‎Fuzzy Model of Smart Toilet Bowl-Bidet System

This paper proposes an application of a Takagi-Sugeno fuzzy model to the prediction of complex mass transfer behavior in smart toilet bidet systems‎. ‎The model is constructed through the integration of fuzzy logic theory‎, ‎nonlinear autoregressive moving average exogenous input models‎, ‎neural networks‎, ‎and data clustering algorithms‎. ‎To develop the model for estimating the air quality of the smart toilet-bidet system‎, ‎many datasets are collected from a smart toilet bidet model equipped with an automatic odor/bacteria suction system using Sulfur hexafluoride ($SF_6$) gas‎. ‎Many case studies were carried out as a function of the suction flow rate‎, ‎suction angle‎, ‎the number of suction holes‎, ‎and suction hole size‎. ‎The inputs for training the fuzzy model are the size‎, ‎number‎, ‎and angles of suction holes‎, ‎whereas its output is the undesirable gas concentration‎. ‎The trained fuzzy model is tested using different datasets‎. ‎Modeling and testing results show the effectiveness of the fuzzy model in predicting the gas concentration of the toilet bowl‎. ‎The proposed fuzzy model is expected to be useful in the implementation of smart toilet bowl systems in the near future‎.