Thesis Abstract

Abstract
The optimization of chemical processes plays a crucial role in enhancing efficiency, reducing costs, and improving product quality. In recent years, the integration of artificial intelligence (AI) techniques has emerged as a powerful tool for optimizing complex chemical processes. This thesis focuses on the application of AI techniques in the optimization of a chemical process to achieve improved performance and productivity. The research explores how AI algorithms can be used to analyze process data, identify inefficiencies, and suggest optimal operating conditions for enhanced process performance. The thesis begins with a comprehensive introduction that highlights the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of terms. The literature review in Chapter Two provides a detailed analysis of existing studies related to the optimization of chemical processes using AI techniques. The review covers various AI algorithms such as machine learning, neural networks, genetic algorithms, and fuzzy logic, and their applications in process optimization. Chapter Three outlines the research methodology employed in this study, including data collection, preprocessing, model development, optimization algorithms, and performance evaluation metrics. The methodology also describes the simulation of the chemical process, the selection of input variables, and the validation of the AI models used for optimization. In Chapter Four, the findings of the study are discussed in detail, highlighting the effectiveness of AI techniques in optimizing the chemical process. The results demonstrate the improvement in process efficiency, reduction in energy consumption, and enhancement of product quality achieved through the application of AI-based optimization strategies. The chapter also discusses the challenges encountered during the optimization process and proposes recommendations for future research in this area. Finally, Chapter Five presents the conclusion and summary of the thesis, outlining the key findings, implications, and contributions of the study. The conclusion highlights the significance of integrating AI techniques in chemical process optimization and emphasizes the potential for further research and application in industrial settings. Overall, this thesis provides valuable insights into the use of AI techniques for optimizing chemical processes and offers practical recommendations for enhancing process efficiency and sustainability. Keywords Optimization, Chemical Process, Artificial Intelligence, Machine Learning, Neural Networks, Genetic Algorithms, Fuzzy Logic, Efficiency, Productivity.

Thesis Overview