Adaptive Kalman filter for estimation of environmental performance variables in an acid gas removal process

Paul, Prokash; Bhattacharyya, Debangsu; Turton, Richard; Zitney, Stephen E.

doi:10.1109/acc.2013.6580245

Cited by 3 publications

(7 citation statements)

References 6 publications

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“…Other example is the residual-based estimation of R k and the adaptive estimation of Q k presented in Wang (1999) and Paul et al (2013). The R k estimation is obtained in a closed-form way as follows:…”

Section: Adaptive Kalman Filtermentioning

confidence: 99%

A Inverted-Wishart Distribution Approach for the Noise Covariance Matrix Estimation Applied to Target Tracking Problems

Melo

Frencl

Oroski

et al. 2019

Anais Do 14º Simpósio Brasileiro De Automação Inteligente

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Improving the precision of an instrument is a hard task. Generally, this can be accomplished using stochastic filters, e.g., Kalman Filter (KF). Normally, these filters have to be tuned by an arbitrary definition of its noise covariance matrices. In this context, the objective of this paper is estimate dynamically the measurement noise covariance matrix, by means of the Bayes Theorem (BT), in a Kalman Filter algorithm, using the Inverted-Wishart distribution (IW-R Kalman Filter). The proposed method is compared with three other classic stochastic filters. In this comparison the IW-R Kalman Filter has achieved the best RMSE (Root Mean Square Error), outperforming the other filters. Therefore, one can conclude that dynamic estimation of noise covariances could improve the performance of the traditional stochastic filters.

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Section: Adaptive Kalman Filtermentioning

confidence: 99%

A Inverted-Wishart Distribution Approach for the Noise Covariance Matrix Estimation Applied to Target Tracking Problems

Melo

Frencl

Oroski

et al. 2019

Anais Do 14º Simpósio Brasileiro De Automação Inteligente

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“…Variations in the syngas flow rate are considered as the disturbance. 19 The disturbance in the inlet flow rate of the acid gas is simulated by changing the inlet pressure of the syngas to the AGR unit. The SND algorithm is generic.…”

Section: Case Studymentioning

confidence: 99%

“…However, both the SSND and DMSND algorithms used a linear process model with the linear Kalman filter for state estimation for computational tractability. 19 However, use of linear models for highly nonlinear processes can lead to suboptimal SND. There are very few works published in the area of nonlinear dynamic model-based SND (NDMSND) using a nonlinear process model.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Dynamic Model-Based Multiobjective Sensor Network Design Algorithm for a Plant with an Estimator-Based Control System

Paul

Bhattacharyya

Turton

et al. 2017

Ind. Eng. Chem. Res.

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A novel sensor network design (SND) algorithm is developed for maximizing process efficiency while minimizing sensor network cost for a nonlinear dynamic process with an estimator-based control system. The multiobjective optimization problem is solved following a lexicographic approach where the process efficiency is maximized first followed by minimization of the sensor network cost. The partial net present value, which combines the capital cost due to the sensor network and the operating cost due to deviation from the optimal efficiency, is proposed as an alternative objective. The unscented Kalman filter is considered as the nonlinear estimator. The large-scale combinatorial optimization problem is solved using a genetic algorithm. The developed SND algorithm is applied to an acid gas removal (AGR) unit as part of an integrated gasification combined cycle (IGCC) power plant with CO 2 capture. Due to the computational expense, a reduced order nonlinear model of the AGR process is identified and parallel computation is performed during implementation.

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“…With this incentive, a computationally efficient DMSND algorithm for the estimator-based control system has been developed in this work for maximizing the efficiency of large-scale processes. In this DMSND algorithm, the Kalman filter (KF) is used for estimating process states (Paul et al, 2013) and particular focus is given to its convergence properties. In addition, several strategies have been developed for significantly reducing the computational expenses for solving large-scale DMSND problems.…”

Section: Literature Reviewmentioning

confidence: 99%

“…This is typically a consideration made after sensor placement and therefore not considered in the DMSND algorithm. The authors of this paper (Paul et al, 2013) have looked into possibility of adapting Q and R for a case similar to the example considered here where optimal performance of the filter was obtained even in the presence of inaccurate knowledge of Q and R. Interested readers are referred to our previous work for more information.…”

Section: Discrete-time Estimator-based Control Systemmentioning

confidence: 99%

Sensor Placement Algorithms for Process Efficiency Maximization

Paul¹

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Adaptive Kalman filter for estimation of environmental performance variables in an acid gas removal process

Cited by 3 publications

References 6 publications

A Inverted-Wishart Distribution Approach for the Noise Covariance Matrix Estimation Applied to Target Tracking Problems

A Inverted-Wishart Distribution Approach for the Noise Covariance Matrix Estimation Applied to Target Tracking Problems

Nonlinear Dynamic Model-Based Multiobjective Sensor Network Design Algorithm for a Plant with an Estimator-Based Control System

Sensor Placement Algorithms for Process Efficiency Maximization

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