| Literature DB >> 17544186 |
Y F Huang1, G Q Wang, G H Huang, H N Xiao, A Chakma.
Abstract
To date, there has been little or no research related to process control of subsurface remediation systems. In this study, a framework to develop an integrated process control system for improving remediation efficiencies and reducing operating costs was proposed based on physical and numerical models, stepwise cluster analysis, non-linear optimization and artificial neural networks. Process control for enhanced in-situ bioremediation was accomplished through incorporating the developed forecasters and optimizers with methods of genetic algorithm and neural networks modeling. Application of the proposed approach to a bioremediation process in a pilot-scale system indicated that it was effective in dynamic optimization and real-time process control of the sophisticated bioremediation systems.Mesh:
Substances:
Year: 2007 PMID: 17544186 DOI: 10.1016/j.envpol.2007.04.010
Source DB: PubMed Journal: Environ Pollut ISSN: 0269-7491 Impact factor: 8.071