Chemical process systems, especially multivariable units like distillation columns, face increasing demands for resilient and adaptive control under stochastic disturbances, nonlinear dynamics, and fluctuating operating conditions [1]. With the rise of novel column configurations enabled by process intensification and customized design, many emerging systems are difficult to control using conventional first-principles models due to structural complexity and limited mechanistic insight [2]. Traditional PID controllers, while industry standard, often struggle to maintain optimal performance under such uncertainty [3]. Hybrid machine learning approaches have thus emerged as promising alternatives for control tasks where partial process knowledge exists. In this work, we present a
Bollinger Band-guided, event-driven reinforcement learning (RL) framework tailored for real-time control of a 25-stage distillation column. Drawing inspiration from high-frequency trading strategies, this approach leverages statistical volatility bands—originally used in financial markets—to trigger dynamic setpoint shifts when process variability breaches learned confidence thresholds. We develop a
hybrid control environment using OpenAI Gym and Stable-Baselines3, combining Proximal Policy Optimization (PPO)-based RL with physics-informed process dynamics, variable volatility vapor-liquid equilibrium, and Kalman filtering. Control actions include reflux and boilup ratio adjustments based on dynamic observations of tray compositions and event-based setpoint updates. Comparative simulations under stochastic noise and step disturbances reveal important trade-offs between conventional PID and RL-based controllers. While PID controllers exhibited lower Integral of Absolute Error (IAE) in baseline tracking performance, the proposed RL controller—augmented with Bollinger Band-based event triggers—demonstrated stronger adaptability to dynamic process fluctuations and unforeseen events. Moreover, the RL agent offers a scalable and model-flexible strategy that is particularly useful when controlling novel column configurations where mechanistic knowledge is incomplete. These results underscore the potential of volatility-aware reinforcement learning as a complementary control paradigm rather than a direct replacement of PID strategies in certain process environments. This study not only introduces a novel control paradigm at the intersection of financial modeling and process engineering but also establishes a scalable methodology for volatility-aware, hybrid-learning-driven control in complex or poorly modeled chemical systems. The framework is broadly applicable across industries where data-driven, resilient, and event-sensitive control strategies are vital.
References
[1] C. Kim, M. Shah, A.M. Sahlodin, Design of multi-loop control systems for distillation columns: review of past and recent mathematical tools, Chemical Product and Process Modeling 17(2) (2022) 171-197.
[2] B. Decardi-Nelson, A.S. Alshehri, A. Ajagekar, F. You, Generative AI and process systems engineering: The next frontier, Computers & Chemical Engineering (2024) 108723.
[3] M.A. Ahmad, G. Yoganathan, M.I.M. Rashid, M.R. Hao, M.H. Suid, M.Z.M. Tumari, Improved Smoothed Functional Algorithms-Optimized PID Controller for Efficient Speed Regulation of Wind Turbines, IEEE Transactions on Industry Applications (2025) 1-15.
