An introduction to computational sensor psychrometrics for the digitization of convective cobed maize drying

Abstract

This study introduces sensor psychrometrics, as opposed to the physically constrained static gravimetric experimentation, for the characterisation of cobed maize drying. Simultaneous spreadsheet integration and Solver analytics were used to interpret the digital drying curve from sensor-sampled psychrometric data. The results were validated gravimetrically at dryer settings of 37, 43, and 53°C. The ear drying curves were reproduced with a goodness-of-fit consistency of 0.997–0.999 across the different calibration settings. The new methodology, presented along with its uncertainty, exploits advances in computing and instrumentation to digitize empirical drying, moving experimentation beyond the rigid confines of the lab to the desktop.