Journal
CHEMICAL ENGINEERING RESEARCH & DESIGN
Volume 200, Issue -, Pages 481-492Publisher
ELSEVIER
DOI: 10.1016/j.cherd.2023.10.033
Keywords
Data-driven state estimation; Nonlinear process; Koopman identification; Moving horizon estimation
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In this paper, a data-driven constrained state estimation method for nonlinear processes is proposed. Utilizing the Koopman operator framework and extended dynamic mode decomposition algorithm, a data-driven model identification procedure is developed to establish a linear state-space model based on historic process data, allowing for effective estimation of states in a higher-dimensional space using a linear moving horizon estimation algorithm. The proposed framework is demonstrated to be effective and superior through two process examples.
In this paper, a data-driven constrained state estimation method is proposed for nonlinear processes. Within the Koopman operator framework, we propose a data-driven model identification procedure for state estimation based on the algorithm of extended dynamic mode decomposition, which seeks an optimal approximation of the Koopman operator for a nonlinear process in a higher-dimensional space that correlates with the original process state-space via a prescribed nonlinear coordinate transformation. By implementing the proposed procedure, a linear state-space model can be established based on historic process data to describe the dynamics of a nonlinear process and the nonlinear dependence of the sensor measurements on process states. Based on the identified Koopman operator, a linear moving horizon estimation (MHE) algorithm that explicitly addresses constraints on the original process states is formulated to efficiently estimate the states in the higher-dimensional space. The states of the treated nonlinear process are recovered based on the state estimates provided by the MHE estimator designed in the higher-dimensional space. Two process examples are utilized to demonstrate the effectiveness and superiority of the proposed framework.
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