Mixing time prediction with artificial neural network model
Jolanta Szoplik, Marta Ciuksza
Abstract
The study presents the methodology for artificial neural network model learning for the purpose of predicting mixing time on a set including 782 data depending on: type, diameter and position of the impeller in the vessel without baffles and the Newtonian fluid flow character and position of the tracer dosing point in the vessel. Numerous models in the form of MLP (multilayer perceptron) were trained, differing in the number of neurons in the hidden layer of the network, and the best quality MLP 15-8-1 model was determined, which can be successfully used to predict mixing time in a vessel of any configuration (mean error MAPE = 13%). Based on the predict error MAPE level, the quality of networks and the quality of the obtained predicts was assessed, whereas based on the model of cumulated risk R sk-20 the level of likelihood of obtaining the predict of mixing time bearing the MAPE error>20% was estimated.