Modeling techniques and processes control application based on Neural Networks with on-line adjustment using Genetic Algorithms

Abstract

-In this work a strategy is presented for the temperature control of the polymerization reaction of styrene in suspension in batch. A three-layer feed forward Artificial Neural Network was trained in an off-line way starting from a removed group of patterns of the experimental system and applied in the recurrent form (RNN) to a Predictive Controller based on a Nonlinear Model (NMPC). This controller presented very superior results to the classic controller PID in the maintenance of the temperature. Still to improve the performance of the model used by NMPC (RNN) that can present differences in relation to the system due to the dead time involved in the control actions, nonlinear characteristic of the system and variable dynamics; an on-line adjustment methodology of the parameters of the exit layer of the Network is implemented, presenting superior results and treating the difficulties satisfactorily in the temperature control. All the presented results are obtained for a real system.

Predictive Control of a Batch Polymerization System Using a Feedforward Neural Network With Online Adaptation by Genetic Algorithm

“…As it is not possible to ensure that the network obtained by adjusting parameters, in each new interval for 140 generations, is always better than what was implemented in a past instant, or even that which was trained offline, the algorithm presented by Marcolla et al (2009) was used in order to avoid, at any given moment, the use of parameters that may lead to a worse performance for the model. This could cause divergences in the solution, since the parameters are adapted from the values obtained in a previous sampling, making the control system unstable.…”

“…Deviations that occur in the system, which are not predicted in the model, cause various control problems, such as high overshoot, offset, and others. In this case, adaptive techniques can be used to adjust the weights of the network and prevent such deviations from occurring (Zeybec et al, 2003;Ng and Hussain;2004;Marcolla et al, 2009, Hosen et al, 2011.…”

Effect of various material parameters on barrier properties of high-density polyethylene/polyamide 6/clay nanocomposites

“…22 –30 On what concerns the polymerization process, various aspects were modeled and optimized with ANNs. 31 –36 Although multiple types of ANNs were developed, the majority of publications are based on the feed-forward neural network and especially on feed forward multilayer perceptron (MLP) neural networks.…”