A hierarchical and role-driven digital twin system with applications to complex chemical process operation

Tao Xinyu; Yao Runjie; Zhu Lingyu; Xie Changrui; Zhang Muqian; Chen Xi

doi:10.1016/j.cjche.2025.09.017

Your Location：

Home >

Browse articles >

A hierarchical and role-driven digital twin system with applications to complex chemical process operation

Updated：2026-03-13

- A hierarchical and role-driven digital twin system with applications to complex chemical process operation
- Chinese Journal of Chemical Engineering Vol. 89, Issue 1, Pages: 302-313(2026)
- Affiliations：
  
  State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China
  Huzhou Institute of Industrial Control Technology, Huzhou 313098, China
  College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, China
- Author bio：
  
  Corresponding authors. E-mail addresses: zhuly@zjut.edu.cn(L. Zhu)
  xi_chen@zju.edu.cn(X. Chen).
- Funds：
- DOI：10.1016/j.cjche.2025.09.017
  CLC：
- Received：26 April 2025，
  
  Revised：2025-08-31，
  
  Accepted：01 September 2025，
  
  Online First：30 October 2025，
  
  Published：2026-01
- Accepted：
Scan QR Code
Tao Xinyu, Yao Runjie, Zhu Lingyu, et al. A hierarchical and role-driven digital twin system with applications to complex chemical process operation[J]. Chinese Journal of Chemical Engineering, 2026, 89(1): 302-313.
DOI：

Tao Xinyu, Yao Runjie, Zhu Lingyu, et al. A hierarchical and role-driven digital twin system with applications to complex chemical process operation[J]. Chinese Journal of Chemical Engineering, 2026, 89(1): 302-313. DOI： 10.1016/j.cjche.2025.09.017.

摘要

Abstract

Digital twin technology brings more opportunities and challenges to chemical engineering in both academic and industry. A complex process could have multiple digitalization needs

including simulation

monitoring

operator training

etc

.; thus

a hierarchical digital twin would be a comprehensive solution to that. In this study

a novel and general framework of the digital twin is proposed for operations in process industry. With the hierarchical structure

the framework can handle various tasks driven by different roles in process industry

including managers

engineers

and operators. To complete these tasks

the framework consists of three modules: OAS(Operation Analysis System)

OMS(Operation Monitoring System)

and OTS (Operator Training System). Each module focuses on one unique type of demand from the staff

as well as interactions among them enabling efficient data sharing. Based on the hierarchical framework

a digital twin system is applied for one complex industrial nitration process

which successfully enhances the operation efficiency and safety in several industrial scenarios with different demands.

关键词

Keywords

references

M. Shafto, M. Conory, R. Dolye, E. Glaessgen, C. Kemp, J. LeMoigne, L. Wang, DRAFT Modeling, Simulation, Information Technology & Processing Rodamap, Technology Area 11, 2010.

D. Jones, C. Snider, A. Nassehi, J. Yon, B. Hicks, Characterising the Digital Twin: a systematic literature review, CIRP J. Manuf. Sci. Technol. 29 (2020) 36—52.

M. Grieves, J. Vickers, Digital twin: mitigating unpredictable, undesirable emergent behavior in complex systems, in: Transdisciplinary Perspectives on Complex Systems: New Findings and Approaches, Springer International Publishing, Cham, 2017, pp.85—113.

M.N. Liu, S.L. Fang, H.Y. Dong, C.Z. Xu, Review of digital twin about concepts, technologies, and industrial applications, J. Manuf. Syst. 58(2021)346—361.

L. Peterson, I.V. Gosea, P. Benner, K. Sundmacher, Digital twins in process engineering: an overview on computational and numerical methods, Comput. Chem. Eng. 193 (2025)108917.

Q. Liu, H. Zhang, J.W. Leng, X. Chen, Digital twin-driven rapid individualised designing of automated flow-shop manufacturing system, Int. J. Prod. Res. 57 (12)(2019)3903—3919.

F. Chinesta, E. Cueto, E. Abisset-Chavanne, J.L. Duval, F. El Khaldi, Virtual, digital and hybrid twins: a new paradigm in data-based engineering and engineered data, Arch. Comput. Methods Eng. 27(1)(2020)105—134.

X.M. Sun, J.S. Bao, J. Li, Y.M. Zhang, S.M. Liu, B. Zhou, A digital twin-driven approach for the assembly-commissioning of high precision products, Robot. Comput. Integrated Manuf. 61 (2020)101839.

C.Z. Li, S. Mahadevan, Y. Ling, S. Choze, L.P. Wang, Dynamic Bayesian network for aircraft wing health monitoring digital twin, AIAA J. 55(3)(2017)930—941.

C.C. Pantelides, F.E. Pereira, P.J. Stanger, N.F. Thornhill, Process operations:from models and data to digital applications, Comput. Chem. Eng. 180(2024) 108463.

R. He, G.M. Chen, C. Dong, S.F. Sun, X.Y. Shen, Data-driven digital twin technology for optimized control in process systems, ISA Trans. 95 (2019) 221—234.

M. El Wajeh, A. Mhamdi, A. Mitsos, Dynamic modeling and plantwide control of a production process for biodiesel and glycerol, Ind. Eng. Chem. Res. 62(27) (2023)10559—10576.

S. Spatenka, M. Matzopoulos, Z. Urban, A. Cano, From laboratory to industrial operation: model-based digital design and optimization of fixed-bed catalytic reactors, Ind. Eng. Chem. Res. 58 (28) (2019) 12571—12585.

J.X. Yu, P. Liu, Z. Li, Data reconciliation-based simulation of thermal power plants for performance estimation and digital twin development, Comput. Chem. Eng. 168(2022)108063.

S.D. Gao, C.M. Bo, C. Jiang, Q.L. Zhang, G.K. Yang, J. Chu, Hybrid modeling for carbon monoxide gas-phase catalytic coupling to synthesize dimethyl oxalate process, Chin. J. Chem. Eng. 70 (2024) 234—250.

W. Wei, P.F. Xia, Z.W. Liu, M. Zuo, A modified active disturbance rejection control for a wastewater treatment process, Chin. J. Chem. Eng. 28(10)(2020) 2607—2619.

M. Vaccari, R.B. di Capaci, E. Brunazzi, L. Tognotti, P. Pierno, R. Vagheggi, G. Pannocchia, Optimally managing chemical plant operations: an example oriented by industry 4.0 paradigms, Ind. Eng. Chem. Res. 60 (21) (2021) 7853—7867.

P.A. Quadros, N.M.C. Oliveira, C.M.S.G. Baptista, Continuous adiabatic industrial benzene nitration with mixed acid at a pilot plant scale, Chem. Eng. J. 108 (1—2) (2005) 1—11.

C.R. Xie, R.J. Yao, L.Y. Zhu, H. Gong, H.Y. Li, X. Chen, Hybrid dynamic modeling of an industrial reactor network with first-principles and data-driven approaches, Chem. Eng. Sci. 289 (2024) 119852.

L.T. Biegler, I.E. Grossmann, A. Westerberg, Systematic methods of chemical process design. Mass and Energy Balances, 1997.

Views

Downloads

CSCD

Alert me when the article has been cited

Submit

Tools

Publicity Resources

Reinforced temperature control of a reactive double dividing-wall distillation column

Total plant performance evaluation based on big data: Visualization analysis of TE process

Enhancing low-temperature NH3-SCR activity via dual-phase MnOx/Cu-SSZ-13 composite catalysts: The different roles of Mn2O3 and Mn3O4

Data scarcity solutions in microreactor modeling co-driven by generic mechanisms and data generation

Preparation of a cellulose nanocrystals/surfactant stabilized Pickering emulsion systems with improved foliar affinity and bioactivity

Related Author

Wei Qin

Lijing Zang

Kejin Huang

Haisheng Chen

Yang Yuan

Xing Qian

Liang Zhang

Shaofeng Wang

Related Institution

College of Information Science and Technology, Beijing University of Chemical Technology

Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology

⁰