An efficient approach based on bi-sensitivity analysis and genetic algorithm for calibration of activated sludge models

2015 ◽  
Vol 259 ◽  
pp. 845-853 ◽  
Author(s):  
Wenliang Chen ◽  
Xiwu Lu ◽  
Chonghua Yao ◽  
Guangcan Zhu ◽  
Zhuo Xu
2019 ◽  
Vol 91 (9) ◽  
pp. 865-876
Author(s):  
Dhan Lord B. Fortela ◽  
Kyle Farmer ◽  
Alex Zappi ◽  
Wayne W. Sharp ◽  
Emmanuel Revellame ◽  
...  

1993 ◽  
Vol 28 (11-12) ◽  
pp. 219-229 ◽  
Author(s):  
Marcos von Sperling

The present work describes an adaptation of the regionalized sensitivity analysis based on Monte Carlo simulations for the parameter estimation and sensitivity analysis of an activated sludge model. The procedure described should be used when observed data are available for the model calibration, which is nevertheless still limited by the problems inherent to activated sludge models (uncertainty and lack of identifiability). The selection between good and bad performance of the model is judged based on the Coefficient of Determination. The application of the procedure to an 11-parameter 4-state dynamic activated sludge model used for operational control was considered satisfactory. The method is simple and yet robust, and the analyst's involvement in the interpretation of the results and decision upon the next steps to be taken increases its controllability.


2006 ◽  
Vol 53 (1) ◽  
pp. 129-138 ◽  
Author(s):  
J.R. Kim ◽  
J.H. Ko ◽  
J.J. Lee ◽  
S.H. Kim ◽  
T.J. Park ◽  
...  

The aim of this study was to suggest a sensitivity analysis technique that can reliably predict effluent quality and minimize calibration efforts without being seriously affected by influent composition and parameter uncertainty in the activated sludge models No. 1 (ASM1) and No. 3 (ASM3) with a settling model. The parameter sensitivities for ASM1 and ASM3 were analyzed by three techniques such as SVM-Slope, RVM-SlopeMA, and RVM-AreaCRF. The settling model parameters were also considered. The selected highly sensitive parameters were estimated with a genetic algorithm, and the simulation results were compared as ΔEQ. For ASM1, the SVM-Slope technique proved to be an acceptable approach because it identified consistent sensitive parameter sets and presented smaller ΔEQ under every tested condition. For ASM3, no technique identified consistently sensitive parameters under different conditions. This phenomenon was regarded as the reflection of the high sensitivity of the ASM3 parameters. But it should be noted that the SVM-Slope technique presented reliable ΔEQ under every influent condition. Moreover, it was the simplest and easiest methodology for coding and quantification among those tested. Therefore, it was concluded that the SVM-Slope technique could be a reasonable approach for both ASM1 and ASM3.


2008 ◽  
Vol 100 (3) ◽  
pp. 516-528 ◽  
Author(s):  
Gürkan Sin ◽  
Dirk J.W. De Pauw ◽  
Stefan Weijers ◽  
Peter A. Vanrolleghem

2003 ◽  
Vol 37 (12) ◽  
pp. 2893-2904 ◽  
Author(s):  
Britta Petersen ◽  
Krist Gernaey ◽  
Martijn Devisscher ◽  
Denis Dochain ◽  
Peter A. Vanrolleghem

2021 ◽  
Author(s):  
Mohammed Ahmed Al-Janabi ◽  
Omar F. Al-Fatlawi ◽  
Dhifaf J. Sadiq ◽  
Haider Abdulmuhsin Mahmood ◽  
Mustafa Alaulddin Al-Juboori

Abstract Artificial lift techniques are a highly effective solution to aid the deterioration of the production especially for mature oil fields, gas lift is one of the oldest and most applied artificial lift methods especially for large oil fields, the gas that is required for injection is quite scarce and expensive resource, optimally allocating the injection rate in each well is a high importance task and not easily applicable. Conventional methods faced some major problems in solving this problem in a network with large number of wells, multi-constrains, multi-objectives, and limited amount of gas. This paper focuses on utilizing the Genetic Algorithm (GA) as a gas lift optimization algorithm to tackle the challenging task of optimally allocating the gas lift injection rate through numerical modeling and simulation studies to maximize the oil production of a Middle Eastern oil field with 20 production wells with limited amount of gas to be injected. The key objective of this study is to assess the performance of the wells of the field after applying gas lift as an artificial lift method and applying the genetic algorithm as an optimization algorithm while comparing the results of the network to the case of artificially lifted wells by utilizing ESP pumps to the network and to have a more accurate view on the practicability of applying the gas lift optimization technique. The comparison is based on different measures and sensitivity studies, reservoir pressure, and water cut sensitivity analysis are applied to allow the assessment of the performance of the wells in the network throughout the life of the field. To have a full and insight view an economic study and comparison was applied in this study to estimate the benefits of applying the gas lift method and the GA optimization technique while comparing the results to the case of the ESP pumps and the case of naturally flowing wells. The gas lift technique proved to have the ability to enhance the production of the oil field and the optimization process showed quite an enhancement in the task of maximizing the oil production rate while using the same amount of gas to be injected in the each well, the sensitivity analysis showed that the gas lift method is comparable to the other artificial lift method and it have an upper hand in handling the reservoir pressure reduction, and economically CAPEX of the gas lift were calculated to be able to assess the time to reach a profitable income by comparing the results of OPEX of gas lift the technique showed a profitable income higher than the cases of naturally flowing wells and the ESP pumps lifted wells. Additionally, the paper illustrated the genetic algorithm (GA) optimization model in a way that allowed it to be followed as a guide for the task of optimizing the gas injection rate for a network with a large number of wells and limited amount of gas to be injected.


2010 ◽  
Vol 61 (4) ◽  
pp. 825-839 ◽  
Author(s):  
H. Hauduc ◽  
L. Rieger ◽  
I. Takács ◽  
A. Héduit ◽  
P. A. Vanrolleghem ◽  
...  

The quality of simulation results can be significantly affected by errors in the published model (typing, inconsistencies, gaps or conceptual errors) and/or in the underlying numerical model description. Seven of the most commonly used activated sludge models have been investigated to point out the typing errors, inconsistencies and gaps in the model publications: ASM1; ASM2d; ASM3; ASM3 + Bio-P; ASM2d + TUD; New General; UCTPHO+. A systematic approach to verify models by tracking typing errors and inconsistencies in model development and software implementation is proposed. Then, stoichiometry and kinetic rate expressions are checked for each model and the errors found are reported in detail. An attached spreadsheet (see http://www.iwaponline.com/wst/06104/0898.pdf) provides corrected matrices with the calculations of all stoichiometric coefficients for the discussed biokinetic models and gives an example of proper continuity checks.


2011 ◽  
Vol 19 (6) ◽  
pp. 18-23
Author(s):  
Seema Sharma ◽  
Rashmi Aggarwal ◽  
Anupriya Jain ◽  
Sachin Sharma

Sign in / Sign up

Export Citation Format

Share Document