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

Krzysztof Lalik,Mateusz Kozek,Paweł Gut,Rafał Figaj,Szymon Podlasek

doi:10.3390/en14175334

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.

Highlights

The problem of optimal control of nonlinear multi-input multi-output (MIMO) systems is very complex [1,2,3,4]
The results demonstrate the effectiveness of the algorithm for uncertain MIMO systems with some external interfering signals, minimal tracking error, and optimal control inputs
This paper presents a proprietary neural controller for a MIMO facility using an actorcritic network

Summary

Introduction

The problem of optimal control of nonlinear multi-input multi-output (MIMO) systems is very complex [1,2,3,4]. The potential associated with improving the control of such systems and methods for experimental optimization of such control are disclosed therein. In most cases, they are based on linearizing the nonlinear model and attempting to tune various controllers to the linearized model. They are based on linearizing the nonlinear model and attempting to tune various controllers to the linearized model In each of these examples, it is necessary to synthesize a system of equations of the controlled object, which is often impossible or subject to considerable error due to parametric uncertainty

Objectives

Methods

Results

Conclusion