Optimization of multiple performance responses of a fish feed pelletizer machine

Osagie Ighodalo; Sebastian O. Amiebenomo; Temabor Esabunor

doi:10.37121/jase.v3i1.71

Osagie Ighodalo Ambrose Alli University
Sebastian O. Amiebenomo Ambrose Alli University
Temabor Esabunor Ambrose Alli University

DOI: https://doi.org/10.37121/jase.v3i1.71

Keywords: Grey relational analysis, Multi response optimization, Orthogonal array, Pelletizer machine, Taguchi methodology

Abstract

This study details the assembling of a prefabricated fish feed pelletizing machine and optimization of some operational parameters such as die thickness, number of die holes, shaft speed and feed rate to produce high-grade fish pellets. The Taguchi methodology and Grey relational analysis (GRA) have been utilized to evaluate the multi-objective functions of interest such as pelletizing efficiency, throughput, energy requirements and pellets bulk density (g/cm³). The pelletizer machine performance evaluation test was carried at 3 levels of die thickness (8, 6 and 12 mm), number of die holes (30, 25, and 35), and feed rates (145, 130 and 160 g/h). The test for the performance indicators was conducted using L9 orthogonal array experimental design. The test data were analyzed using the Taguchi scheme employing the signal-to-noise ratio response with effects deduced. The GRA was utilized to assess multiple responses by fusing the Taguchi technique with the GRA. Thus the multi-objective optimization was transformed to a single equivalent objective function. The results of Taguchi optimization revealed that die thickness was the most influential parameter for the various control factors. In addition, optimum parameter combination was obtainable at medium die thickness (8mm), medium number of die holes (30), low shaft speed (200rpm) and medium feed rate of 145g/h. Analysis of variance for grey relational grade (GRG) reveals that die thickness and feed rate are the dominant parameters. The confirmation test performed shows that the GRG is enhanced by 2.19%.

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Author Biographies

Osagie Ighodalo, Ambrose Alli University

Professor & Head, Department of Mechanical Engineering

Temabor Esabunor , Ambrose Alli University

Postgraduate Student, Department of Mechanical Engineering

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