Achieving Optimal Paper Properties: A Layered Multiscale kMC and LSTM-ANN-Based Control Approach for Kraft Pulping Academic Article

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.

authors

• Kwon, Joseph

published proceedings

• PROCESSES

author list (cited authors)

• Shah, P., Choi, H., & Kwon, J.

citation count

• 4

complete list of authors

• Shah, Parth||Choi, Hyun-Kyu||Kwon, Joseph Sang-Il

publication date

• 2023

publisher

• MDPI  Publisher

published in

• Processes  Journal

keywords

• Layered Kmc Simulation
• Long Short-term Memory
• Machine Learning
• Model Predictive Control
• Multiscale Modeling
• Pulp Digester

Digital Object Identifier (DOI)

• 10.3390/pr11030809

start page

• 809

end page

• 809

volume

• 11

issue

• 3

URL

• http://dx.doi.org/10.3390/pr11030809