Thesis Abstract

Abstract
This thesis explores the optimization of a chemical process using artificial intelligence techniques. The application of artificial intelligence in chemical engineering has gained significant attention due to its potential to enhance process efficiency and productivity. The study focuses on leveraging artificial intelligence algorithms to optimize a specific chemical process, aiming to improve product quality, reduce energy consumption, and minimize production costs. The research begins with a comprehensive literature review to explore the current state-of-the-art in artificial intelligence applications within the field of chemical engineering. Various AI techniques such as machine learning, neural networks, genetic algorithms, and fuzzy logic are examined for their potential to optimize chemical processes. The literature review provides a foundation for understanding the theoretical framework and practical applications of AI in process optimization. In the methodology chapter, the research design and approach for optimizing the chemical process using AI techniques are outlined. The process involves data collection, preprocessing, feature selection, model training, and evaluation. Various AI algorithms are implemented and compared to identify the most effective approach for optimizing the chemical process. The methodology also includes a detailed description of the experimental setup, data sources, and evaluation metrics used to assess the performance of the AI models. The findings chapter presents the results of the optimization process, including improvements in process efficiency, product quality, and cost reduction achieved through the application of AI techniques. The analysis of the experimental data provides insights into the effectiveness of different AI algorithms in optimizing the chemical process. The findings highlight the potential of artificial intelligence to revolutionize traditional methods of process optimization in chemical engineering. The discussion chapter delves into the implications of the research findings, addressing the practical implications for industry and the potential challenges in implementing AI-driven optimization strategies. The chapter also discusses the limitations of the study, areas for future research, and recommendations for integrating AI techniques into industrial chemical processes. In conclusion, this thesis demonstrates the significant potential of artificial intelligence techniques in optimizing chemical processes. By leveraging AI algorithms, chemical engineers can enhance process efficiency, reduce costs, and improve product quality. The research contributes to the growing body of knowledge on the application of AI in chemical engineering and provides valuable insights for industry professionals seeking to adopt advanced optimization techniques.

Thesis Overview