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Industrial Applications using Neural Networks

Published on Aug 15, 2016


Process monitoring and control systems applications

The pressure on the Process Industries to improve yield, reduce wastage, eliminate toxins and above all increase profits makes it essential to increase the efficiency of process operations.

One possible approach for achieving this is through the improvement of existing process monitoring and control systems.Many process monitoring and control schemes are based upon a representation of the dynamic relationship between cause and effect variables.

In such schemes, this representation is typically approximated using some form of linear dynamic model, such as finite impulse response (FIR), autoregressive with exogenous variable (ARX) and auto-regressive, moving average with exogeneous variable (ARMAX) models. Once determined, the dynamic process model of the system can be integrated within a variety of process monitoring and control algorithms. In process control, for example, the model can be incorporated within a model based predictive control (MBPC) algorithm, such as Generalised Predictive Control.

Alternatively, for process monitoring, the residuals (prediction errors) from such models can be analyzed to detect abnormal operation. Such monitoring and control schemes have found widespread application in industry and have led to significant improvements in process operations. Unfortunately, the models employed within the schemes tend to be linear in form. Although linear models can provide acceptable performance for many systems, they may be unsuitable in the presence of significant non-linearities. For such systems it may be beneficial to employ a model that reflects the non-linear relationship between cause and effect variables.

Preliminary studies have indicated that artificial neural networks (ANNs) may provide a generic, non-linear solution for such systems. As with standard linear modelling techniques, ANNs are capable of approximating the dynamic relationships between cause and effect variables. In contrast to linear techniques however, ANNs offer the benefit of being able to capture non-linear relationships. Since the performance of process monitoring and control algorithms are dependent upon the precision of the model embedded within them, ANN models have the potential to provide benefits to these algorithms when applied to nonlinear systems.

Artificial Neural Networks

A mechanistic model derived from first principles is theoretically the most accurate model that can be developed for any system. Unfortunately, the resources required to develop such a model for even the simplest of systems tends to prohibit their use. Consequently engineers tend to rely on system identification techniques to establish process models. The most common approaches to system identification include dynamic process models such as ARX and ARMAX, which are linear in form.

The majority of process systems however contain varying degrees of non-linearity that can reduce the accuracy of such models. To recover this loss in prediction accuracy many research projects in recent years have focused on the use of neural networks as a tool for system identification.

As with linear models, ANNs provide a description of the relationship between cause and effect variables. The benefit ANNs offer over linear models is that they are capable of modelling nonlinear relationships. In fact studies have shown them to be capable of modelling any non-linear function to arbitrary accuracy. Although there exist many different ANN structures, they do possess some common features.

They are generally composed of numerous processing elements, termed nodes, which are arranged together to form a network. The most commonly used processing element is one, which weights the input signals and then sums them together with a bias term. The neuron output is then obtained by passing the summed, weighted inputs through a non-linear activation function, such as the hyperbolic tangent.

A common type of ANN model used in many applications is the feed forward network. This type of network comprises an input layer where input information is presented to the network, one or more hidden layers where neuron processing takes place and an output layer from which the network outputs are obtained.

It is termed a feed forward network because the outputs from one layer are fed forward as inputs to the subsequent layer. The topology of such layered networks is usually described according to the number of nodes in each layer. For example, a network with 2 inputs, 1 hidden layer with 4 nodes and 1 output is referred to as a 2-4-1 network. This basic feedforward network is useful for many applications, however, a number of modifications have been proposed to improve its suitability for application to process systems.

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