A new approach using artificial neural networks for determination of the thermodynamic properties of fluid couples

This paper presents a new approach using artificial neural networks (ANN) to determine the thermodynamic properties of two alternative refrigerant/absorbent couples (LiCl–H2O and LiBr + LiNO3 + LiI + LiCl–H2O). These pairs can be used in absorption heat pump systems, and their main advantage is that they do not cause ozone depletion. In order to train the network, limited experimental measurements were used as training and test data. Two feedforward ANNs were trained, one for each pair, using the Levenberg–Marquardt algorithm. The training and validation were performed with good accuracy. The correlation coefficient obtained when unknown data were applied to the networks was 0.9997 and 0.9987 for the two pairs, respectively, which is very satisfactory. The present methodology proved to be much better than linear multiple regression analysis. Using the weights obtained from the trained network, a new formulation is presented for determination of the vapor pressures of the two refrigerant/absorbent couples. The use of this new formulation, which can be employed with any programming language or spreadsheet program for estimation of the vapor pressures of fluid couples, as described in this paper, may make the use of dedicated ANN software unnecessary

A rare cause of glans penis masses in childhood: Fibroepithelial polyp

Fibroepithelial polyps of glans penis are very rarely seen in childhood. A 6-month-old male admitted to our institution with a slowly enlarging glans penis mass on the ventral side of the glans penis. The mass was totally excised, and hystopathological diagnosis was a fibroepithelial polyp. All of the reported cases published previously, except one, are of adult age and all of them have been associated with the history of long-term condom catheter use. The presence of the case in childhood; however, suggests that the pathology might be congenital. This is the second pediatric case presented in the English literature

Soft computing in absorption cooling systems

Absorption cooling systems make sense in many applications for process water cooling. Instead of mechanically compressing a refrigerant gas, as in the conventional vapor compression process, absorption cooling uses a thermo-chemical process. Two different fluids are used, a refrigerant and an absorbent. Heat directly from natural gas combustion, solar energy, waste-heat source or indirectly from a boiler, drives the process. In recent years, soft computing (SC) methods have been widely utilized in the analysis of absorption cooling systems. Soft computing is becoming useful as an alternate approach to conventional techniques. Soft computing differs from conventional (hard) computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty, partial truth, and approximation. In this chapter, the research of applying soft computing methods for absorption cooling applications is presente

Thermoeconomic optimization of a LiBr absorption refrigeration system

Optimization of thermal systems is generally based on thermodynamic analysis. Thermoeconomic optimization technique combines thermodynamic analysis with economic constraints to obtain an optimum configuration of a thermal system. In this study, the thermoeconomic optimization technique is applied to a LiBr absorption refrigeration system. Various components of the system such as condenser, evaporator, generator, and absorber heat exchangers are optimized. Additionally, optimum heat exchanger areas with corresponding optimum operating temperatures are determined. A cost function is specified for the optimum conditions. Finally, an example for the optimum design of a 20 kW LiBr system is given

Thermodynamic analysis of absorption systems using artificial neural network

Thermodynamic analysis of absorption systems is a very complex process, mainly because of the limited experimental data and analytical functions required for calculating the thermodynamic properties of fluid pairs, which usually involves the solution of complex differential equations. In order to simplify this complex process, Artificial Neural Networks (ANNs) are used. In this study, ANNs are used as a new approach for the determination of the thermodynamic properties of LiBr–water and LiCl–water solutions which have been the most widely used in the absorption heat pump systems. Instead of complex differential equations and limited experimental data, faster and simpler solutions were obtained by using equations derived from the ANN model. It was found that the coefficient of multiple determination (R2-value) between the actual and ANN predicted data is equal to about 0.999 for the enthalpy of both LiBr–water and LiCl–water solutions. As seen from the results obtained, the calculated thermodynamic properties are obviously within acceptable limits. In addition, the coefficient of performance (COP) of absorption systems operating under different conditions with LiBr–water and LiCl–water solutions is calculated. The use of the derived equations, which can be employed with any programming language or spreadsheet program for the estimation of the enthalpy of the solutions, as described in this paper, may make the use of dedicated ANN software unnecessary

Exergy analysis of lithium bromide/water absorption systems

Exergy analysis of a single-effect lithium bromide/water absorption system for cooling and heating applications is presented in this paper. Exergy loss, enthalpy, entropy, temperature, mass flow rate and heat rate in each component of the system are evaluated. From the results obtained it can be concluded that the condenser and evaporator heat loads and exergy losses are less than those of the generator and absorber. This is due to the heat of mixing in the solution, which is not present in pure fluids. Furthermore, a simulation program is written and used for the determination of the coefficient of performance (COP) and exergetic efficiency of the absorption system under different operating conditions. The results show that the cooling and heating COP of the system increase slightly when increasing the heat source temperature. However, the exergetic efficiency of the system decreases when increasing the heat source temperature for both cooling and heating applications

Different methods for modeling absorption heat transformer powered by solar pond

Solar ponds are a type of solar collector used for storing solar energy at temperature below 90°C. Absorption heat transformers (AHTs) are devices used to increase the temperature of moderately warm fluid to a more useful temperature level. In this study, a theoretical modelling of an absorption heat transformer for the temperature range obtained from an experimental solar pond with dimensions 3.5 × 3.5 × 2 m is presented. The working fluid pair in the absorption heat transformer is aqueous ternary hydroxide fluid consisting of sodium, potassium and caesium hydroxides in the proportions 40:36:24 (NaOH:KOH:CsOH). Different methods such as linear regression (LR), pace regression (PR), sequential minimal optimization (SMO), M5 model tree, M5′ rules, decision table and back propagation neural network (BPNN) are used for modelling the absorption heat transformer. The best results were obtained by the back propagation neural network model. A new formulation based on the BPNN is presented to determine the flow ratio (FR) and the coefficient of performance (COP) of the absorption heat transformer. The BPNN procedure is more accurate and requires significantly less computation time than the other methods