Applying artificial neural networks (ANNs) to solve solid waste-related issues: A critical review

Xu, Ankun; Chang, Huimin; Xu, Yawei; Li, Rong; Li, Xiang; Zhao, Yan

doi:10.1016/j.wasman.2021.02.029

Cited by 131 publications

(54 citation statements)

References 145 publications

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“…On the other hand, a single hidden layer with 128 neurons is adopted for the four multi-variate models. Both single-hidden layer and double hidden layers are commonly applied in ANN-based waste studies ( Xu et al 2021 ). The narrower range of lag times (1-6 days) are studied for both single and multi-variate models.…”

Section: Methodsmentioning

confidence: 99%

“…ANN-based models are versatile and applicable to many non-linear problems, provided that a good training data set is provided ( Xu et al 2021 ). Common inputs used in waste studies include socio-economic variables such as earnings and income, education level, employment status, dwelling and household characteristics, workplace and demographic parameters ( Younes et al, 2015 ; Kannangara et al, 2018 ; Kontokosta et al, 2018 ; Wu et al, 2020 ), or climatic variables such as temperature, humidity, wind speed, and precipitation ( Kontokosta et al, 2018 ; Cubillos, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

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The use of a recurrent neural network model with separated time-series and lagged daily inputs for waste disposal rates modeling during COVID-19

Richter

et al. 2021

Sustainable Cities and Society

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A new modeling framework is proposed to estimate mixed waste disposal rates in a Canadian capital city during the pandemic. Different Recurrent Neural Network models were developed using climatic, socio-economic, and COVID-19 related daily variables with different input lag times and study periods. It is hypothesized that the use of distinct time series and lagged inputs may improve modeling accuracy. Considering the entire 7.5-year period from Jan 2013 to Sept 2020, multi-variate weekday models were sensitive with lag times in the testing stage. It appears that the selection of input variables is more important than waste model complexity. Models applying COVID-19 related inputs generally had better performance, with average MAPE of 10.1%. The optimized lag times are however similar between the periods, with slightly longer average lag for the COVID-19 at 5.3 days. Simpler models with least input variables appear to better simulate waste disposal rates, and both ‘Temp-Hum’ (Temperature-Humidity) and ‘Temp-New Test’ (Temperature-COVID new test case) models capture the general disposal trend well, with MAPE of 10.3% and 9.4%, respectively. The benefits of the use of separated time series inputs are more apparent during the COVID-19 period, with noticeable decrease in modeling error.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The use of a recurrent neural network model with separated time-series and lagged daily inputs for waste disposal rates modeling during COVID-19

Richter

et al. 2021

Sustainable Cities and Society

View full text Add to dashboard Cite

show abstract

“…2 (b), an ANN typically consists of an input layer, hidden layers, and an output layer, each of which consists of many nodes linked to each node in the following layers by directed weighted edges (Abdallah et al, 2020 ). It was reported that feedforward neural networks are the most dominant ANN approaches in studying MSW-related issues (Xu et al, 2021 ). Analysis of 147 reviewed studies revealed that multilayer perceptron ANN (MLPANN) and radial basis function ANN (RBFANN) were the two most popular ANN methods, with MLPANN being used in 79% of the reviewed studies (Fig.…”

Section: Collection and Transportation Of Mswmentioning

confidence: 99%

Source separation, transportation, pretreatment, and valorization of municipal solid waste: a critical review

Zhang

Liu

Chen

et al. 2021

Environ Dev Sustain

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“…Machine learning has found a number of applications in chemistry [ 1 , 2 , 3 ] and material science [ 4 , 5 , 6 ]. Among the supervised machine learning methods, the artificial neural networks (ANN) are most popular.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Networks for Pyrolysis, Thermal Analysis, and Thermokinetic Studies: The Status Quo

Muravyev

Luciano²,

Ornaghi

et al. 2021

Molecules

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Artificial neural networks (ANNs) are a method of machine learning (ML) that is now widely used in physics, chemistry, and material science. ANN can learn from data to identify nonlinear trends and give accurate predictions. ML methods, and ANNs in particular, have already demonstrated their worth in solving various chemical engineering problems, but applications in pyrolysis, thermal analysis, and, especially, thermokinetic studies are still in an initiatory stage. The present article gives a critical overview and summary of the available literature on applying ANNs in the field of pyrolysis, thermal analysis, and thermokinetic studies. More than 100 papers from these research areas are surveyed. Some approaches from the broad field of chemical engineering are discussed as the venues for possible transfer to the field of pyrolysis and thermal analysis studies in general. It is stressed that the current thermokinetic applications of ANNs are yet to evolve significantly to reach the capabilities of the existing isoconversional and model-fitting methods.

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Applying artificial neural networks (ANNs) to solve solid waste-related issues: A critical review

Cited by 131 publications

References 145 publications

The use of a recurrent neural network model with separated time-series and lagged daily inputs for waste disposal rates modeling during COVID-19

The use of a recurrent neural network model with separated time-series and lagged daily inputs for waste disposal rates modeling during COVID-19

Source separation, transportation, pretreatment, and valorization of municipal solid waste: a critical review

Artificial Neural Networks for Pyrolysis, Thermal Analysis, and Thermokinetic Studies: The Status Quo

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