Simultaneous data reconciliation and joint bias and leak estimation based on support vector regression

Process data measurements are important for process monitoring, control, optimization, and management decision making. However, process data may be heavily deteriorated by measurement biases and process leaks. Therefore, it is significant to simultaneously estimate biases and leaks with data reconciliation. In this paper, a novel strategy based on support vector regression (SVR) is proposed to achieve simultaneous data reconciliation and joint bias and leak estimation in steady processes. Although the linear objective function of the SVR approach proposed is robust with little computational burden, it would not result in the maximum likelihood estimate. Therefore, to ensure accurate estimates, the maximum likelihood estimate is applied based on the result of the SVR approach. Simulation and comparison results of a linear recycle system and a nonlinear heat-exchange network demonstrate that the proposed strategy is effective to achieve data reconciliation and joint bias and leak estimation with superior performances.

► Support vector regression is used to achieve joint bias and leak estimation.
► The linear objective function proposed is robust with little computational burden.
► Maximum likelihood estimate is then applied to ensure accurate estimates.
► The proposed approach could be applied on both linear and nonlinear systems.
► Comparison results show superior performances of the proposed approach.