  Thank you very much for the attention and concern of colleagues and scholars
in this work. With the comments and guidance of experts, editors, and
reviewers, this work has been accepted for publishing in the journal "Process
Safety and Environmental Protection". The theme of this paper relies on the
Spatial-temporal associations of numerous variables in the same industrial
processes, which refers to numerous variables obtained in dynamic industrial
processes with Spatial-temporal correlation characteristics, i.e., these
variables are not only highly correlated in time but also interrelated in
space. To handle this problem, three key issues need to be well addressed:
variable characteristics modeling and representation, graph network
construction (temporal information), and graph characteristics perception. The
first issue is implemented by assuming the data follows one improved Gaussian
distribution, while the graph network can be defined by the monitoring
variables and their edges which are calculated by their characteristics in
time. Finally, these networks corresponding to process states at different
times are fed into a graph convolutional neural network to implement graph
classification to achieve process monitoring. A benchmark experiment (Tennessee
Eastman chemical process) and one application study (cobalt purification from
zinc solution) are employed to demonstrate the feasibility and applicability of
this paper.
