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 phenomenon is very hard to define, but often many simplified models describing the problem'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 utilized 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 others 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 performs 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 thery. A discussion on new fuzzy decision model is carried out and results on real data are shown.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Artificial Intelligence
- Applied Mathematics
Annunziato, M., & Pizzuti, S. (1999). Fuzzy fusion between fluidodynamic and neural models for monitoring multiphase flows. International Journal of Approximate Reasoning, 22(1), 53 - 71. https://doi.org/10.1016/S0888-613X(99)00018-3