Abstract
Based on the rapid development of intelligent technologies in recent years, the digital transformation of the whole industry and society has become increasingly important. Among them, digital twins and artificial intelligence have great potentials in improving industry processes and further enhancing productivity. This paper proposes an Intelligent Digital Twin System (IDTS) based on artificial intelligence and digital twins for the paper industry. The system includes the prediction models for the stirring speed of the dump chest, the water consumption of the deflaker, the supply air pressure of the dryer, and the exhaust air temperature of the dryer. The sensors, 5G network slices, and other equipment collect related data during the papermaking process for generating twin data, and we use the prediction models to analyze the data and monitor important indicators (stirring speeds, water consumptions, supply air pressures, and exhaust air temperatures) for the manufacturing processes, which are used to improve the energy utilization and production efficiency of the paper industry and thus facilitate cost saving. We apply this intelligent digital twin system and its associated prediction models to an actual paper manufacturing factory and show their effectiveness by improving the operational efficiency and saving labor and maintenance costs.
Introduction
In today's society, paper manufacturing is an important process in the paper industry. In the process of papermaking, a large amount of water and other energy resources are used for raw material preparation, pulp dilution, paper web formation, and equipment cleaning. Therefore, how to reduce resources and energy consumptions and improve the efficiency of paper manufacturing is a research hotspot in the field of the modern paper industry. To reduce the water consumption, the paper manufacturing processes are optimized by studying the life cycle of water used in the paper industry and analyzing the life cycle of water consumptions of different papermaking methods (Man et al., 2018). For the papermaking using recycled paper, some fiber supplements help significantly reduce the water usage and industrial waste pollution (Tarres et al., 2018). Furthermore, decarbonization is proposed for the paper industry and the possible solutions are examined for reducing the pollutant emission (Del Rio et al., 2022). All these efforts aim to effectively reduce high energy consumptions and pollutions in the paper industry. Besides, various technologies are used in the paper industry to prevent molds, improve the paper quality, and enhance the sustainability, such as the nanotechnology (Samyn et al., 2018) and the nano cellulose (Balea et al., 2020).
In addition to the abovementioned traditional papermaking technologies and improved processes, computer technologies have been used in the paper industry to reduce energy consumptions. The decision support system can be used for the interconnection process, which reduces steam consumptions, improves the productivity, and maintains the product quality by optimizing the vacuum pressure of the forming and pressing parts (Afshar et al. 2011). Cloud manufacturing (Li et al., 2010) has been widely adopted in manufacturing industry, the expansion and adoption of the cloud computing concept in the manufacturing field (Li et al., 2012), such as intelligent manufacturing condition monitor platforms (Yang et al., 2020), the integration of services and agents (Zhao et al., 2020), and the service-oriented and interoperable solution (Wang & Xu, 2013). These applications are based on such technologies as cloud technology, edge computing, and 5G wireless communication networks, which enables cloud manufacturing and intelligent manufacturing for more efficient manufacturing processes.
Artificial Intelligence (AI) technologies have been widely used in various applications including manufacturing industries. Integrating artificial intelligence technologies (Dogan & Birant, 2021), information communication, and related product technologies into manufacturing, intelligent manufacturing has entered into a new phase (Li et al., 2017). New architectures based on artificial intelligence and Web programming facilitate the remote and quick access and achieve more flexible design and manufacturing (Cheng et al., 1998). Built on cloud servers, embedded controllers, and mobile terminals based on Internet of Things (IoT) technologies, the could manufacturing system is capable of optimizing and customizing the production procedure (Bu et al., 2021). In the Industry 4.0 era, the physical world and virtual world grow together (Schluse et al., 2018). Digital Twins (DTs) are attracting a lot of attentions in manufacturing. The virtual DTs systems integrate the online and real time data into the real physical systems for the operation efficiency and the performance optimization, such as product-service system of the warehouse (Leng et al., 2021), and complex device systems for the more predictable equipment management (Ren et al., 2022).
The traditional manufacturing industry is not suitable for today's digital age because of its lack of real-time interactions and limited transparency. The applicable AI and DTs technologies can effectively improve the transparency and the interaction ability in the production process, and thus effectively enhance the production efficiency and shorten the production cycle of the traditional manufacturing industry. Although DTs technologies have been used to other manufacturing systems, their integration with paper manufacturing systems is lacking. To the best of our knowledge, there are no applicable DTs-based papermaking systems in the paper industry. The introduction of these technologies helps improve the efficiency, save energies, and reduce emissions during the paper making process in the paper industry.
The papermaking has two main stages, pulping and papermaking. The agitator and the deflaker are used in the pulping process, and the dryer is used in the papermaking process respectively. In the pulping process, the stirring speed needs to be predicted to prevent slurry concentration quality problems. In addition, it is necessary to continuously monitor the current water consumption of the deflaker to achieve abnormal early warning during the dredging process and reduce equipment failure losses. In the papermaking process, some sensors of the dryer are exposed to the humid environment for a long time, which leads to a high damage rate and the difficult monitoring, especially the supply air pressure of the dryer. The paper web should be dried as evenly as possible (vertically and horizontally) in the drying part to avoid wrinkles, edge warping, and other problems, and therefore a good ventilation system is needed. Towards these goals, we infuse digital twins related technologies into the papermaking process to help address those problems.
In this work, we propose an intelligent digital twin system (IDTS) framework to improve the production efficiency in the paper industry. Coupling with different stages in the papermaking procedure, we develop the corresponding prediction models, including the prediction models for the stirring speed of the dump chest, the water consumption of the deflaker, the supply air pressure of the dryer, and the exhaust air temperature of the dryer. The stirring speed model of the dump chest screens out a section of eligible data and obtains the liquid level to analyze the stirring speed. The water consumption model predicts the outlet flow of the deflaker based on extreme random forests. The prediction model for the supply air pressure of the dryer predicts the indicator of the pressure to reduce the maintenance and production costs using the regression model. The prediction model for the exhaust air temperature of the dryer selects the most effective features to model the temperature data using Light Gradient Boosting Machine (LightGBM) and takes the exhaust air temperature of the dryer as the result for the regression model training.
The main contributions of this paper are as follows. Firstly, we propose a framework of an intelligent digital system to detect the important production indicators (stirring speeds, water consumptions, supply air pressures, and exhaust air temperatures) in the paper industry. Various types of sensors, 5G network slices, and other equipment collect related data during the papermaking process for generating twin data, and we use machine learning and other related approaches to analyze the data and monitor important indicators for the improved manufacturing processes. Secondly, we identify four important processes of the papermaking and construct corresponding prediction models to accurately detect their operational indicators used to adjust the processes and their operational equipment and parameters. Thirdly, we apply this proposed framework to a papermaking company in China to improve the operational efficiency and save costs. The practical adoption shows that each prediction model is able to help optimize the corresponding process and verifies the efficiency of the proposed intelligent digital twin system by reducing the cost of product quality losses, equipment abnormal losses, and drying steam consumptions within 8 months. The rest of the paper is organized as follows. Section 2 reviews the related literature in papermaking and digital twin technologies. Section 3 proposes the overall framework of the intelligent digital twin systems in papermaking. Section 4 presents the four prediction models of the intelligent digital twin system. Section 5 provides the experiments and results. Section 6 discusses related issues. Section 7 concludes the paper and points out the future research directions.
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Section snippets
Related work
In recent years, the rapid development of new technologies, such as cloud manufacturing (Ooi et al., 2018), artificial intelligence, and intelligent systems (Gomolka et al., 2022), has greatly impacted many traditional applications including industries and manufacturing. Fisher et al. (2018) introduced the definition, characteristics, and the architecture of cloud manufacturing in details, and identified collaborative design, automation improvement, process improvement, and recycling as four
Overall framework of the intelligent digital twin system of papermaking
This paper aims to develop an intelligent digital twin system (IDTS) for paper manufacturing industries. Below we illustrate the overall framework of the IDTS. Fig. 1 shows the structure of the IDTS. In the figure, there are two spaces including the physical space and the digital (virtual) space. The physical space mainly refers to the actual manufacturing processes and the corresponding equipment, such as the agitator, the deflaker, and the dryer in the paper industry for the two main
The proposed prediction models
To detect the four different indicators (stirring speeds, water consumptions, supply air pressures, and exhaust air temperatures) during the paper manufacturing, we develop the following four prediction models for the three key pieces of equipment, including the stirring speed model of the dump chest, the deflakers water consumption model, the supply air pressure model, and the exhaust air temperature model.
Experimental settings
We apply the proposed intelligent digital twin system to a paper mill and show its effectiveness in improving the production efficiency. The paper mill is located in Zhejiang, China, and mainly manufactures composite-based paper, with 500 employees and about 1 million dollars registered capital. Besides, the factory has a Distributed Control System (DCS) for automated paper production, and we deploy a service program that can regularly collect device data and write them to an open source - study this abstract