Development of Advanced Process Monitoring and Fault Detection Techniques for Specialty Chemicals
Table Of Contents
1. Introduction
1.1 Background and Motivation
1.2 Objectives of the Project
2. Literature Review
2.1 Overview of Specialty Chemical Manufacturing
2.2 Importance of Process Monitoring and Fault Detection
2.3 State-of-the-Art Monitoring Techniques
3. Sensor Technologies for Process Monitoring
3.1 Selection of Suitable Sensors
3.2 Sensor Placement and Integration
4. Data Analytics for Fault Detection
4.1 Data Preprocessing and Feature Extraction
4.2 Statistical Process Control Methods
5. Machine Learning Approaches
5.1 Supervised and Unsupervised Learning Algorithms
5.2 Model Training and Validation
Thesis Abstract
This project focuses on the development of advanced process monitoring and fault detection techniques specifically tailored for the production of specialty chemicals. Specialty chemical manufacturing processes often involve complex reaction pathways and stringent quality requirements, making real-time monitoring and early fault detection crucial for maintaining product quality and process efficiency. The project will explore the application of advanced sensor technologies, data analytics, and machine learning algorithms to detect deviations from normal operating conditions and identify potential issues that could impact product quality. The outcomes of this project will contribute to the enhancement of process safety and product consistency in the specialty chemical industry.
Thesis Overview
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