Adaptive-network-based Fuzzy Inference (ANFIS) Modelling of Particle Image Velocimetry (PIV) Measurements in Stirred Tank Reactors

Mixing represents an energy-intensive unit operation which can significantly influence the performance of the different industrial processes. Efficient mixing is necessary to reduce spatial inhomogeneities within reactors and bioreactors. Particularly, in bioreactors, shear stress on microorganisms and physicochemical gradients might affect the physiological state of the biotic phase and, hence, decrease bioreaction yields. The present work presents an innovative machine-learning modelling approach which uses the adaptive-network-based fuzzy inference system (ANFIS) on experimentally velocity fields data collected through Particle Image Velocimetry (PIV) in STR under different operating conditions. The calibration and optimization of the ANFIS system are performed by dividing the PIV data into two subsets: training and validation data. Then, a sensitivity analysis is carried out varying the percentage of training data and certain features of the membership functions (number and type). The fitness of the produced models was scored by means of the fuzzy Goodness Index (GI), which combines the correlation coefficient (R 2), index of agreement (IA) and relative root mean square error (RRMSE).