A method for detecting cause-effects in data from complex processes

Abstract

When models are developed to aid the decision making in the operation of industrial processes, lack of understanding of the underlying mechanisms can make a first-principles modeling approach infeasible. An alternative is to develop a black-box model on the basis of historical data, and neural networks can be used for this purpose to cope with nonlinearities. Since numerous factors may influence the variables to be modeled, and all potential inputs cannot be considered, one may instead solely focus on occasions where the (input or output) variables exhibit larger changes. The paper describes a modeling method by which historical data can be interpreted with respect to changes in key variables, yielding a model that is well suited for analysis of how changes in the input variables affect the outputs.