Real-time remote monitoring of small-scaled biological wastewater treatment plants by a multivariate statistical process control and neural network-based software sensors

A real-time remote monitoring system for wastewater treatment plants (WWTPs) has been developed to give local operators a guideline that would allow them to arrive at the optimum operational strategy in the early stage of a process disturbance. Especially, small-scaled WWTPs in Korea's rural areas show a large fluctuation in their influent loading and, therefore, they require an efficient operation for treatment of organic matter, nitrogen and phosphorus. However, under requirements to lower running costs, most of the small-scaled WWTPs are being forced to operate with a minimum number of operators. It is too costly for them to employ a local expert to maintain plant systems properly. In recent years, recognition of these problems has raised great interests in real-time remote monitoring systems. They serve the key information needed for efficient operation, and help to transfer knowledge from the experts at a remote control center to local operators in real-time. In this study, both operation data and measurement values from a novel mobile multi-sensor system were transmitted on-line by a telecommunication system. Then multivariate statistical process controls and software sensor techniques were applied to supervise local WWTPs. The developed remote monitoring system makes it possible to monitor the current plants' statuses and to support the operation of local wastewater systems.