CHALLENGES IN UNDERSTANDING MEMBRANE FOULING AND CLEANING

Transcription

1 CHALLENGES IN UNDERSTANDING MEMBRANE FOULING AND CLEANING Charles Liu, Pall Corporation, 25 Harbor Park Dr., Port Washington, NY Ph: Abstract Membrane fouling is ubiquitous and has a significant impact on the operation of membrane plants. Although the impact of fouling may be mitigated via the modification of pretreatment processes, inefficient cleaning can affect the extent of irreversible permeability loss, leading to the premature replacement of membranes. The challenges of having efficient membrane cleaning rise from the complicated and dynamic nature of membrane fouling. In addition, the proper tools to qualify and quantify membrane fouling are in great need of developing. Although fouling materials are usually classified into different types, such as inorganic, organic, biological etc., it is the interactions between different fouling materials and resulting structure of fouling layers that may require cleaning using different and multiple cleaning reagents in certain cleaning sequences. Based on the analyses of membrane cleaning results from 33 membrane plants, in the majority of cases (88%) the causes of membrane fouling are attributed to natural organic matter (NOM) or the combination of NOM and iron. In some cases, the sequence of cleaning steps also has a significant contribution to cleaning efficacy. This may be related to the structure of fouling layers. Concentration, ph, temperature, and cleaning duration were identified as major variables affecting cleaning efficacy. The impacts of those variables on membrane cleaning showed a complex interdependence of different variables and could be case-specific. Introduction Low-pressure membranes (microfiltration and ultrafiltration) are increasingly replacing conventional water treatment processes as the result of more stringent regulations and greatly improved competitive pricing. It has been recognized that the most common operating issue of membrane plant operation is to control membrane fouling. Although the impact of fouling may be mitigated via the modification of pretreatment processes, inefficient cleaning can affect the extent of irreversible permeability loss, leading to the premature replacement of membranes. The challenges of having efficient membrane cleaning rise from the complicated and dynamic nature of membrane fouling. In addition, the proper tools to qualify and quantify membrane fouling are in great need of developing. Although fouling materials are usually classified into 1

2 different types, such as inorganic, organic, biological etc., this classification is more for convenience than the reality - the interactions between different fouling materials that form either homogeneous or heterogeneous fouling layers. Those fouling layers could have different structures (i.e., stratified vs. mixed) that may require cleaning using multiple cleaning reagents in certain cleaning sequences. Factors affecting the membrane cleaning include chemical strength, temperature, and the duration and frequency of cleaning. The impacts of those variables on membrane cleaning showed a complex interdependence of different variables and could be casespecific. Furthermore, practical considerations such as the availability of cleaning chemicals, storage for cleaning chemicals, reuse and/or disposal of spent cleaning solutions, demands for plant production, environmental and regulatory concerns, all can affect how and how often the membrane cleaning can be performed. This paper discusses the lesson learnt for membrane cleaning practices and identifies the knowledge gaps where further investigations should be focused. Approach for Determining the Causes of Membrane Fouling Historically, fouling has been categorized as inorganic, organic, biological etc.. However, this division does not reflect the reality of membrane fouling. Fouling is collective results of the interactions between membranes and fouling materials, and the interactions between fouling materials. Naturally, this view does not dispute that there may be certain predominant factors contributing more significantly to membrane fouling than others. In order to determine the causes of fouling explicitly, the following three conditions have to be met: 1. Knowing what on the membranes. In other words, we need to have methods of analyzing the chemical composition of (clean and fouled) membrane surfaces. Ideally, an analytical method is able to qualify and quantify a substance explicitly. 2. Knowing which method can be used to remove different fouling materials from membranes. Ideally, those methods should be capable of discriminating different type of fouling materials. 3. Verifying the change in membrane permeability as the result of removals of fouling materials. For the first condition, although there are different methods to characterize the various substances (Table 1), the results are far from satisfactory due to lack of specificity, accuracy, and explicitly to qualify and quantify what on the membranes. Many of the methods listed in the table are not direct surface analysis, but the methods to characterize deposited materials after being extracted from membrane surfaces. The characterization typically focuses on some attributes of the deposited materials, but by no means of explicit and conclusive qualification. Furthermore, one needs to take caution in interpreting results of surface analyses and understands their limitations. Nevertheless, those methods provide certain useful information regarding to the nature of fouling, but fail to qualify and quantify fouling explicitly. 2

3 Table 1: Analytical Techniques Potentially Useful for Determine the Causes of Membrane Fouling (Liu, 2014) Method What to Measure Size Exclusion Chromatography (SEC or HPSEC) NOM size/molecular weight (Sequential) Ultrafiltration Resin Fractionation (coupled XAD-8 and XAD-4 resins) UV Spectroscopy (UV 254 and SUVA) Fluorescence Spectroscopy NOM size/molecular weight distribution Hydrophilic/hydrophobic NOM fractions including acids, bases, and neutrals Aromaticity and MW of NOM Autochthonous vs. allocthonous NOM Time of-flight Secondary Ion Mass Spectrometry (TOF-SIMS) Ion mass spectrum of signature fragments of fouling material Attenuated Total Reflectance-Fourier Transform Infrared Spectroscopy (ATR/FTIR) Pyrolysis Gas Chromatography/ Mass Spectrometry (GC/MS) X-Ray Energy Dispersion Spectrograph (X-EDS) Potential Titration 13 C-Nuclear Magnetic Resonance Spectrometry ( 13 C-NMR) NOM functional groups such as C=C, C=O, C-O, C-H etc. NOM structural building blocks such as proteins, polydroxyaromatics, polysaccharides, and aminosugars Elements and their atomic percent. Use ratios of different elements as the indicators of polarity (O/C), degree of saturation (H/C), and source (N/C) Acidic functional groups such as carboxylic and phenolic) and ph-dependent charge density Carbon functionality such as aromatic-c, aliphatic-c, carboxylic-c, phenolic-c etc. For the second condition, different types of chemicals are available to remove fouling materials. However, most of them are unable to discriminate different fouling materials explictly. As such the recovery in membrane permeability may not be attributed to certain type of fouling materials conclusively. For the third condition, it is probably the easiest to meet as the membrane permeability can be measured directly and accurately. However, variations due to the heterogeneous nature of membrane fouling are present and statistical analysis is necessary to ensure the results not a random event. One possible approach for the evaluation on the nature of fouling is to deduce based on two correlations: (a) the correlation of the changes in permeability and the employment of a specific cleaning step, and (b) the correlation between the changes in permeability and the results of surface (elemental) analyses, i.e., the changes in the quantity of substances on membrane surfaces before and after cleaning. Although these two correlations do not necessarily lead 3

4 causation relations, they do narrow the possible cause considerably. The following is an example to illustrate how the approach is employed. The source water in the example is surface water characterized with high TOC (~14 mg/l), high hardness (190 mg/l as CaCO 3 ) and ph (7.9). The pretreatment process contains coagulation- flocculation- sedimentation, prior to microfiltration. Membrane samples was taken after the plant had operated for ~1.5 year for determining what would be the cause for the decline in membrane permeability, and if cleaning could effectively restore the lost permeability. The relative permeability (as a percent of the permeability of new membranes averaged from 5 samples) following steps of cleaning is depicted in Figure 1. Permeability Recovery, % 100% 80% 60% 40% 20% 0% Uncleaned 1st Step 2nd Step A B C Cleaning Regime Cleaning Regime Step 1 (16 o C Step 2 (6 o C A 5,000 ppm NaOCl + 2% citric acid B 2% citric acid 5,000 ppm NaOCl + C 1% Commercial Iron Cleaner 5,000 ppm NaOCl + Figure 1: Examples of determining the probable causes of membrane fouling In Figure 1 letters A, B, and C denote the cleaning regimes that are summarized in the table below the graph; and color bars represent the average relative permeability following cleaning steps. As indicated in the figure, the permeability prior to cleaning was less than 40% of that for new membranes and recovered completely after a two-step cleaning. Notably, the recoveries of membrane permeability were all related to cleaning with the mixture of caustic and chlorine. Figure 1 presents a classic example for which a clear correlation of permeability change and the specific cleaning step (i.e., the mixture of NaOCl and caustic) can be established. The cleaning step using chlorine and caustic mixture in all three cleaning regimes nearly restored membrane permeability completely. The results of surface analysis using Scanning Electron Microscope (SEM) and X-Ray energy Dispersion Spectrograph (EDX) from the example above are presented in Figure 2. 4

5 SEM Uncleaned (outer surface) EDX Uncleaned (outer surface) Figure 2: Examples of surface analyses (SEM and EDX) SEM images of membrane surfaces before and after cleaning showed clear contrasts. The membrane surfaces before cleaning were covered with deposited materials and a few membrane pores are barely visible. On the other hand, the SEM images of membrane surface after cleaning most of deposited materials were removed. The elemental composition of membrane surfaces analyzed with EDX before and after cleaning also showed significant differences. The atomic percent of elements (shadowed area) prior to cleaning showed C, F, O as major elements, Mn, Si, Cu as minor elements, and Ca and Al in trace amount. After cleaning, the remaining elements are C, F, O (major elements), and trace amount of Al. One important indicator for membrane fouling by organic matter is the ratio of C to F. The membranes analyzed in the figures are made of polyvinylidene fluoride (PVDF), with a stoichiometric C:F ratio of 1:1. When the membranes deposit C-containing substances (i.e, NOM), the ratio of C:F is always > 1:1. As such the atomic percent of C:F ratio can be used to indicate if C-containing substances is present on the membrane. If the increase in atomic percent of F as well as the decrease in C:F ratio following the cleaning can be correlate to the significant increase in membrane permeability, it would provide a probable (although not definitive) hypothesis that organic matter was the reason for membrane fouling. Distribution of Probable Causes of Membrane Fouling Membrane samples from 33 microfiltration plants are evaluated using the approach illustrated in the example above. The distribution of probable causes for membrane fouling is illustrated in Figure 3: 5

6 Fe/Mn 9% Other 6% NOM/Fe 33% NOM 52% Figure 3: The distribution of possible causes of membrane fouling based on data from 33 microfiltration plants As illustrated in Figure 3, in more than one half of all analyses the causes of membrane fouling had been identified as organic matter; another one third had been the combination of iron and NOM. It should be noted that not every analysis was as clear-cut as in the example presented above in Figures 1 and 2, in which the cleaning using chlorine/caustic mixture contributed to almost all of recoveries of the lost membrane permeability. In most cases, different cleaning steps contribute to the recovery of lost permeability, although their impacts on permeability recovery may vary. A common phenomenon is the combined fouling caused by iron and NOM in which two constituents may interact. Natural organic matter can form complex with divalent / multivalent cations, presumably via carboxyl and/or phenolic functional groups on NOM (Buffle and Altman, 1987; Stumm and Morgan, 1996; Thurman, 1985). The fouling by iron-nom complex was found to be difficult to clean under certain circumstances (Liu et al., 2007). For fouling by the combined impacts of iron and NOM, their responses to chemical cleaning may vary significantly. This phenomenon is illustrated in Figure 4. In Case (a) in Figure 4, different sequences of cleaning steps did not result in the differences in permeability recovery (sequenceindependent). On contrast, the permeability recovery in Case (b) varied as the sequence of cleaning steps changed (sequence-dependent). 6

7 Percent Permeability, % 100% 80% 60% 40% 20% 0% Uncleaned 1st Step 2nd Step A B C Cleaning Regime Percent Permeability, % 100% 80% 60% 40% 20% 0% Uncleaned 1st Step 2nd Step A B D Cleaning Regime (a) (b) Cleaning 1 st Step (16 hrs) 2 nd Step (16 hrs) Cleaning 1 st Step (16 hrs) 2 nd Step (6 hrs) Regime Regime A 0.5% NaOCl + 2% citric acid A 0.5% NaOCl + 2% citric acid B 2% citric acid 0.5% NaOCl + B 2% citric acid 0.5% NaOCl + C 1% Iron-cleaner 0.5% NaOCl + C 1% Iron-cleaner 0.5% NaOCl + Figure 4: Cleaning of different types of Fe-NOM fouling: (a) sequence independent; (b) sequence - dependent The reasons for the different responses of membrane fouling to the sequence of cleaning steps are yet clear, but presumably relates to how the fouling layer was structured and how different constituents in the fouling layer interacted. There have been limited researches in understanding the structures of fouling layers and how they may affect the efficacy of cleaning (Contreras, 2011; Kim et al., 2009; Kim and Hoek, 2007; Li and Elimelech, 2006). Nevertheless, the sequencedependence for cleaning steps brings additional challenges to the membrane cleaning operation. The Impacts of Key Variables Affecting Cleaning and Their Interactions During the chemical cleaning, operating conditions such as NaOCl concentration, temperature, and cleaning duration all affect the efficacy as well as the costs of the operation (Al-Amoudi and Lovitt, 2007; Porcelli and Judd, 2010). Optimization of operating strategies depends upon the understanding of the interactions of those factors. In order to investigate the impacts of different variable for membrane cleaning and their interactions, a bench-testing plan consisting of four different levels of NaOCl concentration (250 ppm, 500 ppm, 1,000 ppm, and 2,000 ppm) and three level of temperature (20 o C, 30 o C, and 7

8 40 o C) were carried out for a total of twelve different combinations. For each of concentrationtemperature condition, five replicates of samples were tested. To ensure the results to be comparable, the permeability of the samples prior to cleaning was measured. Then the samples were grouped in a way to have similar degree of fouling among the groups of samples in Table 2, as indicated by the percent permeability of that for new membranes. Table 2: Percent Permeability of Samples Prior to Cleaning (Baseline Permeability) 250 ppm 500 ppm 1,000 ppm 2,000 ppm 20 o C 60.8% ± 3.0% 60.8% ± 3.0% 60.3% ± 3.4% 59.7 ± 2.3% 30 o C 59.7% ± 3.6% 60.8% ± 2.3% 60.3% ± 0,0% 60.3% ± 1.9% 40 o C 60.3% ± 2.3% 59.7% ± 3.0% 59.7% ± 2.3% 60.5% ± 2.8% Samples were soaked in the solutions of designated concentration and placed in the incubators for 30 minutes before they were thoroughly rinsed and permeability was measured again. The resulted change in percent permeability as a function of both NaOCl concentration and temperature is plotted in Figure 5. Percent Change in Permeability 25% 20% 15% 10% 5% 0% Figure 5: Interactions of key variables in membrane cleaning: NaOCl concentration and temperature on membrane samples fouled by NOM (duration = 30 minutes) 8

9 As expected, high NaOCl concentration and elevated temperature generally yielded larger recoveries of membrane permeability. For example, at 20 o C, increasing NaOCl concentration from 250 ppm to 2,000 ppm, the gain in permeability recovery increased from 11.5% to 20.3% - nearly doubled. Similarly, for a NaOCl concentration of 250 ppm, increasing temperature from 20 o C to 40 o C, the gain in permeability recovery increased from 11.5% to 15.3%. However, enhanced gain in membrane permeability by increasing NaOCl concentration or temperature diminished at both higher NaOCl concentration and temperature. For example, at 40 o C, increasing NaOCl concentration from 250 ppm to 2,000 ppm, the gain in permeability recovery only increased from 15.3% to 20.5% - only 50% of the gain when compared to the value at 20 o C. For NaOCl concentration of 2,000 ppm, increasing temperature from 20 o C to 40 o C did not result in the gains in the permeability recovery at all. Those results, in spite of being obtained from limited conditions, may provide useful insights for cleaning practices: To achieve the enhanced cleaning, heating the cleaning solution may not be as effective as increasing the strength of the solution. A cost-effective analysis can be performed to evaluate the optimal cleaning conditions after the energy requirement and costs of chemicals are quantified. Concentration of cleaning chemicals and temperature are mutually compensative only to certain degree. Beyond this point, the return in the form of enhanced cleaning diminished. Monitor the strength of cleaning solution is important for preventing the inefficient usage of chemicals due to overdosing on the one hand, and sharp decline in recovery of membrane permeability due to exhaustion of cleaning chemical on the other. Conclusion Remarks The most common operating issue of membrane plant operation is to control membrane fouling. The challenges of having efficient membrane cleaning are due to the complicated and dynamic nature of membrane fouling. In addition, the proper tools to qualify and quantify membrane fouling are in great need of developing. Various foulants interact and form fouling layers with different structures. It is the multiplicity and variety of fouling layer structures that require multiple steps of cleaning with various chemicals and sometimes in a particular sequence to have an effective cleaning. This complexity makes the membrane cleaning a challenging task. While the approach of correlating permeability recovery and specific cleaning step as well as the results of surface analyses has some successes in deducting the probable causes of membrane fouling, more sophisticated and discriminating techniques for fouling analysis are in great demand to better serve the needs for evaluating membrane fouling and optimal cleaning procedures for the industry. In addition, understanding the key variables affecting membrane cleaning and how they may interact during cleaning operation can provide the insights to optimize cleaning operation. 9

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