Utilization of apricot seed in (co-) combustion of lignite coal blends: Numeric optimization, empirical modeling and uncertainty estimation

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dc.authorid0000-0002-1643-2487en_US
dc.authorid0000-0001-6841-6457en_US
dc.contributor.authorBüyükada, Musa
dc.contributor.authorAydoğmuş, Ercan
dc.date.accessioned2021-06-23T19:49:52Z
dc.date.available2021-06-23T19:49:52Z
dc.date.issued2018
dc.departmentBAİBÜ, Mühendislik Fakültesi, Kimya Mühendisliği Bölümüen_US
dc.description.abstractUtilization of apricot seed (AS) in lignite coal (LC)-based (co-)combustion process was aimed in the present study considering the apricot production capacity of Turkey. By this way, an alternative and also ecofriendly way was suggested for coal-based energy production plants located in Turkey. This purpose was tested by thermogravimetric analyses to demonstrate the advantageous sides of AS in reduction of ash amount and also environmental aspects based on harmful gases. The other important contributors of present study was the comparison of both statistical modeling and numeric optimization techniques for maximization of mass loss percentage (MLP, %) in response to (co-) combustion process. For this purpose, multiple non-linear regression (MNLR) and artificial neural network (ANN) models as data-driven modeling techniques, and response surface methodology (RSM) and particle swarm optimization (PSO) as numeric optimization approaches were utilized. Results demonstrated the accuracy of ANN and PSO in prediction of MLP (%) and optimization of operating conditions of (co-)combustion of AS and LC, respectively. Finally, Bayesian approach was applied to the best-fit MNLR model to identify the uncertainties in predictors of proposed model. Bayesian was found quite effective in identification of uncertainties that were not possible to be captured through deterministic ways.en_US
dc.identifier.doi10.1016/j.fuel.2017.12.028
dc.identifier.endpage198en_US
dc.identifier.issn0016-2361
dc.identifier.issn1873-7153
dc.identifier.scopus2-s2.0-85037642024en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage190en_US
dc.identifier.urihttps://doi.org/10.1016/j.fuel.2017.12.028
dc.identifier.urihttps://hdl.handle.net/20.500.12491/9640
dc.identifier.volume216en_US
dc.identifier.wosWOS:000427818100020en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorBüyükada, Musa
dc.language.isoenen_US
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofFuelen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCo-combustionen_US
dc.subjectMultiple Nonlinear Regressionen_US
dc.subjectArtificial Neural Networksen_US
dc.subjectResponse Surface Methodologyen_US
dc.subjectParticle Swarm Optimizationen_US
dc.subjectBayesian Approachen_US
dc.titleUtilization of apricot seed in (co-) combustion of lignite coal blends: Numeric optimization, empirical modeling and uncertainty estimationen_US
dc.typeArticleen_US

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