Method for Automatic Simulation Model Calibration and Maintenance for Brownfield Process Plants

The use of simulation models offers many benefits for process plants, but their application in industrial practice is often hampered by a lacking model fidelity. This lack of model fidelity can either arise during model creation or can result from plant alterations during operation parallel model usage. Calibrating a simulation model during its creation can be challenging, as causes for a lacking fidelity can be difficult to find. Maintaining model fidelity during the operational phase of the plant also presents a challenge, as plant changes caused by plant aging or installation of new equipment can happen constantly and need to be accounted for. Thus, simulation models need to be compared to sensor measurement series continuously during the plant’s operational phase and adjusted accordingly, in order to maintain a high model fidelity. In the approach presented in this paper, a permanently high level of model fidelity during model creation and operation parallel usage is achieved by continuously lowering deviations between simulation model output and plant sensor measurement series by model parameter adaptation. Additionally, a deviation cause analysis can greatly improve deviation reduction by indicating the part of the model that needs to be adapted and can yield interesting insights into plant anomalies, in case the deviation is caused by plant alterations. Therefore, this paper presents a method for simulation model calibration and continuous maintenance, consisting of parameter alignment, deviation detection, deviation cause detection and cause based model adaptation.