Applying Kohonen self-organizing map as a software sensor to predict biochemical oxygen demand

被引:31
作者
Rustum, Rabee [2 ]
Adeloye, Adebayo J. [2 ]
Scholz, Miklas [1 ]
机构
[1] Univ Edinburgh, Sch Engn & Elect, Inst Infrastruct & Environm, Edinburgh EH9 3JL, Midlothian, Scotland
[2] Heriot Watt Univ, Sch Build Environm, Edinburgh, Midlothian, Scotland
关键词
wastewater treatment plant; Kohonen self-organizing map; neural networks; decisionmaking; process control; software sensor; biochemical oxygen demand; bioassay; biosensor; cost saving;
D O I
10.2175/106143007X184500
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The 5 days at 20 degrees C biochemical oxygen demand (BOD5) is an important parameter for monitoring organic pollution in water and assessing the biotreatability of wastewater. Moreover, BOD5 is used for wastewater treatment plant discharge consents and other water pollution control purposes. However, the traditional bioassay method for estimating the BOD5 involves the incubation of sample water for 5 days. It follows that BOD5 is not available for real-time decisionmaking and process control purposes. On the other hand, previous efforts to solve this problem by developing more rapid biosensors had limited success. This paper reports on the development of Kohonen self-organizing map (KSOM)-based software sensors for the rapid prediction of BOD5. The findings indicate that the KSOM-based BOD5 estimates were in good agreement with those measured using the conventional bioassay method. This offers significant potential for more timely intervention and cost savings during problem diagnosis in water and wastewater treatment processes.
引用
收藏
页码:32 / 40
页数:9
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