Experimental Study of Multiscale Fresh Tea Leaf Sorting Device with Intelligently Controlled Drum–Axial Airflow Coupling Based on Response Surface Methodology
This study describes a self-developed intelligently controlled drum–axial airflow coupled sorting device. Using the sorting rate as the evaluation index, single-factor tests were conducted to analyze the influence patterns of axial airflow startup time, drum rotational speed, and axial airflow velocity on the sorting effect. The Box-Behnken response surface design method was adopted to construct a quadratic regression model for the sorting rate, analyze the interaction between factors, and conduct parameter optimization and verification. All three test factors had an extremely significant effect on the sorting rate. Among them, the quadratic effect of axial airflow velocity was the strongest, and parameter fluctuations had the highest sensitivity to the sorting results. Significant coupling interaction between axial airflow startup time and drum rotational speed was observed. Through global optimization, the following optimal process parameter combination was obtained: axial airflow startup time, 6.5 s; drum rotational speed, 16 rpm; and axial airflow velocity, 7.5 m/s. Under these conditions, the measured average sorting rate reached 80.46%, with a relative error of only 1.03% compared with the model predicted value, indicating good model prediction accuracy. These results can provide a theoretical basis and technical support for the research and development of intelligent sorting equipment for multiscale fresh tea leaves and optimization of process parameters.
To address the problems of low sorting accuracy and poor operation stability caused by physical leaf entanglement in traditional drum screening of fresh tea leaves, a multiscale fresh tea leaf sorting system with drum-axial airflow coupling based on intelligent control was designed. The axial moving distances of fresh tea leaves of different scales at wind speeds of 5, 7, and 9 m/s were calibrated through bench tests, and the optimal wind speed parameter for secondary fine screening was determined. A numerical model of the axial airflow field inside the drum was established via Fluent software, and a coupled sorting test platform was built to compare the sorting performance of the traditional pure drum screening mode and that of the coupled intelligent sorting mode. The results showed that 7 m/s is the optimal axial airflow velocity for secondary fine screening of multiscale fresh tea leaves during this test, which can realize effective back-blowing of small-scale materials and accurate screening of large-scale materials. At this velocity, the flow field is evenly distributed, and the effective thrust area highly matched the sorting demand. The average sorting efficiency of the coupled intelligent sorting mode reached 84.8%, which is 25.6% higher than that of traditional pure drum screening, with favorable sorting accuracy and operation stability. These findings can provide a theoretical basis and technical reference for the optimization, upgrading, and intelligent transformation of high-efficiency fresh tea leaf sorting equipment.
Traditional tea harvesting methods often lack precision, leading to significant leaf damage and reduced operational efficiency. To address these limitations, this paper proposes an airflow-driven end-effector designed for selective tea harvesting, utilizing pneumatic transport to move harvested leaves from the shearing interface to storage. A theoretical model based on force equilibrium was developed to determine the critical transport velocity (CTV), calculated to be approximately 3.6 m/s. The theoretical prediction was further evaluated through CFD analysis using ANSYS Fluent with a k–ω Shear Stress Transport (SST) turbulence model across inlet velocities ranging from 2.5 m/s to 4.5 m/s. Simulation results indicate that inlet velocities at or above 3.6 m/s provide airflow conditions favourable for sustained pneumatic leaf transport, with 4.5 m/s achieving outlet velocities up to 6.586 m/s and mass flow rates of approximately 1.59×10−3 kg/s, while lower velocities fail to maintain sufficient transport momentum. Despite considerable velocity decay along the transport path, adequate velocity is retained in critical regions for sustained pneumatic conveyance. These findings demonstrate the feasibility of airflow-based transport for selective harvesting and provide a quantitative basis for optimizing end-effector design.
Sharamjeet Shaurya, Arjun Agarwal, Dhirendra Kumar Verma et al.· International Conference on...· 0 citations
Vertical container-type smart farms offer an effective way to improve productivity in space-limited environments, but their multi-layer arrangement often creates stagnant airflow and nonuniform microclimates. In this study, computational fluid dynamics (CFD) and the Taguchi method were combined to optimize the layout of air-conditioning components in a container-type strawberry smart farm. Three spatial design factors were considered: inlet position, outlet position, and carbon dioxide (CO2) nozzle position. Nine cases were constructed using a three-level L9 orthogonal array, and spatial standard deviation was used as the uniformity index for temperature, CO2 concentration, and local mean age of air (LMA). The baseline model showed low-velocity and recirculation regions around the cultivation beds and structural frame, producing substantial nonuniformity in CO2 distribution. The middle layer had the highest mean CO2 concentration, whereas the lower layer had the lowest concentration, with a maximum interlayer difference of 53.7 ppm. The Taguchi analysis showed that outlet position was the most influential factor, with a delta value of 8.74 and a contribution ratio of 57.78%. The optimal configuration placed the inlet at 1,200 mm, the outlet at 1,500 mm, and the CO2 nozzle at 1,200 mm. Compared with the baseline model, this configuration reduced the standard deviation of CO2 concentration from 75.85 to 7.08 ppm and improved the standard deviation of LMA from 5.63 to 4.98 s. However, temperature uniformity slightly decreased as the analysis used steady-state conditions, fixed operating flow rates, and a simplified crop geometry; the optimal layout should be revalidated when the container size, crop canopy density, or operating strategy changes. These results demonstrate that spatial microclimate uniformity in multi-layer container-type strawberry smart farms can be improved efficiently by optimizing component layout.
Min Jung, Md. Didarul Islam, J. Jeong et al.· Journal of Agricultural Engi...· 0 citations
In wheat threshing, reducing total grain loss and energy consumption is crucial for both economic and sustainable food security. This study investigates the effects of threshing drum type (straight and helical row), drum peripheral speed (36.73–48.98 m s−1), and drum-concave clearance (35–50 mm) on total grain loss and specific fuel consumption in a stationary threshing machine using a full factorial design. The optimum machine settings (drum type, peripheral speed and drum–concave clearance) that simultaneously minimize these two outputs were then determined. We systematically compared three surrogate modelling approaches—Response Surface Model (RSM), Gaussian Process Regression (GPR), and Artificial Neural Network (ANN)—to identify the most effective method for small-dataset optimization in threshing machine design. The best model was selected through cross-validation, and optimization was performed using the NSGA-II multi-objective genetic algorithm. GPR yielded the highest prediction accuracy for both outputs (R2 in prediction data: 0.99 for total grain loss and 0.91 for specific fuel consumption). Multi-objective optimization revealed a conflict between the two objectives; the best balance was achieved for the helical drum at a peripheral speed of approximately 41.5 m s−1 and a drum–concave clearance of 50 mm (predicted total grain loss approximately 3.7%, specific fuel consumption approximately 2.98 mL kg−1). Compared to the straight-row drum, the helical drum provided lower losses and fuel consumption, as well as approximately 3.5 times wider safe operating range. It should be noted that this optimum was predicted by the surrogate model and agreed closely with the best measured treatment; it was not confirmed by an independent validation experiment. The results demonstrated that combining a surrogate model with a genetic algorithm is an effective tool for optimizing threshing machine parameters.
K. Çarman, Ergün Çıtıl, Hasan Özçelik et al.· Agriculture· 0 citations
The convective drying of agricultural materials is an energy-intensive process, and optimizing dryer design is critical for improving efficiency and product quality. This study presents a comprehensive heat and mass transfer model for the convective drying of alfalfa leaves in a rotary drum dryer. Freshly harvested leaves with an initial moisture content of approximately 70% (w.b.) were used as the test material. The proposed system features a simplified drum design aimed at enhancing process efficiency while reducing equipment complexity. The primary objective was to reduce the moisture content of alfalfa leaves to below 50% to ensure their quality during subsequent storage and transportation. To determine the optimal operating conditions, the kinematics of leaf motion inside the rotating drum and the associated heat and mass transfer phenomena were investigated through analytical modeling, numerical simulation, and experimental studies on a laboratory-scale physical model. An analytical model was developed to establish relationships between transverse kinematic characteristics (detachment condition, Froude number, drum inclination angle), average longitudinal velocity, and residence time. Numerical simulations based on the Navier–Stokes equations (continuity, momentum, and energy) provided detailed moisture content distributions within individual leaves under varying airflow orientations and drying durations. The novelty of this work lies in the integrated determination of optimized operating parameters through combined analytical, numerical, and experimental approaches. A regression model relating final moisture content to key process variables (air velocity, temperature of 60 °C, drum rotation frequency and mass of loaded material) was developed from experimental data, yielding practical recommendations for the design and operation of rotary drum dryers for alfalfa and similar agricultural materials.
Gani Zhumatay, O. Zhortuylov, K.A. Moshanov et al.· Applied Sciences· 0 citations
Discharge uniformity and metering accuracy are essential for precision fertilization. An inclined screw fertilizer metering device with a spherical buffering outlet was designed and optimized using the discrete element method (DEM) and bench experiments. A particle-flow model was established using EDEM (DEM simulation software) to evaluate the effects of conveying inclination angle and outlet accumulation length on discharge performance using the coefficient of variation (CV), standard deviation, amplitude, and pulsation degree. Single-factor tests indicated that an inclination angle of 12° and an accumulation length of 32 mm produced favorable discharge performance. Two-factor optimization identified 13.5° and 40 mm as the optimal combination, yielding the lowest CV (2.36%) and standard deviation (51.88). Bench experiments further verified the performance of the optimized device. Compared with a conventional fluted-wheel fertilizer metering device, the optimized screw device reduced the average discharge error from 8.94% to 3.35% and achieved average CV values of 5.91%, 4.39%, and 4.44% under low-, medium-, and high-discharge-rate conditions, respectively. These results show that optimizing the inclination angle and outlet accumulation length can effectively improve fertilizer discharge uniformity and metering accuracy.
Jingyi Zhang, Can Lou, Chen-Xi Duan et al.· Agronomy· 0 citations
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