Agricultural Engineering Research Institute AEnRI

Al Jīzah, Egypt

Agricultural Engineering Research Institute AEnRI

Al Jīzah, Egypt
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Mattar M.A.,King Saud University | Mattar M.A.,Agricultural Engineering Research Institute AEnRI | Alamoud A.I.,King Saud University
Computers and Electronics in Agriculture | Year: 2015

In this paper, we examine the discharge of labyrinth-channel emitters under different operating pressures (P) and water temperatures (T). An artificial neural network (ANN) and multiple linear regression (MLR) model are developed for the emitter flow variation (qvar) and the manufacturer's coefficient of variation (CV). As well as P and T, the structural parameters of the labyrinth emitter are considered as independent variables. The ANN results demonstrate that a feed-forward back-propagation network with five input neurons and 14 neurons in the hidden layer successfully model qvar and CV. The trapezoidal unit spacing and path length of the labyrinth emitter are found to be insignificant. In our ANN model, we use a hyperbolic tangent as the activation function in the hidden layer and the output layer. Statistical criteria indicate that the ANN is better at predicting the hydraulic performance of the labyrinth emitters than MLR. The root mean square errors for qvar and CV are 1.0497 and 0.0044, respectively, for the ANN model, and 2.0703 and 0.0107, respectively, for the MLR model using a test dataset. The relatively low errors obtained by the ANN approach lead to high model predictability and are feasible for modeling the hydraulic performance of labyrinth emitters. © 2015 Elsevier B.V.


Marey S.A.,King Saud University | Marey S.A.,Agricultural Engineering Research Institute AENRI
Bulgarian Journal of Agricultural Science | Year: 2015

Design parameters of the ridger furrow opener directly affecting the furrow profile characteristics and the amount of applied water. Furrow-bed irrigation technique is usually used for water conservation, efficient fertilizer use and many other benefits. This study is to evaluate the impact of design parameters of the ridger furrow opener and planting methods on sugar beet yield and water use efficiency. Therefore, field experiments are conducted to (i) investigate the effects of share rake angles (20°, 25° and 30°), opener wing angles (35° and 45°) and wing shape configurations (straight and curved) on the furrow profile characteristics, transverse scattering, draft force, and (ii) evaluate planting methods (i.e. ridges with 50 cm rows spacing and pair of rows on bed with 30, 35 and 40 cm rows spacing), the wing shape and angles on the emergence, sugar percentage, root and sugar yield, applied water and water use efficiency. The results showed that the curved shape and the wing angle of 45° produced wider furrows than those produced by the straight shape and 35° wing angle. Minimum transverse scattering is associated with the curved wing, wing angle of 35° and share rake angle of 20°. Increasing the share rake and wing angles increased the required draft force. The highest average values of root and sugar yields have been achieved at beet planting in beds with 30 cm rows spacing flowed by beds with 35 and 40 cm rows spacing, respectively. The lowest value of the water use efficiency is achieved at planting on ridges compared to the other planting methods. The maximum emergence percentage, root and sugar yields, sugar percentage and water use efficiency are associated with a wing angle of 45° and the curved wing shape. © 2015, National Centre for Agrarian Sciences. All rights reserved.


Mattar M.A.,King Saud University | Mattar M.A.,Agricultural Engineering Research Institute AEnRI | Alazba A.A.,King Saud University | Zin El-Abedin T.K.,King Saud University | Zin El-Abedin T.K.,Alexandria University
Agricultural Water Management | Year: 2015

An artificial neural network (ANN) was developed for estimating the infiltrated water volume (Z) under furrow irrigation. A feed-forward neural network using back-propagation training algorithm was developed for the prediction. Four variables were used as input parameters; inflow rate (Qo), furrow length (L), waterfront advance time at the end of the furrow (TL) and infiltration opportunity time (To). The Z was the one node in the output layer. The data used to develop the ANN model were taken from published experiments. The ANN model predicted Z over a wide range of the input variables with statistical analysis indicating that it can successfully predict Z with a high degree of accuracy. Performance evaluation criteria indicated that the ANN model was better than the two-point method using a volume balance model. Using testing and validation data sets to compare the ANN model with the two-point method shows that the two-point method had a mean coefficient of determination (R2) value that was about 3.6% less accurate than that from the ANN model. Also, the mean root mean square error (RMSE) value of 0.0135m3m-1 for the two-point method was almost double that of mean values for the ANN model. The relative errors of computed Z values for the ANN model were mostly around ±10%. Therefore, the ANN model is applicable to other soils and to different furrow irrigation hydraulics. © 2014 Elsevier B.V.


Yassin M.A.,King Saud University | Alazba A.A.,King Saud University | Mattar M.A.,King Saud University | Mattar M.A.,Agricultural Engineering Research Institute AEnRI
Computers and Electronics in Agriculture | Year: 2016

This study investigates the ability of gene expression programming (GEP) in modeling of the infiltrated water volume (Z) under furrow irrigation. Field data were collected in the literature study for modeling Z covering wide range of opportunity time. Five variables were used as input parameters; inflow rate (Qo), furrow length (L), waterfront advance time at the end of the furrow (TL), infiltration opportunity time (To) and cross-sectional area of the inflow (Ao). The following statistical parameters that coefficient of determination (R2), overall index of the model performance (OI), root mean square errors (RMSE) and mean absolute errors (MAE) are used as comparing criteria for the evaluation of the models' performances. The best value of the statistical parameters which range in training, testing and validation processes as the following (R2 = 95-97%; OI = 94-97%; RMSE = 0.013-0.009 m3 m-1; and MAE = 0.011-0.007 m3 m-1) implies that the GEP model provides an excellent fit for the measured data. A comparison is made between the estimates provided by the GEP and the two-point method. The comparison results reveal that the GEP models are superior to two-point method. Furthermore, the remarkable advantage of GEP was that it resulted in an explicit equation for the estimation of the Z under furrow irrigation. © 2016 Elsevier B.V.


Farag H.A.,Agricultural Engineering Research Institute AEnRI
Journal of Solid Waste Technology and Management | Year: 2010

Food waste and sawdust were used to produce compost using a composting bioreactor system. The moisture content and C:N ratio of the initial mixture were adjusted at 60% and 30:1, respectively. Three aeration rates and two mixing periods were used in this experiment. The moisture content, dry matter, pH, total Kjeldahl nitrogen, total carbon, bulk density, total phosphate and total potassium were measured on the initial mixture and at the end of composting process. The temperature changes and CO 2 rates were monitored and recorded in all the bioreactors. The results indicated that the maximum temperature ranged from 48 to 52 °C depending on the aeration rate and mixing speed. The maximum temperature that was higher than 50 °C was found only in bioreactors C 1 and C 2 and was maintained for three days. In all reactors the CO 2 emission increased and was proportional to the temperature and aeration rates. The relation between the temperatures, the emissions of carbon dioxide, the aeration rates and the mixing period in this study was found to be (T= 20 + 6.5 CO 2 +24A-0.01 M); T: the compost temperature (°C), A: the aeration rate (m 3/h), M: mixing period (h), CO 2: emission of carbon dioxide (%). An aeration rate of 0.15 m 3/h and mixing period of 12 h produced good quality compost in 18 days and saved 50% of the power consumed in the mixing operation.


Mashaly A.F.,King Saud University | Alazba A.A.,King Saud University | Al-Awaadh A.M.,King Saud University | Mattar M.A.,King Saud University | Mattar M.A.,Agricultural Engineering Research Institute AEnRI
Solar Energy | Year: 2015

A mathematical model to forecast the solar still performance under hyper arid conditions was developed using artificial neural network technique. The developed model expressed by different forms, water productivity (MD), operational recovery ratio (ORR) and thermal efficiency (ηth) requires ten input parameters. The input parameters included Julian day, ambient air temperature, relative humidity, wind speed, solar radiation, ultra violet index, temperature of the feed and brine water, and total dissolved solids of feed and brine water. The developed ANN model was trained, tested and validated based on measured data. The results showed that the coefficient of determination ranged from 0.991 to 0.99 and 0.94 to 0.98 for MD, ORR and ηth during training and testing process, respectively. The average values of root mean-square error for all water were 0.04L/m2/h, 2.60% and 3.41% for MD, ORR and ηth respectively. Findings revealed that the model was effective and accurate in predicting solar still performance with insignificant errors. © 2015 Elsevier Ltd.


Yassin M.A.,King Saud University | Alazba A.A.,King Saud University | Mattar M.A.,King Saud University | Mattar M.A.,Agricultural Engineering Research Institute AEnRI
Agricultural Water Management | Year: 2016

Artificial neural networks (ANNs) and gene expression programming (GEP) were compared to estimate daily reference evapotranspiration (ETref) under arid conditions. The daily climatic variables were collected by 13 meteorological stations from 1980 to 2010. The ANN and GEP models were trained on 65% of the climatic data and tested using the remaining 35%. The generalised Penman-Monteith (PMG) model was used as a reference target for evapotranspiration values, with hc varies from 5 to 105cm with increment of a centimetre. The developed models were spatially validated using climatic data from 1980 to 2010 taken from another six meteorological stations. The results showed that the eight ETref models developed using the ANN technique were slightly more accurate than those developed using the GEP technique. The ANN models' determination coefficients (R2) ranged from 67.6% to 99.8% and root mean square error (RMSE) values ranged from 0.20 to 2.95mmd-1. The GEP models' R2 values ranged from 64.4% to 95.5% and RMSE values ranged from 1.13 to 3.1mmd-1. Although the GEP models performed slightly worse than the ANN models, the GEP models used explicit equations. © 2015 Elsevier B.V.


Marey S.,King Saud University | Marey S.,Agricultural Engineering Research Institute AENRI | Shoughy M.,Agricultural Engineering Research Institute AENRI
International Journal of Food Engineering | Year: 2016

The effects of the drying temperature and the residual moisture content on the drying behavior, energy consumption and quality of dried citrus peels (CPs), which are value-added food ingredients, were studied. The CP samples were dried in a laboratory-scale hot-air dryer at air temperatures from 40 to 70°C under a constant air velocity of 1 m/s until the desired moisture content for safe storage was reached or until the final moisture level was achieved for the specific drying conditions. Cakes prepared from blends containing different proportions (0%, 10%, 15% and 20%) of dried CPs were also evaluated for chemical composition and sensory attributes. The optimal drying temperatures were 50-60°C, and the optimal moisture content was 10±0.2% w.b.; these conditions reduced the drying time and energy consumption and maximized the product quality. In contrast, over-drying CPs with the higher temperatures and to a final moisture level of 5.4±0.2% sharply increased the loss of vitamin C, carotenoids as antioxidants and essential oils. Incorporation of 15% dried orange and mandarin peels in cake formulas increased the dietary fiber by 33.5% and 29.6%, the crude fat by 2.9% and 4.6% and the ash by 30.6% and 29.0%, respectively, whereas the protein and total carbohydrate content decreased slightly. Highly acceptable nutritious cakes could be obtained by incorporating 15% orange or mandarin peel dried to 10% w.b. moisture content into the formulation. © 2016 by De Gruyter.


Saad A.,Agricultural Engineering Research Institute AEnRI | Ibrahim A.,Agricultural Engineering Research Institute AEnRI | El-Bialee N.,Agricultural Engineering Research Institute AEnRI
Agricultural Engineering International: CIGR Journal | Year: 2016

Nondestructive optical methods based on image analysis have been used for determining quality of tomato fruit. It is rapid and requires less sample preparation. A samples of fresh tomatoes were picked at different maturity stages, and determining chromaticity values (L*,a*,b*,a*/b*,h°and ΔE) by image analysis and colorimeter. Total soluble solids (TSS), were measured by refractometer, lycopene extracting and expressed as mg/kg fresh tomato (FW). Results indicated that, during ripening both L*, b*, h°, and ∆E tendency to decline, opposite tendency was determined with a*, a*/b* ratio, TSS and lycopene content. Chromaticity values have an important impact in internal quality parameters. Where, avg. of TSS, entire class and lycopene content had a positive linear correlation with a*/b* ratio. Contrary correlation was determined between avg. of TSS, entire class and both h° and ∆E. Meanwhile, h° and ∆E, had a negative logarithmic correlation with lycopene content. On the other hand, there were positive correlation between chromaticity values performed by image analysis technology and colorimeter. Where, on determining avg. of TSS, entire class, and lycopene content, correlations were linear with a*/b* ratio, and logarithmic with ∆E. Meanwhile, h° had alogarithmic correlation on determining avg. of TSS, entire class, and exponential correlation on determining lycopene content. © 2016, Int. Comm. of Agricultural and Biosystems Engineering. All rights reserved.


Al-Amoud A.I.,King Saud University | Mattar M.A.,King Saud University | Mattar M.A.,Agricultural Engineering Research Institute AEnRI | Ateia M.I.,King Saud University | Ateia M.I.,Agricultural Engineering Research Institute AEnRI
Spanish Journal of Agricultural Research | Year: 2014

The effects of water temperature and structural parameters of a labyrinth emitter on drip irrigation hydraulic performance were investigated. The inside structural parameters of the trapezoidal labyrinth emitter include path width (W) and length (L), trapezoidal unit numbers (N), height (H), and spacing (S). Laboratory experiments were conducted using five different types of labyrinth-channel emitters (three non-pressure compensating and two pressurecompensating emitters) commonly used for subsurface drip irrigation systems. The water temperature effect on the hydraulic characteristics at various operating pressures was recorded and a comparison was made to identify the most effective structural parameter on emitter performance. The pressure compensating emitter flow exponent (x) average was 0.014, while non-pressure compensating emitter's values average was 0.456, indicating that the sensitivity of nonpressure compensating emitters to pressure variation is an obvious characteristic (p < 0.001) of this type of emitters. The effects of water temperature on emitter flow rate were insignificant (p > 0.05) at various operating pressures, where the flow rate index values for emitters were around one. The effects of water temperature on manufacturer's coefficient of variation (CV) values for all emitters were insignificant (p > 0.05). The CV values of the non-pressure compensating emitters were lower than those of pressure compensating emitters. This is typical for most compensating models because they are manufactured with more elements than non-compensating emitters are. The results of regression analysis indicate that N and H are the essential factors (p < 0.001) to affect the hydraulic performance.

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