In the commercial chemical process

many primary product variables cannot be measured online

and soft sensor is an important means to solve this problem.Soft sensing modeling is the core issue of soft sensor.The relationship between soft sensing modeling and identification and nonlinear modeling is presented.The dynamic relationship between quality variables and variables that are easy to measure exists between the increments

and identification depends on incremental data

while soft sensing modeling depends on the measured data to get the relationship.Nonlinear modeling establishes the static relationship between these variables

ignoring the dynamic characteristics

which soft sensing modeling should take into account.With deeper understanding of the chemical process properties

the types and structures of soft sensing model have undergone a great change in the last decades

and soft sensing modeling method evolves from mechanism modeling to data-driven modeling

from linear modeling to nonlinear modeling

and from static modeling to dynamic modeling.The development of the soft sensing modeling method is reviewed.The advantages and disadvantages of the proposed methods are analyzed

and the applications of these methods are shown.In the end

the hot issues and the directions of development of soft sensing modeling method are presented.