Shankar Narasimhan S

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.

Soft sensors are increasingly being used to estimate difficult to measure variables using using a mathematical model and other easily measured variables. Partial Least Squares and Principal Components Regression are two popular methods for developing the linear models used in soft sensors. However, the optimality of these methods has not been established. In this article, we develop a soft sensing technique by combining PCA with conecpts drawn from Data Reconciliation techniques. The solution we propose is a mathematically rigorous approach to the problem when the measurements are corrupted with homoscedastic or heteroscedastic errors. © 2011 Elsevier B.V.