Crossflow Microfiltration of Aqueous Suspensions with Guar and Xanthan Gums: Identification of Solutions Using Artificial Neural Networks

Abstract

Artificial Neural Networks (ANNs) are mathematical models used in the computational area that act in an analogous way to the central nervous system of living beings, which possess the ability of acquiring knowledge in a technique called machine learning, allowing them to recognize patterns and stop numerous applications. Therefore, the objective was to develop Neural Networks capable of identifying aqueous solutions with Guar and Xanthan gums (widely used in the food industry) during the crossflow microfiltration process. The networks were trained in the supervised learning algorithms trainscg, trainlm and traingd, all in the 70/15/15 model, for a range of five to fifteen neurons in the hidden layer, whose datasets were found in the literature, referring to temperature, flow velocity, pressure, transmembrane flow rate, time and membrane pore size. The software used to implement the ANNs was MATLAB and the evaluation criteria consisted of the analysis of the parameters confusion matrix, error histogram,performance and ROC curve. In summary, ten ANNs had satisfactory performances, presenting confusion matrices with accuracies above 98.8%, error histogram graphs being Gaussian centered at 0, decaying performance curves with stopping criterion equal to 6 errors in the validation set and ROC graphs similar to a square with vertices at (0,0), (1,0), (0,1) and (1,1), results considered satisfactory in the literature.