Modeling of cross flow hollow fiber ultrafiltration for treatment of effluent from Railway Workshop

Generalization and Expansion of the Hermia Model for a Better Understanding of Membrane Fouling

One of the most broadly used models for membrane fouling is the Hermia model (HM), which separates this phenomenon into four blocking mechanisms, each with an associated parameter n. The original model is given by an Ordinary Differential Equation (ODE) dependent on n. This ODE is solved only for these four values of n, which limits the effectiveness of the model when adjusted to experimental data. This paper aims extend the original Hermia model to new values of n by slightly increasing the complexity of the HM while keeping it as simple as possible. The extended Hermia model (EHM) is given by a power law for any n ≠ 2 and by an exponential function at n = 2. Analytical expressions for the fouling layer thickness and the accumulated volume are also obtained. To better test the model, we perform model fitting of the EHM and compare its performance to the original four pore-blocking mechanisms in six micro- and ultrafiltration examples. In all examples, the EHM performs consistently better than the four original pore-blocking mechanisms. Changes in the blocking mechanisms concerning transmembrane pressure (TMP), crossflow rate (CFR), crossflow velocity (CFV), membrane composition, and pretreatments are also discussed.

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

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.

Innovative Optical-Sensing Technology for the Online Fouling Characterization of Silicon Carbide Membranes during the Treatment of Oily Water

The oil and gas industry generates a large volume of contaminated water (produced water) which must be processed to recover oil before discharge. Here, we evaluated the performance and fouling behavior of commercial ceramic silicon carbide membranes in the treatment of oily wastewaters. In this context, microfiltration and ultrafiltration ceramic membranes were used for the separation of oil during the treatment of tank dewatering produced water and oily model solutions, respectively. We also tested a new online oil-in-water sensor (OMD-32) based on the principle of light scattering for the continuous measurement of oil concentrations in order to optimize the main filtration process parameters that determine membrane performance: the transmembrane pressure and cross-flow velocity. Using the OMD-32 sensor, the oil content of the feed, concentrate and permeate streams was measured continuously and fell within the range 0.0-200 parts per million (ppm) with a resolution of 1.0 ppm. The ceramic membranes achieved an oil-recovery efficiency of up to 98% with less than 1.0 ppm residual oil in the permeate stream, meeting environmental regulations for discharge in most areas.

Physico-chemical processes

The review scans research articles published in 2018 on physico-chemical processes for water and wastewater treatment. The paper includes eight sections, that is, membrane technology, granular filtration, flotation, adsorption, coagulation/flocculation, capacitive deionization, ion exchange, and oxidation. The membrane technology section further divides into six parts, including microfiltration, ultrafiltration, nanofiltration, reverse osmosis/forward osmosis, and membrane distillation. PRACTITIONER POINTS: Totally 266 articles on water and wastewater treatment have been scanned; The review is sectioned into 8 major parts;  Membrane technology has drawn the widest attention from the research community.