From: harrisp@wl.aecl.ca
Newsgroups: comp.ai.neural-nets
Subject: Advice re suitabilty of ANN's for this application
Date: 14 Aug 92 15:43:33 GMT
Organization: AECL RESEARCH



We are trying to determine if the following problem is one which is suitable
for addressing with a neural network. If so, what sort of network would be
"best" to use? Any advice re: software to use, implementation and testing
of the system, relevant literature etc would be greatly appreciated.

The problem may be stated as follows:

In a nuclear power reactor, a wide variety of chemical processes may occur in
the water used both in the boiler and the steam side of the heat transport 
system. Important chemical parameters to be monitorred include the pH, 
conductivity, dissolved hydrazine, dissolved oxygen, and particulate 
concentrations. Often, transient conditions occur, during changes in reactor
power, which lead to sudden changes in the water chemistry.
These chemical processes can lead corrosion and the formation 
of "crud", leading to boiler fouling. This in turn leads to lost revenue
due to derating, shutdown, cleaning and replacement of components.

Monitorring of the chemistry has been in progress for a number of years,
primarily through the acquisition and analysis of grab samples, although more
online monitoring is being included as time progresses. The reactor
operators are required to monitor the chemistry in the heat transport
systems, to attempt to identify when an undesirable event is likely to
occur, and to take preventive measures to minimize damage and lost revenue.
The process of trying to assimilate the large amounts of data, and to 
qualitatively determine whether this will lead to an undesirable event, is
of course quite difficult.

We have as data (for a potential training set) the chemistry related data
from before, after and during a number of "events". Also included in the 
data sets is the efforts taken by the operators to deal with the event,
and info on what caused the event to happen. Two approaches have
been proposed to make use of this data. One is to develop a mechanistic
model based on the chemical reaction kinetics of the species and surfaces
present in the system. This model could then be used to determine the 
conditions under which fouling is likely to occur. The second approach would
be to use a non-mechanistic approach, such as an Artificial Neural Network,
which would be trained, using the data described previously, in such a way
that the data for the "current" measurements, and perhaps some information 
from "past" measurements, could be used as input to the network, and the
network would provide as output:

	a) Problem  / No Problem    (minimal information needed)

	     This would be what we would consider successful, but not
	     very satisfactory solution to the problem

	b) Problem Identification   (ie there is a problem due to the combined
					 effects of high dissolved oxygen and
			                 low pH {this type of information to
				         have been obtained from the training 
					 sets}	 )

	This would be both successful and satisfactory if achieved.
		
	c) Problem Identification and/or Solution Proposal

		(ie. the network identifies the problem and proposes
		     a method, based on past experience, for resolution
		     of the problem)

	This would be the ideal solution to the problem.

The main desire in this program is provide an automated method for dealing
with the data to minimize the need of the reactor operators to concern
themselves with the chemistry of the heat transport loop, and focus even
more of there attention of reactor operating conditions and safety (which is
already there primary responsibility, they would prefer not to have to worry
to much about the chemistry end of the heat transport)

An expert system approach has been attempted by others for solving this
problem, but it apparently didn't meet with much success. Hard to get much
info about it, as it was done under a proprietary research contract to an
electrical utility.

ANY ADVICE/SUGGESTIONS/INFORMATION re the feasibility of this project
would be greatly appreciated.

				Thanks in advance
					Phil Harris
Newsgroups: comp.ai.neural-nets,comp.ai,comp.compression,comp.theory