Multi-period Planning of Municipal Solid Waste Management: a Case Study in Qingdao

Zhao, Shifen; Ren, Tao; Ma, Lei; Li, Zhiwei

doi:10.1007/s41660-022-00279-7

Cited by 5 publications

(1 citation statement)

References 40 publications

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“…Currently, the GM(1,1) model, with a proper predictive accuracy and a relatively simple calculation, is typically adopted to predict the generation of C&D waste [18]. Zhao et al [19] applied the GM(1,1) model to predict the quantity of building decoration waste in Shenzhen, China. Wang et al [20] proposed a model based on the GM(1,1) model to predict C&D waste generation in Shenyang, China, from 2005 to 2014.…”

Section: Short-term and Medium-term Prediction Methodsmentioning

confidence: 99%

Quantity Prediction of Construction and Demolition Waste Using Weighted Combined Grey Theory and Autoregressive Integrated Moving Average Model

Fang,

Shi,

Chen

et al. 2024

Sustainability

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With rapid urban development, the “waste-free city” concept has emerged. Therefore, the accurate prediction of the amount of C&D waste is of great importance. However, many countries and regions, including China, have not yet established C&D waste databases and standard prediction methods. This study proposed a method using a weighted combination of the grey theory model (GM) and the autoregressive integrated moving average (ARIMA) model to predict the quantity of urban C&D waste in the future. Based on a case study in Guangzhou, this study compared the prediction results of three prediction models, namely the GM, the ARIMA, and the proposed weighted combined model of the GM and the ARIMA (GM-ARIMA). The results of this study proved that the proposed combined GM-ARIMA model had a better predictive performance than both the separated models. The mean absolute percentage errors (MAPE) of the GM and ARIMA models were 12.11% and 14.26%, respectively, whereas the proposed GM-ARIMA model had a lower MAPE (8.5%). This study found that the generation of C&D waste in Guangzhou will continue to grow steadily. From 2024 to 2035, the quantity of C&D waste is expected to reach 850 million tons cumulatively, with an annual growth rate of 7.1%.

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Section: Short-term and Medium-term Prediction Methodsmentioning

confidence: 99%

Quantity Prediction of Construction and Demolition Waste Using Weighted Combined Grey Theory and Autoregressive Integrated Moving Average Model

Fang,

Shi,

Chen

et al. 2024

Sustainability

View full text Add to dashboard Cite

show abstract

Phycoremediation of Industrial Wastewater

Jiménez-Veuthey,

Zapata,

Flores

2024

Developments in Applied Phycology

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Enviro-economic optimization for the design of municipal solid waste management strategies

Puchongkawarin

2024

Environ Syst Decis

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A significant amount of municipal solid waste (MSW) generation and ineffective management can cause adverse impacts on the environment (air and water pollution) and human health. Therefore, a sustainable approach for handling MSW is required. This study aims to develop a tool to select the most promising MSW management system using the superstructure optimization technique to ensure that the selected system can achieve minimization of the total annual cost and environmental impacts. This can lead to a multi-objective superstructure optimization problem solved by using the augmented ϵ-constraint method. The developed model is based on the concept of material balance and all potential technologies include anaerobic digestion, pyrolysis, gasification, incineration, sanitary landfill, material recovery facility and composting. The proposed methodology is applied to synthesize the MSW management system in Warin Chamrap district to determine its applicability. In this research, the composition of MSW include organics, paper, plastic, glass, and others. The results show that the Pareto-optimal solutions optimizing both total annual cost and environmental impacts can be developed. The MSW management system with the lower total annual cost tends to have the higher environmental impacts and the trade offs between each objective can be identified so the decision makers are able to consider before making their decision. Further investigation on sensitivity analysis reveals that the selling prices of products, annual capital cost and operating cost factors have different degree of impacts on the waste management strategies and total annual cost.

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Multi-period Planning of Municipal Solid Waste Management: a Case Study in Qingdao

Cited by 5 publications

References 40 publications

Quantity Prediction of Construction and Demolition Waste Using Weighted Combined Grey Theory and Autoregressive Integrated Moving Average Model

Quantity Prediction of Construction and Demolition Waste Using Weighted Combined Grey Theory and Autoregressive Integrated Moving Average Model

Phycoremediation of Industrial Wastewater

Enviro-economic optimization for the design of municipal solid waste management strategies

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