The paper presents an analysis of water-level control in a thermal power plant (TPP) steam separator. This control structure is vital for the entire plant’s stable, reliable, and efficient operation. This process belongs to processes with an integrator because it concerns a level-control issue, and the control variable is the feedwater flow. Said industrial processes are challenging to control and apply standard methods for tuning the PID controller, so a new procedure has been proposed. A procedure for tuning a PID controller for integrating processes is proposed based on the IFOPDT model, obtained from the wide step response of the process. Based on the process parameters estimated, the tuning of the controller is proposed. Results from the TPP TEKO-B2 (350 MW) are presented as an experimental verification. Compared with standard tuning methods, better results are achieved in the form of rise time and disturbance elimination rate. A significantly less risky and faster experiment for parameter estimation and controller tuning is also obtained. In addition, one adjustable parameter is provided to select the relation between performance and robustness. This method can be applied to various industrial processes with an integrator.

Linear regression is a very simple machine learning model that is supposed to find linear relations between input and output data. Its use is limited since real-world random variables are almost never linearly correlated, but when there is limited amount of data for training, using other larger and more sophisticated models might be impossible. Even though simple linear relations are hard to come by in nature, more complex mathematical dependencies can often be linearized i.e. approximated using linear models. Additionally, by preprocessing the input data using nonlinear mathematical operations linear regression can artificially account for nonlinear relations. This methodology was tested on real data representing measurements in thermal power plant while the goal was predicting the specific energy of the coal that was being burnt in the plant. Estimating coals caloric value is of tremendous value since using this information power plant can be configured to run more efficiently.