A sensitivity-based approach to optimal sensor selection for complex processes

Liu, Siyu; Yin, Xunyuan; Pan, Zhichao; Liu, Jinfeng

doi:10.1016/j.ces.2023.118901

Cited by 4 publications

(5 citation statements)

References 32 publications

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“…In this paper, we apply a sensitivity-based approach for optimal sensor selection to the upstream process of mAb production. This approach, developed by Liu et al [27], was utilized to determine the optimal number and placement of sensors in a wastewater treatment plant. The algorithm involves constructing the sensitivity matrix, as well as employing orthogonalization and a degree of observability for sensor selection.…”

Section: Sensor Selection For State Estimationmentioning

confidence: 99%

“…The individual entries of the matrix, which can be denoted as S ij (k), can be calculated using Equation (41a). Furthermore, Equation (41b) expresses the sensitivity of the state x(k) with respect to the initial state x(0), as mentioned in [27].…”

Section: Construction Of the Sensitivity Matrixmentioning

confidence: 99%

“…The process continues until removing another sensor would render the system unobservable. The orthogonalization procedure used to rank the columns by magnitude, and the final algorithm used to obtain the minimum sensor set were detailed in [27].…”

Section: Procedures To Determine Minimum Number and Optimal Placement...mentioning

confidence: 99%

“…The authors then subsequently proceeded with the re-parametrization of structurally unidentifiable ordinary differential equation (ODE) models. Similarly, in the work by Liu et al [27], a sensitivity-based approach was introduced for determining the optimal sensor combination for state estimation problems. The algorithm seeks to determine an optimal sensor subset that ensures that the entire state information can be reconstructed.…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, we focus on estimating a selected set of parameters within the model to maximize the information extracted from the available measurements. Since the evaluation of each possible sensor combination would not be practical for a system with a large number of states, such as the system under consideration, the sensor selection algorithm developed in the work by Liu et al [27] was applied. Furthermore, simultaneous state and parameter estimations were performed by first augmenting the parameters of the system as additional states and then employing the variable selection technique used in [31] to select the variables to be estimated.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Sensor Selection and State Estimation of Continuous mAb Production Processes

Obiri,

Agyeman,

Debnath

et al. 2023

Mathematics

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The production of monoclonal antibodies (mAbs) plays a pivotal role in therapeutic treatments, and optimizing their production is crucial for minimizing costs and improving their accessibility to patients. One way of improving the production process is to improve model accuracy through the correct estimation of its states and parameters. The contributions of this paper lie in the provision of guidelines for sensor selection in the upstream production process of mAbs to enhance the accuracy of state estimation. Furthermore, this paper applies an effective variable selection technique for simultaneous state and parameter estimations for enhanced estimation results in the biomanufacturing processes of mAbs. An estimation framework of MHE is designed for three different case studies to demonstrate the efficiency of the proposed approach. The estimation performance is compared and assessed using the Root Mean Squared Error (RMSE) as an evaluation criterion.

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Section: Sensor Selection For State Estimationmentioning

confidence: 99%

Section: Construction Of the Sensitivity Matrixmentioning

confidence: 99%

Section: Procedures To Determine Minimum Number and Optimal Placement...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Sensor Selection and State Estimation of Continuous mAb Production Processes

Obiri,

Agyeman,

Debnath

et al. 2023

Mathematics

Self Cite

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State estimation for one‐dimensional agro‐hydrological processes with model mismatch

Liu,

Zhao

et al. 2023

Can J Chem Eng

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The importance of accurate soil moisture data for the development of modern closed‐loop irrigation systems cannot be overstated. Due to the diversity of soil, it is difficult to obtain an accurate model for the agro‐hydrological system. In this study, soil moisture estimation in one‐dimensional (1D) agro‐hydrological systems with model mismatch is the focus. To address the problem of model mismatch, a nonlinear state‐space model derived from the Richards equation is utilized, along with additive unknown inputs. The determination of the number of sensors required is achieved through sensitivity analysis and the orthogonalization projection method. To estimate states and unknown inputs in real‐time, a recursive expectation maximization (EM) algorithm derived from the conventional EM algorithm is employed. During the E‐step, the extended Kalman filter (EKF) is used to compute states and covariance in the recursive Q‐function, while in the M‐step, unknown inputs are updated by locally maximizing the recursive Q‐function. The estimation performance is evaluated using comprehensive simulations. Through this method, accurate soil moisture estimation can be obtained, even in the presence of model mismatch.

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