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Fouling modelling in crude oil heat exchangers is of great importance industrially. Current approaches use empirical or semi-empirical approaches, where fouling rate models are necessary. A series of parameters need to be determined, which directly depend on the nature and type of crude oil. These parameters can be estimated either by using laboratory experiments or, in principle, by measured process-data. This work focuses on the estimation of fouling rate model parameters using measured-data. An optimization-based data reconciliation approach, which accounts for random and gross errors, is integrated with a parameter-fitting algorithm. The methodology is tested in a case study, where a multi-pass heat exchanger is simulated. The effects of measurement error and fouling deposition on both sides are addressed. The fouling resistance is predicted and compared with the simulated data, showing good agreement as well as providing evidence for a successful separation of fouling resistances on both sides of a heat exchanger. Finally, studies are presented to show the isolation process for the minimum gross error magnitude, for different gross error locations.