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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]

This paper presents an application of data-derived approachesforanalyzing and monitoring industrial processes. The discussed methodsare used in visualizing process measurements, extracting operationalinformation, and designing estimation models for primary process variablesotherwise difficult to measure in real-time. Emphasis is given tothe modeling of the data with two classical machine learning paradigms;the self-organizing map (SOM) and the multi-layer perceptron (MLP).The effectiveness of the proposed approach is validated on an industrialdeethanizer, where the goal is to identify operational modes and mostsensitive variables for this full-scale unit, as well as design aninferential model for a critical process variable, the bottom ethaneconcentration. The study led to the definition of a fully automatedmonitoring tool to be implemented online in the plant’s distributedcontrol system. The results confirmed the potential of the data-derivedapproach, and based on the analysis, the existing control configurationof the unit could be redefined toward more consistent operations.Because it is general and modular by design, the tool can be easilyused for other processes. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we overview and discuss the implementation of topology-based approaches to modeling and analyzing industrial process data. Emphasis is given to the representation of the data obtained with the self-organizing map (SOM). The methods are used in visualizing process measurements and extracting relevant information by exploiting the topological structure of the observations. Benefits of the SOM with industrial data are presented for a set of process measurements measured in an industrial gas treatment plant. The practical goal is to identify significant operational modes and most sensitive process variables before developing an alternative control strategy. The results confirmed that the SOM-based approach is capable of providing valuable information and offers possibilities for direct application to other process monitoring tasks. [ABSTRACT FROM AUTHOR]

Recent developments in deep learning have brought huge breakthroughs in the image processing area, which triggered numerous successful applications and positively impacted the current big data context of Industry 4.0. On the other hand, it is widely known that large amounts of training data are required to train a deep learning model with millions of parameters, which limits its application in many industrial applications where sufficient data resources are lacking. Transfer learning is one of the practical solutions to reduce the data required for training, which tries to reuse learned knowledge for similar tasks. Nevertheless, many technical details of transfer learning implementation are not well documented. Therefore, in this work, two datasets collected from plastics manufacturing processes were studied to investigate different transfer learning approaches and implementation details for high-performance model building under the constraint of limited available training data. Different transfer learning implementations are compared and important technical details are also discussed in this study. Through this study, the minimum number of training samples can be estimated. Transfer learning is compared with the newly developed few-shot learning approach as a brief comparative study. Finally, this work summarizes practical guidelines for the development of image classification models with limited data resources. • Transfer learning techniques were investigated in this work on industrial datasets. • Implementation details of transfer learning in deep convolutional neural networks including fine-tuning level, model selection etc. were discussed. • The minimum training data size required for image classification was estimated using the transfer learning approach. • A comparison with few-shot learning was studied to evaluate the feasibility of different approaches. • Practical guidelines were summarized for using transfer learning to build image classification models with limited training data. [ABSTRACT FROM AUTHOR]