Our research group has developed a closed-loop LSTM-based model predictive control system to optimize the Kraft pulping process for desired microscopic properties of paper. A multiscale model combining mass and thermal energy balance equations with a layered kinetic Monte Carlo algorithm accurately captures properties like fiber morphology, kappa number, and Cellulose Degree of Polymerization. Datasets for network training were generated by executing the model under various operating conditions. An LSTM-ANN network achieved a prediction accuracy of over 98% for kappa number and cellulose DP, considering time-varying and time-invariant conditions. A closed-loop LSTM-based multi-objective optimal controller was designed to achieve target set-point values while considering constraints, demonstrating exceptional accuracy and computational efficiency.
Literature:
Shah, Parth, Hyun-Kyu Choi, and Joseph Sang-Il Kwon. “Achieving optimal paper properties: A layered multiscale kMC and LSTM-ANN-based control approach for kraft pulping.” Processes3 (2023): 809.
Choi, Hyun‐Kyu, and Joseph Sang‐Il Kwon. “Multiscale modeling and control of Kappa number and porosity in a batch‐type pulp digester.” AIChE Journal6 (2019): e16589.