Multi-objective Optimization and Development of Mathematical Model for Process Parameters of AmaranthGrains using Regression Analysis

Abstract

Purpose - There are several ways to heat amaranth grains, including oven puffing, gun puffing, extrusion puffing, oil puffing, pan popping, etc. Because of its size, puffing characteristics, poor volume expansion ratio, etc., amaranth grains cannot be popped in an oven, gun, extrusion, oil, or pan. In this study, the amaranth grains were popped or puffed using a Special Purpose Machine (SPM) with a heating plate. This device was created to get around all of the earlier listed restrictions. The temperature, angle, nozzle hole size, heating plate material, and other factors all affect the quality of amaranth grains. Major quality indicators of amaranth grains are the Popping Yield, Ratio of Volume Expansion, and Sensory Score. These quality indicators are examined for the effect of process characteristics on them. Therefore, these characteristics are complete to conduct additional research. The purpose of this study is to examine the more important process variables that affect the quality indicators of Amaranth grains. Methodology – For examining how different process variables affect the grain quality of amaranth, the Taguchi method was used. The significant factors were found using Minitab 17 software. Findings – The design of the experiment has been improved for the major process parameters, according to the experimental analysis and optimization study. To conduct the experimental research, L16 Orthogonal Array has been acquired. The original setup (P4Q2R3S2) for the GRA technique had a GRG of 0.778. By utilizing a new optimum combination (P3Q4R3S2), it was raised to 0.790. It indicates that the grade has increased by 1.89%. As a result, design factors have been successfully optimized utilizing the Gray Relational Analysis approach to produce improved machine parameters for the Lahi machine. Originality: The Gray Relational Analysis approach used to for multi objective optimization which gives 1.89% improvements in quality of grains.