October 2008

Special Report: Process Control and Information Systems

Predicting important parameters using artificial neural networks

Consider them for complex nonlinear processes

Ramakumar, K. R., Gujarat Refinery, Indian Oil Corporation Limited

Artificial neural networks (ANNs) are a powerful tool for modeling nonlinear processes. In a system, various parameters are encountered that do not obey a linear relationship vis-à-vis other parameters. ANNs are one of the answers for such complex systems. In general, ANNs are utilized to establish a relationship between a set of inputs and a set of outputs. Familiarizing the basics. Based on the inputs (x1, x2, . . . , xn) and the weights assigned to each input stream (w1, w2, . . . , wn), the neuron processes this input- (X1 w1 + x2 w2 + . . . + xn wn) and gives an output based on the assigned activation function (also called transfer function, Fig. 1). This output in turn could be a

Log in to view this article.

Not Yet A Subscriber? Here are Your Options.

1) Start a FREE TRIAL SUBSCRIPTION and gain access to all articles in the current issue of Hydrocarbon Processing magazine.

2) SUBSCRIBE to Hydrocarbon Processing magazine in print or digital format and gain ACCESS to the current issue as well as to 3 articles from the HP archives per month. $409 for an annual subscription*.

3) Start a FULL ACCESS PLAN SUBSCRIPTION and regain ACCESS to this article, the current issue, all past issues in the HP Archive, the HP Process Handbooks, HP Market Data, and more. $1,995 for an annual subscription.  For information about group rates or multi-year terms, contact email Peter Ramsay or call +44 20 3409 2240*.

*Access will be granted the next business day.

Related Articles

From the Archive

Comments

Comments

{{ error }}
{{ comment.comment.Name }} • {{ comment.timeAgo }}
{{ comment.comment.Text }}