Research on the Adaptive Cleaning System of a Soybean Combine Harvester

: This study investigates the adaptive cleaning system of a soybean combine harvester, addressing the issue of low adaptability in matching the cleaning parameters of the air-and-screen cleaning device of domestic combine harvesters to varying soybean extract characteristics. This mismatch results in high cleaning loss and impurity rates during soybean machine harvesting. Through cleaning experiments, we examine the impact on soybean machine harvesting, where the cleaning loss rate accounts for approximately 10.08% of the total loss rate. The weight of the cleaning loss rate is lower than that of the impurity rate. Additionally, we establish a linear relationship between cleaning parameters and the corresponding cleaning loss rate and impurity rate. We design an adaptive control strategy workﬂow chart and integrate the adaptive cleaning system into the soybean combine harvester. Veriﬁcation tests conﬁrm the effectiveness of the adaptive control function. Comparative analysis reveals a reduction of 0.19% in cleaning loss rate and 0.98% in impurity rate compared to the air-and-screen cleaning device. The adaptive cleaning system signiﬁcantly improves cleaning quality during soybean machine harvesting and enhances the intelligent capabilities of the air-and-screen cleaning device. The results provide practical insights and theoretical guidance for the development of high-quality, low-loss cleaning technology in soybean machine harvesting in China.


Introduction
Cleaning represents a critical phase in the combined harvesting of soybeans, with the air-and-screen cleaning device being a common piece of equipment in soybean combine harvesters for this purpose [1].The evaluation of cleaning quality relies on two key metrics: the cleaning loss rate and impurity rate associated with the air-and-screen cleaning device [2].Cleaning parameters are operational settings that can be adjusted on the air-andscreen cleaning device, and their accuracy and level of automation directly influence the quality of the cleaning process [3,4].
In China, the degree of automation in adjusting and controlling cleaning parameters is generally low.Manual adjustments are common, but these tend to be inaccurate, timeconsuming, and labor-intensive due to the static nature of the cleaning parameters during harvesting.Given the complexity of working conditions and the significant variation in soybean extract characteristics, the adaptability of the cleaning parameters to these differences is limited.This limitation results in poor cleaning quality during soybean machine harvesting, characterized by high cleaning loss and impurity rates.These issues severely impact the performance of domestic combine harvesters, as well as soybean yield and quality [5][6][7].
To enhance the adaptability of cleaning parameters and improve the cleaning quality of the air-and-screen cleaning device in combine harvesters, both domestic and international researchers have explored intelligent control systems.Domestically, Xiong et al. [8] represented, and the examination of cleaning parameters is often narrowly focused, thus precluding the realization of multi-parameter adaptive control.Conversely, abroad, the application of intelligent control technology in combine harvesters predominantly centers on the precise monitoring and display of cleaning operation performance.These technologies adjust the machine's operating parameters automatically and intelligently when harvesting various crops under diverse field conditions, ensuring that the equipment maintains an optimal operating state.Internationally, advanced combine harvesters have successfully achieved intelligent control and high-quality, high-efficiency cleaning operations for large feed-rate air-and-screen cleaning devices.
In response to the aforementioned analysis, this article proposes an investigation into the adaptive cleaning system of soybean combine harvesters.Such a study is both necessary and urgently needed to address the issues encountered by domestic soybean combine harvesters employing air-and-screen cleaning devices.The objective of this research is to reduce the cleaning loss rate and impurity rate during soybean machine harvesting, enhance the adaptability of cleaning parameter matching to varying characteristics of soybean extracts, and improve overall cleaning quality.The study includes an exploration of the influence of cleaning parameters on cleaning quality, the design of an adaptive control strategy workflow chart, integration of the adaptive cleaning system into soybean combine harvesters, and verification and comparative analysis of the system's effectiveness and superiority.The proposed system allows for the multi-parameter adjustability and measurability of cleaning parameters, as well as the optimization of field cleaning parameters for soybean combine harvesters.This study effectively enhances the adaptability of cleaning parameter matching to the distinct characteristics of soybean extracts, leading to a reduction in the cleaning loss rate and impurity rate during soybean machine harvesting.This improvement is expected to positively impact soybean yield and quality per unit area, with significant implications for soybean safety and the development of the soybean industry.The study also contributes to the enhancement of the intelligent capabilities of air-and-screen cleaning devices, aligning with the trend of intelligent development in combine harvesters.It holds practical significance in advancing the full mechanization of domestic soybean production and promoting technological progress within the domestic harvesting machinery industry, thereby accelerating the modernization of agriculture in the country.

Multi-Parameter Adjustable Measurable Cleaning System
The multi-parameter adjustable and measurable cleaning system comprises an air-andscreen cleaning device, a cleaning parameter control system, a cleaning quality monitoring system, and a power and control system.The overall structure is depicted in Figure 1.The air-sieve cleaning device includes a frame, a fan, a cleaning screen, a grain-collecting screw conveyor, and a re-stripping screw conveyor.This forms the fundamental structure of a multi-parameter adjustable and measurable cleaning system.The cleaning parameter control system includes devices for controlling the cleaning screen crank speed, fan speed, damper opening, and fish scale sieve sheet opening.These devices enable continuous adjustment control and real-time monitoring of cleaning parameters.The cleaning quality monitoring system comprises two components: a cleaning loss rate monitoring system located below the tail of the cleaning screen at the impurity discharge outlet of the airand-screen cleaning device and an impurity rate monitoring system positioned beneath the grain outlet in the grain box.These systems facilitate real-time monitoring of the cleaning loss rate and impurity rate.The power and control system is composed of a power supply, a display terminal, a controller, a GPS receiver, a voltage converter, and integrated circuits.This system provides power, regulates the entire machine, monitors operating speed in real-time, controls the display terminal for cleaning parameters, and handles signal transmission.It also enables the reception, storage, and display of various data, including cleaning parameters, cleaning quality evaluation indicators, and power consumption on the terminal [21].operating speed in real-time, controls the display terminal for cleaning parameters, and handles signal transmission.It also enables the reception, storage, and display of variou data, including cleaning parameters, cleaning quality evaluation indicators, and powe consumption on the terminal [21].The cleaning test items are shown in Table 1.In Table 2, we present the soybean characteristic parameters, harvester structure, operation parameters, and the optimal combination of cleaning parameters, drawing upon relevant studies and previous research [21][22][23][24][25][26].Both the air-and-screen cleaning device and the single-factor test employ a real-time cleaning quality monitoring system to record data for cleaning quality evaluation indices during the harvesting process.Upon completion of the air-and-screen cleaning device test, the tester manually collects 1 m 2 samples of residual loss from the test area corresponding to each test serial number.
(2) Analysis method of evaluation index.
Following established studies, the total loss rate, cleaning loss rate, and impurity rate are computed using Equation (1) and Equation (2), respectively [27].
where Y 0 represents total loss rate in %; W ss is soybean loss in g/m 2 ; W sh is soybean harvest in g/m 2 ; Y 1 denotes cleaning loss rate in %; W sq is cleaning loss in g/m 2 ; W sy is residual loss in g/m 2 ; W sz is natural dropping amount in g/m 2 ; Y 2 is impurity rate in %; W zz is sample quality in g; W zq is sample quality after impurity removal in g.

Test Process
The evaluation of cleaning parameters for soybean machine harvesting is conducted using a multi-parameter adjustable measurable cleaning system and a supporting harvester.The test site is depicted in Figure 2.
where Y0 represents total loss rate in %; Wss is soybean loss in g/m 2 ; Wsh is soybean ha in g/m 2 ; Y1 denotes cleaning loss rate in %; Wsq is cleaning loss in g/m 2 ; Wsy is residual in g/m 2 ; Wsz is natural dropping amount in g/m 2 ; Y2 is impurity rate in %; Wzz is sam quality in g; Wzq is sample quality after impurity removal in g.

Test Process
The evaluation of cleaning parameters for soybean machine harvesting is condu using a multi-parameter adjustable measurable cleaning system and a supporting vester.The test site is depicted in Figure 2.

Industry Standards
In accordance with the pertinent literature, the industry standard for evaluating bean machine harvesting is presented in Table 3 [27].

Industry Standards
In accordance with the pertinent literature, the industry standard for evaluating soybean machine harvesting is presented in Table 3 [27].

Air-and-Screen Cleaning Device Test
The air-and-screen cleaning device test covers a distance of 80 m and includes 16 groups of evaluation indices.The average values for the cleaning loss rate, impurity rate, and total loss rate across these 16 groups are obtained, as detailed in Table 4.The percentage of the cleaning loss rate relative to the total loss rate is computed using Equation (3), as illustrated in Table 4.According to Equation (4), the industry standard for the cleaning loss rate in soybean machine harvesting is Y 1s ≤ 0.5%, as shown in Table 3.
Y 3 represents the cleaning loss rate as a percentage of the total loss rate as a percentage (%), Y 1s is the industry standard for the cleaning loss rate in soybean machine harvesting as a percentage (%), Y 0s is the industry standard for the total loss rate in soybean machine harvesting as a percentage (%), W sq is the cleaning loss in grams per square meter (g/m 2 ), and W ss is the soybean loss in grams per square meter (g/m 2 ).

Weight
Drawing from relevant studies, the evaluation indices and weights for the grain combine harvester are presented in Table 5 [28].Comparing Tables 4 and 5, the cleaning loss rate comprises approximately 10.08% of the total loss rate.The weight for the total loss rate is 0.7, while the weight for the impurity rate is 0.1, with no consideration for the crushing rate.Using Equation ( 5), the weight conversion yields a weight of about 0.4 for the cleaning loss rate (W 1 ) and about 0.6 for the impurity rate (W 2 ), demonstrating that the weight relationship between the cleaning loss rate (Y 1 ) and the impurity rate (Y 2 ) in the quality evaluation index for soybean machine harvesting and cleaning is such that Y 1 weight < Y 2 weight.
W 0 represents the sum of the weight of the initial cleaning loss rate and impurity rate.Y 3 represents the percentage of cleaning loss rate to total loss rate as a percentage (%), W 1 is the weight of the cleaning loss rate, and W 2 is the weight of the impurity rate.

Single Factor Test
The single factor test involves adjusting the level of one cleaning parameter while keeping the other three parameters constant.This process is repeated at five different levels, with each test group conducted at a distance of 25 m [27].In alignment with the relevant literature and prior research on cleaning parameters, combined with the adjustment range of the cleaning parameters for the multi-parameter adjustable measurable cleaning system, the selected cleaning parameter levels are outlined in Table 6, and the data for the single factor test are presented in Table 7 [21][22][23][24][25][26].

Linear Relationship
Based on previous research, the adjustment range for cleaning parameters ensures that the evaluation index values do not fall below zero.Origin 9.1 software is used to analyze the data from the single factor test, which provides the maximum adjustment range for the four cleaning parameters.Additionally, the linear equations and numerical ranges corresponding to the two evaluation indices are determined, as presented in Table 8 [21].To facilitate the implementation of an adaptive control strategy, the monitoring range for evaluating the cleaning quality of soybean machine harvesting is subjected to a fuzzy process.Both Index Y 1 and Index Y 2 employ the same fuzzy rule design method.Four cleaning parameters correspond to seven fuzzy rules: ZO (no adjustment), PS (positive small step adjustment), PM (positive middle step adjustment), PB (positive big step adjustment), NS (negative small step adjustment), NM (negative middle step adjustment), and NB (negative big step adjustment).The fuzzy grade intervals for the evaluation indices, determined by these rules, are categorized into four grades: grade 0, grade 1, grade 2, and grade 3. The fuzzy grade intervals for the two evaluation indices align with the fuzzy rules for the four cleaning parameters, as shown in Table 9 [29][30][31][32][33][34].

Fuzzy Grade Interval and Adjustment Step Division
To determine the fuzzy grade intervals and corresponding adjustment step sizes, we reference the industry standards for soybean machine harvesting cleaning: Y 1s ≤ 0.5% for cleaning loss rate and Y 2s ≤ 3% for impurity rate, as listed in Table 3.We also consider the linear equations and numerical ranges for cleaning parameters presented in Table 8 and the fuzzy rules for cleaning parameters from Table 9.The fuzzy grade intervals for the evaluation indices Y 1 and Y 2 , and the values for the adjustment step sizes of the four cleaning parameters, are established.Grade 0 for Y 1 and Y 2 correspond to the intervals [0, 0.5] and [0, 3], respectively, with the maximum values set to 100%, as indicated in Table 10.Based on the previous study, the influence order of the four cleaning parameters on the cleaning loss rate Y 1 is as follows: cleaning screen crank speed (D) > fan speed (C) > damper opening (B) > fish scale screen sheet opening (A).The influence order on the impurity rate Y 2 is: fish scale screen sheet opening (A) > damper opening (B) > fan speed (C) > cleaning screen crank speed (D) [22].
In line with the influence order of the four parameters on Y 1 (D > C > B > A), when Y 1 exceeds grade 0, parameter D is adjusted first.If the desired effect is not achieved, parameters C, B, and A are adjusted in succession.
Examining Table 8, we find that the parameter with the smallest adjustment range for Y 1 is A, with a range of 0 to 0.65667.To ensure that parameter A can be adjusted in all grade intervals, we set the condition b 1 < 0.65667, with the range 0.5 to 0.65667 covering a length of about 0.15.To allow the other three parameters to effectively divide the adjustment step size into three intervals, we set the constraint "interval range not less than 0.05."Therefore, the length of the grade 1 interval for Y 1 is 0.06, grade 2 is 0.56 to 0.62, and grade 3 is 0.62 to 100.As such, a 1 is set to 0.56 and b 1 is 0.62.Following the same method, we determine that a 2 is 5 and b 2 is 7.For Y2, grade 1 covers 3 to 5, grade 2 is 5 to 7, and grade 3 is 7 to 100.
Analyzing the adjustment step for parameter A, we calculate the step length for grade 0 [0, 0.5] of Y 1 to be approximately 25.When adjusting Y 1 from grade 1, 2, and 3 to 0.5 using parameter A, the minimum adjustments for Y 1 are 0.06, 0.12, and 0.15667, respectively.Corresponding adjustment steps for parameter A when transitioning Y 1 from grade 1, 2, and 3 to grade 0 are calculated as 3, 6, and 7.4, respectively.The adjustment step range for parameter A based on grade 1 of Y 1 is 3 to 28, grade 2 is 6 to 31, and grade 3 is 7.4 to 32.4.Within this range, the step size can be adjusted as needed.In practice, the adjustment step sizes for three fuzzy grade intervals corresponding to parameter A are set to L A11 as 4, L A12 as 7, and L A13 as 10, in accordance with the field operation of the multi-parameter adjustable measurable cleaning system.Using a similar approach, we determine the fuzzy grade intervals for the evaluation index and corresponding adjustment step sizes for the cleaning parameters, as shown in Table 10.Positive adjustment is represented by a positive adjustment step size, and negative adjustment is indicated by a negative step size.10 illustrates that the qualifying conditions for these two indexes are Y 1R ≤ Y 1S and Y 2R ≤ Y 2S , whereas the non-qualifying conditions are Y 1R > Y 1S and Y 2R > Y 2S .Based on the influence order of cleaning parameters on the quality evaluation index of soybean machine harvesting and cleaning, the optimal combination of cleaning parameters for soybean machine harvesting, the fuzzy grade interval of the evaluation index, the adjustment step for corresponding cleaning parameters, and the weight relationship between cleaning loss rate Y 1 and impurity rate Y 2 of soybean machine harvesting are considered in the design of the workflow for the adaptive control strategy, as shown in Figure 3.

Adaptive Control Strategy Workflow Chart
The workflow of the adaptive control strategy for simultaneous monitoring of Y 1 and Y 2 can be given by four parameters.When the real-time monitoring values of Y 1 and Y 2 are more than the 0 level range, the adaptive control of the four parameters is independently and simultaneously carried out.Due to the different ranking of the effects of the four parameters on the two indicators, and the reverse trend of the effects of the four parameters on the two indicators, as shown in Table 8, the regulatory components for these four parameters are unique.Therefore, when adaptively adjusting the four parameters for two indicators simultaneously, there may be situations where the same parameter is adjusted at the same time.For instance, parameter D is adjusted adaptively based on the weight relationship Y 1 < Y 2 of the two indicators.Indicator Y 2 undergoes adaptive adjustment for the corresponding adjustment step size of parameter D within the fuzzy level interval.Indicator Y 1 is sorted based on the impact of the four parameters on indicator Y 1 (E > D > C > B), and it automatically selects the next parameter C. This, in turn, completes adaptive adjustment of the fuzzy level interval corresponding to the adjustment step size of parameter C. When adjusting different parameters, they are adjusted simultaneously according to their respective adaptive control strategy workflow.The workflow of the adaptive control strategy for simultaneous monitoring of Y1 and Y2 can be given by four parameters.When the real-time monitoring values of Y1 and Y2 are more than the 0 level range, the adaptive control of the four parameters is independently and simultaneously carried out.Due to the different ranking of the effects of the four parameters on the two indicators, and the reverse trend of the effects of the four parameters on the two indicators, as shown in Table 8, the regulatory components for these four parameters are unique.Therefore, when adaptively adjusting the four parameters for two indicators simultaneously, there may be situations where the same parameter is adjusted at the same time.For instance, parameter D is adjusted adaptively based on the weight relationship Y1 < Y2 of the two indicators.Indicator Y2 undergoes adaptive adjustment for the corresponding adjustment step size of parameter D within the fuzzy level interval.Indicator Y1 is sorted based on the impact of the four parameters on indicator Y1 (E > D > C > B), and it automatically selects the next parameter C. This, in turn, completes adaptive adjustment of the fuzzy level interval corresponding to the adjustment step size of parameter C. When adjusting different parameters, they are adjusted simultaneously according to their respective adaptive control strategy workflow.

Integration and Working Principle of Adaptive Cleaning System
The adaptive cleaning system is integrated based on the multi-parameter adjustable measurable cleaning system and the workflow chart of the adaptive control strategy.The prototype and the supporting harvester are depicted in Figure 4.

Integration and Working Principle of Adaptive Cleaning System
The adaptive cleaning system is integrated based on the multi-parameter adjustable measurable cleaning system and the workflow chart of the adaptive control strategy.The prototype and the supporting harvester are depicted in Figure 4.
When the adaptive cleaning system is engaged in cleaning during soybean machine harvesting, the power and control system provides electrical power to the entire adaptive cleaning system.It displays various parameters and real-time data, such as cleaning parameters and cleaning quality, on the display terminal.The initial value of the best cleaning parameter combination for soybean machine harvesting is set through the display terminal.The cleaning quality monitoring system monitors two evaluation indexes, cleaning loss rate and impurity rate, in real-time and displays them on the display terminal.If these two evaluation indexes exceed the 0 grade, the display terminal employs the adaptive control strategy workflow to determine the fuzzy grade interval in which these indexes are located.It then automatically selects the corresponding cleaning parameters and adjustment step sizes, and controls the multi-parameter adjustable measurable cleaning system to make real-time adjustments to the cleaning parameters.This process ensures that the cleaning parameters adapt to the characteristic differences of soybean extracts, continuously reducing the cleaning loss rate and impurity rate of soybean machine harvesting until both evaluation indexes reach the 0 grade.This maintains the optimal operation state of the air-and-screen cleaning device of the soybean combine harvester and achieves adaptive cleaning, matching the cleaning parameters to the characteristic differences of soybean extracts during soybean machine harvesting.When the adaptive cleaning system is engaged in cleaning during soybean machine harvesting, the power and control system provides electrical power to the entire adaptive cleaning system.It displays various parameters and real-time data, such as cleaning parameters and cleaning quality, on the display terminal.The initial value of the best cleaning parameter combination for soybean machine harvesting is set through the display terminal.The cleaning quality monitoring system monitors two evaluation indexes, cleaning loss rate and impurity rate, in real-time and displays them on the display terminal.If these two evaluation indexes exceed the 0 grade, the display terminal employs the adaptive control strategy workflow to determine the fuzzy grade interval in which these indexes are located.It then automatically selects the corresponding cleaning parameters and adjustment step sizes, and controls the multi-parameter adjustable measurable cleaning system to make real-time adjustments to the cleaning parameters.This process ensures that the cleaning parameters adapt to the characteristic differences of soybean extracts, continuously reducing the cleaning loss rate and impurity rate of soybean machine harvesting   11.The adaptive cleaning system and supporting harvester were utilized to complete the adaptive cleaning test under the best combination of cleaning parameters for soybean machine harvesting.Initially, Lindou 8 was used for the verification test, followed by separate adaptive cleaning system tests for Lindou 10 and Lindou 8.The test site for Lindou 10 is illustrated in Figure 2, and the test site for Lindou 8 is shown in Figure 5.
Adaptive cleaning test items are shown in Table 11.The adaptive cleaning system and supporting harvester were utilized to complete adaptive cleaning test under the best combination of cleaning parameters for soybean chine harvesting.Initially, Lindou 8 was used for the verification test, followed by sepa adaptive cleaning system tests for Lindou 10 and Lindou 8.The test site for Lindou 1 illustrated in Figure 2, and the test site for Lindou 8 is shown in Figure 5.

Verification Test
The adaptive cleaning system's control function was verified in the field operation of soybean combine harvesters.Cleaning loss rate and impurity rate were significantly increased until they surpassed the 0 grade of the two evaluation indexes.Subsequently, the adaptive cleaning system's control function effectively reduced these evaluation indexes.This verified the system's adaptive control function.
The operation speed of the soybean machine harvester is positively correlated with the cleaning loss rate and impurity rate, with increased operation speed leading to higher cleaning loss rates and impurity rates [35][36][37].The verification test covered a distance of 100 m, with the harvester initially operating at a speed of 6 km/h for the first 30 m.After reaching 30 m, the operator increased the harvester's speed to 7.5 km/h for the remaining 70 m.The verification test details are presented in Table 13.Cleaning screen crank speed /(r/min) 410 0~1500

Data Analysis
The verification test produced 17 sets of evaluation indexes, and their data trends are displayed in Figure 6.Both the cleaning loss rate and impurity rate exhibited an initial increase followed by a decrease, confirming the effectiveness of the adaptive control function of the adaptive cleaning system.

Adaptive Cleaning System Test
The operation speed for the adaptive cleaning system test was fixed at 6 km/h, and each test covered a distance of 80 m, resulting in 16 sets of evaluation indexes for each test.The evaluation indexes for Lindou 10 and Lindou 8 were averaged separately, and subsequently, the evaluation indexes for both soybean varieties were averaged.The data from the adaptive cleaning system test are presented in Table 14.The operation speed for the adaptive cleaning system test was fixed at 6 km/h, and each test covered a distance of 80 m, resulting in 16 sets of evaluation indexes for each test.The evaluation indexes for Lindou 10 and Lindou 8 were averaged separately, and subsequently, the evaluation indexes for both soybean varieties were averaged.The data from the adaptive cleaning system test are presented in Table 14.
The real-time monitoring indexes Y 1 are designated as Y 1R , Y 1R+1 , Y 1R+2 , Y 1R+3 , and Y 1R+4 , while the real-time monitoring indexes Y 2 are denoted as Y 2R , Y 2R+1 , Y 2R+2 , Y 2R+3 , and Y 2R+4 .The industry standard for cleaning loss rate is Y 1S , and the industry standard for impurity rate is Y 2S .Table

Figure 4 .
Figure 4. Prototype of adaptive cleaning system and supporting harvester.

Figure 4 .
Figure 4. Prototype of adaptive cleaning system and supporting harvester.

Figure 6 .
Figure 6.Trend of validation test data.

Figure 6 .
Figure 6.Trend of validation test data.

Table 1 .
Cleaning test items.

Table 3 .
Industry standard of soybean machine harvesting evaluation index.

Table 3 .
Industry standard of soybean machine harvesting evaluation index.

Table 4 .
Air-and-screen cleaning device test data.

Table 5 .
Evaluation index and weight of grain combine harvester.

Table 7 .
Single factor test data.

Table 8 .
Linear equation and numerical range of evaluation index corresponding to cleaning parameters.

Table 9 .
Fuzzy rules of cleaning parameters corresponding to fuzzy grade interval of evaluation index.

Table 10 .
Fuzzy grade interval of evaluation index and adjustment step of corresponding cleaning parameters.

Table 11 .
Adaptive cleaning test items.The adaptive cleaning test employed Lindou 10 and Lindou 8 soybean varieties.characteristic parameters of Lindou 8 soybeans used in the test are presented in Table and the characteristics of Lindou 10 soybeans, the structure and operation parameter the harvester, and the best combination of cleaning parameters for soybean machine vesting are shown in Table 2 [21-26].

Table 14 .
Adaptive cleaning system test data.