Linked Life Data

17544186

Source:http://linkedlifedata.com/resource/pubmed/id/17544186

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2008-3-3
pubmed:abstractText
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.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Feb
pubmed:issn
0269-7491
pubmed:author
pubmed:issnType
Print
pubmed:volume
151
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
460-9
pubmed:meshHeading
pubmed:year
2008
pubmed:articleTitle
IPCS: an integrated process control system for enhanced in-situ bioremediation.
pubmed:affiliation
State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing, China. yuefeihuang@tsinghua.edu.cn
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't