Optimized calibration of RTK parameters in sanitary collection systems for wet weather, a Canadian case study

Model calibration is a ubiquitous challenge that is generally approached with an inefficient trial-and-error process of repeatedly changing calibration parameters, running model simulations, evaluating the fit of model results to observed data, and guessing new calibration parameter values until an acceptable result is achieved. As many collection system model simulations can take hours or even days to run, model calibration is a critical path task that often controls project schedule and demands significant labor hours. 

Jacobs has developed a tool to calibrate collection system models efficiently and automatically. The autocalibration tool performs multi-objective optimization using response surface characterization, in this case RTK values for subcatchments. Using a small number of strategically determined model simulations, it quantifies the mathematical relationship between model inputs and outputs – the model’s response surface. It then executes a multi-objective optimization using this computationally inexpensive response surface instead of the model itself to minimize the difference between modeled and observed outputs. There are many advantages to this approach, one of the foremost being computational cost: the number of model simulations required to arrive at an optimal solution is significantly lower than would be required using traditional optimization algorithms. A single run of the autocalibration tool on a collection system model allows the user to find the set of input parameters that produces the optimal outputs, in this case modeled flow and depth, across multiple meters and calibration events.  

The method is used in a Canadian collection system (with almost 1000 sanitary sewers), with around three months of flow monitoring data in 2021 in multiple strategic locations to perform the calibration task as a part of a bigger sanitary master plan. The results prove the efficiency, speed and accuracy of the calibration that would not have been achieved using the traditional trial and error.  

KEYWORDS 

Autocalibration, collection system modeling, RTK values 

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