Design and simulation of the roller-type device for eel vibratory grading

Monopterus albus (rice field eel) is one of the commercially important fish species in Asian areas. It is often required to efficiently and accurately grade the different body sizes in aquaculture production and standardized processing. In this study, a roller-type machine was proposed for the rice field eel grading. A vibration mechanism and a spraying device were integrated to fully meet the demand for the high-throughput and high-precision size classification. The numerical simulation and experimental validation were conducted to evaluate the grading performance of the equipment. A coupled dynamic model of the grading machine was then established to optimize the operating parameters using RecurDyn–EDEM co-simulation technology. Specifically, the rigid–flexible multibody dynamics of the grading system was modeled in RecurDyn platform, including the vibration exciter, grading sieve, and supporting structure. While the discrete element method was implemented to characterize the motion, collision behavior, and contact interactions of individual rice field eels in EDEM software. The optimal contact models and mechanical parameters were defined to represent the eel–eel and eel–machine interactions. Spraying-assisted lubrication was evaluated to improve material dispersion and then reduce excessive friction during grading. The performance of classification was quantitatively assessed to select grading efficiency and accuracy as the primary indices. A single-factor experiment was conducted to clarify the independent effects of vibration frequency, sieve inclination angle, and feeding quantity on the grading performance. The results showed that the grading efficiency first increased and then decrease, with an increase in the number of rice field eels, while the accuracy decreased gradually. The grading efficiency increased, as the inclination angle of the grading sieve increased, whereas the grading accuracy decreased. Furthermore, the grading efficiency increased continuously, with increasing vibration frequency, while the grading accuracy first increased and then decreased. Subsequently, a multi-factor orthogonal experiment was applied to identify the relative importance of each parameter and their combined influence on grading efficiency and accuracy. Physical experiments were further verified under optimal parameters using a prototype grading machine. A comparison was made on the numerical predictions and measurement. The results showed that the greatest influencing factors on both grading accuracy and efficiency were the grading sieve inclination angle, followed by feeding quantity and vibration frequency. The grading performance was reduced, due to insufficient dispersion, material accumulation, or excessive sliding velocity on the sieve surface. Parameters was then optimized to improve grading performance using Design-Expert software, with the grading efficiency and grading accuracy as the objective variables. The optimal combination of parameters was determined as a vibration frequency of 30 Hz, a grading sieve inclination angle of 2.6°, and a feeding quantity of 27 individuals. The better performance was achieved in the grading efficiency of 0.637 individuals per second, and the average grading accuracy of 0.867. The relative errors between experiment and simulation were 7.378% for the grading efficiency and 3.46% for the grading accuracy, indicating better consistency and prediction of the RecurDyn–EDEM coupled simulation. The optimal grading parameters were obtained from the complex interaction between vibration-driven machinery and elongated aquatic organisms. A reliable basis can provide for the structure and performance of eel vibratory grading equipment. This finding can offer practical technical support for the intelligent, efficient, and scalable grading of aquatic products with similar morphologies.