Applied Chemical Engineering

Data-Driven Prediction of Biofuel Yield and Combustion Emissions Using AI Techniques

Pallavi Vishnu Kharat

Civil Engineering Department, Ajeenkya D. Y. Patil School of Engineering, Lohegaon, Charoli Budruk, Pune - 412105, Maharashtra, India.

Beena Nawghare

Department of First Year Engineering (Engineering Chemistry), 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

N. Alangudi Balaji

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

Vishvas V. Kalunge

Department of Computer Engineering, Dhole Patil College of Engineering, Wagholi, Pune – 412207, Maharashtra, India.

Charu P. Kumbhare

Department of Mathematics, Ramdeobaba University, Ramdeo Tekdi, Katol Road, Nagpur - 440013, Maharashtra, India

Tejasvini Rahul Katkar

Department of Electronics and Telecommunication Engineering, Genba Sopanrao Moze College of Engineering, Balewadi, Pune - 411045, Maharashtra, India.

Sagar Arjun Dalvi

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

Shital Yashwant Waware

Department of Mechanical Engineering, Dr. D. Y. Patil Institute of Technology, Sant Tukaram Nagar, Pimpri - 411018, Pune, 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 - 411018, Pune, 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.v8i4.5841

Keywords: biofuel yield; combustion emissions; machine learning; deep learning; hybrid models; physics-informed ML; soft sensors

---

Abstract

Accurate prediction of biofuel yield and combustion emissions plays a key role in improving conversion efficiency and reducing dependence on trial-and-error experiments. Biofuel systems involve diverse biomass feedstocks and complex thermochemical and combustion processes, which makes modeling difficult. Reliable prediction tools also support cleaner energy practices and informed process control. Existing research shows several clear limitations. Many studies rely on small, single-site datasets, which limit broader applicability. Data preprocessing methods differ widely across publications, leading to inconsistencies in reported results. Validation strategies are often limited to internal testing, which restricts confidence in real-world use. These issues reduce model generalization, reproducibility, and clarity of interpretation. This review examines recent progress in artificial intelligence and machine learning applied to biofuel production and engine emission prediction. It summarizes commonly used data sources, including laboratory experiments and engine tests. The review outlines feature selection and transformation methods adopted in prior work. It also reviews model construction strategies and evaluation practices used to assess performance. Surveyed studies show that ensemble learning methods, neural networks, and physics-informed hybrid models achieve high prediction accuracy at laboratory scale. These models perform well for yield and emission estimation under controlled conditions. At the same time, several persistent challenges remain. Many advanced models show weak extrapolation beyond training ranges. Model transparency is also limited, which affects trust and interpretability. The findings indicate that benchmark datasets and consistent preprocessing protocols are needed. Strong external validation can improve reliability. Incorporating physical constraints into machine learning workflows can enhance stability and realism. Such practices can support real-time implementation and promote wider use of data-driven prediction tools in biofuel research and industrial operations.

---

References

[1]. 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. Chemical Engineering Journal. 2023;471:144503. doi:10.1016/j.cej.2023.144503

[2]. Nguyen TH, Paramasivam P, Dong VH, Le HC, Nguyen DC. Harnessing a better future: exploring AI and ML applications in renewable energy. JOIV International Journal on Informatics Visualization. 2024;8(1):55. doi:10.62527/joiv.8.1.2637

[3]. 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. Bioresource Technology. 2021;343:126099. doi:10.1016/j.biortech.2021.126099

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

[5]. Ascher S, Watson I, You S. Machine learning methods for modelling the gasification and pyrolysis of biomass and waste. Renewable and Sustainable Energy Reviews. 2021;155:111902. doi:10.1016/j.rser.2021.111902

[6]. 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. International Journal of Renewable Energy Development. 2024;13(4):783. doi:10.61435/ijred.2024.60387

[7]. Lee H, Choi IH, Hwang KR. A comprehensive review of machine learning prediction in the production of bio-oil from lignocellulose via pyrolysis. Research Square. 2024. doi:10.21203/rs.3.rs-3830648/v1

[8]. Li F, Li Y, Novoselov KS, Liang F, Meng J, Ho S, et al. Bioresource upgrade for sustainable energy, environment, and biomedicine. Nano-Micro Letters. 2023;15(1). doi:10.1007/s40820-022-00993-4

[9]. Li H, Chen J, Zhang W, Zhan H, He C, Yang Z, Peng H, Leng L. Machine-learning-aided thermochemical treatment of biomass: a review. Biofuel Research Journal. 2023;10(1):1786. doi:10.18331/brj2023.10.1.4

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

[11]. 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. doi:10.15376/biores.17.3.clauser

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

[13]. Gil MV, Jablonka KM, García S, Pevida C, Smit B. Biomass to energy: a machine learning model for optimum gasification pathways. Digital Discovery. 2023;2(4):929. doi:10.1039/d3dd00079f

[14]. Qing-sheng X, Du L, Deng R. Using machine learning to predict biochar yield and carbon content. BioResources. 2024;19(3):6545. doi:10.15376/biores.19.3.6545-6558

[15]. Li J, Chen Y, Wang C, Chen H, Gao Y, Meng J, et al. Optimizing biochar for carbon sequestration using machine learning and NLP. Biochar. 2025;7(1). doi:10.1007/s42773-024-00424-0

[16]. Ali MI, Shabbir K, Ali S, Mohsin M, Kumar A, Aziz M, et al. How artificial intelligence has revolutionized biotechnology. Nepal Journal of Biotechnology. 2024;12(1). doi:10.54796/njb.v12i1.312

[17]. Kant G, Hasan A, Mani P, Pandey A, Srivastava S. The generational shift in biofuels. Biomass and Bioenergy. 2025;196:107757. doi:10.1016/j.biombioe.2025.107757

[18]. Osman AI, Nasr M, Farghali M, Rashwan AK, Abdelkader A, Al-Muhtaseb AH, et al. Optimizing biodiesel production from waste with ML. Environmental Chemistry Letters. 2024;22(3):1005. doi:10.1007/s10311-024-01700-y

[19]. Meena MK, Shubham S, Paritosh K, Pareek N, Vivekanand V. Predicting energy from biomass using AI. Bioresource Technology. 2021;340:125642. doi:10.1016/j.biortech.2021.125642

[20]. Rahimi M, Mashhadimoslem H, Thanh HV, Ranjbar B, Khosrowshahi MS, Rohani A, Elkamel A. Yield prediction of biomass-based products by ML schemes. Energy. 2023;283:128546. doi:10.1016/j.energy.2023.128546

[21]. Ramalingam K, Abdullah MZ, Elumalai PV, Xu Y, Prasad KSN, Chan CK, et al. Maximizing biofuel production using ML. Scientific Reports. 2025;15(1). doi:10.1038/s41598-025-18757-6

[22]. Anbarasu K, Sundaram T, Sathishkumar K, Alam MM, Al-Sehemi AG, Devarajan Y. Harnessing artificial intelligence for sustainable bioenergy. Bioresource Technology. 2024;131893. doi:10.1016/j.biortech.2024.131893

[23]. Arif M, Alalawy AI, Zheng Y, Koutb M, Kareri T, Salama E, Li X. AI and ML models for biodiesel optimization. Sustainable Energy Technologies and Assessments. 2024;73:104097. doi:10.1016/j.seta.2024.104097

[24]. Nath S. Biotechnology and biofuels. Discover Energy. 2024;4(1). doi:10.1007/s43937-024-00032-w

[25]. Aghbashlo M, Peng W, Tabatabaei M, Kalogirou SA, Soltanian S, Hosseinzadeh-Bandbafha H, et al. Machine learning in biodiesel research. Progress in Energy and Combustion Science. 2021;85:100904. doi:10.1016/j.pecs.2021.100904

[26]. Awogbemi O, Kallon DVV. ML technologies in biodiesel production. Frontiers in Energy Research. 2023;11. doi:10.3389/fenrg.2023.1122638

[27]. Nneoma UC, Chukwudi OF, Nnenna UJ, Ugwu OPC. Hybrid biofactories for enhanced biofuel production. Frontiers in Energy Research. 2025;13. doi:10.3389/fenrg.2025.1654079

[28]. Abdullah M, Malik HA, Ali AM, Boopathy R, Vo HNP, Danaee S, et al. AI-driven algae biorefineries. Current Pollution Reports. 2025;11(1). doi:10.1007/s40726-025-00352-y

[29]. Geng Y, Shaukat A, Azhar W, Raza QUA, Tahir A, Abideen MZU, et al. Microalgal biorefineries: systematic review. Biotechnology for Biofuels and Bioproducts. 2025;18(1). doi:10.1186/s13068-025-02694-7

[30]. Çokkızgın H, Çokkızgın A, Girgel Ü. Reducing carbon footprint of field crops. Applied Ecology and Environmental Research. 2025;23(4):7641. doi:10.15666/aeer/2304_76417661

[31]. Ahmad I, Sana A, Kano M, Cheema II, Menezes BC, Shahzad J, et al. Machine learning applications in biofuels’ life cycle. Energies. 2021;14(16):5072. doi:10.3390/en14165072

[32]. Wang C. Biomass fluidized bed gasification model with ML and CFD. arXiv. 2025. doi:10.48550/arXiv.2509.06056

[33]. Costa M, Piazzullo D. Biofuel powering of ICE engines: production and CFD modeling. Frontiers in Mechanical Engineering. 2018;4. doi:10.3389/fmech.2018.00009

[34]. Arumugam S, Muthaiyan R, Dhairiyasamy R, Rajendran S. Enhancing biodiesel stability and performance. Discover Chemical Engineering. 2024;4(1). doi:10.1007/s43938-024-00041-0

[35]. Hong B, Burghout W. Adaptive estimation in NOx emission modelling. Emission Control Science and Technology. 2024;10(2):93. doi:10.1007/s40825-024-00241-8

[36]. Jiang P, Wang C, She WJ, Ye W, Li Y chen, Ji T, et al. ML-accelerated process modelling for biomass conversion. Renewable and Sustainable Energy Reviews. 2025;226:116439. doi:10.1016/j.rser.2025.116439

[37]. Coniglio L, Bennadji H, Glaude P, Herbinet O, Billaud F. Combustion chemical kinetics of biodiesel. Progress in Energy and Combustion Science. 2013;39(4):340. doi:10.1016/j.pecs.2013.03.002

[38]. Jana S, Chatterjee D, Pal N, Pal K, Roy K, Bashak S. AI in soil health monitoring. IJRASET. 2024;12(10):1327. doi:10.22214/ijraset.2024.64871

[39]. Mahanty B, Gharami M, Haldar D. ML modelling for ionic liquid-aided biomass pretreatment. BioEnergy Research. 2024;17(3):1569. doi:10.1007/s12155-024-10747-2

[40]. Zhu L, Chen X, Ouyang B, Yan W, Lei H, Chen Z, Luo Z. ML for hydrodynamics and reactions in multiphase flows. Industrial & Engineering Chemistry Research. 2022;61(28):9901. doi:10.1021/acs.iecr.2c01036

[41]. Ge X, Zhang T, Mukherjee S, Chen Y, Wang X, Chen N, et al. Optimizing hydrothermal treatment with ML. Biochar. 2025;7(1). doi:10.1007/s42773-025-00485-9

[42]. Mahanty B, Gharami M, Haldar D. ML models for ionic liquid biomass pretreatment. Research Square. 2024. doi:10.21203/rs.3.rs-3916542/v1

[43]. Nachippan M, Pathmanabhan P, Nagappan B, Upadhye VJ, Kaliappan N, Balaji V, Priya K. ML predictions for engine performance with nano-additives. Scientific Reports. 2025;15(1). doi:10.1038/s41598-025-02388-y

[44]. Brusa A, Giovannardi E, Barichello M, Cavina N. Data-driven surrogate models for NOx emissions. Energies. 2022;15(21):8088. doi:10.3390/en15218088

[45]. Khandelwal K, Dalai AK. Predicting gas yields of SCWG using ML. Molecules. 2024;29(10):2337. doi:10.3390/molecules29102337

[46]. Wang C. Biomass gasification ML–CFD model. arXiv. 2025. doi:10.48550/arXiv.2509.06056

[47]. Caprio UD, Leblebici ME. SINDybrid: automatic generation of hybrid models. arXiv. 2025. doi:10.48550/arXiv.2506.12498

[48]. Sharma N, Liu YA. Hybrid science-guided ML for chemical processes. arXiv. 2021. doi:10.48550/arXiv.2112.01475

[49]. Driessen JLSP. Integration of biomass conversion, cell factory optimization and bioprocess design. Research Portal Denmark. 2022.

[50]. Spooner MP. Statistical data analysis tools for bio-based manufacturing. Research Portal Denmark. 2018.

[51]. Roy S, Khekare G, Chhajed S, Victor A. ML for biochar safety and pollutant removal. Frontiers in Soil Science. 2025;5. doi:10.3389/fsoil.2025.1661097

[52]. Shahbeik H, Shafizadeh A, Nadian MH, Jeddi D, Mirjalili S, Yang Y, et al. Evolutionary ML for co-pyrolysis optimisation. Journal of Cleaner Production. 2023;387:135881. doi:10.1016/j.jclepro.2023.135881

[53]. Öner İV. ML algorithms applied to internal combustion engine studies. DergiPark. 2025.

[54]. Liu D, Sun N. Prospects of AI in sustainable separation processes. Frontiers in Sustainability. 2023;4. doi:10.3389/frsus.2023.1210209

[55]. Jacobsen C. Enhancing physical modelling with physics-aware ML. University of Michigan Deep Blue. 2024.

[56]. Gupta R, Ouderji ZH, Uzma U, Yu Z, Sloan WT, You S. Machine learning for sustainable organic waste treatment. npj Materials Sustainability. 2024;2(1). doi:10.1038/s44296-024-00009-9

[57]. Ojadi JO, Owulade OA, Odionu CS, Onukwulu EC. Deep learning models for predicting industrial environmental impact. IJSRSET. 2025;12(2):119. doi:10.32628/ijsrset25122109

[58]. Valizadeh A, Amirhosseini MH. Machine learning in lithium-ion batteries. SN Computer Science. 2024;5(6). doi:10.1007/s42979-024-03046-2

[59]. 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

[60]. 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

[61]. 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

[62]. 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

[63]. 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

[64]. 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

[65]. 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

[66]. 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

[67]. 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

[68]. 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

[69]. 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

[70]. 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

[71]. 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

[72]. 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

[73]. 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).

[74]. 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

[75]. 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

[76]. 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

[77]. 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

[78]. 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

[79]. 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

[80]. 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

[81]. 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

[82]. 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

[83]. 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

[84]. 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.

[85]. 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

[86]. 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

[87]. 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

[88]. 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

[89]. 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

[90]. 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

[91]. 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

[92]. 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

[93]. 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

[94]. 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

[95]. 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

[96]. 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

[97]. 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

[98]. 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

[99]. 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

[100]. 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

[101]. 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

[102]. 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

[103]. 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

[104]. 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

[105]. 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

[106]. 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

[107]. 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

[108]. 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

[109]. 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

[110]. 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

[111]. 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

[112]. 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

[113]. 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

[114]. 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

[115]. 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

[116]. 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