1. Introduction
  1.1 Significance of Process Monitoring in Specialty Chemical Production
  1.2 Objectives and Scope of the Project
2. Literature Review
  2.1 Process Monitoring and Fault Detection Techniques
  2.2 Challenges in Monitoring Specialty Chemical Processes
  2.3 Advanced Data Analytics and Machine Learning in Process Monitoring
3. Data Acquisition and Preprocessing
  3.1 Selection of Monitoring Variables
  3.2 Data Acquisition Systems
  3.3 Data Preprocessing and Feature Extraction
4. Development of Monitoring Models
  4.1 Statistical Process Control Techniques
  4.2 Multivariate Data Analysis Methods
  4.3 Machine Learning Algorithms for Fault Detection
5. Real-time Implementation and Validation
  5.1 Integration with Process Control Systems
  5.2 Performance Evaluation and Validation Studies
  5.3 Case Studies of Monitoring Specialty Chemical Processes