Challenges of plastic waste generation and management in sub-Saharan Africa: A review

Ayeleru, Olusola Olaitan; Dlova, Sisanda; Akinribide, Ojo Jeremiah; Ntuli, Freeman; Kupolati, Williams Kehinde; Marina, Paula Facal; Blencowe, Anton; Olubambi, Peter Apata

doi:10.1016/j.wasman.2020.04.017

Cited by 167 publications

(93 citation statements)

References 142 publications

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“…Society is currently grappling with the management of these wastes, as a significant proportion of these end up in landfills. This is especially the case with plastic waste, of which polyethylene terephthalate (PET) forms a major part [ 1 , 2 , 3 ]. To buttress the debilitating impact of PET waste on the environment, these scholars maintain that it takes an estimated 400 years to naturally decompose.…”

Section: Introductionmentioning

confidence: 99%

Durability Assessment and Microstructure of High-Strength Performance Bricks Produced from PET Waste and Foundry Sand

et al. 2021

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Fired clay brickwork in buildings is prone to cracks and deterioration upon exposure to long-time acidic contamination and water absorption, hence decreasing the bearing capacity of masonry walls. As its contribution toward resolving this challenge, this study assessed the durability and morphological characteristics of high-strength performance bricks produced from a mixture of PET waste (PW) and foundry sand (FS). The PET waste bricks (PWBs) were produced through different proportioning (PW: FS) of 20%, 30%, and 40% of the dry mass of FS. The PWBs produced were tested for durability and compressive and tensile strengths and compared to fired clay bricks to evaluate their load-bearing capacity under compression and tension. Furthermore, scanning electron microscopy (SEM) tests were employed to analyze the morphological structure of the bricks. The test results revealed that the PWBs recorded an appreciable strength of 1.5–2 times that of fired clay bricks, and lower water absorption whilst retaining their ultimate strengths after complete immersion in water and acidic concentrations. The morphology of PWB possessed greater intercluster bonds on the surface compared to clay bricks. The findings demonstrate a reasonable methodological approach toward the production of masonry bricks using a mixture of PET waste and spent foundry sands.

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Section: Introductionmentioning

confidence: 99%

Durability Assessment and Microstructure of High-Strength Performance Bricks Produced from PET Waste and Foundry Sand

et al. 2021

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“…Using the same methodology, the research by Qureshi et al (2020) studies analyzes the opportunities and challenges of pyrolysis as an alternative for the treatment of PW. Likewise, Ayeleru et al (2020) use it to provide an overview of the life cycle of plastic and the problems associated with PW management in sub-Saharan Africa.…”

Section: Methodsmentioning

confidence: 99%

Reverse logistics models for the collection of plastic waste: A literature review

et al. 2021

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To support the understanding of recycling models applied to plastics, the main objective of this work is to offer a literature review of the different reverse logistics (RL) models for collecting plastic waste (PW). The methodology used for processing the scientific literature was content analysis, using the google scholar search engine. The main keywords used were RL and PW. This article is divided into two parts: the first part discusses the development of circular economy models and RL networks and raises the conceptual framework of the research, and the second part presents mathematical models and exploratory studies, proposed as a solution for RL problems of PW. Articles published between years 2014 and 2019 were reviewed. In total, 102 references were used, 70 of them are part of the literature review. According to our findings, we can state that the most widely used solution method for mathematical modeling is mixed-integer linear programming, and for exploratory studies, it was evaluations. About 93% of studies evaluated raw materials related to PW; only 13% of studies had models with stochastic processes; and 88% of the investigations used continuous variables, being the multiobjective functions one of the most used to provide solutions to RL problems. Regarding the mathematical models, 49% were evaluations, 9% corresponded to multicriteria analysis, 29% to linear and nonlinear programming, and 4% to another type of evaluation or model.

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“…Plastic mixing generally leads to decreased mechanical properties as most of them are incompatible. For example, almost each plastic has personal melting point, heating several plastics with various melting points will cause unsure changes, or even degrade, especially between crystalline and amorphous plastics (Ayeleru et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Convolutional neural network for identification of plastic solid waste using near infrared spectroscopy

Xia¹,

Huang²,

Li³

et al. 2021

Preprint

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Plastic waste is a relevant challenge for waste management sector and further technological means has to be urgently researched. Near infrared spectroscopy (NIR), as a non-destructive, cost-effective and mature technology, is currently operational in some waste-sorting facilities. However, NIR remains challenging to discriminate different black plastics because black targets have low reflectance in the its spectral region. Therefore, this study aimed to address the problem in black plastic classification. Seven kinds of plastics: High Impact Polystyrene (HIPS), Acrylonitrile Butadiene Styrene (ABS), High Density Polyethylene (HDPE), Polyethylene terephthalate (PET), Polyamide 66 (PA66), Polycarbonate (PC) and Polypropylene (PP), were prepared for analysis, in which black plastic accounted for half of the total samples (84/159). Some methods such as partial least squares discrimination analysis (PLS-DA), soft independent modelling of class analogy (SIMCA), linear discriminant analysis (LDA) and convolutional neural network (CNN) were used to classify the plastics. Coupling with proper preprocessing methods, PLS-DA and LDA could provide an accuracy of 57.00%, and SIMCA of 69.98%. Meanwhile, the NIR spectra fed into the CNN model as one-dimensional data, the accuracy of CNN model could reach 98.00%. Compared to the SIMCA, PLS-DA and LDA, the accuracy of CNN provided an average improvement of 28-41%. Results demonstrated that NIR spectra combined with CNN gave an excellent accuracy which was potential to solve the bottleneck problem of black plastic discrimination.

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Challenges of plastic waste generation and management in sub-Saharan Africa: A review

Cited by 167 publications

References 142 publications

Durability Assessment and Microstructure of High-Strength Performance Bricks Produced from PET Waste and Foundry Sand

Durability Assessment and Microstructure of High-Strength Performance Bricks Produced from PET Waste and Foundry Sand

Reverse logistics models for the collection of plastic waste: A literature review

Convolutional neural network for identification of plastic solid waste using near infrared spectroscopy

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