Emission characteristics and impact factors of air pollutants from municipal solid waste incineration in Shanghai, China

Chen, Xiaojia; Li, Junxiang; Liu, Qizhen; Luo, Huan; Cheng, Jin; Huang, Yuandong

doi:10.1016/j.jenvman.2022.114732

Cited by 32 publications

(11 citation statements)

References 27 publications

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“…Ensuring the stable fluctuation of the main steam parameters is one of the core problems that need to be solved by the waste incinerator automatic control system. Accurate prediction of the values of the main steam parameters in a future period of time can provide control reference for the operator from a decision-making point of view, or can provide parameter prediction for automatic control systems so that control commands can be given rationally to improve the stability of unit operation and boiler thermal efficiency [8].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Main Parameters of Steam in Waste Incinerators Based on BAS-SVM

Chen¹,

Wang²,

Zhong³

et al. 2023

Sustainability

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The main steam parameters of a waste-to-energy plant are the key indicator of the safety and stability of its combustion process. Accurate prediction of the main steam parameters can help the control system to reasonably analyze the combustion conditions and, thus, to greatly improve the combustion efficiency. In this paper, we propose an optimized method for predicting the main steam parameters of waste incinerators. Firstly, a grey relational analysis (GRA) is used to obtain the ranking of the correlation degree between 114 characteristic variables in the furnace and the main steam parameters, and 13 characteristic variables are selected as model inputs. A Spearman-based time delay compensation method is proposed to effectively overcome the influence of time asynchrony on the prediction accuracy. At last, the beetle antennae search (BAS) optimized support vector machine (SVM) model is proposed. Taking advantage of the fast iteration of the beetle antennae search algorithm to find the key hyperparameters of the support vector machine, the optimized main steam parameter prediction model is finally obtained. The simulation results show that the prediction accuracy of this model is greatly improved compared with traditional neural network models, such as long short-term memory neural networks (LSTMs) and convolutional neural networks (CNNs), as well as a single SVM.

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Section: Introductionmentioning

confidence: 99%

Prediction of Main Parameters of Steam in Waste Incinerators Based on BAS-SVM

Chen¹,

Wang²,

Zhong³

et al. 2023

Sustainability

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show abstract

“…As many know, the measurement process of the CEMS system involves sampling, pre-processing, and analysis. As a result, the CEMS encounters two drawbacks in monitoring concentrations: (1) there exists an inherent lag in CEMS measurements. This is primarily due to the practical constraints imposed by the site environment, which frequently prevents the installation of the CEMS system near the measurement point.…”

Section: Introductionmentioning

confidence: 99%

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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“…Therefore, in this study, we focus on the questions of whether the MSW sorting policy in Shanghai significantly improves urban air quality and how large the influence will be. Theoretically, the implementation of MSW sorting policies can promote resource recovery and reduce the total amount of incinerated waste, thereby helping to reduce air pollution [ 4 ]. To prove this hypothesis, we estimate the causal effect of MSW sorting policies on urban air quality by using daily data on air quality, weather and geographical information.…”

Section: Introductionmentioning

confidence: 99%

“…At present, limited research has estimated the causal effect of MSW sorting on air pollution from an economic perspective. Chen et al (2022) [4] used daily air quality data at the air monitoring station level to show that MSW incineration significantly affects the air quality in Shanghai. However, they did not estimate the influence of the MSW sorting policy.…”

Section: Introductionmentioning

confidence: 99%

The impact of municipal solid waste sorting policy on air pollution: Evidence from Shanghai, China

Wang

Shi²

2022

PLoS ONE

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Municipal solid waste (MSW) sorting not only reduces the total quantity of domestic waste but also has positive effects on urban air quality. In this study, using a careful identification strategy and air quality data at the monitoring station level in Shanghai, we estimate the causal effect of the MSW sorting policy on urban air quality. The results show that after the MSW sorting policy was implemented, the air quality index (AQI), nitrogen dioxide (NO2) and carbon monoxide (CO) decreased significantly by 2.71%, 2.07% and 3.62%, respectively. We also find a positive spillover effect from the Shanghai MSW sorting policy on the air quality of adjacent cities. The implementation of this policy has triggered changes in residents’ behaviors. However, the government needs further efforts to maintain the sustainability of MSW sorting policies.

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Emission characteristics and impact factors of air pollutants from municipal solid waste incineration in Shanghai, China

Cited by 32 publications

References 27 publications

Prediction of Main Parameters of Steam in Waste Incinerators Based on BAS-SVM

Prediction of Main Parameters of Steam in Waste Incinerators Based on BAS-SVM

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

The impact of municipal solid waste sorting policy on air pollution: Evidence from Shanghai, China

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