Multiscale modeling and control of Kappa number and porosity in a batch‐type pulp digester

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

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.

Agricultural Residues as Raw Materials for Pulp and Paper Production: Overview and Applications on Membrane Fabrication

The need for pulp and paper has risen significantly due to exponential population growth, industrialization, and urbanization. Most paper manufacturing industries use wood fibers to meet pulp and paper requirements. The shortage of fibrous wood resources and increased deforestation are linked to the excessive dependence on wood for pulp and paper production. Therefore, non-wood substitutes, including corn stalks, sugarcane bagasse, wheat, and rice straw, cotton stalks, and others, may greatly alleviate the shortage of raw materials used to make pulp and paper. Non-woody raw materials can be pulped easily using soda/soda-AQ (anthraquinone), organosolv, and bio-pulping. The use of agricultural residues can also play a pivotal role in the development of polymeric membranes separating different molecular weight cut-off molecules from a variety of feedstocks in industries. These membranes range in applications from water purification to medicinal uses. Considering that some farmers still burn agricultural residues on the fields, resulting in significant air pollution and health issues, the use of agricultural residues in paper manufacturing can eventually help these producers to get better financial outcomes from the grown crop. This paper reviews the current trends in the technological pitch of pulp and paper production from agricultural residues using different pulping methods, with an insight into the application of membranes developed from lignocellulosic materials.

Learning of Iterative Learning Control for Flexible Manufacturing of Batch Processes

Flexible manufacturing as an essential component of smart manufacturing implements the customized production mode, thereby requesting fast controller adaptation for producing different goods but still with high precision. This problem becomes even more acute for batch processes. Here we present a solution called learning of iterative learning control (ILC) based on neural networks. It is able to recommend control parameters for ILC controllers accordingly, so as to yield fast tracking error convergence and smaller steady-state error for disparate set-point profiles, which is deemed an abstraction of different production needs. The method substantially outperforms a benchmark ILC on a variety of systems and cases, thereby showing its potential for deployment in the industrial Internet of Things.

A slip-spring framework to study relaxation dynamics of entangled wormlike micelles with kinetic Monte Carlo algorithm

HYPOTHESIS

Wormlike micelles (WLMs) formed due to the self-assembly of amphiphiles in aqueous solution have similar viscoelastic properties as polymers. Owing to this similarity, in this work, it is postulated that kinetic Monte Carlo (kMC) sampling of slip-springs dynamics, which is able to model the rheology of polymers, can also be extended to capture the relaxation dynamics of WLMs.

THEORY

The proposed modeling framework considers the following relaxation mechanisms: reptation, union-scission, and constraint release. Specifically, each of these relaxation mechanisms is simulated as separate kMC events that capture the relaxation dynamics while considering the living nature of WLMs within the slip-spring framework. As a case study, the model is implemented to a system of sodium oleate and sodium chloride to predict the linear rheology and the characteristic relaxation times associated with the individual relaxation mechanisms at different pH and salt concentrations.

FINDINGS

Linear rheology predictions were found to be in good agreement with experimental data. Furthermore, the calculated relaxation times highlighted that reptation contributed to a continuous increase in viscosity while union-scission contributed to the decrease in viscosity of WLM solutions at a higher salinity and pH. This manifests the proposed model's capability to provide insights into the key processes governing WLM's rheology.