Article Open Access

Knowledge-data Collaborated Digital Twin Model of Papermaking Process

Advanced Materials & Sustainable Manufacturing. 2024, 1(1), 10003;
Zejun Liu    Mengna Hong    Jigeng Li *   
State Key Laboratory of Pulp and Paper Engineering, South China University of Technology, Guangzhou 510640, China
Authors to whom correspondence should be addressed.

Received: 13 Dec 2023    Accepted: 23 Jan 2024    Published: 27 Feb 2024   

(This article belongs to the Topic Collection Circular Economy and Sustainability of Manufacturing )


The structure of the drying section in papermaking process is complex and too compacted to install sensors. In order to monitor the parameters in dynamic and manage the process practically with virtual simulations instead of physical experiments, a digital twin-based process parameter visualization model is constructed in this study. Regarding to the possible missing data in the modeling framework, it is proposed to combine industrial data, and knowledge of mechanism with intelligent algorithms to fill in the missing parameters. Upon which, a digital twin-based data visualization model is established using CADSIM Plus simulation software. Both of the knowledge -based mechanism solution model and the random forest-based parametric prediction model perform well, and the predicted parameters can support the digital twin visualization model in CADSIM Plus. Visual modeling of surface condenser in the paper drying section was realized for example, and results show that the model is capable of monitoring the dynamic changes of parameters in real time, so as to support the optimization and decision making of papermaking process such as formation, drying, et al.


Man Y, Han Y, Li J, Hong M. Review of energy consumption research for papermaking industry based on life cycle analysis. Chin. J. Chem. Eng. 2019, 27, 1543–1553. [Google Scholar]
Qian F, Bogle D, Wang M, Pistikopoulos S, Yan J. Artificial intelligence for smart energy systems in process industries. Appl. Energy 2022, 324, 119684. [Google Scholar]
Antonino PO, Capilla R, Pelliccione P, Schnicke F, Espen D, Kuhn T, et al. A Quality 4.0 Model for architecting industry 4.0 systems. Adv. Eng. Inform. 2022, 54, 101801. [Google Scholar]
Fan Y, Dai C, Huang S, Hu P, Wang X, Yan M. A life-cycle digital-twin collaboration framework based on the industrial internet identification and resolution. Int. J. Adv. Manuf. Technol. 2022, 123, 2883–2911. [Google Scholar]
Hu Y, Li J, Hong M, Ren J, Man Y. Industrial artificial intelligence based energy management system: Integrated framework for electricity load forecasting and fault prediction. Energy 2022, 244, 123195. [Google Scholar]
Li Y, Yang C, Zhang H, Li J. Discussion on key technologies of digital twin in process industry. Acta Automat. Sin. 2021, 47, 501–514. doi:10.16383/j.aas.c200147 (In Chinese).
Zhao L, Fang Y, Lou P, Yan J, Xiao A. Cutting parameter optimization for reducing carbon emissions using digital twin. Int. J. Precis. Eng. Manuf. 2021, 22, 933–949. [Google Scholar]
Huynh TA, Zondervan E. Process intensification and digital twin-the potential for the energy transition in process industries. Phys. Sci. Rev. 2022, 8, 4859–4877. [Google Scholar]
He Z, Xu J, Tran KP, Thomassey S, Zeng X, Yi C. Modeling of textile manufacturing processes using intelligent techniques: a review. Int. J. Adv. Manuf. Technol. 2021, 116, 39–67. [Google Scholar]
Li M, He Z, Xu J. A comparative study of ozonation on aqueous reactive dyes and reactive-dyed cotton. Color. Technol. 2021, 137, 376–388. [Google Scholar]
He Z, Li M, Zuo D, Xu J, Yi C. Effects of color fading ozonation on the color yield of reactive-dyed cotton. Dye Pigments 2019, 164, 417–427. [Google Scholar]
Hu Y, Li J, Hong M, Ren J, Lin R, Liu Y, et al. Short term electric load forecasting model and its verification for process industrial enterprises based on hybrid GA-PSO-BPNN algorithm—A case study of papermaking process. Energy 2019, 170, 1215–1227. [Google Scholar]
Man Y, Yan Y, Wang X, Ren J, Xiong Q, He Z. Overestimated carbon emission of the pulp and paper industry in China. Energy 2023, 273, 127279. [Google Scholar]
Lai C, Wang Y, Fan K, Cai Q, Ye Q, Pang H, et al. An improved forecasting model of short-term electric load of papermaking enterprises for production line optimization. Energy 2022, 245, 123225. [Google Scholar]
He Z, Liu C, Wang Y, Wang X, Man Y. Optimal operation of wind-solar-thermal collaborative power system considering carbon trading and energy storage. Appl. Energy 2023, 352, 121993. [Google Scholar]
He Z, Qian J, Li J, Hong M, Man Y. Data-driven soft sensors of papermaking process and its application to cleaner production with multi-objective optimization. J. Clean. Prod. 2022, 372, 133803. [Google Scholar]
Zhang H, Li J, Hong M, Man Y, He Z. Cost Optimal Production-Scheduling Model Based on VNS-NSGA-II Hybrid Algorithm—Study on Tissue Paper Mill. Processes 2022, 10, 12072. [Google Scholar]
Zhang Z, He X, Man Y, He Z. Multi-objective scheduling in dynamic of household paper workshop considering energy consumption in production process. J. Smart Environ. Green Comput. 2023, 3, 87–105. [Google Scholar]
He Z, Chen G, Hong M, Xiong Q, Zeng X, Man Y. Process Monitoring and Fault Prediction of Papermaking by Learning from Imperfect Data. IEEE Trans. Autom. Sci. Eng. 2023. doi:10.1109/TASE.2023.3290552.
He Z, Tran KP, Thomassey S, Zeng X, Xu J, Yi C. A deep reinforcement learning based multi-criteria decision support system for optimizing textile chemical process. Comput. Ind. 2021, 125, 103373. [Google Scholar]
Zhang Y, Hong M, Li J, Ren J, Man Y. Energy system optimization model for tissue papermaking process. Comput. Chem. Eng. 2021, 146, 107220. [Google Scholar]
He Z, Hong M, Zheng H, Wang J, Xiong Q, Man Y. Towards low-carbon papermaking wastewater treatment process based on Kriging surrogate predictive model. J. Clean. Prod. 2023, 425, 139039. [Google Scholar]
Soares RM, Câmara MM, Feital T, Pinto JC. Digital twin for monitoring of industrial multi-effect evaporation. Processes 2019, 7, 537. [Google Scholar]
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. [Google Scholar]
Schroeder GN, Steinmetz C, Rodrigues RN, Henriques RV, Rettberg A, Pereira CE. A methodology for digital twin modeling and deployment for industry 4.0. Proc. IEEE 2020, 109, 556–567. [Google Scholar]
Shabbir I, Mirzaeian M, Sher F. Energy efficiency improvement potentials through energy benchmarking in pulp and papermaking industry. Clean. Chem. Eng. 2022, 3, 100058. [Google Scholar]
He Z, Tran KP, Thomassey S, Zeng X, Xu J, Yi C. Multi-objective optimization of the textile manufacturing process using Deep-Q-Network based multi-agent reinforcement learning. J. Manuf. Syst. 2021, 62, 939–949. [Google Scholar]
Xu J, He Z, Li S, Ke W. Production cost optimization of enzyme washing for indigo dyed cotton denim by combining Kriging surrogate with differential evolution algorithm. Text. Res. J. 2020, 90, 1860–1871. [Google Scholar]
Xu J, Liu F, He Z, Zhang Z, Li S. Cost optimization of sodium hypochlorite bleaching washing for denim by combining ensemble of surrogates with particle swarm optimization. J. Eng. Fiber. Fabr. 2021, 16. doi:10.1177/15589250211022331.
Li J, Tian X, Liu J. Dynamic Data Scheduling of a Flexible Industrial Job Shop Based on Digital Twin Technology. Discrete Dyn. Nat. Soc. 2022, 2022, 1009507. [Google Scholar]
Bamunuarachchi D, Georgakopoulos D, Banerjee A, Jayaraman PP. Digital twins supporting efficient digital industrial transformation. Sensors 2021, 21, 6829. [Google Scholar]
Zhuang C, Miao T, Liu J, Xiong H. The connotation of digital twin, and the construction and application method of shop-floor digital twin. Robot. Comput. Integr. Manuf. 2021, 68, 102075. [Google Scholar]
Negri E, Berardi S, Fumagalli L, Macchi M. MES-integrated digital twin frameworks. J. Manuf. Syst. 2020, 56, 58–71. [Google Scholar]
Tao F, Sui F, Liu A, Qi Q, Zhang M, Song B, et al. Digital twin-driven product design framework. Int. J. Prod. Res. 2019, 57, 3935–3953. [Google Scholar]
Zhang Y, Wang W, Zhang H, Li H, Liu C, Du X. Vibration monitoring and analysis of strip rolling mill based on the digital twin model. Int. J. Adv. Manuf. Technol. 2022, 122, 3667–3681. [Google Scholar]
Ding G, Guo S, Wu X. Dynamic Scheduling Optimization of Production Workshops Based on Digital Twin. Appl. Sci. 2022, 12, 10451. [Google Scholar]
Goodwin T, Xu J, Celik N, Chen CH. Real-time digital twin-based optimization with predictive simulation learning. J. Simul. 2022, doi:10.1080/17477778.2022.2046520.
Yin Y, Liu J, Wang Y, Zhuo Y, Meng Y. Modeling of Ventilation’s Influence on Energy Consumption in Multi-cylinder Dryer Section Part1: Theoretical Model. Int. J. Comput. Intell. Syst. 2022, 15, 1–13. [Google Scholar]
Marques JP, Cunha DC, Harada LM, Silva LN, Silva ID. A cost-effective trilateration-based radio localization algorithm using machine learning and sequential least-square programming optimization. Comput. Commun. 2021, 177, 1–9. [Google Scholar]
Liu Y, Shen W, Man Y, Liu Z, Seferlis P. Optimal scheduling ratio of recycling waste paper with NSGAII based on deinked-pulp properties prediction. Comput. Ind. Eng. 2019, 132, 74–83. [Google Scholar]
Jadidi A, Menezes R, de Souza N, de Castro Lima AC. Short-term electric power demand forecasting using NSGA II-ANFIS model. Energies 2019, 12, 1891. [Google Scholar]
Verma S, Pant M, Snasel V. A comprehensive review on NSGA-II for multi-objective combinatorial optimization problems. IEEE Access 2021, 9, 57757–57791. [Google Scholar]
Ao Y, Li H, Zhu L, Ali S, Yang Z. The linear random forest algorithm and its advantages in machine learning assisted logging regression modeling. J. Pet. Sci. Eng. 2019, 174, 776–789. [Google Scholar]
Ciulla G, D’Amico A. Building energy performance forecasting: A multiple linear regression approach. Appl. Energy 2019, 253, 113500. [Google Scholar]
Rathore SS. An exploratory analysis of regression methods for predicting faults in software systems. Soft Comput. 2021, 25, 14841–14872. [Google Scholar]
Otchere DA, Ganat TOA, Ojero JO, Tackie-Otoo BN, Taki MY. Application of gradient boosting regression model for the evaluation of feature selection techniques in improving reservoir characterisation predictions. J. Pet. Sci. Eng. 2022, 208, 109244. [Google Scholar]
Cai J, Xu K, Zhu Y, Hu F, Li L. Prediction and analysis of net ecosystem carbon exchange based on gradient boosting regression and random forest. Appl. Energy 2020, 262, 114566. [Google Scholar]
Sun L, Ji Y, Zhu X, Peng T. Process knowledge-based random forest regression for model predictive control on a nonlinear production process with multiple working conditions. Adv. Eng. Inform. 2022, 52, 101561. [Google Scholar]
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