Volume 23
Issue 2
Agronomy
JOURNAL OF
POLISH
AGRICULTURAL
UNIVERSITIES
DOI:10.30825/5.ejpau.185.2020.23.2, EJPAU 23(2), #01.
Available Online: http://www.ejpau.media.pl/volume23/issue2/art01.html
ARTIFICIAL NEURAL NETWORK AND RESPONSE SURFACE METHODOLOGY APPROACHES FOR OPTIMIZATION OF PARAMETERS FOR AN AIRJET SUNFLOWER SEEDS REMOVER MACHINE
DOI:10.30825/5.EJPAU.185.2020.23.2
Amir Hossein Mirzabe^{1}, Gholam Reza Chegini^{2}, Jafar Massah^{2}, Ali Mansouri^{2}, Javad Khazaei^{2}
^{1} Department of Mechanical Engineering of Biosystems, College of Agriculture & Natural Resources, University of Tehran, Tehran, Iran
^{2} Department of Mechanical Engineering of Biosystems, College of Aboureihan, University of Tehran, Tehran, Iran
An impingement jet method was employed for extracting of sunflower seeds from sunflower heads (SHs). The method was based on holding SHs with a rotating plate and extracting the sunflower seeds with the help of pressurized airjets. Artificial neural networks (ANNs) and response surface methodology (RSM) were used to model the effects of operational parameters of impingement airjet on performance of preliminary model of the remover machine. The operational parameters were diameter of nozzle (ND), angle of impingement (AI), distance between nozzle outlet and sunflower head (DBNS), air pressure (A_{P}) and rotational velocity of sunflower head (RV). The final ANN model, 351, successfully modeled the relationship between three operational parameters, ND, AI and RV with removing performance of machine (RP_{AJSSR}) with R^{2}of 0.98 and T value of 0.96. The RSM method was applied for three different locations of SHs at the optimum AP of 7 bar. The maximum value of RP_{AJSSR}, (57%) was obtained for ND of 8 mm, AI of 30°, DBNS of 20 mm and RV of 10 rpm at side region of SH (SR_{SH}). Also, the minimum value (4.49%) belonged to ND of 4 mm, AI of 30°, DBNS of 20 mm and RV of 15 rpm for central region of SH (CR_{SH}).
Key words: Sunflower, Jet impingement, Neural networks, Response surface methodology.
Abbreviations  
AI  Angle of impingement [deg]  MS  Mean squares 
AJSSR  Airjet impingement sunflower seeds remover  MSE  Mean squares error 
ANN  Artificial neural network  ND  Nozzle diameter, mm 
AP  Air pressure [bar]  RE  Relative error 
CRSH  Central region of sunflower head  RSM  Response surface methodology 
D  Sunflower head diameter [mm]  RV  Rotational velocity of SH, rpm 
Da  Arithmetic mean diameter of SH [mm]  RPAJSSR  Removing performance of machine 
DBNS  Distance between nozzle and SH [mm]  RPM  Measured removing performance 
DF  Degree of freedom  RPP  Predicted removing performance 
Dg  Geometric mean diameter of SH [mm]  SH  Sunflower head 
EAP  Predicted extracted area  SRSH  Side region of sunflower head 
EAM  Measured extracted area  SPH  Sphericity of SH, % 
M  Mass of whole seeds of SH [g]  SS  Sum of squares 
MLP  Multilayer perceptron  STD  Standard deviation 
MRSH  Middle region of sunflower head  SV  Source of variations 
T  Thickness of sunflower head, mm 
INTRODUCTION
Sunflower (Helianthus annuus L.) oil is one of the most important seed crops product which can also be a valuable source of protein. In postharvest process of sunflowers, the seeds should be removed from sunflower head (SH). Nowadays, the removing of seeds from SHs is done using traditional manual or mechanized methods.
In traditional methods, SHs are rubbed to each other or over a rigid surface such as: brick, stone, a piece of metal, wood, rubber, etc. Performance and experience of the laborers are the principle factors affecting the efficiency of seeds’ removing [14]. The mechanisms of mechanical machines have been designed based on beating and friction theories. The mechanical machines are categorized into two types: combined harvesters and stationary thresher machines. Pedal operated, wire mesh type and peg tooth and/or raspbar drum threshers are the developed industrial threshing tools available in stationary which are combined harvester machines [27, 30].
The most important problems of these machines are high energy consumption and high amount of damaged seeds and losses. Furthermore, the mentioned tools usually are labor intensive and have high mechanical adjustments. Also, in some areas of the world (in many developing countries), these machines cannot be used due to traditional planting of sunflowers, low level of farmers’ knowhow of using the machines, and complex settings of combined harvester machines.
To overcome these problems, an airjet impingement method was developed. The air and waterjet impingement extraction techniques were successfully used to remove pomegranate fruits arils [16, 25, 26] and juice sacs [2, 15, 17, 23]. Literature reported that the airjet and waterjet impingement methods can sufficiently improve the performance of extraction and reduce the mechanical damage of fruits sacs and arils. When using an airjet impingement method to remove sunflower seeds from the head, each impingement jet parameter may vary in the degree of effect on machine performance. Each of these parameters must be considered while designing machines.
Optimization is an act, process, or methodology of making something as fully perfect, functional, or effective as possible. The aim of optimization of the removing seeds process is to find the specific conditions which the removing process would have the best performance efficiency. Artificial neural networks (ANNs) and response surface methodology (RSM) are the applicable and the two of the most popular methods to model complex nonlinear relationships between independent variables and responses of a system.
ANNs use different sophisticate mathematical functions to process and interpret various sets of erratic data [24]. Therefore, it is being used as an alternative method for the modeling of complex processes. ANNs have been used in various filed, function approximation, pattern recognition, noise reduction, generation of new meaningful pattern and multivariate data analysis [6, 18].
The response surface methodology (RSM) is the collection of mathematical functions that define the relationship between various process parameters and responses with the various desired criteria [11]. It is a sequential experimentation approach to improve efficiency of a process and design of products [5].
The aim of present study was to develop the optimized model of preliminarily airjet sunflower seed remover (AJSSR) parameters in order to design and construct an efficient version of AJSSR machine. ANNs and RSM methods were used to develop a model to optimize effects of nozzle diameter (ND), distance between nozzle and SH (DBNS), angle of impingement (AI), rotational velocity of SH (RV), and air pressure (AP) on AJSSR machine removing performance (RP_{AJSSR}). These are the fundamental steps towards the development of an automated intelligence version of AJSSR machine for industrial usage.
MATERIALS AND METHODS
1. Sample preparation
In the present study, the mature heads of Songhori variety were used. The Songhori variety is native of Iran and is planted in large amounts in many provinces of the country. The sample used in the current work was planted on May 22th, 2012 in local farms of Foodan plain, located on Shahreza, Isfahan province, Iran (longitude of 31.59° N, latitude of 51.50° E, average annual Precipitation 135 mm from 2000 to 2010, height above the sea level of 1845 m, average annual temperature of 14.7°C from 1993 to 2010). The SHs were harvested manually in late October, after they were completely matured.
Since the size of sunflower heads had a great effect on removing force of sunflower seeds [21], in order to avoid experimental errors, the diameter of each SH was measured in 4 directions by a ruler, then mean diameters were calculated. After that, only samples with diameters ranging between 200 and 230 mm were chosen for the tests.
In order to determine the effects of airjet optional parameters on percent of removed seeds from the different areas of SH, the surface of selected SHs were divided into three regions namely central region (CR_{SH}), middle region (MR_{SH}) and side region (SR_{SH}), as shown in Figure 1.
Fig. 1. Three regions of SH, Central region (CR_{SH}), Middle region (MR_{SH}) and Side region (SR_{SH}) 
In order to determine the performance of machine, the weight of removed seeds and residual for each SH were recorded. The weights of the seeds were measured for one rotation of SH with an accurate digital balance (Kern, EMB6002, 600 g ± 0.01 g, Philippines).
2. Physical properties of sunflower heads
The diameter of each SH was measured in 4 directions, then, the mean diameter (D) was calculated. Also, in order to measure the total mass of seeds (M), at the first, all seeds of sunflower heads were extracted manually. After that, the mass of the whole seeds of each SH was measured by an accurate digital balance (Kern, EMB6002, 600 g ± 0.01 g, Philippines).
Moreover, in order to calculate arithmetic mean diameter (Da), geometric mean diameter (Dg), and sphericity (Φ) of sunflower heads, the following equations were used [4, 19]:
3. Experimental setup
The airjet parameter levels were reported in Table 1. All parameters were set at three levels. A schematic diagram of the preliminarily model of airjet impingement sunflower seed remover machine is shown in Figure 2.
Table 1. The adjusted levels of AJSSR parameters 
Parameter  Unit  Levels  
Diameter of nozzle (ND)  mm  4  6 
Angle of impingement (AI)  degree  30  60 
Nozzle distance (DBNS)  mm  10  20 
rotational velocity (RV)  rpm or rev min1  10  15 
Air pressure (PA)  bar  5  6 
Fig. 2. The schematic view of preliminarily model of AJSSR machine. (1) frame, (2) mechanical jack, (3) gear box, (4) electromotor, (5) sloped plate, (6) holder disc, (7), two nozzles, (8) changing angle mechanisms, (9) changing horizontal position of nozzles, (10) valve, (11) pneumatic hoses [20] 
The main parts of the machine consisted of a frame, an electromotor, a worm gear box, a holder disc (Fig. 3a), variable angle of impingement mechanism (Fig. 3b), horizontal position of nozzle mechanism, distance between nozzle outlet and sunflower head (DBNS) change mechanism (mechanical jack), a switching valve, an inclined metallic plate, and transparent polycarbonate talcs.
B) 
Fig. 3. Schematics of mechanisms used to set the holding disk, A, and angle of impingement, B. [20] 
The power required for rotating the sunflower head was provided by electrical motor (Hummer, MS6324, Iran). In order to reduce the rotational velocity of the electro motor and change the rotational direction, a worm gear box (Taizhou Jiaoxing Transmission Equipment Co., Ltd, RV30, China) was used.
On order to hold sunflower heads during the tests, a holder disc was designed and constructed. The holder disc is capable of holding SHs with the diameters of 6 to 36 cm (Fig. 3a). The rotational velocity of the holder disc was adjusted with an inverter (LS industrial systems, SV 015ic51F, China) in the range of 10 to 20 rpm.
The air pressure was increased in piston compressor; the high pressure flow of the air was transferred to the nozzle using pneumatic hoses. To connect and disconnect the air flow, a switching valve was used. To avoid seeds’ throwing out from the machine space, the transparent polycarbonate talc was used. The seeds, after removing from the heads, fall down on inclined plate; then the sunflower seeds were transferred out of the machine.
4. Artificial neural network model development
Nowadays, there are lots of applications of new technologies and new methods in agricultural science. Signal processing techniques, image processing techniques, and computer vision are some of the most important new technologies [3]; neural networks, fuzzy logic, and response surface methodology are some of the most important new methods used for studying and optimizing parameters and processes.
Using the experimental data, multilayer perceptron (MLP) ANNs with one and two hidden layers were developed to model correlation between RPAJSSR and the three airjet operational parameters, namely: ND, AI and RV (Fig. 4). Preliminary trials showed that one hidden layer ANNs performed better than twohiddenlayer ones to learn and predict the input/output parameters correlation. There were a total of 108 patterns (3 levels of ND × 3 levels of AI × 3 levels of RV × 4 replication), each with 4 components, (x1, x2, x3, Y), three of which were the input parameters, while the Y was the single output parameter. The input data were divided into three parts, 60% for training process, 15% for validation phase and 25% for test phase. In order to acquire the same significance for all ranges of data during training, the inputs and outputs were normalized between 1 and 1 value using the MATLAB subroutine mapminmax. The best ANN model was selected on the basis of the lowest error on the training, validation and testing of developed ANNs. The performance of the ANNs was compared by mean square error (MSE), correlation coefficient, R^{2}, and T statistics [12]. ANNs were trained three times and the best values of parameters were recorded. A laptop computer (SONY VAIO VPCEG34F/W, Intel Core i5 processor, China) equipped with MATLAB R2013b software package was used to develop the ANN topologies.
Fig. 4. Topology of multilayer ANN for predicting the removing performance of AJSSR machine 
5. Response surface methodology (RSM)
The optimization by means of RSM approach was performed in six stages: (1) selection of independent variables and possible responses, (2) selection of the experimental design strategy, (3) execution of experiments and obtaining results, (4) fitting the model equation to experimental data, (5) obtaining response graphs and verification of the model, (6) determination of optimal conditions [28].
The mass flow rate of gas, weight ratio of removed seeds to all seeds for each SH and time of the one rotation of holding disk were considered as response of the tests; because, the optimum conditions for machine can be obtained when the weight ratio of removed seeds, mass flow rate of gas and time of the test are maximum, minimum and minimum, respectively. The mass flow rate of gas was calculated as reported by [13].
The experimental strategy was developed with BoxBehnken design method [8]. These designs are more efficient and economical than their corresponding factorial (3^{k}) designs, especially for large numbers of variables [7]. In BoxBehnken design, the number of experiments needed to be done are lower than number of factorial designs; it can be calculated according to following Equation [8–10]:
Where: x is the number of factors and y is the number of the central points; 2) all factor levels have to be adjusted only at three levels with equally spaced intervals between these levels.
In the factorial design, 3^{4} = 81 tests were carried out while response surface method was used and tests were carried out based on Eq. (1), 4^{2} + (2×4) + 5 = 29 (see Table 2).
Table 2. Designed tests based on response surface methodology for each region of sunflower head 
Test number  ND [mm]  DBNS [mm]  AI [deg] 
1  4  10  60 
2  8  10  60 
3  4  30  60 
4  8  30  60 
5  6  20  30 
6  6  20  90 
7  6  20  30 
8  6  20  90 
9  4  20  60 
10  8  20  60 
11  4  20  60 
12  8  20  60 
13  6  10  30 
14  6  30  30 
15  6  10  90 
16  6  30  90 
17  4  20  30 
18  8  20  30 
19  4  20  90 
20  8  20  90 
21  6  10  60 
22  6  30  60 
23  6  10  60 
24  6  30  60 
25  6  20  60 
26  6  20  60 
27  6  20  60 
28  6  20  60 
29  6  20  60 
A second order quadratic model (Eq. 1) was used to describe the effect of independent variables in terms of linear, quadratic and interactions. The result in an empirical model related to the response (ρ) is:
where: ρ is the predicted response (density), b_{0} is the interception coefficient, b_{i}, b_{ii}, and b_{ij} are the linear, quadratic, and interaction terms, θ is the random error and x_{i} is the independent variables studied [29]. Data were analyzed using the response surface regression procedure and fitted to the secondorder quadratic equation. The Design Expert 10 software package (StatEase, Inc., Minneapolis, MN) was used for regression and graphical analysis of the data. The significance of the statistical model was evaluated by the Ftest analysis of variation (ANOVA).
RESULTS
Effects of ND, AI, DBNS, and RV parameters on RPAJSSR were examined with application of ANN and RSM methods. To illustrate of removed areas of sunflower heads and performance of AJSSR machine, three random sunflower heads were chosen which are shown in Figure 5. It is clear from this figure, that the area of separated seeds in B (side region) is more than A (middle region). It is due to higher jet force effect at MR of SHs relative to SR (Fig. 5). Furthermore, increasing of ND leads to an increase in removed area of SHs (Fig. 5 B and C).
Fig. 5. Some removed regions of SHs due to impingement of airjet, A, middle region of SHs (ND = 6 mm), B, side region of SHs (ND = 6 mm), C, side region of SHs (ND = 8 mm) 
1. Physical properties of sunflower heads
Table 3 summarizes the values of some physical properties of sunflower heads that they were measured. The total mass of seeds (M), SH thickness (T) and SH diameter (D) ranged from 344 to 110 gr, 83 to 42 mm and 345 to 170 mm, respectively. The mean values of arithmetic mean diameter (D_{a}), geometric mean diameter (D_{g}), and sphericity (SPH) were equal to 165.61 mm, 140.65 mm and 62.90%, respectively.
Table 3. Some physical properties of sunflower head of Songhori variety 
Parameter  Maximum  Minimum  Mean ± STD  Skewness 
M [g]  344  110  192.83 ± 44.61  1.27 
T [mm]  83  42  57.84 ± 8.12  0.82 
D [mm]  345  170  223.73 ± 33.52  1.44 
Da [mm]  253.33  125.67  165.61 ± 24.94  1.36 
Dg [mm]  209.89  106.83  140.65 ± 20.64  1.24 
SPH [%]  67.87  58.17  62.90 ± 1.71  0.08 
M: total mass of the seeds; T: head thickness; D: head diameter; Da: arithmetic mean diameter; Dg: geometric mean diameter and SPH: sphericity 
2. Artificial neural network model
Various feedforward ANNs were trained and tested with back propagation algorithms for predicting the maximum RPAJSSR based on the three operational parameters of ND, AI and RV. The performance of different error minimization algorithms, transfer functions, nodes number and training iterations on ANN performance were investigated (Table 4). Among the different ANN topologies, the model with the best performance was produced by threelayer ANN structure, 351, onestep secant (oss) error minimization algorithm and logsigmoid activation function. This model produced the smallest MSE in training, 0.0018, validation, 0.0023 and testing, 0.0035. The optimal ANN structure and its parameters are presented in Table 5. The range of ANN parameters tried was: number of neurons (from 1 to 10), activation function (logsigmoid, hyperbolic tangent sigmoid, and linear), and number of epochs (100–1000). Pretests with other ANN topologies indicated that one hidden layer networks produced better results than twohiddenlayer one. The number of hidden nodes required for proper generalization was determined by trial and errors. The value of MSE in both training and test processes for each training ANN models is reported in Figure 6. Initially, the train and test subset errors decreased with increasing the number of neurons in hidden layer (Fig. 6). As the network loses its ability to generalize the test data, the errors increased (see after 5 neuron numbers in Fig. 6). Although the MSE on the test data may not follow a smooth path, the onset of a major increase in the error was considered to represent the optimal number of hidden neurons for that final network architecture [6]. Too few numbers of hidden neurons will hinder the learning process. In contrast, too many hidden neurons will depress prediction abilities through overtraining [1]. The final ANN architecture is obtained at the onset of the increase (5 neurons) in test data errors (Fig. 6).
Table 4. Variation of training and validation MSE for different configurations of the learning algorithms and transfer functions 
Transfer function  Learning algorithm  4 Neuron/ 100 epoch 
6 Neuron/ 400 epoch 
8 Neuron/ 700 epoch 
10 Neurons/ 1000 epoch 

Training  Validation  Training  Validation  Training  Validation  Training  
logsig  gda  0.0045  0.060  0.0038  0.062  0.0043  0.043  0.0097 
gdm  0.0041  0.051  0.0043  0.041  0.0051  0.062  0.0080  
lm  0.0034  0.029  0.0030  0.031  0.0069  0.046  0.0088  
oss  0.0025  0.014  0.0027  0.029  0.0040  0.068  0.0037  
tansig  gda  0.0043  0.040  0.0041  0.041  0.0077  0.045  0.0054 
gdm  0.0040  0.060  0.0042  0.030  0.0073  0.039  0.0048  
lm  0.0039  0.038  0.0032  0.042  0.0058  0.065  0.0042  
oss  0.0037  0.047  0.0037  0.034  0.0059  0.043  0.0065 
Table 5. The optimum values of the ANN model used to predict RPAJSSR 
Optimum  Transfer function 
Mean values  T value 

ANN structure  Algorithm  MSE train  MSE validation  MSE test  
351  oss*  logsig  0.0018  0.0023  0.0035  0.96 
* Onestep secant backpropagation algorithm 
Fig. 6. The performance of training and testing processes for various ANN models as a function of hidden neurons. 
Figure 7 shows the convergence of training, validation and testing errors as function of epochs for final ANN structure, 351. The errors on training, validation and testing subsets data generally decreased with increasing iteration numbers. An initial drop down in MSE values shows the satisfactory learning of input/output relationship by oss error minimization algorithm. Approximately after 60 epochs, MSE arrived a stable phase and no increasing due to memorization and overtraining of trained ANN is observed (Fig. 7). The errors on training, validation and testing sets were in the acceptable range (MSE ≤ 0.0035).
Fig. 7. Convergence of the MSE during training, validation and testing processes of the final ANN structure, 351. 
Figure 8 shows the 18 predicted values of RP_{AJSSR} versus the same set of measured data for final ANN with 1000 epochs.
Fig. 8. Correlation between the measured and predicted removing performance of AJSSR machine using the ANN mode 
It is clear that the network sufficiently learned the correlation between input and output variables. The linear adjustment between the predicted and measured values gave a slop close to 1 (RPP = 1.107×RPM – 9.292). The resulting MSE, correlation of determination and T values were 0.0035, 0.98 and 0.96 for linear regression between measured and predicted values, respectively (Table 5 and Fig. 8). T value represents the distribution of data around line (1:1) and the practically values equal to 1 are desirable [12]. The distribution pattern of training, validation and testing relative errors versus corresponding predicted values are illustrated in Figure 9. It is obvious that the residuals were well scattered on both side of the horizontal line (relative error = 0) without any systematic tendencies and clear pattern (RE = 0.0007×RP_{P}  0.0503, R^{2} = 0.049).
Fig. 9. Relative error distribution of the ANN model for the prediction of removing performance 
If the residual plots indicate a clear pattern, the model could not be accepted [12]. These results prove the sufficient predicting performance of final network for the whole range of data.
3. Response surface methodology
According to the pretest results, performance of machine at AP of 7 bar was better than other AP values, consequently the RSM optimization was performed to determine the interaction effects of ND, AI, DBNS, and RV parameters at AP of 7 bar. The interaction effects of ND, AI, DBNS, and RV on AJSSR machine performance for SR_{SH} are illustrated in Figure 10. Results showed that maximum and minimum values of RPAJSSR were equal to 57.66 and 23.38%, respectively. Table 6 indicates the results of the statistical analysis carried out to examine the effects of AJSSR machine parameters on percentage of the removed seeds for SR_{SH}. ANOVA indicated that ND, AI, DBNS and RV, had a significant effect on machine performance (P≤0.001). For all cases of SR_{SH}, no significant effects were observed for combination of AJSSR parameters.
Table 6. ANOVA results for percentage of the removed seeds from the SH on SR_{SH} 
SV  DF  SS  MS  Fvalue 
Model  14  2471.432  176.531  303.912** 
Diameter of nozzle (ND)  1  641.379  641.379  1104.187** 
Nozzle distance (DBNS)  1  54.955  54.955  94.610** 
Angle of impingement (AI)  1  1006.318  1006.318  1732.458** 
Rotational velocity (RV)  1  544.053  544.053  936.632** 
ND × DBNS  1  0.931  0.931  1.603 
ND × AI  1  1.891  1.891  3.255 
ND × RV  1  5.929  5.929  10.208 
DBNS × AI  1  1.199  1.199  2.064 
DBNS× RV  1  0.689  0.689  1.186 
AI × RV  1  13.432  13.432  23.125 
Residual  14  8.132  0.581  
Lack of Fit  10  8.113  0.811  169.018 s 
Rsquared  0.997  
Adjusted Rsquared  0.993  
Pred Rsquared  0.981  
s and ** show significant and significant difference at probability of % 1, respectively. Numbers have been rounded to three decimal places. 
Fig. 10. Interaction effects between operating parameters of airjet impingement on percentage of removed seeds for SR_{SH} 
The interaction effects between independent parameters of AJSSR machine on removing sunflower seeds for MR_{SH} are shown in Figure 11. The RSM results indicated that maximum and minimum percentages of the removed seeds were equal to 44.55 and 18.46%, respectively. Table 7 represents the results of the statistical analysis carried out to examine the effects of AJSSR parameters on percentage of the removed seeds for MR_{SH}. ANOVA indicated that all airjet operational parameters had significant effects on machine performance (P≤0.001). No significant effects were determined for combination of parameters in MR_{SH} (Table 7).
Table 7. Effects of AJSSR parameter percentage of the removed seeds for MR_{SH} 
SV  DF  SS  MS  Fvalue 
Model  14  1147.752  81.9823  149.267** 
Diameter of nozzle (ND)  1  551.173  551.173  1003.531** 
Nozzle distance (DBNS)  1  14.070  14.070  25.618* 
Angle of impingement (AI)  1  10.498  10.498  19.114 
Rotational velocity (RV)  1  206.579  206.579  376.121** 
ND × DBNS  1  0.343  0.343  0.625 
ND × AI  1  0.261  0.261  0.475 
ND × RV  1  5.394  5.394  9.821 
DBNS × AI  1  0.006  0.006  0.010 
DBNS× RV  1  0.166  0.166  0.302 
AI × RV  1  0.080  0.080  0.145 
Residual  14  7.689  0.549  
Lack of Fit  10  7.563  0.756  23.879 s 
Rsquared  0.993 


Adjusted Rsquared  0.987  
Pred Rsquared  0.962  
s, *, and ** show significant and significant difference at probability of 5% and 1%, respectively. Numbers have been rounded to three decimal places. 
Fig. 11. Interaction effects between operating parameters of airjet impingement on percentage of removed seeds for MR_{SH} 
The interaction effects of airjet impingement parameters on removing sunflower seeds for CR_{SH} are illustrated in Figure 12. The RSM results of CR_{SH} indicated that the maximum and minimum percentages of the removed seeds were equal to 9.24 and 4.49%, respectively. The statistical analyses of AJSSR parameters for MR_{SH} are given in Table 7. Results indicated that all parameters had significant effects on machine performance (P≤0.001). No significant effects were observed for combination of parameters in MR_{SH} (Table 7). Table 8 represents the results of the statistical analysis carried out to examine the effect of AJSSR parameters on percentage of the removed seeds for CR_{SH}. The ND and RV, parameters had significant effects on machine performance (P≤0.001). No significant effects were observed for combination of parameters in CR_{SH} (Table 8).
Table 8. ANOVA results for percentage of the removed seeds from the SH on SR_{SH} 
SV  DF  SS  MS  Fvalue 
Model  14  39.396  2.814  110.894** 
Diameter of nozzle (ND)  1  15.350  15.350  604.906** 
Nozzle distance (DBNS)  1  0.000  0.000  0.000 
Angle of impingement (AI)  1  0.002  0.002  0.089 
Rotational velocity (RV)  1  4.222  4.222  166.386** 
ND × DBNS  1  0.000  0.000  0.000 
ND × AI  1  0.000  0.000  0.000 
ND × RV  1  0.059  0.059  2.337 
DBNS × AI  1  0.000  0.000  0.003 
DBNS× RV  1  0.000  0.000  0.001 
AI × RV  1  0.000  0.000  0.001 
Residual  14  0.355  0.025  
Lack of Fit  10  0.351  0.035  35.851 s 
Rsquared  0.991  
Adjusted Rsquared  0.982  
Pred Rsquared  0.949  
s and ** show significant and significant difference at probability of % 1, respectively. Numbers have been rounded to three decimal places. 
Fig. 12. Interaction effects between operating parameters of airjet impingement on percentage of removed seeds for CR_{SH} 
DISCUSSIONS
In SR_{SH}, the maximum percentage of the removed seeds was obtained when the values of ND, AI, DBNS and RV were equal to 8 mm, 30°, 20 mm and 10 rpm respectively. In contrast, the minimum percentage of the removed seeds with AJSSR machine belonged to ND of 4 mm, AI of 90°, DBNS of 30 mm and RV of 20 rev min^{1}. According to RSM optimization results, the optimal condition of machine operation in SR_{SH} belonged to ND of 4.19 mm, AI of 30°, DBNS of 17.83 mm, and RV of 16.48 rev min^{1}. Additionally, results showed that in all cases, percentage of the removed seeds increased with increasing ND and decreasing AI and RV values.
In MR_{SH}, the maximum performance of AJSSR machine was provided by ND of 8 mm, AI of 60°, DBNS of 20 mm and RV of 10 rpm, respectively. Corresponding values for the minimum percentage of the removed seeds were equal to 4 mm, 90°, 30 mm and 20 rev min^{1}. Results indicated that in all cases, percentage of the removed seeds increased with increasing of ND and decreasing of RV. Consequently, the optimum condition of machine operation for SR_{SH} belonged to ND of 8 mm, AI of 62.42°, DBNS of 15.65 mm and RV of 17.59 rpm (Fig. 11).
In CR_{SH}, the maximum and minimum percent of the removed seeds belonged to ND of 8 mm and 4 mm, AI of 60° and 30°, DBNS of 20 mm and RV of 10 rpm and 15 rpm, respectively. Results indicated that in all cases, percentage of the removed seeds increased with increasing ND and decreasing RV. As well, the optimum condition of machine operation for CR_{SH} were obtained with ND of 8 mm, AI of 62.08°, DBNS of 18.74 mm and RV of 20 rev min^{1}.
Statistical Ftest was performed to determine the effects of three different regions of SHs (SR_{SH}, MR_{SH} and CR_{SH}) on performance of AJSSR machine. The quadratic equations were used to model the percentage of the removed seeds through its lower number of coefficients in compare with third, fourthdegree, etc. equations. The coefficients of developed quadratic model for SR_{SH}, MR_{SH} and CR_{SH} are presented in Table 9.
Table 9. The constant coefficients of the quadratic model to predict percentage of removed seeds for SR_{SH}, MR_{SH} and CR_{SH} 
SV  Side region  Middle region  Central region 
Nozzle diameter (ND)  3.55417  0.53213  0.62850 
Nozzle distance (DBNS)  1.66825  1.39584  0.33780 
Angle of impingement (AI)  0.56425  0.75973  0.20836 
Rotational velocity (RV)  0.22367  1.48828  0.01293 
ND × DBNS  0.02413  0.01465  0.00001 
ND × AI  0.01146  0.00426  0.00000 
ND × RV  0.12175  0.11613  0.01218 
DBNS × AI  0.00183  0.00013  0.00001 
DBNS× RV  0.00830  0.00408  0.00005 
AI × RV  0.01222  0.00094  0.00002 
ND 2  0.25813  0.51759  0.00999 
DBNS2  0.04929  0.03712  0.00848 
AI 2  0.00090  0.00652  0.00174 
RV 2  0.04305  0.05865  0.00115 
Constant coefficient  39.93125  17.99817  5.19375 
Numbers have been rounded to five decimal places. 
According to the experimental results, in the same operation condition, the highest performance belonged to the SR_{SH} and the least performance belonged to the CR_{SH}. This is due to more maturity of SR_{SH} seeds toward the CR_{SH} seeds. Physiological maturity of sunflower seeds starts from the SR_{SH} to the CR_{SH}, so, when the sunflower head is matured, there are immature seeds in central region which are still absorbing nutrition from the plant; therefore, in most cases, in the CR_{SH}, maturity has not occurred completely. Accordingly, a higher picking force for CR_{SH} seeds compared to the SR_{SH} and MR_{SH} is required [22].
With increasing ND and A_{P}, jet momentum and jet force increases; therefore, with increasing ND, percentage of removed seeds increases; the experimental results confirmed this expectation. Furthermore, with increasing DBNS, the covered area by jet increases, but the experimental results showed that percentages of the removed seeds had a decreasing and increasing phases from 10 to 30 mm. Furthermore, with increasing AI from 30° to 90°, the covered area by the jet decreases. This issue was confirmed by experimental results of SR_{SH} compared to MR_{SH} and CR_{SH}. Arrangement of the seeds on SH and distance between adjacent seeds are the principle factors for adjustment of AI parameter.
Results of [16] showed that the air pressure, nozzle diameter, the number of passes, and the route of the nozzle over the surface of halved pomegranate or citrus fruit significantly affected the percentage of removed arils of pomegranate, citrus juice and juice sacs. Their studies also indicated that with increasing reservoir pressure and nozzle diameter, the percentage of removed arils of pomegranate, citrus juice and juice sacs increased. Furthermore, [16] showed that air pressure and nozzle diameter significantly influenced the percentage of damaged pomegranate arils.
Effects of operational parameters of impingement airjet on damaged pomegranate arils and citrus sacs were investigated by [16] and [17], respectively. They cited that nozzle diameter and reservoir pressure had a significant influence on the percentage of damaged pomegranate arils and citrus sacs, while in the present work, in all tests, no seeds have been observed to have been damaged due to airjet impinging, and this is one of the most important advantages of the AJSSR machine.
CONCLUSIONS
Effects of different airjet impingement operational parameters including diameter of nozzle (ND), angle of impingement (AI), distance between nozzle outlet and sunflower heads (DBNS), air pressure (A_{P}), and rotational velocity of the SH (RV) on removing performance of machine (RP_{AJSSR}) for three different areas of the SH were examined.
In order to investigate the mentioned effects and parameters, a preliminarily model machine was designed and constructed based on airjet impingement method. To model and optimize the effects of the mentioned parameters, artificial neural network and response surface methodology were used. The final selected artificial neural network (ANN) model, 351, successfully modeled the relationship between three airjet impingement parameters (ND, AI and RV), and output parameters (RP_{AJSSR}). The final trained ANN model was able to predict simultaneously the removing performance with MSE of 0.0035, R^{2} of 0.98 and T value of 0.96.
Results of RSM optimization indicated that in the same conditions, the maximum percentage of the removed seeds belonged to the SRSH and the minimum percentage belonged to the CRSH. For all regions of SHs, with increasing ND and decreasing RV, performance of machine increased. Furthermore, when DBNS values ranged between 15 and 19 mm and RV values ranged from of 17 to 20 rev min1, the optimum performance of power consumption and removed seeds were obtained. It is interesting to note that in all cases, no damaged seeds during AJSSR machine operation were observed. It can be a significant advantage of jetimpingement method over mechanical methods for removing sunflower seeds from SHs.
For instance, removing success of air jet system for sunflower seeds in sunflower heads is 57% at most, in the present study, which is not low compared to the mechanical systems. But in the present study we used just one nozzle; by increasing number of nozzles we will able to remove high present of sunflower seeds from their heads. Also, on the other hand, most of the seeds that are located on the central region of the SHs are too small and their quality and their marketability are low, and a lot of them are hollow. So, with good regulation of machine’s operating parameters, the machine is capable to sort out low quality seeds by not removing them.
Acknowledgements
The authors would like to thank the University of Tehran for providing technical support for this work. We also want to sincerely thank Dr. Mohammad Hassan Torabi, Mr. Asghar Mirzabe Mr. Fazlollah Ansari, deceased Mr. Feizollah Aghasi, Mr. Ali Kharaji, Mr. Ehsan Afshari and Eng. Javad Yousefi for his technical help and supervision while writing the paper.
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Received: 29.12.2019
Reviewed: 3.04.2020
Accepted: 27.04.2020
Amir Hossein Mirzabe
Department of Mechanical Engineering of Biosystems, College of Agriculture & Natural Resources, University of Tehran, Tehran, Iran
Telephone: 098 3153239185
Cell phone: 0989399442161
a_h_mirzabe@yahoo.com
email: a_h_mirzabe@alumni.ut.ac.ir
Gholam Reza Chegini
Department of Mechanical Engineering of Biosystems, College of Aboureihan, University of Tehran, Tehran, Iran
Telephone: 098 21 360 406 14
Cell phone: 0989126356329
email: chegini@ut.ac.ir
Jafar Massah
Department of Mechanical Engineering of Biosystems, College of Aboureihan, University of Tehran, Tehran, Iran
Telephone: 098 21 360 406 14
Cell phone: 0989198028454
email: jmassah@ut.ac.ir
Ali Mansouri
Department of Mechanical Engineering of Biosystems, College of Aboureihan, University of Tehran, Tehran, Iran
Telephone: 098 3153239185
Cell phone: 0989171837151
email: ali.mansouri@ut.ac.ir
Javad Khazaei
Department of Mechanical Engineering of Biosystems, College of Aboureihan, University of Tehran, Tehran, Iran
Telephone: 098 21 360 406 14
Cell phone: 0989123880128
email: jkhazaei@ut.ac.ir
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