AI-Enhanced Control Systems for Optimizing Chemical Reactor Performance

Thesis Abstract

Optimizing chemical reactor performance remains a critical challenge in the pursuit of increased efficiency, safety, and sustainability in chemical engineering processes. Traditional control systems, often based on proportional-integral-derivative (PID) controllers, are limited in their capacity to adapt dynamically to nonlinear and complex reaction environments, leading to suboptimal operation and increased operational costs. This research aims to develop and evaluate an AI-enhanced control framework that integrates machine learning algorithms with existing control architectures to optimize reactor performance, focusing on maximizing yield, minimizing energy consumption, and maintaining safety parameters. Specific objectives include designing an intelligent control scheme using artificial neural networks (ANNs) trained on historical process data, assessing the robustness of the proposed system under varying operational conditions, and comparing its performance with conventional control strategies through simulation and experimental validation. The study adopts a mixed-methods research design, combining quantitative modeling and simulation with qualitative system analysis. The population comprises operational data from a continuous stirred-tank reactor (CSTR) located within a chemical manufacturing plant, with a sample size of 10,000 data points collected over a six-month period. Data sources include process logs, sensor readings, and control system archives, with data acquisition instruments encompassing PLC-based sensors and SCADA systems. The ANN models are trained using backpropagation algorithms, with hyperparameter tuning conducted via grid search to prevent overfitting. The effectiveness of the AI-based control system is evaluated through statistical techniques such as regression analysis, root mean square error (RMSE) assessments, and ANOVA tests to determine significant improvements over traditional control methods. The modeling framework integrates process simulation using Aspen Plus for system validation, and the analytical framework is grounded in the Theory of Dynamic System Control and Adaptive Control Theory. Expected findings indicate that the AI-enhanced control system significantly outperforms conventional PID controllers in terms of process stability, yield maximization, and energy efficiency. It is anticipated that the neural network-based controllers will demonstrate adaptability to process disturbances and uncertainties, leading to improved operational resilience. The study also expects to reveal insights into the correlation between process variables and optimal control parameters, contributing to the development of more intelligent and flexible control strategies in chemical processing. The findings aim to fill existing gaps in the literature regarding the practical integration of machine learning techniques within real-time control systems for chemical reactors, providing empirical evidence for the benefits and limitations of AI-driven automation. This research contributes to knowledge by advancing the application of artificial intelligence in process control, offering a validated framework for implementing AI-enhanced systems in industrial settings. Additionally, it extends the theoretical foundation of adaptive control strategies by empirically demonstrating the potential of neural networks to manage complex chemical processes. The main conclusion emphasizes that AI-enhanced control systems can substantially improve reactor efficiency and safety, promoting sustainable manufacturing practices. Based on the findings, recommendations include adopting AI-based controllers for high-performance chemical reactors, integrating real-time data analytics with existing control platforms, and fostering further research into hybrid control architectures combining traditional and intelligent control methods. Future studies are suggested to explore the scalability of the proposed framework across different reactor types and diverse chemical processes, as well as to incorporate emerging AI techniques such as reinforcement learning for autonomous process optimization.