Multi-objective thermo-economic optimization of a combined organic Rankine cycle and vapour compression refrigeration cycle

Salim, Mohammad Saad; Kim, Man-Hoe

doi:10.1016/j.enconman.2019.112054

Cited by 44 publications

(6 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A comparison between PSOGA and on-off control showed that PSOGA could decrease the system's total energy consumption by 7.36%. Salim and Kim [16] designed a combined power generation consisting of an organic Rankine cycle (ORC), a VCRS, and then applied MOO. For the ORC, they used three dry refrigerants (R245fa, R245ca, and R236ea), and for the VCRS, they used R410A.…”

Section: Plos Onementioning

confidence: 99%

4E analysis of a two-stage refrigeration system through surrogate models based on response surface methods and hybrid grey wolf optimizer

et al. 2023

View full text Add to dashboard Cite

Refrigeration systems are complex, non-linear, multi-modal, and multi-dimensional. However, traditional methods are based on a trial and error process to optimize these systems, and a global optimum operating point cannot be guaranteed. Therefore, this work aims to study a two-stage vapor compression refrigeration system (VCRS) through a novel and robust hybrid multi-objective grey wolf optimizer (HMOGWO) algorithm. The system is modeled using response surface methods (RSM) to investigate the impacts of design variables on the set responses. Firstly, the interaction between the system components and their cycle behavior is analyzed by building four surrogate models using RSM. The model fit statistics indicate that they are statistically significant and agree with the design data. Three conflicting scenarios in bi-objective optimization are built focusing on the overall system following the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Linear Programming Technique for Multidimensional Analysis of Preference (LINMAP) decision-making methods. The optimal solutions indicate that for the first to third scenarios, the exergetic efficiency (EE) and capital expenditure (CAPEX) are optimized by 33.4% and 7.5%, and the EE and operational expenditure (OPEX) are improved by 27.4% and 19.0%. The EE and global warming potential (GWP) are also optimized by 27.2% and 19.1%, where the proposed HMOGWO outperforms the MOGWO and NSGA-II. Finally, the K-means clustering technique is applied for Pareto characterization. Based on the research outcomes, the combined RSM and HMOGWO techniques have proved an excellent solution to simulate and optimize two-stage VCRS.

show abstract

Section: Plos Onementioning

confidence: 99%

4E analysis of a two-stage refrigeration system through surrogate models based on response surface methods and hybrid grey wolf optimizer

et al. 2023

View full text Add to dashboard Cite

show abstract

“…The analysis of the functional scheme and installation cycle (Figure 1) highlights the following sequence of work processes: (2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20) heating the ORC liquid to the boiling temperature, (20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) evaporating the ORC liquid in the ORC boiler, superheating the vapors in the ORC boiler, (3)(4) expanding the vapors to the condensing pressure in the ORC expander, (7)(8) compressing the superheated vapors to the condensing pressure in the VCRC compressor, (5) mixing the streams in states ( 4) and (8), and (5-1) condensing the saturated liquid in the ORC-VCRC condenser. (1TV) is the part of the stream (1) that expands in the throttling valve (TV) of the refrigeration plant.…”

Section: Mathematical Modeling Of the Systemmentioning

confidence: 99%

“…Salim, M.S. and Kim, M. [23] presented the application of a genetic algorithm (GA) for the thermoeconomic optimization of the combined ORC-VCRC system to determine the optimal component sizes and thermal performance of the system. The authors recommended R245fa as the working fluid for the optimal thermoeconomic performance of the system.…”

Section: Introductionmentioning

confidence: 99%

Exergoeconomic Analysis of a Mechanical Compression Refrigeration Unit Run by an ORC

Taban,

Apostol,

Grosu

et al. 2023

Entropy

View full text Add to dashboard Cite

To improve the efficiency of a diesel internal combustion engine (ICE), the waste heat carried out by the combustion gases can be recovered with an organic Rankine cycle (ORC) that further drives a vapor compression refrigeration cycle (VCRC). This work offers an exergoeconomic optimization methodology of the VCRC-ORC group. The exergetic analysis highlights the changes that can be made to the system structure to reduce the exergy destruction associated with internal irreversibilities. Thus, the preheating of the ORC fluid with the help of an internal heat exchanger leads to a decrease in the share of exergy destruction in the ORC boiler by 4.19% and, finally, to an increase in the global exergetic yield by 2.03% and, implicitly, in the COP of the ORC-VCRC installation. Exergoeconomic correlations are built for each individual piece of equipment. The mathematical model for calculating the monetary costs for each flow of substance and energy in the system is presented. Following the evolution of the exergoeconomic performance parameters, the optimization strategy is developed to reduce the exergy consumption in the system by choosing larger or higher-performance equipment. When reducing the temperature differences in the system heat exchangers (ORC boiler, condenser, and VCRC evaporator), the unitary cost of the refrigeration drops by 44%. The increase in the isentropic efficiency of the ORC expander and VCRC compressor further reduces the unitary cost of refrigeration by another 15%. Following the optimization procedure, the cost of the cooling unit drops by half. The cost of diesel fuel has a major influence on the unit cost of cooling. A doubling of the cost of diesel fuel leads to an 80% increase in the cost of the cold unit. The original merit of the work is to present a detailed and comprehensive model of optimization based on exergoeconomic principles that can serve as an example for any thermal system optimization.

show abstract

“…The organic-based working fluids have many superiorities over steam such as low boiling temperatures, low specific heats, etc. [35][36][37]. In the present study, data obtained from an ORC set up in the laboratory is used to obtain a prediction expression.…”

Section: Description Of the Organic Rankine Cycle (Orc)mentioning

confidence: 99%

Deep neural network approach to estimation of power production for an organic Rankine cycle system

Mert

Bilgiç

Yağlı

et al. 2020

J Braz. Soc. Mech. Sci. Eng.

View full text Add to dashboard Cite

Multi-objective thermo-economic optimization of a combined organic Rankine cycle and vapour compression refrigeration cycle

Cited by 44 publications

References 33 publications

4E analysis of a two-stage refrigeration system through surrogate models based on response surface methods and hybrid grey wolf optimizer

4E analysis of a two-stage refrigeration system through surrogate models based on response surface methods and hybrid grey wolf optimizer

Exergoeconomic Analysis of a Mechanical Compression Refrigeration Unit Run by an ORC

Deep neural network approach to estimation of power production for an organic Rankine cycle system

Contact Info

Product

Resources

About