This study develops a sedimentation process model that simulate the effects of inflow water quality, treatment flow rate and outflow water quality. The model uses transfer function ARIMA (Auto-Regressive Moving Average) for reflecting the dynamic characteristics of the system The sedimentation model for outflow water turbidity are separated into low and high turbidity by input variables, turbidity, pH, alkalinity and flow rate of raw water, and coagulant is used. Determination coefficients of the optimal model selected 0.92 and 0.97 in case of using optimal model for the transfer function model. In conclusion, predictive results were estimated 0.99 and 0.65, respectively.
By analyzing the characters of coagulant dose process and factors related to coagulation, a predictive model of optimal coagulant dosage based on neural network is proposed in this paper. Historical data were used to simulate, the results show that this model is better than the traditional regression model. At last, an effective predictive control strategy of coagulant dosage is put forward based on this model, which offers an effective way for achieve the optimal coagulant dosing.
We prove that groups acting geometrically on delta-quasiconvex spaces contain no essential Baumslag-Solitar quotients as subgroups. This implies that they are translation discrete, meaning that the translation numbers of their nontorsion elements are bounded away from zero.