Performance prediction of a direct expansion solar assisted heat pump using artificial neural networks

This paper presents the suitability of artificial neural network (ANN) to predict the performance of a direct expansion solar assisted heat pump (DXSAHP). The experiments were performed under the meteorological conditions of Calicut city (latitude of 11.15 °N, longitude of 75.49 °E) in India. The performance parameters such as power consumption, heating capacity, energy performance ratio and compressor discharge temperature of a DXSAHP obtained from the experimentation at different solar intensities and ambient temperatures are used as training data for the network. The back propagation learning algorithm with three different variants (such as, Lavenberg–Marguardt (LM), scaled conjugate gradient (SCG) and Pola-Ribiere conjugate gradient (CGP)) and logistic sigmoid transfer function were used in the network. The results showed that LM with 10 neurons in the hidden layer is the most suitable algorithm with maximum correlation coefficients (R²) of 0.999, minimum root mean square (RMS) value and low coefficient of variance (COV). The reported results conformed that the use of ANN for performance prediction of DXSAHP is acceptable.     

Performance analysis of a hybrid photovoltaic thermal double pass air collector using ANN

This paper presents the use of artificial neural network for performance analysis of a semi transparent hybrid photovoltaic thermal double pass air collector for four weather conditions (a, b, c and d type) of New Delhi. The MATLAB 7.1 neural networks toolbox has been used for defining and training of ANN for calculations of thermal energy, electrical energy, overall thermal energy and overall exergy. The ANN model uses ambient air temperature, global solar radiation, diffuse radiation and number of clear days as input parameters for four weather conditions. The transfer function, neural network configuration and learning parameters have been selected based on highest convergence during training and testing of network. About 2000 sets of data from four weather stations (Bangalore, Mumbai, Srinagar, and Jodhpur) have been given as input for training and data of the fifth weather station (New Delhi) has been used for testing purpose. It has been observed that the best transfer function for a given configuration is logsig. The feedforward back-propagation algorithm has been used in this analysis. Further the results of ANN model have been compared with analytical values on the basis of root mean square error.     

Neural network approach for food temperature prediction during solar drying

In the present study, the application of artificial neural network (ANN) for prediction of temperature variation of food product during solar drying is investigated. The important climatic variables namely, solar radiation intensity and ambient air temperature are considered as the input parameters for ANN modeling. Experimental data on potato cylinders and slices obtained with mixed mode solar dryer for 9 typical days of different months of the year were used for training and testing the neural network. A methodology is proposed for development of optimal neural network. Results of analysis reveal that the network with 4 neurons and logsig transfer function and trainrp back propagation algorithm is the most appropriate approach for both potato cylinders and slices based on minimum measures of error. In order to test the worthiness of ANN model for prediction of food temperature variation, the analytical heat diffusion model with appropriate boundary conditions and statistical model are also proposed. Based on error analysis results, the prediction capability of ANN model is found to be the best of all the prediction models investigated, irrespective of food sample geometry.     

Assessment of diffuse solar energy under general sky condition using artificial neural network

In this paper, artificial neural network (ANN) models are developed for estimating monthly mean hourly and daily diffuse solar radiation. Solar radiation data from 10 Indian stations, having different climatic conditions, all over India have been used for training and testing the ANN model. The coefficient of determination (R²) for all the stations are higher than 0.85, indicating strong correlation between diffuse solar radiation and selected input parameters. The feedforward back-propagation algorithm is used in this analysis. Results of ANN models have been compared with the measured data on the basis of percentage root-mean-square error (RMSE) and mean bias error (MBE). It is found that maximum value of RMSE in ANN model is 8.8% (Vishakhapatnam, September) in the prediction of hourly diffuse solar radiation. However, for other stations same error is less than 5.1%. The computation of monthly mean daily diffuse solar radiation is also carried out and the results so obtained have been compared with those of other empirical models. The ANN model shows the maximum RMSE of 4.5% for daily diffuse radiation, while for other empirical models the same error is 37.4%. This shows that ANN model is more accurate and versatile as compared to other models to predict hourly and daily diffuse solar radiation.     

Optimal COP prediction of a solar intermittent refrigeration system for ice production by means of direct and inverse artificial neural networks

A direct and inverse artificial neural network (ANN and ANNi) approach were developed to predict the required coefficient of performance (COP) of a solar intermittent refrigeration system for ice production under various experimental conditions. Ammonia/lithium nitrate was used as a working fluid considering different solution concentrations. The configuration 6-6-1 (6 inputs, 6 hidden and 1 output neurons) presented an excellent agreement (R > 0.986) between experimental and simulated values. The used inputs parameters were: the solution concentration, the cooling water temperature, the generation temperature, the ambient temperature, the generation pressure and the solar radiation. The sensitivity analysis showed that all studied input variables have effect on the COP prediction but the generation pressure is the most influential parameter on the COP, while the rest of input parameters were less significant. COP performance was also determined by inverting ANN to calculate the unknown input parameter from a required COP. Because of the high accuracy and short computing time makes this methodology useful to simulate and to optimize the solar refrigerator system.     

An exergy based test protocol for truncated pyramid type solar box cooker

Developing a test standard/protocol for solar box type cookers has drawn a considerable interest among the researchers throughout the world. Recent publications on solar cookers emphasize the need of introducing the thermal performance indicators determined through exergy analysis. In the present paper, the time variation of instantaneous exergy output and energy output as function of its temperature and also of the instantaneous ambient temperature have been reported for truncated pyramid type solar box cooker and compared with those of box type cooker. Further, variations in the exergy lost with temperature difference have been depicted for the selected water temperature range from 60 °C to 95 °C. Based on this study, quality factor, exergy temperature difference gap product, and heat loss coefficient are determined and are proposed as benchmark parameters of solar cooker’s thermal performance.     

Optimum exergy efficiency of single-effect ideal passive solar stills

There is a complex heat and mass transfer phenomenon in the solar stills. It is desired to examine the ways of maximizing the efficiency with the help of an effective thermodynamic tool, i.e., energy and exergy analysis. In this paper, a thermodynamic model has been developed to estimate the overall instantaneous exergy efficiency of the single-effect horizontal basin-type ideal passive solar stills. Theoretical overall instantaneous exergy efficiency of a passive solar still having 30° tilt angle of glass cover and water depth of 0.04 m on a typical day in June is evaluated and found in the range 0.06 to 5.9 % for the variation of experimental results of energy efficiency from 8 to 87.2 %. The daily energy and exergy efficiency of the solar still is 20.7 and 1.31 %, respectively. An optimum exergy efficiency of the ideal solar still is found to be 21.11 % corresponding to 80 % ultimate energy efficiency and at a typical operating condition. A feasible target of optimum exergy efficiency has been set under assumed ideal conditions to achieve in the future for the real working passive solar stills. It is also confirmed that the overall exergy efficiency increases with the increase of water temperature and decreases with the increase of ambient temperature.     

Multi-objective optimization of an experimental integrated thermochemical cycle of hydrogen production with an artificial neural network

In this study, an experimental lab-scale copper-chlorine (Cu–Cl) cycle of hydrogen production is examined and optimized in terms of exergy efficiency and operational costs of produced hydrogen. The integrated process is modeled and simulated in Aspen Plus incorporating the reaction kinetic parameters with a sensitivity analysis of a range of operating conditions. An artificial neural network (ANN) method with machine learning is used to generate a mathematical function that is optimized based on a multi-objective genetic algorithm (MOGA) method. A sensitivity analysis of variations of each design parameter for both the objective functions and the effectiveness of exergy performance relative to operational costs of produced hydrogen is demonstrated. The sensitivity analysis and optimization results are presented and discussed.     

Exergetic optimization of flat plate solar collectors

In this paper, an exergetic optimization of flat plate solar collectors is developed to determine the optimal performance and design parameters of these solar to thermal energy conversion systems. A detailed energy and exergy analysis is carried out for evaluating the thermal and optical performance, exergy flows and losses as well as exergetic efficiency for a typical flat plate solar collector under given operating conditions. In this analysis, the following geometric and operating parameters are considered as variables: the absorber plate area, dimensions of solar collector, pipes' diameter, mass flow rate, fluid inlet, outlet temperature, the overall loss coefficient, etc. A simulation program is developed for the thermal and exergetic calculations. The results of this computational program are in good agreement with the experimental measurements noted in the previous literature. Finally, the exergetic optimization has been carried out under given design and operating conditions and the optimum values of the mass flow rate, the absorber plate area and the maximum exergy efficiency have been found. Thus, more accurate results and beneficial applications of the exergy method in the design of solar collectors have been obtained.     

小型太阳能热泵地板供暖系统的优化研究

建立了太阳能热泵地板供暖系统的能量分析、可用能分析数学模型,模拟了上海供暖期的气候条件,给出了系统各部件的可用能损失情况。着重从太阳能集热器并联的组数出发对系统进行了优化研究,并给出了系统供暖性能系数和可用能效率,为该系统的设计及应用提供参考。     

Evaluation of exergy and energy efficiencies of photothermal solar radiation conversion

In this paper, a new thermodynamic model for photothermal solar radiation conversion into mechanical through a heat engines is proposed. The developed equations allow for the energy and exergy contents of solar radiation to be found, as well as the energy and exergy efficiencies corresponding to concentration type solar-thermal heat engines operating under a range of conditions. The calculation method remains accurate to other published models when their assumed conditions are imposed to the newly developed model. The heat flux absorbed by the receiver (which is assumed to be a grey body and is placed in the focal point of the solar concentrator) depends on the hemispherical absorptivity and emissivity, concentration ratio and receiver temperature. The model is used to conduct a parametric study regarding the energy and exergy efficiencies of the system for assessing its performance. The use of a selective grey body receiver (having a reduced emissivity and a high absorptivity) for enhancing the conversion efficiency is also studied. If the absorptivity approaches one and the emissivity is low enough the photothermal conversion efficiency becomes superior to the known black body receiver limit of 0.853. It is found that in the limit of receiver emissivity tending to zero and absorptivity lending to one, the present model gives the exergy content of solar radiation because the work generated reaches its maximum. In this situation the energy efficiency approaches the exergy efficiency at 1-ITTIN0/TINS where T_S and T₀ are the sun and ambient temperatures, respectively. The influence of the ambient temperature on the exergy and energy efficiencies becomes apparent, with effects of up to 15%, particularly for high absorptivity and low emissivity. The heat transfer conductances at sink and source of the heat engine have a considerable impact on the efficiency of solar energy conversion. The present model is developed in line with actual power system operations for better practical acceptance. In addition, some irreversibility parameters (absorptivity, emissivity, heat transfer conductivity, etc.) are studied and discussed to evaluate the possible photothermal solar radiation conversion systems and assess their energy and exergy efficiencies.     

Exergy analysis of a passive solar still

This paper presents a steady-state and transient theoretical exergy analysis of a solar still, focused on the exergy destruction in the components of the still: collector plate, brine and glass cover. The analytical approach states an energy balance for each component resulting in three coupled equations where three parameters—solar irradiance, ambient temperature and insulation thickness—are studied. The energy balances are solved to find temperatures of each component; these temperatures are used to compute energy and exergy flows. Results in the steady-state regime show that the irreversibilities produced in the collector account for the largest exergy destruction, up to 615 W/m² for a 935 W/m² solar exergy input, whereas irreversibility rates in the brine and in the glass cover can be neglected. For the same exergy input a collector, brine and solar still exergy efficiency of 12.9%, 6% and 5% are obtained, respectively. The most influential parameter is solar irradiance. During the transient regime, irreversibility rates and still temperatures find a maximum 6 h after dawn when solar irradiance has a maximum value. However, maximum exergy brine efficiency, close to 93%, is found once T_col<T_w (dusk) and the heat capacity of the brine plays an important role in the modeling of collector–brine interaction. Nocturnal distillation is characterized by very low irreversibility rates due to reduced temperature difference between collector and an increase in exergy efficiency towards dawn due to ambient temperature decrease.     

Performance assessment of parabolic dish and parabolic trough solar thermal power plant using nanofluids and molten salts

The present study has been conducted using nanofluids and molten salts for energy and exergy analyses of two types of solar collectors incorporated with the steam power plant. Parabolic dish (PD) and parabolic trough (PT) solar collectors are used to harness solar energy using four different solar absorption fluids. The absorption fluids used are aluminum oxide (Al₂O₃) and ferric oxide (Fe₂O₃)‐based nanofluids and LiCl‐RbCl and NaNO₃‐KNO₃ molten salts. Parametric study is carried out to observe the effects of solar irradiation and ambient temperature on the parameters such as outlet temperature of the solar collector, heat rate produced, net power produced, energy efficiency, and exergy efficiency of the solar thermal power plant. The results obtained show that the outlet temperature of PD solar collector is higher in comparison to PT solar collector under identical operating conditions. The outlet temperature of PD and PT solar collectors is noticed to increase from 480.9 to 689.7 K and 468.9 to 624.7 K, respectively, with an increase in solar irradiation from\ 400 to 1000 W/m². The overall exergy efficiency of PD‐driven and PT‐driven solar thermal power plant varies between 20.33 to 23.25% and 19.29 to 23.09%, respectively, with rise in ambient temperature from 275 to 320 K. It is observed that the nanofluids have higher energetic and exergetic efficiencies in comparison to molten salts for the both operating parameters. The overall performance of PD solar collector is observed to be higher upon using nanofluids as the solar absorbers. Copyright © 2015 John Wiley & Sons, Ltd.     

Parametric optimization design for supercritical CO2 power cycle using genetic algorithm and artificial neural network

Supercritical CO₂ power cycle shows a high potential to recover low-grade waste heat due to its better temperature glide matching between heat source and working fluid in the heat recovery vapor generator (HRVG). Parametric analysis and exergy analysis are conducted to examine the effects of thermodynamic parameters on the cycle performance and exergy destruction in each component. The thermodynamic parameters of the supercritical CO₂ power cycle is optimized with exergy efficiency as an objective function by means of genetic algorithm (GA) under the given waste heat condition. An artificial neural network (ANN) with the multi-layer feed-forward network type and back-propagation training is used to achieve parametric optimization design rapidly. It is shown that the key thermodynamic parameters, such as turbine inlet pressure, turbine inlet temperature and environment temperature have significant effects on the performance of the supercritical CO₂ power cycle and exergy destruction in each component. It is also shown that the optimum thermodynamic parameters of supercritical CO₂ power cycle can be predicted with good accuracy using artificial neural network under variable waste heat conditions.     

Artificial neural networks used for the performance prediction of a thermosiphon solar water heater

Artificial Neural Networks (ANN) are widely accepted as a technology offering an alternative way to tackle complex and ill-defined problems. They can learn from examples, are fault tolerant, are able to deal with non-linear problems, and once trained can perform prediction at high speed. ANNs have been used in diverse applications and they have shown to be particularly effective in system modelling as well as for system identification. The objective of this work is to train an artificial neural network (ANN) to learn to predict the performance of a thermosiphon solar domestic water heating system. This performance is measured in terms of the useful energy extracted and of the stored water temperature rise. An ANN has been trained using performance data for four types of systems, all employing the same collector panel under varying weather conditions. In this way the network was trained to accept and handle a number of unusual cases. The data presented as input were, the storage tank heat loss coefficient (U-value), the type of system (open or closed), the storage volume, and a total of fifty-four readings from real experiments of total daily solar radiation, total daily diffuse radiation, ambient air temperature, and the water temperature in storage tank at the beginning of the day. The network output is the useful energy extracted from the system and the water temperature rise. The statistical coefficient of multiple determination (R²-value) obtained for the training data set was equal to 0.9914 and 0.9808 for the two output parameters respectively. Both values are satisfactory because the closer R²-value is to unity the better is the mapping. Unknown data for all four systems were subsequently used to investigate the accuracy of prediction. These include performance data for the systems considered for the training of the network at different weather conditions. Predictions with maximum deviations of 1 MJ and 2.2°C were obtained respectively. Random data were also used both with the performance equations obtained from the experimental measurements and with the artificial neural network to predict the above two parameters. The predicted values thus obtained were very comparable. These results indicate that the proposed method can successfully be used for the estimation of the performance of the particular thermosiphon system at any of the different types of configuration used here. The greatest advantage of the present model is the capacity of the network to learn from examples and thus gradually improve its performance. This is done by embedding experimental knowledge in the network.     

Technical performance analysis of a micro-combined cooling,heating and power system based on solar energy and high temperature PEMFC

A solar energy and high temperature proton exchange membrane fuel cell (PEMFC)-based micro-combined cooling, heating and power (CCHP) system (named system I) is proposed in this work. This system mainly consists of a PEMFC subsystem, an organic Rankine cycle (ORC) subsystem and a vapor compression cycle (VCC) subsystem. System I would reduce to a high temperature PEMFC-based CCHP system (named system II) if there was no solar energy. With the technical performance analysis models developed, the effects of the current density, operating temperature, solar radiation intensity and ambient temperature on the thermal, economic and environmental performances of the systems are theoretically analyzed. The results show that the current density and solar radiation intensity are the main impact factors that can significantly affect the thermal, economic and environmental performances, while the operating temperature and ambient temperature only have remarkable influences on the thermal performance. The coefficient of performance (COP) of system II is approximately 1.19 in summer and 1.42 in winter, which is always higher than that of system I under the same working conditions. The exergy efficiency of system I and system II are approximately 49.7% and 47.4%, respectively. The primary energy saving rates (PESRs) of system I and system II are 64.9% and 31.8% in summer, and 60.0% and 36.2% in winter, respectively. The payback periods of system I and system II are 9.6 yr and 6.0 yr without government subsidy, respectively. Compared with system II, the pollutant emission reduction rates (ERRs) of system I can be increased by approximately 8.4%–23.5% with the addition of solar energy, which indicates that the development and utilization of clean and renewable energy such as solar energy can significantly reduce pollutant emissions.     

Simulation model of ANN based maximum power point tracking controller for solar PV system

In this paper the simulation model of an artificial neural network (ANN) based maximum power point tracking controller has been developed. The controller consists of an ANN tracker and the optimal control unit. The ANN tracker estimates the voltages and currents corresponding to a maximum power delivered by solar PV (photovoltaic) array for variable cell temperature and solar radiation. The cell temperature is considered as a function of ambient air temperature, wind speed and solar radiation. The tracker is trained employing a set of 124 patterns using the back propagation algorithm. The mean square error of tracker output and target values is set to be of the order of 10⁻⁵ and the successful convergent of learning process takes 1281 epochs. The accuracy of the ANN tracker has been validated by employing different test data sets. The control unit uses the estimates of the ANN tracker to adjust the duty cycle of the chopper to optimum value needed for maximum power transfer to the specified load.     

Improvement in artificial neural network-based estimation of grid connected photovoltaic power output

This paper presents a method to improve the accuracy of artificial neural network (ANN)–based estimation of photovoltaic (PV) power output by introducing two more inputs, solar zenith angle and solar azimuth angle, in addition to the most widely used environmental information, plane-of-array irradiance and module temperature. Solar zenith angle and solar azimuth angle define the solar position in the sky; hence, the loss of modeling accuracy due to impacts of solar angle-of-incidence and solar spectrum is reduced or eliminated. The observed data from two sites where local climates are significantly different is used to train and test the proposed network. The good performance of the proposed network is verified by comparing with existing ANN model, algebraic model, and polynomial regression model which use environmental information only of plane-of-array irradiance and module temperature. Our results show that the proposed ANN model greatly improves the accuracy of estimation in the long term under various weather conditions. It is also demonstrated that the improvement in estimating outdoor PV power output by adding solar zenith angle and azimuth angle as inputs is useful for other data-driven methods like support vector machine regression and Gaussian process regression.     

Computation of beam solar radiation at normal incidence using artificial neural network

In this paper, an artificial neural network (ANN) model is developed for estimating beam solar radiation. Introducing a newly defined parameter, known as reference clearness index (RCI), computation of monthly mean daily beam solar radiation at normal incidence has been carried out. This RCI is defined as the ratio of measured beam solar radiation at normal incidence to the beam solar radiation as computed by Hottel's clear day model. Solar radiation data from 11 stations having different climatic conditions all over India have been used for training and testing the ANN. The feedforward back-propagation algorithm is used in this analysis. The results of ANN model have been compared with measured data on the basis of root mean square error (RMSE) and mean bias error (MBE). It is found that RMSE in the ANN model varies 1.65–2.79% for Indian region.     

Effect of irreversibilities on performance of an absorption heat transformer used to increase solar pond’s temperature

Absorption thermal systems are attractive for using waste heat energy from industrial processes and renewable energy such as geothermal energy, solar energy, etc. The Absorption Heat Transformer (AHT) is a promising system for recovering low-level waste heat. The thermal processes in the absorption system release a large amount of heat to the environment. This heat is evolved considerably at temperature, the ambient temperature results in a major irreversible loss in the absorption system components. Exergy analysis emphasises that both losses and irreversibility have an impact on system performance. Therefore, evaluating of the AHT in exergy basis is a much more suitable approach. In this study, a mathematical model of AHTs operating with the aqua/ammonia was developed to simulate the performance of these systems coupled to a solar pond in order to increase the temperature of the useful heat produced by solar ponds. A heat source at temperatures not higher than 100 °C was used to simulate the heat input to an AHT from a solar pond. In this paper, exergy analysis of the AHT were performed and effects of exergy losses of the system components on performance of the AHT used to increase solar pond’s temperature were investigated. The maximum upgrading of solar pond’s temperature by the AHT, is obtained at 51.5 °C and gross temperature lift at 93.5 °C with coefficients of performance of about 0.4. The maximum temperature of the useful heat produced by the AHT was ˜150 °C. As a result, determining of exergy losses for the system components show that the absorber and the generator need to be improved thermally. If the exergy losses are reduced, use of the AHT to increase the temperature of the heat used from solar ponds will be more feasable.