Technical feasibility assessment of a solar chimney for food drying

Solar dryers use free and renewable energy sources, reduce drying losses (as compared to sun drying) and show lower operational costs than the artificial drying, thus presenting an interesting alternative to conventional dryers. This work proposes to study the feasibility of a solar chimney to dry agricultural products. To assess the technical feasibility of this drying device, a prototype solar chimney, in which the air velocity, temperature and humidity parameters were monitored as a function of the solar incident radiation, was built. Drying tests of food, based on theoretical and experimental studies, assure the technical feasibility of solar chimneys used as solar dryers for agricultural products. The constructed chimney generates a hot airflow with a yearly average rise in temperature (compared to the ambient air temperature) of 13 ± 1 °C. In the prototype, the yearly average mass flow was found to be 1.40 ± 0.08 kg/s, which allowed a drying capacity of approximately 440 kg.     

Neural models of solar collectors for prediction of daily performance

This paper presents the application of an online identification neural technique to the prediction of the in-situ daily performance of solar collectors. First, it is shown that the use of the Laplace transform helps to find the order of an approximated model; the input of the studied system being the solar radiation. Then it is shown that an NNOE model can be more accurate using the right size of the regression vector; the learning database consisting of the data obtained during half a day. Finally, it is shown that a multiple inputs single output (MISO) NNOE model can be accurate; the inputs being the solar radiation and the thermal heat loss conductance that depends on the wind velocity. In any case the differential between the actual value of the daily energy and the value computed by a neural model is less than 0.5%.     

质子交换膜燃料电池膜电极组件温度分布的神经网络预测模型

质子交换膜燃料电池膜电极组件表面的温度分布会影响质子交换膜燃料电池的性能、寿命和可靠性.为探究质子交换膜燃料电池传热规律,本文提出了一种基于神经网络的质子交换膜燃料电池膜电极组件温度分布的预测模型.本研究选取径向基函数神经网络(RBF)和广义回归神经网络(GRNN)两种神经网络,以电流密度、温度点的位置作为网络输入,不同位置的温度作为网络输出,对平行流道质子交换膜燃料电池、蛇形流道质子交换膜燃料电池分别建立了神经网络预测模型.结果显示,RBF神经网络预测的均方根误差平均为0.464、平均绝对百分误差为1.179％,GRNN神经网络预测的均方根误差平均为0.7155、平均绝对百分误差为2.27％;相较于GRNN神经网络,RBF神经网络精度更高;基于RBF神经网络的平行流道质子交换膜燃料电池膜电极组件温度分布预测模型预测值与96％的实验值的相对误差在5％以内.基于RBF神经网络的蛇形流道质子交换膜燃料电池膜电极组件温度分布预测模型预测值与95％的实验值的相对误差在5％以内.     

Neural network approach to estimate 10-min solar global irradiation values on tilted planes

Calculation of solar global irradiation on tilted planes from only horizontal global one is particularly difficult when the time step is small. We used an Artificial Neural Network (ANN) to realize this conversion at a 10-min time step. The ANN is developed and optimized using five years of solar data and the accuracy of the optimal configuration is around 9% for the RMSE and around 5.5% for the RMAE i.e. similar or slightly lower than the errors obtained with empirical correlations available in the literature and used for the estimation of hourly data.     

Functional ceramic for solar drying

Powder with grain size about 1 μm has been prepared from a mullite alloy (2SiO₂ · 3Al₂O₃) containing 4.0 wt % of a ceramic, which converts the initial energy to pulsed infrared (IR) radiation. Photo-photon converters have been fabricated on the base of iron sheet of thickness 0.6 mm and the functional ceramic. It is shown that the use of the metal-ceramic photo-thermo-photon converter accelerates significantly the drying process and the energy transfer rate.     

A solar assisted heat pump drying system for grain in-store drying

For grain in-store drying, a solar assisted drying process has been developed, which consists of a set including a solar-assisted heat pump, a ventilation system, a grain stirrer, etc. In this way, low power consumption, short cycle time and water content uniformity can be achieved in comparison with the conventional method. A solar-assisted heat pump drying system has been designed and manufactured for a practical granary, and the energy consumption performance of the unit is analyzed. The analysis result shows that the solar fraction of the unit is higher than 20%, the coefficient of performance about system (COPS) is 5.19, and the specific moisture extraction rate (SMER) can reach 3.05 kg/kWh.     

A solar assisted heat pump drying system for grain in-store drying

For grain in-store drying, a solar assisted drying process has been developed, which consists of a set including a solar-assisted heat pump, a ventilation system, a grain stirrer, etc. In this way, low power consumption, short cycle time and water content uniformity can be achieved in comparison with the conventional method. A solar-assisted heat pump drying system has been designed and manufactured for a practical granary, and the energy consumption performance of the unit is analyzed. The analysis result shows that the solar fraction of the unit is higher than 20%, the coefficient of performance about system (COP_S) is 5.19, and the specific moisture extraction rate (SMER) can reach 3.05 kg/kWh.     

New approach for thermal testing of solar dryer: Development of generalized drying characteristic curve

It is necessary to develop thermal test procedure that can assist performance comparison between various solar dryer designs on common basis. In order to achieve this objective, the laboratory model of mixed-mode solar dryer is specially designed to perform indoor drying experiments with cylindrical potato samples. Sixteen drying kinetic curves are obtained over a wide range of various drying process variables. The variables investigated are absorbed thermal energy, air mass flow rate, food sample thickness and loading density. Based on experimental data of moisture content ratio-drying time, new concept to develop standard test method for performance evaluation of a given dryer is proposed. The proposed methodology facilitates to generate single generalized characteristic curve representing 16 drying kinetics and dimensionless parameter called dryer performance index (DPI) characterising the effectiveness of dryer system is identified. Different solar dryers with potato, banana and wheat are also tested in real climatic conditions to validate the proposed methodology. DPI with consistent values for all test conditions, indicating high degree of independence of operating conditions and food product characteristics, can therefore be used as a test parameter for performance comparison between different dryer designs on equitable basis.     

Neural network modelling of thermal stratification in a solar DHW storage

In this study an artificial neural network (ANN) model is introduced for modelling the layer temperatures in a storage tank of a solar thermal system. The model is based on the measured data of a domestic hot water system. The temperatures distribution in the storage tank divided in 8 equal parts in vertical direction were calculated every 5 min using the average 5 min data of solar radiation, ambient temperature, mass flow rate of collector loop, load and the temperature of the layers in previous time steps. The introduced ANN model consists of two parts describing the load periods and the periods between the loads. The identified model gives acceptable results inside the training interval as the average deviation was 0.22 °C during the training and 0.24 °C during the validation.     

Heat storage for wood drying using solar heat

储热技术有利于提高太阳能的利用率,降低传统木材干燥的能耗,对木材工业的可持续发展具有重要意义。本文系统地阐述了储热技术的基本原理,简要介绍了其应用概况;结合国内外研究现状,分析了储热技术在木材太阳能干燥中的应用,并指出了储热技术在木材太阳能干燥中的发展趋势。     

A review of solar dryers developed for grape drying

This paper attempts to review various solar dryers developed exclusively for grape drying on a normal scale. Many popular varieties of solar dryers, certain typical models as well as traditional methods practiced for drying grapes are presented in this paper. Technical and economical results have proved that solar drying of grapes is quite feasible. Commercialization of solar drying of grapes has not gained momentum as expected, may be due to high initial investment and low capacity of the dryers. Even, the farmer’s acceptance of solar dryers developed is not encouraging. Exhaustive research and development work has to be carried out in order to make solar drying of grapes economical and user friendly. There has been a remarkable achievement in solar drying of grapes due to sustained research and development associated with the adoption of advanced technologies. A review of various solar drying models for grapes is thus necessitated.     

Neural network applied to prediction of the failure stress for a pressurized cylinder containing defects

An application of the neural network to predict the failure stress of a cylinder with penetrating flaws under internal pressure is studied in this article. The results from the new method are compared with the experimental results. In addition, the neural network method is used to analyze the sensitivity of parameters of a pressurized cylinder with defects. Satisfactory results are obtained. It is shown that the neural network could be a potential tool in engineering practice.     

Forecasting based on neural network approach of solar potential in Turkey

As Turkey lies near the sunny belt between 36 and 42°N latitudes, most of the locations in Turkey receive abundant solar energy. Average annual temperature is 18–20 °C on the south coast, falls down to 14–16 °C on the west coast, and fluctuates 4–18 °C in the central parts. The yearly average solar radiation is 3.6 kW h/m² day, and the total yearly radiation period is 2610 h. The main focus of this study is put forward to solar energy potential in Turkey using artificial neural networks (ANNs). Scaled conjugate gradient (SCG), Pola-Ribiere conjugate gradient (CGP), and Levenberg–Marquardt (LM) learning algorithms and logistic sigmoid transfer function were used in the network. In order to train the neural network, meteorological data for last 4 years (2000–2003) from 12 cities (Çanakkale, Kars, Hakkari, Sakarya, Erzurum, Zonguldak, Balıkesir, Artvin, Çorum, Konya, Siirt, Tekirdağ) spread over Turkey were used as training (nine stations) and testing (three stations) data. Meteorological and geographical data (latitude, longitude, altitude, month, mean sunshine duration, and mean temperature) is used as input to the network. Solar radiation is the output. The maximum mean absolute percentage error was found to be less than 6.78% and R² values to be about 99.7768% for the testing stations. These values were found to be 5.283 and 99.897% for the training stations. The trained and tested ANN models show greater accuracy for evaluating solar resource posibilities in regions where a network of monitoring stations have not been established in Turkey. The predictions from ANN models could enable scientists to locate and design solar energy systems in Turkey and determine the best solar technology.     

Experimental and modelling performances of a roof-integrated solar drying system for drying herbs and spices

This paper presents experimental performance of solar drying of rosella flower and chili using roof-integrated solar dryer and also presents modelling of the roof-integrated solar dryer for drying of chili. Field-level tests for deep bed drying of rosella flower and chili demonstrated that drying in the roof-integrated solar dryer results in significant reduction in drying time compared to the traditional sun drying method and the dry product is a quality dry product compared to the quality products in the markets. The payback period of the roof-integrated solar dryer is about 5 years. To simulate the performance of the roof-integrated solar dryer for drying herbs and spices using hot air from roof-integrated solar collectors, two sets of equations were developed. The first set of equations was solved implicitly and the second set of equations was solved explicitly using finite difference technique. The simulated air temperatures at the collector outlet agreed well with the observed air temperatures. Good agreement was also found between experimental and simulated moisture contents.     

Double chamber solar and fuel drying unit for agricultural products

A service model of a combined double chamber (casing) solar and fuel vertical drying unit was developed based on theoretical and experimental investigations.     

Investigation of a new solar greenhouse drying system for peppers

Solar drying is the oldest preservation technique of agricultural products using several types of solar crop dryers based mostly on solar energy, which is abundant, renewable and sustainable. This study aimed to modeling a new solar greenhouse drying system (SGDS) for the drying of red peppers. The proposed mixed-mode (SGDS) consists of two main parts, namely a flat plate solar air collector and an experimental greenhouse. A mathematical model is developed using the TRNSYS simulation program to predict the change in the drying kinetics during the drying process under our proposed (SGDS). The experimental part consisted in testing the solar air collector to investigate its performance. The test showed that this solar air collector has a good performance; its efficiency varies between 0, 5 and 0, 65. The model was validated with the observed data and showed good agreement with experimental values. The influence of the area of the product to be dried, airflow rate and collector area, on moisture content changes, air temperature and humidity inside the greenhouse was studied. For the case study of this SGDS, the results obtained from simulation showed that the optimum values of area of the product to be dried, the exhaust airflow rate and the collector area were found to be 40 m², 250 kg/h and 2 m², respectively.     

Neural network approach to estimate stable crack growth in welded specimens

This paper describes an application of the hierarchical neural network to the generation phase stable crack growth analysis of two kinds of welded CT specimens using the GE/EPRI simplified method. One of the specimens was machined from a submerged-arc-welded plate of nuclear pressure vessel A533B Class 1 steel, the other from an electron-beam-welded plate of A533B Class 1 steel and high-strength HT80 steel. A ratio of mixture of material constants was introduced to apply the GE/EPRI method to the analysis of crack growth in the welded specimens. The best ratio of mixture was identified using the neural-network-based inverse analysis approach as follows. At first, a number of generation phase crack growth analyses based on the GE/EPRI method were tested by parametrically varying the ratio of mixture. The relationship between the ratio of mixture and the calculated crack growth behavior is called here ‘learning data sets’. The neural network was then ‘trained’ using the learning data sets. In the training process, the calculated crack growth behavior is applied to the input units of the network, while the ratio of mixture is applied to its output units in the form of teaching data. Finally, the best ratio of mixture was estimated by applying measured crack growth behavior to the input units of the ‘trained network’. The effects of material inhomogeneity on crack growth behavior in the welded specimens are discussed with respect to the best ratio of mixture obtained.