科研之友
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摘要
The grain temperature and moisture are non-uniformly distributed in grain stores. This article proposed a distributed parameter model predictive control (DP-MPC) method for the forced air ventilation process through stored grain. The goal of DP-MPC was to control the grain temperature and moisture and to save energy. The grain bulk was divided into different control units to consider the distribution of grain bulk characteristics. The controller was designed with a heat and mass transfer predictive model and an objective function. The objective function was solved with particle swarm optimization (PSO) algorithm. In each control cycle, one control unit was taken as the controlled objective. A control unit shift strategy was proposed to shift controlled objectives. Both simulations and experiments were carried out to validate the control effectiveness. Results showed that the proposed DP-MPC method could control the whole grain bulk temperature and moisture content and optimize the system energy consumption. The maximum grain temperature and moisture content differences between the controlled value and the set point were less than 1 degrees C and 1%, respectively. The energy consumed during ventilation with the DP-MPC was 15.5% less than that consumed during ventilation without control.
