Industrial Applications using Neural Networks
Thread Rating:
  • 0 Vote(s) - 0 Average
  • 1
  • 2
  • 3
  • 4
  • 5
computer science crazy
Super Moderator

Posts: 3,048
Joined: Dec 2008
22-09-2008, 10:38 AM

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 project and implimentations 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.
Use Search at wisely To Get Information About Project Topic and Seminar ideas with report/source code along pdf and ppt presenaion
Active In SP

Posts: 9
Joined: Aug 2010
05-08-2010, 11:13 PM

hey...can you give the download version.?
interesting topic..
i'm new dont kno much of the steps to download.

Important Note..!

If you are not satisfied with above reply ,..Please


So that we will collect data for you and will made reply to the request....OR try below "QUICK REPLY" box to add a reply to this page
Tagged Pages: industrial applications using neural networks doc, the industrial application of neural networks, industrial application using neural networking, industrial applications of neural networks, industrial process control systems, seminar papers on industrial applications of neural networks, industrial applications neural networks,
Popular Searches: trisil applications, industrial applications of anfis, industrial hygiene, applications for droid, to study of industrial chimeny, vlsi for neural networks and their applications pdf, applications of ai,

Quick Reply
Type your reply to this message here.

Image Verification
Please enter the text contained within the image into the text box below it. This process is used to prevent automated spam bots.
Image Verification
(case insensitive)

Possibly Related Threads...
Thread Author Replies Views Last Post
  Cut Detection in Wireless Sensor Networks pdf project girl 2 1,219 16-07-2015, 09:21 PM
Last Post: Guest
  computational intelligence in wireless sensor networks ppt jaseelati 0 351 10-01-2015, 03:10 PM
Last Post: jaseelati
  nanochemistry applications ppt jaseelati 0 254 18-12-2014, 02:11 PM
Last Post: jaseelati
  MANETS: MOBILE ADHOC NETWORKS seminar projects crazy 2 1,961 11-06-2014, 09:44 AM
Last Post: seminar project topic
  Towards Reliable Data Delivery for Highly Dynamic Mobile Ad Hoc Networks seminar ideas 11 3,911 02-04-2014, 12:50 PM
Last Post: Guest
  Computer Science and Applications seminar ideas 2 6,664 20-03-2014, 04:28 PM
Last Post: navasfiroz
  Bluetooth Based Smart Sensor Networks (Download Full Seminar Report) Computer Science Clay 91 68,780 04-03-2014, 12:46 AM
Last Post: nikhil goyal
  Computerized Paper Evaluation using Neural Network computer science crazy 11 8,900 03-02-2014, 03:21 PM
Last Post: Guest
  Vehicular Ad Hoc Networks (VANETs): Challenges and Perspectives seminar poster 0 462 29-10-2013, 01:40 PM
Last Post: seminar poster
  CLUSTERING IN WIRELESS SENSOR NETWORKS PPT project girl 1 1,183 14-10-2013, 09:30 AM
Last Post: Guest