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Numerical Investigation of a modified Kalina cycle system for high-temperature application and genetic algorithm based optimization of the multi-phase expander’s inlet condition

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Energy and AI 2021年第4期6卷 71-87页

作者： mohammad masrur Hossain mdSakib Hossain Niyaz Afnan Ahmed m monjurul ehsa. Department of mechanical and Production Engineering Islamic University of TechnologyBoard BazarGazipur 1704Bangladesh

As an alternative to the conventional steam Rankine Cycle,Kalina Cycle has witnessed a growing interest over the past years for high-temperature applications(A working fluid temperature of 500◦C at the turbine inlet).However,the possibility of implementing an additional multi-phase expander on the weak ammonia-water solution loop of the Kalina cycle was hardly analyzed in the available *** this research,two novel Kalina cycles(Kalina cycle-12A and Kalina cycle-12B)have been presented by integrating a multi-phase expander in addition to the turbine installed downstream of the Kalina *** Kalina cycle-12A,this additional multi-phase expander is positioned downstream of the Kalina separator and on the weak ammonia-water solution loop for Kalina Cycle-12B.A detailed mathematical model based on the thermodynamic laws has been developed to solve and optimize the Kalina *** influence of critical decision parameters,specifically the ammonia concentration on working fluid and evaporation pressure,were *** optimization was performed based on the objective to maximize the net power output from the multi-phase expander under steady-state operating *** the performance of the proposed Kalina cycles was compared with the conventional Kalina Cycle-12,both of them demonstrated superior performance,i.e.,net power output and peak thermal efficiency increased by a maximum value of 3.23%for the proposed Kalina Cycle-12A cycle and 3.94%for the proposed Kalina Cycle-12B *** terms of second law efficiency,Kalina Cycle-12A is 3.68 percent more efficient than Kalina Cycle-12,while Kalina Cycle-12B is 4.04 percent more ***,2nd law analysis also reveals,maximum destruction of exergy occurs at the condensers of the cycles.

关键词： Kalina cycle Genetic algorithm-based optimization Exergy analysis High temperature

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multi-objective performance optimization&thermodynamic analysis of solar powered supercritical CO_(2)power cycles using machine learning methods&genetic algorithm

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Energy and AI 2024年第1期15卷 193-216页

作者： Asif Iqbal Turja md mahmudul Hasan m.monjurul ehsa. Yasin Khan Department of mechanical and Production Engineering(mPE) Islamic University of Technology(IUT)Board BazarGazipur1704Bangladesh

The present study is focused on multi-objective performance optimization&thermodynamic analysis from the perspectives of energy and exergy for Recompression,Partial Cooling&main Compression Intercooling supercritical CO_(2)(sCO_(2))Brayton cycles for concentrated solar power(CSP)applications using machine learning *** novelty of this work lies in the integration of artificial neural networks(ANN)and genetic algorithms(GA)for optimizing the performance of advanced sCO_(2)power cycles considering climatic variation,which has significant implications for both the scientific community and engineering applications in the renewable energy *** methodology employed includes thermodynamic analysis based on energy,exergy&environmental factors including system performance *** system is modelled for net power production of 15 mW thermal output utilizing equations for the energy and exergy balance for each ***,thermodynamic model extracted dataset used for prediction&evaluation of Random Forest,XGBoost,KNN,AdaBoost,ANN and LightGBm ***,considering climate conditions,multi-objective optimization is carried out for the CSP integrated sCO_(2)Power cycle for optimal power output,exergy destruction,thermal and exergetic *** algorithm and TOPSIS(technique for order of preference by similarity to ideal solution),multi-objective decision-making tool,were used to determine the optimum operating *** major findings of this work reveal significant improvements in the performance of the advanced sCO_(2)cycle by 1.68%and 7.87%compared to conventional recompression and partial cooling cycle,*** research could advance renewable energy technologies,particularly concentrated solar power,by improving power cycle designs to increase system efficiency and economic *** advanced supercritical CO_(2)power cycles in concentrated solar power plants might increase renewable energy use

关键词： Supercritical CO_(2) Concentrated solar power Thermodynamic analysis machine learning Artificial neural network multi-objective optimization

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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 ehsa. 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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Analysis of the Adsorption of Toxic Chromium (VI) by Untreated and Chitosan Treated Banana and Areca Fiber

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Journal of Textile Science and Technology 2020年第2期6卷 81-106页

作者： Hosne Ara Begum A. K. m. monjurul Haque md. Didarul Islam m. mehedi Hasan Suza Ahmed md. Razzak Ruhul Amin Khan Department of Yarn Engineering Bangladesh University of Textiles Dhaka Bangladesh Department of Applied Science & Chemical Engineering University of Dhaka Dhaka Bangladesh Department of Fabric Engineering Bangladesh University of Textiles Dhaka Bangladesh Institute of Radiation and Polymer Technology Bangladesh Atomic Energy Commission Dhaka Bangladesh

This paper work involves bunch experiments to investigate the effect of contact time, pH, and adsorbent dose on the extent of adsorption by bio-composites. Adsorption capacity of Chromium (VI) onto chitosan coated with banana and areca fiber was investigated in a batch system by considering the effects of various parameters like contact time, initial concentration, pH and adsorbent dose. The chitosan and fibers (banana and areca) were then cross-linked with glutaraldehyde to remove chromium [Cr (VI)] from water via static adsorption. It was found that optimum chromium absorption capacity of chitosan was assessed at pH of 2.5 to 4.5 and contact time of 30 to 180 minutes for raw banana and areca fiber and for chitosan treated banana & areca fiber. Though optimum adsorption of chromium of chitosan was measured 34.17 ppm (85.42%) from a 100 ml solution containing 40 parts per million (ppm) of Cr (VI) at 120 minutes, pH of 3.0 and 120 milligram (mg) adsorbent dose size, use of composite will be more favorable in the point of environmental concern as well as low cost because chitosan preparation is comparatively costly then banana and areca fiber. On the other hand, untreated banana and areca fiber optimum adsorption of chromium measured 3.65 ppm (9.1%) at 180 min and, pH of 4.5 and 140 mg adsorbent dose size and 3.76 ppm (9.4%) at 180 min, pH of 4.5 and 160 mg adsorbent dose size respectively. In case of chitosan treated banana fiber, it was observed that adsorption was increased from 17.664 to 30.057 (75.14%). On the other hand, for areca fiber with chitosan, it was observed that adsorption was increased from 21.664 to 30.156 (75.39%) ppm. The Langmuir and Freundlich adsorption models were used for the mathematical description of the adsorption of chromium ion onto composites and it was found that the Langmuir adsorption isotherm was more fitted models which mean that a monolayer adsorption surface was created. After analyzing from IBm SPSS 25 software, we got the stand

关键词： Adsorption Heavy metal Chitosan Banana Fiber Areca Fiber Toxicity

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