Applied Chemical Engineering

AI-Supported Forecasting of Biomass Availability under Changing Environmental and Resource Conditions

Sonali Shrikant Patil

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

P. Ramani

Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai – 600089, Tamil Nadu, 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

Prafulla O. Bagde

Shri Ramdeobaba College of Engineering and Management, Ramdeobaba University, 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

N. Alangudi Balaji

Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Greenfields, Vaddeswaram, Guntur — 522502, Andhra Pradesh, 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

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, 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.v9i1.5878

Keywords: Artificial Intelligence; Biomass Availability; Machine Learning; Remote Sensing; Supply Chain Optimization

---

Abstract

Reliable forecasting of biomass availability is essential for sustainable bioenergy planning, climate mitigation, and efficient resource management. Biomass production is influenced by complex interactions among climate variability, land use, management practices, and socioeconomic drivers, which limits the effectiveness of conventional empirical and process-based models. This study reviews recent advances in artificial intelligence (AI) and machine learning approaches for biomass availability forecasting under dynamic environmental and resource conditions. Emphasis is placed on models that integrate multi-source data, including remote sensing, field observations, climate records, management inputs, and socioeconomic indicators. The reviewed literature shows that AI-based methods capture nonlinear and spatiotemporal relationships more effectively than traditional approaches, resulting in improved prediction accuracy, scalability, and adaptability across regions. Ensemble, hybrid, and probabilistic frameworks further support uncertainty-aware forecasting, which is critical for policy formulation and industrial decision-making. From a sustainability perspective, AI-supported biomass forecasting contributes directly to several United Nations Sustainable Development Goals, particularly SDG 7 (Affordable and Clean Energy), SDG 9 (Industry, Innovation, and Infrastructure), SDG 12 (Responsible Consumption and Production), and SDG 13 (Climate Action). By supporting informed decision-making, resilient biomass supply chains, and risk-aware planning, AI-based forecasting frameworks provide a practical pathway toward sustainable and climate-resilient bioenergy systems.

---

References

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

[2]. Esmaeili F, Mafakheri F, Nasiri F. Biomass supply chain resilience: Integrating demand and availability predictions into routing decisions using machine learning. Smart Sci. 2023;11(2):293. https://doi.org/10.1080/23080477.2023.2176749

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

[4]. Zhao J, Wang J, Anderson N. Machine learning applications in forest and biomass supply chain management: A review. Int J Forest Eng. 2024;35(3):371. https://doi.org/10.1080/14942119.2024.2380230

[5]. Djandja OS, He Q. Bridging bioenergy and artificial intelligence for sustainable technological synergies. Energies. 2025;18(19):5293. https://doi.org/10.3390/en18195293

[6]. Wang J. Machine learning applications in biomass supply chain management and optimization. BioResources. 2024;19(4):6961–6963. https://doi.org/10.15376/biores.19.4.6961-6963

[7]. Yelgel ÖC, Yelgel C. The role of machine learning methods for renewable energy forecasting. In: IntechOpen eBooks. 2024. https://doi.org/10.5772/intechopen.1007556

[8]. Le TT, Paramasivam P, Adril E, Quý NV, Le MX, Duong MT, Le HC, Nguyen AQ. Unlocking renewable energy potential: Harnessing machine learning and intelligent algorithms. Int J Renew Energy Dev. 2024;13(4):783. https://doi.org/10.61435/ijred.2024.60387

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

[10]. Saleh Y. Recent advances in biomass valorization through thermochemical processes, bio-oil production and AI strategies: A concise review. RSC Adv. 2025;15(54):45943. https://doi.org/10.1039/d5ra05770a

[11]. Nath S. Biotechnology and biofuels: Paving the way towards a sustainable and equitable energy for the future. Discover Energy. 2024;4(1). https://doi.org/10.1007/s43937-024-00032-w

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

[13]. Jeon PR, Moon JH, Ogunsola NO, Lee SH, Ling JLJ, You S, Park YK. Recent advances and future prospects of thermochemical biofuel conversion processes with machine learning. Chem Eng J. 2023;471:144503. https://doi.org/10.1016/j.cej.2023.144503

[14]. Li F, Li Y, Novoselov KS, Liang F, Meng J, Ho S, Zhao T, Zhou H, Ahmad A, Zhu Y, Hu L, Ji D, Jia L, Liu R, Ramakrishna S, Zhang X. Bioresource upgrade for sustainable energy, environment, and biomedicine. Nano-Micro Lett. 2023;15(1). https://doi.org/10.1007/s40820-022-00993-4

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

[16]. Nair LG, Verma P. Harnessing carbon potential of lignocellulosic biomass: Advances in pretreatments, applications, and the transformative role of machine learning in biorefineries. Bioresour Bioprocess. 2025;12(1). https://doi.org/10.1186/s40643-025-00935-z

[17]. Sulis DB, Lavoine N, Sederoff H, Jiang X, Marques BM, Lan K, Cofre-Vega C, Barrangou R, Wang J. Advances in lignocellulosic feedstocks for bioenergy and bioproducts. Nat Commun. 2025;16(1). https://doi.org/10.1038/s41467-025-56472-y

[18]. Ali MI, Shabbir K, Ali S, Mohsin M, Kumar A, Aziz M, Zubair M, Sultan HM. A new era of discovery: How artificial intelligence has revolutionized biotechnology. Nepal J Biotechnol. 2024;12(1). https://doi.org/10.54796/njb.v12i1.312

[19]. Roni M, Lin Y, Hartley D, Thompson DN, Hoover AN, Emerson R. Importance of incorporating spatial and temporal variability of biomass yield and quality in bioenergy supply chains. Sci Rep. 2023;13(1). https://doi.org/10.1038/s41598-023-28671-4

[20]. Blay-Roger R, Saif M, Bobadilla LF, Reina TR, Nawaz MA, Odriozola JA. Embracing sustainable horizons through bioenergy innovations. Front Chem. 2024;12. https://doi.org/10.3389/fchem.2024.1416102

[21]. Efroymson RA, Langholtz M, Johnson K, et al. 2016 Billion-Ton Report: Advancing domestic resources for a thriving bioeconomy, Vol. 2. 2017. https://doi.org/10.2172/1340460

[22]. Efroymson RA, Langholtz M. 2016 Billion-Ton Report: Environmental sustainability effects. 2017. https://doi.org/10.2172/1338837

[23]. Groundstroem F, Juhola S. Using systems thinking and causal loop diagrams to identify cascading climate change impacts on bioenergy supply systems. Mitig Adapt Strateg Glob Change. 2021;26(7). https://doi.org/10.1007/s11027-021-09967-0

[24]. Langholtz M, Brandt CC, Clark R, et al. 2023 Billion-Ton Report: Assessment of U.S. renewable carbon resources. 2024. https://doi.org/10.2172/2441098

[25]. Taylor G, Thiec DL. Plastic or adaptive? Linking genotype to phenotype to define an ideotype for drought tolerance in Populus nigra. 2017.

[26]. Hartley D, Burli P. Biomass supply, logistics, and factors that affect logistics. In: Springer eBooks. 2024. https://doi.org/10.1007/978-94-007-6308-1_47

[27]. Li W, Ciais P, Stehfest E, et al. Mapping the yields of lignocellulosic bioenergy crops at global scale. Earth Syst Sci Data. 2020;12(2):789. https://doi.org/10.5194/essd-12-789-2020

[28]. Li W, Yue C, Ciais P, et al. ORCHIDEE-MICT-BIOENERGY model for lignocellulosic crop production. Geosci Model Dev. 2018;11(6):2249. https://doi.org/10.5194/gmd-11-2249-2018

[29]. Thomas A, Axelos M, de Vries H, et al. Réflexion prospective interdisciplinaire Bioéconomie. 2019.

[30]. Schauberger B, Jägermeyr J, Gornott C. A systematic review of yield forecasting approaches. Eur J Agron. 2020;120:126153. https://doi.org/10.1016/j.eja.2020.126153

[31]. Ali G, Mijwil MM, Adamopoulos I, Ayad J. Leveraging IoT, remote sensing, and AI for sustainable forest management. Babylonian J IoT. 2025. https://doi.org/10.58496/bjiot/2025/001

[32]. Valipour M, Mafakheri F, Gagnon B, et al. Integrating bio-hubs in biomass supply chains. J Clean Prod. 2024;467:142930. https://doi.org/10.1016/j.jclepro.2024.142930

[33]. Nair SK, Emerson R, Solomon JR. Biomass supply chain risk. In: 2024. https://doi.org/10.1007/978-94-007-6308-1_48

[34]. Pirotti F, González-Olabarria JR, Kutchartt E. Updating aboveground biomass at pan-European scale through AI. ISPRS Arch. 2023. https://doi.org/10.5194/isprs-archives-xlviii-1-w2-2023-1763-2023

[35]. Cândido BM, Mindala U, Ebrahimy H, et al. Integrating proximal and remote sensing with ML for pasture biomass estimation. Sensors. 2025;25(7):1987. https://doi.org/10.3390/s25071987

[36]. Yewle AD, Mirzayeva L, Karakuş O. Multi-modal data fusion and deep ensemble learning for crop yield prediction. 2025.

[37]. Sun C, Zhang W, Zhao G, et al. Mapping rapeseed aboveground biomass using UAV imagery. Front Plant Sci. 2024;15. https://doi.org/10.3389/fpls.2024.1504119

[38]. Sialelli G, Peters T, Wegner JD, Schindler K. AGBD: A global-scale biomass dataset. 2024.

[39]. Saki M, Keshavarz R, Franklin D, et al. Precision soil quality analysis using transformer-based data fusion. 2024.

[40]. Kanga S. Advancements in remote sensing tools for forestry analysis. Sustain Forestry. 2023;6(1). https://doi.org/10.24294/sf.v6i1.2269

[41]. Kamangir H, Sams B, Dokoozlian N, et al. CMAViT: Integrating climate, management, and remote sensing data for crop yield estimation. 2024.

[42]. Jana S, Chatterjee D, Pal N, et al. AI in soil health monitoring: A data-driven approach. IJRASET. 2024;12(10):1327. https://doi.org/10.22214/ijraset.2024.64871

[43]. Shoaib MR, Emara HM, Zhao J. Revolutionizing global food security using AI foundation models. 2023.

[44]. Otamendi U, Maiza M, Olaizola IG, et al. Integrated water resource management using AI. J Environ Manage. 2024;370:122526. https://doi.org/10.1016/j.jenvman.2024.122526

[45]. C.M I, Aigbovbiosa OJ, Olowonubi JA, Fatounde SA. Role of AI in electrification of Africa. Eng Sci Technol J. 2023;4(6):456. https://doi.org/10.51594/estj.v4i6.667

[46]. Hirschmugl M, Sobe C, Traverso L, et al. Energy from biomass: Assessing sustainability by geoinformation technology. GI_Forum. 2021;1:120. https://doi.org/10.1553/giscience2021_01_s120

[47]. Chen T, Lv L, Wang D, et al. Empowering agrifood systems with artificial intelligence. 2023.

[48]. Laaribya S, Alaoui A. Biomass prediction using ML in Google Earth Engine. Geogr Environ Sustain. 2025;18(3):43. https://doi.org/10.24057/2071-9388-2025-3876

[49]. Lungu ON, Chabala LM, Shepande C. Satellite-based crop monitoring and yield estimation. J Agric Sci. 2020;13(1):180. https://doi.org/10.5539/jas.v13n1p180

[50]. Seely H, Coops NC, White JC, et al. Modelling tree biomass using point cloud deep learning. Sci Remote Sens. 2023;8:100110. https://doi.org/10.1016/j.srs.2023.100110

[51]. Huang R, Yao W, Xu Z, et al. Information fusion approach for biomass estimation using UAS and LiDAR. Comput Electron Agric. 2022;202:107420. https://doi.org/10.1016/j.compag.2022.107420

[52]. Kumar R, Aneesh K. Aboveground tree biomass modelling using geospatial data and ML algorithms. 2025. https://doi.org/10.21203/rs.3.rs-7203591/v1

[53]. Ennaji O, Baha S, Vergütz L, Allali AE. Gradient boosting for maize yield prediction. PLoS ONE. 2024;19(12). https://doi.org/10.1371/journal.pone.0315493

[54]. Liu Z, Li M, Li C, Liu Z. Forest aboveground biomass estimation using Landsat 8 and Sentinel-1A. Sci Rep. 2020;10(1). https://doi.org/10.1038/s41598-020-67024-3

[55]. Dube T, Mutanga O, Adam E, Ismail R. Biomass prediction in plantation forests using ML. Sensors. 2014;14(8):15348. https://doi.org/10.3390/s140815348

[56]. Dilmurat K, Sagan V, Moose SP. AI-driven maize yield forecasting using UAV hyperspectral and LiDAR data. ISPRS Ann. 2022. https://doi.org/10.5194/isprs-annals-v-3-2022-193-2022

[57]. Shen Y, Mercatoris B, Cao Z, et al. Improving wheat yield prediction using LSTM-RF. Agriculture. 2022;12(6):892. https://doi.org/10.3390/agriculture12060892

[58]. Zhang L, Li C, Wu X, et al. BO-CNN-BiLSTM model for winter wheat yield estimation. Front Plant Sci. 2024;15:1500499. https://doi.org/10.3389/fpls.2024.1500499

[59]. Lü J, Li J, Fu H, et al. Deep learning for multi-source crop yield prediction. Agriculture. 2024;14(6):794. https://doi.org/10.3390/agriculture14060794

[60]. Xie Y, Huang J. Integration of crop growth models and deep learning for yield estimation. Remote Sens. 2021;13(21):4372. https://doi.org/10.3390/rs13214372

[61]. Mahmoud A, Mohammed A, Wahab MA, Khalil AA. Time-series forecasting of wheat productivity using deep learning. 2023. https://doi.org/10.21203/rs.3.rs-3266492/v1

[62]. Han H, Liu Z, Li J, Zeng Z. Challenges in remote sensing-based climate and crop monitoring using AI. J Cloud Comput. 2024;13(1). https://doi.org/10.1186/s13677-023-00583-8

[63]. Dhal SB, Kar D. Transforming agricultural productivity with AI-driven forecasting. Forecasting. 2024;6(4):925. https://doi.org/10.3390/forecast6040046

[64]. Lionel BM, Musabe R, Gatera O, Twizere C. Comparative study of ML models for crop yield prediction. Discover Agric. 2025;3(1). https://doi.org/10.1007/s44279-025-00335-z

[65]. Shi Y, Han L, Zhang X, et al. Deep learning meets process-based models: A hybrid approach. 2025.

[66]. Beka S, Burgess P, Corstanje R. Robust spatial estimates of biomass carbon on farms. Sci Total Environ. 2022;861:160618. https://doi.org/10.1016/j.scitotenv.2022.160618

[67]. Wang TJ, Zuo Y, Manda T, et al. Harnessing AI, ML and DL for sustainable forestry management. Plants. 2025;14(7):998. https://doi.org/10.3390/plants14070998

[68]. Elufioye OA, Ike CU, Odeyemi O, et al. AI-driven predictive analytics in agricultural supply chains. Comput Sci IT Res J. 2024;5(2):473. https://doi.org/10.51594/csitrj.v5i2.817

[69]. Kabir M, Ekici S. Energy-agriculture nexus: Future AI applications. Energy Nexus. 2023;13:100263. https://doi.org/10.1016/j.nexus.2023.100263

[70]. Martins LOS, Carneiro RAF, Torres EA, et al. Supply chain management of biomass for energy generation. J Agric Sci. 2019;11(13):253. https://doi.org/10.5539/jas.v11n13p253

[71]. Ayadi R, Forouheshfar Y, Moghadas O. Enhancing system resilience to climate change through AI. Front Clim. 2025;7. https://doi.org/10.3389/fclim.2025.1585331

[72]. Dong W, Mitchard ETA, Yu H, et al. Forest aboveground biomass estimation using GEDI and EO data. 2023.

[73]. Hanan A, Khan MN, Fernandez-Añez N, Arghandeh R. DeepBioFusion: Multimodal deep learning for biomass estimation. Ecol Inform. 2025;90:103277. https://doi.org/10.1016/j.ecoinf.2025.103277

[74]. Matiza C, Mutanga O, Peerbhay K, et al. Remote sensing and ML for carbon storage estimation: A review. South For. 2023;85:123. https://doi.org/10.2989/20702620.2023.2251946

[75]. International Journal of Multidisciplinary Research and Growth Evaluation. 2021. https://doi.org/10.54660/anfo

[76]. 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

[77]. 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

[78]. 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

[79]. 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

[80]. 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

[81]. 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

[82]. 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

[83]. 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

[84]. 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

[85]. 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

[86]. 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

[87]. 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

[88]. 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

[89]. 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

[90]. 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).

[91]. 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

[92]. 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

[93]. 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

[94]. 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

[95]. 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

[96]. 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

[97]. 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

[98]. 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

[99]. 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

[100]. 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

[101]. 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.

[102]. 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

[103]. 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

[104]. 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

[105]. 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

[106]. 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

[107]. 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

[108]. 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

[109]. 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

[110]. 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

[111]. 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

[112]. 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

[113]. 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

[114]. 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

[115]. 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

[116]. 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

[117]. 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

[118]. 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

[119]. 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

[120]. 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

[121]. 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

[122]. 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

[123]. 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

[124]. 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

[125]. 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

[126]. 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

[127]. 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

[128]. 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

[129]. 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

[130]. 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

[131]. 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

[132]. 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

[133]. 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

[134]. 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.

[135]. 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

[136]. 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

[137]. 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

[138]. 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

[139]. 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

[140]. 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

[141]. Waware SY, Bote MA, Patil SP, Biradar R, Katke G, Kadam AA, Ghunake KB, Kore SS, Murali G, Kurhade AS. Artificial Intelligence in Extractive and Processing Industries: Applications Across Mining, Metallurgy, and Petroleum Sectors. NJSTR. 2025 Dec. 14 ;7(4):196-208. https://doi.org/10.37933/nipes/7.4.2025.1740

[142]. Waware SY, Nagalli NK, Sugumaran S, Patil PD, Biradar R, Kadam AA, Ghunake KB, Kore SS, Murali G, Kurhade AS. AI-Driven Innovations in the Exploration, Extraction, and Processing of Minerals, Metals, and Petroleum: A Review. NJSTR. 2025 Dec. 14 ;7(4):209-27. https://doi.org/10.37933/nipes/7.4.2025.1717