Predicting performance of grey and neural network in industrial effluent using 1 online monitoring parameters 2 3 ( Running title :
نویسنده
چکیده
16 Grey model (GM) and artificial neural network (ANN) were employed to predict suspended solids 17 (SSeff), chemical oxygen demand (CODeff) and pHeff in the effluent from conventional activated 18 process of an industrial wastewater treatment plant using simple online monitoring parameters (pH 19 in the equalization pond effluent; pH, temperature, and dissolved oxygen in the aeration tank). The 20 results indicated that the minimum mean absolute percentage errors of 20.79 %, 6.09 % and 0.71 % 21 for SSeff, CODeff and pHeff could be achieved using different types of GMs. GM only required a 22 small amount of data (at least 4 data) and the prediction results were even better than those of ANN. 23 According to the results, the online monitoring parameters could be applied on the prediction of 24 effluent quality. It also revealed that GM could predict the industrial effluent variation as its effluent 25 data was insufficient. 26 27
منابع مشابه
Comparisons of grey and neural network prediction of industrial park wastewater effluent using influent quality and online monitoring parameters.
In this study, Grey model (GM) and artificial neural network (ANN) were employed to predict suspended solids (SSeff) and chemical oxygen demand (CODeff) in the effluent from a wastewater treatment plant in industrial park of Taiwan. When constructing model or predicting, the influent quality or online monitoring parameters were adopted as the input variables. ANN was also adopted for comparison...
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