Optimization of mechanical oil extraction process from Camellia oleifera seeds regarding oil yield and energy consumption

Abstract

AbstractMechanical pressing is widely used for producing high‐quality vegetable oil in industry. However, mechanical pressing process faces a challenge from meeting the industrial demand for high yield with low energy consumption. Hence, the objective of this study was to optimize mechanical oil extraction process from Camellia oleifera seeds by maximizing the yield and minimizing the energy consumption. A Box–Behnken design of response surface methodology is adopted for the experiments of mechanical oil extraction from C. oleifera seeds. The effects of experimental factors namely moisture content (0–9% w.b.), applied pressure (10–45 MPa), pressing temperature (40–100°C) and extraction time (10–40 min) on yield and energy consumption were investigated by modeling the oil extraction process. An analysis of variance (ANOVA) revealed that moisture content, applied pressure and extraction time significantly effect on yield while applied pressure, pressing temperature, and extraction time have an obvious influence on energy consumption. In addition, the oil extracted under the processing conditions investigated was validated to be of acceptable quality. The Pareto fronts based on multiobjective genetic algorithm provides a set of optimal process parameters to obtain sustainable end products.Practical ApplicationMoisture content, applied pressure, pressing time, and pressing temperature are primary processing conditions affecting oil extraction from C. oleifera seeds during the mechanical process. The use of inappropriate processing parameters may lead to low yield and high‐energy consumption. Understanding the mechanisms of oil extraction process thoroughly while taking important parameters into consideration is vital for the process to be efficient. This can be obtained by modeling oil extraction process using both experimental and theoretical methods. In this article, how these factors affect the yield and energy consumption of the system were investigated by modeling the oil extraction process. Processing conditions were optimized by multiobjective optimization technique based on NSGA‐II. The oil extracted under the processing conditions investigated was of acceptable quality. The data collected during this study can be utilized to design the equipment and to obtain sustainable end products.