(177b) A Practical Application of Model-Based Techniques to Track Equipment Degradation and Process Drifts: Monitoring of Fouling in Heat Exchangers

Process monitoring and control is a vital component of many industrial processes, required in order to ensure safety and product quality [1]. Process control allows a process engineer to ensure that different streams adhere to strict production standards, through controllers that ensure set-points are being maintained, despite fluctuations in input values or disturbances.

Unfortunately, it is possible for a given process model to also experience degradation over time, e.g., fouling in a heat exchanger. For a heat exchanger model the overall heat transfer coefficient can provide a good indication of fouling, as it decreases with an increase in fouling. In the presence of fouling, the controller increases the amount of cooling utility required in order to meet the desired set-point. In 1982, a conservation estimate of the cost of fouling in the US due to increased operational costs was predicted to be $180 million [2]. This cost was predicted to be closer to $18 billion in 2019 [3]. Since the process is operating under control, monitoring just the output variables, i.e., outlet temperatures of the hot and cold streams, is insufficient, as the controller will continuously adjust the cooling utility to ensure that the set-point is being achieved despite increased operating costs. Therefore, an efficient algorithm needs to be developed in order to monitor both states variables, and the parameter, i.e, overall heat transfer coefficient. This will enable the detection of any drift in the process model, along with the assessment of its impact. The impact is application dependent and can include economic, safety, and environmental losses.

Previous work proposed the use of model-based techniques, namely two Kalman filters: Extended Kalman Filter (EKF), and Unscented Kalman Filter (UKF), in order to monitor deviations in process model through estimation of the overall heat transfer coefficient, in addition to the two states [4]. A simulated synthetic model of a heat exchanger was implemented in Simulink, in order to demonstrate the application of the proposed algorithm. This included the development of a cost contour profile to monitor the economic impact of fouling to the process. This work extends the proposed algorithm to real data, obtained from a heat exchanger at Texas A&M University at Qatar, in order to demonstrate its validity and further highlight its practical applicability.

References

[1] F. Harrou, M. N. Nounou, H. N. Nounou, and M. Madakyaru, “Statistical fault detection using PCA-based GLR hypothesis testing,” J. Loss Prev. Process Ind., vol. 26, no. 1, pp. 129–139, 2013.

[2] W. J. Rebello, S. L. Richlen, and F. Childs, “The cost of heat exchanger fouling in the US industries,” 1988.

[3] “Economics of fouling,” Advanced heat transfer technologies, 2019. [Online]. Available: https://fbhx-usa.com/economics-of-fouling. [Accessed: 10-Oct-2019].

[4] M. Z. Sheriff, H. Nounou, M. Nounou, and M. N. Karim, “Monitoring process degradation through operating regime based process monitoring,” in AIChE Spring Meeting and Global Congress on Process Safety: Process Control Monitoring and Analytics, 2019.