Development of Soft Sensor Models Based on Time Difference of Process Variables with Accounting for Nonlinear Relationship

Soft sensors are widely used to estimate process variables that are difficult to measure online. Though regression models are reconstructed with new data to adapt changes of the plants, some problems remain in practice. Hence, it is attempted to construct soft sensor models based on the time difference of an objective variable and that of explanatory variables for reducing the effects of deterioration with age such as the drift and gradual changes in the state of plants. In this paper, we have proposed to construct time difference models after modeling nonlinear relationship between and among process variables. Variables obtained by physical models or those calculated by statistical nonlinear regression methods are used to consider the nonlinearity, and then, a time difference model is constructed including these variables. We applied these methods to the actual industrial data obtained during an industrial polymer process and confirmed the usefulness of the proposed methods.

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