Stochastic perturbation analysis of thermal food processes with random field parameters

A first-order perturbation algorithm has been used to evaluate the effect o f parameter uncertainties of the random variable and random field type on t he temperature inside a can during a typical thermal sterilization process. The algorithm is based on the finite element formulation of the heat condu ction equation and is considerably faster than a Monte Carlo algorithm for a comparable accuracy. The perturbation algorithm is, however, only applica ble when the coefficient of variation of the random parameters is smaller t han 20%. In the case of random field parameters the finite elements should be smaller than half the scale of fluctuation. It was shown that, in the ca se of random field parameters, the magnitude of the temperature fluctuation s in the can increases with increasing scale of fluctuation. If the scale o f fluctuation becomes very large, the random field degenerates to a random variable and the variance of the temperature at an arbitrary position and t ime is maximal. For a typical sterilization process it appears that the the rmophysical properties are the most important sources of variability.