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

  • Research Article-Chemical Engineering
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Abstract

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%.

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Abbreviations

OFAT:

One-factor-at-a-time

RSM:

Response surface methodology

CCD:

Central composite design

BBD:

Box–Behnken design

GLM:

Generalized linear model

A :

Extraction time (T)

B :

Seed particle size (S)

ANOVA:

Analysis of variance

y :

Predicted response

CSA:

Crow search algorithm

AP:

Awareness probability

fl:

Flight length

N:

Flock size

GC–MS:

Gas chromatography–mass spectrometry

RE:

Relative error

MAE:

Mean absolute error

RMSE:

Root mean squared error

DF:

Desirability function

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Acknowledgements

Authors would like to gratefully acknowledge the support provided by King Abdulaziz City for Science and Technology (KACST) through the Science and Technology Unit at King Fahd University of Petroleum and Minerals (KFUPM) for funding this work through Project No. NSTIP # 13-WAT096-04 as part of the National Science, Technology and Innovation Plan. S. M. Zakir Hossain would also like to thank the Deanship of Scientific Research (Grant #: 10/2014), University of Bahrain, Kingdom of Bahrain.

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Authors and Affiliations

  1. Department of Chemical Engineering, College of Engineering, University of Bahrain, Zallaq, Kingdom of Bahrain

    S. M. Z. Hossain, S. Taher, A. Khan & M. F. Irfan

  2. Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia

    N. Sultana

  3. Department of Petroleum Engineering, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    B. Haq

  4. Department of Chemical Engineering, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    S. A. Razzak

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  1. S. M. Z. Hossain
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Supplemental Figure S1

GC-MS analysis of the fatty acid composition of total lipids extracted from papaya seeds (TIFF 48 kb)

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Hossain, S.M.Z., Taher, S., Khan, A. et al. Experimental Study and Modeling Approach of Response Surface Methodology Coupled with Crow Search Algorithm for Optimizing the Extraction Conditions of Papaya Seed Waste Oil. Arab J Sci Eng 45, 7371–7383 (2020). https://doi.org/10.1007/s13369-020-04551-1

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