OEA41P-Methods to Handle Blend Nonlinearity

SKU: OEA41P Category:

Linear and nonlinear data normalization, or reconciliation, entails a linear system and a nonlinear system with mass and energy scales and constraints. Nonlinear data reconciliation requires more work to create a solution strategy, but nonlinear blend models are closer to reality. This topic will discuss the linear blending model and error correction.

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 will discuss the use of blend model bias, examples of bias for blend grades, use of linear blending model using nonlinear blending values, the lumped parameter blending model, analyzer dynamics, methods to handle blend nonlinearity, use of interaction coefficients (DuPont or ethyl), nonlinear blending indexes, sources of errors in blending operations, lumped bias contributions, flow correction methods, tank prediction bias, etc.

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