Experimental Study and Modeling Approach of Response Surface Methodology Coupled with Crow Search Algorithm for Optimizing the Extraction Conditions of Papaya Seed Waste Oil

摘要

Papaya seed waste can be a reliable feedstock for producing valuable bioproducts (biodiesel, biolubricants, beauty products, etc.) due to its high oil content. This article focuses to explore the effects of Soxhlet extraction process conditions (extraction time and seed particle size) on the percent oil yield obtained from papaya seeds. Initially, two mathematical models were developed using response surface methodology (RSM) via central composite design and regression analysis (generalized linear model, GLM) to predict the oil yield. The prediction performance of RSM model was found to be superior than GLM. The extracted oil was characterized by gas chromatography–mass spectrometry (GC–MS) analysis. The analysis of variance results indicated that both factors were strongly significant. Later, crow search algorithm (nature-motivated metaheuristic algorithm) articulated with RSM was utilized for global optimal solution. The maximum yield of 29.96% was obtained at extraction time of 6.5 h and seed particle size of 0.85 mm. The similar results were obtained by desirability function-based optimization approach. The predicted optimal set was also validated further by experimental yield of 31.1% with the variation of <5%.