Application of nonlinear data reconciliation method for steam generators data

Abstract eng:
In order to improve the accuracy of process data, the data reconciliation method can be used. Data reconciliation takes advantage of physical and chemical balances which can be arranged to decrease the measurement errors. Data processed by data reconciliation are in harmony with the physical model of technology which is further used for optimization of the process. This article claims to formulate the data reconciliation task in case of the heat system - the steam generator. The final form of problem is to minimize quadratic function subject to bilinear constrains. From various possible solutions we chose the gradient projection method and the sequential quadratic programming method (SQP). These too methods are compared in terms of efficiency and robustness in solving the reconciliation of steam generator’s data.

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