Effect of ultrafiltration operating conditions for separation of ferulic acid from arabinoxylans in corn fibre alkaline extract

Corn fibre, a co-product of the starch industry, is rich in compounds with high added value, such as ferulic acid and arabinoxylans, which are released during alkaline extraction. This work aims to optimise an efficient separation method for the recovery of these two compounds from a corn fibre alkaline extract, allowing an efficient valorisation of this co-product. Ultrafiltration was selected as separation method, due to its potential to fractionate these compounds. In order to minimise the loss of membrane permeance, due to mass transfer limitations caused by the high arabinoxylan viscosity, the impact of relevant ultrafiltration operating parameters (membrane molecular weight cut-off, fluid dynamics conditions, transmembrane pressure, and operating temperature) were evaluated. A Nadir UP 150 membrane was found to be an adequate choice, allowing for an efficient separation of ferulic acid from arabinoxylans, with null rejection of ferulic acid, a high estimated rejection of arabinoxylans 98.0% ± 1.7%, and the highest permeance of all tested membranes. A response surface methodology (RSM) was used to infer the effect of ultrafiltration conditions (crossflow velocity, transmembrane pressure and operating temperature) on the rejection of ferulic acid, retention of arabinoxylans (assessed through apparent viscosity of the retentate stream), and permeance. Through mathematical modelling it was possible to determine that the best conditions are the highest operating temperature and initial crossflow velocity tested (66◦C and 1.06 m.s−1, respectively), and the lowest transmembrane pressure tested (0.7 bar).publishersversionpublishe

Fluorescence coupled with chemometrics for simultaneous monitoring of cell concentration, cell viability and medium nitrate during production of carotenoid-rich Dunaliella salina

This work was supported by the Associate Laboratory for Green Chemistry- LAQV which is financed by national funds from FCT / MCTES ( UID/QUI/50006/2019 ); by the European FP7 KBBE project “D-Factory” (contract no. 613870); by KAUST OSR award no. OSR-2016-CPF-2907-05; grant of FCT / MCTES : SFRH/BD/108894/2015. The authors would like to thank The Marine Biological Association (Devon, UK) and NBT Ltd (Israel).Two-dimensional (2D) fluorescence spectroscopy was investigated as a monitoring tool for cultivation, harvesting, and effluent treatment of Dunaliella salina with high carotenoid concentration; aiming to improve the production process and minimise costs. Chemometric analysis, namely Principal Component Analysis (PCA) and Projection to Latent Structures (PLS), were used to build models for estimation of cellular concentration, cellular viability, and nitrate concentration in media. The estimations were based on fluorescence excitation-emission matrices (EEMs) acquired directly from algal suspensions. Cell concentration during cultivation and harvesting can be predicted by a single model capturing 92.0% of the variance, and with R2 of 0.92 and 0.97, for training and validation, respectively. Cell viability during harvesting by ultrafiltration was modelled with 79% of variance and R2 of 0.79 for training and 0.73 for validation. Nitrate concentration was successfully predicted during cultivation and permeate treatment using a single model with 81.8% of variance and R2 of 0.82 for training and 0.80 for validation. Therefore, this work demonstrates the strong potential of combining 2D fluorescence and chemometrics for monitoring different processes during microalgae production.authorsversionpublishe

Development of a monitoring tool based on fluorescence and climatic data for pigments profile estimation in Dunaliella salina

When growing microalgae for biorefinery processes, a high product yield is desired. For that reason, monitoring the concentration of the desired products during growth and products induction procedure is of great interest. 2D Fluorescence spectroscopy is a fingerprinting technique, used in situ and at real time, with a high potential for online monitoring of biological systems. In this work, Dunaliella salina pigment content was monitored using fluorescence data coupled with chemometric tools. Climatic parameters were also used as input variables due to their impact on the pigments profile in outdoor cultivations. Predictive models were developed for chlorophyll content (a, b, and total) with variance captured between 50 and 90%, and R2 varying between 0.6 and 0.9 for both training and validation data sets. Total carotenoids models captured 70 to 80% of variance, and R2 between 0.7 and 0.9, for training and validation. Models for specific carotenoids (zeaxanthin, α-carotene, all-trans-β-carotene, and 9-cis-β-carotene) captured variance between 60 and 90%, with validation and training R2 between 0.6 and 0.9. With this methodology, it was possible to calibrate a monitoring tool for pigments quantification, as a bulk and as individual compounds, proving that 2D fluorescence spectroscopy and climatic data combined with chemometric tools can be used to assess simultaneously and at real time different pigments in D. salina biomass production.info:eu-repo/semantics/publishedVersio

Monitoring of eicosapentaenoic acid (EPA) production in the microalgae Nannochloropsis oceanica

UID/QUI/50006/2019 SFRH/BD/108894/2015With the increase awareness for a healthier food regime and greener environmental processes, microalgae are being looked as a solution for a sustainable production of polyunsaturated fatty acids, such as omega-3 eicosapentaenoic acid (EPA). Nannochloropsis oceanica is an oleaginous microalga, well-known for the ability of EPA accumulation, although higher lipid productivities are still required to make the process competitive. Therefore, three cultivation parameters were tested in the present work (temperature, light cycles and nitrogen supply) in order to study the EPA profile in the polar and neutral fractions of the cells. In addition, an online monitoring tool based on a fluorescence spectroscopy technique was developed with the aim of increasing process knowledge at real time. The results of this work show that nitrogen depletion induces the highest variability in EPA accumulation in the neutral fraction (triacylglycerols). However, to increase the EPA content in the polar fraction a different strategy needs to be implemented, such as decreasing the cultivation temperature or the light available per cell. Chemometric models were developed through PCA (Principal Component Analysis) and PLS (Projection to Latent Structures), using only fluorescence spectra as inputs, enabling the monitoring of EPA in both fractions separately. High explained variance was observed (above 85%) in both fractions, with R2 above 0.81 and slopes above 0.93 for both validation and training data sets. Lower values of cross-validation and prediction errors were observed (between 0.29 and 0.49% g/gDW). The results obtained show that fluorescence spectroscopy is a powerful technique for online monitoring of non-fluorophore molecules, such as EPA, in complex process like microalgae cultivation.authorsversionpublishe

Perspectives of fluorescence spectroscopy for online monitoring in microalgae industry

OSR‐2016‐CPF‐2907‐05 UIDB/50006/2020 UIDP/50006/2020 SFRH/BD/108894/2015Microalgae 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.publishersversioninpres

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

CTM2015-65348-C2-1-R RTI2018-096042-B-C21 CTM2015-74695-JIN (AEI/FEDER/UE) BES-2016-076244 (MCI/AEI/FSE, UE) FCT/MCTES (UIDB/50006/2020).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.publishersversionpublishe

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

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

Optimisation of arsenate removal from water by an integrated ion-exchange membrane process coupled with Fe co-precipitation

UID/QUI/50006/2019 SFRH/BD/103013/2014The present work investigates the performance of an ion-exchange membrane process for arsenate removal, consisting in integrating Donnan dialytic transport of arsenic with its simultaneous precipitation in a separate receiver compartment. The process performance was improved by adding a bicarbonate-carbonate buffer in the receiver solution, where iron (III) chloride was used to precipitate the arsenic. This system allowed to maintain the treated water pH within the acceptable drinking water range of 6–9, without further control. A Response Surface Methodology (RSM) was used to infer about the effect of the supply water characteristics (initial arsenic concentration and pH) and operating conditions (mass ratio of iron to arsenic) on the degree of arsenic removal. It was found that the initial pH of the receiver solution was also a required input to predict accurately the arsenic concentration in the treated water (for a predefined treatment time). The model developed has a fitting R2 value of 0.99 and a prediction error of 6.6 µg/L of As. The methodology presented permits to develop a simple decision tool (either through the use of equations or visual plots) to determine the effective amount of iron to be used in the treatment of As contaminated water.authorsversionpublishe

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 16 S 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