An ML-Based Simulation Capability to Generate Training Data-set of Boiler Subsystems

Abstract

The paper proposes a framework for optimizing and improving steam power plant operations using artificial intelligence (AI). In particular, we will do a case study on the boiler subsystem, which constitutes a significant portion of the steam power plant. Moreover, this power plant has other important subsystems such as; a condenser, a turbine, feed water, and other minor auxiliaries. The boiler subsystem will be studied and simulated using Simulink environment in MATLAB. Our objective is to utilize this simulation model to gather data that may be used in an AI approach. By contrasting our data-set with the real power plant, we may adjust our simulation model. The simulation data set was selected because it enables simple input parameter modification without involving or affecting the real power plant. Consequently, after using the machine learning model, comparing the outputs of the data from the actual power plant with the simulated data set. Furthermore, a validation technique will be used to determine how the data-set of the regression model will optimize the real power plant. This paper discusses the proposed framework and provides an explanation of the machine learning technique with which the data-set will move to a further process of training and testing using different regression analyses. Furthermore, this paper focuses on machine learning algorithms, namely, linear regression, decision tree, and random forest. In addition using r2-score for comparing the performance of these algorithms.