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In this article, multiple reinforcement learning (RL) methods such as value‐based, policy‐based, and actor‐critic algorithms are investigated for typical control tasks found in the chemical industries. Through a critical assessment of these novel techniques, their main advantages are highlighted, but also the challenges that still need to be resolved are discussed. Two batch control tasks are used as benchmarks, namely, production maximization, and setpoint control. Using these testing environments, a direct comparison of different RL approaches is presented, which could guide the algorithm selection in future RL applications for batch process control. Furthermore, the results obtained with a traditional control method, model predictive control (MPC), are shown to provide a baseline for comparison with RL algorithms. The results show that RL has significant applicability in various control tasks and has comparable control performance to traditional methods but with a lower online computational cost. A batch bioreactor simulation and a simulation of an industrial polyol process are used for illustration purposes. [ABSTRACT FROM AUTHOR]

Equipment degradation is ubiquitous in the Chemical Process Industry (CPI), causing significant losses in efficiency, controllability, and plant economy, as well as an increased environmental fingerprint and additional operational safety risks. The case of fouling in heat exchangers, in particular, is well-known and pervasive but still hard to cope with, given the complexity of the underlying mechanisms and the difficulty of assessing its extension in real-time. This problem becomes even more complex in batch processes producing different products, where multiple recipes are used, bringing additional variability and new challenges to the analysis. In this work, we propose a functional data-driven approach for streamlining the analysis and monitoring of the progression of fouling taking place in heat exchangers in multiproduct batch processes. With the approach developed and presented in this paper, process analysis can be efficiently conducted by integrating historical data with engineering knowledge. Furthermore, a surrogate measure of fouling extension in heat exchangers is proposed, that can be readily implemented as an equipment health indicator (EHI) leading to a safer operation of the heat exchanger. [ABSTRACT FROM AUTHOR]

We present a data analytics framework for offline analysis of batch processes. The framework provides a unified setting for implementing several variants of feature oriented analysis proposed in the literature, including a new methodology based on the process variable's profiles presented in this article. It also integrates feature generation and feature analysis, in order to speed up the data exploration cycle, which is especially relevant for complex batch processes. The FOBA (Feature Oriented Batch Analytics platform) is described in detail and applied to several case studies regarding different analysis goals: visualization of the differences between the operation of two industrial units (dryers), quality prediction and end-of-batch process monitoring. The performance of the proposed methodology is also critically assessed and compared with other alternative analytical approaches currently in use. [ABSTRACT FROM AUTHOR]

A significant number of batch process monitoring methods have been proposed since the first groundbreaking approaches were published in the literature, two decades ago. The proper assessment of all the alternatives currently available requires a rigorous and robust assessment framework, in order to assist practitioners in their difficult task of selecting the most adequate approach for the particular situations they face and in the definition of all the optional aspects required, such as the type of preprocessing, infilling, alignment, etc. However, comparison methods currently adopted present several limitations and even some flaws that make the variety of studies available not easily generalizable or, in extreme situations, fundamentally wrong. Without a proper comparison tool decisions are made on a subjective basis and therefore are prone to be at least suboptimal. In this article we present a structured review of comparison methods and figures of merit adopted to assess batch process monitoring approaches, analyzing their strengths and limitations, as well as some common missuses. Furthermore, we propose a comparison and assessment methodology (CAM) and figures of merit that should be adopted in order to circumvent some of the limitations of the current procedures. We begin by addressing, in this article, the analysis of the methods' "detection strength". Detection strength regards the ability to correctly detect abnormal situations without resulting in excessive false alarms. We describe in detail how the comparison framework is implemented and illustrate its application in two case studies, encompassing a rich variety of testing scenarios (99 000 batch runs) and monitoring methods (2-way, 3-way, and dynamic methods, amounting to a total of 60 different combinations of methods and their variants). From this analysis, it stands out that the CAM methodology is suitable for comparing different monitoring methods, even when the number of methods and variants is very large. It provides simple, informative, and statistical-based metrics to assess a given method's performance and is able to quantify the added value of alternative approaches. When applied to the two case studies considered, 2-way methods (with batch-wise unfolding) combined with missing data (MD) infilling and dynamic time warping (DTW) were found to provide adequate solutions. If a more parsimonious model is required, dynamic methods such as autoregressive principal components analysis (ARPCA) provide good alternatives and Tucker3 with current deviations (CD) infilling also presents an interesting performance for some fault scenarios. In general, no method can claim absolute supremacy in all faulty scenarios. [ABSTRACT FROM AUTHOR]