  During the operation of a chemical plant, product quality must be
consistently maintained, and the production of off-specification products
should be minimized. Accordingly, process variables related to the product
quality, such as the temperature and composition of materials at various parts
of the plant must be measured, and appropriate operations (that is, control)
must be performed based on the measurements. Some process variables, such as
temperature and flow rate, can be measured continuously and instantaneously.
However, other variables, such as composition and viscosity, can only be
obtained through time-consuming analysis after sampling substances from the
plant. Soft sensors have been proposed for estimating process variables that
cannot be obtained in real time from easily measurable variables. However, the
estimation accuracy of conventional statistical soft sensors, which are
constructed from recorded measurements, can be very poor in unrecorded
situations (extrapolation). In this study, we estimate the internal state
variables of a plant by using a dynamic simulator that can estimate and predict
even unrecorded situations on the basis of chemical engineering knowledge and
an artificial intelligence (AI) technology called reinforcement learning, and
propose to use the estimated internal state variables of a plant as soft
sensors. In addition, we describe the prospects for plant operation and control
using such soft sensors and the methodology to obtain the necessary prediction
models (i.e., simulators) for the proposed system.
