Dynamic modeling for NOx emission sequence prediction of SCR system outlet based on sequence to sequence long short-term memory network

Xie, Peiran; Gao, Mingming; Zhang, Hongfu; Niu, Yuguang; Wang, Xiaowen

doi:10.1016/j.energy.2019.116482

Cited by 88 publications

(34 citation statements)

References 24 publications

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“…Yang et al used two LSTMs to build the DL-based NOx prediction model [13]. Xie et al used the LSTM variant called bidirectional LSTM as the building block to build encoder-decoder architecture to predict NOx emissions [14]. LSTM can capture long-term temporal dependencies from data by storing the history information, which leads to increased storage cost and computing cost.…”

Section: Introductionmentioning

confidence: 99%

The Deep Convolutional Neural Network for NOx Emission Prediction of a Coal-Fired Boiler

2020

IEEE Access

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This paper presents a methodology for predicting NOx emissions of a coal-fired boiler by using real operation data, coal properties and CNN (Convolutional Neural Network). Two building blocks are carefully designed following the practical guidelines for the light weight CNN architecture design. Furthermore, the building blocks are used to develop the deep CNN-based model for NOx prediction. A comprehensive comparison among different prediction models based on DL (Deep Learning) shows that the proposed deep CNN-based prediction model outperforms other prediction models in terms of RMSE (Root Mean Square Error) criteria. The results indicate that the developed deep CNN-based prediction model has more excellent accuracy and better numerical stability. Besides, the architecture design of the DL-based prediction model has a significant impact on the performance of the prediction model. INDEX TERMS Coal-fired boiler, convolutional neural network, deep learning, NOx emission prediction.

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Section: Introductionmentioning

confidence: 99%

The Deep Convolutional Neural Network for NOx Emission Prediction of a Coal-Fired Boiler

2020

IEEE Access

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“…In DNN, data quantity for training and input/output definitions are well known for their impacts for problem-solving, and there are some studies to predict NOx emissions using DNN. However, despite the importance of the hyperparameter definitions, these values for DNN designs have often been overlooked or found manually (intuition or trial & error) in previous researches [29][30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Prediction of engine NOx for virtual sensor using deep neural network and genetic algorithm

Kim

Park

Shin

et al. 2021

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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The Nitrogen Oxides (NOx) from engines aggravate natural environment and human health. Institutional regulations have attempted to protect the human body from them, while car manufacturers have tried to make NOx free vehicles. The formation of NOx emissions is highly dependent on the engine operating conditions and being able to predict NOx emissions would significantly help in enabling their reduction. This study investigates advanced method of predicting vehicle NOx emissions in pursuit of the sensorless engine. Sensors inside the engine are required to measure the operating condition. However, they can be removed or reduced if the sensing object such as the engine NOx emissions can be accurately predicted with a virtual model. This would result in cost reductions and overcome the sensor durability problem. To achieve such a goal, researchers have studied numerical analysis for the relationship between emissions and engine operating conditions. Also, a Deep Neural Network (DNN) is applied recently as a solution. However, the prediction accuracies were often not satisfactory where hyperparameter optimization was either overlooked or conducted manually. Therefore, this study proposes a virtual NOx sensor model based on the hyperparameter optimization. A Genetic Algorithm (GA) was adopted to establish a global optimum with DNN. Epoch size and learning rate are employed as the design variables, and R-squared based user defined function is adopted as the object function of GA. As a result, a more accurate and reliable virtual NOx sensor with the possibility of a sensorless engine could be developed and verified.

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“…e RNN introduces the concept of time series into the design of the network structure, making it more adaptive in modeling the time series data (Mocanu et al, 2016;Zhang et al, 2018). As a famous deep learning model, RNN provides more powerful feature representation and mapping capabilities to model time sequence, because it has more hidden layers to learn the relationship between input and output vectors (Wen et al, 2020). Besides, RNN with the gated recurrent unit (GRU) and long short-term memory (LSTM) unit, is chosen as the network structure to process longterm time series data in this study.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Optimization of NO<i><sub>x</sub></i> Emission from a 660 MW Coal-Fired Boiler Based on the Deep Learning Algorithm

Wang

Xie

Liu

et al. 2021

J. Chem. Eng. Japan / JCEJ

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With the increasingly strict environmental protection policies, restrictions on NO x emissions are becoming increasingly stringent. This paper focuses on modeling and optimizing NO x emission for a coal-red boiler with advanced deep learning approaches. Three types of deep recurrent neural network models, including recurrent neural network (RNN), long short-term memory (LSTM), and gate recurrent unit (GRU), are developed to model the relationship between operational parameters and NO x emission of a 660 MW boiler. The hyperparameters of the models are selected by grid search and the e ects of the hyperparameters on the prediction results are analyzed. Compared with the traditional back propagation neural network (BP), support vector machine (SVM) models and deep belief network (DBN), the deep recurrent neural network models have higher prediction accuracy. The experimental results show that the GRU-based NO x prediction model has the best prediction performance among the proposed models. Then, the predicted NO x emission is used as the objective of searching the optimal parameters for the boiler combustion through the grey wolf optimization (GWO) algorithm. The searching process of GWO is convergent. According to the simulation results, the declines in the NO x emissions in the two selected cases were 19.49% and 17.96%, which are reasonable achievements for the boiler combustion process.

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Dynamic modeling for NOx emission sequence prediction of SCR system outlet based on sequence to sequence long short-term memory network

Cited by 88 publications

References 24 publications

The Deep Convolutional Neural Network for NOx Emission Prediction of a Coal-Fired Boiler

The Deep Convolutional Neural Network for NOx Emission Prediction of a Coal-Fired Boiler

Prediction of engine NOx for virtual sensor using deep neural network and genetic algorithm

Modeling and Optimization of NO<i><sub>x</sub></i> Emission from a 660 MW Coal-Fired Boiler Based on the Deep Learning Algorithm

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