Regression-based planning

Regression-Based Planning Example of the Fluid Catalytic Cracker... 

For this case of second order complete plan, the specificity of the matrix of the coefficients results in an assembly of relations directly giving the regression values of the coefficients. In this example, where the complete second order plan is based on a 2 CFE, these relations are written as follows ... 

On a practical level, the heuristic approach includes first collecting all the possible data during the experiments as a function of the parameters which are deemed to be important, i.e. concentrations, temperature, pressures, pH, catalyst concentration, volume, etc. Then the rate constants are estimated by regression analysis and the adequacy of the model is judged based on some criteria (like residual sums and parameter significance, which will be discussed further). If a researcher is not satisfied, then additional experiments are performed, followed by parameter estimation and sometimes simulations outside the studied parameter domain. The latter procedure provides the possibility to test the predictive power of a kinetic model. The kinetic model is then gradually improved and the experimental plan is modified, if needed. This process continues until the researcher is satisfied with the kinetic model. 

The choice of process of disc extrusion is depended upon the opportunity of realization of high deformation. The optimisation of composition of multycomponent electrical conductive composite material on modified PP bases with low inflame, getting adequate regressive models were held by methods of optimal planning of experiment. 

Last but not least, the status view component is used to visualize the content of the local part of the knowledge base. It provides an interactive insight to the queried process plan. This process plan may also contain gaps if no solution was found. Also assumptions are included if a new process segment was derived from an existing one by the use of regression functions. 

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