Based on the changes of catalyst properties, a model was constructed to estimate the rate constants of Standard-SCR, NH3 oxidation, and NO oxidation reactions over hydrothermally aged Cu-CHA catalyst. Moreover, the rate constants obtained by the constructed model were applied to the model for estimating NOx conversion over fresh catalyst constructed in previous studies, and the NOx conversion over hydrothermally aged catalyst was estimated. As a result, the calculated NOx conversion over hydrothermally aged Cu-CHA catalyst was in good agreement with the experimental results.
Knocking is the abnormal combustion of a gasoline engine, it generates a metallic noise. Engine knocking can damage the engine, so workers detect knocking by listening to the sound. There is a need to develop a way to automate this kind of work. We developed the deep learning model which separates Knocking sound from engine radiation noise measured by a microphone. This model obtains the time-frequency mask from the paired data of engine emissions and cylinder pressure. The time-frequency mask enables the separation of knocking sound from engine radiation noise. By training various rotation speeds, the proposed model can separate the knocking sound without training target engine speed.
