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Liang Z.,Guangdong University of Technology | Luo X.,Guangdong University of Technology | Chen Y.,Soft Matter Center | Mo S.,Soft Matter Center
Reneng Dongli Gongcheng/Journal of Engineering for Thermal Energy and Power | Year: 2016

Double-pressure organic Rankinecyclewithagas-liquid separator (DSORC) is proposed. Parametric optimization of DSORC used binary zeotropic mixtures, basic organic Rankine cycle (BORC) used zeotropic mixtures and DSORC used pure fluids isconducted. The second law efficiencymaximization is used as objective function. The optimization variables are mixing ratios of zeotropic mixtures, evaporating temperature and vapor quality in separator. And the optimizationmodel is solved using genetic algorithm. The working fluids under study are: R245fa, isopentane, hexane, cyclohexane, isohexane, pentane, isobutane, R365mfc and their binary mixture. The results show that DSORC used zeotropicmixtureshasan increase of 14.1~17.2% in second law efficiency compared to basic ORC (BORC) usedzeotropic mixtures, and an increase of 5.3~8.7% compared to DSORC with pure fluids. The efficiency differences among 8 zeotropic mixtures are less than 3%. The second law efficiency changes linearly with the inlet temperatureof heat source. © 2016, Harbin Research Institute. All right reserved. Source


Luo X.,Guangdong University of Technology | Luo X.,Soft Matter Center | Xu J.,Guangdong University of Technology | Chen Y.,Guangdong University of Technology | And 3 more authors.
Energy Procedia | Year: 2015

The liquid separation condenser (LSC) is superior in terms of performance because of its relatively low pressure drop without reducing heat transfer coefficients. It is compact, material saving, and suitable for use in the organic Rankine cycle. However, the total performance of LSC is dependent on tube and fin configurations. A suitable combination of these structural parameters can improve LSC performance significantly. In this study, a mixed-integer nonlinear programming (MINLP) model is formulated to optimize the tube pass, tube number per pass, tube length, tube diameter, and fin number simultaneously. The objective function is the annual total cost of investment and operation. A solution is proposed to solve the formulated MINLP model. A case study is presented to verify the model and solution algorithm. © 2015 The Authors. Published by Elsevier Ltd. Source


Xu J.,Guangdong University of Technology | Luo X.,Guangdong University of Technology | Luo X.,Soft Matter Center | Chen Y.,Guangdong University of Technology | And 3 more authors.
Energy Procedia | Year: 2015

Evaporator and condenser are two main components of ORC and they are predominant in exergy destruction, cost and material consumption. The optimization and screening of evaporator and condenser is important to achieve the optimal performance of ORC. In this paper, the popular used shell and tube heat exchangers and plate heat exchangers are selected as candidate heat exchangers for ORC. The detailed parametric and structural models of heat exchangers are formulated. The candidate heat exchangers are optimized and screened under the objective of maximum thermal efficiency (THE), minimum specific cost (SIC) and minimum heat exchanger area per unit power output (APR). Genetic algorithm (GA) is used to solve the model. Then two candidates of ORC schemes with different heat exchangers are screened by fuzzy multi-criteria decision making process. Single objective and fuzzy multi-criteria design optimization and screening cases are demonstrated to testify the proposed methodology. © 2015 The Authors. Published by Elsevier Ltd. Source


Luo X.,Guangdong University of Technology | Luo X.,Soft Matter Center | Yi Z.,Guangdong University of Technology | Chen Z.,Guangdong University of Technology | And 4 more authors.
Applied Thermal Engineering | Year: 2016

Organic Rankine cycle (ORC)-based power generation is a promising technology for recovering waste energy and effectively utilizing renewable energy with low enthalpy. A condenser is an important unit in ORC. The screening and design optimization of the condenser is significant in achieving high efficiency and low cost. Liquid-vapor separation (LSC) is a newly developed air-cooled fin-tube condenser with low pressure drop, low investment cost, and high compactness. The advantages of LSC have been proven in the refrigeration system. This study theoretically investigates the comprehensive performance of LSC in ORC. A mathematical model of LSC and an ORC that contains LSC is formulated. The effects of some key geometric parameters on the total heat transfer surface area and pressure drop of the condenser are examined. The total cost of investment and operation is selected as the objective function to identify the comprehensive performance of the heat transfer coefficient and presser drop at a constant heat transfer rate. The LSC is also compared with the serpentine condenser (SC) and parallel flow condenser (PFC) in terms of heat transfer coefficients, pressure drop, heat transfer area, and economic cost. The systematic performance of ORC containing different types of condensers is examined and compared in the context of ORC at fixed heat resource parameters and environment parameter. The structural parameter analysis shows that the tube length, tube inner diameter and tube pass arrangement with minimum cost of LSC are 1.5 m, 11 mm and 17-15-10-5-1, respectively. The optimal total cost of LSC is 3.74% and 34.50% lower than that of PFC and SC under a given design condition. The comparison of ORC with different condensers shows that the thermal efficiency (exergy efficiency) of ORC-LSC and ORC-PFC are 0~13.75% (0~11.82%) and 25.25~65.53% (21.83~52.3%) higher than those of ORC-SC, respectively. The sensitivity analysis of heat resource parameters and environment parameter is also performed, and quantitative comparisons of three ORCs are provided. © 2015 Elsevier Ltd. All rights reserved. Source


Zhu Q.,Guangdong University of Technology | Luo X.,Guangdong University of Technology | Luo X.,Soft Matter Center | Zhang B.,Sun Yat Sen University | And 4 more authors.
Energy | Year: 2016

Complex turbine (i.e., steam turbine with multiple controlled and uncontrolled extractions) is one of the most important components in industrial utility system. The function of complex turbine is to generate power electricity, provide steam for the processes, and heat boiler feedwater using uncontrolled extraction steam. Although many studies have focused on the modeling and optimization of utility systems of different scales, few works have been reported on the modeling and operation optimization for utility system containing multiple complex steam turbines. In this paper, a superstructure of utility network consists of multiple interconnected various types of complex turbines (ICSTUN) is constructed. The superstructure demonstrates not only an external utility network that supplies utility energy for the processes but also an internal utility network that supplies boiler feedwater. A systematic modeling approach is developed for robust simulation, validation and operation optimization of ICSTUN. A mixed-integer nonlinear programming (MINLP) model based on an improved modeling principle of complex turbine is formulated for the operation optimization of ICSTUN. The MINLP model is able to simultaneously optimize the external and internal utility networks. A solving strategy incorporating model decomposition, model relaxation and case initialization is proposed to find a feasible solution. A large utility network in a petrochemical complex is studied to test the accuracy of the complex turbine model and the effectiveness of the proposed operation optimization methodology. The validation results of the complex turbine model show that the mean relative error between field operation data and simulation data is less than 1.18% and is acceptable in engineering application. The optimization of a single utility plant yields a maximum coal reduction rate of 4.59%, and the optimization of the total utility system consisting of two utility plants yields a maximum coal reduction rate of 6.01%. The optimization contribution is discussed in detail, and several conclusions and suggestions are presented. © 2015 Elsevier Ltd. Source

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