Random Surface Methodology: Process Optimization for Peanut Oil Extraction in A Mechanical Oil Expeller

Abstract

The extraction process of peanut oil has been a major concern for local processors due to the difficult task it constitutes during processing. The use of oil expellers has been found to reduce the difficulty in this task yet different processing factors tend to affect the efficiency of those oil expellers. In this study, the optimum peanut oil processing factors and their interaction were investigated using Response Surface Methodology (RSM) with fractional factorial design (33) model of Central Composite Design (CCD). Processing factors such as Moisture Content (10, 12, and 14% db), Peanut Temperature (50, 65, and 80°C), and Water Quantity added during extraction (12, 14, and 16 ml). This aimed at providing the optimum parameter needed to obtain the optimum oil yield using a peanut oil expeller. From this study, it was observed that all three factors considered affecting the oil yield of peanuts during extraction. Only water quantity added during extraction is statistically different. The optimum condition of the oil extraction processing parameter was observed at 50oC, 10 db, and 120 ml. The correlation coefficient (R-squared) of the model analysis was found to be 0.8901.

Kariya Biodiesel Process Optimization Using Kariya Pod-husks Bio-catalyzed

“…The transesterification process of kariya seed oil was modeled using fractional factorial design (three-factor-three-level) of Central Composite Design (CCD) thereby giving 15 experimental runs as depicted in Table 1. Response surface method (RSM) was used in optimizing the biodiesel production process and in evaluating the influence of process parameters on the model response [13].…”