Nonlinear Dynamic Soft-Sensing Modeling of NOx Emission of a Selective Catalytic Reduction Denitration System

Wu, Xiuliang; Yu, Xiaodong; Xu, Rui; Cao, Maoyong; Sun, Kai

doi:10.1109/tim.2022.3141154

Cited by 12 publications

(6 citation statements)

References 40 publications

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“…Cross-validation is an effective method of evaluating the model’s performance and selecting the hyperparameters. Considering the time series characteristics of industrial processes, a moving window cross-validation (MWCV) method 35 was used to determine the optimal λ. Briefly, this method comprises two loops.…”

Section: Proposed Methodologiesmentioning

confidence: 99%

“…34 Furthermore, Wu et al developed an effective soft sensor by combing the LASSO with the LSTM network to predict the NO x emissions from a denitration system. 35 To establish an accurate data-driven model of the NO x emissions from a practical denitration system in a thermal power plant, a soft-sensing algorithm based on the LSTM network and regularized STA mechanism is proposed. The main contributions are summarized as follows:…”

Section: Introductionmentioning

confidence: 99%

“… 34 Furthermore, Wu et al developed an effective soft sensor by combing the LASSO with the LSTM network to predict the NO x emissions from a denitration system. 35 …”

Section: Introductionmentioning

confidence: 99%

“…Yang et al proposed a dynamic model based on the least square SVM and mutual information to predict the NO x concentration of a coal-fired boiler . Furthermore, Wu et al developed an effective soft sensor by combing the LASSO with the LSTM network to predict the NO x emissions from a denitration system …”

Section: Introductionmentioning

confidence: 99%

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A New Regularized Spatiotemporal Attention-Based LSTM with Application to Nitrogen Oxides Emission Prediction

Sun

Cao

2023

ACS Omega

Self Cite

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The data collected from complex process industries are usually time series with considerable nonlinearities and dynamics, as well as excessive redundancy. Moreover, there are temporal and spatial correlations between input variables and key performance variables. These characteristics bring great difficulties to data-driven modeling of the key performance variables. To overcome the problems, a new regularized spatiotemporal attention (STA)-based long short-term memory (LSTM) was developed. First, a standard LSTM network with an STA module was trained to capture the dynamic relationship between input and target variables. Second, the least absolute shrinkage and selection operator was introduced to optimize the STA module. Third, the hyperparameter representing the regularization strength of the algorithm was determined using a moving window cross-validation strategy. Finally, the proposed algorithm was compared to other state-of-the-art algorithms using artificial data, and then it was used to predict the nitrogen oxide emissions of a selective catalytic reduction denitration system. Simulation results showed that the proposed algorithm achieved more accurate predictions than the other algorithms. Furthermore, the statistics and analysis of the importance of the variables are consistent with known chemical-reaction mechanisms and observations of field experts. Thus, the proposed method can provide technical support for the predictive control and optimization of such systems.

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Section: Proposed Methodologiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“… 34 Furthermore, Wu et al developed an effective soft sensor by combing the LASSO with the LSTM network to predict the NO x emissions from a denitration system. 35 …”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A New Regularized Spatiotemporal Attention-Based LSTM with Application to Nitrogen Oxides Emission Prediction

Sun

Cao

2023

ACS Omega

Self Cite

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“…Liu et al [18] selected 22 key variables of the power plant and built the multiple linear regression (LR) model with three hidden layers. Wu et al [19] proposed the least absolute shrinkage and selection operator to select input variables and used long short-term memory to establish the model. Yuan et al [20] combined PCA and SGEM method to build an inlet NO x concentration model.…”

Section: Introductionmentioning

confidence: 99%

Prediction and Control of the Nitrogen Oxides Emission for Environmental Protection Goal Based on Data-Driven Model in the SCR de-NOx System

Liu

Song

et al. 2022

Sustainability

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In order to reduce the nitrogen oxides (NOx) emission of flue gas, a selective catalytic reduction (SCR) system must be installed. In general, the lag of the inlet NOx analyzer, the action of the NH3 injection valve and the feedforward signal are seriously delayed. Therefore, it is necessary to consider the measurement lag of inlet NOx on the NH3 injection flowrate control system. In this paper, the data-driven model of inlet NOx is proposed to improve control system, so as to avoid excessive or insufficient NH3 injection. First, the measurement lag time of inlet NOx is estimated by the blowback signal of a CEMS and the change process of the inlet O2 content. Then, an exponential model is used to predict the inlet NOx in advance, and recursive LSSVM is proposed to revise the output of the exponential model. Finally, the output of the final model is used as the feedforward signal for improved feedforward (IF) control. Based on IF control and PID control, the IF-PID control strategy for NH3 injection is proposed. The results show that the outlet NOx are close to the set value and meet the national environmental regulation. Furthermore, the average value of the NH3 injection flowrate remains unchanged. It shows that a better control effect and environmental sustainability are achieved without increasing the cost of NH3 injection.

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Fast Prediction of Heat Flux Distribution in Boilers Using Computational Fluid Dynamics Simulation Data via Multi-Extreme Learning Machines

Tang,

Yang,

Pan

et al. 2023

Journal of Chemical Engineering of Japan

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Nonlinear Dynamic Soft-Sensing Modeling of NOx Emission of a Selective Catalytic Reduction Denitration System

Cited by 12 publications

References 40 publications

A New Regularized Spatiotemporal Attention-Based LSTM with Application to Nitrogen Oxides Emission Prediction

A New Regularized Spatiotemporal Attention-Based LSTM with Application to Nitrogen Oxides Emission Prediction

Prediction and Control of the Nitrogen Oxides Emission for Environmental Protection Goal Based on Data-Driven Model in the SCR de-NOx System

Fast Prediction of Heat Flux Distribution in Boilers Using Computational Fluid Dynamics Simulation Data via Multi-Extreme Learning Machines

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