scholarly journals Modelling of energy and exergy analysis of ORC integrated systems in terms of sustainability by applying artificial neural network

2020 ◽  
Author(s):  
Zafer Utlu ◽  
Mert Tolon ◽  
Arif Karabuga
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Solar Energy ◽  
Ambient Temperature ◽  
Exergy Analysis ◽  
Energy System ◽  
Exergy Efficiency ◽  
Energy And Exergy ◽  
Energy And Exergy Analysis ◽  
Artificial Neural

Abstract The present study focuses on the organic Rankine cycle (ORC) integrated into an evacuated tube heat pipe (ETHP), whose systems are an alternative solar energy system to low-efficiency planary collectors. In this work, a detailed thermodynamic and artificial neural network (ANN) analysis was conducted to evaluate the solar energy system. One of the key parameters of sustainable approaches focused on exergy efficiency is application of thermal engineering. In addition to this, sustainable engineering approaches nowadays are a necessity for improving the efficiency of all of the engineering research areas. For this reason, the ANN model is used to forecast different types of energy efficiency problems in thermodynamic literature. The examined system consists of two main parts such as the ETHP system and the ORC system used for thermal energy production. With this system, it is aimed to evaluate energy and exergy analysis results by the ANN method in the case of integrating the ORC system to ETHP, which is one of the planar collectors suitable for the roofs of the buildings. Within the scope of this study, the exergy efficiency was evaluated on the developed ANN algorithm. The effect rates of parameters such as pressure, temperature and ambient temperature affecting the exergy efficiency of ORC integrated ETHP were calculated. Ambient temperature was found to be the most influential parameter on exergy efficiency. The exergy efficiency of the whole system has been calculated as ~23.39%. The most suitable BPNN architecture for this case study is recurrent networks with dampened feedback (Jordan–Elman nets). The success rate of the developed BPNN model is 95.4%.

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.

scholarly journals Active and Reactive Powers Control of DFIG Based WECS Using PI Controller and Artificial Neural Network Based Controller

2020 ◽  
Vol 93 (1-4) ◽  
pp. 31-38
Author(s):  
Bilal Boudjellal ◽  
Tarak Benslimane
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wind Energy ◽  
Control Strategy ◽  
Energy System ◽  
Maximum Power ◽  
Point Tracking ◽  
Pi Controllers ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The purpose of this study is to improve the control performance of a Doubly Fed Induction Generator (DFIG) in a Wind Energy Conversion System (WECS) by using both of the conventional Proportional-Integral (PI) controllers and an Artificial Neural Network (ANN) based controllers. The rotor-side converter (RSC) voltages are controlled using a stator flux oriented control (FOC) to achieve an independent control of the active and reactive powers, exchanged between the stator of the DFIG and the power grid. Afterward, the PI controllers of the FOC are replaced with two ANN based controllers. A Maximum Power Point Tracking (MPPT) control strategy is necessary in order to extract the maximum power from the of wind energy system. A simulation model was carried out in MATLAB environment under different scenarios. The obtained results demonstrate the efficiency of the proposed ANN control strategy.

scholarly journals Energy and Exergy Analysis of Sensible Thermal Energy Storage—Hot Water Tank for a Large CHP Plant in Poland

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4842 ◽  
Author(s):  
Ryszard Zwierzchowski ◽  
Marcin Wołowicz
Keyword(s):  
Energy Storage ◽  
Ambient Temperature ◽  
Thermal Energy ◽  
Thermal Energy Storage ◽  
Exergy Analysis ◽  
Hot Water ◽  
Water Tank ◽  
Exergy Destruction ◽  
Energy And Exergy ◽  
Energy And Exergy Analysis

The paper contains a simplified energy and exergy analysis of pumps and pipelines system integrated with Thermal Energy Storage (TES). The analysis was performed for a combined heat and power plant (CHP) supplying heat to the District Heating System (DHS). The energy and exergy efficiency for the Block Part of the Siekierki CHP Plant in Warsaw was estimated. CHP Plant Siekierki is the largest CHP plant in Poland and the second largest in Europe. The energy and exergy analysis was executed for the three different values of ambient temperature. It is according to operation of the plant in different seasons: winter season (the lowest ambient temperature Tex = −20 °C, i.e., design point conditions), the intermediate season (average ambient temperature Tex = 1 °C), and summer (average ambient temperature Tex = 15 °C). The presented results of the analysis make it possible to identify the places of the greatest exergy destruction in the pumps and pipelines system with TES, and thus give the opportunity to take necessary improvement actions. Detailed results of the energy-exergy analysis show that both the energy consumption and the rate of exergy destruction in relation to the operation of the pumps and pipelines system of the CHP plant with TES for the tank charging and discharging processes are low.

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