A medium-sized wastewater treatment plant in Italy must purify all of the water produced before it is discharged downstream into a river to ensure that it meets or exceeds required quality regulations.
The plant purifies 1,000 m3/hour of water and runs 24 hours a day, seven days a week. It diverts the water flowing into the river, splits the flow into two parallel lines, and directs the two flows to the treatment tanks. The activated sludge purification system is a biological type where organic substances and ammonia are oxidized in the presence of oxygen by the activated sludge. The nitrate products, typically eutrophying nutrients, are later removed in the absence of oxygen. Consequently, the oxygen content, the active sludge concentration, the nitrates, and the ammonia are key data inputs of the plant process control system.
The water treatment plant needed to upgrade its control system to improve water quality control, reduce energy usage and gather data insights to optimize the overall process. The old plant was run according to a fixed time logic. This consisted of making the sewage water stand in the various vessels for a predetermined length of time and controlling the operation of the process-related machines (aerators, blowers, pumps, etc.) according to dissolved oxygen measurements and laboratory test data only. The goal set by the plant was to use the data collected by various sensors to control the transit times of the sewage in the tanks and machine operation according to the values of oxygen, ammonia, suspended solids, and nitrates to improve plant processing and energy efficiency. Furthermore, the new control system had to enable data visibility to an operator at the plant and it had to relay data to the control room from where all the plants are monitored. The plants are manned during the day, but the control room alone monitors the operation of all water treatment plants during the night, so the new automation control system had to allow for this.
The water treatment plant is subject to seasonal variations determined by rainfall, so the water quality cannot be determined before treatment. Furthermore, the plant collection basin includes a number of industries, which introduce large amounts of waste, thus the water chemistry and flow vary greatly. Another criticality of a plant like this, with such an extensive coverage, is that it operates 24/7 in order to prevent the risk of releasing polluted water into the river and to avoid fines by the water quality monitoring authority.
Critical requirements for the automation and control solution included: high plant availability and reliability; data access by operators; and improved process management in terms of better results and more efficient use of energy resources. To reach these goals, the plant selected an Emerson control solution. The “brain” of the system is the Emerson PACSystemsTM RX3i controller in redundant hot backup configuration, which interfaces with all the field instruments on a Profibus network (part optical fibers and part copper wires); there are several new and old sensors in total, amounting to approximately 600 controlled tags. The two redundant CPUs ensure high availability required by the criticality of the application. The PACSystems RX3i controller establishes the standing times of the slurry in the various stages of the plant. By means of a direct Modbus/TCP link, the PAC communicates data to the control room, where it is stored in a SQL database and concisely displayed so that the operator (present 24 hours a day) can be warned of faults and act accordingly.
At this particular plant, a local computer running software from Emerson, part of the HMI/SCADA suite, monitors and displays information and data in the form of trend graphs or logs, in addition to alarms, which may be silenced by the users according to their access levels. The application allows for set up and program control parameters. Many fault-detecting functions have been implemented in the program running at the water treatment plant today to signal measurements deviating from expected values and to collect and use self-diagnostic data from the field sensors.
The new system collects plant data for constant monitoring. Predictive control, sensor data collection, and use and control system response rapidity have been used to optimize machine running times and consequently decrease energy consumption while ensuring high water quality. After only 50 days, for example, an energy consumption reduction of 30% was already observed. The plant was shut down for approximately half an hour to allow the new system to be installed. Personnel training was swift, thanks to intuitive, self-explanatory graphic displays, and was carried out over several shifts to account for staff turnover. In addition to the reduced energy usage, the system improved water quality and enabled the plant to maintain better control for crucial river habitat parameters.