process control, modelling, uncertainty, robust model predictive control, neuro-fuzzy system, energy savings
The research project deals with the development of advanced control methods and algorithms for systems with uncertainties whose implementation will provide significant energy savings in control of energy intensive processes in chemical, biochemical and food technologies. The core of the project is the development of methods and design of algorithms for predictive control, robust predictive control and fuzzy control of systems with uncertainty. Computational efficiency and feasibility in practice will be taken into account when designing control algorithms. Designed control algorithms, controllers, and control structures will be tested by simulations and experiments in laboratory conditions and will be compared according to energy consumption with conventional control approaches. The controlled processes will be chemical reactors, biochemical reactors, heat exchangers, distillation columns and other energy intensive processes typical for chemical, biochemical and food technologies.
The main objective is to design effective algorithms and control systems using advanced control methods that will address not only the problems of processes with uncertainties in chemical, biochemical and food production, and the problems of the boundaries on manipulated and output variables, but also the high energy consumption in these processes, while algorithms and control systems will ensure not only the required control quality but also significant energy savings compared to conventional control.
- development of mathematical models, fuzzy and neuro-fuzzy models of chemical and biochemical energy intensive processes with various types of uncertainty
- extension of the toolbox of mathematical models of chemical and biochemical processes
- development of model-based predictive control (MPC) and robust predictive control (RMPC) methods, which take into account boundaries on manipulated and controlled variables already in the design of control algorithms
-development of MPC and RMPC algorithms reducing computational effort in order to increase efficiency of their real-time implementation and energy saving
- development of control algorithms for energy intensive processes with uncertainty using fuzzy and neuro-fuzzy systems
- development of software tools that will enable effective implementation of the proposed advanced control algorithms with consideration of their implementation within the Industrial Internet of Things concept
- implementation of advanced control algorithms dealing with boundaries on manipulated variables that directly affect energy consumption, as well as boundaries on controlled outputs that directly affect quality of control and efficiency in simulations and real-time laboratory experiments
- implementation of fuzzy and neuro-fuzzy control in simulations and real-time laboratory experiments
- analysis of the control quality and energy savings achieved with the designed control algorithms and comparison of the designed advanced control with conventional PID based control.
Responsibility for content: doc. Ing. Juraj Oravec, PhD.