Discriminating signal from background using neural networks: Application to top-quark search at the Fermilab Tevatron

GRAPHICAL REPRESENTATION OF CAUSE-EFFECT RELATIONSHIPS AMONG CHEMICAL PROCESS VARIABLES USING A NEURAL NETWORK APPROACH

The extraction of information from tabular data is not a natural task for human beings, which is worse when dealing with high dimensional systems. On the other hand, graphical representations make the understanding easier by exploring the human capacity of processing visual information. Such representations can be used for many purposes, e.g., complex systems structuring which contributes to a better understanding of it. This paper constructs a cause-effect map relating the influence of each input process variable on the steam generated by a boiler. The real case study is based on the operations of a chemical recovery boiler of a Kraft pulp mill in Brazil. The map is obtained by two steps, namely the identification of a neural predictive model for the steam and a study of sensitivity analysis. The numerical results are then depicted in a graphical format using a cause-effect map. This representation highlights the relative importance of the predictor variables to the steam generation. The results, in agreement with the literature, show the higher contribution of the heat released during the fuel burning, and the lower influence of both the fuel temperature and the operating variables associated with the primary level of injection of the combustion air.