| 在废水处理过程中基于过程分析技术的多重响应优化 |
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| 作者姓名： | Abbas AL-REFAIE |
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| 作者单位： | Department of Industrial Engineering, The University o f Jordan, Amman 11942, Jordan |
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| 摘 要： | 研究目的：基于过程分析技术框架，优化家禽业的废水处理工艺的性能。研究方法：生产商主要关注浊度和污泥容积指数（SVI）这两个响应。首先为每个响应建立移动平均（MA）和移动范围（MR）控制图，并在实验工作中重复两次3。全因子设计。基于模糊目标规划建立加权相加模型，并用于确定最佳的因子设置组合。最后，在最佳的因子设置组合条件下，对实验结果进行跟踪确认。重要结论：在絮凝剂为18mg／L、凝结剂为40．0mg／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6．184NTU和73．21ml／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。
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| 关 键 词： | 模糊目标优化 多反应 废水 过程分析技术(PAT) |
Applying process analytical technology framework to optimize multiple responses in wastewater treatment process |
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| Abbas AL-REFAIE.Applying process analytical technology framework to optimize multiple responses in wastewater treatment process[J].Journal of Zhejiang University Science,2014,15(5):374-384. |
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| Authors: | Abbas Al-Refaie |
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| Institution: | 1. Department of Industrial Engineering, The University of Jordan, Amman, 11942, Jordan
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| Abstract: | 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. |
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| Keywords: | Fuzzy goal optimization Multiple responses Wastewater Process analytical technology （PAT） |
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