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Machine learning-based multi-objective optimization and thermal assessment of supercritical CO_(2) Rankine cycles for gas turbine waste heat recovery

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Energy and AI 2024年第2期16卷 363-384页

作者： Asif Iqbal Turja ishtiak ahmed khan Sabbir Rahman Ashraf Mustakim Mohammad Ishraq Hossain M Monjurul Ehsan Yasin khan Department of Mechanical and Production Engineering(MPE) Islamic University of Technology(IUT)Board BazarGazipur1704Bangladesh

Technologies for utilizing waste heat for power generation have attracted significant attention in recent years due to their potential to enhance energy efficiency and reduce greenhouse gas *** research focuses on the comparative and optimization analysis of three supercritical carbon dioxide(sCO_(2))Rankine cycles(simple,cascade,and split)for gas turbine waste heat *** study begins with parametric analysis,investigating the significant effects of key variables,including turbine inlet temperature,condenser inlet temperature,and pinch point temperature,on the thermal performance of advanced sCO_(2) power *** identify the most efficient cycle configuration,a multi-objective optimization approach is *** approach combines a Genetic Algorithm with machine learning regression models(Random Forest,XGBoost,Artificial Neural Network,Ridge Regression,and K-Nearest Neighbors)to predict cycle performance using a dataset extracted from cycle *** decision-making process for determining the optimal cycle configuration is facilitated by the TOPSIS(technique for order of preference by similarity to the ideal solution)*** study's major findings reveal that the split cycle outperforms the simple and cascade configurations in terms of power generation across various operating *** optimized split cycle not only demonstrates superior power output but also exhibits enhanced net power output,heat recovery,system and exergy efficiency of 7.99 MW,76.17%,26.86%and 57.96%,respectively,making it a promising choice for waste heat recovery *** research has the potential to contribute to the advancement and widespread adoption of waste heat recovery in energy technologies boosting system efficiency and economic *** provides a new perspective for future research,contributing to the improvement of energy generation infrastructure.

关键词： Supercritical co2 Rankine cycle Waste heat recovery Gas turbine Thermodynamic analysis Machine Learning Optimization

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Papillary thyroid carcinoma:A bibliometric analysis of the top 100 cited articles

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World Journal of Otorhinolaryngology-Head and Neck Surgery 2024年第3期10卷 225-230页

作者： Mahamud ishtiak ahmed Jake deCastro Francis Sanu Ameeth Department of Ear Nose and Throat SurgeryMorriston HospitalHeol Maes Eglwys Cwmrhydyceirw SwanseaSwanseaUK

Objectives:To describe the current trends and key themes that are shaping the literature surrounding papillary thyroid ***:The Thomson Reuters Web of Science citation database was used to identify manuscripts relevant to papillary thyroid *** were then subdivided into title,author,journal,publication date,theme,and manuscript *** were then ***:A total of 1506 manuscripts were *** mean citation number was 163(range 885–74),with the highest citation rate of *** highest impact factor was JAMA(impact factor of 56.27).85%were original research,10%review articles,and 5%were *** highest number of manuscripts were published in South Korea(21%).Conclusions:This bibliometric analysis describes the most influential papers published in the topic of papillary thyroid *** key themes have been endocrinology and metabolism,followed by surgical management and oncology(subdivided into surgery,pathology,radiology and public health/epidemiology).South Korea leads the world with the largest number of publications in this field;with an incidence rate of papillary thyroid carcinoma that is significantly higher than most parts of the *** concerns with overdiagnosis,a collaborative radiological team skilled in ultrasonography of the neck appears to be of great value to the workup of thyroid cancers.

关键词： bibliometric carcinoma citation literature review thyroid

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