Artificial intelligence methods for modeling gasification of waste biomass: a review

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dc.authoridCINER, MIRAC NUR/0000-0002-9920-928X
dc.authoridozcan, hasan/0000-0002-0135-8093
dc.contributor.authorAlfarra, Fatma
dc.contributor.authorOzcan, H. Kurtulus
dc.contributor.authorCihan, Pinar
dc.contributor.authorOngen, Atakan
dc.contributor.authorGuvenc, Senem Yazici
dc.contributor.authorCiner, Mirac Nur
dc.date.accessioned2024-10-29T17:58:19Z
dc.date.available2024-10-29T17:58:19Z
dc.date.issued2024
dc.departmentTekirdağ Namık Kemal Üniversitesi
dc.description.abstractGasification is a highly promising thermochemical process that shows considerable potential for the efficient conversion of waste biomass into syngas. The assessment of the feasibility and comparative advantages of different biomass and waste gasification schemes is contingent upon a multifaceted combination of interrelated criteria. Conventional analytical approaches employed to facilitate decision-making rely on a multitude of inadequately defined parameters. Consequently, substantial efforts have been directed toward enhancing the efficiency and productivity of thermochemical conversion processes. In recent times, artificial intelligence (AI)-based models and algorithms have gained prominence, serving as indispensable tools for expediting these processes and formulating strategies to address the growing demand for energy. Notably, machine learning (ML) and deep learning (DL) have emerged as cutting-edge AI models, demonstrating exceptional effectiveness and profound relevance in the realm of thermochemical conversion systems. This study provides an overview of the machine learning (ML) and deep learning (DL) approaches utilized during gasification and evaluates their benefits and drawbacks. Many industries and applications related to energy conversion systems use AI algorithms. Predicting the output of conversion systems and subjects linked to optimization are two of this science's critical applications. This review sheds light on the burgeoning utility of AI, particularly ML and DL, which have garnered significant attention due to their applications in productivity prediction, process optimization, real-time process monitoring, and control. Furthermore, the integration of hybrid models has become commonplace, primarily owing to their demonstrated success in modeling and optimization tasks. Importantly, the adoption of these algorithms significantly enhances the model's capability to tackle intricate challenges, as DL methodologies have evolved to offer heightened accuracy and reduced susceptibility to errors. Within the scope of this study, an exhaustive exploration of ML and DL techniques and their applications has been conducted, uncovering existing research knowledge gaps. Based on a comprehensive critical analysis, this review offers recommendations for future research directions, accentuating the pivotal findings and conclusions derived from the study.
dc.description.sponsorshipScientific Research Projects Coordination Unit of Istanbul University-Cerrahpasa Rectorate [37323]
dc.description.sponsorshipThis study was funded by the Scientific Research Projects Coordination Unit of Istanbul University-Cerrahpasa Rectorate. Project number: 37323.
dc.identifier.doi10.1007/s10661-024-12443-2
dc.identifier.issn0167-6369
dc.identifier.issn1573-2959
dc.identifier.issue3en_US
dc.identifier.pmid38407668
dc.identifier.urihttps://doi.org/10.1007/s10661-024-12443-2
dc.identifier.urihttps://hdl.handle.net/20.500.11776/14226
dc.identifier.volume196
dc.identifier.wosWOS:001173230300004
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofEnvironmental Monitoring and Assessment
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectGasification
dc.subjectArtificial intelligence
dc.subjectMachine learning
dc.subjectDeep learning
dc.subjectHybrid model
dc.subjectOptimization
dc.titleArtificial intelligence methods for modeling gasification of waste biomass: a review
dc.typeReview Article

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