Papers by Farzaneh talebkeikhah
Sinter‐Resistant Nickel Catalyst for Lignin Hydrogenolysis Achieved by Liquid Phase Atomic Layer Deposition of Alumina
Advanced Energy Materials
International Journal of Environmental Analytical Chemistry, 2020
This study evaluates and compares several machine learning methods on the effects of different pa... more This study evaluates and compares several machine learning methods on the effects of different parameters in lead adsorption capacity. pH, contact time, adsorbent dosage and initial lead concentration were considered as inputs and adsorption capacity was regarded as output. For analysing the input parameters, the response surface methodology was used for experimental designs and the obtained results were utilised here as training sets. Various data mining approaches like support vector machine (SVM), group method of data handling (GMDH), decision tree, random forest, radial basis function (RBF), adaptive neuro-fuzzy inference system (ANFIS) and multilayer perceptron (MLP) neural network were implemented to model the problem and two different optimisation techniques named bat algorithm (BAT) and Grasshopper Optimisation Algorithm (GOA) were employed with MLP and ANFIS model for optimising. By comparing different statistical parameters such as Average Absolute Relative Deviation (AARD), coefficient of determination (R 2), Root Mean Square Error (RMSE) and Standard Deviation (SD), it is found out that SVM method has a considerably better performance relative to other methods for estimating adsorption capacity parameter. Furthermore, coupling of MLP and ANFIS with GOA increases the accuracy of these models.
International Journal of Environmental Analytical Chemistry, 2019
This study evaluates and compares several machine learning methods on the effects of different pa... more This study evaluates and compares several machine learning methods on the effects of different parameters in the hydrothermal carbonisation (HTC) process of macroalgae Sargassum horneri. Reaction temperature, residence time, biomass particle size, the amount of catalyst and loaded biomass were considered as inputs and three variables of BET, higher heating value (HHV) and energy recovery were regarded as outputs. For analysing the input parameters, the Taguchi method was used for experimental designs and the obtained results were utilised here as training sets. Various data mining approaches like support vector machine (SVM), group method of data handling, decision tree, random forest, radial basis function, adaptive neuro-fuzzy inference system (ANFIS) and multilayer perceptron (MLP) neural network were implemented to model the problem and two different optimisation techniques named BAT and Grasshopper Optimisation Algorithm (GOA) were employed with MLP and ANFIS model for optimising. By comparing different statistical parameters such as Average Absolute Relative Deviation (AARD), coefficient of determination (R 2), Root Mean Square Error (RMSE) and Standard Deviation (SD), It is found out that SVM method has a considerably better performance relative to other methods for estimating both the BET, HHV and energy recovery parameters. Furthermore, coupling of MLP and ANFIS with GOA increases the accuracy of these models for BET, HHV and energy recovery estimations.
Journal of Molecular Liquids, 2019
This is a PDF file of an article that has undergone enhancements after acceptance, such as the ad... more This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Advanced Materials, 2019
50 W. The C1s peak at 284.8 eV was used as a reference and the spectra were recorded with steps o... more 50 W. The C1s peak at 284.8 eV was used as a reference and the spectra were recorded with steps of 0.2 eV. The data were processed using CasaXPS.
Journal of Petroleum Science and Engineering, 2019
One of the most prevalent problems in drilling industry is lost circulation which causes intense ... more One of the most prevalent problems in drilling industry is lost circulation which causes intense increase in drilling expenditure as well as operational obstacles such as well instability and blowout. The aim of this research is to develop smart systems for estimating amount of lost circulation making able to use appropriate prevention and remediation methods. To obtain this aim, a large data set were collected from 61 recently drilled wells in Marun oil field in Iran to be used for developing relevant models. After that, using the extracted data set consisting of 1900 data subset, intelligent prediction models including decision tree (DT), adaptive neuro-fuzzy inference systems (ANFIS), artificial neural networks (ANN) and also hybrid artificial neural network namely genetic algorithm-multi-layer perception (GA-MLP) were developed to make a quantitative prediction on lost circulation. The model outputs are then analyzed by various performance indices such as variance accounted for (VAF), root mean square error (RMSE), performance index (PI) and coefficient of determination (R 2). Eventually, it is found that developed models are highly applicable in lost circulation prediction. Concluding remark is that DT model having determination coefficient of 0.9355 and RMSE of 0.091 is superior comparing to other developed models and hybrid ANN (GA-MLP) exhibits lowest prediction performance among other implemented models.
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Papers by Farzaneh talebkeikhah