Distributed Extended Kalman Filtering for Wastewater Treatment Processes

Zeng, Jing; Liu, Jinfeng; Zou, Tao; Yuan, Dong

doi:10.1021/acs.iecr.6b00529

Cited by 30 publications

(13 citation statements)

References 18 publications

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“…In [8,26,27,28], distributed data fusion was introduced on the basis of EKF to solve different projects. However, how the EKF uses the first-order Taylor expansion to linearize the system yields the error of the high-order part.…”

Section: Algorithm Descriptionmentioning

confidence: 99%

Real-Time Weighted Data Fusion Algorithm for Temperature Detection Based on Small-Range Sensor Network

Zhang

Nan

Wang

2018

Sensors

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Biological oxidation pretreatment, which can improve the yield of gold, is the main gold extraction technology for disposing refractory gold ore with high arsenic and sulfur. The temperature of the oxidation tank influences the oxidation efficiency between the ore pulp and bacteria, including the yield of gold. Therefore, measurement has consistently been an important subject for researchers. As an effective data processing method, data fusion has been used extensively in many fields of industrial production. However, the interference of equipment or external factors such as the diurnal temperature difference or powerful wind may constantly increase measurement errors and damage certain sensors, which may transmit error data. These problems can be solved by following a pretreatment process. First, we establish a heat transfer mechanism model. Second, we design a small-range sensor network for the pretreatment process and present a layered fusion structure of sharing sensors using a multi-connected fusion structure. Third, we introduce the idea of iterative operation in data processing. In addition, we use prior data for predicting state values twice in order to improve the effectiveness of extended Kalman filtering in one time step. This study also proposes multi-fading factors on the basis of a weighted fading memory index to adjust the prediction error covariance. Finally, the state estimation accuracy of each sensor can be used as a weighting principle for the predictive confidence of each sensor by adding a weighting factor. In this study, the performance of the proposed method is verified by simulation and compared with the traditional single-sensor method. Actual industrial measurement data are processed by the proposed method for the equipment experiment. The performance index of the simulation and the experiment shows that the proposed method has a higher global accuracy than the traditional single-sensor method. Simulation results show that the accuracy of the proposed method has a 55% improvement upon that of the traditional single-sensor method, on average. In the equipment experiment, the accuracy of the industrial measurement improved by 37% when using the proposed method.

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Section: Algorithm Descriptionmentioning

confidence: 99%

Real-Time Weighted Data Fusion Algorithm for Temperature Detection Based on Small-Range Sensor Network

Zhang

Nan

Wang

2018

Sensors

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“…In a distributed control system, a distributed state estimation scheme is preferred from a fault tolerance perspective. In the study of distributed state estimation, there are many distributed Kalman filtering research results [7,8]. These methods are mainly based on the consensus algorithm and have great limitation for nonlinear systems [9].…”

Section: Introductionmentioning

confidence: 99%

Distributed State Estimation Based Distributed Model Predictive Control

Zeng¹,

Liu²

2021

Mathematics

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In this work, we consider output-feedback distributed model predictive control (DMPC) based on distributed state estimation with bounded process disturbances and output measurement noise. Specifically, a state estimation scheme based on observer-enhanced distributed moving horizon estimation (DMHE) is considered for distributed state estimation purposes. The observer-enhanced DMHE ensures that the state estimates of the system reach a small neighborhood of the actual state values quickly and then maintain within the neighborhood. This implies that the estimation error is bounded. Based on the state estimates provided by the DMHE, a DMPC algorithm is developed based on Lyapunov techniques. In the proposed design, the DMHE and the DMPC are evaluated synchronously every sampling time. The proposed output DMPC is applied to a simulated chemical process and the simulation results show the applicability and effectiveness of the proposed distributed estimation and control approach.

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“…Communication with a remote control centre and the associated delays also make central estimation unattractive compared to its distributed counterparts. It has indeed been shown that distributed estimation is more accurate than central estimation in the case of communication under poor weather conditions [1]. These disadvantages of cen-tral estimation provide a compelling need for distributed estimation schemes.…”

Section: Introductionmentioning

confidence: 99%

Distributed state estimation through co‐acting Kalman filters

Kanagala

Detroja

2020

Asian Journal of Control

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Distributing calculations of a central Kalman filter requires subsystem level expressions for the propagation and update steps of the Kalman filter. It is difficult to obtain subsystem level expressions due to the inverse term present in the update step. In this manuscript, a non-iterative way of decomposing the inverse of a matrix is presented. This decomposition allows rewriting the update equations of the Kalman filter subsystem-wise. Subsequently, a Co-acting Kalman Filter (CoKF) is proposed using these decomposed central Kalman filter equations to perform distributed state estimation. The convergence of the CoKF algorithm is established under the assumption that each subsystem is observable. Two variants of the proposed CoKF, namely (m-CoKF and p-CoKF), suitable for applications on opposite ends of computation and communication resource spectrum, are presented along with the trade-offs involved. A comparison of the proposed method with existing distributed Kalman filters is also presented. The proposed CoKF algorithm is implemented on a standard wireless sensor network example with 200 nodes. The simulation results demonstrate the accuracy of the proposed CoKF algorithm relative to the central Kalman filter. KEYWORDS distributed state estimation, distributed systems, Kalman filter 1. It should distribute the calculations of a central estimator in such a way that the computations involved in central estimation are equally distributed among each distributed estimator 2. It needs to duly consider the interactions betweensubsystems and systematically incorporate information received from other subsystems 3. While keeping the communication between distributed estimators to a minimum, the estimates produced by

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Distributed Extended Kalman Filtering for Wastewater Treatment Processes

Cited by 30 publications

References 18 publications

Real-Time Weighted Data Fusion Algorithm for Temperature Detection Based on Small-Range Sensor Network

Real-Time Weighted Data Fusion Algorithm for Temperature Detection Based on Small-Range Sensor Network

Distributed State Estimation Based Distributed Model Predictive Control

Distributed state estimation through co‐acting Kalman filters

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