STM Journals Peer-reviewed research from Consortium e-Learning Network

Emerging Trends in Chemical Engineering Review Article

Digital Transformation in Chemical Engineering: A Comprehensive Review

  1. Ravuri Hema Krishna ✉ Professor, Department of Chemistry, Amrita Sai Institute of Science and Technology, Bathinapadu, Andhra Pradesh, India

Abstract

Digital transformation has emerged as a transformative paradigm in chemical engineering, fundamentally redefining process design, operation, optimization, and sustainability through the integration of advanced digital technologies. The convergence of Artificial Intelligence (AI), Machine Learning (ML), Industrial Internet of Things (IIoT), Digital Twins, Big Data Analytics, Cloud Computing, Cyber-Physical Systems (CPS), robotics, and advanced process automation has accelerated the transition from conventional manufacturing to intelligent, data-driven, and autonomous chemical production. These technologies enable real-time monitoring, predictive maintenance, process optimization, energy management, quality control, and enhanced operational safety, thereby improving productivity while reducing costs, emissions, and resource consumption. Furthermore, digital transformation supports the implementation of Industry 4.0 and the emerging Industry 5.0 framework by fostering human–machine collaboration, resilient manufacturing systems, and sustainable industrial practices. This review comprehensively examines the evolution of digital transformation in chemical engineering, highlighting key enabling technologies, smart manufacturing strategies, digital process modeling, advanced control systems, and industrial applications across petrochemical, pharmaceutical, biochemical, food, and environmental engineering sectors. Recent advances in digital twins, AI-assisted process systems engineering, intelligent sensors, cloud-based manufacturing, and autonomous laboratories are critically discussed along with their contributions to process efficiency and decision-making. The review also addresses significant challenges, including cybersecurity, data quality, interoperability, regulatory compliance, high implementation costs, workforce skill gaps, and the integration of legacy infrastructure with modern digital platforms. Finally, future research directions are presented, focusing on explainable artificial intelligence, edge computing, quantum computing, autonomous chemical plants, carbon-neutral manufacturing, and sustainable digital process engineering. The integration of intelligent digital technologies is expected to play a pivotal role in achieving safer, more efficient, economically competitive, and environmentally sustainable chemical industries, positioning digital transformation as a cornerstone of next-generation chemical engineering.

Keywords

References (62)

  1. 1. Lee J, Bagheri B, Kao HA. A cyber-physical systems architecture for Industry 4.0-based manufacturing systems. Manuf Lett. 2015;3:18–23. doi:10.1016/j.mfglet.2014.12.001.
  2. 2. Qin J, Liu Y, Grosvenor R. A categorical framework of manufacturing for Industry 4.0 and beyond. Procedia CIRP. 2016;52:173–178. doi:10.1016/j.procir.2016.08.005.
  3. 3. Tao F, Zhang H, Liu A, Nee AYC. Digital twin in industry: state-of-the-art. IEEE Trans Ind Inform. 2019;15(4):2405–2415. doi:10.1109/TII.2018.2873186.
  4. 4. Kritzinger W, Karner M, Traar G, Henjes J, Sihn W. Digital twin in manufacturing: a categorical literature review. IFAC-PapersOnLine. 2018;51(11):1016–1022. doi:10.1016/j.ifacol.2018.08.474.
  5. 5. Lu Y. Industry 4.0: a survey on technologies, applications and open research issues. J Ind Inf Integr. 2017;6:1–10. doi:10.1016/j.jii.2017.04.005.
  6. 6. Frank AG, Dalenogare LS, Ayala NF. Industry 4.0 technologies: implementation patterns in manufacturing companies. Int J Prod Econ. 2019;210:15–26. doi:10.1016/j.ijpe.2019.01.004.
  7. 7. Monostori L. Cyber-physical production systems: roots, expectations and R&D challenges. Procedia CIRP. 2014;17:9–13. doi:10.1016/j.procir.2014.03.115.
  8. 8. Kagermann H, Wahlster W, Helbig J. Recommendations for implementing the strategic initiative Industry 4.0. Frankfurt: Acatech; 2013.
  9. 9. Varshney KR. Engineering safety in machine learning. Information. 2016;7(3):40. doi:10.3390/info7030040.
  10. 10. Goodfellow I, Bengio Y, Courville A. Deep learning. Cambridge (MA): MIT Press; 2016.
  11. 11. Venkatasubramanian V. The promise of artificial intelligence in chemical engineering: is it here, finally? AIChE J. 2019;65(2):466–478. doi:10.1002/aic.16489.
  12. 12. Venkatasubramanian V. Artificial intelligence for process systems engineering: challenges and opportunities. Comput Chem Eng. 2019;132:106630. doi:10.1016/j.compchemeng.2019.106630.
  13. 13. Mohammadi M, Al-Fuqaha A, Sorour S, Guizani M. Deep learning for IoT big data and streaming analytics. IEEE Commun Mag. 2018;56(5):110–116. doi:10.1109/MCOM.2018.1700625.
  14. 14. Tao F, Qi Q, Liu A, Kusiak A. Data-driven smart manufacturing. J Manuf Syst. 2018;48:157–169. doi:10.1016/j.jmsy.2018.01.006.
  15. 15. Wang S, Wan J, Li D, Zhang C. Implementing smart factory of Industry 4.0. Int J Distrib Sens Netw. 2016;12:3159805. doi:10.1155/2016/3159805.
  16. 16. Lee J, Davari H, Singh J, Pandhare V. Industrial artificial intelligence for Industry 4.0-based manufacturing systems. Manuf Lett. 2018;18:20–23. doi:10.1016/j.mfglet.2018.09.002.
  17. 17. Zhou K, Liu T, Zhou L. Industry 4.0: towards future industrial opportunities and challenges. In: Proceedings of the 12th International Conference on Fuzzy Systems and Knowledge Discovery; 2015. p. 2147–2152. doi:10.1109/FSKD.2015.7382284.
  18. 18. Xu X, Xu Y, Li L. Industry 4.0 and smart manufacturing for the chemical industry. Engineering. 2018;4(4):532–535. doi:10.1016/j.eng.2018.07.015.
  19. 19. Carvalho TP, Soares FAC, Vita R, Francisco R, Basto JP, Alcalá SG. A systematic literature review of machine learning methods applied to predictive maintenance. Comput Ind Eng. 2019;137:106024. doi:10.1016/j.cie.2019.106024.
  20. 20. Javaid M, Haleem A, Singh RP, Haq MI, Raina A, Suman R. Industry 5.0: Potential applications in COVID-19. Journal of Industrial Integration and Management. 2020 Dec 19;5(04):507-30.
  21. 21. Xu LD, Xu EL, Li L. Industry 4.0: state of the art and future trends. Int J Prod Res. 2018;56(8):2941–2962. doi:10.1080/00207543.2018.1444806.
  22. 22. Breque M, De Nul L, Petridis A. Industry 5.0: towards a sustainable, human-centric and resilient European industry. Luxembourg: European Commission; 2021.
  23. 23. Vaidya S, Ambad P, Bhosle S. Industry 4.0: a glimpse. Procedia Manuf. 2018;20:233–238. doi:10.1016/j.promfg.2018.02.034.
  24. 24. Tanner J, Newbery C. Digital Twins in the Chemical Process Industries. TCE: The Chemical Engineer. 2022 Oct 1(976).
  25. 25. MOSKOVENKO A. Remote Recognition of Human Emotions Using Deep Machine Learning of Artificial Neural Networks.
  26. 26. Chellappa R, Madhavan G, Schlesinger TE, Anderson JL. Engineering and AI: Advancing the synergy. PNAS nexus. 2025 Mar;4(3):pgaf030.
  27. 27. Bailey RL, MacFarlane AJ, Field MS, Tagkopoulos I, Baranzini SE, Edwards KM, Rose CJ, Schork NJ, Singhal A, Wallace BC, Fisher KP. Artificial intelligence in food and nutrition evidence: The challenges and opportunities. PNAS nexus. 2024 Nov 28;3(12):pgae461.
  28. 28. Dey S, Santra M, Ghosh AR, Sen K. Green Chemical Engineering for a Net-Zero Future: Integrating Circular Economy Principles with a Sustainable Process Design. ACS Sustainable Resource Management. 2026 Sep 24;3(9):2904-28.
  29. 29. Yuan Q, Wang X, Xu D, Liu H, Zhang H, Yu Q, Bi Y, Li L. Machine learning-assisted catalysts for advanced oxidation processes: Progress, challenges, and prospects. Catalysts. 2025 Mar 17;15(3):282.
  30. 30. Hu Y, Jia Q, Yao Y, Lee Y, Lee M, Wang C, Zhou X, Xie R, Yu FR. Industrial internet of things intelligence empowering smart manufacturing: A literature review. IEEE Internet of Things Journal. 2024 Feb 20;11(11):19143-67.
  31. 31. Pal PK, Hens A, Behera N, Lahiri SK. Digital twins: transforming the chemical process industry—a review. Can J Chem Eng. 2025;103(8):3611–3636. doi:10.1002/cjce.25611.
  32. 32. Chew JW. AI in chemical engineering: from promise to practice. AIChE J. 2026;72(7). doi:10.1002/aic.70358.
  33. 33. Chowdhury S, Ghosh A, Acharya S, Saha S, Pal PK, Roy S, et al. Transforming chemical process engineering: the role of AI and machine learning in revolutionizing process systems. Can J Chem Eng. 2026;104(2):699–731. doi:10.1002/cjce.70032.
  34. 34. Mane S, Dhote RR, Sinha A, Thirumalaiswamy R. Digital twin in the chemical industry: a review. Digital Twins Appl. 2024;1(2):118–130. doi:10.1049/dgt2.12019.
  35. 35. Massaro A. Electronic artificial intelligence and digital twins in Industry 5.0: a systematic review and perspectives. Machines. 2025;13(9):755. doi:10.3390/machines13090755.
  36. 36. Tao F, Qi Q, Wang L, Nee AYC. Digital twins and cyber-physical systems toward smart manufacturing and Industry 4.0. Engineering. 2019;5(4):653–661.
  37. 37. Perno M, Hvam L, Haug A. A review of digital twin applications in manufacturing. Comput Ind. 2022;134:103558.
  38. 38. Liu M, Fang S, Dong H, Xu C. Review of digital twin about concepts, technologies and industrial applications. J Manuf Syst. 2021;58:346–361.
  39. 39. Errandonea I, Beltrán S, Arrizabalaga S. Digital twin for maintenance: a literature review. Comput Ind. 2020;123:103316.
  40. 40. Liu C, Le Roux L, Körner C, Tabaste O, Lacan F, Bigot S. Digital twin-enabled smart manufacturing: a review. J Manuf Syst. 2022;62:857–878.
  41. 41. Peric M. Leveraging AI for Sustainability in the Fashion Industry: Anticipated and Unanticipated Consequences. PQDT-Global. 2024.
  42. 42. Alrowaie F. Digital twins and artificial intelligence for HAZOP enhancement in process safety: a critical literature review. Processes. 2026;14(14):2334. doi:10.3390/pr14142334.
  43. 43. 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. doi:10.1016/j.compchemeng.2025.109178.
  44. 44. Thelen A, Zhang X, Fink O, Lu Y, Ghosh S, Youn BD, et al. A comprehensive review of digital twin—Part 1: modeling and twinning enabling technologies. Struct Multidiscip Optim. 2023;66:12.
  45. 45. Sharma A, Kosasih E, Zhang J, Brintrup A, Calinescu A. Digital twins: state of the art theory and practice, challenges, and open research questions. J Ind Inf Integr. 2022;25:100215.
  46. 46. Nölle C, Kannisto P. Timeseries on IIoT platforms: requirements and survey for digital twins in process industry. Comput Chem Eng. 2024;182:108594.
  47. 47. Ismail L, Abdelmoti A, Basu A, Berini ADE, Naouss M. A systematic review of digital twin-driven predictive maintenance in industrial engineering. J Manuf Syst. 2025;76:102–130.
  48. 48. Tao F, Qi Q. Make more digital twins. Nature. 2019;573:490–491.
  49. 49. Fuller A, Fan Z, Day C, Barlow C. Digital twin: enabling technologies, challenges and open research. IEEE Access. 2020;8:108952–108971.
  50. 50. Grieves M, Vickers J. Digital twin: mitigating unpredictable, undesirable emergent behavior in complex systems. In: Kahlen FJ, Flumerfelt S, Alves A, editors. Transdisciplinary perspectives on complex systems. Cham: Springer; 2017. p. 85–113.
  51. 51. Porter ME, Heppelmann JE. How smart, connected products are transforming competition. Harv Bus Rev. 2014;92(11):64–88.
  52. 52. Porter ME, Heppelmann JE. How smart, connected products are transforming companies. Harv Bus Rev. 2015;93(10):96–114.
  53. 53. Kagermann H. Change through digitization—value creation in the age of Industry 4.0. In: Albach H, Meffert H, Pinkwart A, Reichwald R, editors. Management of permanent change. Wiesbaden: Springer; 2015. p. 23–45.
  54. 54. Hermann M, Pentek T, Otto B. Design principles for Industry 4.0 scenarios. In: Proceedings of the 49th Hawaii International Conference on System Sciences; 2016. p. 3928–3937.
  55. 55. Lu Y, Morris KC, Frechette S. Current standards landscape for smart manufacturing systems. Gaithersburg (MD): National Institute of Standards and Technology; 2016. NISTIR 8107. p. 1–39.
  56. 56. Ivanov D, Dolgui A, Sokolov B. The impact of digital technology and Industry 4.0 on resilient supply chains. Int J Prod Res. 2019;57(3):829–846.
  57. 57. Kusiak A. Smart manufacturing. Int J Prod Res. 2018;56(1–2):508–517.
  58. 58. Wang L, Wang G. Big data in cyber-physical systems, digital manufacturing and Industry 4.0. Int J Eng Manuf. 2021;11(2):1–18.
  59. 59. Sarker IH. Machine learning: algorithms, real-world applications and research directions. SN Comput Sci. 2021;2:160.
  60. 60. Bishop CM. Pattern recognition and machine learning. New York: Springer; 2006.
  61. 61. Sutton RS, Barto AG. Reinforcement learning: an introduction. 2nd ed. Cambridge (MA): MIT Press; 2018.
  62. 62. Russell SJ, Norvig P. Artificial intelligence: a modern approach. 4th ed. Hoboken (NJ): Pearson; 2021.

Other papers on this site that share keywords with this one, closest match first.

  1. Emerging Trends in Chemical Engineering

    Activated Carbon-Based Adsorptive Desulphurization of Diesel for Ultra-Low Refractory Sulfur Fuel Production

    2026

  2. Emerging Trends in Chemical Engineering

    Artificial Intelligence–Assisted Reduced-Order Modeling and Stability Control in Granular Couette Flow

    2026

  3. Emerging Trends in Chemical Engineering

    Co-Digestion of Sewage Sludge and Food Waste for Enhanced Bio-fertilizer Production

    2026

  4. Emerging Trends in Chemical Engineering

    An Extensive Analysis of CO₂ Capture Technologies and Advanced Sorbent Materials: Advances, Difficulties, and Prospects for The Future

    2026

  5. Emerging Trends in Chemical Engineering

    Framework-Based Optimization of Catalysis Efficiency through Reaction Pathway Engineering

    2026

Read more of it

Subscribe to one journal or build your own list of several. One invoice, one payment, access for a full year.

Subscribe to journals →

Publish your article

Peer-reviewed journals across every subject, with open access options. Send your manuscript to the right editor today.

Publish with us → Calculate your APC
Support