Fuzzy fusion between fluidodynamic and neural models for monitoring multiphase flows

Abstract In most of industrial applications and in the fields of scientific research phenomena are highly non-linear and/or they have high dimensionality. In such cases a model which describes exactly the phenomena is very hard to define, but often many simplified models describing the problems's phenomenology in particular conditions are available. The problem of the multiphase flow rate estimation in oil extraction and transport processes fills in with this class of problems. At present the most utilised approach to solve such problem is that of comparing all the available models and techniques and then choose the one which behaves better than the other in all different conditions. In our work we propose an approach in which all models are utilized with the task of getting a system which performes better than the best available model. In particular different mathematical models of multiphase flow rate estimation and neural models co-operate by using a meta-decision maker based on fuzzy theory. A discussion on new fuzzy discusion models is curried out and results on real data are shown.