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<p>This work explores the application of state-of-the-art techniques of time series forecasting to the delignification process in the pulp and paper industry, aiming to enhance sustainability and efficiency. While traditional machine learning models, such as Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM), have been widely used, recent advancements in time series architectures provide significant improvements in prediction accuracy. This study adopts cutting-edge time series architectures and integrates them with explainability techniques (Explainable Artificial Intelligence, XAI) to analyze critical features and their temporal saliency, providing insights into the most influential variables in the delignification process. For a sequence length of 96, Crossformer attains the lowest error of 0.625±0.032, while for a sequence length of 24, LMS-AutoTSF achieves the lowest error of 0.281±0.0001. Additionally, we perform simulation analyses using the identified important features to evaluate the effects of input parameter changes–such as temperature or H-factor adjustments–on correlated variables and the target Kappa number. To model these interdependencies and generate realistic input scenarios, we employ a Conditional Variational Autoencoder (CVAE), which enables the adjustment of one input feature while automatically adapting correlated features in a coherent and interpretable manner. This allows for counterfactual simulations that help operators understand the dynamic impact of process modifications on the overall system behavior. By leveraging advanced forecasting models, XAI-driven feature analysis, and CVAE-based simulation studies, we aim to improve prediction accuracy, optimize resource usage, and enhance operational efficiency. This work underscores the potential of combining modern time series forecasting, explainability techniques, and generative modeling to advance delignification processes, contributing to a more sustainable future for the pulp and paper industry. We further extend our experimental analysis to another industrial pulp dataset comprising a large number of instances, where Crossformer achieves the lowest error of 0.283 ± 0.112, followed by LMS-AutoTSF with the second-lowest error of 0.367 ± 0.136, across average prediction lengths of 12, 24, and 48.</p>

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Advancing delignification in the pulp and paper industry: Multivariate time series forecasting, explainability, and simulation analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月24日

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  • Ibrahim Delibasoglu,
  • Sanjay Chakraborty,
  • Fredrik Heintz,
  • Fredrik Wernersson Brodin,
  • Andreas Darnell

摘要

This work explores the application of state-of-the-art techniques of time series forecasting to the delignification process in the pulp and paper industry, aiming to enhance sustainability and efficiency. While traditional machine learning models, such as Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM), have been widely used, recent advancements in time series architectures provide significant improvements in prediction accuracy. This study adopts cutting-edge time series architectures and integrates them with explainability techniques (Explainable Artificial Intelligence, XAI) to analyze critical features and their temporal saliency, providing insights into the most influential variables in the delignification process. For a sequence length of 96, Crossformer attains the lowest error of 0.625±0.032, while for a sequence length of 24, LMS-AutoTSF achieves the lowest error of 0.281±0.0001. Additionally, we perform simulation analyses using the identified important features to evaluate the effects of input parameter changes–such as temperature or H-factor adjustments–on correlated variables and the target Kappa number. To model these interdependencies and generate realistic input scenarios, we employ a Conditional Variational Autoencoder (CVAE), which enables the adjustment of one input feature while automatically adapting correlated features in a coherent and interpretable manner. This allows for counterfactual simulations that help operators understand the dynamic impact of process modifications on the overall system behavior. By leveraging advanced forecasting models, XAI-driven feature analysis, and CVAE-based simulation studies, we aim to improve prediction accuracy, optimize resource usage, and enhance operational efficiency. This work underscores the potential of combining modern time series forecasting, explainability techniques, and generative modeling to advance delignification processes, contributing to a more sustainable future for the pulp and paper industry. We further extend our experimental analysis to another industrial pulp dataset comprising a large number of instances, where Crossformer achieves the lowest error of 0.283 ± 0.112, followed by LMS-AutoTSF with the second-lowest error of 0.367 ± 0.136, across average prediction lengths of 12, 24, and 48.