Graphical representation of cause-effect relationships among chemical process variables using a neural network approach

The visualization of relevant information from numerical data is not a natural task for human beings, mainly in case of multivariate systems. In compensation, graphical representations make the understanding easier since it explores the human capacity of processing visual information. Based on that, this study constructs a cause-effect map relating effects of operating process variables over the steam generated by a boiler. This is done after the identification of a neural predictive model for this response. The use of such data-driven technique is due to its capacity of performing a non linear input-output mapping given a reliable database. The case study is based on the operations of a chemical recovery boiler belonging to a Kraft pulp mill located in Brazil. The utility of the obtained map is clear, once the visualization of the contributions of each process variable over the output steam, from this graphical representation, is more intuitive.