Modification and Optimization of Groundnut (Arachis hypogaea) Roasting Machine

Main Article Content

A. Musa
O. A. Adetola
O. J. Olukunle
A. M. Akintade

Abstract

Roasting of groundnut is essential to ensure quality improvement, easy handling, safe storage, further processing, and value addition of the product. Therefore, the aim of the study was modified and optimized a groundnut roasting machine. Standard design parameters were used for the design modification. The design of the experiment had 27 runs. Machine speed (6.60, 12.80 and 19 rpm), roasting temperature (120, 160 and 200℃), and feed rate (120, 180 and 240 kg/h) were used as independent parameters, and the response variables include the moisture content of the groundnut, roasting efficiency, mechanical damage, throughput and quality efficiency of the machine. Response Surface Methodology (RSM) of the Design Expert Version 11 was adopted for the optimization process by applying the central composite design method for the Analysis of Variance (ANOVA) and optimized responses within the limit of the independent factors tested. Roasting temperature (200℃), machine speed (19 rpm) and feed rate (240 kg/h) were found as the optimum operational conditions which will optimally result in the optimal machine performance of 8.76% moisture content (r2 = 0.94), 76.99% roasting efficiency (r2 = 0.90), 2.46% mechanical damage (r2 = 0.86), 62.32 kg/h machine throughput (r2 = 0.98), 74.3% quality performance efficiency (r2 = 0.86) with the high desirability of 88%. An increase in machine speed increased the moisture content of the groundnut, roasting efficiency, mechanical damage, throughput, and quality efficiency of the machine. The study showed the optimal machine parameters for a groundnut roasting machine.

Keywords:
Modification, groundnut roasting, optimization, machine parameters, process variables

Article Details

How to Cite
Musa, A., Adetola, O. A., Olukunle, O. J., & Akintade, A. M. (2020). Modification and Optimization of Groundnut (Arachis hypogaea) Roasting Machine. Journal of Scientific Research and Reports, 26(5), 71-83. https://doi.org/10.9734/jsrr/2020/v26i530260
Section
Original Research Article

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