Toward Intelligent and Green Ethylene Manufacturing: An AI-Based Multi-Objective Dynamic Optimization Framework for the Steam Thermal Cracking Process

Yao Zhang; Peng Sha; Meihong Wang; Cheng Zheng; Shengyuan Huang; Xiao Wu; Joan Cordiner

doi:10.1016/j.eng.2025.06.045

Engineering ›› 2025, Vol. 52 ›› Issue (9) :160 -171. DOI: 10.1016/j.eng.2025.06.045

Research

Article

Toward Intelligent and Green Ethylene Manufacturing: An AI-Based Multi-Objective Dynamic Optimization Framework for the Steam Thermal Cracking Process

Author information +

History +

PDF (2173KB)

Abstract

With growing concerns over environmental issues, ethylene manufacturing is shifting from a sole focus on economic benefits to an additional consideration of environmental impacts. The operation of the thermal cracking furnace in ethylene manufacturing determines not only the profitability of an ethylene plant but also the carbon emissions it releases. While multi-objective optimization of the thermal cracking furnace to balance profit with environmental impact is an effective solution to achieve green ethylene manufacturing, it carries a high computational demand due to the complex dynamic processes involved. In this work, artificial intelligence (AI) is applied to develop a novel hybrid model based on physically consistent machine learning (PCML). This hybrid model not only reduces the computational demand but also retains the interpretability and scalability of the model. With this hybrid model, the computational demand of the multi-objective dynamic optimization is reduced to 77 s. The optimization results show that dynamically adjusting the operating variables with coke formation can effectively improve profit and reduce CO₂ emissions. In addition, the results from this study indicate that sacrificing 28.97 % of the annual profit can significantly reduce the annual CO₂ emissions by 42.89 %. The key findings of this study highlight the great potential for green ethylene manufacturing based on AI through modeling and optimization approaches. This study will be important for industrial practitioners and policy-makers.

Keywords

Green manufacturing / Thermal cracking furnace / Artificial intelligence / Hybrid modeling / Multi-objective optimization / Process modeling

Cite this article

Download citation ▾

Yao Zhang, Peng Sha, Meihong Wang, Cheng Zheng, Shengyuan Huang, Xiao Wu, Joan Cordiner. Toward Intelligent and Green Ethylene Manufacturing: An AI-Based Multi-Objective Dynamic Optimization Framework for the Steam Thermal Cracking Process. Engineering, 2025, 52(9): 160-171 DOI:10.1016/j.eng.2025.06.045

登录浏览全文

4963

注册一个新账户忘记密码

References

Publishing order | Descend order by publishing year | Descend order by cited within

[1]	Sadrameli S. Thermal/catalytic cracking of hydrocarbons for the production of olefins: a state-of-the-art review I: thermal cracking review. Fuel 2015; 140:102-15.

[2]	Fakhroleslam M, Sadrameli SM. Thermal cracking of hydrocarbons for the production of light olefins; a review on optimal process design, operation, and control. Ind Eng Chem Res 2020; 59(27):12288-303.

[3]	Yuan B, Zhang Y, Du W, Wang M, Qian F. Assessment of energy saving potential of an industrial ethylene cracking furnace using advanced exergy analysis. Appl Energy 2019; 254:113583.

[4]	Hu G, Li X, Liu X, Hu J, Otitoju O, Wang M, et al. Techno-economic evaluation of post-combustion carbon capture based on chemical absorption for the thermal cracking furnace in ethylene manufacturing. Fuel 2023; 331:125604.

[5]	Li Z, Åhman M, Nilsson LJ, Bauer F. Towards carbon neutrality: transition pathways for the Chinese ethylene industry. Renew Sustain Energy Rev 2024; 199:114540.

[6]	Zhang Y, Vangaever S, Theis G, Henneke M, Heynderickx GJ, Van Geem KM. Feasibility of biogas and oxy-fuel combustion in steam cracking furnaces: experimental and computational study. Fuel 2021; 304:121393.

[7]	Tijani M, Zondag H, Van Delft Y. Review of electric cracking of hydrocarbons. ACS Sustain Chem Eng 2022; 10(49):16070-89.

[8]	Amghizar I, Dedeyne JN, Brown DJ, Marin GB, Van Geem KM. Sustainable innovations in steam cracking: CO₂ neutral olefin production. React Chem Eng 2020; 5(2):239-57.

[9]	Masoumi ME, Sadrameli SM, Towfighi J, Niaei A. Simulation, optimization and control of a thermal cracking furnace. Energy 2006; 31(4):516-27.

[10]	Gao GY, Wang M, Ramshaw C, Li XG, Yeung H. Optimal operation of tubular reactors for naphtha cracking by numerical simulation. Asia Pac J Chem Eng 2009; 4(6):885-92.

[11]	Berreni M, Wang M. Modelling and dynamic optimization of thermal cracking of propane for ethylene manufacturing. Comput Chem Eng 2011; 35 (12):2876-85.

[12]	Yu K, While L, Reynolds M, Wang X, Liang JJ, Zhao L, et al. Multiobjective optimization of ethylene cracking furnace system using self-adaptive multiobjective teaching-learning-based optimization. Energy 2018; 148:469-81.

[13]	Shen W, Tian Z, Zhao L, Qian F. Life cycle assessment and multiobjective optimization for steam cracking process in ethylene plant. ACS Omega 2022; 7 (18):15507-17.

[14]	Zendehboudi S, Rezaei N, Lohi A. Applications of hybrid models in chemical, petroleum, and energy systems: a systematic review. Appl Energy 2018; 228:2539-66.

[15]	Rezaei N, Rahimpour MR, Khayatian A, Jahanmiri A. A new hybrid approach in the estimation of end-states of a tubular plug-flow reactor by Kalman filter. Chem Eng Process 2008; 47(5):770-9.

[16]	Bui L, Joswiak M, Castillo I, Phillips A, Yang J, Hickman D. A hybrid modeling approach for catalyst monitoring and lifetime prediction. ACS Eng Au 2022; 2 (1):17-26.

[17]	Xiao T, You F. Building thermal modeling and model predictive control with physically consistent deep learning for decarbonization and energy optimization. Appl Energy 2023; 342:121165.

[18]	Xiao T, You F. Physically consistent deep learning-based day-ahead energy dispatching and thermal comfort control for grid-interactive communities. Appl Energy 2024;353:122133.

[19]	Ren T, Patel MK, Blok K. Steam cracking and methane to olefins: energy use, CO₂ emissions and production costs. Energy 2008; 33(5):817-33.

[20]	Fakhroleslam M, Sadrameli SM. Thermal/catalytic cracking of hydrocarbons for the production of olefins; a state-of-the-art review III: process modeling and simulation. Fuel 2019; 252:553-66.

[21]	Mynko O, Amghizar I, Brown DJ, Chen L, Marin GB, de Alvarenga RF, et al. Reducing CO₂ emissions of existing ethylene plants: evaluation of different revamp strategies to reduce global CO2 emission by 100 million tonnes. J Clean Prod 2022; 362:132127.

[22]	Sarris SA, Symoens SH, Olahova N, Reyniers MF, Marin GB, Van Geem KM. Alumina-based coating for coke reduction in steam crackers. Materials 2020; 13(9):2025.

[23]	Zhang Y, Yan H, Chong D, Cordiner J, Wang M. Modelling, simulation, exergy and economic analyses of thermal cracking of propane using CO₂ and steam as diluents. Comput Aided Chem Eng 2024; 53:691-6.

[24]	Zhang Y, Yan H, Chong D, Guo C, Huang S, Cordiner J, et al. Modelling, simulation, thermodynamic and economic performance analysis of steam and CO₂ as diluents in thermal cracking furnace for ethylene manufacturing. Fuel 2025; 381:133656.

[25]	Sundaram K, Froment G. Kinetics of coke deposition in the thermal cracking of propane. Chem Eng Sci 1979; 34(5):635-44.

[26]	Zhao H, Ierapetritou MG, Rong G. Production planning optimization of an ethylene plant considering process operation and energy utilization. Comput Chem Eng 2016; 87:1-12.

[27]	Van Damme P, Narayanan S, Froment G. Thermal cracking of propane and propane-propylene mixtures: pilot plant versus industrial data. AIChE J 1975; 21(6):1065-73.

[28]	Cui Y, Han Y, Geng Z, Zhu Q, Fan J. Production optimization and energy saving of complex chemical processes using novel competing evolutionary membrane algorithm: emphasis on ethylene cracking. Energ Conver Manage 2019; 196:311-9.

[29]	Bhaskar V, Gupta SK, Ray AK. Applications of multiobjective optimization in chemical engineering. Rev Chem Eng 2000; 16(1):1-54.

[30]	Deb K, Pratap A, Agarwal S, Meyarivan TA. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 2002; 6(2):182-97.

[31]

Hamdani TM, Won JM, Alimi AM, Karray F. Multi-objective feature selection with NSGA II. In: Beliczynski B, Dzielinski A, Iwanowski M, Ribeiro B, editors. Proceedings of the 8th International Conference on Adaptive and Natural Computing Algorithms, 2007 Apr 11-14, Warsaw, Poland. Berlin:Springer; 2007. p. 240-7.

[32]	Xu XF, Wang K, Ma WH, Wu CL, Huang XR, Ma ZX, et al. Multi-objective particle swarm optimization algorithm based on multi-strategy improvement for hybrid energy storage optimization configuration. Renew Energy 2024; 223:120086.

[33]	Chaudhari P, Thakur AK, Kumar R, Banerjee N, Kumar A. Comparison of NSGAIII with NSGA-II for multi objective optimization of adiabatic styrene reactor. Mater Today Proc 2022; 57:1509-14.

[34]	Geng Z, Wang Z, Zhu Q, Han Y. Multi-objective operation optimization of ethylene cracking furnace based on AMOPSO algorithm. Chem Eng Sci 2016; 153:21-33.

[35]	Agrawal N, Rangaiah GP, Ray AK, Gupta SK. Multi-objective optimization of the operation of an industrial low-density polyethylene tubular reactor using genetic algorithm and its jumping gene adaptations. Ind Eng Chem Res 2006; 45(9):3182-99.

[36]	Nabavi SR, Rangaiah GP, Niaei A, Salari D. Multiobjective optimization of an industrial LPG thermal cracker using a first principles model. Ind Eng Chem Res 2009; 48(21):9523-33.

[37]	Berreni M, Wang M. Modelling and dynamic optimisation for optimal operation of industrial tubular reactor for propane cracking. Comput Aided Chem Eng 2011; 29:955-9.

[38]	Shen F, Wang M, Huang L, Qian F. Exergy analysis and multi-objective optimisation for energy system: a case study of a separation process in ethylene manufacturing. J Ind Eng Chem 2021; 93:394-406.