Optimization of biodiesel production by alkali-catalyzed transesterification of used frying oil

Atapour, Mehdi; Kariminia, Hamid‐Reza; Moslehabadi, P Moslehi

doi:10.1016/j.psep.2012.12.005

Cited by 90 publications

(25 citation statements)

References 17 publications

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“…The higher viscosity is caused by their larger molecular mass and chemical structure [9]. Transesterification process has been reported to be an effective method of viscosity reduction through the conversion of the waste cooking oil (WCO) to waste cooking oil methyl ester (WCOME) [10,11]. However, application of the resulting biodiesel in diesel engine leads to higher oxides of nitrogen (NO x ) emissions [12].…”

Section: Introductionmentioning

confidence: 99%

Experimental Investigation on Performance of a Compression Ignition Engine Fueled with Waste Cooking Oil Biodiesel–Diesel Blend Enhanced with Iron-Doped Cerium Oxide Nanoparticles

et al. 2019

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The effect of iron-doped cerium oxide (FeCeO2) nanoparticles as a fuel additive was experimentally investigated with waste cooking oil methyl ester (WCOME) in a four-stroke, single cylinder, direct injection diesel engine. The study aimed at the reduction of harmful emissions of diesel engines including oxides of nitrogen (NOx) and soot. Two types of nanoparticles were used: cerium oxide doped with 10% iron and cerium oxide doped with 20% iron, to further investigate the influence of the doping level on the nanoparticle activity. The nanoparticles were dispersed in the tested fuels at a dosage of 90 ppm with the aid of an ultrasonic homogenizer. Tests were conducted at a constant engine speed of 2000 rpm and varying loads (from 0 to 12 N.m) with neat diesel (D100) and biodiesel–diesel blends of 30% WCOME and 70% diesel by volume (B30). The engine combustion, performance, and emission characteristics for the fuel blends with nanoparticles were compared with neat diesel as the base fuel. The test results showed improvement in the peak cylinder pressure by approximately 3.5% with addition of nanoparticles to the fuel. A reduction in NOx emissions by up to 15.7% were recorded, while there was no noticeable change in unburned hydrocarbon (HC) emissions. Carbon monoxide (CO) emission was reduced by up to 24.6% for B30 and 15.4% for B30 with nano-additives. Better engine performance was recorded for B30 with 20% FeCeO2 as compared to 10% FeCeO2, in regard to cylinder pressure and emissions. The brake specific fuel consumption was lower for the fuel blend of B30 with 10% FeCeO2 nanoparticles, in low-to-medium loads and comparable to D100 at high loads. Hence, a higher brake thermal efficiency was recorded for the blend in low-to-medium loads compared to D100.

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Section: Introductionmentioning

confidence: 99%

Experimental Investigation on Performance of a Compression Ignition Engine Fueled with Waste Cooking Oil Biodiesel–Diesel Blend Enhanced with Iron-Doped Cerium Oxide Nanoparticles

et al. 2019

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“…The production of biodiesel was increased up to a certain critical level with increasing temperature and reaction time. With increasing reaction temperature, mass transfer restrictions occurred between the liquid and solid phase in the heterogeneous catalytic system [15,16]. The higher quantity of methanol in the reaction mixture facilitated the rate of diffusion of methanol towards the catalyst pore.…”

Section: Interaction Effectsmentioning

confidence: 99%

Optimization and Mathematical Modeling of Biodiesel Production using Homogenous Catalyst from Waste Cooking Oil

S*¹,

Anbessa²

2019

IJEAT

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In the present investigation, the transesterification of waste cooking oil (WCO) to biodiesel over homogenous catalyst KOH have been carried out. To optimize the transesterification process variables both response surface method (RSM) and artificial neural network (ANN) mathematical models were applied to study the impact of process variables temperature, catalyst loading, methanol to oil ratio and the reaction time on biodiesel yield. The experiments were planned with a central composite design matrix using 24 factorial designs. A performance validation assessment was conducted between RSM and ANN. ANN models showed a high precision prediction competence in terms of coefficient of determination (R2 = 0.9995), Root Mean Square Error (RMSE = 0.5702), Standard Predicted Deviation (SEP = 0.0133), Absolute Average Deviation (AAD = 0.0115) compared to RSM model. The concentration of catalyst load was identified as the most significant factor for the base catalyzed transesterification. Under optimum conditions, the maximum biodiesel yield of 88.3% was determined by the artificial neural network model at 60 ºC, 1.05 g catalyst load, 7:1 methanol to oil ratio and 90 min transesterification reaction time. The biodiesel was analyzed by GCMS and it showed the presence of hexadecanoic acid, 9- octadecenoic acid, 9, 12, 15-octadecatrienoic acid, eicosenoic acid, methyl 18-methyl-nonadecanoate, docosanoic acid, and tetracosanoic acid as key fatty acid methyl esters.

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“…The DoE that is defined in Table 1 indicates that a design matrix was generated with 56 experiments and their corresponding factors and levels ( Table 2). The variable levels that appear in Table 1 address the intervals that commonly appear in the literature in regards to biodiesel production [37][38][39][40][41].…”

Section: Design Of Experiments and Design Matrixmentioning

confidence: 99%

Optimizing Biodiesel Production from Waste Cooking Oil Using Genetic Algorithm-Based Support Vector Machines

et al. 2018

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The ever increasing fuel demands and the limitations of oil reserves have motivated research of renewable and sustainable energy resources to replace, even partially, fossil fuels, which are having a serious environmental impact on global warming and climate change, excessive greenhouse emissions and deforestation. For this reason, an alternative, renewable and biodegradable combustible like biodiesel is necessary. For this purpose, waste cooking oil is a potential replacement for vegetable oils in the production of biodiesel. Direct transesterification of vegetable oils was undertaken to synthesize the biodiesel. Several variables controlled the process. The alkaline catalyst that is used, typically sodium hydroxide (NaOH) or potassium hydroxide (KOH), increases the solubility and speeds up the reaction. Therefore, the methodology that this study suggests for improving the biodiesel production is based on computing techniques for prediction and optimization of these process dimensions. The method builds and selects a group of regression models that predict several properties of biodiesel samples (viscosity turbidity, density, high heating value and yield) based on various attributes of the transesterification process (dosage of catalyst, molar ratio, mixing speed, mixing time, temperature, humidity and impurities). In order to develop it, a Box-Behnken type of Design of Experiment (DoE) was designed that considered the variables that were previously mentioned. Then, using this DoE, biodiesel production features were decided by conducting lab experiments to complete a dataset with real production properties. Subsequently, using this dataset, a group of regression models—linear regression and support vector machines (using linear kernel, polynomial kernel and radial basic function kernel)—were constructed to predict the studied properties of biodiesel and to obtain a better understanding of the process. Finally, several biodiesel optimization scenarios were reached through the application of genetic algorithms to the regression models obtained with greater precision. In this way, it was possible to identify the best combinations of variables, both independent and dependent. These scenarios were based mainly on a desire to improve the biodiesel yield by obtaining a higher heating value, while decreasing the viscosity, density and turbidity. These conditions were achieved when the dosage of catalyst was approximately 1 wt %.

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Optimization of biodiesel production by alkali-catalyzed transesterification of used frying oil

Cited by 90 publications

References 17 publications

Experimental Investigation on Performance of a Compression Ignition Engine Fueled with Waste Cooking Oil Biodiesel–Diesel Blend Enhanced with Iron-Doped Cerium Oxide Nanoparticles

Experimental Investigation on Performance of a Compression Ignition Engine Fueled with Waste Cooking Oil Biodiesel–Diesel Blend Enhanced with Iron-Doped Cerium Oxide Nanoparticles

Optimization and Mathematical Modeling of Biodiesel Production using Homogenous Catalyst from Waste Cooking Oil

Optimizing Biodiesel Production from Waste Cooking Oil Using Genetic Algorithm-Based Support Vector Machines

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