Full Text:   <2564>

Summary:  <1569>

CLC number: X703

On-line Access: 2014-05-04

Received: 2013-10-29

Revision Accepted: 2014-02-17

Crosschecked: 2014-04-25

Cited: 3

Clicked: 5175

Citations:  Bibtex RefMan EndNote GB/T7714

-   Go to

Article info.
Open peer comments

Journal of Zhejiang University SCIENCE A 2014 Vol.15 No.5 P.374-384


Applying process analytical technology framework to optimize multiple responses in wastewater treatment process

Author(s):  Abbas Al-Refaie

Affiliation(s):  . Department of Industrial Engineering, The University of Jordan, Amman 11942, Jordan

Corresponding email(s):   abbas.alrefai@ju.edu.jo

Key Words:  Fuzzy goal optimization, Multiple responses, Wastewater, Process analytical technology (PAT)

Share this article to: More <<< Previous Article|

Abbas Al-Refaie. Applying process analytical technology framework to optimize multiple responses in wastewater treatment process[J]. Journal of Zhejiang University Science A, 2014, 15(5): 374-384.

@article{title="Applying process analytical technology framework to optimize multiple responses in wastewater treatment process",
author="Abbas Al-Refaie",
journal="Journal of Zhejiang University Science A",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Applying process analytical technology framework to optimize multiple responses in wastewater treatment process
%A Abbas Al-Refaie
%J Journal of Zhejiang University SCIENCE A
%V 15
%N 5
%P 374-384
%@ 1673-565X
%D 2014
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A1300262

T1 - Applying process analytical technology framework to optimize multiple responses in wastewater treatment process
A1 - Abbas Al-Refaie
J0 - Journal of Zhejiang University Science A
VL - 15
IS - 5
SP - 374
EP - 384
%@ 1673-565X
Y1 - 2014
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A1300262

In this research, the process analytical technology (PAT) framework is used to optimize the performance of the wastewater treatment process in poultry industry. Two responses, turbidity and sludge volume index (SVI), are of main manufacturer’s interest. Initially, the moving average (MA) and moving range (MR) control charts are established for each response. The 33 full factorial design with two replicates is then used for conducting experimental work. The weighted additive model in fuzzy goal programming is formulated, and then employed to determine the combination of optimal factor settings. Finally, confirmation experiments follow at the combination of optimal factor settings. The results show that the actual process index for turbidity is improved from 1.34 to 5.5, while it is enhanced from 1.46 to 1.93 for SVI. Moreover, the multiple process capability index is improved significantly from 1.95 to 10.6, which also indicates that the treatment process becomes highly capable with both responses concurrently. Further, the process standard deviations at initial (optimal) factor settings are 2.16 (1.27) and 6.02 (3.39) for turbidity and SVI, respectively. These values show significant variability reductions in turbidity and SVI by 41.22% and 77.75%, respectively. Such improvements will lead to huge savings in quality and productivity costs. In conclusion, the PAT framework is found to be an effective approach for optimizing the performance of the wastewater treatment process with multiple responses.


重要结论:在絮凝剂为18 mg/L、凝结剂为40.0 mg/L和pH=4.0的最佳工艺条件下,得到的浊度和污泥体积指数的最佳值分别为6.184 NTU和73.21 ml/g。在此条件下得到的浊度和污泥沉降指数能够满足设计要求,它们的过程变异性分别显著下降了41.22%和77.75%,且多重能力指数从1.95显著增加至10.6。这表明此过程可行性很高。总之,加权相加模型是一种用于优化多个响应流程性能的有效技术,并且可以考虑工程师的首选设置流程。


Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1] Al-Refaie, A., 2009. Optimizing SMT performance using comparisons of efficiency between different systems technique in DEA. IEEE Transactions on Electronics Packaging Manufacturing, 32(4):256-264. 

[2] Al-Refaie, A., 2010. A grey-DEA approach for solving the multi-response problem in Taguchi method. Journal of Engineering Manufacture, 224(1):147-158. 

[3] Al-Refaie, A., 2011. Optimizing correlated QCHs using principal components analysis and DEA techniques. Production Planning & Control, 22(7):676-689. 

[4] Al-Refaie, A., 2012. Optimizing performance with multiple responses using cross-evaluation and aggressive formulation in DEA. IIE Transactions, 44(4):262-276. 

[5] Al-Refaie, A., Li, M.H., 2011. Optimizing the performance of plastic injection molding using weighted additive model in goal programming. International Journal of Fuzzy System Applications, 1(2):43-54. 

[6] Al-Refaie, A., Al-Tahat, M., 2011. Solving the multi-response problem in Taguchi method by benevolent formulation in DEA. Journal of intelligent Manufacturing, 22(4):505-521. 

[7] Al-Refaie, A., Li, M.H., Tai, K.C., 2008. Optimizing SUS 304 wire drawing process by grey analysis utilizing Taguchi method. Journal of University of Science and Technology Beijing, Mineral, Metallurgy, Material, 15(6):714-722. 

[8] Al-Refaie, A., Wu, T.H., Li, M.H., 2009. DEA approaches for solving the multi-response problem in Taguchi method. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 23(2):159-173. 

[9] Al-Refaie, A., Rawabdeh, I., Alhajj, R., 2012. A fuzzy multiple-regression approach for optimizing multiple responces in the Taguchi method. International Journal of Fuzzy System Applications, 2(3):13-34. 

[10] CDER (Center for Drug Evaluation and Research), 2004. Guidance for Industry: PAT-A Framework for Innovative Pharmaceutical Development, Manufacturing, and Quality Assurance, US Department of Health and Human Services Food and Drug Administration,:

[11] Muhammad, N., Manurng, Y.H., Hafidzi, M., 2012. Optimization and modeling of spot welding parameters with simultaneous multiple response consideration using multi-objective Taguchi method and RSM. Journal of Mechanical Science and Technology, 26(8):2365-2370. 

[12] Salmasnia, A., Kazemzadeh, R.B., Tabrizi, M.M., 2012. A novel approach for optimization of correlated multiple responses based on desirability function and fuzzy logics. Neurocomputing, 91:56-66. 

[13] Wang, J.P., Chen, Y.Z., Ge, X.W., 2007. Optimization of coagulation-flocculation process for a paper-recycling wastewater treatment using response surface methodology. Colloids and Surfaces A: Physicochemical and Engineering Aspects, 302(1-3):204-210. 

[14] Wang, J.P., Chen, Y.Z., Ge, X.W., 2011. Optimization of coagulation-flocculation process for pulp mill wastewater treatment using a combination of uniform design and response surface methodology. Water Research, 45(17):5633-5640. 

[15] Yaghoobi, M.A., Jones, D.F., Tamiz, M., 2008. Weighted additive models for solving fuzzy goal programming problems. Asia-Pacific Journal of Operational Research, 25(5):715-733. 

[16] Ycel, A., Guneri, A.F., 2011. A weighted additive fuzzy programming approach for multi-criteria supplier selection. Expert Systems with Applications, 38(5):6281-6286. 

Open peer comments: Debate/Discuss/Question/Opinion


Please provide your name, email address and a comment

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2022 Journal of Zhejiang University-SCIENCE