Optimization of recycled polypropylene concrete aggregates processing using water-assisted melt compounding via response surface methodology

Plastic waste aggregate for concrete is a vast communicating topic nowadays due to overusing and depletion of natural sands and gravels. Despite efforts, plastic waste aggregates (PWA) still weaken concrete due to inadequate particle interaction. Incorporating clay particles into plastic particles...

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Main Authors: Mohamad, Noraiham, Abd Ghani, Anis Aqilah, Anen, Marvrick Anak, Abd Razak, Jeefferie, Raja Abdullah, Raja Izamshah, Mohd Ali, Mohd Amran, Ab Maulod, Hairul Effendy, Se, Sian Meng
Format: Conference or Workshop Item
Language:en
Published: 2023
Online Access:http://eprints.utem.edu.my/id/eprint/28030/1/Optimization%20of%20recycled%20polypropylene%20concrete%20aggregates%20processing%20using%20water-assisted%20melt%20compounding%20via%20response%20surface%20methodology.pdf
http://eprints.utem.edu.my/id/eprint/28030/
https://link.springer.com/book/10.1007/978-981-19-9267-4
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Summary:Plastic waste aggregate for concrete is a vast communicating topic nowadays due to overusing and depletion of natural sands and gravels. Despite efforts, plastic waste aggregates (PWA) still weaken concrete due to inadequate particle interaction. Incorporating clay particles into plastic particles is a wise step to mitigate this issue. Water-assisted melt compounding is a promising process for intercalating clay particles into polymer particles. The performance of recycled biaxially- oriented polypropylene composite aggregate (PCA) is highly dependent on the manufacturing processing parameters. In this study, the effect of water-assisted melt compounding process and formulation parameters using a Haake internal mixer on the tensile property of the PCA was investigated. The processing parameters (temperature and duration), clay and water content, called independent variables, were optimized to maximize the response (tensile strength) using response surface methodology (RSM) via a two-level full factorial design. The selected model accurately analysed the interaction between the parameters, with the coefficient of determination approaching a unity of more than 0.9633.