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Seeking simplicity and the use of standard tools, we used the virtual plant to explore whether we could replace the fuzzy system with straightforward model predictive control. The goal as illustrated in Figure 2 was to minimize reagent consumption by lowering the pH set point of the inline systems without causing a pH excursion in the tank below the RCRA environmental limit. To insure the second stage could quickly do its job, a secondary objective was to keep the second-stage reagent valves at a minimum throttle position good enough to eliminate the valve and reagent delivery delays associated with getting a valve off its seat and flushing out an injector. To back up the fuzzy logic, a kicker algorithm would step open the second-stage pH controller output to 50% if the second-stage pH hit a low pH trigger point.
Before we could effectively use an online pH model, we needed to get the concentration right for the major strong acid in the waste. To kill two birds with one stone, we used a single input and output model predictive control (MPC) in an innovative way to adapt the concentration online. The set point and the controlled variable of the MPC was the reagent-to-waste flow ratio for the actual and virtual plant, respectively. The manipulated variable of the MPC was the strong acid concentration in the process model. The set points of the inline pH system came from the actual plant. Figure 3 shows this setup of the virtual plant running in unison with the actual plant. While a single model parameter was used here, the multivariable capability of the MPC offers the ability to extend the adaptation to several model parameters.
The goal of this arrangement was to minimize reagent consumption by lowering the pH set point of the in-line systems.
The first few hours after the model was put online, the adaptive MPC homed in on a relatively constant concentration. When we returned from lunch, the plant was rocking and rolling, and the pH system was dealing with frequent rapid changes in waste concentration. The kicker was activating about every 20 minutes. The MPC did a much better than expected job of chasing the acid concentration, as shown in Figure 4. We confirmed later that the production unit that was the source of most of the strong acid was having issues. A comparison of the virtual plant and actual plant control valve positions and pH response revealed there was no flow going through one of the second-stage reagent valves. The problem cleared a day after a phone call. Although not confirmed for this case, a technician said the tiny trim in these reagent valves had gotten plugged.
This is the setup of the virtual plant running in unison with the actual plant.
The kicker algorithm was made smarter by incrementing the output by 0.5% per second instead of a single step in the output of 50% (For more details, see item 9 in the sidebar located at the end of this story.). Also, a larger filter was added to the pH trigger for the second stage to emulate the filtering action of the attenuation tank. The result was smoother operation and a significant reagent savings. Making the kicker less disruptive and putting the reagent valve optimization in the same MPC used for tank pH control has eliminated potentially debilitating interactions.
It takes more and more interesting opportunities to get weathered engineers excited. However, the almost limitless opportunities to explore advanced control ideas make us downright tingly. For example, tests have shown model predictive control can make fine and coarse split-ranged reagent valves just a distant memory—or nightmare) (See item 8 in the sidebar located at the end of this story). The reagent savings in environmental systems are potentially big, since a lot is left unknown, and the loops don’t get as much attention as the more up-front and glamorous applications in production units.
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