Mathematical simulation of one-stage grinding of products frozen in blocks

Abstract

One-stage grinding by milling is energy-and resource-saving process. It allows to create the line for the production of finished meat products with automatic control system (ACS) for assessing quality of minced meat based on artificial intelligence within the "untended manufacturing" principle. To design the line, methods of mathematical simulation of both individual machines operation and the entire technological process were used. In the article, the problem is considered of mathematical simulation of the milling tool (milling shaft) speed control circuit to construct ACS for grinding process of raw materials frozen in blocks. The objectives of the ACS analysis are as follows: 1) establishing the covariance of the controlled variable with the driving signal, i.e. the exact correspondence of the milling tool rotational speed to the specified (optimal) value for a given type of grinded raw material; 2) construction of the transient process performance curve in the system for given parameters of adjustment (stabilization). The scheme for cutting speed control circuit of the ACS is shown, where the controlled object (CO) is a set of devices: asynchronous short-circuited electric motor (AM) of the cutting mechanism; frequency transformer (FT) of supply voltage that changes the frequency of AD rotation (the frequency of the milling tool rotation); the grinder milling tool. The parameters of the control circuit are calculated at rated AM power of 22 kW and 11 kW for two possible configurations of the grinder. The task for synthesis of the control circuit is to minimize the deviation from the setting of milling tool rotation speed under the influence of external disturbances, which leads to decrease in size (thickness) variance of the meat chips with the improvement of the meat product quality. A technique is proposed for calculating the degree of raw material grinding for prediction of its change under the influence of external factors using Monte Carlo computing experiment. This technique allows to increase the amount of statistical information (grinding of "virtual" meat blocks) about the change in the rotation speed of the grinder milling tool under the influence of external disturbances. These estimates determine the prediction for the degree of grinding to plan further processing of ground raw materials.