(411d) Cause and Effect Modeling from Plant Data
AIChE Annual Meeting
2007
2007 Annual Meeting
Computing and Systems Technology Division
Data Analysis: Design, Algorithms and Applications II
Wednesday, November 7, 2007 - 1:45pm to 2:10pm
Plant data bases are filled with information on the relationships of state (output) and input variables. However, due to natural high multi-collinearities of the inputs and low signal to noise ratios for the outputs, modelers are challenged in developing cause and effect relationships using plant data. As a result, dynamic models are commonly developed from plant tests which incur costs and risks to the operation. Thus, the purpose of this work is to introduce a modeling approach that is capable of developing accurate cause and effect models from plant data. The proposed method is a special application of the Wiener block-oriented system and the unique and powerful attributes of this approach over existing techniques are demonstrated in a mathematically simulated process study.
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