Data Reconciliation in Reaction Systems using the Concept of Extents

Abstract

Concentrations measured during the course of a chemical reaction are corrupted with noise, which reduces the quality of information. When these measurements are used for identifying kinetic models, the noise impairs the ability to identify accurate models. The noise in concentration measurements can be reduced using data reconciliation, exploiting for example the material balances as constraints. However, additional constraints can be obtained via the transformation of concentrations into extents and invariants. This paper uses the transformation to extents and invariants and formulates the data reconciliation problem accordingly. This formulation has the advantage that non-negativity and monotonicity constraints can be imposed on selected extents. A simulated example is used to demonstrate that reconciled measurements lead to the identification of more accurate kinetic models.

Identification of Multiphase Reaction Systems with Instantaneous Equilibria

“…For batch and semibatch reactors, the numbers of invariants are q̅ f = S̅ f – R f,k – p m and q̅ f = S̅ f – R f,k – p m – p f , respectively, with the invariant relationships being P̅ f T ( n̅ f ( t ) – n̅ f0 ) = 0 q̅ f (see ref for details).…”

A New Data Reconciliation Strategy Based on Mutual Information for Industrial Processes

“…Over the past decades, numerous researches have been carried out on data reconciliation. Moreover, data reconciliation has been largely applied to various industrial processes, such as chemical processes, − mineral and metal processes, , and steam turbine systems, , etc. In early data reconciliation studies, researchers mainly focused on data reconciliation model with linear constraints.…”