Thesis Abstract

Abstract
The field of Chemical Engineering has witnessed significant advancements in recent years, with an increasing emphasis on optimization techniques to enhance process efficiency and productivity. This thesis focuses on the optimization of a chemical process using artificial intelligence techniques, which have shown great potential in improving process performance. The study aims to investigate the application of artificial intelligence algorithms, such as machine learning and optimization algorithms, to optimize a complex chemical process. Chapter 1 provides an introduction to the research topic, highlighting the background of the study, the problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. The background of the study discusses the current challenges faced in chemical process optimization and the potential benefits of artificial intelligence techniques in addressing these challenges. The problem statement identifies the gap in existing literature and the need for further research in this area. Chapter 2 presents a comprehensive literature review covering ten key aspects related to chemical process optimization and artificial intelligence techniques. The review includes discussions on the principles of chemical process optimization, different optimization algorithms, applications of artificial intelligence in chemical engineering, and case studies demonstrating the effectiveness of these techniques. Chapter 3 outlines the research methodology employed in this study, which includes the selection of the chemical process for optimization, data collection, preprocessing, feature selection, model development, and validation. The chapter also discusses the artificial intelligence algorithms used, such as neural networks, genetic algorithms, and reinforcement learning, and their implementation in the optimization process. Chapter 4 delves into the detailed discussion of the findings obtained from the application of artificial intelligence techniques to optimize the selected chemical process. The chapter presents the results of the optimization process, including performance metrics, comparisons with traditional optimization methods, and sensitivity analysis to evaluate the robustness of the optimized process. Chapter 5 concludes the thesis by summarizing the key findings, discussing the implications of the research, and providing recommendations for future studies in this area. The conclusion highlights the significance of utilizing artificial intelligence techniques for chemical process optimization and emphasizes the potential for further advancements in this field. In conclusion, this thesis contributes to the growing body of knowledge on the application of artificial intelligence techniques in optimizing chemical processes. The findings of this study demonstrate the effectiveness of these techniques in improving process efficiency, reducing costs, and enhancing overall sustainability. The research presented in this thesis opens up new avenues for exploration and innovation in the field of chemical engineering, paving the way for future advancements in process optimization using artificial intelligence.

Thesis Overview