Waste to energy – An evaluation of the environmental impact

Pavlas, M.; Touš, Michal; Bébar, L.; Stehlı́k, Petr

doi:10.1016/j.applthermaleng.2009.10.019

Cited by 84 publications

(31 citation statements)

References 16 publications

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“…other processes [31], [32]. According to Chang et al [12], the quality of flue gas in RDF incineration is better than using MSW as fuel, since the presence of heavy metals in the fly ash is lower as well.…”

Section: A C C E P T E D Accepted Manuscriptmentioning

confidence: 99%

Analysis and comparison of municipal solid waste and reject fraction as fuels for incineration plants

Montejo

Costa

Ramos

et al. 2011

Applied Thermal Engineering

108

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Section: A C C E P T E D Accepted Manuscriptmentioning

confidence: 99%

Analysis and comparison of municipal solid waste and reject fraction as fuels for incineration plants

Montejo

Costa

Ramos

et al. 2011

Applied Thermal Engineering

108

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“…The impact on the environment of thermal treatment of waste with energy recovery was evaluated by Pavlas et al (2010) who concluded that thermal treatment of MSW with energy recovery was undoubtedly one of the best techniques. WtE not only offers an alternative to treat the waste but also produces clean energy which can offset primary energy consumption in conventional heat and power units.…”

mentioning

confidence: 99%

Artificial neural network based modelling approach for municipal solid waste gasification in a fluidized bed reactor

et al. 2016

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In this paper, multi-layer feed forward neural networks are used to predict the lower heating value of gas (LHV), lower heating value of gasification products including tars and entrained char (LHVp) and syngas yield during gasification of municipal solid waste (MSW) during gasification in a fluidized bed reactor. These artificial neural networks (ANNs) with different architectures are trained using the Levenberg-Marquardt (LM) back-propagation algorithm and a cross validation is also performed to ensure that the results generalise to other unseen datasets. A rigorous study is carried out on optimally choosing the number of hidden layers, number of neurons in the hidden layer and activation function in a network using multiple Monte Carlo runs. Nine input and three output parameters are used to train and test various neural network architectures in both multiple output and single output prediction paradigms using the available experimental datasets. The model selection procedure is carried out to ascertain the best network architecture in terms of predictive accuracy. The simulation results show that the ANN based methodology is a viable alternative which can be used to predict the performance of a fluidized bed gasifier.2

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“…The proposed site-areas are strongly related to existing well-developed district heating (DH) networks, which enhances the positive environmental impact of its operation (discussed in Ref. [14]). The connection between proper placement and its effect on WTE plants' efficiency is described in Grosso [15].…”

Section: Introductionmentioning

confidence: 99%

“…A study [14] stresses potential locations of new WTE plants for year 2020. The proposed site-areas are strongly related to existing well-developed district heating (DH) networks, which enhances the positive environmental impact of its operation (discussed in Ref.…”

Section: Introductionmentioning

confidence: 99%