The Unit supports the educational needs of the following courses:

• Process Control
• Advanced Process Control

The research interests of the Unit cover a wide range of topics related to the modeling, automatic control, and optimization of chemical engineering systems, with a focus on the development and application of artificial intelligence technologies. More specifically, the main directions of the Unit’s research activities can be summarized as follows:

• Design, Simulation, and Analysis of Linear and Nonlinear Automatic Control Systems: The Unit’s research in this area focuses on the development of methodologies for Model Predictive Control (MPC) systems. These computer-based systems use a dynamic process model to predict future behavior. The controller’s objective is to minimize the deviation between the desired values of the process-controlled variables and the model’s predictions for these variables.
• Neural Networks: Neural networks are multilayer structures composed of neurons, used for the parameterization of nonlinear system mappings. They constitute a powerful mathematical tool that mimics the functioning of the human brain. Particular emphasis is placed on the development of Radial Basis Function (RBF) network training algorithms.
• Fuzzy Logic Systems: Fuzzy logic has emerged in recent years as one of the most successful technologies for developing intelligent systems. The Unit applies fuzzy logic for the development of dynamic models, sales forecasting, pattern recognition, and the design of fuzzy controllers.
• System Optimization: Development of evolutionary algorithms for solving complex optimization problems arising in chemical engineering systems. Such algorithms are of particular importance, as real industrial problems often involve a mixture of integer and continuous variables, making their solution challenging through conventional methods.
• Production Scheduling and Inventory Control: Development of methodologies aimed at optimal decision-making during production scheduling, considering key parameters such as raw material and utility costs, storage capacity, production capacity, and customer service targets. These methodologies are based on automatic control techniques.