Environmental Assessment of Municipal Solid Waste by Two-Stage Plasma Gasification

Ramos, Ana; Teixeira, Carlos Afonso; Rouboa, Abel

doi:10.3390/en12010137

Cited by 57 publications

(18 citation statements)

References 43 publications

(73 reference statements)

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“…Slightly improved results for this technique have been published ( Evangelisti et al, 2015a ; Evangelisti et al, 2015b ), given the inclusion of a by-product within the boundaries, constituting an extra avoided burden. Further discussion about the environmental results, a thorough analysis of the impact categories as well as sensitivity analysis for each may be found in ( Ramos et al, 2018b ; Ramos et al, 2018c ; Ramos et al, 2019b ).…”

Section: Resultsmentioning

confidence: 99%

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Renewable energy from solid waste: life cycle analysis and social welfare

Ramos

Rouboa

2020

Environmental Impact Assessment Review

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In this study, municipal solid waste (MSW) composition in distinct world locations is compared and a case study is assessed. Three waste-to-energy (WtE) techniques are employed within the framework of an industrial partnership. Life cycle assessment (LCA) and a brief social contextualization including the production of renewable energy from the waste generated worldwide were held to attain a holistic view and attract the interest of multiple stakeholders. Incineration depicted a sustainable profile with improved results for global warming potential and terrestrial ecotoxicity potential. Regular gasification revealed the best results for eutrophication, acidification, marine aquatic ecotoxicity and human toxicity potential. Two-stage plasma gasification showed negative values for all impact categories i.e. achieving environmental credits. The estimate of the electricity produced from the waste generated per capita showed a fair coverage of the electrical demand in distinct world areas. To the best of the authors' knowledge, there are no reports connecting the electricity use, the waste production and the renewable energy achieved from WtE for different world regions. Therefore, this study supports the replacement of fossil fuels with renewable alternatives, reducing greenhouse gas emissions while maintaining the comfort and commodities suitable for a comfortable quality of life.

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Section: Resultsmentioning

confidence: 99%

“…The functional unit (fu) was defined as the thermal treatment of 1 t of MSW, its characterization being shown in Table 2 . Further information on the experimental conditions is described in ( Ramos et al, 2018b ; Ramos et al, 2018c ; Ramos et al, 2019b ).…”

Section: Methodsmentioning

confidence: 99%

Renewable energy from solid waste: life cycle analysis and social welfare

Ramos

Rouboa

2020

Environmental Impact Assessment Review

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“…Many studies summarise new information for the treatment processes of polypropylene waste with a comparison between the different technologies [ 46 , 47 , 48 ]. The thermic treatment process (pyrolysis, incineration, gasification, and plasma-based technology) can be considered on the basis of the environmental burden and energy efficiency [ 49 , 50 , 51 ]. The new technologies enable greater market penetration, since these secondary energy sources are compatible with gas turbines and gas motors [ 52 , 53 ].…”

Section: Resultsmentioning

confidence: 99%

Total Life Cycle of Polypropylene Products: Reducing Environmental Impacts in the Manufacturing Phase

Mannheim

Siménfalvi

2020

Polymers

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This paper assesses the environmental burdens of a polypropylene product throughout the product’s life cycle, especially focusing on the injection-moulding stage. The complete life cycle model of the polypropylene product has been developed from the raw material extraction and production phase through its usage to the end-of-life stage with the help of the life cycle assessment method. To find the answers to the posed problems, different impacts were analysed by GaBi 8.0 software. The analysis lasted from the cradle to the grave, expanding the analysis of the looping method. The aim of the research was to determine the energy and material resources, emissions, and environmental impact indicators. Basically, the article tried to answer three questions: (1) How can we optimize the production phase for the looping method? (2) Which materials and streams are recyclable in the design of the production process? (3) What is the relationship between life cycle stages and total life cycle of the product? As we inspect the life cycle of the product, the load on the environment was distributed as follows: 91% in the production phase, 3% in the use phase, and 6% in the end-of-life phase. The results of the research can be used to develop technologies, especially the injection-moulding process, with a lower environmental impact.

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“…There are many factors that affect the slag viscosity at different temperatures, and the governing factor is the chemical composition of ash. The compositions of hazardous wastes vary in a wide range [10][11][12][13][14]. For the raw materials with high SiO 2 content, the polymeric network structure composed of long silicate chains formed during the ash transformation process is dominant so that the flow resistance of the slag should increase at high temperature [15,16].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Slag Characteristics Based on Artificial Neural Network for Molten Gasification of Hazardous Wastes

Lin

Dai

et al. 2020

Energies

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Molten gasification is considered as a promising technology for the processing and safe disposal of hazardous wastes. During this process, the organic components are completely converted while the hazardous materials are safely embedded in slag via the fusion-solidification-vitrification transformation. Ideally, the slag should be glassy with low viscosity to ensure the effective immobilization and steady discharge of hazardous materials. However, it is very difficult to predict the characteristics of slag using existing empirical equations or conventional mathematical methods, due to the complex non-linear relationship among the phase transformation, vitrification transition and chemical composition of slag. Equipped with a strong nonlinear mapping ability, an artificial neural network may be able to predict the properties of slags if a large amount of data is available for training. In this work, over 10,000 experimental data points were used to train and develop a slag classification model (glassy vs. non-glassy) based on a neural network. The optimal structure of the neural network was figured out and validated. The results suggest that the classification accuracy for the independent test samples reached 93.3%. Using 1 and 0 as model inputs to represent mildly reducing and inert atmospheres, a double hidden layer structure in the neural network enabled the accurate classification of slags under various atmospheres. Furthermore, the neural network for the prediction of glassy slag viscosity was optimized; it featured a double hidden layer structure. Under a mildly reducing atmosphere, the absolute error from the independent test data was generally within 4 Pa·s. By adding a gas atmosphere into the input of the neural network using a simple normalization method, a multi-atmosphere slag viscosity prediction model was developed. Said model is much more accurate than its counterpart that does not consider the effect of the atmosphere. In summary, the artificial neural network proved to be an effective approach to predicting the slag properties under different atmospheres. The data-driven models developed in this work are expected to facilitate the commercial deployment of molten gasification technology.

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Environmental Assessment of Municipal Solid Waste by Two-Stage Plasma Gasification

Cited by 57 publications

References 43 publications

Renewable energy from solid waste: life cycle analysis and social welfare

Renewable energy from solid waste: life cycle analysis and social welfare

Total Life Cycle of Polypropylene Products: Reducing Environmental Impacts in the Manufacturing Phase

Prediction of Slag Characteristics Based on Artificial Neural Network for Molten Gasification of Hazardous Wastes

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