A Review of Computational Modeling in Wastewater Treatment Processes.

Wastewater treatment companies are facing several challenges related to the optimization of energy efficiency, meeting more restricted water quality standards, and resource recovery potential. Over the past decades, computational models have gained recognition as effective tools for addressing some of these challenges, contributing to the economic and operational efficiencies of wastewater treatment plants (WWTPs). To predict the performance of WWTPs, numerous deterministic, stochastic, and time series-based models have been developed. Mechanistic models, incorporating physical and empirical knowledge, are dominant as predictive models. However, these models represent a simplification of reality, resulting in model structure uncertainty and a constant need for calibration. With the increasing amount of available data, data-driven models are becoming more attractive. The implementation of predictive models can revolutionize the way companies manage WWTPs by permitting the development of digital twins for process simulation in (near) real-time. In data-driven models, the structure is not explicitly specified but is instead determined by searching for relationships in the available data. Thus, the main objective of the present review is to discuss the implementation of machine learning models for the prediction of WWTP effluent characteristics and wastewater inflows as well as anomaly detection studies and energy consumption optimization in WWTPs. Furthermore, an overview considering the merging of both mechanistic and machine learning models resulting in hybrid models is presented as a promising approach. A critical assessment of the main gaps and future directions on the implementation of mathematical modeling in wastewater treatment processes is also presented, focusing on topics such as the explainability of data-driven models and the use of Transfer Learning processes.

merlin, an improved framework for the reconstruction of high-quality genome-scale metabolic models.

Genome-scale metabolic models have been recognised as useful tools for better understanding living organisms' metabolism. merlin (https://www.merlin-sysbio.org/) is an open-source and user-friendly resource that hastens the models' reconstruction process, conjugating manual and automatic procedures, while leveraging the user's expertise with a curation-oriented graphical interface. An updated and redesigned version of merlin is herein presented. Since 2015, several features have been implemented in merlin, along with deep changes in the software architecture, operational flow, and graphical interface. The current version (4.0) includes the implementation of novel algorithms and third-party tools for genome functional annotation, draft assembly, model refinement, and curation. Such updates increased the user base, resulting in multiple published works, including genome metabolic (re-)annotations and model reconstructions of multiple (lower and higher) eukaryotes and prokaryotes. merlin version 4.0 is the only tool able to perform template based and de novo draft reconstructions, while achieving competitive performance compared to state-of-the art tools both for well and less-studied organisms.

Quantitative image analysis as a robust tool to assess effluent quality from an aerobic granular sludge system treating industrial wastewater.

Quantitative image analysis (QIA) is a simple and automated method for process monitoring, complementary to chemical analysis, that when coupled to mathematical modelling allows associating changes in the biomass to several operational parameters. The majority of the research regarding the use of QIA has been carried out using synthetic wastewater and applied to activated sludge systems, while there is still a lack of knowledge regarding the application of QIA in the monitoring of aerobic granular sludge (AGS) systems. In this work, chemical oxygen demand (COD), ammonium (N-NH4+), nitrite (N-NO2-), nitrate (N-NO3-), salinity (Cl-), and total suspended solids (TSS) levels present in the effluent of an AGS system treating fish canning wastewater were successfully associated to QIA data, from both suspended and granular biomass fractions by partial least squares models. The correlation between physical-chemical parameters and QIA data allowed obtaining good assessment results for COD (R2 of 0.94), N-NH4+ (R2 of 0.98), N-NO2- (R2 of 0.96), N-NO3- (R2 of 0.95), Cl- (R2 of 0.98), and TSS (R2 of 0.94). While the COD and N-NO2- assessment models were mostly correlated to the granular fraction QIA data, the suspended fraction was highly relevant for N-NH4+ assessment. The N-NO3-, Cl- and TSS assessment benefited from the use of both biomass fractions (suspended and granular) QIA data, indicating the importance of the balance between the suspended and granular fractions in AGS systems and its analysis. This study provides a complementary approach to assess effluent quality parameters which can improve wastewater treatment plants monitoring and control, with a more cost-effective and environmentally friendly procedure, while avoiding daily physical-chemical analysis.

Monitoring morphological changes from activated sludge to aerobic granular sludge under distinct organic loading rates and increasing minimal imposed sludge settling velocities through quantitative image analysis.

Quantitative image analysis (QIA) was used for monitoring the morphology of activated sludge (AS) during a granulation process and, thus, to define and quantify, unequivocally, structural changes in microbial aggregates correlated with the sludge properties and granulation rates. Two sequencing batch reactors fed with acetate at organic loading rates of 1.1 ± 0.6 kgCOD m-3 d-1 (R1) and 2.0 ± 0.2 kgCOD m-3 d-1 (R2) and three minimal imposed sludge settling velocities (0.27 m h-1, 0.53 m h-1, and 5.3 m h-1) induced distinct granulation processes and rates. QIA results evidenced the turning point from flocculation to granulation processes by revealing the differences in the aggregates' stratification patterns and quantifying the morphology of aggregates with equivalent diameter (Deq) of 200 µm ≤ Deq ≤ 650 µm. Multivariate statistical analysis of the QIA data allowed to distinguish the granulation status in both systems, by clustering the observations according to the sludge aggregation and granules maturation status, and successfully predicting the sludge volume index measured at 5 min (SVI5) and 30 min (SVI30). These results evidence the possibility of defining unequivocally the granulation rate and anticipating the sludge settling properties at early stages of the process using QIA data. Hence, QIA could be used to predict episodes of granules disruption and hindered settling ability in aerobic granulation sludge processes.

Effect of ibuprofen on extracellular polymeric substances (EPS) production and composition, and assessment of microbial structure by quantitative image analysis.

A Sequencing Batch Reactor (SBR) with activated sludge was operated with synthetic wastewater containing ibuprofen (IBU) to investigate the biomass stress-responses under long-term IBU exposure. There were 3 different phases: phase I as the control without IBU for 56 days, phase II (40 days), and phase III (60 days) containing IBU at 10 and 5 mg L-1 each. The overall performance of the SBR as well as the extracellular polymeric substances (EPS) in terms of polysaccharides, proteins, and humic acid substances were estimated. Morphological parameters of microbial aggregates in the presence of IBU (phase II and phase III) were assessed by quantitative image analysis (QIA). Removal efficiencies of chemical oxygen demand (COD) and ammonium (NH4+) were significantly reduced by IBU. Loosely bound EPS (LB-EPS) decreased during phase II and phase III, and tightly bound EPS (TB-EPS) was slightly higher in phase II than phase I. TB-EPS proteins were greater in phase II, perhaps to protect microbial cells from IBU exposure. These findings provided insight into both activated sludge stress-responses and EPS composition under long-term IBU exposure. Spearman correlation showed that EPS and morphological parameters significantly affected sludge settleability and flocculation. QIA also proved to be a powerful technique in investigating dysfunctions in activated sludge under IBU exposure.

COVID-19, Chikungunya, Dengue and Zika Diseases: An Analytical Platform Based on MALDI-TOF MS, IR Spectroscopy and RT-qPCR for Accurate Diagnosis and Accelerate Epidemics Control.

COVID-19 and arboviruses (ARBOD) epidemics co-occurrence is a great concern. In tropical and subtropical regions, ARBOD diseases such as chikungunya, dengue, and Zika are frequent. In both COVID-19 and ARBOD cases, an accurate diagnosis of infected patients is crucial to promote adequate treatment and isolation measures in COVID-19 cases. Overlap of clinical symptoms and laboratory parameters between COVID-19 and ARBOD present themselves as an extra challenge during diagnosis. COVID-19 diagnosis is mainly performed by quantitative reverse polymerase chain reaction (RT-qPCR), while ARBOD diagnosis is performed by serology, detection of antigen or antibody, and molecular diagnosis. In this review, the epidemiologic profile of arboviruses and SARS-CoV-2 is analyzed, and potential risks of symptom overlap is addressed. The implementation of an analytical platform based on infrared (IR) spectroscopy, MALDI-TOF mass spectrometry, and RT-qPCR is discussed as an efficient strategy for a fast, robust, reliable, and cost-effective diagnosis system even during the co-occurrence of virus outbreaks. The spectral data of IR spectroscopy and MALDI-TOF MS obtained from COVID-19 infected and recovered patients can be used to build up an integrated spectral database. This approach can enable us to determine quickly the groups that have been exposed and have recovered from COVID-19 or ARBOD, avoiding misdiagnoses.

Assessment of an aerobic granular sludge system in the presence of pharmaceutically active compounds by quantitative image analysis and chemometric techniques.

In this study, a sequencing batch reactor (SBR) with aerobic granular sludge (AGS) was operated with synthetic wastewater containing environmental relevant concentrations of 17ß-estradiol (E2), 17α-ethinylestradiol (EE2) and sulfamethoxazole (SMX). Despite the presence of the studied PhAC, the granular fraction clearly predominated (TSSgran/TSS ranging from 0.82 to 0.98) throughout the monitoring period, presenting aggregates with high organic fraction (VSS/TSS above 0.83) and good settling characteristics (SVI5 ranging from 15 to 39 mL/gTSS). A principal component analysis (PCA) with quantitative image analysis (QIA) based data allowed to distinguish the different operational periods, namely with mature granules (CONT), and the E2, EE2, and SMX feeding periods. It further revealed a positive relationship between the biomass density, sludge settling ability, overall and granular biomass contents, granulation properties, granular biomass fraction and large granules fraction and size. Moreover, a discriminant analysis (DA) allowed to successfully discriminate not only the different operational periods, mainly by using the floccular apparent density, granular stratification and contents data, but also the PhAC presence in samples. The filamentous bacteria contents, sludge settling properties, settling properties stability and granular stratification, structure and contents parameters were found to be crucial for that purpose.

Long-term stability of a non-adapted aerobic granular sludge process treating fish canning wastewater associated to EPS producers in the core microbiome.

The tolerance of aerobic granular sludge (AGS) to variable wastewater composition is perceived as one of its greatest advantages compared to other aerobic processes. However, research studies select optimal operational conditions for evaluating AGS performance, such as the use of pre-adapted biomass and the control of wastewater composition. In this study, non-adapted granular sludge was used to treat fish canning wastewater presenting highly variable organic, nutrient and salt levels over a period of ca. 8 months. Despite salt levels up to 14 g NaCl L-1, the organic loading rate (OLR) was found to be the main factor driving AGS performance. Throughout the first months of operation, the OLR was generally lower than 1.2 kg COD m-3 day-1, resulting in stable nitrification and low COD and phosphorous levels at the outlet. An increase in OLR up to 2.3 kg COD m-3 day-1 disturbed nitrification and COD and phosphate removal, but a decrease to average values between 1 and 1.6 kg COD m-3 day-1 led to resuming of those processes. Most of the bacteria present in the AGS core microbiome were associated to extracellular polymeric substances (EPS) production, such as Thauera and Paracoccus, which increased during the higher OLR period. Ammonium-oxidizing bacteria (AOB) and nitrite-oxidizing bacteria (NOB) species were detected in AGS biomass; while AOB were identified throughout the operation, NOB were no further identified after the period of increased OLR. Different polyphosphate-accumulating organisms (PAOs) were detected along the process: CandidatusAccumulibacter, Tetrasphaera and Gemmatimonas. A non-adapted granular sludge was able to treat the fish canning wastewater and to tolerate salinity fluctuations up to 14 g L-1. Overall, a high microbial diversity associated to EPS producers allowed to preserve bacterial groups responsible for nutrients removal, contributing to the adaptation and long-term stability of the AGS system.

Bio-Based Nanoparticles as a Carrier of ß-Carotene: Production, Characterisation and In Vitro Gastrointestinal Digestion.

ß-carotene loaded bio-based nanoparticles (NPs) were produced by the solvent-displacement method using two polymers: zein and ethylcellulose. The production of NPs was optimised through an experimental design and characterised in terms of average size and polydispersity index. The processing conditions that allowed to obtain NPs (<100 nm) were used for ß-carotene encapsulation. Then ß-carotene loaded NPs were characterised in terms of zeta potential and encapsulation efficiency. Transmission electron microscopy, Fourier transform infrared spectroscopy and X-ray diffraction analysis were performed for further morphological and chemical characterisation. In the end, a static in vitro digestion following the INFOGEST protocol was performed and the bioaccessibility of ß-carotene encapsulated in both NPs was determined. Results show that the best conditions for a size-controlled production with a narrow size distribution are lower polymer concentrations and higher antisolvent concentrations. The encapsulation of ß-carotene in ethylcellulose NPs resulted in nanoparticles with a mean average size of 60 ± 9 nm and encapsulation efficiency of 74 ± 2%. ß-carotene loaded zein-based NPs resulted in a mean size of 83 ± 8 nm and encapsulation efficiency of 93 ± 4%. Results obtained from the in vitro digestion showed that ß-carotene bioaccessibility when encapsulated in zein NPs is 37 ± 1%, which is higher than the value of 8.3 ± 0.1% obtained for the ethylcellulose NPs.

Optimization of bacterial nanocellulose fermentation using recycled paper sludge and development of novel composites.

In this work, recycled paper sludge (RPS), composed of non-recyclable fibres, was used as a carbon source for bacterial nanocellulose (BNC) production. The biomass was enzymatically hydrolysed with Cellic CTec 2 to produce a sugar syrup with 45.40 g/L glucose, 1.69 g/L cellobiose and 2.89 g/L xylose. This hydrolysate was used for the optimization of BNC fermentation by static culture, using Komagataeibacter xylinus ATCC 700178, through response surface methodology (RSM). After analysis and validation of the model, a maximum BNC yield (5.69 g/L, dry basis) was obtained using 1.50% m/v RPS hydrolysate, 1.0% v/v ethanol and 1.45% m/v yeast extract/peptone (YE/P). Further, the BNC obtained was used to produce composites. A mixture of an amino-PolyDiMethylSiloxane-based softener, polyethyleneglycol (PEG) 400 and acrylated epoxidized soybean oil (AESO), was incorporated into the BNC membranes through an exhaustion process. The results show that BNC composites with distinct performances can be easily designed by simply varying the polymers percentage contents. This strategy represents a simple approach towards the production of BNC and BNC-based composites.

SamPler - a novel method for selecting parameters for gene functional annotation routines.

BACKGROUND: As genome sequencing projects grow rapidly, the diversity of organisms with recently assembled genome sequences peaks at an unprecedented scale, thereby highlighting the need to make gene functional annotations fast and efficient. However, the (high) quality of such annotations must be guaranteed, as this is the first indicator of the genomic potential of every organism. Automatic procedures help accelerating the annotation process, though decreasing the confidence and reliability of the outcomes. Manually curating a genome-wide annotation of genes, enzymes and transporter proteins function is a highly time-consuming, tedious and impractical task, even for the most proficient curator. Hence, a semi-automated procedure, which balances the two approaches, will increase the reliability of the annotation, while speeding up the process. In fact, a prior analysis of the annotation algorithm may leverage its performance, by manipulating its parameters, hastening the downstream processing and the manual curation of assigning functions to genes encoding proteins. RESULTS: Here SamPler, a novel strategy to select parameters for gene functional annotation routines is presented. This semi-automated method is based on the manual curation of a randomly selected set of genes/proteins. Then, in a multi-dimensional array, this sample is used to assess the automatic annotations for all possible combinations of the algorithm's parameters. These assessments allow creating an array of confusion matrices, for which several metrics are calculated (accuracy, precision and negative predictive value) and used to reach optimal values for the parameters. CONCLUSIONS: The potential of this methodology is demonstrated with four genome functional annotations performed in merlin, an in-house user-friendly computational framework for genome-scale metabolic annotation and model reconstruction. For that, SamPler was implemented as a new plugin for the merlin tool.

Mapping Salmonella typhimurium pathways using 13C metabolic flux analysis.

In the last years, Salmonella has been extensively studied not only due to its importance as a pathogen, but also as a host to produce pharmaceutical compounds. However, the full exploitation of Salmonella as a platform for bioproduct delivery has been hampered by the lack of information about its metabolism. Genome-scale metabolic models can be valuable tools to delineate metabolic engineering strategies as long as they closely represent the actual metabolism of the target organism. In the present study, a 13C-MFA approach was applied to map the fluxes at the central carbon pathways of S. typhimurium LT2 growing at glucose-limited chemostat cultures. The experiments were carried out in a 2L bioreactor, using defined medium enriched with 20% 13C-labeled glucose. Metabolic flux distributions in central carbon pathways of S. typhimurium LT2 were estimated using OpenFLUX2 based on the labeling pattern of biomass protein hydrolysates together with biomass composition. The results suggested that pentose phosphate is used to catabolize glucose, with minor fluxes through glycolysis. In silico simulations, using Optflux and pFBA as simulation method, allowed to study the performance of the genome-scale metabolic model. In general, the accuracy of in silico simulations was improved by the superimposition of estimated intracellular fluxes to the existing genome-scale metabolic model, showing a better fitting to the experimental extracellular fluxes, whereas the intracellular fluxes of pentose phosphate and anaplerotic reactions were poorly described.

Erratum to: Quantitative physiology and elemental composition of Kluyveromyces lactis CBS 2359 during growth on glucose at different specific growth rates.

In the original publication of the article, the below mentioned errors have appeared. The correct text is provided in this erratum, In the abstract section, the sentence "This dataset serve" should be replaced as "This dataset serves". Also, the reference "Basso TO, Gomes FS, Lopes ML, et al (2014) Homo- and heterofermentative lactobacilli differently affect sugarcane-based fuel ethanol fermentation. Antonie Van Leeuwenhoek 105:169-177. doi: 10.1007/s10482-013-0063-6 " should be replaced as "Basso TO, Dario MG, Tonso A, Stambuk BU, Gombert AK (2010) Insufficient uracil supply in fully aerobic chemostat cultures of Saccharomyces cerevisiae leads to respiro-fermentative metabolism and double nutrient-limitation. Biotechnol Lett 32:973-977. doi: 10.1007/s10529-010-0248-2 ". Finally, in the Table 2 footnote, "according to (Heijnen 1981)" should be replaced as "according to Heijnen (1981)".

Quantitative physiology and elemental composition of Kluyveromyces lactis CBS 2359 during growth on glucose at different specific growth rates.

The yeast Kluyveromyces lactis has received attention both from academia and industry due to some important features, such as its capacity to grow in lactose-based media, its safe status, its suitability for large-scale cultivation and for heterologous protein synthesis. It has also been considered as a model organism for genomics and metabolic regulation. Despite this, very few studies were carried out hitherto under strictly controlled conditions, such as those found in a chemostat. Here we report a set of quantitative physiological data generated during chemostat cultivations with the K. lactis CBS 2359 strain, obtained under glucose-limiting and fully aerobic conditions. This dataset serves [corrected] as a basis for the comparison of K. lactis with the model yeast Saccharomyces cerevisiae in terms of their elemental compositions, as well as for future metabolic flux analysis and metabolic modelling studies with K. lactis.

An Overview of the Evolution of Infrared Spectroscopy Applied to Bacterial Typing.

The sustained emergence of new declared bacterial species makes typing a continuous challenge for microbiologists. Molecular biology techniques have a very significant role in the context of bacterial typing, but they are often very laborious, time consuming, and eventually fail when dealing with very closely related species. Spectroscopic-based techniques appear in some situations as a viable alternative to molecular methods with advantages in terms of analysis time and cost. Infrared and mass spectrometry are among the most exploited techniques in this context: particularly, infrared spectroscopy emerged as a very promising method with multiple reported successful applications. This article presents a systematic review on infrared spectroscopy applications for bacterial typing, highlighting fundamental aspects of infrared spectroscopy, a detailed literature review (covering different taxonomic levels and bacterial species), advantages, and limitations of the technique over molecular biology methods and a comparison with other competing spectroscopic techniques such as MALDI-TOF MS, Raman, and intrinsic fluorescence. Infrared spectroscopy possesses a high potential for bacterial typing at distinct taxonomic levels and worthy of further developments and systematization. The development of databases appears fundamental toward the establishment of infrared spectroscopy as a viable method for bacterial typing.

Quantitative image analysis of polyhydroxyalkanoates inclusions from microbial mixed cultures under different SBR operation strategies.

Polyhydroxyalkanoates (PHAs) produced from mixed microbial cultures (MMC), regarded as potential substitutes of petrochemical plastics, can be found as intracellular granules in various microorganisms under limited nutrient conditions and excess of carbon source. PHA is traditionally quantified by laborious and time-consuming chromatography analysis, and a simpler and faster method to assess PHA contents from MMC, such as quantitative image analysis (QIA), is of great interest. The main purpose of the present work was to upgrade a previously developed QIA methodology (as reported by Mesquita et al. (Anal Chim Acta 770:36-44, 2013a, Anal Chim Acta 865:8-15, 2015)) for MMC intracellular PHA contents quantification, increase the studied intracellular PHA concentration range, and extend to different sequencing batch reactor (SBR) operation strategies. Therefore, the operation of a new aerobic dynamic feeding (ADF) SBR allowed further extending the studied operating conditions, dataset, and range of the MMC intracellular PHA contents from the previously reported anaerobic/aerobic cycle SBR. Nile Blue A (NBA) staining was employed for epifluorescence microscope visualization and image acquisition, further fed to a custom developed QIA. Data from each of the feast and famine cycles of both SBR were individually processed using chemometrics analysis, obtaining the correspondent partial least squares (PLS) models. The PHA concentrations determined from PLS models were further plotted against the results obtained in the standard chromatographic method. For both SBR, the predicted ability was higher at the end of the feast stage than for the famine stage. Indeed, an independent feast and famine QIA data treatment was found to be fundamental to obtain the best prediction abilities. Furthermore, a promising overall correlation (R 2 of 0.83) could be found combining the overall QIA data regarding the PHA prediction up to a concentration of 1785.1 mg L-1 (37.3 wt%). Thus, the results confirm that the presented QIA methodology can be seen as promising for estimating higher intracellular PHA concentrations for a larger reactors operation systems and further extending the prediction range of previous studies.

Simultaneous partial nitrification and 2-fluorophenol biodegradation with aerobic granular biomass: Reactor performance and microbial communities.

An aerobic granular bioreactor was operated for over 4months, treating a synthetic wastewater with a high ammonium content (100mgNL-1). The inoculum was collected from a bioreactor performing simultaneous partial nitrification and aromatic compounds biodegradation. From day-56 onwards, 2-fluorophenol (2-FP) (12.4mgL-1) was added to the feeding wastewater and the system was bioaugmented with a 2-FP degrading bacteria (Rhodococcus sp. FP1). By the end of operation, complete 2-FP biodegradation and partial nitrification were simultaneously achieved. Aerobic granules remained stable over time. During the 2-FP loading, a shift in the community structure occurred, coinciding with the improvement of 2-FP degradation. DGGE analysis did not allow to infer on the bioaugmented strain presence but pyrosequencing analysis detected Rhodococcus genus by the end of operation. Together with other potential phenolic-degraders within granules, these microorganisms were probably responsible for 2-FP degradation.

Genome-Wide Semi-Automated Annotation of Transporter Systems.

Usually, transport reactions are added to genome-scale metabolic models (GSMMs) based on experimental data and literature. This approach does not allow associating specific genes with transport reactions, which impairs the ability of the model to predict effects of gene deletions. Novel methods for systematic genome-wide transporter functional annotation and their integration into GSMMs are therefore necessary. In this work, an automatic system to detect and classify all potential membrane transport proteins for a given genome and integrate the related reactions into GSMMs is proposed, based on the identification and classification of genes that encode transmembrane proteins. The Transport Reactions Annotation and Generation (TRIAGE) tool identifies the metabolites transported by each transmembrane protein and its transporter family. The localization of the carriers is also predicted and, consequently, their action is confined to a given membrane. The integration of the data provided by TRIAGE with highly curated models allowed the identification of new transport reactions. TRIAGE is included in the new release of merlin, a software tool previously developed by the authors, which expedites the GSMM reconstruction processes.

A Comparative Proteome Analysis of Escherichia coli ΔrelA Mutant Cells.

The bacterial RelA-dependent stringent response exerts a strong influence over various processes. In this work, the impact of the relA gene mutation in Escherichia coli cells was evaluated by a quantitative proteomics analysis, employing stable-isotope labeling and high-resolution mass spectrometry. Chemostat cultures of E. coli W3110 and ΔrelA mutant strains were performed at two dilution rates (0.1 and 0.2 h-1) to assess the influence of the relA gene mutation in steady-state protein levels. A total of 121 proteins showed significant alterations in their abundance when comparing the proteome of mutant to wild-type cells. The relA gene mutation induced changes on key cellular processes, including the amino acids and nucleotide biosynthesis, the lipid metabolism, transport activities, transcription and translation processes, and responses to stress. Furthermore, some of those changes were more pronounced under specific growth conditions, as the most significant differences in protein ratios were observed at one of the dilution rates. An effect of the relA gene mutation in the acetate overflow was also observed, which confers interesting characteristics to this mutant strain that could be useful in the production of recombinant proteins. Overall, these results provide a valuable insight into the E. coli stringent response under defined steady-state conditions, suggesting that this stress response might influence multiple metabolic processes like the acetate overflow or the catabolite repression.

Economic analysis and environmental impact assessment of three different fermentation processes for fructooligosaccharides production.

Three different fermentation processes for the production of fructooligosaccharides (FOS) were evaluated and compared in terms of economic aspects and environmental impact. The processes included: submerged fermentation of sucrose solution by Aspergillus japonicus using free cells or using the cells immobilized in corn cobs, and solid-state fermentation (SSF) using coffee silverskin as support material and nutrient source. The scale-up was designed using data obtained at laboratory scale and considering an annual productivity goal of 200 t. SSF was the most attractive process in both economic and environmental aspects since it is able to generate FOS with higher annual productivity (232.6 t) and purity (98.6%) than the other processes; reaches the highest annual profit (6.55 M€); presents the lowest payback time (2.27 years); and is more favourable environmentally causing a lower carbon footprint (0.728 kg/kg, expressed in mass of CO2 equivalent per mass of FOS) and the lowest wastewater generation.