Techniques for solving industrial nonlinear data reconciliation problems

The focus of this short note is to highlight several techniques to solve industrial nonlinear data reconciliation problems. The main areas of discussion are starting value generation, row and column scaling, regularization of the kernel matrix, using different and independent unconstrained solving methods such as ridge regression, matrix projection, Newton's method and singular value decomposition and infeasibility handling. These techniques are usually necessary to arrive at solutions to nonlinear reconciliation problems which are poorly initiated, ill-conditioned and even inconsistent. A relatively large and well-studied numerical example is solved taken from the mining process industry which demonstrates some of the techniques discussed. (C) 2004 Elsevier Ltd. All rights reserved.