Modeling of a mechanical cooling system with variable cooling capacity by using artificial neural network

Capacity modification in mechanical cooling systems can be performed by various methods. Changing condenser temperature also changes the capacity of the cooling system. In this study, a series of experiments were performed in order to determine the effects of changing cooling water flow rate (changing condenser temperature) in a mechanical heat pump experimental setup on the cooling capacity of the system. Power consumption, thermal efficiency, coefficient of performance (COP) of the system in various cooling capacities were estimated theoretically by using the data acquired from the experiments performed. Performance values obtained were used for training Artificial neural network (ANN) whose structure was designed for this operation. The Network, which has three layers as input, output, and hidden layer, has one input and four output cells. Six cells were used in hidden layers. Training was continued until the square error became (ε ⩽ 0.005) in this ANN, for which back propagation algorithm was used for training. Desired error value was achieved in ANN and, ANN was tested with both data used for training ANN and data not used. Resultant low relative error value of the test indicates the usability of ANNs in this area.     

侧风环境下湿式冷却塔性能参数变化规律的试验研究

外界侧风在很大程度上影响自然通风逆流湿式冷却塔的传热传质性能,根据相似理论,通过热态模型试验,以冷却数和刘易斯数为出发点,研究了外界侧风对冷却塔传热传质性能的影响.研究发现:冷却数和刘易斯数随着外界侧风的变化呈现先减小后增大的趋势,拐点风速值为0.4 m/s;当外界侧风风速小于0.7 ～0.8m/s时,侧风的存在严重恶...     

Daily means ambient temperature prediction using artificial neural network method: A case study of Turkey

The objective of this paper is to develop an artificial neural network (ANN) model which can be used to predict daily mean ambient temperatures in Denizli, south-western Turkey. In order to train the model, temperature values, measured by The Turkish State Meteorological Service over three years (2003–2005) were used as training data and the values of 2006 were used as testing data.In order to determine the optimal network architecture, various network architectures were designed; different training algorithms were used; the number of neuron and hidden layer and transfer functions in the hidden layer/output layer were changed. The predictions were performed by taking different number of hidden layer neurons between 3 and 30. The best result was obtained when the number of the neurons is 6. The selected ANN model of a multi-layer consists of 3 inputs, 6 hidden neurons and 1 output. Training of the network was performed by using Levenberg–Marquardt (LM) feed-forward backpropagation algorithms. A computer program was performed under Matlab 6.5 software. For each network, fraction of variance (R²) and root-mean squared error (RMSE) values were calculated and compared. The results show that the ANN approach is a reliable model for ambient temperature prediction.     

Modelling of a direct evaporative air cooler using artificial neural network

This paper proposes the use of artificial neural networks (ANNs) to predict various performance parameters of a direct evaporative air cooler. For this aim, an experimental evaporative cooler was operated at steady‐state conditions, while varying the dry bulb temperature and relative humidity of the entering air along with the flow rates of air and water streams. Using some of the experimental data for training, a three‐layer feed‐forward ANN model based on back propagation algorithm was developed. This model was used for predicting various performance parameters of the cooler, namely the dry bulb temperature and relative humidity of the leaving air, mass flow rate of the water evaporated into the air stream, sensible cooling rate, and effectiveness of the cooler. Then, the performance of the ANN predictions was tested by applying a set of new experimental data. The predictions usually agreed well with the experimental values with correlation coefficients in the range of 0.969–0.993, mean relative errors in the range of 0.66–4.04%, and very low root mean square errors. This study reveals that, as an alternative to classical modelling techniques, the ANN approach can be used successfully for predicting the performance of direct evaporative air coolers. Copyright © 2007 John Wiley & Sons, Ltd.     

B-P网络隐含层对水质评价结果的影响分析

运用人工神经网络理论和方法，建立了水质评价的B－P网络模型；重点探讨了隐含层节点数的确定方法。通过对某市不同水域水质评价及多个实例的检验分析表明，隐含层节点数在一定范围内取值对水质评价结果无影响。     

Prediction of thermal conductivity of ethylene glycol–water solutions by using artificial neural networks

The objective of this study is to develop an artificial neural network (ANN) model to predict the thermal conductivity of ethylene glycol–water solutions based on experimentally measured variables. The thermal conductivity of solutions at different concentrations and various temperatures was measured using the cylindrical cell method that physical properties of the solution are being determined fills the annular space between two concentric cylinders. During the experiment, heat flows in the radial direction outwards through the test liquid filled in the annual gap to cooling water. In the steady state, conduction inside the cell was described by the Fourier equation in cylindrical coordinates, with boundary conditions corresponding to heat transfer between the solution and cooling water. The performance of ANN was evaluated by a regression analysis between the predicted and the experimental values. The ANN predictions yield R² in the range of 0.9999 and MAPE in the range of 0.7984% for the test data set. The regression analysis indicated that the ANN model can successfully be used for the prediction of the thermal conductivity of ethylene glycol–water solutions with a high degree of accuracy.     

Artificial neural networks for automotive air-conditioning systems performance prediction

In this study, ANN model for a standard air-conditioning system for a passenger car was developed to predict the cooling capacity, compressor power input and the coefficient of performance (COP) of the automotive air-conditioning (AAC) system. This paper describes the development of an experimental rig for generating the required data. The experimental rig was operated at steady-state conditions while varying the compressor speed, air temperature at evaporator inlet, air temperature at condenser inlet and air velocity at evaporator inlet. Using these data, the network using Lavenberg–Marquardt (LM) variant was optimized for 4–3–3 (neurons in input–hidden–output layers) configuration. The developed ANN model for the AAC system shows good performance with an error index in the range of 0.65–1.65%, mean square error (MSE) between 1.09 × 10^?5 and 9.05 × 10^?5 and the root mean square error (RMSE) in the range of 0.33–0.95%. Moreover, the correlation which relates the predicted outputs of the ANN model to the experimental results has a high coefficient in predicting the AAC system performance.     

Optimum operating conditions for a water purification process integrated to a heat transformer with energy recycling using neural network inverse

Artificial neural network inverse (ANNi) is applied to calculate the optimal operating conditions on the coefficient of performance (COP) for a water purification process integrated to an absorption heat transformer with energy recycling. An artificial neural network (ANN) model is developed to predict the COP which was increased with energy recycling. This ANN model takes into account the input and output temperatures for each one of the four components (absorber, generator, evaporator, and condenser), as well as two pressures and LiBr + H₂O concentrations. For the network, a feedforward with one hidden layer, a Levenberg–Marquardt learning algorithm, a hyperbolic tangent sigmoid transfer function and a linear transfer function were used. The best fitting training data set was obtained with three neurons in the hidden layer. On the validation data set, simulations and experimental data test were in good agreement (R > 0.99). This ANN model can be used to predict the COP when the input variables (operating conditions) are well known. However, to control the COP in the system, we developed a strategy to estimate the optimal input variables when a COP is required from ANNi. An optimization method (the Nelder–Mead simplex method) is used to fit the unknown input variable resulted from the ANNi. This methodology can be applied to control on-line the performance of the system.     

Evaporative cooling of water in a mechanical draft cooling tower

A new mathematical model of a mechanical draft cooling tower performance has been developed. The model represents a boundary-value problem for a system of ordinary differential equations, describing a change in the droplets velocity, its radii and temperature, and also a change in the temperature and density of the water vapor in a mist air in a cooling tower. The model describes available experimental data with an accuracy of about 3%. For the first time, our mathematical model takes into account the radii distribution function of water droplets.Simulation based on our model allows one to calculate contributions of various physical parameters on the processes of heat and mass transfer between water droplets and damp air, to take into account the cooling tower design parameters and the influence of atmospheric conditions on the thermal efficiency of the tower. The explanation of the influence of atmospheric pressure on the cooling tower performance has been obtained for the first time.It was shown that the average cube of the droplet radius practically determines thermal efficiency. The relative accuracy of well-defined monodisperse approximation is about several percent of heat efficiency of the cooling tower. A mathematical model of a control system of the mechanical draft cooling tower is suggested and numerically investigated. This control system permits one to optimize the performance of the mechanical draft cooling tower under changing atmospheric conditions.     

火电厂自然通风冷却塔性能研究

冷却塔是汽轮发电机组重要的冷端设备之一,其冷却性能对电站的经济和安全运行有重要的影响。以双曲线型自然通风冷却塔为研究对象,根据实际运行参数,通过对冷却塔热力性能的计算,得到了冷却水出塔水温及其主要影响因素——填料层淋水密度不均对出塔水温的影响。计算结果与实测值相符。同时,对冷却塔内气体流场建立热态模型并进行了空气动力性能的数值计算。冷却塔热力和空气动力性能的变化规律为冷却塔的运行、检修和改造提供了依据。     

Simplification of analytical models and incorporation with CFD for the performance predication of closed‐wet cooling towers

Simplified analytical models are developed for evaluating the thermal performance of closed‐wet cooling towers (CWCTs) for use with chilled ceilings in cooling of buildings. Two methods of simplification are used with regard to the temperature of spray water inside the tower. The results obtained from these models for a prototype cooling tower are very close to experimental measurements. The thermal performance of the cooling tower is evaluated under nominal conditions. The results show that the maximum difference in the calculated cooling water heat or air sensible heat between the two simplified methods and a general computational model is less than 3%. The analytical model distribution of the sensible heat along the tower is then incorporated with computational fluid dynamics (CFD) to assess the thermal performance of the tower. It is found that CFD results agree well with the analytical results when the air flow is simulated with air supply from the bottom of the tower, which represents a uniform air flow. CFD shows the importance of the uniform distribution of air and spray water to achieve optimum design. Copyright © 2002 John Wiley & Sons, Ltd.     

Assessing long-term performance of centralized thermal energy storage system

A validated computational fluid dynamics simulation tool is used to study the long-term performance of a centralized latent heat thermal energy storage system (LHTES). The LHTES system is integrated with a building mechanical ventilation system. Paraffin RT20 was used as a phase change material (PCM) and fins are used to enhance its performance.To reduce the computational time, artificial neural networks (ANN) was used to relate the relationships between the LHTES inputs and output parameters. Extensive CFD simulations were carried out to identify all the influential parameters for the development of ANN. They include phase change temperature range, air flow rate, the geometrical configuration of a LHTES system, fin size, and the unit's length. Further CFD simulations were carried out to provide sufficient data for proper training and testing of the ANN. The ANN model was used to predict the LHTES's outlet air-temperature. There was a good agreement between the ANN prediction and CFD model's prediction.The ANN model then was used to study the annual performance of a LHTES for application in Montreal. We found that the potential of use the centralized LHTES system to reduce the cooling load is high with a wider phase change temperature range. The centralized LHTES system contributes to reducing the cooling load from 21% to 36% when the length of the centralized LHTES system is increased from 500 to 650 mm at a flow rate of 1.5 m/s.     

基于预测的复合地源热泵系统控制方法实现

在冬冷夏热且夏季冷负荷远大于冬季热负荷的地区常采用带有冷却塔的复合式地源热泵系统,其控制策略存在极大的优化空间。文章提出了直接比较冷却塔和与土壤换热器相连的板式换热器的出口温度的控制方法,并通过人工神经网络预测板式换热器机组侧的出口水温来实现此控制方法。通过FLUENT软件建立复合式地源热泵系统动态数值模型,获取建立神经网络的数据,采用3层BP网络,建立了多个预测板式换热器机组侧出口温度的模型。研究结果表明,采用神经网络可以准确实现此预测,绝对误差不超过0.4℃。     

Heat transfer using a correlation by neural network for natural convection from vertical helical coil in oil and glycerol/water solution

A physical-empirical model is designed to describe heat transfer of helical coil in oil and glycerol/water solution. It includes an artificial neural network (ANN) model working with equations of continuity, momentum and energy in each flow. The discretized equations are coupled using an implicit step by step method. The natural convection heat transfer correlation based on ANN is developed and evaluated. This ANN considers Prandtl number, Rayleigh number, helical diameter and coils turns number as input parameters; and Nusselt number as output parameter. The best ANN model was obtained with four neurons in the hidden layer with good agreement (R > 0.98). Helical coil uses hot water for the inlet flow; heat transfer by conduction in the internal tube wall is also considered. The simulated outlet temperature is carried out and compared with the experimental database in steady-state. The numerical results for the simulations of the heat flux, for these 91 tests in steady-state, have a R ≥ 0.98 with regard to experimental results. One important outcome is that this ANN correlation is proposed to predict natural convection heat transfer coefficient from helical coil for both fluids: oil and glycerol/water solution, thus saving time and improving general system performance.     

Performance and exhaust emissions of a gasoline engine using artificial neural network

This study deals with artificial neural network (ANN) modelling of a gasoline engine to predict the brake specific fuel consumption, brake thermal efficiency, exhaust gas temperature and exhaust emissions of the engine. To acquire data for training and testing the proposed ANN, a four-cylinder, four-stroke test engine was fuelled with gasoline having various octane numbers (91, 93, 95 and 95.3), and operated at different engine speeds and torques. Using some of the experimental data for training, an ANN model based on standard back-propagation algorithm for the engine was developed. Then, the performance of the ANN predictions were measured by comparing the predictions with the experimental results which were not used in the training process. It was observed that the ANN model can predict the engine performance, exhaust emissions and exhaust gas temperature quite well with correlation coefficients in the range of 0.983–0.996, mean relative errors in the range of 1.41–6.66% and very low root mean square errors. This study shows that, as an alternative to classical modelling techniques, the ANN approach can be used to accurately predict the performance and emissions of internal combustion engines.     

Prediction of Heat Transfer Rates for Shell-and-Tube Heat Exchangers by Artificial Neural Networks Approach

This work used artificial neural network(ANN)to predict the heat transfer rates of shell-and-tube heatexchangers with segmental baffles or continuous helical baffles,based on limited experimental data.The BackPropagation (BP) algorithm was used in training the networks.Different network configurations were alsostudied.The deviation between the predicted results and experimental data was less than 2%.Comparison withcorrelation for prediction shows ANN superiority.It is recommended that ANN can be easily used to predict theperformances of thermal systems in engineering applications,especially to model heat exchangers for heattransfer analysis.     

Artificial neural networks approach on solar parabolic dish cooker

This paper presents heat transfer analysis of solar parabolic dish cooker using Artificial Neural Network (ANN). The objective of this study to envisage thermal performance parameters such as receiver plate and pot water temperatures of the solar parabolic dish cooker by using the ANN for experimental data. An experiment is conducted under two cases (1) cooker with plain receiver and (2) cooker with porous receiver. The Back Propagation (BP) algorithm is used to train and test networks and ANN predictions are compared with experimental results. Different network configurations are studied by the aid of searching a relatively better network for prediction. The results showed a good regression analysis with the correlation coefficients in the range of 0.9968–0.9992 and mean relative errors (MREs) in the range of 1.2586–4.0346% for the test data set. Thus ANN model can successfully be used for the prediction of the thermal performance parameters of parabolic dish cooker with reasonable degree of accuracy.     

Thermodynamic study of wet cooling tower performance

An analytical model was developed to describe thermodynamically the water evaporation process inside a counter‐flow wet cooling tower, where the air stream is in direct contact with the falling water, based on the implementation of the energy and mass balance between air and water stream describing thus, the rate of change of air temperature, humidity ratio, water temperature and evaporated water mass along tower height. The reliability of model predictions was ensured by comparisons made with pertinent experimental data, which were obtained from the literature. The paper elaborated the effect of atmospheric conditions, water mass flow rate and water inlet temperature on the variation of the thermodynamic properties of moist air inside the cooling tower and on its thermal performance characteristics. The analysis of the theoretical results revealed that the thermal performance of the cooling tower is sensitive to the degree of saturation of inlet air. Hence, the cooling capacity of the cooling tower increases with decreasing inlet air wet bulb temperature whereas the overall water temperature fall is curtailed with increasing water to air mass ratio. The change of inlet water temperature does not affect seriously the thermal behaviour of the cooling tower. Copyright © 2005 John Wiley & Sons, Ltd.     

Performance and exhaust emissions of a gasoline engine with ethanol blended gasoline fuels using artificial neural network

The purpose of this study is to experimentally analyse the performance and the pollutant emissions of a four-stroke SI engine operating on ethanol–gasoline blends of 0%, 5%, 10%, 15% and 20% with the aid of artificial neural network (ANN). The properties of bioethanol were measured based on American Society for Testing and Materials (ASTM) standards. The experimental results revealed that using ethanol–gasoline blended fuels increased the power and torque output of the engine marginally. For ethanol blends it was found that the brake specific fuel consumption (bsfc) was decreased while the brake thermal efficiency (η_b.th.) and the volumetric efficiency (η_v) were increased. The concentration of CO and HC emissions in the exhaust pipe were measured and found to be decreased when ethanol blends were introduced. This was due to the high oxygen percentage in the ethanol. In contrast, the concentration of CO₂ and NO_x was found to be increased when ethanol is introduced. An ANN model was developed to predict a correlation between brake power, torque, brake specific fuel consumption, brake thermal efficiency, volumetric efficiency and emission components using different gasoline–ethanol blends and speeds as inputs data. About 70% of the total experimental data were used for training purposes, while the 30% were used for testing. A standard Back-Propagation algorithm for the engine was used in this model. A multi layer perception network (MLP) was used for nonlinear mapping between the input and the output parameters. It was observed that the ANN model can predict engine performance and exhaust emissions with correlation coefficient (R) in the range of 0.97–1. Mean relative errors (MRE) values were in the range of 0.46–5.57%, while root mean square errors (RMSE) were found to be very low. This study demonstrates that ANN approach can be used to accurately predict the SI engine performance and emissions.     

基于焓差理论的带填料逆流闭塔热力性能分析

为了分析不同风量和喷淋水量对填料逆流闭式冷却塔热力性能的影响,建立和验证了带填料逆流闭式冷却塔热质交换的数学模型,基于焓差理论对模型计算的结果进行分析。结果表明：加入填料相当于对盘管区进口的喷淋水进行预冷,降低了喷淋水的平均温度,使带填料闭式冷却塔的冷却性能优于纯盘管闭式冷却塔;风量的增加可以提高带填料逆流闭式冷却塔和纯盘管逆流闭式冷却塔的热力性能,两种塔的冷却性能随风量增加的提升速率相同;喷淋水量的增加对纯盘管逆流闭式冷却塔的热力性能的影响较小,却可以较大幅度提高带填料逆流闭式冷却塔的热力性能。