Multi-objective exergetic and technical optimization of a piezoelectric ultrasonic reactor applied to synthesize biodiesel from waste cooking oil (WCO) using soft computing techniques

•Exergetic and technical optimization of ultrasound-assisted biodiesel production.•Excellent capability of ANFIS for estimating exergetic parameters of the process.•10 min time, 60 °C temperature, and 6.20 methanol/oil ratio as the best conditions. This work was devoted to optimizing the operating c...

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Bibliographic Details
Published in:Fuel (Guildford) Vol. 235; pp. 100 - 112
Main Authors: Aghbashlo, Mortaza, Hosseinpour, Soleiman, Tabatabaei, Meisam, Mojarab Soufiyan, Mohamad
Format: Journal Article
Language:English
Published: Kidlington Elsevier Ltd 01.01.2019
Elsevier BV
Subjects:
Biodiesel fuels
Biodiesel production
Biofuels
Chemical synthesis
Classification
Computing time
Cooking
Cooking oils
Diesel
Efficiency
Energy consumption
Exergy
Exergy analysis
Fuzzy sets
Genetic algorithms
Methanol
Multi-objective optimization
Multiple objective analysis
Oil wastes
Optimization
Piezoelectric ultrasonic reactor
Piezoelectricity
Reactors
Soft computing
Soft computing techniques
Sorting algorithms
Thermodynamics
Transesterification
Ultrasonic technology
Waste cooking oil
Soft computing techniques
Waste cooking oil
Multi-objective optimization
Biodiesel production
Exergy analysis
Piezoelectric ultrasonic reactor
ISSN:0016-2361, 1873-7153
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Summary:•Exergetic and technical optimization of ultrasound-assisted biodiesel production.•Excellent capability of ANFIS for estimating exergetic parameters of the process.•10 min time, 60 °C temperature, and 6.20 methanol/oil ratio as the best conditions. This work was devoted to optimizing the operating conditions of a low-energy consumption, high frequency piezoelectric ultrasonic reactor used for biodiesel production from waste cooking oil (WCO). The optimization was conducted on the basis of exergetic and technical constraints using a consolidated combination of soft computing techniques. Transesterification temperature, residence time, methanol/oil molar ratio were input variables, while functional exergy efficiency (FEE), universal exergy efficiency (UEE), normalized exergy destruction (NED), and conversion efficiency (CE) were considered as output parameters. Four independent ANFIS models were first developed to map the outputs as a function of the inputs. The developed ANFIS models were then introduced to a hybridized optimization paradigm obtained by consolidating the elitist non-dominated sorting genetic algorithm (NSGA-II) and linear interdependent fuzzy multi-objective optimization (ALIFMO) approaches. The optimization was set to maximize the FEE and UEE and to minimize the NED, while satisfying the ASTM standard on CE (i.e., biodiesel content >96.5%). All the developed ANFIS models excellently estimated the outputs with an R2 ≈ 1.0. The transesterification temperature of 60 °C, residence time of 10 min, and methanol/oil molar ratio of 6.20 were selected as the best operating conditions among the spectrum of the solutions suggested by the developed approach.
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ISSN:0016-2361
1873-7153
DOI:10.1016/j.fuel.2018.07.095