Towards an innovative virtual optimisation machine for the power industry Online publication date: Mon, 18-Jul-2005
by Benedetto Risio, Frank Blum, Jens Hetzer, Alexander Berreth, Uwe Schnell Klaus R.G. Hein
Progress in Computational Fluid Dynamics, An International Journal (PCFD), Vol. 5, No. 7, 2005
Abstract: The present work reports the development of a fully automated, innovative, optimisation environment (optimisation machine) based on evolutionary algorithms. The evolutionary algorithm uses the same mechanisms (selection, recombination, and mutation) as the natural evolution to identify fittest (=optimised) individuals (=operational settings and designs). The optimisation machine is capable of automatically determining potential better scenarios, evaluating the achieved results, and working out future development strategies. The applicability of this machine for industrial problem solving is demonstrated by the computer-aided repetition of successful optimisation exercises in power plants. The optimisation machine is able to identify the same optimised settings that can be found experimentally. A comparison of the required time and manpower effort clearly shows the advantages of the computer-aided approach.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the Progress in Computational Fluid Dynamics, An International Journal (PCFD):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook