scholarly journals Thermodynamic analysis and simulation of a new combined power and refrigeration cycle using artificial neural network

2011 ◽  
Vol 15 (1) ◽  
pp. 29-41 ◽  
Author(s):  
Abdolreza Fazeli ◽  
Hossein Rezvantalab ◽  
Farshad Kowsary
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Heat Source ◽  
Exergy Efficiency ◽  
Combined Cycle ◽  
Cycle Performance ◽  
Working Fluid ◽  
Refrigeration Cycle ◽  
Water Mixture ◽  
Artificial Neural

In this study, a new combined power and refrigeration cycle is proposed, which combines the Rankine and absorption refrigeration cycles. Using a binary ammonia-water mixture as the working fluid, this combined cycle produces both power and refrigeration output simultaneously by employing only one external heat source. In order to achieve the highest possible exergy efficiency, a secondary turbine is inserted to expand the hot weak solution leaving the boiler. Moreover, an artificial neural network (ANN) is used to simulate the thermodynamic properties and the relationship between the input thermodynamic variables on the cycle performance. It is shown that turbine inlet pressure, as well as heat source and refrigeration temperatures have significant effects on the net power output, refrigeration output and exergy efficiency of the combined cycle. In addition, the results of ANN are in excellent agreement with the mathematical simulation and cover a wider range for evaluation of cycle performance.

A Totally Heat-Driven Refrigeration System Using Low-Temperature Heat Sources for Low-Temperature Applications

2019 ◽  
Vol 27 (02) ◽  
pp. 1950012 ◽  
Author(s):  
Zeynab Seyfouri ◽  
Mehran Ameri ◽  
Mozaffar Ali Mehrabian
Keyword(s):  
Low Temperature ◽  
Heat Source ◽  
Rankine Cycle ◽  
Exergy Efficiency ◽  
Working Fluid ◽  
Refrigeration Cycle ◽  
Water Mixture ◽  
Refrigeration System ◽  
Power Requirement ◽  
Temperature Heat

In the present study, a totally heat-driven refrigeration system is proposed and thermodynamically analyzed. This system uses a low-temperature heat source such as geothermal energy or solar energy to produce cooling at freezing temperatures. The proposed system comprises a Rankine cycle (RC) and a hybrid GAX (HGAX) refrigeration cycle, in which the RC provides the power requirement of the HGAX cycle. An ammonia–water mixture is used in both RC and HGAX cycles as the working fluid. A comparative study is conducted in which the proposed system is compared with two other systems using GAX cycle and/or a single stage cycle, as the refrigeration cycle. The study shows that the proposed system is preferred to produce cooling at temperatures from 2∘C to [Formula: see text]C. A detailed parametric analysis of the proposed system is carried out. The results of the analysis show that the system can produce cooling at [Formula: see text]C using a low-temperature heat source at 133.5∘C with the exergy efficiency of about 20% without any input power. By increasing the heat source temperature to 160∘C, an exergy efficiency of 25% can be achieved.

Analysis of a Combined Power and Refrigeration Cycle Working on Solar Energy

2008 ◽  
Author(s):  
Bijan Kumar Mandal ◽  
Kousik Sadhukhan ◽  
Achin Kumar Chowdhuri ◽  
Arup Jyoti Bhowal
Keyword(s):  
Solar Energy ◽  
Working Conditions ◽  
Exergy Efficiency ◽  
Combined Cycle ◽  
Cooling Effect ◽  
Second Law ◽  
Carnot Cycle ◽  
Refrigeration Cycle ◽  
Water Mixture ◽  
The Ideal

Thermodynamic analysis of a combined cycle producing power and refrigeration (cooling) effect simultaneously using solar energy has been analyzed. The working substance of the cycle is a binary mixture of ammonia and water. The effect of variation of absorber pressure, boiler pressure, boiler temperature and superheater temperature on the turbine work, refrigeration (cooling) effect and net output has been investigated. For different conditions of the above variables, the first law efficiency, the second law efficiency and the exergy efficiency of the cycle have been investigated. Since the ammonia water mixture boils at varying temperatures, Lorenz cycle instead of Carnot cycle is considered as the ideal cycle for this analysis. It is observed that the first law and the second law efficiencies are maximum under the same working conditions, but the exergy efficiency is maximum at some other working conditions. The maximum values of the first law, the second law and the exergy efficiencies are found to be 21.72%, 27.30% and 62.53% respectively.

scholarly journals Efficiency Enhancement in Double-Pass Perforated Glazed Solar Air Heaters with Porous Beds: Taguchi-Artificial Neural Network Optimization and Cost–Benefit Analysis

2021 ◽  
Vol 13 (21) ◽  
pp. 11654
Author(s):  
Roozbeh Vaziri ◽  
Akeem Adeyemi Oladipo ◽  
Mohsen Sharifpur ◽  
Rani Taher ◽  
Mohammad Hossein Ahmadi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mass Flow Rate ◽  
Mass Flow ◽  
Flow Rate ◽  
Thermal Efficiency ◽  
Exergy Efficiency ◽  
Wire Mesh ◽  
Double Pass ◽  
Artificial Neural

Analyzing the combination of involving parameters impacting the efficiency of solar air heaters is an attractive research areas. In this study, cost-effective double-pass perforated glazed solar air heaters (SAHs) packed with wire mesh layers (DPGSAHM), and iron wools (DPGSAHI) were fabricated, tested and experimentally enhanced under different operating conditions. Forty-eight iron pieces of wool and fifteen steel wire mesh layers were located between the external plexiglass and internal glass, which is utilized as an absorber plate. The experimental outcomes show that the thermal efficiency enhances as the air mass flow rate increases for the range of 0.014–0.033 kg/s. The highest thermal efficiency gained by utilizing the hybrid optimized DPGSAHM and DPGSAHI was 94 and 97%, respectively. The exergy efficiency and temperature difference (∆T) indicated an inverse relationship with mass flow rate. When the DPGSAHM and DPGSAHI were optimized by the hybrid procedure and employing the Taguchi-artificial neural network, enhancements in the thermal efficiency by 1.25% and in exergy efficiency by 2.4% were delivered. The results show the average cost per kW (USD 0.028) of useful heat gained by the DPGSAHM and DPGSAHI to be relatively higher than some double-pass SAHs reported in the literature.

A novel solar combined cycle integration: An exergy-based optimization using artificial neural network

2021 ◽  
Author(s):  
Ahmad M. Abubaker ◽  
Adnan Darwish Ahmad ◽  
Ahmad A. Salaimeh ◽  
Nelson K. Akafuah ◽  
Kozo Saito
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Combined Cycle ◽  
Artificial Neural

Optimization of the exergy efficiency, exergy destruction, and engine noise index in an engine with two direct injectors using NSGA-II and artificial neural network

2021 ◽  
pp. 146808742110577
Author(s):  
Saeid Shirvani ◽  
Sasan Shirvani ◽  
Seyed Ali Jazayeri ◽  
Rolf Reitz
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Exergy Efficiency ◽  
Base Case ◽  
Optimal Solutions ◽  
Exergy Destruction ◽  
Engine Noise ◽  
Nsga Ii ◽  
Noise Index ◽  
Artificial Neural

Direct Dual Fuel Stratification (DDFS) strategy is a novel Low Temperature Combustion (LTC) strategy that has comparable thermal efficiency to the Reactivity Controlled Compression Ignition (RCCI) strategy, while it offers more control over the combustion process and the rate of heat release. The DDFS strategy uses two direct injectors for the low- and high-reactivity fuels (gasoline and diesel) to benefit from the RCCI concept. In this study, the injection strategy of the injectors of a gasoline/diesel DDFS engine was optimized from the thermodynamic perspective to maximize exergy efficiency and minimize exergy destruction and an engine noise index. An artificial neural network was developed with 576 samples from a CFD code to predict the DDFS mode behavior, and the non-dominated sorting genetic algorithm (NSGA-II) was used to obtain the Pareto Front and the optimal solutions. Compared to the base case, the exergy efficiency of the optimal cases increased by up to 2%, exergy destruction and Peak Pressure Rise Rate (PPRR) reduced by about 2.3%, and 2 bar/deg, respectively, in the optimal solutions. NOX and soot emissions were reduced by 40% and 35%, respectively, in the best-case scenarios.

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