The strength of GPS-X TM lies in its ability to rapidly model and simulate wastewater processes. This makes it possible to use the MATLAB Link to control a GPS-X TM model using a controller that is implemented in MATLAB, thereby leveraging MATLAB's extensive library of control-related functions. In effect, MATLAB is placed "in the loop" during a simulation, as depicted in the diagram.
#Python analyze gps tracks code#
The MATLAB Link allows the user to call MATLAB code from a GPS-X TM model, sending GPS-X TM model variables as inputs to a special function M-file, and mapping the outputs of that function to other GPS-X TM model variables. It can be seen from the results of this optimization that the plant can meet an effluent TN objective of 7.0 mg/L, if the current operating conditions are modified to the values determined by the GPS-X TM Optimizer. The iteration number and the three optimized parameter values are shown along with the associated effluent TN concentration. The table in Figure 4 shows the results of the optimization. This optimization required 70 iterations to converge. The objective of this optimization is to minimize the effluent TN concentration by finding the best combination of the three flows, while ensuring that the maximum installed pumping capacities at the plant are not exceeded. In this example, the optimizer was used to vary the RAS flow, the WAS flow and the mixed-liquor internal recycle (MLIR) flow to meet a more stringent effluent TN objective of 7.0 mg/L. Under current operating conditions, this plant has an effluent total nitrogen (TN) of 10.0 mg/L. The plant shown in Figure 3 is designed for biological nutrient removal. The GPS-X TM Optimizer can be used to find appropriate control methods to minimize effluent concentrations from an activated sludge process.
0 kommentar(er)
