Data Reconciliation Using Nonlinear Programming Techniques

DYNAMIC DATA RECONCILIATION USING NONLINEAR PROGRAMMING TECHNIQUES 

As was previously shown, Kalman filtering techniques can be, and have been, successfully used on dynamic process data, to smooth measurement data recursively and 

FIGURE 4 Speed of response (from Porras and Romagnoli, 1987). 

Note that the measurements and estimates include both measured state variables and measured input variables. The inclusion of the input variables among those to be estimated establishes the error-in-variable nature of the data reconciliation problem. 

The lengths of y(t) and r are equal to the total number of variables (state and input). The vector y comprises all yt, where y represent the measurements at discrete time tk- The lengths of vectors f, t], and cu are problem specific. 

---

Dynamic Data Reconciliation Using Nonlinear Programming Techniques 148... 

In this chapter, the data reconciliation problem for dynamic/quasi-steady-state evolving processes is considered. The problem of measurement bias is extended to consider dynamic situations. Finally in this chapter, an alternative approach for nonlinear dynamic data reconciliation using nonlinear programming techniques will be discussed. 

Finally, an approach for nonlinear dynamic data reconciliation using nonlinear programming techniques was discussed. This formulation involves the optimization of an objective function through the adjustment of estimate functions constrained by differential and algebraic equalities and inequalities. 

Liebman, M. J. T. F. Edgar and L. S. Lasdon. Efficient Data Reconciliation and Estimation for Dynamic Processes Using Nonlinear Programming Techniques. Comput Chem Eng 16(10/11) 963-986 (1992). 

Ramamurthi, Y. and B.W. Bequette (1990), Data reconciliation of systems with unmeasured variables using nonlinear programming techniques, AIChE 1990 Spring National Meeting, Orlando, FL... 

Several researchers [e.g., Tjoa and Biegler (1992) and Robertson et al. (1996)] have demonstrated advantages of using nonlinear programming (NLP) techniques over such traditional data reconciliation methods as successive linearization for steady-state or dynamic processes. Through the inclusion of variable bounds and a more robust treatment of the nonlinear algebraic constraints, improved reconciliation performance can be realized. 

In this section the extension of the use of nonlinear programming techniques to solve the dynamic joint data reconciliation and parameter estimation problem is briefly discussed. As shown in Chapter 8, the general nonlinear dynamic data reconciliation (NDDR) formulation can be written as ... 

---