Optimisation of Recycled Thermoplastic Plate (Tile)

Document Type : Regular Article

Authors

Department of Civil Engineering, University of Ilorin, Ilorin, Nigeria

Abstract

The purpose of this paper is to perform a structural optimization of a flat thermoplastic plate (tile). This task is developed computationally through the interface between an optimization algorithm and the finite element method with the goal of minimizing the equivalent stress with specified target stress of 2 MPa when applied with a load intensity of 1000N. A 300 x 300 x 20 mm thermoplastic plate was selected for the optimization, which was performed with a tool in MATLAB R2012b known as genetic algorithm accompanied with static analysis in ANSYS 15. The results produced the optimum equivalent stress (δopt) of 2.136 MPa with the optimum dimensions of 305 x 302 x 20 mm. Also, the dimensions of the plate with the optimum value of the equivalent stress were discovered to be within the lower and upper bound dimensions of the plate. The thermoplastic plate object of the optimization was a square plate of 300 x 300mm, and 20 mm thick with isotropic properties and a particular load and boundary conditions were applied on the entire plate.

Highlights

Google Scholar

Keywords

Main Subjects


[1]     Achilias DS, Roupakias C, Megalokonomos P, Lappas AA, Antonakou ΕV. Chemical recycling of plastic wastes made from polyethylene (LDPE and HDPE) and polypropylene (PP). J Hazard Mater 2007;149:536–42. doi:10.1016/j.jhazmat.2007.06.076.
[2]     Babatunde MA, Biala MI. Externality Effects of Sachet Water Consumption and the Choice of Policy Instruments in Nigeria: Evidence from Kwara State. J Econ 2010;1:113–31. doi:10.1080/09765239.2010.11884931.
[3]     Aguado J, Serrano DP, San Miguel G. European trends in the feedstock recycling of plastic wastes. Glob NEST J 2007;9:12–9.
[4]     Alter L. Africa wages war on scourge of plastic bags 2007.
[5]     Edoga MO, Onyeji LI, Oguntosin OO. Achieving Vision 20: 2020 through waste produce candle. J Eng Appl Sci 2008;3:642–6.
[6]     Hussein AA, Sultan AA, Matoq QA. Mechanical Behaviour of Loe Density polyethylene/Shrimp Shells Composite. J Basrah Res Sci 2011;37.
[7]     Jiménez A, Zaikov GE. Recent advances in research on biodegradable polymers and sustainable composites. Nova Science Publishers; 2009.
[8]     Olanrewaju OO, Ilemobade AA. Waste to wealth: A case study of the ondo state integrated wastes recycling and treatment project, Nigeria. Eur J Soc Sci 2009;8:7–16.
[9]     Williams PT, Slaney E. Analysis of products from the pyrolysis and liquefaction of single plastics and waste plastic mixtures. Resour Conserv Recycl 2007;51:754–69. doi:10.1016/j.resconrec.2006.12.002.
[10]    Sarker M, Rashid MM, Rahman MS. Agricultural waste plastics conversion into high energy liquid hydrocarbon fuel by thermal degradation process. J Pet Technol Altern Fuels 2011;2:141–5.
[11]    Nwachukwu S, Obidi O, Odocha C. Occurrence and recalcitrance of polyethylene bag waste in Nigerian soils. African J Biotechnol 2010;9:6096–104.
[12]    Subbo WK, Moindi MN. Recycling of wastes as a strategy for environmental conservation in the Lake Victoria Basin: The case of women groups in Kisumu, Kenya. African J Environ Sci Technol 2008;2:318–25.
[13]    Tamboli SM, Mhaske ST, Kale DD. Crosslinked polyethylene. Indian J Chem Technol 2004;11:853–64.
[14]    Olesya P. Global Optimisation Genetic Algorithms. McMaster Unversity Hamilton, Ontaria ppt presentation, pp 25 2007.
[15]    Bhattacharjya RK. Introduction to genetic algorithms. vol. 12. Indian Institute of Technology Guwahati: 2012.
[16]    Chen G, Yu J. Particle Swarm Optimization Neural Network and Its Application in Soft-Sensing Modeling. In: Wang L, Chen K, Ong YS, editors. Int. Conf. Nat. Comput., Berlin, Heidelberg: Springer Berlin Heidelberg; 2005, p. 610–7.
[17]    Ding S, Xu L, Su C, Zhu H. Using genetic algorithms to optimize artificial neural networks. J Converg Inf Technol 2010;5:54–62. doi:10.1.1.645.8178.
[18]    He A, Kyung Kyoon Bae, Newman TR, Gaeddert J, Kyouwoong Kim, Menon R, et al. A Survey of Artificial Intelligence for Cognitive Radios. IEEE Trans Veh Technol 2010;59:1578–92. doi:10.1109/TVT.2010.2043968.