Environmental Pollution Prediction of NOx by Process Analysis and Predictive Modelling in Natural Gas Turbine Power Plants

TL;DR

This paper presents a KNN-based machine learning model that predicts NOx emissions from natural gas turbines by incorporating weather, performance, and operational data, aiming to optimize emissions reduction and efficiency.

Contribution

It introduces a KNN approach trained on small datasets for accurate NOx emission prediction in natural gas turbines, integrating ambient and operational factors.

Findings

KNN achieves high prediction accuracy with small datasets.

Inclusion of weather and performance data improves model reliability.

The model can be used for operational optimization and emissions control.

Abstract

The main objective of this paper is to propose K-Nearest-Neighbor (KNN) algorithm for predicting NOx emissions from natural gas electrical generation turbines. The process of producing electricity is dynamic and rapidly changing due to many factors such as weather and electrical grid requirements. Gas turbine equipment are also a dynamic part of the electricity generation since the equipment characteristics and thermodynamics behavior change as the turbines age. Regular maintenance of turbines are also another dynamic part of the electrical generation process, affecting the performance of equipment. This analysis discovered using KNN, trained on relatively small dataset produces the most accurate prediction rates. This statement can be logically explained as KNN finds the K nearest neighbor to the current input parameters and estimates a rated average of historically similar observations as prediction. This paper incorporates ambient weather conditions, electrical output as well as turbine performance factors to build a machine learning model to predict NOx emissions. The model can be used to optimize the operational processes for reduction in harmful emissions and increasing overall operational efficiency. Latent algorithms such as Principle Component Algorithms (PCA) have been used for monitoring the equipment performance behavior change which deeply influences process paraments and consequently determines NOx emissions. Typical statistical methods of machine learning performance evaluations such as multivariate analysis, clustering and residual analysis have been used throughout the paper.