AI-Driven Green Biolubricant Production from Microalgal Oil: Process Intensification via WOA-Optimized ANN Modeling

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Kutluk T., Gürkaya Kutluk B.

6th International Symposium of Scientific Research and Innovative Studies, Balıkesir, Türkiye, 11 - 14 Mart 2026, ss.520-521, (Tam Metin Bildiri)

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Basıldığı Şehir: Balıkesir
  • Basıldığı Ülke: Türkiye
  • Sayfa Sayıları: ss.520-521
  • Kocaeli Üniversitesi Adresli: Evet

Özet

This study presents a statistically robust framework for the green synthesis of

biolubricants from Chlorella protothecoides oil via enzymatic esterification using Resinase HT,

a fungal lipase derived from Aspergillus oryzae and immobilized through an optimized crosslinking

method. Process modeling and optimization were performed using Artificial Neural

Networks (ANNs) trained with the Whale Optimization Algorithm (WOA). The WOAenhanced

ANN model exhibited the highest predictive performance (R² = 0.8245), effectively

capturing the non-linear behavior of the system compared to conventional gradient-based

algorithms and Response Surface Methodology (RSM). Temperature was identified as the most

influential parameter affecting FAME conversion, while enzyme loading and molar ratio

showed relatively minor effects. The integrated ANN–WOA framework successfully defined

optimal operating conditions, providing a reliable and sustainable strategy for biolubricant

production under solvent-free conditions.