The Kalman Filter for the Supervision of Cultivation Processes

Yousefi-Darani, Abdolrahim; Emire, Shimelis Admassu; Hitzmann, Bernd

doi:10.1007/10_2020_145

Cited by 9 publications

(21 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several examples in the literature present use of the EKF algorithm for DT purposes in different aspects and applications. DT of the backer’s yeast batch cultivation system is analyzed in [ 20 ] based on the linear discrete Kalman filter, and its nonlinear variants such as EKF and UKF are used to predict the bioprocess variables employed for optimization and control of the real entity. Various aspects of Li-ion battery prognostics and lifespan monitoring are reviewed in [ 21 ] using the DT technology based on the EKF algorithm for estimation of state-of-charge, capacity, current/voltage, and remaining-useful-life.…”

Section: Introductionmentioning

confidence: 99%

Digital Twin Model of Electric Drives Empowered by EKF

Ebadpour

Jamshidi

Talla

et al. 2023

Sensors

View full text Add to dashboard Cite

Digital twins, a product of new-generation information technology development, allows the physical world to be transformed into a virtual digital space and provide technical support for creating a Metaverse. A key factor in the success of Industry 4.0, the fourth industrial revolution, is the integration of cyber–physical systems into machinery to enable connectivity. The digital twin is a promising solution for addressing the challenges of digitally implementing models and smart manufacturing, as it has been successfully applied for many different infrastructures. Using a digital twin for future electric drive applications can help analyze the interaction and effects between the fast-switching inverter and the electric machine, as well as the system’s overall behavior. In this respect, this paper proposes using an Extended Kalman Filter (EKF) digital twin model to accurately estimate the states of a speed sensorless rotor field-oriented controlled induction motor (IM) drive. The accuracy of the state estimation using the EKF depends heavily on the input voltages, which are typically supplied by the inverter. In contrast to previous research that used a low-precision ideal inverter model, this study employs a high-performance EKF observer based on a practical model of the inverter that takes into account the dead-time effects and voltage drops of switching devices. To demonstrate the effectiveness of the EKF digital twinning on the IM drive system, simulations were run using the MATLAB/Simulink software (R2022a), and results are compared with a set of actual data coming from a 4 kW three-phase IM as a physical entity.

show abstract

Section: Introductionmentioning

confidence: 99%

Digital Twin Model of Electric Drives Empowered by EKF

Ebadpour

Jamshidi

Talla

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…In various data analysis methods, the Kalman filter and its non-linear extensions, such as the extended Kalman filter, are powerful tools for predicting values of the unobserved states. Although there are several applications of the extended Kalman filter for mAb production [22,28] and other cultivation processes [29,30], its application to the rAAV production process has not been reported.…”

Section: Introductionmentioning

confidence: 99%

Monitoring the Recombinant Adeno-Associated Virus Production using Extended Kalman Filter

Iglesias

Mehta

et al. 2022

Processes

View full text Add to dashboard Cite

The recombinant adeno-associated virus (rAAV) is a viral vector technology for gene therapy that is considered the safest and most effective way to repair single-gene abnormalities in non-dividing cells. However, improving the viral titer productivity in rAAV production remains challenging. The first step to this end is to effectively monitor the process state variables (cell density, GLC, GLN, LAC, AMM, and rAAV viral titer) to improve the control performance for an enhanced productivity. However, the current approaches to monitoring are expensive, laborious, and time-consuming. This paper presents an extended Kalman filter (EKF) approach used to monitor the rAAV production using the online viable cell density measurements and estimating the other state variables measured at a low frequency. The proposed EKF uses an unstructured mechanistic kinetic model applicable in the upstream process. Three datasets were used for parameter estimation, calibration, and testing, and the data were collected from the production of rAAV through a triple-plasmid transfection of HEK293SF-3F6 cells. Overall, the proposed approach accurately estimated metabolite concentrations and the rAAV production yield. Therefore, the approach has a high potential to be extended to an online soft sensor and to be classified as a cost-effective and fast approach to the monitoring of rAAV production.

show abstract

“…To estimate the system states that cannot be measured due to physical limitations or the high cost of implementing extra sensors, state observers have received considerable attention. For linear systems, different methods such as the Luenberger observer [15], Proportional Multi-Integral observer [16], Sliding Mode Observer [17], and Kalman filter [18] can be employed. The main advantage of linear observers is their simplicity of implementation.…”

Section: Introductionmentioning

confidence: 99%

State Estimation in a Hydraulically Actuated Log Crane Using Unscented Kalman Filter

et al. 2022

View full text Add to dashboard Cite

Multibody system dynamics approaches together with state estimation methods can reduce the need for a large number of sensors, especially in the digital twin of working mobile machinery. To demonstrate this, a hydraulically actuated machine was modeled using the double-step semi-recursive multibody formulation and lumped fluid theory in terms of system independent states. Next, because of the high non-linearity of the modeled system and with respect to the reported performance degradation of the Extended Kalman Filters (EKF), which are mostly related to the linearization procedure of this filter, the Unscented Kalman Filter (UKF) was implemented to achieve high accuracy and performance. The methodology of the proposed approaches was applied to a mobile log crane model PATU 655. The implementation of the proposed estimation algorithms is demonstrated with three different multibody based simulation models: the synthetic real system producing the artificial measurements, the simulation model, and the estimation model. Encoders and pressure sensors, installed on the synthetic real system, provided synthetic sensor measurement data. To mimic real-world conditions, the estimation and simulation models used in the processing of the state estimation algorithm were assumed to have errors in the initial conditions and force model. The proposed UKF was applied to the estimation model with the synthetic sensor measurement data. The minimum percent normalized root mean square errors in the associated measured and unmeasured states of case example were 0.11% and 1.86%, respectively. The UKF using the multibody system dynamics formulations is able to estimate the case example states despite 15% and 60% errors in mass and inertial properties of bodies and Payload, respectively, confirming the accuracy and performance of the algorithm.INDEX TERMS state estimation, multibody system dynamics, hydraulic actuators, unscented Kalman filter, working machine, digital twin

show abstract

The Kalman Filter for the Supervision of Cultivation Processes

Cited by 9 publications

References 56 publications

Digital Twin Model of Electric Drives Empowered by EKF

Digital Twin Model of Electric Drives Empowered by EKF

Monitoring the Recombinant Adeno-Associated Virus Production using Extended Kalman Filter

State Estimation in a Hydraulically Actuated Log Crane Using Unscented Kalman Filter

Contact Info

Product

Resources

About