Optimal selection of measuring points in complex plants by linear models



For reliable information on operating plants it is essential to design measuring points well by selecting directly measured quantities from the set of all measurable quantities.

This article deals with a new method for optimizing measurement design. It is based on multiple Gauss-Jordan elimination of the system of linear mathematical model equations and solves the problem of instrumentation design in new plants as well as the problem of optimizing existing measuring systems. Optimization methods for linear objective functions and for objective functions of general type are proposed. The method also offers a complex classification of quantities (observability and redundancy). After the optimization, the problem is presolved and is ready for an optimal processing of measured data. The mathematical model is reduced to the minimum set of equations and quantities relevant to the solution of a given problem. From a numerical standpoint, the solution is efficient.