Optimization of Sewage Treatment Plants by Advanced Process Control

Transcription

1 Optimization of Sewage Treatment Plants by Advanced Process Control How can the operation of sewage treatment plants, especially the aeration of biological treatment steps, be optimized using the Advanced Process Control functions of SIMATIC PCS 7? White Paper 27 March 215 You want to optimize operation of your sewage treatment plant, for example with respect to process stability, energy consumption, and compliance with environmental regulations? Do you want an automation solution that is uniform, straightforward, and easy to adapt? This White Paper provides an overview of the available closed-loop control concepts for this task, and how they can be implemented transparently and with minimal effort using the SIMATIC PCS 7 Advanced Process Library.

2 Content Introduction... 3 Basic information on the activated sludge process... 3 Challenges in the automation of sewage treatment plants 4 Advanced Process Control for sewage treatment plants... 4 Optimization of a small sewage treatment plant with intermittent operation... 6 Plant description... 6 Simulation model... 6 Challenges for automation... 6 Solution concept... 7 Simulation results... 7 Outlook... 8 Optimization of a large sewage treatment plant in continuous operation... 9 Plant description... 9 Simulation model... 9 Challenges for automation... 9 Solution concept... 1 Simulation results... 1 Outlook Conclusion References... 12

3 Because the sewage treatment plant discharges directly into the environment, the operator is responsible for complying with legal limits in the purified wastewater." Introduction Basic information on the activated sludge process Generally, sewage treatment plants based on the activated sludge process are used for purification of wastewater. A typical design of these plants is shown in Figure 1 [9.]. Preliminary mechanical purification, consisting of a screen, grit chamber and preliminary sedimentation tank, initially removes coarse contaminants and substances that deposit on the bottom. The preliminarily purified wastewater then goes to the activated sludge tank where it is purified biologically by the activated sludge process. The activated sludge is separated from the purified water by a settling process in the secondary sedimentation tank. Most of the activated sludge is then fed back to the activated sludge tank. The purified water is usually introduced into rivers and lakes. Closed-loop control of the biological operations in the activated sludge tank poses the greatest challenge for sewage treatment plant automation. The materials in the wastewater are biologically degraded using activated sludge in the respective tanks of a sewage treatment plant. Aerobic bacteria (bacteria requiring oxygen) break down the carbon compounds primarily into carbon dioxide and biomass. As part of the nitrification process, nitrogen from organic compounds, which exists predominantly in the form of inorganic ammonium NH4, is split off by other bacteria first as ammonia (NH3) and then oxidized with oxygen into nitrite and then nitrate. NH O 2 NO 3 + H + + H 2 O. The nitrosomonas and nitrobacter bacteria involved in nitrification grow much more slowly than the heterotrophic bacteria involved in carbon elimination. Because Figure 1: Typical process diagram of a medium-sized sewage treatment plant

4 they also need more oxygen and are more sensitive to temperature fluctuations, they must be promoted in a targeted manner. The nitrate produced during nitrification is reduced to elemental nitrogen under anoxic conditions, that is, in the absence of molecular oxygen: 2 NO H e N H 2 O. For this the bacteria must convert their metabolism from oxygen respiration to nitrate respiration, which has to be forced through a lack of oxygen and is referred to as denitrification. This also requires a sufficient number of easily degradable carbon compounds as electron donors. A variety of activated sludge process techniques are available, including upstream, simultaneous, and intermittent denitrification. In upstream denitrification, an initial activated sludge tank is operated under anoxic conditions, and the sludge/wastewater mixture is pumped back from the aerated tanks, which contain a high oxygen content. This is also shown in Figure 1. As a result, adequate carbon is available from the influent to the first tank and nitrate from the recirculation flow. In intermediate denitrification, anoxic conditions are produced temporarily in a single tank by phased shutdown of the aeration. Both an intermittently operated sewage treatment plant and one with upstream denitrification will be presented as example plants. Challenges in the automation of sewage treatment plants The largely automated operation of sewage treatment plants is considered state of the art. Compared to process engineering plants in other industries, such as the chemical industry, a sewage treatment plant has fewer sensors and control loops. Nevertheless, sewage treatment plant automation has its own special challenges: The sewage treatment plant discharges directly into receiving waters. The sewage treatment plant operator is therefore liable for complying with legal limits in purified wastewater, such as limits for ammonia nitrogen NH4-N, total nitrogen, chemical oxygen demand (COD), and phosphate. The biological processes for purification of the wastewater using different bacteria in the nitrification and denitrification sub-steps are complex and not easily be modeled [1.]. Careful attention must be paid that the bacteria, as living beings, are provided with the right environmental conditions so that they fulfill exactly the desired task with their metabolic processes. Many of the variables important for closed-loop control, especially concentrations, are not measured online but are available only at longer intervals as lab samples. The influent to a sewage treatment plant is subject to strong fluctuations in terms of flow rate and components. This is due to weather-related and seasonal fluctuations as well as the behavior of the high numbers of private individuals and industrial companies that discharge wastewater to the sewer network. Sewage treatment plants and, in particular, the aeration of biological purification steps represent the largest communal energy consumer in many cities and municipalities. Measures that reduce energy consumption can therefore pay for themselves after a short amount of time. Based on the typical size of mainly municipal sewage treatment plants, engineers with relevant know-how about wastewater-specific, biotechnological issues are usually available on-site, but not control engineers. That is why any control solution must have a clear and transparent structure so that it can be operated and maintained by the available personnel. Advanced Process Control for sewage treatment plants The use of I&C (instrumentation and control) technology in sewage treatment plants generally has the following objectives: Improvement of purification performance for compliance with discharge limits. Minimization of operating costs, especially energy costs. Saving of investment costs through optimal use of existing infrastructures. Sophisticated control engineering methods, which have become known under the keyword Advanced Process Control (APC) in other industry sectors such as the chemical industry and oil refining industry, offer potential for optimization of process control in the water/wastewater industry as well. Ever since these methods have been seamlessly integrated into modern distributed control systems such as SIMATIC PCS 7 [2.] and made available at low cost as standard software blocks [3.], nothing more stands in the way of their successful application in sewage treatment plants. Model Predictive Control (MPC) is particularly attractive in this context. MPC allows "predictive operation" of the plant because it takes into consideration both the physical/chemical/biological interactions between different variables and measurable disturbance effects, for example from the influent. MPC is integrated as a standard function block in the SIMATIC PCS 7 Advanced Process Library with the name ModPreCon. This paper shows, on the basis of specific real world examples, how this optimization potential can be tapped.

5 The case studies are pilot projects implemented with a uniform approach: Configuration and parameter assignment of a simulation model of the specific sewage treatment plant, build on an industry-specific library of plant components [4.]. Simulation study on the current state of automation (baseline). Specification of requirements for optimization of process control. Design and configuration of an APC solution for the existing plant type through a combination of standard function blocks and use of associated software tools for computer-aided commissioning of closed-loop control functions. Benchmarking simulation in order to quantify the improvement potential of the APC solution. The goal of the pilot projects is to develop a generalizable APC solution for widespread plant types. The studies necessary for this can only be performed using a detailed simulation model. In the meantime, extensive field experience exists so that APC solutions can be created for comparable sewage treatment plants without new simulation studies. The fact that the laborious modeling step is eliminated not only saves costs but also valuable time until commissioning of the new closed-loop control concept at a new sewage treatment plant.

6 "Sewage treatment plants and, in particular, the aeration of biological purification steps represents the largest communal energy consumer in many cities." Optimization of a small sewage treatment plant with intermittent operation Plant description The example to be examined is a small sewage treatment plant with intermittent denitrification. Figure 2 shows the structure of the sewage treatment plant. The activated sludge tank is operated alternately for nitrification and denitrification by switching the aeration on and off. The timing of the phase changeover is based on a measurement of the ammonium and nitrate concentrations (hereinafter always indicated as ammonium or nitrate nitrogen concentration) in the activated sludge tank. The denitrification phase is ended when the nitrate concentration falls below a specified value. The aeration is then switched on in order for the ammonium not treated in the denitrification phase to be degraded again in the nitrification phase. This is ended when the ammonium concentration falls below a specified level. Figure 2: Flow diagram of the sewage treatment plant Simulation model A model is created for the sewage treatment plant in a Siemens internal tool for simulation of biological and chemical process technology. For modeling of the biological processes in the activated sludge tank, the "Activated Sludge Model No. 1" (ASM1) is used [5.]. The modeling of the secondary sedimentation tank is based on the "Secondary settler" model from "Benchmark Simulation Model No. 1" (BSM1) [6.]. The simulation, which is compared with measured data of the real sewage treatment plant, describes the essential dynamic processes of the sewage treatment plant effectively. Challenges for automation The current control solution, which is referred to hereinafter as conventional closed-loop control, consists on the one hand of the changeover of the two phases of the activated sludge tank. The changeover between nitrifica-

7 tion and denitrification in the intermittently operated sewage treatment plant is triggered on the basis of a combination ammonium/nitrate probe. The aeration is switched off to end the nitrification phase as soon as enough ammonium has been broken down. This is the case when the ammonium concentration has dropped below a threshold value of.5 mg/l. Denitrification is ended as soon as the nitrate concentration has dropped below a threshold value of 8 mg/l indicating that enough nitrate has been broken down. The changeover logic is supplemented with minimum and maximum phase durations to prevent excessively fast changeovers and excessively long dwell times. The aeration control loop during the nitrification phase represents the second part of the conventional closed-loop control. A constant dissolved oxygen (DO) setpoint is specified and is controlled by a PI controller via a blower. The typical duration of the nitrification phase, however, is not always sufficient for the oxygen controller to reach a steady-state oxygen concentration by varying the blower speed. At the same time, the blower speed at the start of the nitrification phase is very high, which uses a significant amount of energy. The influent flow rate has a considerable effect on the dynamic processes in the sewage treatment plant. However, the current automation cannot react directly to these fluctuations because the influent flow rate is not taken into account in conventional closed-loop control. A significant variation in the concentrations in the activated sludge tank must occur before the phase durations will be modified and the oxygen controller will adjust the blower speed as needed. Solution concept The oxygen concentration in the nitrification phase is only an auxiliary controlled variable for providing the appropriate environmental conditions for aerobic metabolism of bacteria. An oxygen concentration that is constant via an extended time period is not always achievable, but it is also not really necessary from process point of view. With the new concept, therefore, the blower speed is controlled directly by the process variable of primary interest, that is, the ammonium concentration. The goal is to drop ammonium from a measured value at the start of the nitrification phase to a specified target value within a specified time in order to end the nitrification phase. Moreover, the influent flow rate is interpreted as a measurable disturbance variable. If the effects on the processes in the nitrification phase are known, the controller can adjust the aeration in time to prevent, or at least reduce, the negative effects of influent fluctuation. The ModPreCon MPC function block is ideally suited for the described tasks. It includes a setpoint prefilter that specifies the desired transition time of the ammonium concentration. Due to the above-described limitations of the PI controller, the MPC is not cascaded with the existing oxygen controller. Accordingly, the MPC uses the blower speed directly as the manipulated value in order to control the ammonium concentration while taking into account influent as a measurable disturbance variable. The conventional PI oxygen controller used to date is kept as a backup solution. The passive controller in each case is run in tracking mode so that a bumpless changeover is ensured at any time. The MPC Configurator included as a standard feature in SIMATIC PCS 7 can be used for parameter assignment of the MPC block. Measured data of the manipulated, disturbance, and controlled variables must be recorded in which excitations of the manipulated and disturbance variables occur. Since this example includes only one manipulated variable (blower speed) and one disturbance variable (influent), the data recording can be carried out in parallel with normal operation. The nitrification phase may need to be extended, but this has no negative effects on the biological processes in the sewage treatment plant. The MPC Configurator provides automatic MPC design, using a few transparent parameters for adjustment of the dynamic behavior. Simulation results The conventional closed-loop control implemented in SIMATIC PCS 7 and the automation with MPC are connected to the simulator. A time period of 25 hours is simulated, which is shown in Figure 3. Since there is no real influent data for the plant, a synthetic influent profile is used. The conventional closed-loop control is shown in red and the MPC solution in blue. The durations of the individual phases can differ with the two control methods, so the phases are shifted in the figure. The simulation starts in a non-aerated denitrification phase. Once enough nitrate has been broken down, the aeration starts. The curves now differ because the two controllers specify different blower speeds. The purification performance is comparable in the two control methods because the ammonium and nitrate concentrations remain the same on average. It can be clearly seen that the MPC solution provides a lower oxygen concentration in the activated sludge tank than conventional closed-loop control. Because the blower often runs at maximum speed with the conventional closed-loop control, this means significant energy savings, amounting to 33% in the time period examined.

8 NH4/mgL Time/h 6 Outlook The simulation results are promising. Although the quality of the effluent values is the same with the new MPC solution as when using conventional closed-loop control, significant energy savings for aeration are possible. NO3/mgL Time/h O2/mgL -1 n/% Time/h Time/h 8 6 classic MPC Feed/m 3 h Time/h Figure 3: Simulation in intermittent operation with influent fluctuations. Red: conventional closed-loop control, blue: MPC; ammonium concentration (NH4), nitrate concentration (NO3), dissolved oxygen concentration (O2) in the nitrification tanks, blower speed (n), and influent flow rate (Feed) The increases of the influent flow rates after 2.5 and 14.5 hours trigger an increase in the blower speed by means of the closed-loop ammonium control and feedforward control of the MPC. The behavior of the MPC control loop thus approaches that of the conventional oxygen control loop, which blows at maximum speed nearly all the time anyway. Of interest, in contrast, are the decreases in the influent. In the case of small influent flows, the MPC throttles back the blower speed considerably, which explains the significant energy savings.

9 "Any control solution must have a clear and transparent structure so that it can be operated and maintained by the available personnel." Optimization of a large sewage treatment plant in continuous operation Plant description The second example to be examined is a large sewage treatment plant whose basic structure is shown in Figure 4. After the inflow and preliminary sedimentation, the wastewater initially goes to the upstream denitrification. Nitrification then takes place in the aerated tank. Before secondary sedimentation, a portion of the water is pumped back into the denitrification tank as an internal recirculation stream. Simulation model A simulation model exists for this sewage treatment plant based on Matlab/Simulink and the SIMBA library [7.]. The model simulates using a variable cycle time that can be reduced down to 5 s. The simulation model operates with real measured influent flow rates and concentrations as well as temperatures in a time period up to 1.5 years and reflects the real plant characteristics during this time period very effectively. The existing automation is also integrated into the simulation model. Figure 4: Flow diagram of the sewage treatment plant Challenges for automation Two independent manipulated variables are available for closed-loop control in this sewage treatment plant: the aeration of the nitrification tank and the recirculation rate. In the conventional closed-loop control of the automation to date, the aeration is manipulated by a PI controller that controls an oxygen concentration corresponding to the constant oxygen setpoint in the nitrification tank. The recirculation is controlled using a characteristic curve based on the nitrate concentration.

10 The conventional closed-loop control cannot influence the effluent concentrations directly. Moreover, the operation of the two controllers is not coordinated even though the controlled systems are strongly interacting from the process engineering perspective. Because the wastewater composition is largely constant, it is the influent flow rate above all that affects the plant behavior. The existing closed-loop control, however, can only react to fluctuations after the oxygen concentration or nitrate concentration move away from their setpoints. Solution concept The most important criterion for automation of sewage treatment plants is compliance with legal effluent limits. The measured values of the ammonium and nitrate concentrations in the discharge of the secondary sedimentation tank are thus selected as controlled variables. Closedloop control on the basis of concentration measurements in the nitrification tank would also be possible. The necessary manipulated variables of the MPC are the oxygen concentration setpoint in the nitrification tank, which is controlled by a slave control loop with a PI oxygen controller, and the recirculation rate. The influent flow rate is also measured and used for dynamic feedforward control. Unlike in the first plant example, the oxygen controller can continue to be used here and is employed as a slave controller of a cascade structure because it quickly and reliably achieves the desired oxygen concentration. This results in a multi-variable problem for closed-loop control in which both manipulated variables affect both controlled variables. The disturbance variable also affects both controlled variables. A satisfactory solution to this control engineering problem is not possible with singleloop controllers, such as PI controllers. A multi-variable controller must therefore be used. The ModPreCon function block from the Advanced Process Library in SIMATIC PCS 7 is ideally suitable for this task and is therefore connected to the existing Matlab/Simulink simulation of the sewage treatment plant. To assign the MPC parameters, suitable training data must first be recorded, just like in the first sewage treatment plant example. The manipulated and disturbance variables are individually excited in a targeted manner for this. A mathematical model of the plant behavior can be obtained from this data with the MPC Configurator and then used to assign the MPC function block parameters. Simulation results In an idealized simulation in which all external influencing factors are kept constant, it can be effectively proven that the MPC is able to directly control the multi-variable problem presented. Targeted excitations of the setpoints and the disturbance variable for this are shown in Figure 5. After one day, the setpoint of the ammonium concentration is modified, whereby all setpoints are chosen arbitrarily. The MPC reacts with an adjustment of the oxygen concentration and the recirculation, which leads to fast tracking of the ammonium concentration setpoint. At the same time, the nitrate concentration is hardly affected. After three days, the setpoint of the nitrate concentration is modified, whereupon the MPC again adjusts the oxygen concentration and the recirculation. Within one day, the new nitrate concentration reaches a steady-state value and fluctuation of the ammonium concentration is corrected. NH4/mgL -1 NO3/mgL -1 O2/mgL -1 Reci./m 3 Day -1 Feed/m 3 Day x x Figure 5: Idealized simulation with constant external influencing factors. Ammonium concentration (NH4), nitrate concentration (NO3) in the discharge from the secondary sedimentation tank, blue: setpoint, red: actual value; dissolved oxygen concentration setpoint in the nitrification tank (O2), recirculation rate (Reci.) and influent flow rate (Feed) The influent flow rate is changed on the fifth day. The MPC uses both manipulated variables again to correct the am-

11 monium and nitrate concentration errors and achieves a steady-state within two days. The suitability of the MPC for closed-loop control of the sewage treatment plant under real environmental conditions is demonstrated by a long-term simulation over 24 days, which is summarized in Figure 6. Here, the MPC is designed in such a way that it only intervenes when high concentrations in the controlled variables threaten to violate the legal limits. The rest of the time, it strives to reach an oxygen setpoint of 1.3 mg/l, which is less than the constant setpoint of 1.5 mg/l of the conventional closed-loop control. Overall, it can be clearly seen that the ammonium and nitrate concentration curves are largely similar. However, the MPC reacts to higher concentrations with active attenuation so that the maximum pollution loads are reduced. The very low inflow rate near day 5 results in a high nitrate concentration in the conventional closed-loop control. The effect of the disturbance variable compensation via MPC is clearly visible: it reduces the nitrate concentration significantly. Because the MPC commands a lower oxygen setpoint in the nitrification tank on average, less energy is used for aeration of the nitrification tank compared to conventional closed-loop control. This amounts to an energy saving of 5.4% over the examined time period of 24 days. Outlook The simulation results show that the MPC can also reliably control a large and complex sewage treatment plant. In doing so, not only are the effluent concentrations improved but a significant amount of energy is saved for aeration. Overall, the MPC enables straightforward, transparent, and uniform automation of the sewage treatment plant. Up to now, only the influent flow rate has been integrated in the automation concept using MPC. Because the temperature, in particular, has a large, but identifiable effect on plant behavior, additional studies will analyze how temperature can be integrated into the closed-loop control concept. NH4/mgL -1 NO3/mgL -1 Reci./m 3 Day -1 O2/mgL -1 Feed/m 3 Day -1 Temp./ C 2 1 classic control MPC x x Figure 6: Long-term simulation. Red: conventional closed-loop control, blue: MPC; ammonium concentration (NH4), nitrate concentration (NO3) in the discharge from the secondary sedimentation tank, dissolved oxygen concentration setpoint in the nitrification tank (O2), recirculation rate (Reci.), influent flow rate (Feed), and temperature (Temp.):

12 Conclusion The simulation of two sewage treatment plants one small plant with intermittent denitrification and one large plant in continuous operation demonstrates clear advantages of automation with the Advanced Process Library of SIMATIC PCS 7. Compliance with discharge limits is ensured or even improved. Moreover, potential for significant energy savings is revealed. In addition, the MPC block allows a uniform and straightforward automation solution that can react to fluctuations in the inflow rate without user intervention. Implementation and parameter assignment of the MPC is managed very easily through the use of standard function blocks and off-the-shelf commissioning tools. As a result, automation with SIMATIC PCS 7 contributes significantly to "operational excellence" [8.] and supports efficient operation of water and wastewater treatment plants with functions for Advanced Process Control integrated into the distributed control system. Siemens AG Process Industries and Drives Water and Wastewater PD PA AE W&W 3 P.O. Box Nuremberg, Germany All rights reserved. All trademarks used are owned by Siemens or their respective owners. Siemens AG 215 References [1.] Hansen, J.: Der Einsatz von Fuzzy Control für Regelungsaufgaben im Bereich der Nährstoffelimination in kommunalen Kläranlagen. Dissertation Kaiserslautern University, Department of Urban Water Management, [2.] Siemens AG, SIMATIC PCS 7. [3.] Siemens AG, Industry Sector, Industrial Automation: White Paper "How to Improve the Performance of your Plant Using the Appropriate Tools of SIMATIC PCS 7 APC-Portfolio?" s7/support/marktstudien/wp_pcs7_apc_en.pdf, 28. [4.] Siemens AG, Water Library. [5.] Jeppsson, U.: A general description of the Activated Sludge Model No. 1 (ASM1), Lund Institute of Technology, Lund, [6.] Alex, J., L. Benedetti, J. Copp, K. Gernaey, U. Jeppsson, I. Nopens, M.-N. Pons, L. Rieger, C. Rosen, J. Steyer, P. Vanrolleghem and S. Winkler: Benchmark Simulation Model no. 1 (BSM1), Dept. of Industrial Electrical Engineering and Automation, Lund University, 28. [7.] ifak system GmbH, SIMBA, [8.] Siemens AG, Industry Sector, Industrial Automation: White Paper "Which contributions to "operational excellence" and efficient operation of process plants can be expected from automation with SIMATIC PCS 7?". support/pdf/76/wp_op-eff-pcs7_en.pdf, 211. [9.] Pfeiffer, B-M., Labisch, D., Grieb, H., Brandstetter, V., Wehrstedt, J.C., Pirsing, A.: Optimierungspotentiale bei Kläranlagen durch den Einsatz modellbasierter prädiktiver Regelungen. Automation 215, Baden-Baden. Conference CD, VDI-Verlag, Düsseldorf.