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Mineral Scaling Monitoring for reverse osmosis desalination via real-time membrane surface image analysis

An approach to real-time analysis of mineral scale formation on reverse osmosis (RO) membranes was developed using an ex-situ direct observation membrane monitor (MeMo). The purpose of such monitoring is to signal the onset of mineral scaling and provide quantitative information in order to appropriately initiate system cleaning/scale dissolution. The above is enabled by setting the MeMo operating conditions (cross flow velocity and transmembrane pressure) to closely match the conditions in the monitored membrane plant (e.g., in the tail RO element) in order to mimic the surface scaling processes taking place inside the monitored RO plant element. Mineral scale in the MeMo system is monitored by comparison of consecutive images of the membrane surface for the purpose of determining the evolution of the fractional coverage by mineral salt crystals and the corresponding crystal count in the monitored region. Through online image analysis, once crystal growth is determined to be above a prescribed threshold, one can then initiate any number of cleaning protocols. Through early detection of membrane scaling (i.e., before permeate flux decline is observed), enabled by the present monitoring approach, the system operator can prevent irreversible membrane damage and loss of system productivity.

Bartman A., Lyster E., Rallo R., Christofides PD., Cohen Y. Mineral Scaling Monitoring for reverse osmosis desalination via real-time membrane surface image analysis. Desalination, 273(1):64-71

  • DOI: 10.1016/j.desal.2010.10.021
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Coupled 3-D Hydrodynamics and Mass Transfer Analysis of Mineral Scaling-Induced Flux Decline in a Laboratory Plate-and-Frame Reverse Osmosis Membrane Module

A 3-D (3-dimensional) numerical modeling approach to analyze the coupling of mineral scale, concentration polarization and permeate flux was developed and demonstrated for a gypsum membrane scaling test in a plate-and-frame RO membrane module geometry. The impact of concentration polarization on mineral gypsum scaling, and in turn the impact of mineral scale on the concentration polarization field, were explored via 3-D finite-element numerical solutions of the coupled fluid hydrodynamics and solute mass transfer equations, along with detailed experimental data on the extent and location of mineral scale. Numerical simulations of the concentration field for a scale-free membrane revealed that the regions of highest supersaturation with respect to calcium sulfate corresponded to regions of highest gypsum scale density, as observed through real-time imaging of the membrane surface in the gypsum scaling test. 3-D simulations of the concentration field in the presence of mineral scale revealed that the concentration polarization modulus and permeate flux were largely unaffected in contiguous scale-free regions of the membrane. However, near and just downstream of individual crystal formations, the local concentration polarization modulus decreased (by similar to 5%) and the permeate flux increased (by similar to 2%) relative to the same positions in the absence of scale. The model-calculated and experimental permeate flux decline agreed closely with an average absolute error of 1.8%. The present study suggests that flux decline due to mineral scaling can be reasonably described by the surface blockage mechanism.

Lyster E, Au J, Rallo R, Giralt, F, Cohen Y (2009). Coupled 3-D Hydrodynamics and Mass Transfer Analysis of Mineral Scaling-Induced Flux Decline in a Laboratory Plate-and-Frame Reverse Osmosis Membrane Module. Journal of Membrane Science, 339,39:48.

Neural Network Approach for Modeling the Performance of Reverse Osmosis Membrane Desalting

A neural network-based modeling approach with back-propagation and support vector regression algorithms was investigated as a mean of developing data-driven models for forecasting reverse osmosis (RO) plant performance and for potential use for operational diagnostics. The concept of plant “short-term memory” time-interval was introduced to capture the time-variability of plant performance since both a state of the plant model and standard time-series analyses for both flux decline and salt passage did not result in realistic predictive horizons for practical purposes. Past information of normalized permeate flux and salt passage were introduced as unique input variables along with process operating parameters to capture short-term plant performance variability. Sequential models, where the time-variation within each forecasting time-interval was also taken as input information, and marching forecasting models, where target values were predicted at fixed future times from past plant information, were developed. Models were trained, with normalized permeate flux and salt passage, for various model architectures, memory time-intervals and forecasting times using both back-propagation and support vector regression approaches. State of the plant models (without forecasting) were able to describe the relatively small permeate flux variations but were unable to capture salt passage trends (for any present time condition) since unsteady state phenomena could not be properly described without plant memory information. Forecasting of plant performance, with both sequential and marching models, yielded good predictive accuracy for short-term memory time-intervals in the range of 8-24 h for permeate flux and salt passage for forecasting times up to 24 h. Current work is ongoing to extend the approach for longer time scales and to incorporate data-driven forecasting models of RO plant into control strategies and process diagnostics.

Libotean D., Giralt J., Giralt, F. Rallo R., Wolfe, T., Cohen Y. (2008). Neural Network Approach for Modeling the Performance of Reverse Osmosis Membrane Desalting. Journal of Membrane Science, (326)2,408:419

Organic Compounds Passage through RO Membranes

Organic solute permeation, sorption, and rejection by reverse osmosis membranes, from aqueous solutions, were Studied experimentally and via artificial neural networks (ANN)-based quantitative structure-property relations (QSPR), for a set of fifty organic compounds for polyamide and cellulose acetate membranes. Membrane solute sorption and passage for dead-end filtration model experiments were quantified based on radioactivity measurements for radiolabeled compounds in the feed, permeate and the membrane, while solute rejection was determined from a mass balance on the permeated solution Volume. Artificial neural networks-based quantitative structure-property relations models were developed for the organic passage (P), sorbed (M) and rejected (R) fractions using the most relevant set of molecular descriptors selected from a pool of 45 molecular descriptors by means of a correlation-based feature selection method and self-organizing maps (SOM). The analysis included pre-screening with principal components analysis and SOM of the chemical domain for the study chemicals as defined by chemical descriptors to identify the applicability domain and chemical similarities. The QSPR models predicted the P and M mass fractions within the range of the standard deviations of measurements for the experimental data set of fifty compounds. Mass balance closure (requiring that M, P and R sum to unity) was satisfactory for the experimental data set of fifty compounds and for an external set of 144 test chemicals, which were not included in the model development. Somewhat higher prediction errors were encountered for a few chemicals that were not well represented within the present chemical domain. The quality of the QSPR/NN models developed suggests that there is merit in extending both the present compound database and the present approach to develop a comprehensive toot for assessing organic solute behavior in RO water treatment processes.

Libotean D., Giralt J., Rallo R., Cohen Y., Giralt F., Ridgway H.F., Rodriguez G., Phipps D. (2008). Organic Compounds Passage through RO Membranes. Journal of Membrane Science, (313)1-2:23-43