Learning Dynamic Reconfiguration for Distributed Modular Process Systems

Distributed modular process systems are gaining prominence across various industries due to their flexibility, scalability, and potential for localized deployment. These architectures are particularly relevant in contexts such as biomass processing, where decentralized, small-scale units are better suited to handle heterogeneous and spatially distributed feedstocks. However, such systems present complex control challenges, particularly in managing real-time configuration decisions. To address this, we propose a machine learning-enhanced model predictive control (MPC) framework tailored for modular reactor systems. Classical classifiers, including k-nearest neighbors, decision trees, and support vector machines, are employed to predict optimal system configurations at each control step. To improve robustness, we incorporate a modified AdaBoost algorithm guided by a performance metric, which favors configuration decisions that minimize performance degradation even under misclassification. The framework is validated on a benchmark nonisothermal CSTR system with multiple feasible configurations. Results show that k-nearest neighbors offers the best overall prediction accuracy, while support vector machines demonstrate superior robustness in worst-case scenarios, revealing a trade-off between accuracy and resilience. The AdaBoost-enhanced MPC further improves tracking performance and reduces the degradation of misclassifications. While applicable to a broad range of modular process systems, this approach is particularly promising for biomass processing applications, where heterogeneity and decentralized operations make robust, flexible control essential.