• Energy Research

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.

AbstractOnline 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.

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).

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.