OPTIMIZATION OF SIFTING CAPACITY OF AN IMPROVED DEWATERED CASSAVA MASH SIFTING MACHINE USING DESIGN OF EXPERIMENT

Abstract

The aim of this study was to optimize the sifting capacity of an enhanced dewatered cassava mash sifting machine through experiment design and application. The goal is to determine the best performance indicators between the response variable (sifting capacity) and the operational variables (cassava mash mass and operating time). General full factorial design (GFFD) is used in experiment design to accomplish this. Nine treatments with three replicates were included in the design, for a total of twenty-seven treatments. The mass of cassava mash, operating time, and sifting capacity were among the experimental test parameters that were established. The experiment was carried out at the Nigerian Stored Product Research Institute's postharvest engineering research department, located in the Port Harcourt Zonal Office in Nigeria. Statistical analyses, including analysis of variance (ANOVA), main and interaction effects, multiple linear regression model, and response optimization using MINITAB 21 software were used. Also, the validity of the model was checked using standard error (SE), coefficient of determination (r²), Adjusted r², and prediction r².  The results revealed that the application of cassava mash mass and time of operation have the highest scapacity at 90 kg and 0.6 hr using the machine. According to ANOVA, sifting capacity was significantly impacted (P<0.05) by cassava mash mass and time of operation. The sifting capacity multiple regression  model was created with coefficients determined in the models. The models' above 95% prediction accuracy was substantiated. At a cassava mash mass of 90 kg and a time of operation of 0.6 hr, the optimal sifting capacity during sifting was attained