| Cause and Effect Modeling from Plant Data | ||
| Swee-Teng Chin, Chemical and Biological Engineering, Iowa State University, 2114 Sweeney, Ames, IA 50011, Nidhi Bhandari, Department of Chemical Engineering, Indian Institute of Technology, Roorkee, 247667, India and Derrick Rollins, Iowa State University, Department of Chemical Engineering, 2114 Sweeney Hall, Ames, IA 50011-2230 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. Extended Abstract Status: File Uploaded | ||