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A heat exchanger network (HEN) is fundamental for reducing industrial processes’ external heating and cooling utility consumption, leading to lower operational costs and environmental impacts. HEN efficiency depends on the conditions of the heat exchangers, whose performance is reduced over time due to the constant deposit of impurities over the heat exchangers’ wall. This deposit, known as fouling, generates heat transfer resistance and reduces the corresponding performance and heat recovery. Therefore, the real-time estimation of fouling resistance is a crucial operational issue. The main industrial problem for monitoring in real-time the fouling in a HEN of an industrial process is the lack of measurements (e.g. temperatures and flow rates) and knowledge of the oil’s physical-chemical properties. In this work, a constrained Kalman filter with smoothing is applied to estimate the unavailable measurements and the fouling resistance to overcome these problems. This technique is applied to a real case study of a refinery industry’s crude oil distillation unit preheating train. The results show that the technique efficiently estimates the desired variables, increasing the process monitoring capability.

Fouling monitoring of a heat exchanger network of an actual crude oil distillation unit by constrained extended Kalman filter with smoothing