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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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Factors Associated with Peripheral Neuropathy in Type 2 Diabetes： Subclinical versus Confirmed Neuropathy

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Journal of Huazhong University of Science and Technology(Medical Sciences) 2017年第3期37卷 337-342页

作者： Yogendranathsing Unmar mohammad ishraq Zafar 高峰 Department of Endocrinology Union Hospital Tongji Medical College Huazhong University of Science and Technology

This study determined the prevalence of diabetic peripheral neuropathy(DPN) and subclinical DPN(s DPN) in patients with type 2 diabetes mellitus(T2DM) using nerve conduction study(NCS) as a diagnostic tool. We also investigated the factors associated with the development of s DPN and compared factors between the sD PN and confirmed DPN(cDPN). This cross-sectional study involved 240 T2DM patients who were successively admitted to the endocrinology wards of Wuhan Union Hospital over the period of January to December 2014. Data on the medical history, physical and laboratory examinations were collected. DPN was diagnosed using NCS. One-way ANOVA with least significant difference(LSD) analysis or chi-square tests was used to compare parameters among DNP-free, s DPN and c DPN patients. Independent factors associated with s DPN were determined using logistic regression. The results showed that 50.8% of the participants had DPN, and among them, 17.1% had sDPN. sDPN showed significant independent associations with age, height, HbA1c, presence of atherosclerosis and diabetic retinopathy. Patients with DPN differed significantly from those without DPN with respect to age, duration of disease(DOD), HbA1c, presence of atherosclerosis, diabetic retinopathy, nephropathy and hypertension. Patients with cDPN, relative to those with sDPN, had significantly longer DOD and higher prevalence of peripheral artery disease(PAD) and coronary artery disease(CAD). Our study suggests that a significant number of T2DM patients are affected by s DPN, and the development of this condition is associated with advanced age, tall stature, poor glycaemic control, presence of diabetic retinopathy and atherosclerosis. On the other hand, patients with cDPN tend to have a longer DOD and are more likely to suffer from PAD and CAD.

关键词： diabetic peripheral neuropathy type 2 diabetes mellitus subclinical diabetic peripheral neuropathy nerve conduction study

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