東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Optimal design for groundwater remed...

Adaramola, Olufemi Olawale.

FindBook

Google Book

Amazon

博客來

Optimal design for groundwater remediation under uncertainty.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Optimal design for groundwater remediation under uncertainty./
作者:	Adaramola, Olufemi Olawale.
面頁冊數:	208 p.
附註:	Adviser: Teresa B. Culver.
Contained By:	Dissertation Abstracts International68-09B.
標題:	Engineering, Civil. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3283288
ISBN:	9780549258261

Optimal design for groundwater remediation under uncertainty.
Adaramola, Olufemi Olawale.

Optimal design for groundwater remediation under uncertainty. - 208 p.

Adviser: Teresa B. Culver.

Thesis (Ph.D.)--University of Virginia, 2008.

Optimization modeling has been shown to be an important tool in achieving cost-effective groundwater remediation. This study addresses two of the primary remaining challenges for optimal remedial design: the inherent uncertainty in the system and the significant computational demand. Currently most optimal remediation design tools are deterministic, assuming full knowledge of all inputs. Those that include uncertainty typically utilize a random sampling of possible conductivity fields as input.

ISBN: 9780549258261Subjects--Topical Terms:

783781
Engineering, Civil.

Optimal design for groundwater remediation under uncertainty.
LDR:03360nam 2200313 a 45 001 957496
005 20110630
008 110630s2008 ||||||||||||||||| ||eng d
020 $a 9780549258261
035 $a (UMI)AAI3283288
035 $a AAI3283288
040 $a UMI $c UMI
100 1 $a Adaramola, Olufemi Olawale. $3 1280844
245 1 0 $a Optimal design for groundwater remediation under uncertainty.
300 $a 208 p.
500 $a Adviser: Teresa B. Culver.
500 $a Source: Dissertation Abstracts International, Volume: 68-09, Section: B, page: 6146.
502 $a Thesis (Ph.D.)--University of Virginia, 2008.
520 $a Optimization modeling has been shown to be an important tool in achieving cost-effective groundwater remediation. This study addresses two of the primary remaining challenges for optimal remedial design: the inherent uncertainty in the system and the significant computational demand. Currently most optimal remediation design tools are deterministic, assuming full knowledge of all inputs. Those that include uncertainty typically utilize a random sampling of possible conductivity fields as input.
520 $a In the existing robust remediation design approach, the initial contaminant plume prior to remediation and the fitness of the cleanup policies during optimization are estimated by assuming that the random samples of the possible conductivity fields have equal likelihood of occurrence. However, when the information from concentration samples is utilized, the scaled likelihood of occurrence of conductivity realizations can be established. In the first steps of this research, a scaled likelihood (ScL) approach to robust remediation design is developed by considering the ScL of random conductivity realization when estimating the initial contaminant plume and when determining the fitness of the cleanup policies during optimization. Thus, the developed ScL-based robust remediation design approach uses multiple conductivity realizations, but places greater weight on conductivity realizations that result in more accurate representations of the initial concentration measurements. In addition, robust optimal designs are developed using sets of unconditional and conditional conductivity fields. Different conditional conductivity sets are conditioned to varying numbers of field samples. The performance, in terms of costs and reliability, of the robust optimizer as a function of the number of field samples are explored.
520 $a Toward the second major objective, a robust feed-forward artificial neural network (ANN) is developed and used in Genetic Algorithm (GA) in place of the computationally intensive simulation model. The ANN is trained to translate a pumping policy into various forms of the magnitude and likelihood of the concentration violations. Finally, the performance of the ANN based GAs are tested on a range of remediation problems. The factors investigated include the degree of variability in the conductivity fields, the number and location of wells, the water quality standard and the requisite level of reliability.
590 $a School code: 0246.
650 4 $a Engineering, Civil. $3 783781
650 4 $a Engineering, Environmental. $3 783782
650 4 $a Engineering, System Science. $3 1018128
690 $a 0543
690 $a 0775
690 $a 0790
710 2 $a University of Virginia. $3 645578
773 0 $t Dissertation Abstracts International $g 68-09B.
790 $a 0246
790 1 0 $a Culver, Teresa B., $e advisor
791 $a Ph.D.
792 $a 2008
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3283288