Multi-objective optimization of a novel crude lipase-catalyzed fatty acid methyl ester (FAME) production using low-order polynomial and Kriging models

KULA C., SAYAR N. A.

INTERNATIONAL JOURNAL OF GREEN ENERGY, cilt.16, sa.8, ss.657-665, 2019 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 16 Sayı: 8
  • Basım Tarihi: 2019
  • Doi Numarası: 10.1080/15435075.2019.1608443
  • Dergi Adı: INTERNATIONAL JOURNAL OF GREEN ENERGY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.657-665
  • Anahtar Kelimeler: Biodiesel, crude lipase, multi-objective optimization, response surface methodology, Kriging, ENZYMATIC BIODIESEL PRODUCTION, CENTRAL COMPOSITE DESIGN, BIOCATALYTIC SYNTHESIS, PROCESS SIMULATION, TRANSESTERIFICATION, OIL, SILICA, IMMOBILIZATION, STATE
  • Marmara Üniversitesi Adresli: Evet

Özet

In this paper, conventional response surface methodology (RSM) based on low-order polynomials and an alternative Kriging-based method are used for the model-based single and multi-objective optimization of fatty-acid methyl ester (FAME) production catalyzed by a novel crude lipase from the yeast Cryptococcus diffluens (D44). The coefficient of determination for the two modeling approaches was calculated as 0.97 for the Kriging method, and 0.86 for RSM; showing a more reliable representation of experimental data by Kriging. Both models were used to perform single (maximizing FAME titer and temporal productivity separately) and multi-objective (maximizing FAME titer and temporal productivity simultaneously) optimizations of four important operating conditions (reaction time and temperature; amount of crude enzyme; and volume of methanol used). In all cases, the highest temperature considered (60 degrees C) gave the best results. A reduction of reaction time in half was seen to be necessary to achieve optimum productivity compared to titer, when the two objectives were considered separately. The observed trade-off between the two objectives was quantified via multi-objective optimization using Pareto-front analysis.