ANN modeling of tincal ore dehydration

Abstract

Abstract Tincal ore is a preferred material in many industrial applications, especially without water. It is important to dehydrate boron ores so that they can be used in materials engineering. For this purpose, some heat treatments must be carried out. Heat treatments are associated with additional costs. It is possible to model heat treatments using artificial intelligence methods, determine optimal process conditions and achieve the desired results with much less processing effort. In this study, a dehydration process was first carried out to dehydrate tincal ore and ANN (artificial neural networks) modeling of this process was investigated using the parameters of temperature, time and amount of ore. The possibility of achieving the desired H2O, B2O3 and Na2O concentration values in the dewatering process in the shortest time and by the shortest route was investigated using the ANN model. In the modeling, a single model was designed for the changes in concentrations and this model was trained separately for each. The result of the modeling was that the R 2 values for all three models were close to each other and were approximately 0.98. It was thus shown that the ANN method can be successfully modeled for dewatering processes.