Design and Parameter Optimization of Fresh Chili Seed Extractor

Abstract

There is a poor mechanization level among the existing chili seed extractors. The separation operation still relies on manual labor, with low efficiency and high costs. In this study, a fresh chili seed extractor for small-scale operations was designed, and the relevant parameters were optimized. The rotational speed of the drum, feeding speed, sieve diameter, threshing gap, number of peg teeth, and inclination angle of the frame were used as test factors, and the comprehensive score (loss rate, crushing rate, and impurity rate) of the effect of the chili seed extractor was set as an evaluation index. The initial parameters were selected via the Plackett–Burman test. The steepest climb test was carried out to determine the ranges of significance for the parameters. Moreover, a Box–Behnken test were conducted to obtain the optimal parameter combination: the drum rotation speed was 661 r/min, the sieve diameter was 8.5 mm, and the disengagement gap was 9.4 mm. The test results showed that the loss rate was 3.83%, the crushing rate was 2.01%, and the impurity rate was 11.31%, which met the actual production requirements for chili seeds. This study is expected to provide a necessary reference for the design of chili seed extractors.

1. Introduction

China is the world’s largest producer and consumer of chili, and it is popular among consumers as a vegetable and as a condiment [1,2]. The separation efficiency of existing chili seed extractors is low, and the loss rate and crushing rate are high. On the one hand, this reduces the utilization rate and germination rate of seeds, resulting in the waste of seeds and increasing the production costs. On the other hand, it also affects the quality of mechanized precision sowing, which affects the yield and the economic benefits. Furthermore, it even restricts the development of the chili industry [3,4].

Currently, there are few machines in China applied in the separation of chili peppers’ seeds and skins. Ma et al. [5] designed a chili seed extractor. However, it relied on manual crushing; then, materials were separated through sieves and a fan. In most areas of China, chili seed–skin separation is mainly completed using rice–wheat separators through parameter adjustment and the replacement of operating parts. However, field measurement results have shown that the harvest loss is as high as 10% due to inappropriate machine parameters and part structures, and the crushing and impurity content is also excessive [6,7]. Seed vigor may also be damaged, but this is one of the essential elements during the separation process of chili pepper seeds [8,9].

Scholars at home and abroad have carried out a large number of studies on the structure and working parameters of the threshing and cleaning system. In the research on chili seed separation and crushing devices, the crushing rate has been reduced by adjusting the rotational speed of the drum and the threshing gap. Chayalakshmi et al. designed a combined cleaning and separation device consisting of a crushing device and a double-layer vibrating screen; however, there was a high impurity rate [10]. At the same time, it is necessary to ensure that the health of the seeds is not harmed. In 2022, Din et al. [11] studied the engineering properties of chili fruits and seeds, and a chili seed extractor was designed. It showed that the extraction efficiency was 76.21% when there was moisture content of 3–4%, blade spacing of 1.5 cm, and an amplitude of 4 cm. Therefore, the current research is mainly focused on how to reduce the loss rate of chili seeds in the sieving process, and the methods used mainly involve adjusting the size of the sieve mesh holes and the vibration amplitude of the sieve mesh. Regarding the influence of structural and motion parameters on the separation effect of chili seeds, the main focus is on the effects of the feeding rate, drum speed, and different forms of separation components on seed loss and the separation efficiency [12].

To promote the mechanized separation of chili seeds, a chili seed separator was designed in this work. Through preliminary experiments, the factors with more significant effects on the results were obtained. We studied the effects of various factors on the seed separation of chili peppers using the Box–Behnken test method [13]. Then, field experiments were conducted, and it was found that the chili seed separator performed well. This work is expected to provide a reference for the detailed testing and optimization of the subsequent chili seed separator [14].

2. Materials and Methods

2.1. Test Equipment

This study mainly focuses on the chili seed cleaning and separating operation in small fields, so a chili seed separator with a small feeding amount was designed. As shown in Figure 1, the chili seed separator mainly included the feeding hopper, drum, spike teeth, perforated screen, and discharge hopper. The structural parameters of the whole machine are shown in

Table 1. Main technical parameters of chili seed separator.

Parameter	Value
Rated power (kW)	3
Overall dimensions (mm × mm × mm)	730 × 250 × 600
Working capacity (g/s)	500
Length of drum (mm)	310
Diameter of drum (mm)	74
Length of concave screen (mm)	320
Diameter of concave screen (mm)	180
Motor speed (r/min)	1400

. The equipment required for the experiment included electronic scales, vernier calipers, and an MJ33 moisture meter with accuracy of 0.01. In addition, non-contact tachometers and stopwatches were also provided.

When the experiment was conducted, the motor was first turned on. The initially broken chili pepper was fed from the feeding hopper after the motor was running smoothly. The breaking mixture was hit by the rotating spike teeth on the drum. They fell into the concave sieve mesh along the inclined drum. The smaller chili pepper seeds and small pieces of impurities passed through the holes of the concave sieve mesh. Other larger impurities, such as chili pepper pulp and chili pepper stalks, were discharged from the waste discharge hopper.

2.2. Test Materials

The seed separation experiment was carried out at the School of Agricultural Engineering, Jiangsu University, Zhenjiang, China. Pod pepper was taken as the experimental chili pepper. The main parameters of the materials were measured before the experiments (

Table 2. Main parameters of the test materials.

Variety	Moisture Content	Length (mm)	Diameter (mm)	Weight of 100 Grains (g)	Thickness (mm)
Pod pepper	77%	60.4 ± 3.1	9.5 ± 1.1	237.7 ± 8.5	-
Pepper seed	18%	4.24 ± 1.07	3.15 ± 0.62	-	1.03 ± 0.93

). Ten chili pepper seeds from different chili peppers were extracted. The quality of the chili peppers was better.

2.3. Test Factors

In the process of chili seed cleaning and separating operations, there are many factors affecting the operational performance of the equipment. According to the relevant literature on chili pepper seed separation, six factors were selected as the experimental variables, namely the rotational speed of the drum, feeding speed, sieve diameter, threshing gap, number of spike teeth, and inclination angle of the frame [15,16,17]. The schematic structure of the key factor dimensions is shown in Figure 2. Among these, d is the diameter of the sieve hole, the threshing gap x is the vertical distance from the top of the rod teeth to the concave screen, y is the spacing between two spike teeth, and the inclination angle of frame θ is the angle between the frame and the horizontal direction.

Based on the size of the chili seeds, the sieve diameter d was selected. The threshing gap x was determined by measuring the size of the chili pepper. To ensure the crushing effect, the spacing of the nail teeth y was inversely proportional to the number. The sieve holes were arranged in an equilateral triangle with hole spacing of 12 mm; therefore, the number of holes could change with a change in their diameter.

2.4. Test Indicators

Three experimental indicators were selected to evaluate the quality of the pepper seed separator, namely the loss rate, the broken rate, and the impurity rate of chili seeds. The lost seeds consisted of all chili pepper seeds discharged from the waste discharge hopper; the broken seeds were those with incomplete cotyledons (including the whole half of the grain), transverse cracks, and rupture of the particles. The impurities included broken chili pepper pulp, pedicels, placenta, and so on. The separation effect of chili seeds was measured as a combination of the loss rate Y₁, broken rate Y₂, and impurity rate Y₃ [18]. The smaller the value of the comprehensive score, the better the separation effect. The equation for the calculation of the comprehensive score was as follows (1)–(4):

$$\mathrm{Y}={0.4Y}_1+0.4Y_2+{0.2Y}_3$$

(1)

$$Y_1={\displaystyle \frac{W_{ss}}{W_{sh}+W_{ss}}}\times 100\%$$

(2)

$$Y_2={\displaystyle \frac{W_{pz}-W_{pq}}{W_{pz}}}\times 100\%$$

(3)

$$Y_3={\displaystyle \frac{W_{zz}-W_{pq}}{W_{zz}}}\times 100\%$$

(4)

Note: $W_{\mathrm{s}\mathrm{s}}$ is the mass of all chili seeds discharged from the waste discharge hopper (g); $W_{\mathrm{s}\mathrm{h}}$ is the mass of all chili seeds discharged from the discharge hopper (g); $W_{\mathrm{p}\mathrm{z}}$ is the mass of the sample after the removal of impurities (g); $W_{\mathrm{p}\mathrm{q}}$ is the mass of the sample after the removal of impurities and broken seeds (g); and $W_{\mathrm{z}\mathrm{z}}$ is the mass of the sample (g).

2.5. Experimental Design and Methodology

In this study, the Plackett–Burman test was conducted using the Design-Expert software, and the experimental parameters that had a significant effect on the response value were selected [19]. In each group of experiments, 500 g of pod pepper was fed into the separator. Samples were taken after the chili seed separator was running smoothly. Each group was sampled three times, and the test results were averaged.

Combined with the biological characteristics of chili peppers and the parameters and machine design, the value of each factor was determined [20,21]. The middle value of the drum speed was 700 r/min, the middle value of the feeding speed was 50 g/s, the middle value of the sieve diameter was 8 mm, the middle value of the threshing gap was 8 mm, the middle value of the number of spike teeth was 24, and the inclination angle of the frame was 5°~35°. The limit value of this factor was taken to be the lowest and highest level. The maximum and minimum values of the six test factors were coded as levels +1 and −1. The test factors and coding are shown in

Table 3. Test factor codes.

Code	Rotational Speed of Drum (r/min)	Feeding Speed (g/s)	Screen Hole Diameter (mm)	Threshing Gap (mm)	Number of Spike Teeth	Inclination Angle (°)
−1	600	25	7	6	20	5
0	700	50	8	8	24	20
1	800	75	9	10	28	35

[22,23,24].

3. Results and Discussion

3.1. Plackett–Burman Test

The experimental design scheme and results are shown in

Table 4. The scheme and results of the Plackett–Burman test.

Code	Drum Speed (X1)	Feeding Speed (X2)	Sieve Diameter (X3)	Threshing Gap (X4)	Number of Spike Teeth (X5)	Inclination Angle of Frame (X6)	Loss Rate Y1 (%)	Broken Rate Y2 (%)	Impurity Rate Y3 (%)	Combined Score Y (%)
1	−1	−1	1	−1	1	1	3.74	2.77	11.98	5.00
2	−1	1	1	−1	1	1	4.76	2.33	11.81	5.20
3	−1	1	−1	1	1	−1	6.05	2.15	10.68	5.42
4	1	−1	1	1	−1	1	4.46	1.86	11.29	4.66
5	1	1	−1	1	1	1	6.83	2.44	10.81	5.87
6	1	1	−1	−1	−1	1	6.97	2.78	10.92	6.08
7	1	1	1	−1	−1	−1	3.74	2.96	11.95	5.07
8	−1	1	1	1	−1	−1	4.65	1.14	11.03	4.52
9	−1	−1	−1	1	−1	1	6.87	1.33	10.11	5.30
10	1	−1	1	1	1	−1	3.13	3.02	12.04	4.87
11	1	−1	−1	−1	1	−1	4.14	4.27	11.87	5.74
12	−1	−1	−1	−1	−1	−1	5.16	2.86	10.98	5.40

. The test factors were the rotational speed of the drum (X₁), feeding speed (X₂), sieve diameter (X₃), threshing gap (X₄), number of spike teeth (X₅), and inclination angle of the frame (X₆). The broken rate was higher in group 11. This was because, when the drum speed was higher, the chili peppers were hit more frequently due to the larger number of spike teeth and smaller threshing gap. The broken rate was lower in group 8.

Determination of Parameter Significance

Based on Table 4, a variance analysis of the test results was carried out on the comprehensive score of the separation effect. The significance of each test parameter was obtained via the Design-Expert 13 software, as shown in

Table 5. Significance analysis of Plackett–Burman test parameters.

Parameter	Degrees of Freedom	Sum of Squares	F-Value	p-Value (α = 0.05)	η2
X1	1	0.1752	24.44	0.0006	0.0714
X2	1	0.118	16.46	0.0023	0.0481
X3	1	1.68	234.37	<0.0001	0.6848
X4	1	0.2852	39.79	<0.0001	0.1163
X5	1	0.0954	13.31	0.0047	0.0389
X6	1	0.099	13.81	0.0041	0.0403

. It was found that the most significant effects on the comprehensive score were exhibited for three parameters (drum speed X₁, sieve diameter X₃, and threshing gap X₄). The p-values of these parameters were all less than 0.05. Moreover, for X₃, the eta-squared η² was larger than 0.14, which indicates that there were large effects. There were medium effects for X₁ and X₄, because the values of η² were larger than 0.06.

3.2. Analysis of the Results of the Steepest Climb Test with Combined Scores

The three parameters with the most significant effects were screened for the steepest climb test. For the other parameters, the middle values were used: the feeding speed was 50 g/s, the number of spike teeth was 24, and the inclination angle was 20°. The comprehensive score was used as an evaluation index to determine the optimal range intervals of the three parameters with the most significant impacts. The scheme and results of the steepest climb experimental design are shown in

Table 6. The steepest climb test design scheme and results.

No.	Drum Speed (X1)	Sieve Diameter (X3)	Threshing Gap (X4)	Comprehensive Score Y (%)
1	600	7	8	5.33
2	650	7.5	8.5	5.07
3	700	8	9	4.68
4	750	8.5	9.5	4.96
5	800	9	10	5.25

.

According to Table 6, it can be seen that, with an increase in the three test factors, the comprehensive score Y decreases first and then increases. Moreover, the comprehensive score Y of No. 3 was the smallest, which was 4.68%.

3.3. Box–Behnken Test Analysis and Results for Combined Scores

The three most significant factors X₁, X₃, and X₄ were determined in the Plackett–Burman test, and their values at the −1, 0, and 1 levels were selected with the steepest climb test. The other simulation parameters were kept the same as in the steepest climb test. The Box–Behnken experimental design was implemented in Design-Expert 13, and a total of 17 sets of simulation experiments were conducted. The experimental design scheme and results are shown in

Table 7. Box–Behnken experimental design scheme and results.

No.	Drum Speed (X1)	Sieve Diameter (X3)	Threshing Gap (X4)	Comprehensive Score Y (%)
1	0	0	0	4.64
2	−1	1	0	4.61
3	0	−1	−1	5.25
4	0	0	0	4.71
5	0	0	0	4.55
6	0	1	1	4.41
7	0	−1	1	5.01
8	−1	0	1	4.39
9	1	0	1	5.57
10	−1	−1	0	5.02
11	−1	0	−1	4.88
12	1	0	−1	5.04
13	0	0	0	4.51
14	1	−1	0	5.34
15	0	1	−1	4.45
16	0	0	0	4.75
17	1	1	0	4.59

[25,26].

A multivariate regression was fitted to the results of the Box–Behnken test to obtain a quadratic polynomial regression model equation of the comprehensive score Y with the three significant parameters:

$$Y=4.63+0.205X_1-0.32X_3-0.03X_4-0.085X_1{X}_3+0.255X_1{X}_4+{0.05X}_3{X}_4+0.224X_1^2+0.034X_3^2+0.114X_4^2$$

(5)

The results of the ANOVA analysis for the Box–Behnken test regression model are shown in

Table 8. The regression variance analysis of the significant influencing factors.

Source of Variance	Sum of Squares	Degrees of Freedom	Mean Square	F-Value	p-Value
Model	1.75	9	0.1946	6.92	0.0092
X1	0.3362	1	0.3362	11.95	0.0106
X3	0.8192	1	0.8192	29.13	0.001
X4	0.0072	1	0.0072	0.256	0.6284
X1 X3	0.0289	1	0.0289	1.03	0.3445
X1 X4	0.2601	1	0.2601	9.25	0.0188
X3 X4	0.01	1	0.01	0.3555	0.5698
X12	0.2113	1	0.2113	7.51	0.0289
X32	0.0049	1	0.0049	0.1731	0.6899
X42	0.0547	1	0.0547	1.95	0.2057
Residual	0.1969	7	0.0281
Lack of fit	0.1552	3	0.0517	4.96	0.0778
Pure error	0.0417	4	0.0104
Sum	1.95	16

. Then, p-values were used to analyze the significance of the object: p < 0.01 indicated that the response model was highly significant, and p < 0.05 indicated that the response model was more significant. The regression model indicated p < 0.01 for the combined score Y and the misfit term p = 0.0778 > 0.05. These values showed that the model was highly significant and well fitted, with no misfit occurring. The p-value of X₃ < 0.01 indicated that it had an extremely significant effect on the comprehensive score Y; the p-values of X₁, X₁X₄, and X₁² < 0.05 indicated that they also had a significant effect on the comprehensive score Y. The regression equation coefficient of determination R² was 0.8990, and the corrected coefficient of determination adjusted R² was 0.7690. Their values were close to 1. The coefficient of variation (CV) was 5.23%, and the precision was 8.2410. These results indicated that the regression model had a good fit, and it was able to reliably and realistically reflect the real situation, with high credibility.

To further improve the reliability of the model, it was ensured that the model was significant and the misfit term was not significant, and the insignificant regression terms in the model were removed. Then, a new second-order regression model equation was obtained by optimization:

$$Y=4.70+0.205X_1-0.32X_3+0.255X_1{X}_4+0.2322X_1^2$$

(6)

To ensure that model assumptions held, a residual plot was constructed (Figure 3). In the residual analysis, the residual plot showed no distinct pattern, indicating that the regression model fitted the data well.

3.4. Response Surface Analysis of Combined Scores and Significant Parameters

The 3D response surface was obtained using the Model Graphs module of the Design-Expert 13 software, as shown in Figure 4.

According to the response surface plot, the impact of the interaction between the drum speed, sieve hole diameter, and shelling gap on the combined score Y could be analyzed. Figure 4a shows the effect of the interaction between the drum speed and sieve diameter on the composite score when the threshing gap was at the center level (X₄ = 9 mm). As can be seen from the figure, when the drum speed was certain, with an increase in the diameter of the sieve hole, the combined score gradually decreased, and the magnitude of the change was more obvious. The reason was that more impurities passed through the screen holes due to the larger diameter. Moreover, when the diameter of the sieve hole was certain, with a gradual increase in the drum speed, the combined score was first reduced and then increased. Therefore, the interaction between the threshing drum rotational speed and the sieve diameter had a significant effect on the combined score. The response surface curves varied more significantly along the direction of the sieve diameter. They also indicated that the effect of the sieve diameter on the combined score was more significant than the drum rotational speed.

Figure 4b shows the effect of the interaction between the drum speed and disengagement gap on the combined score when the sieve diameter was at the middle level (X₃ = 8 mm). It can be seen that, when the drum speed was certain, the combined score decreased first and then rose with the increasing threshing gap, and the magnitude of change was relatively flat. When the threshing gap was certain, with a gradual increase in the drum speed, the combined score first decreased and then rose. This was because the contact time between the chili seeds and the screen was greater with a lower drum speed. Meanwhile, when the speed was higher, the materials could not accumulate in the drum. This indicates that the interaction between the two factors had a similar effect on the combined score. Moreover, the effect of the drum speed on the combined score was relatively more significant than that of the detachment clearance.

Figure 4c shows the effect of the interaction between the sieve diameter and disengagement gap on the combined score when the drum speed was at the center level (X₁ = 700 r/min). When the diameter of the sieve hole was certain, the comprehensive score was essentially maintained. When the threshing clearance was certain, with a gradual increase in the diameter of the sieve hole, the combined score decreased significantly. It was found that when the drum rotational speed was located at the center level and the threshing gap was constant, the sieve diameter had a significant effect on the combined score.

3.5. Parameter Optimization and Experimental Validation

3.5.1. Significant Parameter Optimization

To enable the chili seed separator to work optimally, the combined score Y needs to reach the minimum value. Through the analysis of the response surface plot, it can be seen that when the drum rotational speed is small, the diameter of the sieve is large, and the threshing gap is larger, the combined score Y is smaller. Considering the differences in the factor effects and factor interactions on the response values of the test indicators, the multiobjective optimization of the regression model was required [27,28,29,30].

Based on the Optimization module of the Design-Expert 13 software, multiobjective optimization was carried out for the regression model of the test indices. The response value of the combined score Y was analyzed, and constraints for each test factor were set: the upper sieve drum rotational speed was 650–750 r/min, the sieve diameter was 7.5–8.5 mm, and the threshing clearance was 8.5–9.5 mm. The combined score Y was taken as the minimum value of the objective function at 100%. The optimization results of the three factor levels obtained were as follows: when the drum speed was 661 r/min, the sieve diameter was 8.5 mm, and the detachment gap was 9.4 mm, the surface response value of the regression model was the best, and the combined score of the model prediction value was 4.32% at this time.

3.5.2. Experimental Validation

To verify the accuracy of the above model, three field validation tests were conducted. The test materials and test methods were as before. The feeding speed was 50 g/s, the number of spike teeth was 24, and the inclination angle was 20°.

During the tests, the seed content in 500 g of chili peppers was measured to be 75.45 g, 68.74 g, and 81.56 g, respectively. According to Equations (1)–(4), the test results were as shown in

Table 9. Results of three validation tests.

No.	Wss (g)	Wsh (g)	Wzz (g)	Wpz (g)	Wpq (g)	Y1 (%)	Y2 (%)	Y3 (%)	Y (%)
1	2.38	73.07	82.98	75.45	73.44	3.15	2.66	11.50	4.624
2	2.74	66.00	75.40	68.74	67.40	3.98	1.95	10.61	4.366
3	3.56	78.00	91.18	81.56	80.40	4.36	1.42	11.82	4.676

. Based on the People’s Republic of China’s machinery industry standard [31], the loss rate and crushing rate are required to be less than 5%, and the impurity rate must be less than 3%. The average values of the loss rate and crushing rate measured in the test were 3.83% and 2.01%, respectively, which are lower than the standard. Because chili pepper seeds also need to be washed, dried, wind-cleaned, and subjected to other operations in the actual cleaning and separation process, the average value of the impurity rate of 11.31% is in line with the actual production requirements. The relative errors between the experimental values and the optimized values of the combined scores were 6.96%, 3.96%, and 8.17%, respectively, and the experimental results were closer to the predicted values of the model, which indicated that the optimized regression model of the above parameters was highly reliable.

4. Conclusions

In this work, to improve the separation efficiency of chili seeds, a pepper seed separator was designed.

The steepest climb test was conducted to optimize the ranges of values of the three most significant parameters. Through the response surface method, it was found that the combined score was smaller when the drum speed was small, the sieve diameter was large, and the threshing gap was large. After obtaining the regression model for multiobjective optimization, the optimal results were obtained as follows: the drum rotational speed was 661 r/min, the sieve diameter was 8.5 mm, and the disengagement gap was 9.4 mm. Then, validation experiments were conducted. The relative errors between the experimental and optimization values for the combined score were 6.96%, 3.96%, and 8.17%, respectively. Therefore, it was found that the predictive model was relatively reliable.

The values of the loss rate and impurity rate were below the standard requirements, thus meeting the actual production needs for chili pepper seeds. This study is expected to provide a reference for research on chili seed separators.