Selection of the most influential flow and thermal parameters for predicting the efficiency of activated carbon filters using neuro-fuzzy technique

This paper analyzes the influence of various flow and thermal variables on the performance of the activated carbon filter in the air-conditioning system. The ANFIS (adaptive neuro fuzzy inference system) method was applied to the data obtained from the experimental apparatus in order to select the most influential parameters for assessing the efficiency of the activated carbon filter. Acetone was selected as the target pollutant component. Experiments were performed for different temperature, humidity, and flow rate conditions, as well as for acetone concentrations. A set of four potential inputs was considered: velocities of gas mixture through ventilation duct (flow), temperature, humidity, and concentration of test chemical pollutants ahead of the filter module. The results show that the most influential parameter for predicting outlet concentrations of acetone is temperature.