The catalytic performance of heterogeneous catalysts is dependent on many factors and the mechanism of catalyst
deactivation is very complicated. Moreover, the limitation of getting time series data during the deactivation process of
catalysts reduces the modeling efficiency and prediction precision. Therefore, a method for nonlinear time series forecasting
of the catalyst deactivation based on phase space reconstruction and support vector regression is presented. The method is
applied to predict the deactivation process of the Cu-Si-Al based catalysts for the synthesis of dimethyl carbonate(DMC).
The simulation results show that the prediction error of catalyst deactivation model is in a range of tolerance. The prediction
space-time yield value of DMC can provide important information for the design and operation of reactors as well as the
optimization of the reaction conditions.