processes
Article Achieving Optimal Paper Properties: A Layered Multiscale kMC and LSTM-ANN-Based Control Approach for Kraft Pulping ParthShah 1,2 , Hyun-Kyu Choi 1,2 and Joseph Sang-Il Kwon 1,2, *
1 Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77843, USA 2 Texas A&M Energy Institute, Texas A&M University, College Station, TX 77845, USA * Correspondence: kwonx075@tamu.edu; Tel.: +1-(979)-862-5930 Abstract: The growing demand for various types of paper highlights the importance of optimizing the kraft pulping process to achieve desired paper properties. This work proposes a novel multiscale model to optimize the kraft pulping process and obtain desired paper properties. The model combines mass and energy balance equations with a layered kinetic Monte Carlo (kMC) algorithm to predict the degradation of wood chips, the depolymerization of cellulose, and the spatio-temporal evolution of the Kappa number and cellulose degree of polymerization ( DP ). A surrogate LSTM-ANN model is trained on data generated from the multiscale model under different operating conditions, dealing with both time-varying and time-invariant inputs, and an LSTM-ANN-based model predictive controller is designed to achieve desired set-point values of the Kappa number and cellulose DP while considering process constraints. The results show that the LSTM-ANN-based controller is able to drive the process to desired set-point values with the use of a computationally faster surrogate model with high accuracy and low offset.
Keywords: pulp digester; multiscale modeling; model predictive control; machine learning; long short-term memory; layered kMC simulation
Citation: Shah, P.; Choi, H.-K.; Kwon, J.S.-I. Achieving Optimal Paper Properties: A Layered Multiscale kMC and LSTM-ANN-Based Control Approach for Kraft Pulping. Processes 2023 , 11 , 809. https://doi.org/ 10.3390/pr11030809
1. Introduction The pulp and paper industry (PPI) is undergoing rapid modernization to meet the growing demand for pulp and paper production as shown in Figure 1 [1]. This demand can be attributed to the increase in the use of packaging paper by companies, such as Amazon and Walmart, the growing manufacturing industry in emerging markets, such as Asia and Africa, and the shift towards online shopping by consumers. As a result, the PPI is focusing on using advanced first-principles models and artificial-intelligence techniques to develop digital twins and process-control systems for improved efficiency, reduced waste, and safer operations [2–5]. Paper is made up of three substances, lignin, hemicellulose, and cellulose, and is typically produced from wood chips. Lignin, the main component of the cell wall, is a highly branched three-dimensional polymer that acts as a glue to hold cellulose microfibrils together [6]. In order to separate lignin from these cellulosic fibers, a process called delignification is used in a large chemical reactor called a pulp digester [6,7]. The Kappa number, a metric that describes the degree of delignification, is used to evaluate the process, where a Kappa number below 100 for cardboard papers and below 60 for kraft and bleachable white papers is favorable [8–10]. Despite efforts to manage waste and recycle fibers, the PPI still generates a large amount of waste product, which poses a significant burden on the industry [11]. Based on a report, 17% of the total global waste comes from paper industries, and discarded paper and paperboard make up roughly 26% of solid municipal waste in landfill sites [12,13].
Academic Editor: Zhe Wu
Received: 28 January 2023 Revised: 21 February 2023 Accepted: 24 February 2023 Published: 8 March 2023
Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).
Processes 2023 , 11 , 809. https://doi.org/10.3390/pr11030809
https://www.mdpi.com/journal/processes
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