Inverse estimation of thermophysical properties and initial moisture content of cereal grains during deep-bed grain drying

This paper proposes and demonstrates the use of inverse methods to estimate grain properties during deep-bed drying. An inverse analysis was performed to estimate the bulk density, specific heat and initial moisture content of cereal grains, using only grain temperature measurements as inputs. Grain temperature data obtained from numerically solving the direct problem were used to generate the temperature measurements. An iterative procedure, based on minimizing a sum of squares function with the conjugate gradient method and the discrepancy principle, was used to solve the inverse problem. A statistical analysis was performed to evaluate the accuracy of the estimated results. The effects of measurement errors and the sensor location on the inverse solution were also investigated. The close agreement between the exact and the estimated results shows the capability of the proposed method in estimating unknown parameters.