Q-Learning Neural Controller for Steam Generator Station in Micro Cogeneration Systems

Abstract

This article presents the results of the optimization of steam generator control systems powered by mixtures of liquid fuels containing biofuels. The numerical model was based on the results of experimental research of steam generator operation in an open system. The numerical model is used to build control algorithms that improve performance, increase efficiency, reduce fuel consumption and increase safety in the full range of operation of the steam generator and the cogeneration system of which it is a component. In this research, the following parameters were monitored: temperature and pressure of the circulating medium, exhaust gas temperature, oxygen content in exhaust gas, percentage control of oil burner power. Two methods of controlling the steam generator were proposed: the classic one, using the PID regulator, and the advanced one, using artificial neural networks. The work shows how the model is adapted to the real system and the impact of the control algorithms on the efficiency of the combustion process. The example is considered for the implementation of advanced control systems in micro-, small- and medium-power cogeneration and trigeneration systems in order to improve their final efficiency and increase the profitability of implementation.

Optimal Neural Network PID Approach for Building Thermal Management

“…With intelligent and continuous learning methods, this approach can learn sophisticated nonlinear relationships between several parameters, allowing real identification of the systems. Several parametric system identification algorithms have been designed using Neural Networks to overcome standard identification limitations [14,27], such as the nonlinearity of building systems.…”

“…Many studies have opted for optimal solutions based on artificial intelligence techniques facilitated by the development of computer technology, such as genetic algorithms [10], Fuzzy Logic [11,12], Neural Networks [13,14], decoupling control, and others. They are used in several areas such as controlling electrical DC motors [15,16], automatic and robot manipulation systems [17], controlling temperature performance [18], and controlling systems in agriculture [19].…”