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.
Process modularization is an alternative process design
and construction
framework, in which modular units are independent and replaceable
blocks of a process system. While modular plants have higher efficiency
and are safer to construct than conventional stick-built plants (Roy,
S. Chem. Eng. Prog. 2017, 113, 28–31), they are significantly more challenging to operate
because of the loss in the control degrees of freedom that comes with
process integration and intensification (Bishop, B. A.; Lima, F. V. Processes
2021, 9, 2165).
To address this challenge, in this work, operability analyses are
performed to consider the design and operation of modular units. Initially,
a steady-state operability analysis is employed to find a set of feasible
modular designs that are able to operate considering different modular
plant conditions. A dynamic operability analysis is then applied to
the feasible designs to identify the operable designs that are capable
of rejecting the operational disturbances. Lastly, a closed-loop control
measure is introduced to compare the performances of the different
operable designs. The proposed approach is implemented in a modular
membrane reactor to find a set of operable designs considering different
natural gas wells, and the respective closed-loop nonlinear model
predictive control performance of these units is evaluated.
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