Article Knowledge-data Collaborated Digital Twin Model of Papermaking Process Zejun Liu, Mengna Hong and Jigeng Li * State Key Laboratory of Pulp and Paper Engineering, South China University of Technology, Guangzhou 510640, China; msliuzejun@mail.scut.edu.cn (Z.L.); femnhong@scut.edu.cn (M.H.) * Corresponding author. E-mail: jigengli@scut.edu.cn (J.L.) Received: 13 December 2023; Accepted: 23 January 2024; Available online: 27 February 2024 ABSTRACT: 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.
Keywords: Digital twin; Model; Papermaking; Parameter prediction; Simulation
© 2024 The authors. This is an open access article under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).
1. Introduction The papermaking process involves a large number of complex physical and chemical reactions [1], accompanied with characteristics of multi-variable, strong coupling and non-linear etc., hindering the intelligent development process of the papermaking industry [2]. At present, the process models and control systems, established by paper-making enterprises based on a new generation of information technology, have not yet solved the problem of “ data silos ” and cannot integrate material flow and energy flow information to achieve real-time monitoring and control of paper production, which affects the sustainability of the process dramatically. With the development of Industry 4.0 era [3,4], digital twin is gradually being studied and applied in the process industry [5]. In the steel industry, with the applications of technologies such as intelligent data sensing, multi-source heterogeneous data integration, efficient data transmission, digital twin creation, enhanced interaction, and conversion applications, a production line that combines reality with virtuality can be established to realize the optimization of the production process [6]. In the machine building industry, simulation and optimization based on the digital twin ’ s dynamic perception of the physical machine tool, it can be effectively optimized machining conditions such as cutting parameters and reduce carbon emissions [7]. It is worth noting that, the establishment of a digital twin model of the process industry, can facilitate the simulation, analysis, monitoring and optimization of manufacturing processes in real time, turns out the dynamic management without physical efforts [8 – 11]. The papermaking industry is a typical process-oriented industry [12,13], and the information technologies such as big data and machine learning have been widely used in its energy-saving renovation [14,15], modeling [16], scheduling [17,18], fault prediction [19], decision-making support [20], and process optimization [21 – 23] sectors. However, most of the previous studies only focus on a single process or a single equipment of it, without considering the modeling and control of the whole process systematically, which tends to achieve local optimizations. Therefore,
https://doi.org/10.35534/amsm.2024.10003
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