Marcos Calle Suárez

Artificial neural networks as alternative tool for minimizing error predictions in manufacturing ultradeformable nanoliposome formulations

Abstract This work was aimed at determining the feasibility of artificial neural networks (ANN) by implementing backpropagation algorithms with default settings to generate better predictive models than multiple linear regression (MLR) analysis. The study was hypothesized on timolol-loaded liposomes. As tutorial data for ANN, causal factors were used, which were fed into the computer program. The number of training cycles has been identified in order to optimize the performance of the ANN. The optimization was performed by minimizing the error between the predicted and real response values in the training step. The results showed that training was stopped at 10 000 training cycles with 80% of the pattern values, because at this point the ANN generalizes better. Minimum validation error was achieved at 12 hidden neurons in a single layer. MLR has great prediction ability, with errors between predicted and real values lower than 1% in some of the parameters evaluated. Thus, the performance of this model was compared to that of the MLR using a factorial design. Optimal formulations were identified by minimizing the distance among measured and theoretical parameters, by estimating the prediction errors. Results indicate that the ANN shows much better predictive ability than the MLR model. These findings demonstrate the increased efficiency of the combination of ANN and design of experiments, compared to the conventional MLR modeling techniques.

Improved virtual build to order

The evolution of the markets in recent years caused the evolution of production systems to adapt to a new kind of demand. This demand is characterized by requiring products more varied, available within a short time and at a reasonable price. We observed an evolution of productive systems from mass production to mass customization strategies. The adaptation of order fulfilment and production strategies to this new paradigm of mass customization offers new opportunities for companies improving their competitive advantages. This work is devoted to perform a simulation study of a new order fulfilment procedure (i-VBTO) to be used in mass customized environments. The results are promising, although additional studies are needed to bring this system into practice.