Dynamic hybrid model for ultrafiltration membrane processes

Díaz, Víctor Hugo Grisales; Prado-Rúbio, Oscar Andrés; Willis, M.J.; von, Stosch

doi:10.1016/b978-0-444-63965-3.50034-9

Cited by 7 publications

(2 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Piron et al applied the serial model configuration to yeast filtration [ 28 ]. Díaz et al used a serial neural network and Darcy law configuration for flux prediction, but did not use the model for process control and optimization [ 29 ]. Chew et al used a similar model structure for the prediction of the fouling and filtration resistances of an industrial water clarification process; however, the model was not yet further used for process optimization [ 30 ].…”

Section: Introductionmentioning

confidence: 99%

Regression Metamodel-Based Digital Twin for an Industrial Dynamic Crossflow Filtration Process

Heusel,

Grim,

Rauhut

et al. 2024

Bioengineering

View full text Add to dashboard Cite

Dynamic crossflow filtration (DCF) is the state-of-the-art technology for solid–liquid separation from viscous and sensitive feed streams in the food and biopharma industry. Up to now, the potential of industrial processes is often not fully exploited, because fixed recipes are usually applied to run the processes. In order to take the varying properties of biological feed materials into account, we aim to develop a digital twin of an industrial brownfield DCF plant, allowing to optimize setpoint decisions in almost real time. The core of the digital twin is a mechanistic–empirical process model combining fundamental filtration laws with process expert knowledge. The effect of variation in the selected process and model parameters on plant productivity has been assessed using a model-based design-of-experiments approach, and a regression metamodel has been trained with the data. A cyclic program that bidirectionally communicates with the DCF asset serves as frame of the digital twin. It monitors the process dynamics membrane torque and transmembrane pressure and feeds back the optimum permeate flow rate setpoint to the physical asset in almost real-time during process runs. We considered a total of 24 industrial production batches from the filtration of grape juice from the years 2022 and 2023 in the study. After implementation of the digital twin on site, the campaign mean productivity increased by 15 % over the course of the year 2023. The presented digital twin framework is a simple example how an industrial established process can be controlled by a hybrid model-based algorithm. With a digital process dynamics model at hand, the presented metamodel optimization approach can be easily transferred to other (bio)chemical processes.

show abstract

Section: Introductionmentioning

confidence: 99%

Regression Metamodel-Based Digital Twin for an Industrial Dynamic Crossflow Filtration Process

Heusel,

Grim,

Rauhut

et al. 2024

Bioengineering

View full text Add to dashboard Cite

show abstract

“…Hybrid models combine the advantages of data-driven black box models (such as ANNs), correlating input with output variables (such as the concentration of impurity with the decrease in flux) with knowledge-based mechanistic models (white box models) derived from conservation of kinetic laws [17]. Hybrid models have been applied to bioprocesses for upstream [18] and downstream applications [19,20].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Modeling for Simultaneous Prediction of Flux, Rejection Factor and Concentration in Two-Component Crossflow Ultrafiltration

et al. 2020

View full text Add to dashboard Cite

Ultrafiltration is a powerful method used in virtually every pharmaceutical bioprocess. Depending on the process stage, the product-to-impurity ratio differs. The impact of impurities on the process depends on various factors. Solely mechanistic models are currently not sufficient to entirely describe these complex interactions. We have established two hybrid models for predicting the flux evolution, the protein rejection factor and two components’ concentration during crossflow ultrafiltration. The hybrid models were compared to the standard mechanistic modeling approach based on the stagnant film theory. The hybrid models accurately predicted the flux and concentration over a wide range of process parameters and product-to-impurity ratios based on a minimum set of training experiments. Incorporating both components into the modeling approach was essential to yielding precise results. The stagnant film model exhibited larger errors and no predictions regarding the impurity could be made, since it is based on the main product only. Further, the developed hybrid models exhibit excellent interpolation properties and enable both multi-step ahead flux predictions as well as time-resolved impurity forecasts, which is considered to be a critical quality attribute in many bioprocesses. Therefore, the developed hybrid models present the basis for next generation bioprocessing when implemented as soft sensors for real-time monitoring of processes.

show abstract

Hybrid Semiparametric Modeling: A Modular Process Systems Engineering Approach for the Integration of Available Knowledge Sources

Azevedo¹,

Diaz²,

Prado-Rúbio³

et al. 2019

Systems Engineering in the Fourth Industrial Revolution

View full text Add to dashboard Cite

Dynamic hybrid model for ultrafiltration membrane processes

Cited by 7 publications

References 2 publications

Regression Metamodel-Based Digital Twin for an Industrial Dynamic Crossflow Filtration Process

Regression Metamodel-Based Digital Twin for an Industrial Dynamic Crossflow Filtration Process

Hybrid Modeling for Simultaneous Prediction of Flux, Rejection Factor and Concentration in Two-Component Crossflow Ultrafiltration

Hybrid Semiparametric Modeling: A Modular Process Systems Engineering Approach for the Integration of Available Knowledge Sources

Contact Info

Product

Resources

About