The modeling problem is one of the important topics in engineering applications.
In various applications, it is required to find a mathematical model to represent the
relationship between output and the associated input variables. In this study, an approach
on basis of a new deep learned type-3 (T3) fuzzy logic system (FLS) is introduced.
The modeling of CO2 solubility on basis of temperature, molality of NaCl, and pressure is
considered as an application. The monitoring of carbon dioxide (CO2) solubility in brine is
one of the effective approaches in carbon capture and sequestration technique to reduce it
in the atmosphere. A new hybrid learning method is presented to optimize the suggested
model. The new adaptation laws are carry-out to tune the rule parameters and centers of
membership functions (MFs). The values of horizontal slices and α- cuts are learned by the
unscented Kalman filter (UKF). By the real-world experimental data sets, several
statistical examinations, and comparison with conventional well-known fuzzy neural
networks (NNs) and learning methods, the reliability and good performance of the
suggested method are demonstrated. Also, the sensitivity of the input variables is analyzed
by the use of the Sobol approach