Literature DB >> 30343811

Estimation of the generation rate of different types of plastic wastes and possible revenue recovery from informal recycling.

Atul Kumar1, S R Samadder2, Nitin Kumar1, Chandrakant Singh1.   

Abstract

Plastic waste generation is an inevitable product of human activities, however its management faces challenges in many cities. Understanding the existing patterns of plastic waste generation and recycling is essential for effective management planning. The present study established a relationship between plastic waste generation rate and the identified socioeconomic groups, higher socioeconomic group (HSEG), middle socioeconomic group (MSEG), and lower socioeconomic group (LSEG) of the study area (Dhanbad, India). For identification of the socioeconomic groups, four different socioeconomic parameters were considered (total family income, education, occupation and type of houses). The information related to the identified parameters were obtained using questionnaire survey conducted in the selected households. One week plastic waste sampling was carried out in the households of all the socioeconomic groups. The plastic waste generated in the study area was 5.7% of the total municipal solid waste. In terms of total plastic waste generation rate, it was found that HSEG had maximum (51 g/c/d) and LSEG had minimum (8 g/c/d) generation rate. The present study area does not have any formal waste recycling system. Thus, the amount of plastic waste recovered and the revenue generated from recycling of plastic waste by the active informal recyclers (waste pickers, itinerant waste buyers and scrap dealers) in the study area have been evaluated. Additionally, three non-linear machine learning models i.e., artificial neural network (ANN), support vector machine (SVM) and random forest (RF) have been developed and compared for the prediction of plastic waste generation rate.
Copyright © 2018 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Informal; Machine learning models; Plastic waste; Recycling; Revenue generation; Socioeconomic groups

Mesh:

Substances:

Year:  2018        PMID: 30343811     DOI: 10.1016/j.wasman.2018.08.045

Source DB:  PubMed          Journal:  Waste Manag        ISSN: 0956-053X            Impact factor:   7.145


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Review 4.  Plastic waste recycling: existing Indian scenario and future opportunities.

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Review 5.  Application of machine learning algorithms in municipal solid waste management: A mini review.

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6.  Automated waste-sorting and recycling classification using artificial neural network and features fusion: a digital-enabled circular economy vision for smart cities.

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  6 in total

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