State and parameter estimation is essential for process monitoring and control. Observability
plays an important role in both state and parameter estimation. In simultaneous state and …

In most chemical processes only some measurements are available online while other
measurements are available infrequently and often with long delays. Multirate state …

A moving horizon estimation (MHE) approach to simultaneously estimate states and
parameters is revisited. Two different noise models are considered, one with measurement …

A suitable design of state estimators for advanced control requires a detailed and
representative mathematical model for capturing the nonlinear process behavior. The …

This study focuses on the development and application of a hybrid moving horizon estimator
(HMHE) to achieve simultaneous estimation of both continuum and noncontinuum states in …

State estimation is a crucial part of the monitoring and/or control of all chemical processes.
Among various approaches for this problem, moving horizon estimation (MHE) has the …

Moving‐horizon estimation (MHE) is an optimization‐based strategy for process monitoring
and state estimation. One may view MHE as an extension for Kalman filtering for constrained …

In current model predictive control (MPC) applications, the accuracy of the models utilized is
usually the main factor for the final successes. These models, especially for complex …

A moving horizon state estimation algorithm (MHE) is applied to the nonlinear and unstable
Tennessee Eastman process, a well-known benchmark problem in the chemical process …

The development and acceptance of model-based monitoring tools in the bioprocess
industry is made difficult by the usually large uncertainty associated with the process model …