Model predictive controller for cryogenic tunnel freezers Original Research Article

Cryogenic freezing is an upcoming food processing technology that is gaining popularity because of the lower setup costs and improved food quality when compared to mechanical freezing. However, high operating costs are its major deterrent: the cost of cryogenic freezing is almost eight times that of its mechanical counterpart, and this is mainly attributed to the cost of the cryogen that is used. When the variability in the input heat load and/or the product characteristics is high, the economics become highly unfavorable due to either over or under freezing, which in turn imply either excess use of cryogen or reduced throughput. There is therefore a need for a good control mechanism that will minimize the losses due to over or under freezing while maintaining the required throughput. Current industrial freezers use programmable logic controllers (PLCs), which have conservative set-points and consequently significant operational costs. This paper proposes and tests the design of a model predictive control (MPC) algorithm with a zero absolute error (ZAE) minimizer that addresses these issues simultaneously. The controller combines features of feedback–feedforward control to adjust cryogen consumption and throughput rate of the tunnel freezers to minimize the deviation of the end temperature of the food product from the desired set point temperature at the outlet. The stability, accuracy and robustness of the proposed method are tested on a simulation model. The controller guarantees stability, and for an input variance of 10%, the average deviation of the temperature from the set point was found to be less that 0.25%.