Claudia F. Galinha

Multivariate statistically-based modelling of a membrane bioreactor for wastewater treatment using 2D fluorescence monitoring data

This work presents the development of multivariate statistically-based models for monitoring several key performance parameters of membrane bioreactors (MBR) for wastewater treatment. This non-mechanistic approach enabled the deconvolution of 2D fluorescence spectroscopy data, a powerful technique that has previously been shown to capture important information regarding MBR performance. Projection to latent structure (PLS) modelling was used to integrate 2D fluorescence data, after compression through parallel factor analysis (PARAFAC), with operation and analytical data to describe an MBR fouling indicator (transmembrane pressure, TMP), five descriptors of the effluent quality (total COD, soluble COD, concentration of nitrite and nitrate, total nitrogen and total phosphorus in the permeate) and the biomass concentration in the bioreactor (MLSS). A multilinear correlation was successfully established for TMP, CODtp and CODsp, whereas the optimised models for the remaining outputs included quadratic and interaction terms of the compressed 2D fluorescence matrices. Additionally, the coefficients of the optimised models revealed important contributions of some of the input parameters to the modelled outputs. This work demonstrates the applicability of 2D fluorescence and statistically-based models to simultaneously monitor multiple key MBR performance parameters with minimal analytical effort. This is a promising approach to facilitate the implementation of MBR technology for wastewater treatment.

Real-time monitoring of membrane bioreactors with 2D-fluorescence data and statistically based models

The application of membrane bioreactors (MBR) for wastewater treatment is growing worldwide due to their compactness and high effluent quality. However, membrane fouling, mostly associated to biological products, can reduce MBR performance. Therefore, it is important to monitor MBRs as close to real-time as possible to accelerate control actions for maximal biological and membrane performance. 2D-fluorescence spectroscopy is a promising on-line tool to simultaneously monitor wastewater treatment efficiency and the formation of potential biological fouling agents. In this study, 2D-fluorescence data obtained from the wastewater and the permeate of a MBR was successfully modelled using projection to latent structures (PLS) to monitor variations in the influent and effluent total chemical oxygen demand (COD). Analysis of the results also indicated that humic acids and proteins highly contributed to the measured COD in both streams. Nevertheless, this approach was not valid for other performance parameters of the MBR system (such as influent and effluent ammonia and phosphorus), which is usually characterised through a high number of analytical and operating parameters. Principal component analysis (PCA) was thus used to find possible correlations between these parameters, in an attempt to reduce the analytical effort required for full MBR characterisation and to reduce the time frame necessary to obtain monitoring results. The 3 first principal components, capturing 57% of the variance, indicated and confirmed expected relationships between the assessed parameters. However, this approach alone could not provide robust enough correlations to enable the elimination of parameters for process description (PCA loadings ≤ 0.5). Nevertheless, it is possible that the information captured by 2D-fluorescence spectroscopy could replace some of the analytical and operating parameters, since this technique was able to successfully describe influent and effluent total COD. It is thus proposed that combined modelling of 2D-fluorescence data and selected performance/operating parameters should be further explored for efficient MBR monitoring aiming at rapid process control.

2D fluorescence spectroscopy for monitoring ion-exchange membrane based technologies – Reverse electrodialysis (RED)

Reverse electrodialysis (RED) is one of the emerging, membrane-based technologies for harvesting salinity gradient energy. In RED process, fouling is an undesirable operation constraint since it leads to a decrease of the obtainable net power density due to increasing stack electric resistance and pressure drop. Therefore, early fouling detection is one of the main challenges for successful RED technology implementation. In the present study, two-dimensional (2D) fluorescence spectroscopy was used, for the first time, as a tool for fouling monitoring in RED. Fluorescence excitation-emission matrices (EEMs) of ion-exchange membrane surfaces and of natural aqueous streams were acquired during one month of a RED stack operation. Fouling evolvement on the ion-exchange membrane surfaces was successfully followed by 2D fluorescence spectroscopy and quantified using principal components analysis (PCA). Additionally, the efficiency of cleaning strategy was assessed by measuring the membrane fluorescence emission intensity before and after cleaning. The anion-exchange membrane (AEM) surface in contact with river water showed to be significantly affected due to fouling by humic compounds, which were found to cross through the membrane from the lower salinity (river water) to higher salinity (sea water) stream. The results obtained show that the combined approach of using 2D fluorescence spectroscopy and PCA has a high potential for studying fouling development and membrane cleaning efficiency in ion exchange membrane processes.

Chitin–glucan complex production by Komagataella (Pichia) pastoris: impact of cultivation pH and temperature on polymer content and composition

Chitin-glucan complex (CGC) is a valuable biomaterial that can be extracted from the cell wall of several yeast and fungi. In this work, the yeast Komagataella (Pichia) pastoris was grown on glycerol as the sole carbon source in batch cultivation experiments to evaluate the effect of pH (3.5-6.5) and temperature (20-40°C) on CGC production and polymer composition. The CGC content in the biomass and the volumetric productivity (rp) were not significantly affected within the tested pH and temperature ranges. Nevertheless, both parameters could be maximized (CGC ≥14wt% and rp ≥ 3.0 gCGC L(-1)day(-1)) for temperatures within 27-34°C and pH above 6.0 or below 4.0. In contrast, the effect of pH and temperature on the polymers chitin:β-glucan molar ratio was more pronounced. The highest chitin:β-glucan molar ratio (>14:86) was obtained for the mid-range pH (4.5-5.8) and temperatures (26-33°C), while a drastic reduction of chitin to ≤ 6%mol was observed outside those ranges. Therefore, a compromise between maximal CGC production and the synthesis of polymers enriched in chitin must be achieved, depending on the final application of this product.

Dynamic change of pH in acidogenic fermentation of cheese whey towards polyhydroxyalkanoates production: Impact on performance and microbial population.

Polyhydroxyalkanoates (PHA) are a sustainable alternative to conventional plastics that can be obtained from industrial wastes/by-products using mixed microbial cultures (MMC). MMC PHA production is commonly carried out in a 3-stage process of acidogenesis, PHA culture selection and accumulation. This research focused on the possibility of tailoring PHA by controlling the acidogenic reactor operating conditions, namely pH, using cheese whey as model feedstock. The objective was to investigate the impact that dynamically varying the acidogenic pH, when targeting different PHA monomer profiles, had on the performance and microbial community profile of the anaerobic reactor. To accomplish this, an anaerobic reactor was continuously operated under dynamic pH changes, ranging from pH 4 to 7, turning to pH 6 after each change of pH. At pH 6, lactate and acetate were the dominant products (41-48% gCOD basis and 22-44% gCOD basis, respectively). At low pH, lactate production was higher while at high pH acetate production was favoured. Despite the dynamic change of pH, the fermentation product composition at pH 6 was always similar, showing the resilience of the process, i.e. when the same pH value was imposed, the culture produced the same metabolic products independently of the history of changes occurring in the system. The different fermentation product fractions led to PHAs of different compositions. The microbial community, analysed by high throughput sequencing of bacterial 16S rRNA gene fragments, was dominated by Lactobacillus, but varied markedly when subjected to the highest and lowest pH values of the tested range (4 and 7), with increase in the abundance of Lactococcus and a member of the Candidate Division TM7. Different bacterial profiles obtained at pH 6 during this dynamic operation were able to produce a consistent profile of fermentation products (and consequently a constant PHA composition), demonstrating the communitys functional redundancy.

Development of a hybrid model strategy for monitoring membrane bioreactors.

In the present study, the performance of a membrane bioreactor (MBR) was modelled using a hybrid approach based on the activated sludge model number 3 (ASM3) combined with projection to latent structures (PLS) to predict the residuals of the ASM. The application of ASM to MBRs requires frequent re-calibration to adjust the model to variations in influent characteristics, determined through time-consuming analysis and batch tests. Considering this problem, the objective of this study was to improve ASM prediction ability with minimal additional monitoring effort. Hybrid models were developed to predict three MBR performance parameters: mixed liquor suspended solids (MLSS), COD in the permeate (CODp) and nitrite and nitrate concentration in the permeate (NOxp). For PLS modelling of ASM residuals three input strategies were used: (1) analytic and operating data; (2) operating data plus 2D fluorescence spectroscopy; (3) all the data. The first input strategy improved ASM prediction of the three selected outputs, and highlighted the lack of detailed and real-time information from wastewater and operating parameters in the ASM used in this study. In the second input strategy, the incorporation of updated data from 2D fluorescence spectroscopy resulted on better model fitting than in the first input strategy, for all the output parameters studied. Through the hybrid modelling approach it was possible to significantly improve the ASM predictions in real-time using 2D fluorescence measurements and other relevant parameters acquired on-line, without requiring further laboratory analysis. Furthermore, the third input strategy, incorporating all the collected data, did not significantly improve the prediction of the outputs beyond the second strategy. This shows that 2D fluorescence spectroscopy is a comprehensive monitoring tool, able to capture on-line the required information to complement, through hybrid modelling, the mechanistic information described by an ASM.