Uğurlutepe K. M., Alkhaled A. Y., Beyhan M. A., Sauk H., Selvi K. Ç., Gheorghita N.
APPLIED SCIENCES, vol.16, no.13, pp.376-396, 2026 (SCI-Expanded, Scopus)
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Publication Type:
Article / Article
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Volume:
16
Issue:
13
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Publication Date:
2026
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Doi Number:
10.3390/app16136821
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Journal Name:
APPLIED SCIENCES
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Journal Indexes:
Applied Science & Technology Source, Scopus, Science Citation Index Expanded (SCI-EXPANDED), Compendex, INSPEC, Directory of Open Access Journals
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Page Numbers:
pp.376-396
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Open Archive Collection:
AVESIS Open Access Collection
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Ondokuz Mayıs University Affiliated:
Yes
Abstract
Efficient removal of stones and soil from harvested hazelnuts remains a critical challenge in postharvest processing, especially in regions where mechanization is limited. There is a growing need to optimize cleaning systems to improve grain quality, reduce labor, and support scalable operations. This study investigates the optimization of air velocity, feed rate, drop distance, and impurity mixture in a horizontal wind tunnel pneumatic separation system designed for hazelnut postharvest cleaning. Using both classical statistical analysis and Random Forest (RF) modeling, the performance metrics, grain purity, grain loss, and net contaminant removal, were evaluated across variable settings. The results reveal significant influences of air velocity and drop distance on cleaning efficiency, with optimal performance achieved at 25 m/s, 500 kg/h, and a 60–70 cm drop range. Machine learning models achieved high predictive accuracy (R2 > 0.9), confirming their utility for performance forecasting. This integrated approach offers robust recommendations for machine parameter settings, supporting mechanized cleaning solutions to enhance efficiency and reduce manual labor in hazelnut production.