Data Analysis and Symbolic Regression Models for Predicting CO and NOx Emissions from Gas Turbines

Abstract

Predictive emission monitoring systems (PEMS) are software solutions for the validation and supplementation of costly continuous emission monitoring systems for natural gas electrical generation turbines. The basis of PEMS is that of predictive models trained on past data to estimate emission components. The gas turbine process dataset from the University of California at Irvine open data repository has initiated a challenge of sorts to investigate the quality of models of various machine learning methods to build a model for predicting CO and NOx emissions depending on ambient variables and the parameters of the technological process. The novelty and features of this paper are: (i) a contribution to the study of the features of the open dataset on CO and NOx emissions for gas turbines, which will enable one to more objectively compare different machine learning methods for further research; (ii) for the first time for the CO and NOx emissions, a model based on symbolic regression and a genetic algorithm is presented—the advantage of this being the transparency of the influence of factors and the interpretability of the model; (iii) a new classification model based on the symbolic regression model and fuzzy inference system is proposed. The coefficients of determination of the developed models are: R2=0.83 for NOx emissions, R2=0.89 for CO emissions.

Analysis of approximations of the gas compressibility factor derived from genetic algorithms

“…In contrast to neural networks and other machine learning algorithms, the obtained models are not a "black box" and allow you to analyze the interaction between the input variables. There is a fairly large number of works describing the application of this method to solving problems encountered in practice in a wide variety of subject areas, for instance [13,14,15,16]. The terminology of the method comes from biology.…”

Tabular Machine Learning Methods for Predicting Gas Turbine Emissions

“…Azzam et al [17] utilised evolutionary artificial neural networks and support vector machines to model NOx emissions from gas turbines, finding that use of their genetic algorithm results in a high-enough accuracy to offset the computational cost compared to the cheaper support vector machines. Kochueva et al [18] developed a model based on symbolic regression and a genetic algorithm with a fuzzy classification model to determine "standard" or "extreme" emissions levels to further improve their prediction model. Botros et al [19][20][21] developed a predictive emissions model based on neural networks with an accuracy of ±10 parts per million.…”