Estimation of Fouling Model Parameters for Shell Side and Tube Side of Crude Oil Heat Exchangers Using Data Reconciliation and Parameter Estimation

Abstract

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.

Industrial data science – a review of machine learning applications for chemical and process industries

“…melt viscosity, temperature profile, and flow index). More widely, this is primarily due to well-established statistical practices, as encompassed by data reconciliation and validation approaches, 90,91 model selection, validation tools, 92 data assimilation practice, 93,94 and the field of estimation theory (which is generally concerned with identifying models of systems from data). 95,96 In the following, we discuss data-driven techniques to briefly illustrate a general approach to reduce redundant tags with similar effect size, quantify the historical variability or uncertainty, to provide insight into possible future process conditions.…”

“…melt viscosity, temperature profile, and flow index). More widely, this is primarily due to well-established statistical practices, as encompassed by data reconciliation and validation approaches, 90,91 model selection, validation tools, 92 data assimilation practice, 93,94 and the field of estimation theory (which is generally concerned with identifying models of systems from data). 95,96…”