OEA41P-Methods to Handle blend model errors

Description

Linear and nonlinear data normalization, or reconciliation, entails a linear system and a nonlinear system with mass and energy scales and constraints. A considerable sum of work is required to devise a solution strategy or develop a nonlinear data reconciliation model, as it is more complex than linear models. On the other hand, nonlinear blend models are closer to reality. This topic discusses blend model bias, blend grades, and linear blending model using nonlinear blending values, the lumped parameter blending model, and analyzer dynamics. It will also discuss the methods to handle blend nonlinearity, interaction coefficients (DuPont or ethyl), nonlinear blending indexes, sources of errors in blending operations, lumped bias contributions, flow correction methods, tank prediction bias, etc.