Statistical model for heating value of municipal solid waste in Brazil based on gravimetric composition

Drudi, Kelly C.R.; Drudi, R.; Martins, Gilberto; Antonio, Graziella Colato; Leite, Juliana T.C.

doi:10.1016/j.wasman.2019.03.012

Cited by 48 publications

(26 citation statements)

References 7 publications

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“…In the context of environmental impact, the authors tried to estimate: the illegal disposal of waste (Yang, Fan, et al, 2019), the emissions of polluting gases from the inadequate final disposal of waste (Dimishkovsk et al, 2019;Kumar et al, 2016;Vu et al, 2018), soil pollution by heavy metals (Perez-Alonso et al, 2017) and the calorific value of waste as a potential source of energy (Baghban and Shamshirband, 2019;Bagheri et al, 2019;Boumanchar et al, 2019;Drudi et al, 2019;Li et al, 2020;Rostami and Baghban, 2018). In terms of economic impact, studies sought to predict the generation of waste to promote cost efficiency in the provision of services and to identify the best form of service provision (Pérez-López et al, 2016).…”

Section: Main Challenges and Opportunitiesmentioning

confidence: 99%

Data mining and knowledge discovery in databases for urban solid waste management: A scientific literature review

et al. 2021

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The processes related to solid waste management (SWM) are being revised as new technologies emerge and are applied in the area to achieve greater environmental, social and economic sustainability for society. To achieve our goal, two robust review protocols (Population, Intervention, Comparison, Outcome, and Context (PICOC) and Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA)) were used to systematically analyze 62 documents extracted from the Web of Science database to identify the main techniques and tools for Knowledge Discovery in Databases (KDD) and Data Mining (DM) as applied to SWM and explore the technological potential to optimize the stages of collecting and transporting waste. Moreover, it was possible to analyze the main challenges and opportunities of KDD and DM for SWM. The results show that the most used tools for SWM are MATLAB (29.7%) and GIS (13.5%), whereas the most used techniques are Artificial Neural Networks (35.8%), Linear Regression (16.0%) and Support Vector Machine (12.3%). In addition, 15.3% of the studies were conducted with data from China, 11.1% from India and 9.7% of the studies analyzed and compared data from several other countries. Furthermore, the research showed that the main challenges in the field of study are related to the collection and treatment of data, whereas the opportunities appear to be linked mainly to the impact on the pillars of sustainable development. Thus, this study portrays important issues associated with the use of KDD and DM for optimal SWM and has the potential to assist and direct researchers and field professionals in future studies.

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Section: Main Challenges and Opportunitiesmentioning

confidence: 99%

Data mining and knowledge discovery in databases for urban solid waste management: A scientific literature review

et al. 2021

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“…USW from domestic activities in urban residences may be eligible for energy recovery. These residues have a high calorific value, and one of the treatment possibilities is energy recovery through thermochemical processes (Drudi et al, 2019). For Mamede (2013), the local valuation of the USW and its use for energy purposes may assist in diverting a significant amount of waste for more noble purposes, as they would be destined for landfills.…”

Section: Urban Solid Waste Energeticmentioning

confidence: 99%

Multicritery analysis for prioritizing the energy use of urban solid waste as a fuel in Paulista-PE

Guedes

El-Deir

Jucá

et al. 2021

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Paper aims: To carry out a multi-criteria analysis to assist decision making regarding the indication of the best alternative for converting solid waste energy into fuel. Originality:The innovative and advantageous approach analyzes the feasibility of deploying the technology, mainly due to new and significant information on waste energy recovery. Research method:The application of the system based on multi-criteria analysis, through the method of hierarchical analysis, according to previously established criteria, enables decision-making based on multiple criteria. Main findings:The criteria generated by the mathematical process presented the priorities of heat treatment technologies and energy potential according to the levels of preference and order of importance. Implications for theory and practice:The production refused derived fuel replaces petroleum-derived fuel sources, and reduces the amount of waste disposed of in landfills.

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“…Hidalgo et al (2019) [34] determined an LHV of 23.2 MJ/kg (d.w.) and 15.7% ash from rejects from MBT plants. Nevertheless, in the work of Drudi et al (2019)[35], the LHV in waste extracted from a Brazilian landfill was 7.03 MJ/kg (w.w.) Chan et al (2019).…”

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confidence: 93%

Behavior of Rejects from a Biological-Mechanical Treatment Plant on the Landfill to Laboratory Scale

et al. 2020

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This paper describes the laboratory-scale simulation of the behaviour of rejects from a biological-mechanical treatment (BMT) plant in Castellón (Spain). For this purpose, four lysimeters were built, with different densities. Simulations were carried out for 7 weeks and leachate recirculation was applied to two of them. The experimental results allowed us to determine: (i) dirt in fractions, which was relatively high (up to 15% in some fractions) due to biological processes; (ii) the field capacity for this waste with similar values to other works, which varied depending on the experiment; (iii) variation in the biomass percentage which lowered after experiments in all cases (59.5% lower on average), and the rejects’ calorific value was higher after experiments (28.2% on average); (iv) the evolution of leachate properties with or without recirculation, where percolation, in addition to the dragging of soluble materials, stabilised waste, which diminished its biological activity. Rejects’ increased calorific value will allow combustible material to be recovered in the future as a way to exploit the energy potential stored in landfills.

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Statistical model for heating value of municipal solid waste in Brazil based on gravimetric composition

Cited by 48 publications

References 7 publications

Data mining and knowledge discovery in databases for urban solid waste management: A scientific literature review

Data mining and knowledge discovery in databases for urban solid waste management: A scientific literature review

Multicritery analysis for prioritizing the energy use of urban solid waste as a fuel in Paulista-PE

Behavior of Rejects from a Biological-Mechanical Treatment Plant on the Landfill to Laboratory Scale

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