Development and Implementation of MBR Monitoring: Use of 2D Fluorescence Spectroscopy.

The monitoring of a membrane bioreactor (MBR) requires the assessment of both biological and membrane performance. Additionally, the development of membrane fouling and the requirements for frequent membrane cleaning are still major concerns during MBR operation, requiring tight monitoring and system characterization. Transmembrane pressure is usually monitored online and allows following the evolution of membrane performance. However, it does not allow distinguishing the fouling mechanisms occurring in the system or predicting the future behavior of the membrane. The assessment of the biological medium requires manual sampling, and the analyses involve several steps that are labor-intensive, with low temporal resolution, preventing real-time monitoring. Two-dimensional fluorescence spectroscopy is a comprehensive technique, able to assess the system status at real-time without disturbing the biological system. It provides large sets of data (system fingerprints) from which meaningful information can be extracted. Nevertheless, mathematical data analysis (such as machine learning) is essential to properly extract the information contained in fluorescence spectra and correlate it with operating and performance parameters. The potential of 2D fluorescence spectroscopy as a process monitoring tool for MBRs is, therefore, discussed in the present work in view of the actual knowledge and the authors' own experience in this field.

Nitrate Removal by Donnan Dialysis and Anion-Exchange Membrane Bioreactor Using Upcycled End-of-Life Reverse Osmosis Membranes.

This work explores the application of Reverse Osmosis (RO) upcycled membranes, as Anion Exchange Membranes (AEMs) in Donnan Dialysis (DD) and related processes, such as the Ion Exchange Membrane Bioreactor (IEMB), for the removal of nitrate from contaminated water, to meet drinking water standards. Such upcycled membranes might be manufactured at a lower price than commercial AEMs, while their utilization reinforces the commitment to a circular economy transition. In an effort to gain a better understanding of such AEMs, confocal µ-Raman spectroscopy was employed, to assess the distribution of the ion-exchange sites through the thickness of the prepared membranes, and 2D fluorescence spectroscopy, to evaluate alterations in the membranes caused by fouling and chemical cleaning The best performing membrane reached a 56% average nitrate removal within 24 h in the DD and IEMB systems, with the latter furthermore allowing for simultaneous elimination of the pollutant by biological denitrification, thus avoiding its discharge into the environment. Overall, this work validates the technical feasibility of using RO upcycled AEMs in DD and IEMB processes for nitrate removal. This membrane recycling concept might also find applications for the removal and/or recovery of other target negatively charged species.

Perspectives of fluorescence spectroscopy for online monitoring in microalgae industry.

Microalgae industrial production is viewed as a solution for alternative production of nutraceuticals, cosmetics, biofertilizers, and biopolymers. Throughout the years, several technological advances have been implemented, increasing the competitiveness of microalgae industry. However, online monitoring and real-time process control of a microalgae production factory still require further development. In this mini-review, non-destructive tools for online monitoring of cellular agriculture applications are described. Still, the focus is on the use of fluorescence spectroscopy to monitor several parameters (cell concentration, pigments, and lipids) in the microalgae industry. The development presented makes it the most promising solution for monitoring up-and downstream processes, different biological parameters simultaneously, and different microalgae species. The improvements needed for industrial application of this technology are also discussed.

Predicting the concentration of hazardous phenolic compounds in refinery wastewater-a multivariate data analysis approach.

The present study focused on the methodology for identification of the wastewater stream that presents the highest phenolic impact at a large oil refinery. As a case-study, the oil refinery, Petrogal S.A., in Sines, Portugal, was selected. Firstly, stripped sour water from the cracking complex was identified as the most relevant wastewater stream concerning phenolic emission. Secondly, multivariate data analysis was used, through projection to latent structures (PLS) regression, to find existing correlations between process parameters and phenols content in stripped sour water. The models developed allowed the prediction of phenols concentration with predictive errors down to 20.16 mg/L (corresponding to 8.2% average error), depending on the complexity of the correlation used, and R2 values as high as 0.85. Models were based in input parameters related to fluid catalytic crackers (FCC) feedstock quality, crudemix and steam injected in the catalyst stripper. The studied data analysis approach showed to be useful as a tool to predict the phenolic content in stripped sour water. Such prediction would help improve the wastewater management system, especially the units responsible for phenol degradation. The methodology shown in this work can be used in other refineries containing catalytic cracking complexes, providing a tool which allows the online prediction of phenols in stripped sour water and the identification of the most relevant process parameters. An optimised system at any refinery leads to an improvement in the wastewater quality and costs associated with pollutant discharge; thus, the development of monitoring online tools, as proposed in this work, is essential.

From Black Box to Machine Learning: A Journey through Membrane Process Modelling.

Membrane processes are complex systems, often comprising several physicochemical phenomena, as well as biological reactions, depending on the systems studied. Therefore, process modelling is a requirement to simulate (and predict) process and membrane performance, to infer about optimal process conditions, to assess fouling development, and ultimately, for process monitoring and control. Despite the actual dissemination of terms such as Machine Learning, the use of such computational tools to model membrane processes was regarded by many in the past as not useful from a scientific point-of-view, not contributing to the understanding of the phenomena involved. Despite the controversy, in the last 25 years, data driven, non-mechanistic modelling is being applied to describe different membrane processes and in the development of new modelling and monitoring approaches. Thus, this work aims at providing a personal perspective of the use of non-mechanistic modelling in membrane processes, reviewing the evolution supported in our own experience, gained as research group working in the field of membrane processes. Additionally, some guidelines are provided for the application of advanced mathematical tools to model membrane processes.

Fluorescence spectroscopy and chemometrics for simultaneous monitoring of cell concentration, chlorophyll and fatty acids in Nannochloropsis oceanica.

Online monitoring of algal biotechnological processes still requires development to support economic sustainability. In this work, fluorescence spectroscopy coupled with chemometric modelling is studied to monitor simultaneously several compounds of interest, such as chlorophyll and fatty acids, but also the biomass as a whole (cell concentration). Fluorescence excitation-emission matrices (EEM) were acquired in experiments where different environmental growing parameters were tested, namely light regime, temperature and nitrogen (replete or deplete medium). The prediction models developed have a high R2 for the validation data set for all five parameters monitored, specifically cell concentration (0.66), chlorophyll (0.78), and fatty acid as total (0.78), saturated (0.81) and unsaturated (0.74). Regression coefficient maps of the models show the importance of the pigment region for all outputs studied, and the protein-like fluorescence region for the cell concentration. These results demonstrate for the first time the potential of fluorescence spectroscopy for in vivo and real-time monitoring of these key performance parameters during Nannochloropsis oceanica cultivation.

Valorization of raw brewers' spent grain through the production of volatile fatty acids.

This work is focused on production of volatile fatty acids (VFA) through anaerobic digestion (AD) using raw (without pre-treatment) brewers' spent grain (BSG) as feedstock. VFAs are by-products from AD of organic wastes with wide potential industrial application in bioplastic production. A long term fed batch stirred-tank reactor was operated and the impact of three hydraulic retention times (HRT) and two organic loading rates (OLR) on VFA production was assessed. Results showed clearly that AD of raw BSG is possible without a pre-treatment step. The maximum volumetric VFA productivity of 91.3 ± 9.1 mgCODL-1 h-1 and VFA concentration of 24.9 ± 2.6 g L-1 were obtained for 16 days of HRT and 16 gTSinL-1d-1 of OLR. This is the highest value of VFA concentration so far reported for BSG. Propionic, acetic and butyric acids were the main VFAs produced. Community identification by FISH and its correlation with process parameters was performed by principal component analysis.

An efficient method for anthocyanins lipophilization based on enzyme retention in membrane systems.

Anthocyanin lipophilization emerged as an efficient technique to improve their chemical stability, liposolubility and antioxidant properties for novel technological applications. This work describes an efficient method for the synthesis of cyanidin-3-glucoside-fatty acid conjugate using a Candida antarctica lipase B-rich extract, without further purification and retained in a porous membrane. Due to the enzyme retention within the membrane structure it was possible to improve the yield of the lipophilization reaction by 2.5-fold as well as obtaining the product in a shorter period of time comparing with its free form. Furthermore, the membrane retention allowed for enzyme reusability, since the same conversion yield was obtained in three consecutive reaction cycles.

Bioremoval of priority polycyclic aromatic hydrocarbons by a microbial community with high sorption ability.

The treatment of large volumes of wastewater during oil refining is presently a challenge. Bioremediation has been considered an eco-friendly approach for the removal of polycyclic aromatic hydrocarbons (PAHs), which are one of the most hazardous groups of organic micropollutants. However, it is crucial to identify native PAH-removing microorganisms for the development of an effective bioremediation process. This study reports the high potential of an anaerobic microbial consortium enriched from a petrochemical refinery wastewater to remove two priority PAHs-acenaphthene and phenanthrene. Seventy-seven percent of acenaphthene was removed within 17 h, whereas phenanthrene was no longer detected after 15 h. Bioremoval rates were extremely high (0.086 and 0.156 h-1 for acenaphthene and phenanthrene, respectively). The characterization of the microbial communities by next-generation sequencing and fluorescence in situ hybridization showed that the PAH-removing consortium was mainly composed by bacteria affiliated to Diaphorobacter and Paracoccus genera, independently of the PAH tested. Moreover, besides biodegradation, biosorption was a relevant mechanism involved in the removal of both PAHs, which is an important finding since biosorption is less expensive than biodegradation and can be carried out with dead biomass. Although biodegradation is the most commonly reported biological mechanism for PAH removal, this study demonstrated that biosorption by this microbial community may be extremely efficient for their removal. Given the outstanding ability of this microbial consortium to quickly remove the compounds addressed, it could be further applied for the bioremediation of PAHs in refinery wastewaters and other contaminated environments.

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 community's functional redundancy.

Impact of sludge retention time on the fine composition of the microbial community and extracellular polymeric substances in a membrane bioreactor.

Membrane bioreactors (MBRs) are an advanced technology for wastewater treatment whose wide application has been hindered by rapid fouling of the membranes. MBRs can be operated with long sludge retention time (SRT), a crucial parameter impacting microbial selection in the reactor. This also affects filtration performance, since a major fouling agent are the extracellular polymeric substances (EPS). In this study, the impact of the SRT on the ecophysiology of the MBRs and, consequently, on membrane fouling was evaluated. A MBR was operated under a SRT of 60 days followed by a SRT of 20 days. A comprehensive analysis of the microbial community structure and EPS proteins and polysaccharide profiles of the mixed liquor and cake layer was carried out throughout both operation periods. The results of this study showed that the imposition of a shorter SRT led to a shift in the dominant bacterial populations. The mixed liquor and cake layer communities were very different, with Actinomycetales order standing out in the cake layer at SRT of 20 days. Overall, higher EPS concentrations (particularly proteins) were found at this SRT. Furthermore, EPS profiles were clearly affected by the SRT: it was possible to correlate a group of soluble EPS proteins with the SRT of 60 days, and a lower sludge age led to a lower diversity of polysaccharide sugar monomers, with an increase of glucose and galactose in the cake layer. This study improves our knowledge regarding the molecular reasons for fouling, which may contribute to improve MBR design and operation.

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 polymer's chitin:ß-glucan molar ratio was more pronounced. The highest chitin:ß-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.

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.

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.