Towards Sustainable Flux Determination for Dynamic Ultrafiltration through Multivariable System Identification

Prado-Rúbio, Oscar Andrés; von, Stosch

doi:10.1016/b978-0-444-63965-3.50455-4

Cited by 7 publications

(3 citation statements)

References 2 publications

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“…Recently, Ohanessian et al [ 81 ] proposed hybrid models to evaluate the performances of UF for the treatment of CMP effluents where the models were able to predict the filtration number of cycles adjustable according to the permeability recovery rate after physical washes of the membrane, the duration of physical and chemical washes and many operating parameters such as the transmembrane pressure, the nanoparticles concentration, the temperature, and the tangential velocity (for crossflow mode) [ 81 ]. Additionally, several efforts have been made in the modeling of permeate flux based on the analysis of phenomenological data [ 7 , 15 , 73 , 180 ] obtained experimentally, avoiding the use of specific transport mechanisms [ 69 ]. Among them, artificial neural networks (ANNs) have been applied in the field of membrane science and in other areas, including marketing, accounting, finance, health and medicine, engineering, and manufacturing [ 181 , 182 , 183 ].…”

Section: Theorymentioning

confidence: 99%

“…Even though there are some phenomenological [ 6 , 9 , 11 , 68 , 70 , 71 ] and non-phenomenological models [ 12 , 15 , 72 , 73 ] with applications at the pilot scale, the majority of developed models have been tested with ideal matrices (e.g., PEG, BSA, Dextran), and their predictions have been validated at the laboratory scale for short-term operations [ 69 ]. In this regard, Chew et al [ 74 ] mentioned that, commonly, rigorous pilot-scale studies are not usually performed in the industrial practice due to the urgency of production and insufficient allocation for pilot studies.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Permeate Flux in Ultrafiltration Processes: A Review of Modeling Approaches

et al. 2021

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In any membrane filtration, the prediction of permeate flux is critical to calculate the membrane surface required, which is an essential parameter for scaling-up, equipment sizing, and cost determination. For this reason, several models based on phenomenological or theoretical derivation (such as gel-polarization, osmotic pressure, resistance-in-series, and fouling models) and non-phenomenological models have been developed and widely used to describe the limiting phenomena as well as to predict the permeate flux. In general, the development of models or their modifications is done for a particular synthetic model solution and membrane system that shows a good capacity of prediction. However, in more complex matrices, such as fruit juices, those models might not have the same performance. In this context, the present work shows a review of different phenomenological and non-phenomenological models for permeate flux prediction in UF, and a comparison, between selected models, of the permeate flux predictive capacity. Selected models were tested with data from our previous work reported for three fruit juices (bergamot, kiwi, and pomegranate) processed in a cross-flow system for 10 h. The validation of each selected model's capacity of prediction was performed through a robust statistical examination, including a residual analysis. The results obtained, within the statistically validated models, showed that phenomenological models present a high variability of prediction (values of R-square in the range of 75.91–99.78%), Mean Absolute Percentage Error (MAPE) in the range of 3.14–51.69, and Root Mean Square Error (RMSE) in the range of 0.22–2.01 among the investigated juices. The non-phenomenological models showed a great capacity to predict permeate flux with R-squares higher than 97% and lower MAPE (0.25–2.03) and RMSE (3.74–28.91). Even though the estimated parameters have no physical meaning and do not shed light into the fundamental mechanistic principles that govern these processes, these results suggest that non-phenomenological models are a useful tool from a practical point of view to predict the permeate flux, under defined operating conditions, in membrane separation processes. However, the phenomenological models are still a proper tool for scaling-up and for an understanding the UF process.

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Section: Theorymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of Permeate Flux in Ultrafiltration Processes: A Review of Modeling Approaches

et al. 2021

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“…In general, cycle times and cleaning routines can and should be analyzed and optimized based on statistical evidence in process data. 166,167 An interesting trade-off between capacity and costs or environmental impact can arise, which is, for instance, discussed in an optimal scheduling context in ref 168.…”

Section: Prevalence Of Batch Bioprocessesmentioning

confidence: 99%

Challenges in Optimization and Control of Biobased Process Systems: An Industrial-Academic Perspective

Bähner

Prado-Rúbio

Huusom

2021

Ind. Eng. Chem. Res.

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Biobased slurry processes and large-scale continuous chemical production share many similarities. These concern not only process technologies and equipment but also staffing (positions/ educations) and plant operations, including methods and technologies for planning, scheduling, and control. Process Systems Engineering (PSE) approaches have been applied successfully in large-scale chemical production to optimize processes of high complexity. By replacing rules-of-thumb and oversimplified calculations with rigorous quantification and automation, PSE increases the profitability of a wide range of processes. Such improvement potential also exists in the bioindustry, which traditionally comprises many small-scale and batch plants deployed in food or pharmaceutical production. The article elucidates the most important operational challenges of such processes. It brings together academic research and industrial perspectives by contrasting a careful revision of literature with a survey of personnel working in industrial biobased production settings in Denmark. This analysis allows showing the existence of a gap between academic research focus and industrial need, for instance, concerning production processes with humans-in-the-loop. Furthermore, differences between chemical and biobased processes are pointed out, highlighting some limitations of applying PSE to biobased processes. A deeper understanding of these limitations is fundamental to assessing risks when identifying process improvement potential. Finally, the article suggests a possible roadmap for the development of frameworks for the optimization of industrial biobased processes based on existing works in chemical processing.

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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

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Towards Sustainable Flux Determination for Dynamic Ultrafiltration through Multivariable System Identification

Cited by 7 publications

References 2 publications

Prediction of Permeate Flux in Ultrafiltration Processes: A Review of Modeling Approaches

Prediction of Permeate Flux in Ultrafiltration Processes: A Review of Modeling Approaches

Challenges in Optimization and Control of Biobased Process Systems: An Industrial-Academic Perspective

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

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