Applied Chemical Engineering

Artificial Intelligence in Chemical Process Optimization: Techniques, Applications, Challenges, and Future Directions

Manjusha Tatiya

Department of Artificial Intelligence and Data Science, Indira College of Engineering and Management, Pune, 410506, Maharashtra, India

Pragati Choudhari

School of Computer Science Engineering & Applications, D Y Patil International University, Akurdi, Pune, 412101, Maharashtra, India

Rupali Ramdas Kawade

Department of Electronics and Telecommunication Engineering, PCET's Pimpri Chinchwad College of Engineering and Research, Ravet, Pune, 412101, Maharsahtra, India

Prafulla O. Bagde

Shri Ramdeobaba College of Engineering and Management, Ramdeobaba University, Nagpur, 440013, Maharashtra, India

Rupali Ashok Patil

Department of Computer Science, Dombivali shikshan prasarak mandal's K.V. Pendharkar College of Arts, Science and Commerce (Autonomous), Dombivli (E), 421203, Maharashtra, India

Vasundhara Vasudev Sutar

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, 411 018, 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, 411 018, Maharashtra, India

DOI: https://doi.org/10.59429/ace.v9i2.5949

Keywords: Artificial Intelligence, Chemical Process Optimization, Deep Learning, Digital Twins, Machine Learning, Reinforcement Learning

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Abstract

Chemical processes are often complex nonlinear reactions, with large numbers of operating variables, and high energy consumption; therefore, optimization is not an easy task. With the industrial data from sensors and monitoring systems becoming more widely available, artificial intelligence (AI) is being deployed to enable process efficiency as well as better decision making. Further, unlike the more traditional optimization methods used, especially for complex and high dimensional systems as in molecular design, AI techniques can do so much more efficiently than exploring the whole computational landscape thanks to their powerful predictive capabilities. This review provides an overview of recent developments in machine learning, deep learning (DL), reinforcement learning, and evolutionary algorithms for chemical process optimization across four key industrial applications: reactor design; distillation; energy management; and predictive maintenance. Results suggest that AI-driven solutions enhance process performance, energy savings and promote sustainable industrialization in the context of SDGs 7, 9, 12 and 13.

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References

[1]. Gao Q, Schweidtmann AM. Deep reinforcement learning for process design: Review and perspective. Curr Opin Chem Eng. 2024;44:101012.

[2]. Al-Sakkari EG, Ragab A, Dagdougui H, Boffito DC, Amazouz M. Carbon capture utilization and sequestration systems design and optimization: Artificial intelligence opportunities. Sci Total Environ. 2024;917:170085.

[3]. Zhou T, Sundmacher K. Multiscale process systems engineering—analysis and design of chemical and energy systems from molecular design up to process optimization. Front Chem Sci Eng. 2022;16(2):137.

[4]. Pal PK, Hens A, Behera N, Lahiri SK. Digital twins transforming the chemical process industry: A review. Can J Chem Eng. 2025;103(8):3611.

[5]. Kim HW, Lee S, Na GS, Han SJ, Kim SK, Shin JH, Chang H, Kim YT. Reaction condition optimization for non-oxidative conversion of methane using artificial intelligence. React Chem Eng. 2021;6(2):235.

[6]. Chowdhury SH, Ghosh A, Acharya S, Saha SC, Pal PK, Roy S, Lahiri SK. Transforming chemical process engineering: The role of AI and machine learning in revolutionizing process systems. Can J Chem Eng. 2025;104(2):699.

[7]. Ding C, Gui X, Jiang J. Advancing chemical engineering technology with artificial intelligence. Clean Energy. 2025;9(5):55.

[8]. Zeng T, Badrinarayanan S, Ock J, Lai CK, Farimani AB. LLM-guided chemical process optimization with a multi-agent approach. Mach Learn Sci Technol. 2025;6(4):045067.

[9]. Mitrai I, Daoutidis P. Accelerating process control and optimization via machine learning: A review. Rev Chem Eng. 2025;41(4):401.

[10]. Liang HY, Yan T, Zhao W. Comprehensive assessment of recent major chemical accidents in China and path to sustainable solutions. Smart Constr Sustain Cities. 2024;2(1).

[11]. Wu Z, Christofides PD, Wu W, Wang Y, Abdullah F, Alnajdi A, Kadakia YA. A tutorial review of machine learning-based model predictive control methods. Rev Chem Eng. 2024.

[12]. Arinze CA, Jacks BS. AI-driven optimization techniques enhancing sustainability in oil and gas production processes. Eng Sci Technol J. 2024;5(3):962.

[13]. Decardi-Nelson B, Alshehri AS, Ajagekar A, You F. Generative AI and process systems engineering: The next frontier. Comput Chem Eng. 2024;187:108723.

[14]. Huang Q, Peng S, Deng J, Zeng H, Zhang Z, Liu Y, Yuan P. Artificial intelligence in nuclear reactors: Current status and future directions. Heliyon. 2023;9(3).

[15]. Granacher J, Kantor I, Maréchal F. Increasing superstructure optimization capacity through self-learning surrogate models. Front Chem Eng. 2021;3.

[16]. Anyebe AP, Yeboah OKK, Bakinson OI, Adeyinka TY, Okafor FC. Optimizing carbon capture efficiency through AI-driven process automation for predictive maintenance and CO₂ sequestration in oil and gas facilities. Am J Environ Clim. 2024;3(3):44.

[17]. Schweidtmann AM, Esche E, Fischer A, Kloft M, Repke J, Säger S, Mitsos A. Machine learning in chemical engineering: A perspective. Chem Ing Tech. 2021;93(12):2029.

[18]. Ma J, Han Y, Wang M, Zhong W, Du W, Qian F. Artificial intelligence for carbon capture utilization and storage in the petrochemical industry. Carbon Capture Sci Technol. 2025;16:100471.

[19]. Galeazzi A, Prifti K, Cortellini C, Pretoro AD, Gallo F, Manenti F. Development of a surrogate model of an amine scrubbing digital twin using machine learning methods. Comput Chem Eng. 2023;174:108252.

[20]. Toniato A, Schilter O, Laino T. The role of AI in driving the sustainability of the chemical industry. Chimia (Aarau). 2023;77(3):144.

[21]. Hakim BAE, Abdel-Goad M, Awad ME, Shoaib AM. AI enhanced model predictive control for optimizing LPG recovery through integrated computational modeling design of experiments and multivariate regression. Sci Rep. 2025;15(1).

[22]. Kubosawa S, Onishi T, Tsuruoka Y. AI and simulation for soft sensors and process control. Kagaku Kogaku Ronbunshu. 2022;48(4):141.

[23]. Yan Y, Borhani TN, Subraveti SG, Pai KN, Prasad V, Rajendran A, Nkulikiyinka P, Asibor JO, Zhang Z, Shao D, Wang L, Zhang W, Yan Y, Ampomah W, You J, Wang M, Anthony EJ, Manović V, Clough PT. Machine learning for carbon capture, utilisation, and storage: A state-of-the-art review. Energy Environ Sci. 2021;14(12):6122.

[24]. Sajadieh SMM, Noh SD. From simulation to autonomy: Integration of artificial intelligence and digital twins. Int J Precis Eng Manuf Green Technol. 2025;12(5):1597.

[25]. Namdeti R. Artificial Intelligence in Chemical Engineering: Past, Present, and Future Perspectives. J Chem Health Risks. 2023;13(6).

[26]. Özdemir P, Yıldırım R. ML@ChemE: Past, present, and future of machine learning in chemical engineering. ChemBioEng Rev. 2025.

[27]. Nabil T, Noaman M, Morosuk T. Data-driven structural synthesis of supercritical CO₂ power cycles. Front Chem Eng. 2023;5.

[28]. Alhazmi K, Sarathy SM. Dynamic optimizers for complex industrial systems via direct data-driven synthesis. Commun Eng. 2025;4(1):25.

[29]. Smart E, Olayiwola DE, Okwor UD, Asere JB, Takele MPA, Dirisu CD, Dada DM. AI-driven optimization strategies for CO₂ capture and storage processes. Int J Sci Technol Res Arch. 2025;9(1):18.

[30]. Bussemaker J, Saves P, Bartoli N, Lefèbvre T, Lafage R. System architecture optimization strategies: Dealing with expensive hierarchical problems. J Glob Optim. 2024.

[31]. Devarakonda VS, Sun W, Tang X, Tian Y. Recent advances in reinforcement learning for chemical process control. Processes. 2025;13(6):1791.

[32]. Zhu L, Chen X, Ouyang B, Yan W, Lei H, Chen Z, Luo Z. Review of machine learning for hydrodynamics, transport, and reactions in multiphase flows and reactors. Ind Eng Chem Res. 2022;61(28):9901.

[33]. Decardi-Nelson B, Alshehri AS, You F. Generative artificial intelligence in chemical engineering across multiple scales. Front Chem Eng. 2024;6.

[34]. Adeyeye O, Akanbi I. Artificial intelligence for systems engineering complexity: A review on the use of AI and machine learning algorithms. Comput Sci IT Res J. 2024;5(4):787.

[35]. Shi H, Zhou T. Computational design of heterogeneous catalysts and gas separation materials for advanced chemical processing. Front Chem Sci Eng. 2021;15(1):49.

[36]. Liao M, Lan K, Yao Y. Sustainability implications of artificial intelligence in the chemical industry: A conceptual framework. J Ind Ecol. 2022;26(1):164.

[37]. Cai T, Fang J, Daida S, Lou HH. Review of synergy between machine learning and first principles models for asset integrity management. Front Chem Eng. 2023;5.

[38]. Jul-Rasmussen P. Hybrid modeling for process systems simulation: Integrating process understanding and machine learning. 2025.

[39]. Almazrouei SM, Dweiri F, Aydın R, Alnaqbi A. Advancements and challenges of artificial intelligence models for predictive maintenance of water injection pumps in oil and gas industry. SN Appl Sci. 2023;5(12).

[40]. Tüzün U. Artificial intelligence assisted dynamic control of environmental emissions from hybrid energy process plants. Front Energy Res. 2020;8.

[41]. Daniel C, Gehin JC, Laurin-Kovitz KF, Morreale BD, Stevens R, Tumas W, Anitescu M, Poczatek A, Siegel A, Som S, Vilim R, Grout R, Kroposki B, Yue M, Rose K, Rashdan AA, Ritter C, Balaprakash P, Jain P, Kuruganti T, Piette MA, Hong T, Corley C, Ralló R, Grosh J, Essen BV, Reno M, Viswanathan H, Alexander F, Dietrich E. Advanced research directions on artificial intelligence for energy. 2024.

[42]. Liu D, Sun N. Prospects of artificial intelligence in the development of sustainable separation processes. Front Sustain. 2023;4.

[43]. Mao Y, Zhang N, Shi P, Zhong W, Lin X. A review of smart integrated energy systems towards industrial carbon neutrality: Opportunity and challenge. npj Therm Sci Eng. 2026;1(1).

[44]. Ji C, Ma F, Rao J, Wang J, Sun W. Bridging the gap in chemical process monitoring: Toward industrial deployment. Processes. 2025;13(12):3809.

[45]. Arinze CA, Izionworu VO, Isong DE, Daudu CD, Adefemi A. Predictive maintenance in oil and gas facilities leveraging AI for asset integrity management. Int J Front Eng Technol Res. 2024;6(1):16.

[46]. Ananikov VP. Top 20 influential AI-based technologies in chemistry. Artif Intell Chem. 2024;2(2):100075.

[47]. Gärtler M, Khaydarov V, Klöpper B, Urbas L. The machine learning life cycle in chemical operations: Status and open challenges. Chem Ing Tech. 2021;93(12):2063.

[48]. Katterbauer K, Qasim A, Alhashboul A. An AI-aided carbon conversion framework for efficient carbon storage. SSRN Electron J. 2025.

[49]. Rebello CM, Nogueira IBR. Digital twins in chemical engineering: An integrated framework for identification, implementation, online learning and uncertainty assessment. Comput Chem Eng. 2025;200:109178.

[50]. Berg D van de, Savage T, Petsagkourakis P, Zhang D, Shah N, Rio-Chanona EA del. Data-driven optimization for process systems engineering applications. Chem Eng Sci. 2021;248:117135.

[51]. Russell JM, Allman A. Sustainable decision making for chemical process systems via dimensionality reduction of many objective problems. AIChE J. 2023;69(2).

[52]. Lam AYS, Li VOK. Chemical reaction optimization: A tutorial. Memet Comput. 2012;4(1):3.

[53]. Park H, Kwon H, Cho H, Kim J. A framework for energy optimization of distillation process using machine learning-based predictive model. Energy Sci Eng. 2022;10(6):1913.

[54]. Neveux T, Commenge J, Vermeire FH. Editorial: Artificial intelligence-assisted design of sustainable processes. Front Chem Eng. 2024;6.

[55]. Bishnu SK, Alnouri SY, Mohannadi DMA. Stochastic algorithm-based optimization using artificial intelligence/machine learning models for sorption enhanced steam methane reformer reactor. Comput Chem Eng. 2025;196:109060.

[56]. Hwangbo S, Al R, Sin G. An integrated framework for plant data-driven process modeling using deep learning with Monte Carlo simulations. Comput Chem Eng. 2020;143:107071.

[57]. Chong X, Li L, Zhang C, Zhao Y, Kraft M, Wang X. AI-enhanced multi-scale smart systems for decarbonization in the chemical industry: A pathway to sustainable and efficient production. Technol Rev Carbon Neutral. 2025.

[58]. Zhu L, Du L, Cao G, Cai Z. AI-guided electro-decomposition of persistent organic pollutants. Environ Sci Adv. 2023;2(10):1302.

[59]. Chiang LH, Braun B, Wang Z, Castillo I. Towards artificial intelligence at scale in the chemical industry. AIChE J. 2022;68(6).

[60]. Demirhan CD, Tso WW, Ogumerem GS, Pistikopoulos EN. Energy systems engineering: A guided tour. BMC Chem Eng. 2019;1(1).

[61]. Montastruc L, Belletante S, Pagot A, Negny S, Raynal L. From conceptual design to process design optimization: A review on flowsheet synthesis. Oil Gas Sci Technol. 2019;74:80.

[62]. Sethi H, Ahmad I, Khan MM, Qazi A, Ayub A, Zulkefal M, Shutaywi M. Applications of computer intelligence in hydrogen production. ACS Omega. 2025;10(31):33982.

[63]. Edaugal JP, Zhang D, Liu D, Glezakou V, Sun N. Solvent screening for separation processes using machine learning and high-throughput technologies. Chem Bio Eng. 2025;2(4):210.

[64]. Tan S, Zhou X, Zhou H, Hao Z, Xie Y, Cao L, Shen G, Gao Y, Shen Q, Wei W. Reasoning-agent-driven process simulation, optimization, carbon accounting and decarbonization of distillation. Commun Eng. 2026.

[65]. Demirhan CD, Tso WW, Ogumerem GS, Pistikopoulos EN. Energy systems engineering: A guided tour. BMC Chem Eng. 2019;1(1).

[66]. Srinivasan KK, Puliyanda A, Thosar D, Bhakte A, Singh K, Addo P, Srinivasan R, Prasad V. Artificial intelligence and machine learning at various stages and scales of process systems engineering. Can J Chem Eng. 2025;103(3):1004.

[67]. 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;8(4):ACE-5791.

[68]. 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;8(4):ACE-5841.

[69]. Manjusha Tatiya, Milind Manikrao Darade, Babaso A. Shinde, Mahesh Prakash Kumbhare, Rupali Dineshwar Taware, Sukhadip Mhankali Chougule, et al. AI Applications in Tailings and Waste Management: Improving Safety, Recycling, and Water Utilization. Appl Chem Eng. 2025;8(4):ACE-5789.

[70]. Dipa Dattatray Dharmadhikari, Avani Ray, Babaso A. Shinde, Sandeep V. Raut, Rupali Dineshwar Taware, Smita Desai, et al. Machine Learning Applications in Ore Grade Estimation and Blending Optimization for Modern Mining. Appl Chem Eng. 2025;8(4):ACE-5790.

[71]. 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;8(4):ACE-5792.