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Predicting the instant moisture content of the low-rank coals under the different drying conditions is crucial to construct the optimal system design and operation related to drying processes. Even if the thin-layer drying models are at the center of the field studies, the disadvantage of these type models is that the prediction results are valid only for the conditions of the drying experiment. Conversely, artificial intelligence models can provide accurate prediction results under a wide range of different conditions. Nevertheless, they are not practical because of their implicit forms and require both the specific software and experts. In this study, the GMDH-type neural network is applied for the first time in developing explicit model equations for the prediction of coal moisture at any time during the drying process. 223 experimental instances are used, representing coal moisture contents obtained under the different drying conditions. The considered parameters are bed height (80–150 mm), coal sample size (20–50 mm), drying air velocity (0.4–1.1 m/s), drying air temperature (70–160 °C), and drying time (0–270 minute). The developed equation is nonlinear and provides satisfactory prediction accuracy (R 2  is 0.96–0.99) for different drying conditions. Additionally, its usage is quite practical due to the explicit form.

Explicit equation derivation for predicting coal moisture content in convective drying process by GMDH-type neural network