Optimization of a Chemical Process using Machine Learning Techniques

Thesis Abstract

Abstract
This thesis focuses on the optimization of a chemical process using machine learning techniques. The application of machine learning in chemical engineering has gained significant attention due to its potential to improve process efficiency and productivity. The objective of this study is to explore the use of machine learning algorithms to optimize a specific chemical process and analyze the impact on process performance. The research methodology involves a comprehensive literature review to understand the current state of the art in machine learning applications in chemical engineering. Various machine learning algorithms, such as neural networks, support vector machines, and decision trees, are studied to determine their suitability for process optimization. Additionally, the study includes the collection of data from the target chemical process and the development of a predictive model using the selected machine learning algorithm. Chapter 1 provides an introduction to the research problem, background of the study, problem statement, objectives, limitations, scope, significance, and the structure of the thesis. Chapter 2 presents a detailed literature review covering ten key aspects related to machine learning applications in chemical engineering. Chapter 3 outlines the research methodology, including data collection, data preprocessing, model development, and validation techniques. The findings from the study are discussed in Chapter 4, which includes the analysis of the optimized chemical process performance compared to the traditional methods. The discussion also evaluates the effectiveness of the machine learning model and its potential for real-world applications in chemical process optimization. Finally, Chapter 5 provides a conclusion and summary of the thesis, highlighting the key findings, contributions, limitations, and future research directions. The study demonstrates the feasibility and effectiveness of using machine learning techniques for optimizing chemical processes, offering insights for improving process efficiency and productivity in the chemical engineering domain. In conclusion, this thesis contributes to the growing body of knowledge in the field of chemical engineering by showcasing the potential benefits of integrating machine learning into process optimization. The findings of this study can inform industry practitioners and researchers on the practical applications of machine learning in enhancing chemical process performance.