Abstract
This paper provides an overview of nonlinear state estimation techniques along with a discussion on the challenges and opportunities for future work in the field. Emphasis is given on Bayesian methods such as moving horizon estimation (MHE) and extended Kalman filter (EKF). A discussion on Bayesian, deterministic, and hybrid methods is provided and examples of each of these methods are listed. An approach for nonlinear state estimation design is included to guide the selection of the nonlinear estimator by the user/practitioner. Some of the current challenges in the field are discussed involving covariance estimation, uncertainty quantification, time-scale multiplicity, bioprocess monitoring, and online implementation. A case study in which MHE and EKF are applied to a batch reactor system is addressed to highlight the challenges of these technologies in terms of performance and computational time. This case study is followed by some possible opportunities for state estimation in the future including the incorporation of more efficient optimization techniques and development of heuristics to streamline the further adoption of MHE.
Highlights
Modern chemical and biochemical plants must satisfy technical, economic, and environmental requirements that have become increasingly challenging
This paper aims to present a current overview of the main nonlinear state estimators used to monitor chemical in and biochemical
Some important challenges nonlinear are. This discussion is followed by a section containing a nonlinear batchonreactor case state studyestimation for illustrating discussed. This discussion is followed by a section containing a nonlinear batch reactor case study extended Kalman filter (EKF) and moving horizon estimation (MHE) implementations
Summary
Modern chemical and biochemical plants must satisfy technical, economic, and environmental requirements that have become increasingly challenging. Stronger economic competition and sustainable development goals have imposed stringent environmental and safety regulations, as well as the need for more profitable plant operation and tighter product quality specifications. In this scenario, process monitoring, estimation, and control have gained increased attention, driving the development of new technologies to operate modern plants safely and profitably. Measuring instruments and mathematical tools are generally employed to monitor process variables, providing online state/output information for feedback control systems and for detecting abnormal process operations.
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