Study on the Influence Mechanism of Energy Consumption of Sugarcane Harvester Extractor by Fluid Simulation and Experiment

Abstract

Previous studies on sugarcane harvester extractors have mainly focused on improving harvest quality and reducing the impurity rate and loss rate, which often ignored the issue of high energy consumption. To reduce the energy consumption of the extractor while maintaining the original impurity rate and loss rate stable, firstly, a blade element analysis method with aerodynamic theory was put forward to analyze the stress of the extractor blade, and the energy consumption equation and influencing factors of extraction were obtained. Subsequently, the computational fluid dynamics model of the exhaust extractor was established. Computational fluid dynamics (CFD) and the SST k-ω model were used to analyze the mechanism of various influencing factors on the energy consumption and internal flow characteristics of the extractor. The changes in various parameters were analyzed and discussed with respect to the resulting variations in internal pressure, velocity, vortex structure, and lift–drag coefficient of the extractor. A test bench of the extractor was built, and orthogonal tests were carried out with energy consumption, impurity rate, and loss rate as test indicators. Considering the results of the simulation and bench test comprehensively, the combination of a rotational speed of 1450 RPM, a blade number of 3, an installation angle of 25°, and a blade chord length of 200 mm was optimal for the extractor. Finally, a comparative test was carried out between the optimized extractor and the original extractor. The results demonstrated that the energy consumption of the optimized extractor was reduced by 15.49%. The impurity rate decreased by 3.51%, and the loss rate decreased by 12.39% compared to the original extractor. The study can provide a theoretical and experimental basis for designing and optimizing extractor performance.

1. Introduction

Sugarcane is an essential sugar crop with high efficiency of light energy conversion, high photosynthetic rate, and high biomass yield. The sugarcane industry is one of the pillar industries in developing countries to promote farmer incomes. Global sugar production increased from 5.72 × 10⁷ t in 1959 to 1.81 × 10⁸ t in 2021, of which sugarcane sugar production increased by 280.5%. As a result, sugarcane is an important cash crop and sugar source. At present, China is the third largest sugarcane producer in the world. Although China has a relatively high level of sugarcane production in terms of the cultivation area and total output, the yield per unit area still lags behind sugarcane production powerhouses such as Brazil, Australia, and the United States. Sugarcane sugar production in China accounts for approximately 90% of the total domestic sugar output. However, the domestic sugar production is only able to meet around 70% of the demand, resulting in a remaining 30% reliance on imports [1,2,3]. The main reason for the downturn of the sugarcane industry in China is the low mechanization level, especially the low level of mechanization harvesting, one of the main reasons is that the impurity rate and loss rate of mechanization harvesting are high [4,5,6]. As one of the core components of the sugarcane harvester, the performance of the extractor has a significant impact on the quality of sugarcane harvest and is directly related to the quality of sugar and farmers’ income [7,8,9,10]. The energy consumption of the extractor accounts for about 20% of the total energy consumption of the sugarcane harvester; however, the impurity removal performance reaches 8% of the impurity rate. The wind cleaning performance of sugarcane harvesters is unsatisfactory, resulting in high energy consumption and low energy utilization efficiency in sugarcane harvesting. These findings indicate a significant potential for improvement in extractor performance and overall field operations [11,12,13]. Therefore, many researchers have studied the mechanism of impurity removal, and related parameters of the extractor have been studied to optimize the performance of the extractor.

Xing et al. conducted a simulation analysis and structural optimization on the internal flow field of the 4GZQ-180 (Guangxi Agricultural Machinery Research Institute Co., Ltd., Nanning, China) segmented sugarcane harvester extractor. The outlet chamber structure was redesigned, and the leading and trailing edges of the blades were rounded to enhance the lift–drag ratio of the blades and the aerodynamic performance of the extractor. The highest impurity rate can be reduced by 20.42%, and the highest loss rate can be reduced by 28.08% [14,15]. Xia et al. used a crossflow fan and blowing method to remove impurities. The issue of chaotic streamline at the outlet of an axial flow fan is basically solved using this design. In addition, the study also determined the optimal structural operating parameters of crossflow impurity removal fans. The wind speed at the fan outlet is increased, and the impurity rate and loss rate are reduced [16]. Tian et al. studied the influence of hub ratio, tip radial clearance, blade installation angle, and other parameters of the axial flow fan on aerodynamic performance, and they plotted the extractor characteristic curve under each parameter [17]. The study had a positive impact on improving the aerodynamic performance and reducing noise of axial flow fans. Wang et al. installed a leading-edge slat on a wind turbine blade, which effectively suppressed the flow separation and significantly increased the lift coefficient of the blade [18]. Chai et al. used the response surface method to optimize the performance of the cleaning fan of the rice combine harvester. The research improved the three structural parameters of the blade inlet installation angle, the blade curvature, and the blade inlet curvature. The CFD simulation test was used to obtain the flow field data, and it finally improved the wind speed and uniformity of the fan outlet [19]. Ye et al. studied the blade tip shape of the axial flow fan and compared the effect of different blade tip shapes on fan performance through flow efficiency and flow total pressure curve. The study improved three structural parameters of blade inlet installation angle, blade curvature, and blade inlet curvature. CFD simulation tests were used to obtain the flow field data, which eventually improved the fan flow and impeller efficiency [20].

Previous studies had provided abundant and valuable information for improving extractor performance, but most studies focused on how to reduce the impurity rate and loss rate without considering how to reduce energy consumption. Therefore, it is significant to reduce the energy consumption of the extractor based on maintaining the stability of the impurity rate and loss rate. In this study, a blade element analysis method with aerodynamic theory was proposed, and the energy consumption equation of the extractor was obtained. The impact of various parameters on the energy consumption and internal flow characteristics of the extractor was analyzed through fluid simulation and the bench test. The better structure and operation parameter combination of the extractor were determined. The relevant results are expected to provide the theoretical and experimental basis for the optimization of energy consumption of the extractor.

2. Extractor Energy Consumption Analysis

To obtain the factors that affect the energy consumption of the sugarcane harvester extractor, the load on the extractor blades during the operation needs to be analyzed. During the rotation of blades, the blades are mainly subjected to aerodynamic load, centrifugal load, and gravity load [21,22]. As most of the extractor blades on the sugarcane harvester are placed horizontally, the aerodynamic load makes the blades subject to tensile, bending, and torsional forces. The centrifugal load makes the blades subject to tensile and torsional forces, and the gravity load makes the blades subject to torsional and bending forces [23,24]. The blade rotation needs to overcome the torsional force; therefore, the factors influencing the energy consumption of the extractor are obtained by analyzing the torsional force of the blades under aerodynamic load and centrifugal load.

2.1. Aerodynamic Load Analysis

When the aerodynamic load on the extractor blades is analyzed, the extractor blades are divided radially into many micro-segments, and each micro-segment is a blade element with a length of dr. The forces acting on each segment are determined only by the aerodynamic performance of the micro-segments, without considering the interference between adjacent micro-segments along the blade radial direction. The forces and inlet velocity on the blade are shown in Figure 1.

Considering the rotating effect of the extractor impeller wake, radial and axial airflow velocity at extractor blades can be obtained as follows.

$$V=wr(1+b)$$

(1)

$$U=V_0(1-a)$$

(2)

$$a=\frac{1}{\frac{4{\mathrm{s}\mathrm{i}\mathrm{n}}^2 \varphi }{{\sigma }_r{C}_n}+1}$$

(3)

$$b=\frac{1}{\frac{4\sin \varphi \cos \varphi }{{\sigma }_r{C}_t}-1}$$

(4)

$${\sigma }_r=\frac{c\left(r\right)B}{2\pi r}$$

(5)

• V = radial airflow velocity at extractor blades (m/s)
• U = axial airflow velocity at extractor blades (m/s)
• r = distance from micro-segments to impeller center (m)
• w = impeller rotation angular speed (rad/s)
• $V_0$ = flow velocity before impeller (m/s)
• a = axial induction factor
• b = radial induction factor
• ϕ = inflow angle (°)
• $C_n$ = lift characteristic coefficient
• $C_t$ = drag characteristic coefficient
• ${\sigma }_r$ = micro-segment chord length solidity (m)
• B = blade number
• $c\left(r\right)$ = chord length at a given micro-segment (m)

The inflow angle can be expressed using blade attack angle and blade installation angle, as shown in the Equation (6).

$$\varphi =\alpha +\beta$$

(6)

• α = blade attack angle (°)
• $\beta$ = blade installation angle (°)

In fact, the velocity of the air flow through the extractor blade is the combined velocity of the axial air flow velocity and the radial air flow velocity. The equation of the relative combined velocity is as follows [25].

$$\Omega =\sqrt{V^2+U^2}=\sqrt{(1+b{)}^2(wr{)}^2+(1-a{)}^2{V}_0^2}$$

(7)

The blades are subjected to the lift and drag exerted by the airflow when the extractor works. The lift is perpendicular to the direction of the combined velocity, and the drag is parallel to the direction of the combined velocity. The aerodynamic force on the micro-segment with the length of dr can be expressed as Equations (8) and (9).

$$d{F}_L=\frac{1}{2}\rho C{\Omega }^2{C}_{n}dr$$

(8)

$$d{F}_D=\frac{1}{2}\rho C{\Omega }^2{C}_{t}dr$$

(9)

• C = chord length (m)
• $\rho$ = air density (kg/m³)

The lift and drag are projected to the vertical and tangential directions of the rotating plane of the extractor blade, respectively. The components of aerodynamic forces in the radial and axial directions of the blades can be obtained using Equations (10) and (11).

$$d{F}_{\mathrm{x}}=d{F}_D\sin \phi -d{F}_L\cos \phi =\frac{1}{2}{\Omega }^{2}C\rho C_{\mathrm{x}}dr$$

(10)

$$d{F}_y=d{F}_L\sin \phi +d{F}_D\cos \phi =\frac{1}{2}{\Omega }^{2}C\rho C_{y}dr$$

(11)

• C_x = radial force coefficient
• C_y = axial force coefficient

The aerodynamic resistance torque acting on the blade micro-segment dr is found using Equation (12).

$$d{M}_a=Bd{F}_{\mathrm{y}}r=\frac{1}{2}B{\Omega }^{2}C{C}_{\mathrm{y}}\rho rdr$$

(12)

The aerodynamic resistance torque on the whole extractor blade is found using Equation (13).

$$M_a=\frac{1}{2}{\int }_{r_0}^R\rho BC{\Omega }^2{C}_{y}rdr$$

(13)

• R = impeller radius (m)
• r₀ = distance between blade root and hub center (m)

2.2. Centrifugal Load Analysis

Centrifugal force is a mass force generated by the rotation of blades, which is perpendicular to the axis of rotation. Centrifugal force can be decomposed into longitudinal and transverse components. The transverse force is along the spreading direction of the blade, which makes the blade subject to tensile stress. The longitudinal force is perpendicular to the blade deployment direction, which makes the blade subject to centrifugal torque. Centrifugal force and centrifugal torque can be obtained using Equations (14) and (15).

$$F_r={ w}^{2}B{\int }_{r_0}^R{\rho }_{i}r{s}_{i}dr$$

(14)

$$M_p=w^{2}B{\int }_{r_0}^R{\rho }_i{r}^2{s}_{i}dr$$

(15)

• $F_r$ = centrifugal force (N)
• ${\rho }_i$ = density of micro-segment (kg/m³)
• $s_i$ = profile area of micro-segment (m²)

2.3. Energy Consumption Analysis

The total resistance torque of extractor blades is obtained by synthesizing pneumatic torque and centrifugal torque. The total resistance torque is shown in Equation (16).

$$M_t={M_a-M}_p$$

(16)

The extractor energy consumption can be obtained using Equation (17).

$$W=w{M}_t{\eta }_{m}T=wT(\frac{1}{2}{\int }_{r_0}^R\rho BC{\Omega }^2{C}_{y}rdr-w^{2}B{\int }_{r_0}^R{\rho }_i{r}^2{s}_{i}dr)$$

(17)

• T = extractor operation time (s)
• ${\eta }_m$ = transmission efficiency (%)

Based above equations, it can be concluded that the energy consumption is related to the parameters such as extractor rotational speed, blade number, installation angle, blade chord length, and impeller radius, which is consistent with the research conclusions of Wang et al. [26]. Since the impeller radius is not convenient to change due to the limitation of the extractor structure, the effect of extractor rotational speed, blade number, installation angle, and chord length on energy consumption is mainly discussed in the following sections.

3. Materials and Methods

The fluid simulation and bench test were used to test the extractor energy consumption under different combinations of impeller structural and operating parameters. The effect and mechanism of impeller structure and operating parameters on energy consumption were analyzed to optimize extractor energy consumption.

3.1. Physical Model and Numerical Calculation Method

Reynold’s-Averaged Navier–Stokes (RANS) equations were used to calculate the flow fields used [27]. The assumptions adopted in the calculation process were that the air is incompressible regardless of the influence of gravity and other volume forces. The steady state incompressible governing RANS equations are stated as follows [28,29].

$$\frac{\partial \rho }{\partial t}+\frac{\partial \left(\rho u_i\right)}{\partial x_i}=0$$

(18)

$$\frac{\partial u_i}{\partial t}+u_j\frac{\partial u_i}{\partial x_j}=-\frac{1}{\rho }\frac{\partial p}{\partial x_i}+\mu \frac{{\partial }^2{u}_i}{\partial x_j\partial x_j}-\frac{\partial }{\partial x_j}\left(u_i^{\prime }{u}_j^{\prime }\right)$$

(19)

• ρ = air density (kg/m³)
• $t$ = flow time (s)
• $u_i$ = the time-averaged velocity (m/s)
• $x_i$ = the spatial coordinate
• $\mu$ = dynamic viscosity (Pa s)
• $u_i^{\prime }$ = fluctuating velocity component in the ith direction (m/s)

Since the governing equation is not closed, a turbulence model must be introduced to solve the flow field. The standard k-ε model is more accurate in calculating two-dimensional flow with pressure gradient and three-dimensional boundary layer flow. However, the simulation results of rotating flow, large curvature flow, and separated flow will deviate from the real flow [30]. The k-ω model does not involve the complex nonlinear attenuation function in the k-ε model. The k-ω model is more accurate and has a better convergence effect for a low Re number and compressible and shear flow. However, the k-ω model is sensitive to the condition of free air flow, and the calculated results will also change greatly when the ω (specific dissipation rate) of the inlet changes. The SST k-ω model is obtained by combining the k-ω model and the k-ε model through the mixing function. The k-ω model is used near the wall of the boundary layer, and the k-ε model is used outside the boundary layer and in the free flow. Therefore, the SST k-ω model can predict the reverse pressure gradient flow more sensitively y [31]. The calculation model is as follows.

$$\frac{\partial \left(\rho k\right)}{\partial t}+\frac{\partial \left(\rho k{u}_i\right)}{\partial x_i}=\frac{\partial }{\partial x_j}\left[\left(\mu +\frac{{\mu }_t}{{\sigma }_k}\right)\frac{\partial k}{\partial x_{k3}}\right]-{\beta }^{\ast }\rho k\omega$$

(20)

$$\frac{\partial \left(\rho \omega \right)}{\partial t}+\frac{\partial \left(\rho \omega u_i\right)}{\partial x_i}=\frac{\partial }{\partial x_j}\left[\left(\mu +\frac{{\mu }_t}{{\sigma }_{\omega 3}}\frac{\partial \omega }{\partial x_j}\right]+\left(1-F_1\right)2\rho \frac{1}{{\sigma }_{\omega 2}\omega }\frac{\partial k}{\partial x_j}\frac{\partial \omega }{\partial x_j}-\beta \rho {\omega }^2\right.$$

(21)

where $k$ = the turbulent kinetic energy; $\epsilon$ = the turbulent dissipation rate; ${\mu }_t$ = turbulence viscosity coefficient; $\partial k$ = $F_1{\sigma }_{k1}+(1-F_1){\sigma }_{k2}$; $F_1$ = thah(${\Phi }_2^2$); ${\Phi }_2=max\left[2\frac{\sqrt{k}}{0.09\omega y},\frac{500\mu }{\rho y^2\omega }\right]$; ${\sigma }_{\omega }={ F}_1{\sigma }_{\omega 1}+\left(1-F_1\right){\sigma }_{\omega 2}$; ${\sigma }_{\omega 1}$ = 2; ${\sigma }_{\omega 2}$ = 1.17; $\beta =F_1{\beta }_1+(1-F_1){\beta }_2$; and ${\beta }^{\ast }$ = 0.09.

The second-order upwind difference scheme is adopted as the numerical discretization method. The commercial CFD software Fluent (ANSYS 2022 R1, Canonsburg, PA, USA) was applied to implement the above algorithms.

CFD simulation was used to study the influence of extractor speed, blade number, installation angle, and blade chord length on the aerodynamic performance and energy consumption of the extractor. It is necessary to model and mesh the extractor before simulation. The boundary condition of the inlet is set to the pressure inlet with an initial pressure inlet of 0. The boundary condition of the outlet is set to the pressure outlet that is set as 0. The extractor speed can be set in the unit area conditions. A low turbulent intensity (5%) and low turbulent viscosity ratio (10) were chosen. The under-relaxation factors of pressure, momentum, turbulence kinetic energy, and turbulent dissipation rate are 0.4, 0.7, 0.8, and 0.8, respectively. The simulation uses multiphase flow and chooses the Eulerian model. The model uses dynamic mesh, and the mesh method selects smoothing.

To eliminate the influence of grid division accuracy on the simulation results, it is necessary to verify the independence of the grid. When mesh independence was considered, the total pressure and impeller torque with no extracting load were taken as indicators, the revolving speed of the impeller was set to 1650 r/min, and six types of meshes were verified. The result is shown in Figure 2. The evaluation index tends to be stable when the number of grids is more than 0.52 million. A fifth type of mesh with a total number of 0.64 million was adopted after the accuracy and calculation time of the results were considered comprehensively, and the grid size was 15 mm.

The extractor model and grid division are shown in the Figure 3. The extractor inlet diameter is 0.9 m, the outlet length is 1.11 m, the impeller diameter is 0.85 m, and the overall size is 1.61 m × 1.11 m × 0.88 m. This step was crucial to ensuring accurate and reliable predictions of aerodynamic performance and energy consumption. The use of CFD simulations enabled us to gain deeper insights into the operation of the extractor and make informed decisions on how best to optimize its performance.

3.2. Sample Preparation and Test Device

This study was carried out from November 2022 to January 2023 at the China Agricultural University Professor Workstation (22.513231° N, 107.684859° E) in Fusui County, Guangxi Zhuang Autonomous Region, China. The sugarcane variety used in the test was Guitang 43, with a cylindrical stem, a height of 2700 $\pm$ 50 mm, a moisture content of 67.5 $\pm$ 0.5%, and a diameter of 30 $\pm$ 2.5 mm. The sugarcane for the test was obtained from a cane field near the workstation with an area of 0.5 hectares and row spacing of 1.2 m. The sampling point was determined according to the five-point sampling method based on the Chinese National Standard GB/T 5262-2008 (China, 2008) [32].

The extractor test bench used in the energy consumption test is shown in Figure 4. The bench consists of pressure sensors, extractor, conveying roller, conveyor belt, impurity collecting device, data acquisition component, bench controller, and hydraulic pump station. The conveying speed of the conveyor belt is kept at 0.5 m/s during the test.

The functionality of the pressure sensor is to detect the hydraulic pressure at the inlet and outlet of the extractor hydraulic motor. The conveying roller and extractor on the bench are the same as the conveying roller and extractor on the sugarcane harvester (4GZQ-130, Luoyang Chenhan Agricultural Equipment Technology Co., Ltd., Luoyang, China). The conveyor belt and conveying roller are used to transport sugarcane for cutting and impurity removal. The extractor is driven by a hydraulic motor. The impurity collecting device is used to collect the discharged impurities to calculate the impurity rate and loss rate. The data acquisition component includes a computer and a data acquisition card, which is used to collect the pressure data detected by the pressure sensor.

Orthogonal experimental design is a design method to study multi-factors and multi-levels. It can systematically analyze the effect of each parameter and obtain the best parameter combination with the least scheme, the least cost, and the shortest test period. It is an efficient, rapid, and economical experimental design method. This paper studies four factors, which are suitable for the orthogonal test. The energy consumption, impurity rate, and loss rate are taken as indicators in the test.

To determine the impurity content, the mixture falling from the bottom of the extractor is collected during the test. The impurity rate is calculated as follows.

$$P_i=\frac{m_i}{m_t}\times 100\%$$

(22)

• $m_i$ = impurity content (kg)
• $m_t$ = total mass of mixture falling from the bottom of the extractor (kg)

To determine the loss rate, the sugarcane is weighed before the test, and the mixture falling from the bottom of the extractor and the mixture collected by the impurity collection device are collected during the test. The loss rate is calculated as follows.

$$P_l=(1-\frac{m_d}{m_c-m_j})\times 100\%$$

(23)

• $m_c$ = total mass of sugarcane before test (kg)
• $m_j$ = mass of collected impurity (kg)
• $m_d$ = mass of sugarcane in the mixture falling from the bottom of the extractor (kg)

The energy consumption of the extractor during the test can be calculated according to the pressure difference between the oil inlet and the oil outlet of the hydraulic motor. The principle of obtaining the oil inlet and outlet pressure is shown in Figure 5.

The energy consumption of the extractor can be calculated using Equation (18).

$$W_t=1000Pt=1000\Delta pq{\eta }_{\mathrm{v}}t$$

(24)

$$q=\frac{nQ}{{\eta }_q}$$

(25)

• $W_t$ = energy consumption of the extractor (J)
• $P$ = power of the extractor (kw)
• $t$ = operating time of the extractor (s)
• $q$ = flow of hydraulic motor (L/min)
• $\Delta p$ = pressure difference between oil inlet and outlet of hydraulic motor (Mpa)
• $n$ = rotational speed of hydraulic motor (RPM)
• $Q$ = displacement of hydraulic motor (ml/r)
• ${\eta }_{\mathrm{v}}$ = mechanical efficiency of hydraulic motor (%)
• ${\eta }_q$ = volumetric efficiency of hydraulic motor (%)

4. Results and Discussion

4.1. Fluid Simulation

The impact mechanism of various structural parameters and operating parameters on the internal flow field and energy consumption of the extractor is explored through fluid simulation. A pressure nephogram of the suction and pressure surfaces of impellers operating at different rotational speeds is presented in Figure 6. The pressure variation curve is shown in Figure 7.

As illustrated in Figure 6, significant pressure variations occur on the suction and pressure surfaces of the impeller at different rotational speeds. Figure 6a demonstrates that the area of low-pressure zones on the blade gradually increases with the increase in rotational speed. The concentration of low-pressure zones on the upper and middle parts of the blade indicates that the blade primarily relies on the upper and middle parts of the blade to perform work. As shown in Figure 6b, the area of high-pressure zones on the blade also gradually increases with the increase in rotational speed. Both the low-pressure and high-pressure zones are mainly concentrated on the middle and upper parts of the blade, where a higher pressure difference can be produced to generate higher energy. Therefore, at a rotational speed of 1650, the performance of the extractor blade is the most powerful, which can produce a greater wind speed and lower impurities. However, the energy consumption and loss rate will also be increased. It can also be observed from Figure 6 that when the rotational speed increased from 1250 to 1450, both the areas of low-pressure zones and high-pressure zones increased significantly. However, when the rotational speed increased from 1450 to 1650, the area of low-pressure zones changed only slightly.

Figure 7a illustrates that a significant pressure gradient occurs at the blade tip on the suction surface at a rotational speed of 1250, while a larger gradient appears at the blade base at a rotational speed of 1650, which requires more energy consumption to drive. However, the overall extractor performance is not greatly improved. Figure 7b shows that a significant pressure gradient occurs at the blade top on the pressure surface at a rotational speed of 1650, which easily leads to internal turbulence in the extractor. Based on the results, it would be better to control the rotational speed at around 1450, where the area of the low-pressure zone is relatively large and the pressure gradient changes more smoothly, ultimately reducing turbulence and improving the extractor performance.

To better observe the effect of the change in blade installation angle on the internal flow field of the extractor, the pressure and velocity nephogram of the cross section at the center of the blade is shown in Figure 8. The variation curves of lift and drag coefficients are shown in Figure 9.

According to Figure 8, the area of the low flow speed area in the center of the extractor first decreases and then increases as the installation angle increases. When the installation angle is 20, the low flow speed area reaches maximum size, and the outside wind speed is also lower than at angles of 25 and 30, which is not conducive to the removal of sugarcane leaves. A shunt phenomenon occurs in the center of the low flow speed area when the installation angle is 30, which may cause impurity accumulation. Additionally, the low-pressure area of the extractor gradually expands as the installation angle increases, resulting in lower pressure at the top of the blade. When the installation angle increases from 20 to 25, the range of the low-pressure area expands significantly, and the blade’s work ability is also greatly improved. However, when the installation angle increases to 30, a high-speed area appears at the blade top, and the pressure is also low, which is not conducive to the removal of sugarcane leaves. From Figure 9, it is evident that the lift coefficient initially increases rapidly and then decreases slowly as the installation angle increases. This not only enhances the aerodynamic performance of the extractor but also increases the blade’s ability to resist stalling. When the installation angle is around 25, the lift coefficient reaches its maximum value, and the energy required to achieve the same cleaning effect is lower, which is conducive to reducing energy consumption. In addition, the drag coefficient also changes significantly with the installation angle, showing a trend of first increasing, then decreasing, and then increasing again. When the installation angle is 20, the drag coefficient is the lowest, and another low value appears at 25. Based on the comprehensive analysis, an installation angle around 25 is the most conducive to improving performance and reducing energy consumption as the lift coefficient is the highest and the drag coefficient is relatively low. Therefore, the installation angle should be maintained at approximately 25.

The previous research indicates that the change in chord length can easily lead to a change in turbulent coherent structure on the surface of the extractor impeller, thus affecting the performance of the extractor. The identification of vortex structures near impellers with different chord lengths using Q criterion is shown in Figure 10.

As depicted in Figure 10, a vortex structure is observed near the root of the impeller for all three chord lengths. The pressure inside the vortex structure is lower than in other positions, and a notable pressure gradient exists near the vortex structure resulting in reduced impeller efficiency and increased energy consumption. As the chord length increases, the low-pressure area gradually expands. When the chord length is 150, the vortex structure appears curved and folded, which is prone to disrupting the streamlined shape near the root of the blade, leading to airflow separation and reduced extractor performance. With increasing chord length, the size of the vortex structure gradually increases, and the vortex structure breaks near the root of the blade, leading to reduced blade performance and increased extractor energy consumption, which ultimately reduces the impeller’s performance. Therefore, it is not recommended to increase the blade chord length excessively. Instead, an appropriate increase in the chord length is beneficial for expanding the low-pressure area, improving the structure size of vortex structure, reducing energy consumption, and improving extractor performance.

To further explore the influence of blade number change on extractor performance, the vortex structures and pressure nephograms near impellers with different blade numbers are shown in Figure 11.

As shown in Figure 11, the lower pressure area can extend to a smaller blade height as the number of blades decreases, which is beneficial for increasing the pressure difference between the suction and pressure surfaces. Overall, similar pressure distribution patterns are observed for all blade numbers. A high-pressure zone occurs at the rear of the blade, and the low-pressure area gradually increases toward the top of the blade. The differences in the vortex structure among the blades are relatively apparent, with the high-pressure zone occupying around one third of the total blade area. The high-pressure area near the root of the three-bladed and four-bladed impellers is smaller, indicating that blades with fewer numbers lead to a less extended high-pressure area. Moreover, the high-pressure area’s extension length, caused by the root vortex, can exceed 0.5 times the blade height. As the number of blades increases, the high-pressure zone at the root of the blades gradually expands, and the vortex stripe breaks. Therefore, the wind speed reduces with the increase in the number of blades.

Although the number of blades has no significant influence on the pressure and vortex structure of the blades, it will cause great changes in the lift coefficient when the chord length and installation angle change, which will lead to changes in energy consumption and aerodynamic performance. The change in lift coefficient under different blade numbers is shown in Figure 12.

As shown in Figure 12a, the lift coefficient first increases and then decreases with the increase in chord length at different blade numbers. The lift coefficient fluctuates greatly with five blades, which easily leads to an airflow disorder inside the extractor and reduces the work capacity. For three blades, the lift coefficient will increase to the maximum, then slowly decrease and then stabilize, which is beneficial for improving the ability of the blade to resist stalling. Figure 12b demonstrates that the lift coefficient first increases and then decreases as the installation angle increases for the three blades and four blades. However, the lift coefficient for the five blades first decreases and then increases with an increasing installation angle. Additionally, with the change in chord length and installation angle, the lift coefficient of three blades and four blades increases first and then decreases, and the change is relatively stable. In contrast, for the five blades, the lift coefficient significantly fluctuates as the vortex intensity at the root of the impeller increases with increasing blade numbers. In addition, the changes of chord length and installation angle also aggravate the changes of vortex, resulting in excessive fluctuation of lift coefficient. In summary, the results indicate that the three blades and four blades perform better than the five blades. Moreover, a moderate increase in the chord length and installation angle is beneficial in improving the lift coefficient and enhancing the aerodynamic performance.

The extractor blades used for a period on the sugarcane harvester are shown in Figure 12.

It can be seen from Figure 13 that the wear of the extractor blades mainly occurs at the right top and left bottom of the blades. It shows that the pressure and speed in these areas are high when the extractor is working, which is consistent with the simulation results. The wear degree and wear range of the extractor with three blades is smaller, making it a preferable choice for extractor blade design. By considering simulation results alongside practical observations such as blade wear, engineers and operators can optimize extractor performance, minimize energy consumption, and extend the life of the equipment.

4.2. Bench Test

A three-level orthogonal table L9(3⁴) with four factors and three levels was selected for this test, and each test was repeated five times.

To further explore the influence mechanism of extractor structure and operation parameters on energy consumption, impurity content, and loss rate, a bench orthogonal test was carried out. The influence degree of each factor and level on the test indices can be obtained using range analysis. The optimal parameter combination of each factor was obtained via visual analysis diagram. The results are expected to provide a theoretical basis for the optimization of energy consumption of the extractor. The orthogonal test results are shown in

Table 1. Orthogonal test results of four factors and three levels of energy consumption.

Number	A (RPM)	B	C (°)	D (mm)	Energy Consumption (KJ)	Impurity Rate (%)	Loss Rate (%)
1	1250	3	20	150	523.5	9.50%	8.27%
2	1250	4	25	200	537.3	9.44%	8.32%
3	1250	5	30	250	569.46	10.23%	8.35%
4	1450	3	25	250	597.93	7.38%	8.16%
5	1450	4	30	150	587.16	8.13%	8.05%
6	1450	5	20	200	613.92	8.45%	8.07%
7	1650	3	30	200	661.02	7.41%	9.44%
8	1650	4	20	250	691.65	8.28%	9.16%
9	1650	5	25	150	637.56	8.63%	9.84%

.

According to the orthogonal test results, the energy consumption range analysis shown in

Table 2. Energy consumption range analysis.

Category	A (KJ)	B (KJ)	C (KJ)	D (KJ)
Ⅰj	1630.26	1782.45	1829.07	1748.22
Ⅱj	1799.01	1816.11	1772.79	1812.24
Ⅲj	1990.23	1820.94	1817.64	1859.04
Ⅰj/3	543.42	594.15	609.69	582.74
Ⅱj/3	599.67	605.37	590.93	604.08
Ⅲj/3	663.41	606.98	605.88	619.68
Rj	119.99	12.83	18.76	36.94

was obtained through range analysis. The Ⅰ_j, Ⅱ_j, and Ⅲ_j (j = A, B, C, and D) in the table are the sum of the test results of the first, second, and third levels of the test factors, respectively. The Ⅰ_j/3, Ⅱ_j/3, and Ⅲ_j/3 are the average values of test results of various factors and levels, respectively. R_j (range) is the difference between the maximum value and the minimum value of the average value of each factor.

The range reflects the influence degree of factors on the test indices. A greater range indicates that this factor has a greater impact on the test indices [33]. According to the values of R_j given in

Table 3. Summary of test results of energy consumption, impurity rate, and loss rate.

Index	Factor Importance Order	Better Level
		A (RPM)	B	C (°)	D (mm)
Energy consumption	A > D > C > B	1250	3	25	150
Impurity rate	A > B > D > C	1450	3	25	200
Loss rate	A > C > B > D	1450	4	20	200

, it can be concluded that R_A > R_D > R_C > R_B. The order of the factors affecting the energy consumption of the extractor is A > D > C > B. Extractor rotational speed had a greatest effect on energy consumption, followed by blade chord length and blade installation angle, and the number of blades had the least effect on energy consumption.

The average distribution of energy consumption at different levels of influencing factors is illustrated according to Table 2, as shown in Figure 14.

It can be seen from Figure 14 that extractor rotational speed and blade chord length had a great effect on energy consumption, and both were positively correlated with energy consumption. The energy consumption changed greatly when the extractor rotational speed increased from 1450 RPM to 1650 RPM, so extractor rotational speed should be controlled between 1250 RPM and 1450 RPM, which is also consistent with the simulation analysis results. The energy consumption changed greatly when the chord length of blades increased from 150 mm to 200 mm, because the vortex structure size increases at the root of the blade and the vortex structure separates. The number of blades had little effect on energy consumption. With the increase in blade installation angle from 20 to 25, the impact of loss of airflow entering the blade flow channel became smaller, which led to the reduction of energy consumption. When the installation angle was increased from 25 to 30, the relative windward area of the blade became larger. The airflow is greatly hindered, and it is easy to produce secondary flow and separated flow, which leads to the increase in energy consumption.

Similarly, the average distribution of impurity rate and loss rate at different levels of influencing factors is shown in Figure 15 and Figure 16.

Based on the comprehensive analysis of energy consumption and impurity rate and loss rate tests, the test results are summarized in Table 3.

It is necessary to obtain lower energy consumption on the premise of ensuring the stability of impurity rate and loss rate. The results of fluid simulation analysis and bench test show that the combination of a rotational speed of 1450 RPM, a blade number of 3, an installation angle of 25°, and a blade chord length of 200 mm is optimal for an extractor.

It can be seen from

Table 4. Performance comparison between optimized extractor and original extractor.

Category	Energy Consumption (KJ)	Impurity Rate (%)	Loss Rate (%)
Original extractor	661.02	7.41%	9.44%
Optimized extractor	558.57	7.15%	8.27%
Effect	15.49% (↓)	3.51% (↓)	12.39% (↓)

Note: (↓) represents a decrease.

that the energy consumption after optimization was 558.57 KJ, which was 15.49% lower than that before optimization. The impurity rate decreased by 3.51%, so it can be considered that the impurity rate was almost unchanged and remained stable. The loss rate of the optimized extractor was 12.39% lower than the original extractor. It can be concluded that the optimized extractor parameters not only ensure the stability of the impurity rate, but they also effectively reduce the loss rate and working energy consumption. The study results are expected to provide a theoretical and experimental basis for the optimization of energy consumption of extractors.

5. Conclusions

In this study, the influence of various influencing factors on the energy consumption and internal flow characteristics of the extractor was analyzed through theoretical analysis, fluid simulation, and bench test, and the performance of the extractor was optimized. The main conclusions are as follows:

• A blade element analysis method with aerodynamic theory was put forward to analyze the stress of the extractor blade, and the energy consumption equation and influencing factors of extraction were obtained. The main influencing factors of energy consumption are rotational speed, blade number, installation angle, and chord length.
• The influence of various factors on the flow characteristics of the extractor was studied comprehensively using fluid simulation analysis. The rotation speed significantly affected the pressure distribution on the suction and pressure surfaces of the blades. Installation angle mainly affected the low-pressure area of the blades and the lift–drag coefficient, thereby affecting the performance of the extractor. The vortex structure near the root of the blade was largely affected by the chord length. The number of blades had an influence on the stress of blades and the vortex structure at the blade root. Furthermore, the influence of the number of blades on the lift coefficient became greater with the change in chord length and installation angle.
• The results of the bench test indicated that the order of influence of various factors on energy consumption was rotational speed > chord length > installation angle > number of blades.
• Comprehensive fluid simulation and bench test results indicated that the combination of a rotational speed of 1450 RPM, a blade number of 3, an installation angle of 25°, and a blade chord length of 200 mm was optimal for the extractor. The performances of the optimized extractor and the original extractor were compared, and the results indicated that the energy consumption of the optimized extractor was reduced by 15.49%. The impurity rate decreased by 3.51%, and the loss rate decreased by 12.39%. The study results are expected to provide a theoretical and experimental basis for the optimization of energy consumption of the extractor.