Multi-objective Optimization of Succinic Acid  Production from Empty Fruit Bunch

R. H. Hafyan; W. D.  Prasetyo; L.  Bhullar; Z. A.  Putra; M. R.  Bilad; M. D. H.  Wirzal; N. A. H. M.  Nordin

R. H. Hafyan Department of Chemical Engineering, Universiti Teknologi PETRONAS, 32610 Bandar Seri Iskandar, Perak Darul Ridzuan, Malaysia
W. D. Prasetyo Department of Chemical Engineering, Universiti Teknologi PETRONAS, 32610 Bandar Seri Iskandar, Perak Darul Ridzuan, Malaysia
L. Bhullar Department of Chemical Engineering, Universiti Teknologi PETRONAS, 32610 Bandar Seri Iskandar, Perak Darul Ridzuan, Malaysia
Z. A. Putra Department of Chemical Engineering, Universiti Teknologi PETRONAS, 32610 Bandar Seri Iskandar, Perak Darul Ridzuan, Malaysia
M. R. Bilad Department of Chemical Engineering, Universiti Teknologi PETRONAS, 32610 Bandar Seri Iskandar, Perak Darul Ridzuan, Malaysia
M. D. H. Wirzal Department of Chemical Engineering, Universiti Teknologi PETRONAS, 32610 Bandar Seri Iskandar, Perak Darul Ridzuan, Malaysia
N. A. H. M. Nordin Department of Chemical Engineering, Universiti Teknologi PETRONAS, 32610 Bandar Seri Iskandar, Perak Darul Ridzuan, Malaysia

Keywords: empty fruit bunch, multi-objective optimization, succinic acid, genetic algorithm

Abstract

Empty Fruit Bunch (EFB) produced in plantation mill activities in Malaysia creates a major disposal problem. On the other hand, sustainability issues have driven industries to overcome the depletion of fossil fuels and reduction of greenhouse gases emissions. Therefore, as a renewable source, EFB can be an attractive option to address the above problems by converting it into fuels and chemicals. Succinic acid, one of 12 chemical building blocks identified by DOE to be used in synthesis of high-value materials, can be produced from biochemical conversion of the EFB. The present study evaluates succinic acid production process using EFB as the raw material from the perspective of three pillars of sustainability, namely economic, environment, and safety. Flowsheet modeling and techno-economic analysis methods are applied, followed by a multi-objective optimization using genetic algorithm method that simultaneously accounts for maximization of Net Present Value (NPV) and minimization of both Global Warming Potential (GWP) and Toxicity Damage Index (TDI). The pareto frontier reveals a trade-off among all objectives that the maximum NPV is 1,619 MMSD at the maximum EFB of 71,900 kg/hour. Meanwhile, the minimum GWP (12.4 kg CO2-eq/kg succinic acid) and TDI (4.5) are acquired at the minimum EFB of 50,000 kg/hour.

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