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Design and Data-Driven Optimization of Groundnut Oil Extraction Machine’s Efficiency: ANOVA and Predictive Modeling Techniques

Author

Listed:
  • Jamiu Ayodeji Bakri

    (Yaba College of Technology, Nigeria)

  • Kolawole Olagbami Oladerin

    (Yaba College of Technology, Nigeria)

  • Temitope Bamidele Ajayi

    (Yaba College of Technology, Nigeria)

  • Ridwan Abiola Oyetunde

    (Yaba College of Technology, Nigeria)

  • Adeniyi Oluwadamilola Ajimo

    (Yaba College of Technology, Nigeria)

  • Ebunoluwa Modupe Arowolo

    (Western Illinois University, USA)

Abstract

This study details the design, optimization, and performance evaluation of a locally fabricated groundnut oil extractor, focusing on improving extraction efficiency and by-product utilization. The optimization process targeted enhancing oil yield, minimizing residual cake weight, and maximizing efficiency. The extractor’s design includes essential components such as a power shaft, heater band, speed gear reduction unit, and screw shaft with a press barrel. Validation of the optimized extractor revealed significant results: the predicted weight of the cake produced was 4.7 kg, while the experimental value was 3.968 kg, reflecting an 18.45% decrease. The machine’s oil yield was predicted at 1.1 kg but achieved a reduced value of 2.155 kg, indicating a 48.96% maximization error. Efficiency improved to 69.645%, against a predicted value of 66.9%, reflecting a 3.94% increase. These results confirm that the optimized extractor effectively meets its performance objectives, demonstrating its viability for efficient, small-scale groundnut oil production.

Suggested Citation

  • Jamiu Ayodeji Bakri & Kolawole Olagbami Oladerin & Temitope Bamidele Ajayi & Ridwan Abiola Oyetunde & Adeniyi Oluwadamilola Ajimo & Ebunoluwa Modupe Arowolo, 2026. "Design and Data-Driven Optimization of Groundnut Oil Extraction Machine’s Efficiency: ANOVA and Predictive Modeling Techniques," European Journal of Engineering and Technology Research, European Open Science, vol. 11(1), pages 22-30, January.
    • Handle: RePEc:epw:ejeng0:v:11:y:2026:i:1:id:63222
    DOI: 10.24018/ejeng.2026.11.1.63222
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