This article presents the data of recovered lipid from microalgae using fuzzy logic based-modelling and particle swarm optimization (PSO) algorithm. The details of fuzzy model and optimization process were discussed in our work entitled “Application of Fuzzy Modelling and Particle Swarm Optimization to Enhance Lipid Extraction from Microalgae” (Nassef et al., 2019) [1]. The presented data are divided into two main parts. The first part represents the percentage of recovered lipid using fuzzy logic model and ANOVA. However, the second part shows the variation of the cost function (recovered lipid) for the 100 runs of PSO algorithm during optimization process. These data sets can be used as references to analyze the data obtained by any other optimization technique. The data sets are provided in the supplementary materials in Tables 1–2

Abdalla

Diab

Inayat

Mohamed

Nassef

Onumaegbu

Rezk

English

Abdelkareem, Mohammad Ali

Rezk, Hegazy

Sayed, Enas T.

Alaswad, A.

Nassef, Ahmed M.

Olabi, A. G.

Aston Publications Explorer

Data in brief 28 (2020) 104931Contents lists available at ScienceDirectData in briefjournal homepage: www.elsevier .com/locate/dibData ArticleData on fuzzy logic based-modelling andoptimization of recovered lipid from microalgaeMohammad Ali Abdelkareem a, b, c, *, Hegazy Rezk d, e, **,Enas T. Sayed a, A. Alaswad f, Ahmed M. Nassef d, g,A.G. Olabi b, fa Dept. of Sustainable and Renewable Energy Engineering, University of Sharjah, P.O. Box 27272, Sharjah,United Arab Emiratesb Center for Advanced Materials Research, University of Sharjah, PO Box 27272, Sharjah, United ArabEmiratesc Chemical Engineering Department, Faculty of Engineering, Minia University, Egyptd College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Saudi Arabiae Electrical Engineering Department, Faculty of Engineering, Minia University, Egyptf Mechanical Engineering and Design, Aston University, School of Engineering and Applied Science, AstonTriangle, Birmingham, B4 7ET, UKg Computers and Automatic Control Engineering Department, Faculty of Engineering, Tanta University, Egypta r t i c l e i n f oArticle history:Received 29 August 2019Accepted 27 November 2019Available online 4 December 2019Keywords:Lipid extractionMicroalgaeFuzzy logic modelingParticle swarm optimizationRenewable energy* Corresponding author. Dept. of Sustainable anSharjah, United Arab Emirates.** Corresponding author. College of EngineeringE-mail addresses: mabdulkareem@sharjah.ac.aehttps://doi.org/10.1016/j.dib.2019.1049312352-3409/© 2019 The Author(s). Published by Ecreativecommons.org/licenses/by/4.0/).a b s t r a c tThis article presents the data of recovered lipid from microalgaeusing fuzzy logic based-modelling and particle swarm optimiza-tion (PSO) algorithm. The details of fuzzy model and optimizationprocess were discussed in our work entitled “Application of FuzzyModelling and Particle Swarm Optimization to Enhance LipidExtraction fromMicroalgae” (Nassef et al., 2019) [1]. The presenteddata are divided into two main parts. The ﬁrst part represents thepercentage of recovered lipid using fuzzy logic model and ANOVA.However, the second part shows the variation of the cost function(recovered lipid) for the 100 runs of PSO algorithm during opti-mization process. These data sets can be used as references toanalyze the data obtained by any other optimization technique.d Renewable Energy Engineering, University of Sharjah, P.O. Box 27272,at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Saudi Arabia.(M.A. Abdelkareem), hegazy.hussien@mu.edu.eg (H. Rezk).lsevier Inc. This is an open access article under the CC BY license (http://M.A. Abdelkareem et al. / Data in brief 28 (2020) 1049312Speciﬁcations TableSubject area EnergyMore speciﬁc subject area Renewable Energy;Type of data Excel ﬁlesHow data was acquired The input parametthe numerical simuData format Filtered and analyzExperimental factors The fuzzy model hasamples for 50 epoExperimental features The fuzzy logic bascompared with ANData source location Wadi Addawaser, PData accessibility Data are providedValue of the data The data presented in this paper can be utilizedthe recovered lipid from Microalgae. By using these data sets, researchers can manetworks (ANNs) These data sets are very useful for making compcuckoo search.The data sets are provided in the supplementary materials inTables 1e2.© 2019 The Author(s). Published by Elsevier Inc. This is an openaccess article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).Artiﬁcial Intelligence; Swarm Optimizationers of PSO from Refs. [2,3]. Data of fuzzy model from Refs. [4,5]. Afterwards,lation was conducted by MATLAB/Simulink software packageeds 13 fuzzy rules. The model's training process has been done with 13chsed model has minimum RMSE and maximum coefﬁcient of determinationOVArince Sattam Bin Abdulaziz University, Saudi Arabiain supplementary materials with this articledirectly without spending time to initiate any further simulations to studyke comparisons with other modelling techniques like artiﬁcial neuralarisons with other optimization algorithms such as genetic algorithm and1. DataThis article presents the numerical data generated during the maximizing of recovered lipid frommicroalgae using fuzzy logic based-modelling and PSO algorithm. The simulationwas carried out usingMatlab/Simulink software package on a Core i7 computer with Win10 operating system. The datageneration process comes with some stages. First, by using the experimental data from Refs. [1,6], arobust model that describes the lipid extraction is generated using fuzzy logic technique. Table 1(supplementary materials) shows a comparison of fuzzy based model with ANOVA. Second, theoptimal decision variables for extracting the lipids are determined using PSO algorithm. During opti-mization process, three different operating parameters; power (W), heating time (minutes), andextraction time (hours) have been used as a decision variables in order to maximize the percentage ofthe recovered-lipid which is used as a cost function. Due to the stochastic behavior of the swarmoptimizers, the optimizer results cannot be trusted unlessmany trials have been done [7e9]. Therefore,the optimization process was executed for 100 times. The data of the 100 runs is presented in Table 2(supplementary materials).2. Experimental design, materials and methodsA sample of 500 ml of the wet algae was subjected to microwave pre-treatment using a roundbottom open glass. The samples were pre-treated at a microwave power ranging between 180 W and600 W for times ranging between 2 minutes and 8 minutes. Furthermore, different extraction timeswere tested between 3 and 4 hours. More information about the experimental design and data can beM.A. Abdelkareem et al. / Data in brief 28 (2020) 104931 3found in Refs. [1,2]. Then, based on these experimental data sets, an accurate fuzzy logic based model iscreated to simulate the process. Finally, PSO algorithm has been used to identify the optimal operatingparameters to maximize the recovered lipid. Tables 1 and 2, in Appendix-A, show the outputs of thefuzzy model and the results of optimization process, respectively.Conﬂict of InterestThe authors declare that they have no known competing ﬁnancial interests or personal relation-ships that could have appeared to inﬂuence the work reported in this paper.Appendix A. Supplementary dataSupplementary data to this article can be found online at https://doi.org/10.1016/j.dib.2019.104931.References[1] A.M. Nassef, H. Rezk, M.A. Abdelkareem, A. Al-Aswad, A.G. Olabi, Application of fuzzy modelling and particle swarmoptimization to enhance lipid extraction from microalgae, Sustain. Energy Technol. Assess. 35 (2019) 73e79.[2] A.M. Nassef, E.T. Sayed, H. Rezk, M.A. Abdelkareem, C. Rodriguez, A.G. Olabi, Fuzzy-modeling with particle swarm opti-mization for enhancing the production of biodiesel from microalga, Energy Sources, Part A Recovery, Util. Environ. Eff. 41(2019) 2094e2103.[3] O. Abdalla, H. Rezk, E.M. Ahmed, Wind driven optimization algorithm based global MPPT for PV system under non-uniformsolar irradiance, Sol. Energy 180 (2019) 429e444.[4] H. Rezk, A.M. Nassef, A. Inayat, E.T. Sayed, M. Shahbaz, A.G. Olabi, Improving the environmental impact of palm kernel shellthrough maximizing its production of hydrogen and syngas using advanced artiﬁcial intelligence, Sci. Total Environ. 658(2019) 1150e1160.[5] A. Inayat, A.M. Nassef, H. Rezk, E. T Sayed, M.A. Abdelkareem, A.G. Olabi, Fuzzy modeling and parameters optimization forthe enhancement of biodiesel production from waste frying oil over montmorillonite clay K-30, Sci. Total Environ. 666(2019) 821e827.[6] C. Onumaegbu, A. Alaswad, C. Rodriguez, A.G. Olabi, Modelling and optimization of wet microalgae Scenedesmus quad-ricauda lipid extraction using microwave pre-treatment method and response surface methodology, Renew. Energy 132(2019) 1323e1331.[7] A.A.Z. Diab, H. Rezk, Global MPPT based on ﬂower pollination and differential evolution algorithms to mitigate partialshading in building integrated PV system, Sol. Energy 157 (2017) 171e186.[8] H. Rezk, A.S. Alsaman, M. Al-Dhaifallah, A.A. Askalany, M.A. Abdelkareem, A.M. bassef, Identifying optimal operatingconditions of solar-driven silica gel based adsorption desalination cooling system via modern optimization, Sol. Energy 181(2019) 475e489.[9] M.A. Mohamed, A.A.Z. Diab, H. Rezk, Partial shading mitigation of PV systems via different meta-heuristic techniques,Renew. Energy 130 (2019) 1159e1175.

Data on fuzzy logic based-modelling and optimization of recovered lipid from microalgae

Crossref

https://publications.aston.ac.uk/id/eprint/41106/1/1_s2.0_S2352340919312867_main.pdf

Data on fuzzy logic based-modelling and optimization of recovered lipid from microalgae

Abstract

Similar works

Full text

Available Versions

Aston Publications Explorer

Crossref