuel Res J. 2025;12(1):2319. https://doi.org/10.18331/BRJ2025.12.1.4

[52]. Jiang P, Wang C, She WJ, et al. ML-accelerated data generation and optimization for biomass conversion. Renew Sustain Energy Rev. 2025;226:116439. https://doi.org/10.1016/j.rser.2025.116439

[53]. Li H, Chen J, Zhang W, et al. ML-aided thermochemical treatment of biomass: a review. Biofuel Res J. 2023;10(1):1786. https://doi.org/10.18331/BRJ2023.10.1.4

[54]. Mulomba CM, Choi H. Air quality forecasting with ML: global perspective. SSRN. 2024.

[55]. Murmu S, Sinha D, Chaurasia H, et al. AI-assisted omics techniques in plant defense. Front Plant Sci. 2024;15. https://doi.org/10.3389/fpls.2024.1292054

[56]. Aryandoust A, Rigoni T, Stefano FD, Patt A. Unified ML tasks and datasets for renewable energy. arXiv. 2023.

[57]. Duong-Trung N, Born S, Kim JW, et al. Bioprocess engineering and machine learning: automated bioprocess development. Biochem Eng J. 2022;190:108764. https://doi.org/10.1016/j.bej.2022.108764

[58]. Duong-Trung N, Born S, Kim JW, et al. Bioprocess engineering meets machine learning. arXiv. 2022.

[59]. Abbasi AF, Asim MN, Dengel A. Systematic review of AI predictors in CRISPR. J Transl Med. 2025;23(1). https://doi.org/10.1186/s12967-024-06013-w

[60]. Wang T, Wang Y, Zhou J, et al. Exploring uncertainty quantification techniques in AI. arXiv. 2025.

[61]. Kurhade AS, Gadekar T, Siraskar GD, Jawalkar SS, Biradar R, Kadam AA, Yadav RS, Dalvi SA, Waware SY, Mali CN. Thermal performance analysis of electronic components on different substrate materials. J Mines Met Fuels. 2024 Oct 1;72(10). https://doi.org/10.18311/jmmf/2024/45569

[62]. Kurhade AS, Siraskar GD, Jawalkar SS, Gadekar T, Bhambare PS, Biradar R, Yadav RS, Waware SY, Mali CN. The impact of circular holes in twisted tape inserts on forced convection heat transfer. J Mines Met Fuels. 2024 Oct 16;72(9):1005-12. https://doi.org/10.18311/jmmf/2024/45505

[63]. Kurhade AS, Bhambare PS, Desai VP, Murali G, Yadav RS, Patil P, Gadekar T, Biradar R, Kirpekar S, Charwad GA, Waware SY. Investigating the effect of heat transfer influenced by wavy corrugated twisted tape inserts in double pipe heat exchangers. J Adv Res Fluid Mech Therm Sci. 2024;122:146-55. https://doi.org/10.37934/arfmts.122.2.146155

[64]. Kurhade AS, Murali G, Jadhav PA, Bhambare PS, Waware SY, Gadekar T, Yadav RS, Biradar R, Patil P. Performance analysis of corrugated twisted tape inserts for heat transfer augmentation. J Adv Res Fluid Mech Therm Sci. 2024;121(2):192-200. https://doi.org/10.37934/arfmts.121.2.192200

[65]. Yadav RS, Nimbalkar A, Gadekar T, Patil P, Patil VN, Gholap AB, Kurhade AS, Dhumal JR, Waware SY. Comparison of experimental and numerical investigation of mono-composite and metal leaf spring. J Mines Met Fuels. 2024 Aug 1;72(8). https://doi.org/10.18311/jmmf/2024/45325

[66]. Kurhade AS, Warke P, Maniyar K, Bhambare PS, Waware SY, Deshpande S, Harsur S, Ingle M, Kolhe P, Patil PA, Jadhav P. Wind rose analysis of temperature variation with sensor implantation technique for wind turbine. J Adv Res Fluid Mech Therm Sci. 2024;122(1):1-8. https://doi.org/10.37934/arfmts.122.1.118

[67]. Kurhade AS, Siraskar GD, Bhambare PS, Kaithari DK, Dixit SM, Waware SY. Enhancing smartphone circuit cooling: a computational study of PCM integration. J Adv Res Numer Heat Trans. 2024 Nov 30;27(1):132-45. https://doi.org/10.37934/arnht.27.1.132145

[68]. Kurhade AS, Darade MM, Siraskar GD, Biradar R, Mahajan RG, Kardile CS, Waware SY, Yadav RS. State-of-the-art cooling solutions for electronic devices operating in harsh conditions. J Mines Met Fuels. 2024 Aug 1;72(8). https://doi.org/10.18311/jmmf/2024/45374

[69]. Yadav RS, Gadekar T, Gundage V, Patil P, Patil A, Patil P, Patil A, Sutar R, Kurhade AS. Numerical and experimental investigation of the effect of overlapping angle on strength and deformation of curved plate joined using arc welding process. J Mines Met Fuels. 2024 Oct 1;72(10). https://doi.org/10.18311/jmmf/2024/45697

[70]. Kurhade AS, Bhambare PS, Siraskar GD, Dixit SM, Purandare PS, Waware SY. Computational study on thermal management of IC chips with phase change materials. J Adv Res Numer Heat Trans. 2024;26(1):34-43. https://doi.org/10.37934/arnht.26.1.3443

[71]. Yadav RS, Gandhi P, Veeranjaneyulu K, Gaji R, Kirpekar S, Pawar D, Khairnar YS, Patil S, Kurhade AS, Patil SP. Influence of plate thickness on the mechanical behaviour of mild steel curved plates: an experimental study. J Mines Met Fuels. 2024 Dec 1;72(12). https://doi.org/10.18311/jmmf/2024/46253

[72]. Chippalkatti S, Chekuri RB, Ohol SS, Shinde NM, Barmavatu P, Shelkande VD, Murali G, Kurhade AS. Enhancing heat transfer in micro-channel heat sinks through geometrical optimization. J Mines Met Fuels. 2025 Mar 1;73(3). https://doi.org/10.18311/jmmf/2025/47773

[73]. Raut PN, Dolas AS, Chougule SM, Darade MM, Murali G, Waware SY, Kurhade AS. Green adsorbents for heavy metal removal: a study on zinc ion uptake by Tinospora cordifolia biocarbon. J Mines Met Fuels. 2025 Jan 1;73(1). https://doi.org/10.18311/jmmf/2025/47121

[74]. Kurhade AS, Siraskar GD, Bhambare PS, Murali G, Deshpande SV, Warke PS, Waware SY. Simulation and analysis of heat transfer in counter-flow helical double-pipe heat exchangers using CFD. Int J Mod Phys C. 2025 Mar 15. https://doi.org/10.1142/S0129183125500433

[75]. Patil Y, Tatiya M, Dharmadhikari DD, Shahakar M, Patil SK, Mahajan RG, Kurhade AS. The role of AI in reducing environmental impact in the mining sector. J Mines Met Fuels. 2025 May 1;73(5).

[76]. Waware SY, Ahire PP, Napate K, Biradar R, Patil SP, Kore SS, Kurhade AS. Advancements in heat transfer enhancement using perforated twisted tapes: a comprehensive review. J Mines Met Fuels. 2025 May 1;73(5). https://doi.org/10.18311/jmmf/2025/48438

[77]. Chougule SM, Murali G, Kurhade AS. Finite element analysis and design optimization of a paddle mixer shaft. J Mines Met Fuels. 2025 May 1;73(5). https://doi.org/10.18311/jmmf/2025/48664

[78]. Chougule SM, Murali G, Kurhade AS. Failure investigation of the driving shaft in an industrial paddle mixer. J Mines Met Fuels. 2025 May 1;73(5). https://doi.org/10.18311/jmmf/2025/48627

[79]. Kurhade AS, Sugumaran S, Kolhalkar NR, Karad MM, Mahajan RG, Shinde NM, Dalvi SA, Waware SY. Thermal management of mobile devices via PCM. J Mines Met Fuels. 2025 May 1;73(5):1313-20. https://doi.org/10.18311/jmmf/2025/48437

[80]. Kurhade AS, Bhavani P, Patil SA, Kolhalkar NR, Chalapathi KS, Patil PA, Waware SY. Mitigating environmental impact: a study on the performance and emissions of a diesel engine fueled with biodiesel blend. J Mines Met Fuels. 2025 Apr 1;73(4):981-9. https://doi.org/10.18311/jmmf/2025/47669

[81]. Kurhade AS, Siraskar GD, Chekuri RB, Murali G, Pawar P, Patil AR, Waware SY, Yadav RS. Biodiesel blends: a sustainable solution for diesel engine performance improvement. J Mines Met Fuels. 2025 Mar 1;73(3). https://doi.org/10.18311/jmmf/2025/47628

[82]. Kurhade AS, Siraskar GD, Darade MM, Murali G, Katkar TR, Patil SP, Charwad GA, Waware SY, Yadav RS. Enhancement in heat transfer with nanofluids in double-pipe heat exchangers. J Mines Met Fuels. 2025 Jan 7;73(1):165-72. https://doi.org/10.18311/jmmf/2025/47225

[83]. Napte K, Kondhalkar GE, Patil SV, Kharat PV, Banarase SM, Kurhade AS, Waware SY. Recent advances in sustainable concrete and steel alternatives for marine infrastructure. Sustain Mar Struct. 2025 Jun 4:107-31. https://doi.org/10.36956/sms.v7i2.2072

[84]. Kurhade AS, Chougule SM, Kharat PV, Kondhalkar GE, Murali G, Raut PN, Charwad GA, Waware SY, Yadav RS. Integrated approach to enhance vehicle safety: a novel bumper design with energy-absorbing mechanisms. J Mines Met Fuels. 2025 Jan 1;73(1). https://doi.org/10.18311/jmmf/2025/47168

[85]. Yadav R, Nimbalkar A, Kirpekar S, Patil PJ, Dalvi SA, Jadhav PA, Kurhade AS, Wakchaure GN. Effect of transformed-induced plasticity steel plate thickness on ultimate tensile strength of butt welded joint using Nd:YAG laser. Int J Veh Struct Syst. 2024;16(6):857-62. https://doi.org/10.4273/ijvss.16.6.08

[86]. Deshpande SV, Pawar RS, Keche AJ, Kurhade A. Real-time surface finish measurement of stepped holding shaft by automatic system. J Adv Manuf Syst. 2025 Feb 25:1-26.

[87]. Ramani P, Reji V, Sathish Kumar V, Murali G, Kurhade AS. Deep learning-based detection and classification of moss and crack damage in rock structures for geo-mechanical preservation. J Mines Met Fuels. 2025 Mar 1;73(3). https://doi.org/10.18311/jmmf/2025/47760

[88]. Kurhade AS, Siraskar GD, Deshmukh MT, Patil PA, Chaudhari SS, Kadam AA, Dolas AS, Mahajan RG, Waware SY, Yadav RS. Impact of PCM on heat dissipation from IC chips. J Mines Met Fuels. 2025 Mar 1;73(3). https://doi.org/10.18311/jmmf/2025/47522

[89]. Kurhade AS, Kharat PV, Chougule SM, Darade MM, Karad MM, Murali G, Charwad GA, Waware SY, Yadav RS. Harnessing the power of plastic waste: a sustainable approach to fuel production. J Mines Met Fuels. 2025 Feb 1;73(2). https://doi.org/10.18311/jmmf/2025/47354

[90]. Sarode GC, Gholap P, Pathak KR, Vali PSNM, Saharkar U, Murali G, Kurhade AS. Edge AI and explainable models for real-time decision-making in ocean renewable energy systems. Sustain Mar Struct. 2025 Jun 24;7(3):17-42. https://doi.org/10.36956/sms.v7i3.2239

[91]. 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

[92]. 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

[93]. 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

[94]. 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

[95]. Keloth Kaithari D, Kaulage A, Ayyappadas MT, Gholap P, Puri A, Bhandari MA, et al. A review of smart AI systems for real-time monitoring and optimization of ocean-based carbon capture, utilization, and storage networks. Appl Chem Eng. 2025 Sep 17;8(3):ACE-5747. https://doi.org/10.59429/ace.v8i3.5747

[96]. Dhamdhere P, Dixit SM, Tatiya M, Shinde BA, Deone J, Kaulage A, et al. AI-based monitoring and management in smart aquaculture for ocean fish farming systems. Appl Chem Eng. 2025 Sep 17;8(3):ACE-5746. https://doi.org/10.59429/ace.v8i3.5746

[97]. 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

[98]. 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

[99]. Dharmadhikari DD, Ray A, Shinde BA, Raut SV, Taware RD, Desai S, et al. Machine learning applications in ore grade estimation and blending optimization for modern mining. Appl Chem Eng. 2025 Nov 6;8(4):ACE-5790. https://doi.org/10.59429/ace.v8i4.5790

[100]. 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

[101]. 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

[102]. 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

[103]. 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

[104]. 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

[105]. 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

[106]. 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

[107]. 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

[108]. 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

[109]. 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

[110]. 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

[111]. 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

[112]. 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

[113]. 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

[114]. 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

[115]. 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

[116]. 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

[117]. 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

[118]. 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

[119]. 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.

[120]. 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

[121]. 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

[122]. Sajal Suhane, Rushali Rajaram Katkar, Smita Suhane, S. Sugumaran, Santosh Bhauso Takale, Surekha Dehu Khetree, et al. AI-Driven Optimization of Bio-Energy Systems: Models for Resource Assessment and Emission Reduction. Appl. Chem. Eng. 2025 Dec. 29;9(1):ACE-5837. https://doi.org/10.59429/ace.v9i1.5837

[123]. 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