An explainable AI model for power plant NOx emission control  

作　　者：Yuanye Zhou Ioanna Aslanidou Mikael Karlsson Konstantinos Kyprianidis 

机构地区：[1]School of Business,Society and Engineering,Mälardalens University,Universitetsplan 1,72220,Västerås,Sweden [2]Future Energy Centre,Mälardalens University,Universitetsplan 1,72220,Västerås,Sweden [3]School of Innovation,Design and Engineering,Mälardalens University,Universitetsplan 1,72220,Västerås,Sweden

出　　处：《Energy and AI》2024年第1期171-180,共10页能源与人工智能（英文）

摘　　要：In recent years,developing Artificial Intelligence(AI)models for complex system has become a popular research area.There have been several successful AI models for predicting the Selective Non-Catalytic Reduction(SNCR)system in power plants and large boilers.However,all these models are in essence black box models and lack of explainability,which are not able to give new knowledge.In this study,a novel explainable AI(XAI)model that combines the polynomial kernel method with Sparse Identification of Nonlinear Dynamics(SINDy)model is proposed to find the governing equation of SNCR system based on 5-year operation data from a power plant.This proposed model identifies the system’s governing equation in a simple polynomial format with polynomial order of 1 and only 1 independent variable among original 68 input variables.In addition,the explainable AI model achieves a considerable accuracy with less than 21%deviation from base-line models of partial least squares model and artificial neural network model.

关 键 词：Explainable AI SINDy KERNEL SNCR Power plant BOILER 

分 类 号：X51[环境科学与工程—环境工程] TP18[自动化与计算机技术—控制理论与控制工程]