Industrial Applications using Neural Networks : Seminar Report and PPT
Process monitoring and control
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.
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.
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.
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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