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Design and Implementation of Autonomous Control Systems that Utilize AI to Make Decisions and Take Control Actions Without Human Intervention

Geku, Diton

Abstract

The main challenge of overfitting during the training phase significantly hinders the application of machine learning for characterizing chemical processes utilizing noisy datasets. This study aims to develop long short-term memory (LSTM) neural networks capable of identifying the genuine underlying dynamics of processes from noisy data by incorporating a dropout technique along with a co-teaching learning approach. To generate operational datasets influenced by sensor noise, depicted through industrial data, we employ an industrial chemical reactor as a case study along with Aspen Plus Dynamics, a comprehensive process simulator that circumvents the usual assumptions regarding reactor characteristics in first-principles model derivations. This method enables us to assess the effectiveness and robustness of the proposed modeling approaches. LSTM models initially exposed to noisy datasets tend to overfit; nevertheless, we apply the dropout technique to address this challenge. Next, we utilize the co-teaching strategy to refine the LSTM models by leveraging cleaner data derived from reactor simulations based on fundamental principles, which include certain general modeling assumptions that are not present in the Aspen model. Our results from both open-loop and closed-loop simulations illustrate that enhancing LSTM neural network models with dropout and co-teaching considerably improves performance relative to conventional training methods that utilize noisy datasets, thereby boosting the accuracy of model predictions in scenarios involving model predictive controllers.

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