Development of advanced process monitoring techniques using artificial intelligence
Table Of Contents
<p>1. Introduction<br> 1.1 Background and motivation<br> 1.2 Objectives of the project<br>2. Literature review<br> 2.1 Process monitoring and control techniques<br> 2.2 Artificial intelligence in chemical engineering<br> 2.3 Sensor data integration and analysis<br>3. AI-based process monitoring<br> 3.1 Data preprocessing and feature selection<br> 3.2 Machine learning algorithms for process monitoring<br> 3.3 Neural network-based monitoring systems<br>4. Case studies and applications<br> 4.1 Monitoring of chemical reactors<br> 4.2 Fault detection in process equipment<br> 4.3 Real-time process optimization using AI<br>5. Performance evaluation<br> 5.1 Robustness and reliability analysis<br> 5.2 Comparison with traditional monitoring techniques<br></p>
Thesis Abstract
<p> This project focuses on the development of advanced process monitoring techniques using artificial intelligence (AI) for chemical engineering applications. The project aims to leverage AI algorithms, such as machine learning and neural networks, to monitor and predict process behavior, detect anomalies, and optimize process performance. The project will explore the integration of sensor data, process models, and AI algorithms to develop robust and reliable monitoring systems for chemical processes. <br></p>Thesis Overview
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