Temperatures and flow rates are the most frequently measured variables in industrial processes. They generally form the basis of data reconciliation based on mass and energy balances. While flow rate data rectification is routinely carried out using rigorous linear models of mass balances, energy balances, necessary for the reconciliation of temperature values, introduce two main difficulties. Indeed the presence of the product of two variables that need reconciling (temperature and flow rate) changes the original linear equations into a system of bilinear equations. Additionally energy balances are subject to modelling errors due to the presence of parameters (specific heats, latent heats, heats of reaction). The uncertainties on these parameters can affect the reliability of the data reconciliation considerably. It is shown in this article that interval analysis can provide a useful tool for reducing the sensitivity of the reconstructed values of the process variables. An important simple case is examined for illustration purposes. Copyright © 2014, AIDIC Servizi S.r.l.

Application of interval analysis to the reconciliation of process data when models subject to uncertainties are used

VOCCIANTE, MARCO;REVERBERI, ANDREA;
2014

Abstract

Temperatures and flow rates are the most frequently measured variables in industrial processes. They generally form the basis of data reconciliation based on mass and energy balances. While flow rate data rectification is routinely carried out using rigorous linear models of mass balances, energy balances, necessary for the reconciliation of temperature values, introduce two main difficulties. Indeed the presence of the product of two variables that need reconciling (temperature and flow rate) changes the original linear equations into a system of bilinear equations. Additionally energy balances are subject to modelling errors due to the presence of parameters (specific heats, latent heats, heats of reaction). The uncertainties on these parameters can affect the reliability of the data reconciliation considerably. It is shown in this article that interval analysis can provide a useful tool for reducing the sensitivity of the reconstructed values of the process variables. An important simple case is examined for illustration purposes. Copyright © 2014, AIDIC Servizi S.r.l.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11567/810789
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