Multilevel Modeling for Data Mining of Downstream Bio-Industrial Processes

Klimkiewicz, Anna; Cervera-Padrell, Albert E.; Berg, Frans van den

doi:10.1016/j.chemolab.2016.03.020

Cited by 6 publications

(22 citation statements)

References 17 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%

“…For these reasons, membrane processes are often recognized as the best available technology (BAT) in the food industry [ 2 , 3 , 4 ]. Among pressure-driven membrane processes, ultrafiltration (UF) has been extensively applied in the treatment of industrial effluents [ 5 , 6 , 7 , 8 , 9 , 10 ], oil-based emulsions [ 11 , 12 , 13 , 14 ], biological macromolecules [ 15 , 16 , 17 ], milk [ 18 , 19 , 20 ], sugar cane [ 21 , 22 ], extracts of soybean flour [ 23 ], clay suspensions [ 24 ], black kraft liquor [ 25 ], and fruit juices [ 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 ] among others. Within the fruit juice industry, bergamot, kiwifruit, and pomegranate have great importance in the market, not only for their volume of production, but also because they are characterized by a high concentration of phytochemicals which are recognized to be associated with antioxidant activities within others.…”

Section: Introductionmentioning

confidence: 99%

“…In the majority of cases, these models are based on convective transport under a pressure gradient and separation by size exclusion [ 61 ], diffusive transport through the cake layer [ 62 , 63 , 64 ], and fouling mechanisms [ 60 , 65 , 66 , 67 ] as well. Other models, unrelated to the phenomenological aspects, are based on statistical tools such as artificial neural networks, data mining, computational models of system dynamics, and principal component analysis (PCA) [ 8 , 12 , 15 , 68 , 69 ].…”

Section: Introductionmentioning

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%

Section: Introductionmentioning

confidence: 99%

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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“…This study uses flux in ultrafiltration (UF) as a capacity measure of a process and focuses on the block level to investigate the overall flux values, or more specific flux decline as a function of process time. Significant attention, in both public research and industry, has been paid to better understand the mechanisms of membrane fouling observed as flux decline in UF. − These problems clearly affect the production scheduling and hence economics in downstream biomanufacturing. In the Novozymes production facilities at Kalundborg (Denmark), a project was initiated to investigate the flux decline issue based on historic full-scale processing data.…”

Section: Introductionmentioning

confidence: 99%

Modeling of the Flux Decline in a Continuous Ultrafiltration System with Multiblock Partial Least Squares

Klimkiewicz

Cervera-Padrell

Berg

2016

Ind. Eng. Chem. Res.

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This study investigates flux decline in ultrafiltration as a capacity measure for the process. A continuous ultrafiltration is a multistage process where a considerable coupling between the stages is expected due to similar settings on the subsequent recirculation loops and recirculation of parts of the process streams. To explore the flux decline issue from an engineering perspective, two ways of organizing process signals into logical blocks are identified and used in a multiblock partial least-squares regression: (1) the “physical location” of the sensors on the process layout and (2) “engineering type of tags”. Abnormal runs are removed iteratively from the original data set, and then the multiblock parameters are calculated based on the optimized regression model to determine the role of the different data building units in flux prediction. Both blocking alternatives are interpreted alongside offering a compact overview of the most important sections related to the flux decline. This way one can zoom in on the smaller sections of the process, which gives an optimization potential.

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References

2019

Data Science for Business and Decision Making

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Multilevel Modeling for Data Mining of Downstream Bio-Industrial Processes

Cited by 6 publications

References 17 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

Modeling of the Flux Decline in a Continuous Ultrafiltration System with Multiblock Partial Least Squares

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