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During the operation of a chemical plant, product quality must beconsistently maintained, and the production of off-specification productsshould be minimized. Accordingly, process variables related to the productquality, such as the temperature and composition of materials at various partsof the plant must be measured, and appropriate operations (that is, control)must be performed based on the measurements. Some process variables, such astemperature and flow rate, can be measured continuously and instantaneously.However, other variables, such as composition and viscosity, can only beobtained through time-consuming analysis after sampling substances from theplant. Soft sensors have been proposed for estimating process variables thatcannot be obtained in real time from easily measurable variables. However, theestimation accuracy of conventional statistical soft sensors, which areconstructed from recorded measurements, can be very poor in unrecordedsituations (extrapolation). In this study, we estimate the internal statevariables of a plant by using a dynamic simulator that can estimate and predicteven unrecorded situations on the basis of chemical engineering knowledge andan artificial intelligence (AI) technology called reinforcement learning, andpropose to use the estimated internal state variables of a plant as softsensors. In addition, we describe the prospects for plant operation and controlusing such soft sensors and the methodology to obtain the necessary predictionmodels (i.e., simulators) for the proposed system.

Soft Sensors and Process Control using AI and Dynamic Simulation