This paper presents a novel solution, using a vision-sensor, to a challenging control problem in cryogenic food freezing industry. This industrial application is characterized by significant variation in input food products because cryogenics-freezing technology with its inherent process flexibility is typically used in low volume/high mix applications. Current industrial controllers use PLCs for regulating the belt speed of the tunnel, which leads to conservative set-points and consequently significant operational cost and frequent over-freezing. Servo control of the process is difficult because of the complicated non-linear dynamics of cryogenic freezing caused by phase-change, and thermal dynamics between the frozen products and the tunnel. The solution presented in this paper uses a vision-sensor to estimate the shape, size, and heat load of food products that will enter the freezing tunnel. An analysis of the sensor location and its impact on disturbance feed-forward control is also presented. Efficacies of these developments are verified in an industrial case study using a commodity webcam for capturing and processing two-dimensional streaming images, and integrating the processed information with an industrial control system using model-predictive control architecture. The proposed solution is especially attractive for the food industry because of the low-cost and non-contact features of webcam, operational cost savings through reduced consumption of cryogen, and improved quality through reduction in variation of temperature of the frozen products.
All Science Journal Classification (ASJC) codes
- Computer Science(all)