Prediction of SO
            <sub>2</sub>
            and NO
            <sub>x</sub>
            in sintering flue gas based on PSO-BP neural network model

Li, Xin; Liu, Xiaojie; Li, Hongyang; Liu, Ran; Zhang, Zhifeng; Li, Hongwei; Lyu, Qing; Chen, Shujun

doi:10.1080/03019233.2023.2185739

Cited by 3 publications

(3 citation statements)

References 10 publications

(11 reference statements)

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“…Notably, the interdependency between the input variables hinders the model's performance due to multicollinearity [25,26]. Hence, the Pearson correlation coefficient (r) was used to perform a redundancy analysis for the input variables and is expressed as follows [27]:…”

Section: Database Description and Variable Analysismentioning

confidence: 99%

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The Prediction of Cu(II) Adsorption Capacity of Modified Pomelo Peels Using the PSO-ANN Model

Jiao,

Jacquemin,

Zhang

et al. 2023

Molecules

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It is very well known that traditional artificial neural networks (ANNs) are prone to falling into local extremes when optimizing model parameters. Herein, to enhance the prediction performance of Cu(II) adsorption capacity, a particle swarm optimized artificial neural network (PSO-ANN) model was developed. Prior to predicting the Cu(II) adsorption capacity of modified pomelo peels (MPP), experimental data collected by our research group were used to build a consistent database. Then, a PSO-ANN model was established to enhance the model performance by optimizing the ANN’s weights and biases. Finally, the performances of the developed ANN and PSO-ANN models were deeply evaluated. The results of this investigation revealed that the proposed hybrid method did increase both the generalization ability and the accuracy of the predicted data of the Cu(II) adsorption capacity of MPPs when compared to the conventional ANN model. This PSO-ANN model thus offers an alternative methodology for optimizing the adsorption capacity prediction of heavy metals using agricultural waste biosorbents.

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Section: Database Description and Variable Analysismentioning

confidence: 99%

“…The inertia factor ω indicates the ability of a particle to continue the previous velocity, which is a fixed value in the conventional PSO algorithm [27]. Its numerical value is positively related to the global search, and inversely related to the local search [36].…”

Section: Psomentioning

confidence: 99%

The Prediction of Cu(II) Adsorption Capacity of Modified Pomelo Peels Using the PSO-ANN Model

Jiao,

Jacquemin,

Zhang

et al. 2023

Molecules

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“…The iron ore sintering is an important production step of ironmaking and is an agglomeration process mixing iron ore, returning sinter ore, fluxes and coke. 1,2 The purpose of the sintering process is to manufacture a product with suitable characteristics to be fed to the blast furnace. 3,4 During the sintering process, the liquid phase is distributed in a region with a certain width and complex physical and chemical changes occur in this region.…”

Section: Introductionmentioning

confidence: 99%

Partial differential equation modelling of the solid–liquid phase process in iron sintering and interface control with backstepping method

Chen,

Jiang,

Kang

et al. 2024

Ironmaking & Steelmaking: Processes, Products and Applicati

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Due to the highly continuous temporal and spatial distribution characteristics of the sintering process, the solid–liquid phase interface during sintering is uneven and discontinuous, which seriously affects the quality of sintered ore. In order to achieve real-time and effective control of the solid–liquid interface in the sintering system, this study proposes a spatiotemporally distributed partial differential equation (PDE) thermal system with Stefan interface boundaries. Slice the 3D sintering mixing space and establish a discrete 1D space multi-distributed system to achieve distributed control of the sintering thermal system. Based on this system, a distributed boundary controller is designed using the backstepping method to drive the solid–liquid phase interface to the desired position. This method fully adapts to the nonlinear problems of the sintering thermal system and can quickly respond to system changes. Based on real data from the sintering site, a multi-physics simulation model was designed to verify the correctness of the theoretical research. Comparing the simulation results with the actual sintering state shows that this control method can effectively control the position of the sintering solid–liquid interface, which plays a vital role in improving the quality of sinter and sintering efficiency.

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Atmospheric SO2 pollutant prediction using mutual information based TCNN-GRU model for flue gas desulfurization process

Liu,

Li,

Wang

2024

IDA

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Over the past several years, sulfur dioxide (SO2) has raised growing concern in China owing to its adverse impact on atmosphere and human respiratory system. The major contributor to SO2 emissions is flue gas generated by fossil-fired electricity-generating plants, and as a consequence diverse flue gas desulphurization (FGD) techniques are installed to abate SO2 emissions. However, the FGD is a dynamic process with serious nonlinearity and large time delay, making the FGD process modeling problem a formidable one. In our research study, a novel hybrid deep learning model with temporal convolution neural network (TCNN), gated recurrent unit (GRU) and mutual information (MI) technique is proposed to predict SO2 emissions in an FGD process. Among those technique, MI is applied to select variables that are best suited for SO2 emission prediction, while TCNN and GRU are innovatively integrated to capture dynamics of SO2 emission in the FGD process. A real FGD system in a power plant with a coal-fired unit of 1000 MW is used as a study case for SO2 emission prediction. Experimental results show that the proposed approach offers satisfactory performance in predicting SO2 emissions for the FGD process, and outperforms other contrastive predictive methods in terms of different performance indicators.

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Prediction of SO ₂ and NO _x in sintering flue gas based on PSO-BP neural network model

Cited by 3 publications

References 10 publications

The Prediction of Cu(II) Adsorption Capacity of Modified Pomelo Peels Using the PSO-ANN Model

The Prediction of Cu(II) Adsorption Capacity of Modified Pomelo Peels Using the PSO-ANN Model

Partial differential equation modelling of the solid–liquid phase process in iron sintering and interface control with backstepping method

Atmospheric SO2 pollutant prediction using mutual information based TCNN-GRU model for flue gas desulfurization process

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Prediction of SO 2 and NO x in sintering flue gas based on PSO-BP neural network model

Cited by 3 publications

References 10 publications

The Prediction of Cu(II) Adsorption Capacity of Modified Pomelo Peels Using the PSO-ANN Model

The Prediction of Cu(II) Adsorption Capacity of Modified Pomelo Peels Using the PSO-ANN Model

Partial differential equation modelling of the solid–liquid phase process in iron sintering and interface control with backstepping method

Atmospheric SO2 pollutant prediction using mutual information based TCNN-GRU model for flue gas desulfurization process

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Prediction of SO ₂ and NO _x in sintering flue gas based on PSO-BP neural network model