Forecasting municipal solid waste generation using artificial intelligence modelling approaches

Abbasi, Maryam; Hanandeh, Ali El

doi:10.1016/j.wasman.2016.05.018

Cited by 261 publications

(143 citation statements)

References 34 publications

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“…Meanwhile, the process of predicting HSW generation is challenging and often intensified by uncontrollable parameters [10,13]. In recent years, various conventional, regression, non-algorithm and descriptive statistical methods of forecasting municipal solid waste (MSW) generation have been reported [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Comparative Modelling and Artificial Neural Network Inspired Prediction of Waste Generation Rates of Hospitality Industry: The Case of North Cyprus

Azarmi

Oladipo²,

Vaziri³

et al. 2018

Sustainability

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This study was undertaken to forecast the waste generation rates of the accommodation sector in North Cyprus. Three predictor models, multiple linear regression (MLR), artificial neural networks (ANNs) and central composite design (CCD), were applied to predict the waste generation rate during the lean and peak seasons. ANN showed highest prediction performance, specifically, lowest values of the standard error of prediction (SEP = 2.153), mean absolute error (MAE = 1.378) and highest R 2 value (0.998) confirmed the accuracy of the model. The analysed waste was categorised into recyclable, general waste and food residue. The authors estimated the total waste generated during the lean season at 2010.5 kg/day, in which large hotels accounted for the largest fraction (66.7%), followed by medium-sized hotels (19.4%) and guesthouses (2.6%). During the peak season, about 49.6% increases in waste generation rates were obtained. Interestingly, 45% of the waste was generated by British tourists, while the least waste was generated by African tourists (7.5%). The ANN predicted that small and large hotels would produce 5.45 and 22.24 tons of waste by the year 2020, respectively. The findings herein are promising and useful in establishing a sustainable waste management system.

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Section: Introductionmentioning

confidence: 99%

Comparative Modelling and Artificial Neural Network Inspired Prediction of Waste Generation Rates of Hospitality Industry: The Case of North Cyprus

Azarmi

Oladipo²,

Vaziri³

et al. 2018

Sustainability

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“…The major challenges related to MSW modeling concern the prediction of MSW output based on either the statistics of MSW generation or construction of complex prediction models relying on an available (although extended) number of input parameters (Table 1). The first group of these type of models, such as the one proposed in (Abbasi & El Hanandeh 2016), struggles to capture changes in future MSW trends since their estimations are based on MSW historical data. They also do not account for the impact of other explanatory variables, such as taxes.…”

Section: Expanded Literature Reviewmentioning

confidence: 99%

“…However, the relationship between MSW generation and explanatory variables is not usually explicitly identified. In view of this, additional data collection (Keser et al 2012;Li et al 2011;Lebersorger & Beigl 2011) and model training (Abbasi & El Hanandeh 2016) may be required to perform MSW estimations for predictions over different time horizons. In addition, only a limited number of studies, such as (Keser et al 2012), have attempted to provide global estimations of MSW outputs or other subcategories, or to model MSW distribution for urban territories.…”

Section: Expanded Literature Reviewmentioning

confidence: 99%

Integrated decision-support methodology for combined centralized-decentralized waste-to-energy management systems design

Kuznetsova

Cardin

Diao

et al. 2019

Renewable and Sustainable Energy Reviews

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The rapid expansion of urban populations and concomitant increase in the generation of municipal solid waste (MSW) exert considerable pressure on the conventional centralized MSW management system and are beginning to exceed disposal capacities. To tackle this issue, the conventional centralized MSW management system is more likely to evolve toward a more decentralized system with smaller capacity waste treatment facilities that are integrated at different levels of the urban environment, e.g., buildings, districts, and municipalities. In addition, MSW can become an important urban resource to address the rising energy consumption through waste-to-energy (WTE) technologies capable of generating electricity, heat, and biogas. This shift toward the combined centralizeddecentralized waste-to-energy management system (WtEMS) requires an adapted decisionsupport methodology (DSM) that can assist decision-makers in analyzing MSW generation across large urban territories and designing optimal long-term WtEMS.The proposed integrated DSM for WtEMS planning relies on: i) an MSW segregation and prediction methodology, ii) an optimization methodology for the deployment of multi-level urban waste infrastructure combining centralized and decentralized facilities, and iii) a multicriterion sustainability framework for WtEMS assessment. The proposed DSM was tested on a case study that was located in Singapore. The proposed WtEMS not only reduced the total operational expenses by about 50%, but also increased revenues from electricity recovery by two times in comparison with the conventional MSW management system. It also allowed more optimal land use (capacity-land fragmentation was reduced by 74.8%) and reduced the size of the required transportation fleet by 15.3% in comparison with the conventional MSW system. The Global Warming Potential (GWP) was improved by about 18.7%. Sets |T|,t ∈T -Life span period of a WTF [year]|I|,i ∈ I -Number of waste generators [unit] |J|, j ∈ J -Number of candidate sites where decentralized (on-site) and centralized (offsite) treatment facilities can be installed [unit] |A|, a ∈ A -Set of technologies available for the deployment |L|, l∈ L -Possible number of units of each technology that can be deployed at each candidate site [unit] Parameters and variables q i , t -Amount of waste generated at each time step, t, by each waste generator, i [tons of waste/year] k a 0 -Unit transformation capacity of treatment facility [tons of processed waste/day] K a , j -Limitation of land space represented by the maximum number of units of technology, a, to be installed at candidate site, j [units] λ a ,r 0 -Amount of recovered resource per ton of treated waste of technology, a [Amount of recovered energy/material/ton of processed waste] − +, u t ❑ u t ❑-Additional continuous variables to determine the smaller value between the quantity of waste, q i , t , generated at WGS i at time step t is greater than or equal to the system capacity, x j ,l , t , installed at candidate site, j v t -Binary decision ...

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“…Furthermore, a quadratic model might not provide the proper response to describe the variables. Hence, computational intelligence methods have a significant role in this matter …”

Section: Introductionmentioning

confidence: 99%

“…Hence, computational intelligence methods have a significant role in this matter. [14][15][16] The genetic algorithm (GA) and genetic programming (GP) are important optimization processes based on the genetic and natural selection. [8,17] The ability to present a simple relationship and no assumption for a base form is considered the main advantages of this method.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Prediction of Biohydrogen Production by Dark Fermentation

Akhbari

Ibrahim

Zinatizadeh

et al. 2018

CLEAN Soil Air Water

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The present work is a study of the performance and effect of operational parameters on biohydrogen production from palm oil mill effluent by dark fermentation in batch mode. The process parameters examined are pH (5, 5.5, and 6), temperature (30, 35, and 40 °C), substrate concentration (5000, 12 500, and 20 000 mg L−1) and inoculum volume (20, 25, and 30 mL). The inoculum concentration prepared was 10 000 mg L−1 volatile suspended solids. The experiments were designed by response surface methodology (RSM). The highest chemical oxygen demand (COD) removal, hydrogen percentage (H2%) and hydrogen yield (HY) obtained were 58.3%, 80%, and 4.83 mol H2/mole of COD consumed, respectively. Based on the experimental data obtained with the RSM design, gene expression programming (GEP) was developed to predict the COD removal, hydrogen production, and hydrogen yield as process responses. The projected models were assessed based on the correlation coefficient (R2), root mean square error, mean absolute relative error, scatter index, and BIAS. The results demonstrate that the GEP model outperformed the RSM model and was superior in predicting the response variables. Partial derivative sensitivity analysis was also employed to assess the effect of each variable on COD%, H2%, and HY prediction. The prediction uncertainty for COD%, H2%, and HY was quantified, and the results were ±0.11, ±0.17, and 0.015, respectively. According to the results, the GEP model is more efficient than the RSM model in predicting the experimental data for biological hydrogen production in the dark fermentation process.

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Forecasting municipal solid waste generation using artificial intelligence modelling approaches

Cited by 261 publications

References 34 publications

Comparative Modelling and Artificial Neural Network Inspired Prediction of Waste Generation Rates of Hospitality Industry: The Case of North Cyprus

Comparative Modelling and Artificial Neural Network Inspired Prediction of Waste Generation Rates of Hospitality Industry: The Case of North Cyprus

Integrated decision-support methodology for combined centralized-decentralized waste-to-energy management systems design

Evolutionary Prediction of Biohydrogen Production by Dark Fermentation

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