When there is a fault in the chemical plant

the operators may not be able to find the root cause of the fault. Data-driven method can be a great help to reduce the uncertainty of root cause

even to locate the root cause. By analysis of chemical process data

the disturbance propagation way can be detected

which can help to locate the root cause. In this article

convergent cross mapping(CCM) with the characteristic of time is proposed to detect the causality in dynamic chemical process. Furthermore

the usage of Akaike information criterion is proposed to determine the most proper embedding dimension. To prove the effectiveness of the method

the new method is applied to the ecosystem examples

causality testing benchmark model and CSTR model. Comparing the result calculated by the original CCM

the effectiveness of the new method is found.