On-line monitoring of ORP has been proved to be a practical and useful technique for process control of wastewater treatment systems. This paper presents the feasibility of using on-line ORP monitoring system on a laboratory scale single tank continuous-flow activated sludge batch reactor, which is capable of removing carbon, nitrogen and phosphorus pollutants. Two control strategies, fixed-time and real-time, are applied for process control. Results obtained from fixed-time control study indicate that the variations and the ORP profile can accurately represent dynamic characteristics of system; the pH profile can also indicate some of those characteristics. Also, the breakpoints, setpoints and settime on the ORP and pH profiles are used to establish the real-time control strategy to determine the transfer of operation stages. The real-time experiments show a better performance than fixed-time, thus, on-line ORP and pH monitoring and control is practical for continuous-flow batch activated sludge process control.

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Conventional operations of wastewater treatment systems use the concepts of steady-state control, and often lead to unnecessary resource consumption for maintaining system functions. Real-time control was examined as a useful approach for improving the operation of wastewater treatment systems. This paper presents the application of real-time control to enhance the performance of nitrogen removal in a continuous-flow SBR system. A real-time control system combining on-line measurement of ORP and pH with Artificial Neural Network (ANN) model was proposed to carry out unsteady-state regulation of the hydraulic retention time of different operation phases. The result of this study shows that the performance of nitrogen removal was enhanced under real-time operation. Compared with fixed-time operation, the retention time of aerobic and anoxic phases can be reduced by approximately 45% and 15.5% in real-time operation respectively, also meaning that 45% aeration energy can be saved. The real-time operation also reveals a higher total nitrogen removal in a relative short retention time. Moreover, some dynamics and kinetics of nitrogen were investigated. These indicate the occurrence of nitrite-type nitrification under real-time operation. This nitrite-type nitrification results in the enhancement of denitrification performance with less carbon resource requirement and higher denitrification efficiency.

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Coagulant dosing is one of the major operation costs in water treatment plant, and conventional control of this process for most plants is generally determined by the jar test. However, this method can only provide periodic information and is difficult to apply to automatic control. This paper presents the feasibility of applying artificial neural network (ANN) to automatically control the coagulant dosing in water treatment plant. Five on-line monitoring variables including turbidity (NTUin), pH (pHin) and conductivity (Conin) in raw water, effluent turbidity (NTUout) of settling tank, and alum dosage (Dos) were used to build the coagulant dosing prediction model. Three methods including regression model, time series model and ANN models were used to predict alum dosage. According to the result of this study, the regression model performed a poor prediction on coagulant dosage. Both time-series and ANN models performed precise prediction results of dosage. The ANN model with ahead coagulant dosage performed the best prediction of alum dosage with a R2 of 0.97 (RMS=0.016), very low average predicted error of 0.75 mg/L of alum were also found in the ANN model. Consequently, the application of ANN model to control the coagulant dosing is feasible in water treatment.