Prediction of optimized pretreatment process parameters for biodiesel production using ANN and GA

Machavaram, Rajendra; Jena, Prakash Chandra; Raheman, Hifjur

doi:10.1016/j.fuel.2008.12.008

Cited by 167 publications

(80 citation statements)

References 12 publications

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“…The coefficient of determination (R 2 ), root mean square error (RMSE) and model predictive error (MPE) are calculated as cited by Rajendra et al [33] and García-Gimeno et al [34] as shown in Eq. (5), Eq.…”

Section: Comparison Approach Between Rsm and Ann Modelsmentioning

confidence: 99%

On the Modeling of Surface Roughness and Cutting Force when Turning of Inconel 718 Using Artificial Neural Network and Response Surface Methodology: Accuracy and Benefit

Tebassi¹,

Yallese²,

Meddour³

et al. 2017

Period. Polytech. Mech. Eng.

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IntroductionThe Inconel 718 is one of the most important materials used in modern industries. In addition of the best properties in terms of high strength, corrosion resistance, heat resistance and fatigue resistance, the Inconel 718 has, also a low thermal conductivity as it is mentioned by Lynch [1]. Certainly, this type of alloy is difficult to machine for the following reasons as it is presented by Alauddin [2]: High work hardening rates at machining, strain rates leading to high cutting forces; abrasiveness; toughness, gummy and strong tendency to weld to the tool with forming the built-up edge; low thermal properties leading to high cutting temperatures. However, it has a wide variety of applications such as aircraft gas turbines stack gas reheaters, reciprocating engines and others.In order to respond to the requirements of those applications, it is very important to forecasting surface roughness and cutting force. Consequently, it is necessary to search the best modeling approach of these output parameters. To obtain this objective, several approaches can be used as well as response surface methodology (RSM) and artificial neural network (ANN). Response surface methodology (RSM) is considered as a quick and useful procedure for the investigation and optimization of complex processes as well as modeling machining output parameters. Certainly, Davoodi and Eskandari [3] found that response surface methodology represents a better approach to predict tool life and productivity when turning of N-155 iron-nickel-base superalloy. Shihab et al. [4], investigated cutting temperature during hard turning of AISI 52100 alloy steel using multilayer coated carbide insert; they concluded that the developed RSM model is able to predict cutting temperature for different combination of input parameters very close to experimental values. Arokiadass et al. [5], used response surface methodology to modeling tool flank wear when end milling of LM 25 Al/SiCp with carbide tool. They concluded that the developed relationship can be effectively used to predict flank wear of carbide tool at a confidence level of 95%. Sahoo et al. [6], confirmed this conclusion when studying the development of flank wear model in turning hardened EN 24 steel with PVD TiN coated mixed ceramic insert under dry environment.

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Section: Comparison Approach Between Rsm and Ann Modelsmentioning

confidence: 99%

On the Modeling of Surface Roughness and Cutting Force when Turning of Inconel 718 Using Artificial Neural Network and Response Surface Methodology: Accuracy and Benefit

Tebassi¹,

Yallese²,

Meddour³

et al. 2017

Period. Polytech. Mech. Eng.

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“…One key advantage of ANN is that it can be used to predict outputs of a new input data set once a data set has been trained well by a neural network. ANNs have been successfully applied as a modeling tool in many research areas, including esterification [19] and transesterification [8,20,21] processes. In the work of Betiku and Ajala [8] in which the efficacy of ANN was tested, the authors used calcined unripe plantain peel as a heterogeneous catalyst, yellow oleander oil and methanol for the transesterification reaction.…”

Section: Introductionmentioning

confidence: 99%

Potential of Ripe Plantain Fruit Peels as an Ecofriendly Catalyst for Biodiesel Synthesis: Optimization by Artificial Neural Network Integrated with Genetic Algorithm

et al. 2018

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The present work was aimed at assessing the possible use of ripe plantain fruit peel as a green-base catalyst in synthesizing Azadirachta indica oil methyl esters (AIOME). The free fatty acid content of the oil (5.81 wt %) was initially reduced to 0.90 wt % using methanol: oil at 2.19 v/v, Fe 2 (SO 4 ) 3 at 6 wt %, time of 15 min and temperature of 65 • C. The pretreated oil was converted to AIOME in a transesterification process with calcined ripe plantain peel ash (CRPPA) at 700 • C as catalyst. The process was modeled by artificial neural network and optimized using genetic algorithm. The effectiveness of the developed CRPPA is ascribable to its high K content and microstructural transformation. The reliability of the model obtained was confirmed with a high coefficient of determination (R 2 ) of 0.996 and a low mean relative percentage deviation (MRPD) of 8.10%. The best operating variables combination for the process was methanol:oil of 0.73 v/v, CRPPA of 0.65 wt % and time of 57 min while the temperature was kept constant at 65 • C with a corresponding AIOME yield of 99.2 wt %. The results of this work demonstrated the potentials of ripe plantain peels and neem oil as cheap feedstocks for biodiesel production.

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“…It is capable of handling even incomplete data and is effective in executing fast predictions and even for non-linear generalizations [27,28]. In 2011, performance of linear and non-linear calibration techniques were compared in predicting biodiesel properties from near IR spectra by Balabin et al [29].…”

Section: Introductionmentioning

confidence: 99%

Biodiesel production from castor oil: ANN modeling and kinetic parameter estimation

et al. 2017

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This research work concerns with the transesterification of castor oil with methanol to form biodiesel. As the free fatty acid content in castor oil is more than 1%, an acid catalyst namely, H 2 SO 4 has been used for esterification. The experimental conditions were determined using central composite design method and the experiments were conducted in a 2 L working volume fully controlled reactor. The input conditions namely, catalyst concentration, methanol to oil molar ratio and temperature were varied, and % fatty acid methyl ester (FAME) content was determined. Based upon the experimental data, an ANN model has been developed which is used to predict %FAME yield for a given set of input conditions. The experimental data and the data predicted by the ANN model have been used to estimate the rate constants of a kinetic model. The ANN model predicts the % FAME yield within ±8% deviation, and the developed kinetic model shows successfully the effect of methanol to oil molar ratio on % FAME yield at 60°C and 3% (v/v) catalyst loading.

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Prediction of optimized pretreatment process parameters for biodiesel production using ANN and GA

Cited by 167 publications

References 12 publications

On the Modeling of Surface Roughness and Cutting Force when Turning of Inconel 718 Using Artificial Neural Network and Response Surface Methodology: Accuracy and Benefit

On the Modeling of Surface Roughness and Cutting Force when Turning of Inconel 718 Using Artificial Neural Network and Response Surface Methodology: Accuracy and Benefit

Potential of Ripe Plantain Fruit Peels as an Ecofriendly Catalyst for Biodiesel Synthesis: Optimization by Artificial Neural Network Integrated with Genetic Algorithm

Biodiesel production from castor oil: ANN modeling and kinetic parameter estimation

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