Estimation of reduced greenhouse gas emission from municipal solid waste incineration with electricity recovery in prefecture- and county-level cities of China

Zhao, Qing; Tang, Weihao; Han, Mengjie; Cui, Wenjing; Zhu, Lei; Xie, Huaijun; Li, Wei; Wu, Fengchang

doi:10.1016/j.scitotenv.2023.162654

Cited by 15 publications

(5 citation statements)

References 41 publications

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“…Given that incineration plants basically generate electricity for energy recovery in China, we calculate the GHG emission reduction benefits brought about by replacing coal-fired power generation with incineration power generation and include it in the final calculation of incineration GHG emissions, as shown in eq E GHG , FI , y , p = E GHG , normalI , y , p − MSW normalI , y , p × prefix∑ i = 1 n = 9 PCMSW i , y , p × hlv i × δ × E ele , coal 3600 where E GHG,FI, y , p is the amount of final GHG emissions of incineration in city p in year y (Tg a –1 ), δ is the electricity conversion efficiency for incineration, hlv i is the heat value of PCMSW i (kJ/kg), and E ele,coal is the GHG emission generated by coal-fired power generation (kg/kWh) …”

Section: Methods and Datamentioning

confidence: 99%

“…where E GHG,I,y,p is the amount of direct GHG emissions of incineration in city p in year y (Tg a −1 ), MSW I,y,p is the amount of MSW disposed of through incineration in city p in year y (Tg a −1 ), PCMSW i,y,p is physical composition of municipal solid waste in city p in year y, PCMSW i includes the organic fraction, ash and stone, paper, plastic and rubber, textile, wood, metal, glass, and others, dry i is the proportion of combustible dry matter in PCMSW i , and CF i , FCF i , and O i are the fraction of carbon, the fraction of fossil carbon, and oxidation rate of combustible dry matter in PCMSW i , respectively. Given that incineration plants basically generate electricity for energy recovery in China, 32 we calculate the GHG emission reduction benefits brought about by replacing coal-fired power generation with incineration power generation and include it in the final calculation of incineration GHG emissions, as shown in eq 4…”

Section: ■ Methods and Datamentioning

confidence: 99%

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Decreasing Greenhouse Gas Emissions from the Municipal Solid Waste Sector in Chinese Cites

Ma,

Deng,

Zhao

et al. 2024

Environ. Sci. Technol.

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Section: Methods and Datamentioning

confidence: 99%

Section: ■ Methods and Datamentioning

confidence: 99%

Decreasing Greenhouse Gas Emissions from the Municipal Solid Waste Sector in Chinese Cites

Ma,

Deng,

Zhao

et al. 2024

Environ. Sci. Technol.

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“…The emissions in this scenario are higher, compared to Scenario 3, because the residual waste is treated through incineration. However, if the incineration process is utilised for renewable energy generation based on waste, it can help reduce GHG emissions [19]. If the energy system relies more on renewable energy sources, the electricity generated can contribute to significant GHG reductions.…”

Section: Scenariomentioning

confidence: 99%

Potential reduction of greenhouse gas emissions from waste banks and 3R waste treatment facilities in Bandung City

Nursetyowati,

Rachmanissa,

Fairus

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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The Indonesian government has established a 30% reduction by 2030 target for reducing GHG emissions, through the 3R (Reduce, Reuse, Recycle) approach. In the City of Bandung, various institutions are involved in waste handling and reduction. The objectives of this study were to determine the GHG emissions, resulting from household waste disposal at Sarimukti Landfill, without (scenario 1) and with (scenario 2) the utilisation of Waste Banks, 3R Waste Treatment Facility (TPS 3R), and an incinerator in a TPS 3R, at Ciwastra Market, as well as to estimate the GHG emissions, once Bandung City successfully achieves its waste reduction and waste management target in 2025, as stated in the Bandung Mayor Regulation No. 1426 of 2018, leveraging all existing facilities with the landfilling (scenario 3) and incineration (scenario 4) method. The calculations made were aligned with the Intergovernmental Panel on Climate Change (IPCC) 2019 Guidelines Tier 1. The findings of this study revealed that, in the first scenario, the GHG emissions amount to 185,271.934 tons of CO2eq. In the second scenario, the emissions amount to 150,423.096 tons of CO2eq. Consequently, the reduction in GHG emissions achieved is 34,848.847 tons CO2eq. In the third scenario, the emissions are 64,373.560 tons of CO2eq, while in the fourth scenario, GHG emissions are 70,001.973 tons of CO2eq. These findings highlight the substantial GHG emission reductions achievable through the implementation of waste management strategies. By adopting these approaches, it is possible to mitigate the environmental impact of waste disposal and contribute to the reduction of GHG emissions, well in tandem with the climate change mitigation goals.

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“…Various testing methods, including manual sorting, balance method, and radiocarbon dating through a 14 C analysis, have been used to assess the direct emissions of fossil CO 2 during incineration. Manual sorting entails the separation of MSW into several components, such as food waste, paper, and plastic, and then determination of the fossil carbon content of each component to estimate the direct emissions of fossil CO 2 during incineration.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Prediction of the CO₂ Concentration in the Flue Gas and Carbon Emissions from a Waste Incineration Plant

Ma,

He,

Lü

et al. 2024

ACS EST Eng.

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Monitoring the CO2 concentration in flue gas (CO2_G) is crucial to accurately calculate the direct carbon emissions associated with waste incineration. In this study, random forest (RF) and extreme gradient boosting (XGBoost) algorithms were used to predict CO2_G, using 21 operating variables from a municipal solid waste (MSW) incineration plant as input variables. The results showed a strong prediction performance for both the RF and XGBoost-based models with R 2 values of 0.932 and 0.903, respectively. A feature importance analysis identified key variables used for model retraining, resulting in R 2 values of 0.917 and 0.894, respectively. Based on the predicted and measured values of CO2_G and a balance calculation, the direct carbon emissions from waste incineration were determined. The emissions based on the predicted CO2_G value ranged from 283.38 to 348.39 kgCO2-eq/t, while the emission based on the measured value was 269.21 kgCO2-eq/t. To further validate the accuracy of the calculation results, the physical composition of MSW in the incineration plant was analyzed, resulting in a direct carbon emission estimate of 257.59 kgCO2-eq/t. These findings demonstrate the effective application of machine learning (ML)-based CO2_G predictions and overcome the labor-intensive and data-lagging aspects of carbon emission accounting in waste incineration.

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Estimation of reduced greenhouse gas emission from municipal solid waste incineration with electricity recovery in prefecture- and county-level cities of China

Cited by 15 publications

References 41 publications

Decreasing Greenhouse Gas Emissions from the Municipal Solid Waste Sector in Chinese Cites

Decreasing Greenhouse Gas Emissions from the Municipal Solid Waste Sector in Chinese Cites

Potential reduction of greenhouse gas emissions from waste banks and 3R waste treatment facilities in Bandung City

Machine Learning-Based Prediction of the CO₂ Concentration in the Flue Gas and Carbon Emissions from a Waste Incineration Plant

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Estimation of reduced greenhouse gas emission from municipal solid waste incineration with electricity recovery in prefecture- and county-level cities of China

Cited by 15 publications

References 41 publications

Decreasing Greenhouse Gas Emissions from the Municipal Solid Waste Sector in Chinese Cites

Decreasing Greenhouse Gas Emissions from the Municipal Solid Waste Sector in Chinese Cites

Potential reduction of greenhouse gas emissions from waste banks and 3R waste treatment facilities in Bandung City

Machine Learning-Based Prediction of the CO2 Concentration in the Flue Gas and Carbon Emissions from a Waste Incineration Plant

Contact Info

Product

Resources

About

Machine Learning-Based Prediction of the CO₂ Concentration in the Flue Gas and Carbon Emissions from a Waste Incineration Plant