MF joined the meeting and gave an overview of UKWIR's
role. UKWIR was set up by the UK water industry to
provide a common research programme for UK water
that Wallingford Software's role is between water companies, academic research
and end users. The software has a need to be profit driven and the most
saleable software is usually original software that has been
built upon. DF stated that the future is in different
kinds of modelling and optimisation software.
AS from Ewan Associates
introduced his role as consultancy. He said that there was
a difficulty with access routes into water companies but that
the DTI funded schemes provided a beneficial access route.
His role is generally as a partner or part of a framework with
water companies, strengthening relationships between academia
The discussion turned to research funding
mechanisms and ways of filtering projects originating from
MF spoke of the Global Water
Research coalition where UKWIR is one of twelve research funders.
He said that water industry problems are similar worldwide, money is not being effectively
spent and return needs to be maximised.
that although Liverpool use EPSRC as a source of funds they
increasingly need industrial resources. If research goes
into responsive mode there is less than 20% success in proposal
LB stated that research ideas from
industry are more successful because they are bigger, the
problem being that industry needs to focus on research areas and does not have the time
to wait for funding.
MF said UKWIR's 2002/2003 programme had 160
expressions of interest, out of which have come 31 new
projects starting from April 2002. RB suggested pulling
out some of the projects that were not funded. MF agreed
that recycling the unfunded projects seemed a good idea and
would look into it. Action MF.
The subject of data was discussed. AS said that there is a lack of acknowledgement in
water companies as to how data rich they are. GAW mentioned that it takes longer than anticipated to
obtain data sets and also in the format needed.
LK stated that a lot of data has
already been collected and is not being made the most of. DF added that
a large amount of data is poor quality. AC said that until
data is applied can poor data be found.
LB said that all types of data were needed for
network modelling backgrounds through to data mining techniques.
For example network calibration, online measurements, offline
measurements, leakage control, risk analysis,
discoloration. We should think about how we can use the
data and information to build better models and come to