Abstract
This study used a predictive controller based on an empirical nonlinear model comprising a three-layer feedforward neural network for temperature control of the suspension polymerization process. In addition to the offline training technique, an algorithm was also analyzed for online adaptation of its parameters. For the offline training, the network was statically trained and the genetic algorithm technique was used in combination with the least squares method. For online training, the network was trained on a recurring basis and only the technique of genetic algorithms was used. In this case, only the weights and bias of the output layer neuron were modified, starting from the parameters obtained from the offline training. From the experimental results obtained in a pilot plant, a good performance was observed for the proposed control system, with superior performance for the control algorithm with online adaptation of the model, particularly with respect to the presence of off-set for the case of the fixed parameters model.
Highlights
Control systems based on models have proven to be efficient in chemical processes, especially in cases with strong interactions between input and output, high dead time, and physical constraints on the variables (García et al, 1989)
In the online controller application, the Feedforward Artificial Neural Network (FANN) is transformed into a RNN (Recurrent Neural Network) due to the feedback of the output value, i.e., the network is retrained in real time and used as a predictor of multiple steps ahead
In this work we sought to emphasize that, even when a nonlinear controller is used, there is a need for online adaptation of an empirical model that was adjusted from data obtained for a system where no variations occur in the dynamic function of time processing
Summary
Control systems based on models have proven to be efficient in chemical processes, especially in cases with strong interactions between input and output, high dead time, and physical constraints on the variables (García et al, 1989). It is well established that phenomenological models typically provide a more accurate description of the process, especially for extrapolation, and empirical models are easier to obtain and manipulate during online applications in real time, especially when obtaining experimental data is facilitated (Vieira et al, 2003; Cubillos et al, 2007; Janakiraman et al, 2013). For this reason, some applications require an optimization/adaptation of the model developed and eventually the use of hybrid structures, which take into account empirical knowledge plus phenomeno-. When the system treated has strong nonlinearities, neural networks have been widely applied for identification and modeling (Ng and Hussain, 2004; Qiao and Han, 2012)
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