Multi-objective prediction for denitration systems in cement: an approach combining process analysis and bi-directional long short-term memory network

Hao, Xiaochen; Di, Yinlu; Xu, Qin; Liu, Pengfei; Wang, Xin

doi:10.1007/s11356-022-24021-5

Cited by 7 publications

(3 citation statements)

References 47 publications

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“…Setting appropriate search ranges for each parameter to enhance search efficiency is crucial. The following guidelines, derived from relevant literature, [ 27,30,31 ] are considered: (1) The time series length of input variables range from 1 to 6, with a fine search step of 1. (2) The number of neurons ranges from 10 to 300, with a fine search step of 1.…”

Section: Methodsmentioning

confidence: 99%

“…[39] Setting appropriate search ranges for each parameter to enhance search efficiency is crucial. The following guidelines, derived from relevant literature, [27,30,31]…”

Section: Model Parameters Optimizationmentioning

confidence: 99%

“…Kang et al [ 30 ] combined dynamic joint MI and a Bi‐LSTM algorithm to achieve NOx emission prediction at time t and time t + 3, respectively. Hao et al [ 31 ] developed a multi‐input‐multi‐output Bi‐LSTM to predict NOx emissions from a cement process SNCR system for the next three moments. These researchers showed that the Bi‐LSTM networks outperform LSTM networks in feature mining and analysis of time series data.…”

Section: Introductionmentioning

confidence: 99%

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Modelling nitrogen oxide emission trends from the municipal solid waste incineration process using an adaptive bi‐directional long and short‐term memory network

Li,

Yao,

Chen

et al. 2023

Can J Chem Eng

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Accurately predicting trends in NOx emission is essential for effectively controlling pollution in municipal solid waste incineration (MSWI) power plants. However, the MSWI process exhibits notable dynamic nonlinearity, time series characteristics, and fluctuations that are distinct from those present in fossil fuel combustion processes. Therefore, the model must possess excellent capabilities in handling time series and nonlinear features while achieving adaptive updates to account for complex working conditions. To address these issues, we have developed a robust prediction model for NOx emission trends using the bi‐directional long short‐term memory (Bi‐LSTM) deep learning algorithm. This model encompasses maximum information coefficient and expert experience for input variables selection, parameter optimization using the linear inertial weight particle swarm algorithm (LDWPSO), and an adaptive update strategy based on probabilistic statistics. The prediction performance of this model was compared to that of the traditional and widely used backpropagation neural network (BPNN), extreme learning machine (ELM), and LSTM. Furthermore, we verified the adaptive update effect of the proposed model using additional data. The results demonstrate that the proposed model exhibits robust prediction and adaptive capabilities. This study's originality is presenting a satisfactory trend prediction for NOx emission from the MSWI process using an adaptive LDWPSO‐(Bi‐LSTM) model. It will be essential for the optimization and control of NOx emissions from the MSWI process.

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Section: Methodsmentioning

confidence: 99%

“…[39] Setting appropriate search ranges for each parameter to enhance search efficiency is crucial. The following guidelines, derived from relevant literature, [27,30,31]…”

Section: Model Parameters Optimizationmentioning

confidence: 99%