Artificial Intelligence For Air Quality Control Systems: A Holistic Approach

Recent environmental regulations introduced by the United States environmental protection agency such as the Mercury Air Toxics Standards and Hazardous Air Pollution Standards have challenged environmental particulate control equipment especially the electro-static precipitators to operate beyond their design specifications. The impact is exacerbated in power plants burning a wide range of low and high-ranking fossil fuels relying on co-benefits from upstream processes such as the selective catalytic reactor and boilers. To alleviate and mitigate the challenge, this manuscript presents the utilization of modern and novel algorithms in machine learning and artificial intelligence for improving the efficiency and performance of electrostatic precipitators reflecting a holistic approach by considering upstream processes as model parameters. In addition, the paper discusses input relevance algorithms for neural networks and random forests such as partial derivatives, input perturbation and GINI importance comparing their performance and applicability for our case study. Our approach comprises of applying random forests and neural network algorithms to an electrostatic precipitator extending the model to include upstream process parameters such as the selective catalytic reactor and the air heaters. To study variable importance differences and model generalization performance between our employed algorithms, we developed a statistical approach to compare features data distributions impact on input relevance.

[1]  Jacek M. Zurada,et al.  Sensitivity analysis for minimization of input data dimension for feedforward neural network , 1994, Proceedings of IEEE International Symposium on Circuits and Systems - ISCAS '94.

[2]  Yannis Dimopoulos,et al.  Use of some sensitivity criteria for choosing networks with good generalization ability , 1995, Neural Processing Letters.

[3]  Mohammad Hassan Shojaeefard,et al.  Sensitivity Analysis of the Artificial Neural Network Outputs in Friction Stir Lap Joining of Aluminum to Brass , 2013 .

[4]  Julian D. Olden,et al.  Illuminating the “black box”: a randomization approach for understanding variable contributions in artificial neural networks , 2002 .

[5]  Pierre Geurts,et al.  Proteomic mass spectra classification using decision tree based ensemble methods , 2005, Bioinform..

[6]  Harshinder Singh,et al.  Application of the Random Forest Method in Studies of Local Lymph Node Assay Based Skin Sensitization Data. , 2005 .

[7]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[8]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[9]  N. Sivakumaran,et al.  Internal model controller for CFBC boiler using neural networks , 2018, 2018 Indian Control Conference (ICC).

[10]  S. Dudoit,et al.  Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data , 2002 .

[11]  Ramón Díaz-Uriarte,et al.  Gene selection and classification of microarray data using random forest , 2006, BMC Bioinformatics.

[12]  Anna Jankowska,et al.  Early detection and prediction of leaks in fluidized-bed boilers using artificial neural networks , 2015 .

[13]  Chong Jin Ong,et al.  A Feature Selection Method for Multilevel Mental Fatigue EEG Classification , 2007, IEEE Transactions on Biomedical Engineering.

[14]  M. Gevrey,et al.  Review and comparison of methods to study the contribution of variables in artificial neural network models , 2003 .

[15]  Constantin F. Aliferis,et al.  A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification , 2008, BMC Bioinformatics.

[16]  Russell G. Death,et al.  An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data , 2004 .

[17]  Jingtao Yao,et al.  Forecasting and Analysis of Marketing Data Using Neural Networks , 1998, J. Inf. Sci. Eng..

[18]  Jun Chen,et al.  Joint analysis of two microarray gene-expression data sets to select lung adenocarcinoma marker genes , 2004, BMC Bioinformatics.

[19]  H. Clack.  Simultaneous removal of particulate matter and gas-phase pollutants within electrostatic precipitators: Coupled in-flight and wall-bounded adsorption , 2015 .

[20]  Bjoern H. Menze,et al.  A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data , 2009, BMC Bioinformatics.

[21]  Paolo Vezza,et al.  A comparison of artificial neural networks and random forests to predict native fish species richness in Mediterranean rivers , 2013 .

[22]  I. Dimopoulos,et al.  Role of some environmental variables in trout abundance models using neural networks , 1996 .

[23]  G. David Garson,et al.  Interpreting neural-network connection weights , 1991 .

[24]  Mohamed A. Zohdy,et al.  Comparative Performance of Several Recent Supervised Learning Algorithms , 2018 .

[25]  R. Srivastava,et al.  Flue gas desulfurization: the state of the art. , 2001, Journal of the Air & Waste Management Association.

[26]  Bjoern H Menze,et al.  Multivariate feature selection and hierarchical classification for infrared spectroscopy: serum-based detection of bovine spongiform encephalopathy , 2007, Analytical and bioanalytical chemistry.