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http://dx.doi.org/10.9713/kcer.2020.58.4.588

Development of Prediction Model for Nitrogen Oxides Emission Using Artificial Intelligence  

Jo, Ha-Nui (Department of Chemical Engineering, Pohang University of Science and Technology)
Park, Jisu (Department of Chemical Engineering, Pohang University of Science and Technology)
Yun, Yongju (Department of Chemical Engineering, Pohang University of Science and Technology)
Publication Information
Korean Chemical Engineering Research / v.58, no.4, 2020 , pp. 588-595 More about this Journal
Abstract
Prediction and control of nitrogen oxides (NOx) emission is of great interest in industry due to stricter environmental regulations. Herein, we propose an artificial intelligence (AI)-based framework for prediction of NOx emission. The framework includes pre-processing of data for training of neural networks and evaluation of the AI-based models. In this work, Long-Short-Term Memory (LSTM), one of the recurrent neural networks, was adopted to reflect the time series characteristics of NOx emissions. A decision tree was used to determine a time window of LSTM prior to training of the network. The neural network was trained with operational data from a heating furnace. The optimal model was obtained by optimizing hyper-parameters. The LSTM model provided a reliable prediction of NOx emission for both training and test data, showing an accuracy of 93% or more. The application of the proposed AI-based framework will provide new opportunities for predicting the emission of various air pollutants with time series characteristics.
Keywords
$NO_x$; Artificial intelligence; Long short-term memory; Decision tree; Time series;
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Times Cited By KSCI : 2  (Citation Analysis)
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