By Topic

Parallel real-valued genetic algorithms for bioremediation optimization of TCE-contaminated groundwater

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
C. A. Garrett ; Inst. of Technol., Wright-Patterson AFB, OH, USA ; Junqi Huang ; M. N. Goltz ; G. B. Lamont

Remediation of groundwater contamination remediation can involve very expensive processes. A recent field evaluation of in situ aerobic cometabolic bioremediation demonstrated that this in situ (meaning “in place”) technology has promise as an inexpensive and effective alternative to current technologies for the remediation of trichloroethylene (TCE)-contaminated groundwater. A mathematical fate and transport model has been developed to simulate the relevant physical, chemical, and biological processes that affect TCE fate and transport during the application of in situ bioremediation. In order to cost-effectively implement the technology developers must understand how to adjust engineered parameters in order to optimize system performance. While the fate and transport model can provide system performance information, the complexity of the system and the number of engineered parameters that can be varied make determining an optimal design problematic. Thus, real-valued genetic algorithms are used to determine those design parameter values that attempt to optimize the performance of this complex treatment system. In augmenting the remediation objective function with time-varying penalty weights associated with the model constraints, “acceptable” design parameter values were obtained. By executing the genetic algorithm in parallel using a “farming model” on an efficient parallel computational platform with MPI, we were able to obtain reasonable run times

Published in:

Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on  (Volume:3 )

Date of Conference: