ARTIFICIAL INTELLIGENCE IN INDUSTRIAL CONTROL SYSTEMS

Syllabus

Principles of Artificial Intelligence (AI). Aspects of Design and Software for AI systems. Directions in the application of AI to industrial control systems. Machine Learning applications for Real Time Control of industrial processes. Cognitive Approaches for Self‐Optimizing Machines. Neural network control software platforms. Fuzzy control software platforms. Stepwise Safe Switching. Simulating annealing and Metaheuristic Optimization Algorithms for controller regulation. Expert industrial control systems. AI based Industrial Decision support systems. Artificial intelligence and predictive maintenance. Fault Detection and Diagnostics. AΙ approaches for product and process quality control and inspection. Industrial applications in Chemical Processes and Manufacturing. Simulations for AI control systems and Software Platforms.

Learning Outcomes

Aim of the course is to familiarize students with the application of artificial intelligence tools in industrial systems, toward control systems design and performance optimization. Special emphasis is placed on chemical and manufacturing processes. Upon successful completion of the course, students will be able to:

• Understand the function of artificial intelligence tools, tuning performance variables of industrial processes,
• Apply machine learning tools for real-time control in industrial processes,
• Design and implement fuzzy controllers,
• Design and implement safe switching controllers for industrial processes,
• Apply simulating annealing, neural networks, and metaheuristic techniques to optimize the degrees of freedom in control schemes.
• Design and implement expert systems and decision support systems, being based on artificial intelligence tools,
• Apply artificial intelligence tools to develop fault diagnosis and predictive maintenance systems.
   

For the outline of the course press here.