|
Abstract
|
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
|