Search

The wastewater industry and the energy system are undergoing significant transformations to address climate change and environmental pollution. Green hydrogen, which will be mainly obtained from renewable electricity water electrolysis (Power-to-Hydrogen, PtH), has been considered as an essential energy carrier to neutralize the fluctuations of renewable energy sources. PtH, or Power-to-X (PtX), has been allocated to multiple sectors, including industry, transport and power generation. However, considering its large potential for implementation in the wastewater sector, represented by Water Resource Recovery Facilities (WRRFs), the PtX concept has been largely overlooked in terms of planning and policymaking. This paper proposes a concept to implement PtX at WRRFs, where sourcing of water, utilization of the oxygen by-product, and PtX itself can be sustainable and diversified strategies. Potential value chains of PtX are presented and illustrated in the frame of a WWRF benchmark simulation model, highlighting the applications of oxygen from PtX through pure oxygen aeration and ozone disinfection. Opportunities and challenges are highlighted briefly, and so is the prospective outlook to the future. Ultimately, it is concluded that 'coupling PtX to WRRFs' is a promising solution, which will potentially bring sustainable opportunities for both WRRFs and the energy system. Apart from regulatory and economic challenges, the limitations in coupling PtX to WRRFs mainly come from energy efficiency concerns and the complexity of the integration of the water framework and the energy system., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Qipeng Liu reports financial support was provided by European Union., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Raman spectroscopy has been used to measure the concentration of a pharmaceutically relevant model amine intermediate for positive allosteric modulators of nicotinic acetylcholine receptor in a ω-transaminase-catalyzed conversion. A model based on a one-dimensional convolutional neural network was developed to translate raw data augmented Raman spectra directly into substrate concentrations, with which the conversion from ketone to amine by ω-transaminase could be determined over time. The model showed very good predictive capabilities, with R 2 values higher than 0.99 for the spectra included in the modeling and 0.964 for an independent dataset. However, the model could not extrapolate outside the concentrations specified by the model. The presented work shows the potential of Raman spectroscopy as a real-time monitoring tool for biocatalytic reactions., (© 2024 The Authors. Biotechnology Progress published by Wiley Periodicals LLC on behalf of American Institute of Chemical Engineers.)

The upcoming change of legislation in some European countries where wastewater treatment facilities will start to be taxed based on direct greenhouse gas (GHG) emissions will force water utilities to take a closer look at nitrous oxide (N 2 O) production. In this study, we report for the first time N 2 O emissions from two full-scale size membrane aerated biofilm reactors (MABR) (R1, R2) from two different manufacturers treating municipal wastewater. N 2 O was monitored continuously for 12 months in both the MABR exhaust gas and liquid phase. Multivariate analysis was used to assess process performance. Results show that emission factors (EF N2O ) for both R1 and R2 (0.88 ± 1.28 and 0.82 ± 0.86 %) were very similar to each other and below the standard value from the Intergovernmental Panel on Climate Change (IPCC) 2019 (1.6 %). More specifically, N 2 O was predominantly emitted in the MABR exhaust gas (NTR exh ) and was strongly correlated to the ammonia/um load (NH x,load ). Nevertheless, the implemented Oxidation Reduction Potential (ORP) control strategy increased the bulk contribution (NTR bulk ), impacting the overall EF N2O . A thorough analysis of dynamic data reveals that the changes in the external aeration (EA)/loading rate patterns suggested by ORP control substantially impacted N 2 O mass transfer and biological production processes. It also suggests that NTR exh is mainly caused by ammonia-oxidizing organisms (AOO) activity, while ordinary heterotrophic organisms (OHO) are responsible for NTR bulk . Different methods for calculating EF N2O were compared, and results showed EF N2O would range from 0.6 to 5.5 depending on the assumptions made. Based on existing literature, a strong correlation between EF N2O and nitrogen loading rate (R 2  = 0.73) was found for different technologies. Overall, an average EF N2O of 0.86 % N 2 O-N per N load was found with a nitrogen loading rate >200 g N m -3 d -1 , which supports the hypothesis that MABR technology can achieve intensified biological nutrient removal without increasing N 2 O emissions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Motivated by the limited attention given to water management in industrial symbiosis research, this study presents the first global review of water innovation practices in the implemented industrial symbiosis cases reported in literature. We analyze the prevalence of global water innovation practices extending beyond the commonly used broad practices of water treatment and reuse to propose six categories, including utility sharing for alternative water supply, utility sharing for wastewater treatment, water recovery, energy recovery from water, material recovery from water, and material exchange to enhance water/wastewater treatment. Our findings highlight regional variations in adoption, with Asian and Europe showcasing diverse practices. Additionally, they indicate that most symbiosis cases center on the extensive role of public utilities and shared water facilities in pursuing water innovation, while 'pure' interfirm water-related symbiosis is limited. Finally, this review highlights extensive knowledge gaps and research needs in advancing sustainable water management and innovation in industrial symbiosis. Overall, our study contributes to the development of a comprehensive framework for water innovation practices in industrial symbiosis and emphasizes the need for future research in this area., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Cleaning Validation is a crucial process in pharmaceutical manufacturing, as it verifies that cross-contamination levels are below acceptable limits. The use of Mid-Infrared (IR) spectroscopy has long been proposed as a rapid method enabling real-time release, but only with the recent emergence of a handheld Fourier-Transform IR (FTIR) does a feasible solution exist for practical implementation in pharmaceutical manufacturing plants. This paper address the model development challenges for multi-product plants without complete traceability of production equipment and produced product. This is done by developing a partial least squares discriminant analysis model determining if the sample is clean or not clean compared to a residual acceptance limit, based on total organic carbon (TOC) measurements. The model is built and tested on artificial samples printed with a chemical printer for multiple products with different spectral peaks based on 91 samples in the calibration set and tested on 30 samples in the validation set. Furthermore, the model also incorporates spectra from surfaces with different surface roughness. The evaluation of the model is based on sensitivity, specificity and class error. The model outperforms or performs equally well as TOC swab with sampling error, dependent on the sampling error distribution for the TOC swab.

Membrane Aerated Biofilm Reactors (MABR) are gaining more and more acceptance in the plethora of wastewater process intensification technologies. Mathematical modelling has contributed to show their feasibility in terms of reduced energy consumption and footprint. Nevertheless, most simulation studies published until now are still focused on analyzing MABR as single units and not fully integrated within the flow diagram of the water treatment plant (WWTP). In this paper, the prediction capabilities of an integrated modelling approach is tested using full-scale data from Ejby Mølle WWTP+MABR site (Odense, Denmark). Mass balances, data reconciliation methods, process simulation and the different evaluation criteria were used to adjust influent, effluent and process indicators. Results show 10 % mismatch between flow, COD, N and P predictions and measurements in different plant locations. Using the adopted hydraulic retention time (HRT), nitrogen load (NL), membrane surface area (MA) and oxygen transfer rate (OTR), it was possible to predict nitrification rates (NR) within the interquartile range. This has been done under two different MABR operational conditions: with (#S2) and without (#S1) external aeration (EA) in the bulk liquid. The model provides additional process insights about biofilm structure, substrate gradients, weak acid base chemistry and precipitation potential. More specifically, simulations suggest the potential undesirable effects of sulfate (SRB) and iron reducing bacteria (IRB) on both microbial activity and composition of the biofilm. The latter may have a strong impact on ammonium (NH x ), sulfate (SO x ) and ferrous ion (Fe +2 ) conversion processes. The change of operational strategy in the scenario analysis highlights that the denitrifying activity of phosphorus accumulating organisms (PAOs) can enhance nutrient removal in MABR tanks. In addition, it was possible to assess the chance of success (in terms of energetic cost of nitrogen removal) of adding several MABR units in one tank of the WWTP under study before full-scale implementation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

Industrial biotechnology encompasses a large area of multi-scale and multi-disciplinary research activities. With the recent megatrend of digitalization sweeping across all industries, there is an increased focus in the biotechnology industry on developing, integrating and applying digital models to improve all aspects of industrial biotechnology. Given the rapid development of this field, we systematically classify the state-of-art modelling concepts applied at different scales in industrial biotechnology and critically discuss their current usage, advantages and limitations. Further, we critically analyzed current strategies to couple cell models with computational fluid dynamics to study the performance of industrial microorganisms in large-scale bioprocesses, which is of crucial importance for the bio-based production industries. One of the most challenging aspects in this context is gathering intracellular data under industrially relevant conditions. Towards comprehensive models, we discuss how different scale-down concepts combined with appropriate analytical tools can capture intracellular states of single cells. We finally illustrated how the efforts could be used to develop digitals models suitable for both cell factory design and process optimization at industrial scales in the future., Competing Interests: Declaration of Competing Interest For the review paper titled “Digital models in biotechnology: Towards multi-scale integration and implementation” to be considered for publication in Biotechnology Advances, the authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The paper is original work, and has been conducted with support of a grant from the Novo Nordisk Foundation (Fermentation Based Biomanufacturing Initiative). However, the paper only reflects the opinion of the authors, and not of the Novo Nordisk Foundation. The Foundation has not been involved in developing and writing the paper. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022. Published by Elsevier Inc.)

Mathematical modeling is a powerful and inexpensive approach to provide a quantitative basis for improvements that minimize the negative effects of bioreactor heterogeneity. For a model to accurately represent a heterogeneous system, a flow model that describes how mass is channeled between different zones of the bioreactor volume is necessary. In this study, a previously developed compartment model approach based on data from flow-following sensor devices was further developed to account for dynamic changes in volume and flow rates and thus enabling simulation of the widely used fed-batch process. The application of the dynamic compartment model was demonstrated in a study of an industrial fermentation process in a 600 m3 bubble column bioreactor. The flow model was used to evaluate the mixing performance by means of tracer simulations and was coupled with reaction kinetics to simulate concentration gradients in the process. The simulations showed that despite the presence of long mixing times and significant substrate gradients early in the process, improving the heterogeneity did not lead to overall improvements in the process. Improvements could, however, be achieved by modifying the dextrose feeding profile., (© The Author(s) 2022. Published by Oxford University Press on behalf of Society of Industrial Microbiology and Biotechnology.)

The use of mathematical models is a well-established procedure in the field of (waste) water engineering to "virtually" evaluate the feasibility of novel process modifications. In this way, only options with the highest chance of success are further developed to be implemented at full-scale, while less interesting proposals can be disregarded at an early stage. Nevertheless, there is still lack of studies, where different plant-wide model predictions (effluent quality, process economics, and technical aspects) are comprehensibly verified in the field with full-scale data. In this work, a set of analysis/evaluation tools are used to assess alternative retrofitting options in the largest industrial wastewater treatment plant in Northern Europe. A mechanistic mathematical model is simulated to reproduce process behavior (deviation < 11%). Multiple criteria are defined and verified with plant data (deviation < 5%). The feasibility of three types of scenarios is tested: (1) stream refluxing, (2) change of operational conditions and (3) the implementation of new technologies. Experimental measurements and computer simulations show that the current plant´s main revenues are obtained from the electricity produced by the biogas engine (54%) and sales of the inactivated bio-solids for off-site biogas production (33%). The main expenditures are the discharge fee (39%), and transportation and handling of bio-solids (30%). Selective treatment of bio-solid streams strongly modifies the fate of COD and N compounds within the plant. In addition, it increases revenues (+3%), reduces cost (-9%) and liberates capacity in both activated sludge (+25%) and inactivation reactors (+50%). Better management of the buffer tank promotes heterotrophic denitrification instead of dissimilatory nitrate conversion to ammonia. In this way, 11% of the incoming nitrogen is removed within the anaerobic water line and does not overload the activated sludge reactors. Only a marginal increase in process performance is achieved when the anaerobic granular sludge reactor operates at full capacity. The latter reveals that influent biodegradability is the main limiting factor rather than volume. Usage of either NaOH or heat (instead of CaO) as inactivation agents allows anaerobic treatment of the reject water, which substantially benefits revenues derived from higher electricity recovery (+44%). However, there is a high toll paid on chemicals (+73%) or heat recovery (-19%) depending on the inactivation technology. In addition, partial nitration/Anammox and a better poly-aluminum chloride (PAC) dosage strategy is necessary to achieve acceptable (< 2%) N and P levels in the effluent. The scenarios are evaluated from a sustainability angle by using life cycle impact assessment (LCIA) in form of damage stressors grouped into three categories: human health, ecosystems quality, and resource scarcity. The presented decision support tool has been used by the biotech company involved in the study to support decision-making on how to handle future expansions., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Cleaning verification is a critical process for patient safety in pharmaceutical manufacturing in order to keep cross-contamination below acceptable limits. A common cleaning verification method is the total organic carbon (TOC) swab. Others have studied the variances of different factors on the TOC swab in order to establish the best swab method. This paper attempts to quantify the sampling error of the TOC swab in the actual sampling situation using simulation. The study investigates the variability on the drug product recovery due to different analysts, concentration, steel finish and position, as well as the estimation of the given swab area. The results demonstrate that the sampling error leads to a large variation in TOC results. For areas estimated in the laboratory, it leads to an increase in limit of detection, LOD, with 60%, while for areas estimated in a tank, the LOD cannot be determined due to the large heteroscedasticity. Thus, this paper is also to be considered as an invitation to discuss and further investigate the TOC sampling error in the pharmaceutical industry., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

This study presents an extensive plant-wide model-based assessment of four alternative activated sludge (AS) configurations for biological nitrogen (N) and phosphorus (P) removal under uncertain influent loads and characteristics. Zeekoegat wastewater treatment plant (WWTP) in South Africa was chosen as case study due to its flexible design that enables operation in four different AS configurations: 3-stage Bardenpho (A2O), University of Cape Town (UCT), UCT modified (UCTM), and Johannesburg (JHB). A metamodeling based global sensitivity analysis was performed on a steady-state plant-wide simulation model using Activated Sludge Model No. 2d with the latest extension of physico-chemical processes describing the plant-wide P transformations. The simulation results showed that the predictions of effluent chemical oxygen demand (COD), N and P using the proposed approach fall within the interquartile range of measured data. The study also revealed that process configuration can affect: 1) how influent uncertainty is reflected in model predictions for effluent quality and cost related performances, and 2) the parameter rankings based on variance decomposition, particularly for effluent phosphate, sludge disposal and methane production. The results identified UCT and UCTM as more robust configurations for P removal (less propagated uncertainty and less sensitivity to N load) in the expense of incomplete denitrification. Moreover, based on the results of Monte-Carlo based scenario analysis, the balanced SRT for N and P removal is more sensitive to influent load variation/uncertainty for the A2O and JHB configurations. This gives a more operational flexibility to UCT and UCTM, where a narrow SRT range can ensure both N and P removal., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

The ever-increasing demand for biopharmaceuticals has created the need for improving the overall productivity of culture processes. One such operational concept that is considered is fed-batch operations as opposed to batch operations. However, optimal fed-batch operations require complete knowledge of the cell culture to optimize the culture conditions and the nutrients feeding. For example, when using high-throughput small-scale bioreactors to test multiple clones that do not behave the same, depletion or overfeeding of some key components can occur if the feeding strategy is not individually optimized. Over the recent years, various solutions for real-time measuring of the main cell culture metabolites have been proposed. Still, the complexity in the implementation of these techniques has limited their use. Soft-sensors present an opportunity to overcome these limitations by indirectly estimating these variables in real-time. This manuscript details the development of a new soft-sensor-based fed-batch strategy to maintain substrate concentration (glucose and glutamine) at optimal levels in small-scale multiparallel Chinese Hamster Ovary Cells cultures. Two alternatives to the standard feeding strategy were tested: an OUR soft-sensor-based strategy for glucose and glutamine (Strategy 1) and a dual OUR for glutamine and CO 2 /alkali addition for glucose soft-sensor strategy (Strategy 2). The results demonstrated the applicability of the OUR soft-sensor-based strategy to optimize glucose and glutamine feedings, which yielded a 21% increase in final viable cell density (VCD) and a 31% in erythropoietin titer compared with the reference one. However, CO 2 /alkali addition soft-sensor suffered from insufficient data to relate alkali addition with glucose consumption. As a result, the culture was overfed with glucose resulting in a 4% increase on final VCD, but a 9% decrease in final titer compared with the Reference Strategy., (© 2022 Wiley Periodicals LLC.)

The non-spore forming Gram-positive actinomycetes Amycolatopsis keratiniphila subsp. keratiniphila D2 T (DSM 44,409) has a high potential for keratin valorization as demonstrated by a novel biotechnological microbial conversion process consisting of a bacterial growth phase and a keratinolytic phase, respectively. Compared to the most gifted keratinolytic Bacillus species, a very large number of 621 putative proteases are encoded by the genome of Amycolatopsis keratiniphila subsp. keratiniphila D2 T , as predicted by using Peptide Pattern Recognition (PPR) analysis. Proteome analysis by using LC-MS/MS on aliquots of the supernatant of A. keratiniphila subsp. keratiniphila D2 T culture on slaughterhouse pig bristle meal, removed at 24, 48, 96 and 120 h of growth, identified 43 proteases. This was supplemented by proteome analysis of specific fractions after enrichment of the supernatant by anion exchange chromatography leading to identification of 50 proteases. Overall 57 different proteases were identified corresponding to 30% of the 186 proteins identified from the culture supernatant and distributed as 17 metalloproteases from 11 families, including an M36 protease, 38 serine proteases from 4 families, and 13 proteolytic enzymes from other families. Notably, M36 keratinolytic proteases are prominent in fungi, but seem not to have been discovered in bacteria previously. Two S01 family peptidases, named T- and C-like proteases, prominent in the culture supernatant, were purified and shown to possess a high azo-keratin/azo-casein hydrolytic activity ratio. The C-like protease revealed excellent thermostability, giving promise for successful applications in biorefinery processes. Notably, the bacterium seems not to secrete enzymes for cleavage of disulfides in the keratinous substrates. KEY POINTS: • A. keratiniphila subsp. keratiniphila D2 T is predicted to encode 621 proteases. • This actinomycete efficiently converts bristle meal to a protein hydrolysate. • Proteome analysis identified 57 proteases in its secretome., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Sewage sludge management has garnered interest in both academia and industry due to the challenges of overpopulation and its potential as a bioenergy source. Thermal hydrolysis is a promising technology for sludge pre-treatment prior to anaerobic digestion to enhance biogas production. However, the technology is facing two main problems; the dark colour of sludge can affect UV disinfection and the formation of methanogenesis inhibitors such as free ammonia and refractory compounds have a significant impact on methane production in anaerobic digestion processes. Advanced thermal hydrolysis, which is an oxidative thermal hydrolysis process, has been introduced to overcome these challenges. This study provides a comprehensive review of the mechanisms and reactions which occur during the hydrothermal hydrolysis and advanced thermal hydrolysis processes. Technical and implementation challenges of both technologies are discussed. Additionally, the prospects of the technologies are assessed through their technology readiness levels. An assessment of the relevant literature is also provided to illuminate the aspects in which research gaps exist and areas where additional studies could be performed., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Chemical conditioning prior to disposal is a common practice in biotech companies to stabilize the biological waste generated during production. Nevertheless, the state of the art models used to analyze management strategies in water treatment systems (WTS) do not include the effect of high alkaline conditions during bio-solids processing. In this paper, the prediction capabilities of a novel model-based approach describing the effect of quicklime addition (CaO) on the waste streams of an industrial WTS is assessed. Two measuring campaigns were carried out taking samples of TSS, VSS and total/soluble COD, N, P, S and multiple metals before and after chemical stabilization, and dewatering under and overflow. Mass balances were set up and Sankey diagrams were generated to represent the occurrence, transformation and fate of the major compounds within the studied facility. A simulation model was used to predict plant at different locations. Next, a scenario analysis was carried out in order to assess potential alternatives to the current operational practice. The resulting mass balances show a mismatch between the system's input and output up to 17%. It was also possible to identify different types of compound-behavior depending on the effect that high pH induced on the soluble and particulate fractions: hydrolysis, precipitation and unaltered. Model predictions and measurements differed 9.6% (steady state) and 12.4% (dynamic state) respectively. Finally, in the scenario analysis, the model suggested that the change from quicklime to sodium hydroxide (NaOH) would increase the quantity of organics in the dewatered cake (+23%), but with a considerable increase in chemical consumption (+50%). The selective stabilization of the incoming streams has the lowest use of chemicals (-30%) and reduces the load of CODsol (-13%) and TNsol (-14%) recirculated to the water line of the WWTP., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Harnessing the potential of biocatalytic conversion of renewable biomass into value-added products is still hampered by unfavorable process economics. This has promoted the use of biofilms as an alternative to overcome the limitations of traditional planktonic systems. In this paper, the benefits and challenges of biofilm fermentations are reviewed with a focus on the production of low-value bulk chemicals and fuels from waste biomass. Our study demonstrates that biofilm fermentations can potentially improve productivities and product yields by increasing biomass retention and allowing for continuous operation at high dilution rates. Furthermore, we show that biofilms can tolerate hazardous environments, which improve the conversion of crude biomass under substrate and product inhibitory conditions. Additionally, we present examples for the improved conversion of pure and crude substrates into bulk chemicals by mixed microbial biofilms, which can benefit from microenvironments in biofilms for synergistic multi-species reactions, and improved resistance to contaminants. Finally, we suggest the use of mathematical models as useful tools to supplement experimental insights related to the effects of physico-chemical and biological phenomena on the process. Major challenges for biofilm fermentations arise from inconsistent fermentation performance, slow reactor start-up, biofilm carrier costs and carrier clogging, insufficient biofilm monitoring and process control, challenges in reactor sterilization and scale-up, and issues in recovering dilute products. The key to a successful commercialization of the technology is likely going to be an interdisciplinary approach. Crucial research areas might include genetic engineering combined with the development of specialized biofilm reactors, biofilm carrier development, in-situ biofilm monitoring, model-based process control, mixed microbial biofilm technology, development of suitable biofilm reactor scale-up criteria, and in-situ product recovery., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Membrane-aerated biofilm reactor (MABR) technology is an exciting alternative to conventional activated sludge, with promising results in bench and pilot-scale systems. Nevertheless, there is still a lack of long-term and full-scale data under different operational conditions. This study aims to report the performance of a full-scale hybrid MABR located in the North of Europe. Influent, effluent, and exhaust data were collected for 1 year (September 2019 to September 2020) using online sensors/gas-analyzers and off-line laboratory analysis. Next, oxygen transfer rate (OTR), oxygen transfer efficiency (OTE), and nitrification rates (NR) were quantified as process indicators. Finally, multivariate methods were used to find patterns among monitored variables. Observations revealed that lower airflows achieved higher OTE at the same values of OTR and OTR was strongly correlated to ammonia/um concentration in the MABR tank (NH x,eff ). The dynamics between oxygen concentration in the exhaust (O 2,exh ) and NH x,eff indicated that a nitrifying biofilm was established within 3 weeks. Average NR were calculated using four different methods and ranged between 1 and 2 g N m -2 d -1 . Principal component analysis (PCA) explained 81.4% of the sample variance with the first three components and cluster analysis (CA) divided the yearly data into five distinctive periods. Hence, it was possible to identify typical Nordic episodes with high frequency of heavy rain, low temperature, and high variations in pollution load. The study concludes that nitrification capacity obtained with MABR is robust during cold weather conditions, and its volumetric value is comparable to other well-established biofilm-based technologies. Moreover, the aeration efficiency (AE) obtained in this study, 5.8 kg O 2 kW h -1 , would suppose an average reduction in energy consumption of 55% compared to fine pore diffused aeration and 74% to the existing surface aeration at the facility., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021. Published by Elsevier B.V.)

Experimental characterization of solid-liquid mixing for a high shear wet granulation process in a twin-screw granulator (TSG) is very challenging. This is due to the opacity of the multiphase system and high-speed processing. In this study, discrete element method (DEM) based simulations are performed for a short quasi-two-dimensional simulation domain, incorporating models for liquid bridge formation, rupture, and the effect of the bridges on inter-particular forces. Based on the knowledge gained from these simulations, the kneading section of a twin-screw wet granulation process was simulated. The time evolution of particle flow and liquid distribution between particles, leading to the formation of agglomerates, was analyzed. The study showed that agglomeration is a rather delayed process that takes place once the free liquid on the particle surface is well distributed.

Gaining an in-depth understanding of the response of Saccharomyces cerevisiae to the different inhibitors generated during the pretreatment of lignocellulosic material is driving the development of new strains with higher inhibitor tolerances. The objective of this study is to assess, using flow cytometry, how three common inhibitors (vanillin, furfural, and acetic acid) affect the membrane potential, the membrane permeability and the concentration of reactive oxygen species (ROS) during the different fermentations. The membrane potential decreased during the detoxification phase and reflected on the different mechanisms of the toxicity of the inhibitors. While vanillin and furfural caused a metabolic inhibition and a gradual depolarization, acetic acid toxicity was related to fast acidification of the cytosol, causing an immediate depolarization. In the absence of acetic acid, ethanol increased membrane permeability, indicating a possible acquired tolerance to ethanol due to an adaptive response to acetic acid. The intracellular ROS concentration also increased in the presence of the inhibitors, indicating oxidative stress. Measuring these features with flow cytometry allows a real-time assessment of the stress of a cell culture, which can be used in the development of new yeast strains and to design new propagation strategies to pre-adapt the cell cultures to the inhibitors.

The objective of this paper is to use plant-wide modeling to assess the net impacts of varying sludge management strategies. Special emphasis is placed on effluent quality, operational cost and potential resource recovery (energy, nutrients). The study is particularly focused on a centralized bio-solids beneficiation facility (BBF), which enables larger, more capital intensive sludge management strategies. Potential barriers include the ability to process reject streams from multiple donor plants in the host plant. Cape Flats (CF) wastewater treatment works (WWTW) (Cape Town, South Africa) was used as a relevant test case since it is currently assessing to process sludge cake from three nearby facilities (Athlone, Mitchells Plain and Wildevoelvlei). A plant-wide model based on the Benchmark Simulation Model no 2 (BSM2) extended with phosphorus transformations was adapted to the CF design / operational conditions. Flow diagram and model parameters were adjusted to reproduce the influent, effluent and process characteristics. Historical data between January 2014 and December 2019 was used to compare full-scale measurements and predictions. Next, different process intensification / mitigation technologies were evaluated using multiple criteria. Simulation values for COD, TSS, VSS/TSS ratio, TN, TP, NH 4 + /NH 3 , H x PO4 3-x , NO x alkalinity and pH fall within the interquartile ranges of measured data. The effects of the 2017 severe drought on influent variations and biological phosphorus removal are successfully reproduced for the entire period with dynamic simulations. Indeed, 80% of all dynamically simulated values are included within the plant measurement uncertainty ranges. Sludge management analysis reveals that flow diagrams with thermal hydrolysis pre-treatment (THP) result in a better energy balance in spite of having higher heat demands. The flow diagram with THP is able to i) increase biodegradability/solubility, ii) handle higher sludge loads, iii) change methanogenic microbial population and iv) generate lower solids volumes to dispose by improving sludge dewaterability. The study also reveals the importance of including struvite precipitation and harvesting (SPH) technology, and the effect that pH in the AD and the use of chemicals (NaOH, MgO) may have on phosphorus recovery. Model-based results indicate that the current aerobic volume in the water line (if properly aerated) would be able to handle the returns from the sludge line and the contribution of a granular partial nitritation/Anammox (PN/ANX) reactor on the overall nitrogen removal would be marginal. However autotrophic N denitrification generates a much lower sludge production and therefore increases AD treatment capacity. The study shows for the very first time in Africa how the use of a (calibrated) plant-wide model could assist water utilities to decide between competing plant layouts when upgrading a WWTW., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Operating lignocellulosic fermentation processes to produce fuels and chemicals is challenging due to the inherent complexity and variability of the fermentation media. Real-time monitoring is necessary to compensate for these challenges, but the traditional process monitoring methods fail to deliver actionable information that can be used to implement advanced control strategies. In this study, a hybrid-modeling approach is presented to monitor cellulose-to-ethanol (EtOH) fermentations in real-time. The hybrid approach uses a continuous-discrete extended Kalman filter to reconciliate the predictions of a data-driven model and a kinetic model and to estimate the concentration of glucose (Glu), xylose (Xyl), and EtOH. The data-driven model is based on partial least squares (PLS) regression and predicts in real-time the concentration of Glu, Xyl, and EtOH from spectra collected with attenuated total reflectance mid-infrared spectroscopy. The estimations made by the hybrid approach, the data-driven models and the internal model were compared in two validation experiments showing that the hybrid model significantly outperformed the PLS and improved the predictions of the internal model. Furthermore, the hybrid model delivered consistent estimates even when disturbances in the measurements occurred, demonstrating the robustness of the method. The consistency of the proposed hybrid model opens the doors towards the implementation of advanced feedback control schemes., (© 2020 The Authors. Biotechnology and Bioengineering published by Wiley Periodicals LLC.)

Industrial parks have a high potential for recycling and reusing resources such as water across companies by creating symbiosis networks. In this study, we introduce a mathematical optimization framework for the design of water network integration in industrial parks formulated as a large-scale standard mixed-integer non-linear programming (MINLP) problem. The novelty of our approach relies on i) developing a multi-level incremental optimization framework for water network synthesis, ii) including prior knowledge of water demand growth and projected water scarcity to evaluate the significance of water-saving solutions, iii) incorporating a comprehensive formulation of the water network synthesis problem including multiple pollutants and different treatment units and iv) performing a multi-objective optimization of the network including freshwater savings and relative cost of the network. The significance of the proposed optimization framework is illustrated by applying it to an existing industrial park in a water-scarce region in Kenya. Firstly, we illustrated the benefits of including prior knowledge to prevent an over-design of the network at the early stages. In the case study, we achieved a more flexible and expandable water network with 36% lower unit cost at the early stage and 15% lower unit cost at later stages for overall maximum freshwater savings of 25%. Secondly, multi-objective analysis suggests an optimum freshwater savings of 14% to reduce the unit cost of the network by half. Moreover, the significance of symbiosis networks is highlighted by showing that intra-company connections can only achieve a maximum freshwater savings of 17% with significantly higher unit cost (+45%). Finally, we showed that the values of symbiosis connectivity index in the Pareto front correspond to higher freshwater savings, indicating the significant role of the symbiosis network in the industrial park under study. This is the first study, where all the above elements have been taken into account simultaneously for the design of a water reuse network., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Gradients in industrial bioreactors have attracted substantial research attention since exposure to fluctuating environmental conditions has been shown to lead to changes in the metabolome, transcriptome as well as population heterogeneity in industrially relevant microorganisms. Such changes have also been found to impact key process parameters like the yield on substrate and the productivity. Hence, understanding gradients is important from both the academic and industrial perspectives. In this review the causes of gradients are outlined, along with their impact on microbial physiology. Quantifying the impact of gradients requires a detailed understanding of both fluid flow inside industrial equipment and microbial physiology. This review critically examines approaches used to investigate gradients including large-scale experimental work, computational methods and scale-down approaches. Avenues for future work have been highlighted, particularly the need for further coordinated development of both in silico and experimental tools which can be used to further the current understanding of gradients in industrial equipment., (Copyright © 2020 Elsevier Inc. All rights reserved.)

The bio-manufacturing industry, along with other process industries, now has the opportunity to be engaged in the latest industrial revolution, also known as Industry 4.0. To successfully accomplish this, a physical-to-digital-to-physical information loop should be carefully developed. One way to achieve this is, for example, through the implementation of digital twins (DTs), which are virtual copies of the processes. Therefore, in this paper, the focus is on understanding the needs and challenges faced by the bio-manufacturing industry when dealing with this digitalized paradigm. To do so, two major building blocks of a DT, data and models, are highlighted and discussed. Hence, firstly, data and their characteristics and collection strategies are examined as well as new methods and tools for data processing. Secondly, modelling approaches and their potential of being used in DTs are reviewed. Finally, we share our vision with regard to the use of DTs in the bio-manufacturing industry aiming at bringing the DT a step closer to its full potential and realization.

A novel approach for electrochemical tuning of alcohol oxidase (AOx) and alcohol dehydrogenase (ADH) biocatalysis towards butanol-1 oxidation by incorporating enzymes in various designs of amperometric biosensors is presented. The biosensors were developed by using commercial graphene oxide-based screen-printed electrodes and varying enzyme producing strains, encapsulation approaches (layer-by-layer (LbL) or one-step electrodeposition (EcD)), layers composition and structure, operating conditions (applied potential values) and introducing mediators (Meldola Blue and Prussian Blue) or Pd-nanoparticles (Pd-NPs). Simultaneous analysis/screening of multiple crucial system parameters during the enzyme engineering process allowed to identify within a period of one month that four out of twelve proposed designs demonstrated a good signal reproducibility and linear response (up to 14.6 mM of butanol) under very low applied potentials (from -0.02 to -0.32 V). Their mechanical stability was thoroughly investigated by multi-analytical techniques prior to butanol determination in cell-free samples from an anaerobic butanol fermentation. The EcD-based biosensor that incorporates ADH, NAD + , Pd-NPs and Nafion showed no loss of enzyme activity after preparation and demonstrated capabilities towards low potential (-0.12 V) detection of butanol-1 in fermentation medium (4 mM) containing multiple electroactive species with almost 15 times enhanced sensitivity (0.2282 μA/mM ± 0.05) when compared to the LbL design. Furthermore, the ADH-Nafion bonding for the S. cerevisiae strain was confirmed to be 3 times higher than for E. coli., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Background: The diauxic growth of Saccharomyces cerevisiae on glucose and xylose during cellulose-to-ethanol processes extends the duration of the fermentation and reduces productivity. Despite the remarkable advances in strain engineering, the co-consumption of glucose and xylose is still limited due to catabolite repression. This work addresses this challenge by developing a closed-loop controller that is capable of maintaining the glucose concentration at a steady set-point during fed-batch fermentation. The suggested controller uses a data-driven model to measure the concentration of glucose from 'real-time' spectroscopic data. The concentration of glucose is then automatically controlled using a control scheme that consists of a proportional, integral, differential (PID) algorithm and a supervisory layer that manipulates the feed-rates to the reactor accounting for the changing dynamics of fermentation., Results: The PID parameters and the supervisory layer were progressively improved throughout four fed-batch lignocellulosic-to-ethanol fermentations to attain a robust controller able of maintaining the glucose concentration at the pre-defined set-points. The results showed an increased co-consumption of glucose and xylose that resulted in volumetric productivities that are 20-33% higher than the reference batch processes. It was also observed that fermentations operated at a glucose concentration of 10 g/L were faster than those operated at 4 g/L, indicating that there is an optimal glucose concentration that maximises the overall productivity., Conclusions: Promoting the simultaneous consumption of glucose and xylose in S. cerevisiae is critical to increase the productivity of lignocellulosic ethanol processes, but also challenging due to the strong catabolite repression of glucose on the uptake of xylose. Operating the fermentation at low concentrations of glucose allows reducing the effects of the catabolite repression to promote the co-consumption of the two carbon sources. However, S. cerevisiae is very sensitive to changes in the glucose concentration and deviations from a set-point result in notable productivity losses. The controller structure developed and implemented in this work illustrates how combining data-driven measurements of the glucose concentration and a robust yet effective PID-based supervisory control allowed tight control of the concentration of glucose to adjust it to the metabolic requirements of the cell culture that can unlock tangible gains in productivities.

The biomanufacturing industry has now the opportunity to upgrade its production processes to be in harmony with the latest industrial revolution. Technology creates capabilities that enable smart manufacturing while still complying with unfolding regulations. However, many biomanufacturing companies, especially in the biopharma sector, still have a long way to go to fully benefit from smart manufacturing as they first need to transition their current operations to an information-driven future. One of the most significant obstacles towards the implementation of smart biomanufacturing is the collection of large sets of relevant data. Therefore, in this work, we both summarize the advances that have been made to date with regards to the monitoring and control of bioprocesses, and highlight some of the key technologies that have the potential to contribute to gathering big data. Empowering the current biomanufacturing industry to transition to Industry 4.0 operations allows for improved productivity through information-driven automation, not only by developing infrastructure, but also by introducing more advanced monitoring and control strategies.

Production-scale fermentation processes in industrial biotechnology experience gradients in process variables, such as dissolved gases, pH and substrate concentrations, which can potentially affect the production organism and therefore the yield and profitability of the processes. However, the extent of the heterogeneity is unclear, as it is currently a challenge at large scale to obtain representative measurements from different zones of the reactor volume. Computational fluid dynamics (CFD) models have proven to be a valuable tool for better understanding the environment inside bioreactors. Without detailed measurements to support the CFD predictions, the validity of CFD models is debatable. A promising technology to obtain such measurements from different zones in the bioreactors are flow-following sensor devices, whose development has recently benefitted from advancements in microelectronics and sensor technology. This paper presents the state of the art within flow-following sensor device technology and addresses how the technology can be used in large-scale bioreactors to improve the understanding of the process itself and to test the validity of detailed computational models of the bioreactors in the future., Competing Interests: Tue Rasmussen, Tannaz Tajsoleiman (industrial Postdoc), and Jonas Bisgaard (industrial PhD student) are full-time employees at Freesense, a company that has a commercial interest in sensor devices. Monica Muldbak, Jakob K. Huusom, and Krist V. Gernaey are employed at the Technical University of Denmark, with no commercial interests in the sensor devices. Sjef Cornelissen is employed at Novozymes and has an interest in the sensor devices as an end user of the technology., (© 2020 The Author(s).)

Fermentation processes are still compromised by a lack of monitoring strategies providing integrated process data online, ensuring process understanding, control, and thus, optimal reactor efficiency. The crucial demand for online monitoring strategies, not only encouraged by the PAT initiative but also motivated by modern paradigms such as circular economy and sustainability, has driven research and industry to provide "next-generation process technology": in other words, technology tailored toward industrial needs. Mid-infrared (MIR) spectroscopy as such is superior to near-infrared (NIR) spectroscopy since it provides significantly enhanced selectivity. However, due to high costs and a lack of instrumental robustness, MIR spectroscopy is outcompeted by NIR when it comes to industrial application. The lack of chemometric expertise, model understanding, and practical guidance might add to the slow acceptance of industrial MIR application. This work demonstrates the use of novel MIR, so-called non-linear infrared (NLIR) technology and the importance of model understanding, exemplarily investigated on a lab-scale yeast fermentation process. The six analytes glucose, ethanol, glycerol, acetate, ammonium, and phosphate were modeled by partial least squares (PLS) based on spectral data, demonstrating the potential of the novel technology facilitating online data acquisition and the necessity of investigating indirect predictions. KEY POINTS: • NLIR spectra were acquired online during a yeast fermentation process • PLS models were constructed for six components based on uncorrelated samples • Glucose, ethanol, ammonium, and phosphates were modeled with errors of less than 15% • Acetate and glycerol were shown to rely on indirect predictions.

Design and development of scale-down approaches, such as microbioreactor (μBR) technologies with integrated sensors, are an adequate solution for rapid, high-throughput and cost-effective screening of valuable reactions and/or production strains, with considerably reduced use of reagents and generation of waste. A significant challenge in the successful and widespread application of μBRs in biotechnology remains the lack of appropriate software and automated data interpretation of μBR experiments. Here, it is demonstrated how mathematical models can be usedas helpful tools, not only to exploit the capabilities of microfluidic platforms, but also to reveal the critical experimental conditions when monitoring cascade enzymatic reactions. A simplified mechanistic model was developed to describe the enzymatic reaction of glucose oxidase and glucose in the presence of catalase inside a commercial microfluidic platform with integrated oxygen sensor spots. The proposed model allowed an easy and rapid identification of the reaction mechanism, kinetics and limiting factors. The effect of fluid flow and enzyme adsorption inside the microfluidic chip on the optical sensor response and overall monitoring capabilities of the presented platform was evaluated via computational fluid dynamics (CFD) simulations. Remarkably, the model predictions were independently confirmed for μL- and mL- scale experiments. It is expected that the mechanistic models will significantly contribute to the further promotion of μBRs in biocatalysis research and that the overall study will create a framework for screening and evaluation of critical system parameters, including sensor response, operating conditions, experimental and microbioreactor designs., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Monitoring and control of fermentation processes remain a crucial challenge for both laboratory and industrial-scale experiments. Reliable identification and quantification of the key process parameters in on-line mode allow operation of the fermentation at optimal reactor efficiency, maximizing productivity while minimizing waste. However, state-of-the-art fermentation on-line monitoring is still limited to a number of standard measurements such as pH, temperature and dissolved oxygen, as well as off-gas analysis as an advanced possibility. Despite the availability of commercial biosensor-based platforms that have been established for continuous monitoring of glucose and various biological variables within healthcare, on-line glucose quantification in fermentation processes has not been implemented yet to a large degree. For the first time, this work presents a complete study of a commercial flow-through-cell with integrated electrochemical glucose biosensors (1 st generation) applied in different media, and importantly, at- and on-line during a yeast fed-batch fermentation process. Remarkably, the glucose biosensor-based platform combined with the developed methodology was able to detect glucose concentrations up to 150 mM in the complex fermentation broth, on both cell-free and cell-containing samples, when not compromised by oxygen limitations. This is four to six-fold higher than previously described in the literature presenting the application of biosensors predominately toward cell-free fermentation samples. The automated biosensor platform allowed reliable glucose quantification in a significantly less resource and time (<5 min) consuming manner compared to conventional HPLC analysis with a refractive index (RI) detector performed as reference measurement. Moreover, the presented biosensor platform demonstrated outstanding mechanical stability in direct contact with the fermentation medium and accurate glucose quantification in the presence of various electroactive species. Coupled with the developed methodology it can be readily considered as a simple, robust, accurate and inexpensive tool for real-time glucose monitoring in fermentation processes., (Copyright © 2020 Pontius, Semenova, Silina, Gernaey and Junicke.)

Novel wastewater treatment plants (WWTPs) are expected to be less energetically demanding than conventional ones. However, scarce information is available about the fate of organic micropollutants (OMPs) in these novel configurations. Therefore, the objective of this work is to assess the fate of OMPs in three novel WWTP configurations by using a plant-wide simulation that integrates multiple units. The difference among the three configurations is the organic carbon preconcentration technology: chemically enhanced primary treatment (CEPT), high-rate activated sludge (HRAS) combined or not with a rotating belt filter (RBF); followed by a partial-nitritation (PN-AMX) unit. The simulation results show that the three selected novel configurations lead mainly to comparable OMPs removal efficiencies from wastewater, which were similar or lower, depending on the OMP, than those obtained in conventional WWTPs. However, the presence of hydrophobic OMPs in the digested sludge noticeably differs among the three configurations. Whereas the configuration based on sole HRAS to recover organic carbon leads to a lower presence of OMPs in digested sludge than the conventional WWTP, in the other two novel configurations this presence is noticeable higher. In conclusion, novel WWTP configurations do not improve the OMPs elimination from wastewater achieved in conventional ones, but the HRAS-based WWTP configuration leads to the lowest presence in digested sludge so it becomes the most efficient alternative., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Two proteases, named C- and T-like proteases, respectively, were purified from the culture supernatant of Amycolatopsis keratinophila subsp. keratinophila D2 T grown on a keratinous slaughterhouse by-product of pig bristles and nails as sole nitrogen and carbon source. The two proteases belong to peptidase family S1 as identified by mass spectrometric peptide mapping, have low mutual sequence identity (25.8%) and differ in substrate specificity. T-like protease showed maximum activity at 40 °C and pH 8-9, and C-like protease at 60 °C and pH 8-10. Peptides released from the keratinous by-product were identified by mass spectrometry and indicated P1 specificity for arginine and lysine of T-like and alanine, valine and isoleucine of C-like protease as also supported by the activity of the two proteases towards synthetic peptide and amino acid substrates. The specific activities of the C- and T-like proteases and proteinase K on keratin azure and azokeratin were comparable. However, C- and T-like proteases showed 5-10-fold higher keratin/casein (K/C) activity ratios than that of another S1 and two keratin-degrading S8 peptidases used for comparison. The findings support that the range of peptidase families considered to contain keratinases should be expanded to include S1 peptidases. Furthermore, the results indicated the quite thermostable C-like protease to be a promising candidate for use in industrial degradation of keratinous slaughterhouse by-products.

The manufacturing of conventional enzymatic biosensors produced via a layer-by-layer (LbL) approach requires expensive instrumentation, and in most cases involves a complex, resource and time-consuming fabrication process. Moreover, LbL assemblies are prone to mechanical instability that leads to irreversible changes in sensor architecture and morphology resulting in degradation of enzymatic activities and insufficient signal reproducibility. Hence, novel fabrication techniques for the production of enzymatic biosensors that are instrumentally controlled and allow reproducible, simultaneous multi-analyte detection with high specificity, temporal and spatial resolution are greatly required. Herein, we report on the development of a novel, fully instrumentally controlled, one-step synthesis approach for the production of nanoparticle-based enzymatic biosensors. The approach relies on a simultaneous encapsulation of the enzyme (glucose and alcohol oxidases), a fluoropolymer (Nafion) and noble metal nanoparticles via co-deposition from a phosphate multiple electrolyte on top of the sensor surface. Remarkably, electrochemical studies revealed that nanoparticle-based biosensors produced by this novel fabrication approach display a significantly enhanced mechanical stability (more than several orders of magnitude higher) without loss of biological activity or leakage of the enzyme or Nafion, and advanced synthesis reproducibility (40 times higher) in comparison to LbL analogues.

The methanotrophic bacterium Methylococcus capsulatus is capable of assimilating methane and oxygen into protein-rich biomass, however, the diverse metabolism of the microorganism also allows for several undesired cometabolic side-reactions to occur. In this study, the ammonia cometabolism in Methylococcus capsulatus is investigated using pulse experiments. Surprisingly Methylococcus capsulatus oxidizes ammonia to nitrate through a yet unknown mechanism and fixes molecular nitrogen even at a high dissolved oxygen tension. The observed phenomena can be modeled using 14 ordinary differential equations and 18 kinetic parameters, of which 6 were revealed by Morris screening to be identifiable from the experimental data. Monte Carlo simulations showed that the model was robust and accurate even with uncertainty in the parameter values as confirmed by statistical error analysis., (© 2019 Wiley Periodicals, Inc.)

Microbial cells in bioprocesses are usually described with averaged parameters. But in fact, single cells within populations vary greatly in characteristics such as stress resistance, especially in response to carbon source gradients. Our aim was to introduce tools to quantify population heterogeneity in bioprocesses using a combination of reporter strains, flow cytometry, and easily comprehensible parameters. We calculated mean, mode, peak width, and coefficient of variance to describe distribution characteristics and temporal shifts in fluorescence intensity. The skewness and the slope of cumulative distribution function plots illustrated differences in distribution shape. These parameters are person-independent and precise. We demonstrated this by quantifying growth-related population heterogeneity of Saccharomyces cerevisiae and Escherichia coli reporter strains in steady-state of aerobic glucose-limited chemostat cultures at different dilution rates and in response to glucose pulses. Generally, slow-growing cells showed stronger responses to glucose excess than fast-growing cells. Cell robustness, measured as membrane integrity after exposure to freeze-thaw treatment, of fast-growing cells was strongly affected in subpopulations of low membrane robustness. Glucose pulses protected subpopulations of fast-growing but not slower-growing yeast cells against membrane damage. Our parameters could successfully describe population heterogeneity, thereby revealing physiological characteristics that might have been overlooked during traditional averaged analysis.

Miniaturized stirred bioreactors (MSBRs) are gaining popularity as a cost-effective approach to scale-down experimentation. However, realizing conditions that reflect the large-scale process accurately can be challenging. This article highlights common challenges of using MSBRs for scale-down. The fundamental difference between oxygen mass transfer coefficient (k L a) and oxygen transfer rate scaling is addressed and the difficulty of achieving turbulent flow and industrially relevant tip speeds is described. More practical challenges of using MSBR systems for scale-down are also discussed, including the risk of vortex formation, changed volume dynamics, and wall growth. By highlighting these challenges, the article aims to create more awareness of these difficulties and to contribute to improved design of scale-down experiments., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

The objective of this paper is to present the main results of an engineering-research project dealing with model-based evaluation of waste streams treatment from a biotech company. This has been extensively done in domestic treatment systems, but is equally important, and with different challenges in industrial wastewater treatment. A new set of biological (activated sludge, anaerobic digestion), physicochemical (aqueous phase, precipitation, mass transfer) process models and model interfaces are required to describe removal of organics in an upflow anaerobic sludge blanket (UASB) reactor plus either traditional nitrification/denitrification (A 1 ) or partial nitritation (PN)/anammox (ANX) (A 2 ) processes. Model-based analysis shows that option A 1 requires a decrease in digestion energy recovery (E recovery ) in order to have enough organic substrate for subsequent post NO 3 reduction treatment (95 kWh.kg N -1 ). In contrast, A 2 in an aerobic granular sludge reactor allows for higher UASB conversion since N removal is carried out autotrophically. The study also reveals that the addition of an aerated pre-treatment unit prior to the PN/ANX (A 2 ) reactor promotes COD and H 2 S oxidation, CO 2 and CH 4 stripping, a pH increase (up to 8.5) and a reduction of the risk of intra-granular precipitation as well as sulfide inhibition. Simulations indicate clear differences regarding the microbial distribution/abundance within the biofilm in A 2 when comparing the two operational modes. Final results show the effects of different loading and operational conditions; dissolved oxygen (DO), Total Suspended Solids (TSS op ), energy recovery (E recovery ); on the overall process performance; N removal, aeration energy (E aeration ), net energy production (E recovery ); using response surfaces, highlighting the need of integrated approaches to avoid sub-optimal outcomes. The study shows the benefits of virtual plant simulation and demonstrates the potential of model-based evaluation when process engineers in industry have to decide between competing options., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Stringent phosphorus discharge standards (i.e. 0.15-0.3 g P.m -3 ) in the Baltic area will compel wastewater treatment practice to augment enhanced biological phosphorus removal (EBPR) with chemical precipitation using metal salts. This study examines control of iron chemical dosing for phosphorus removal under dynamic loading conditions to optimize operational aspects of a membrane biological reactor (MBR) pilot plant. An upgraded version of the Benchmark Simulation Model No. 2 (BSM2) with an improved physico-chemical framework (PCF) is used to develop a plant-wide model for the pilot plant. The PCF consists of an equilibrium approach describing ion speciation and pairing, kinetic minerals precipitation (such as hydrous ferric oxides (HFO) and FePO 4 ) as well as adsorption and co-precipitation. Model performance is assessed against data sets from the pilot plant, evaluating the capability to describe water and sludge lines across the treatment process under steady-state operation. Simulated phosphorus differed as little as 5-10% (relative) from measured phosphorus, indicating that the model was representative of reality. The study also shows that environmental factors such as pH, as well operating conditions such as Fe/P molar ratios (1, 1.5 and 2), influence the concentration of dissolved phosphate in the effluent. The time constant of simultaneous precipitation in the calibrated model, due to a step change decrease/increase in FeSO 4 dosage, was found to be roughly 5 days, indicating a slow dynamic response due to a multi-step process involving dissolution, oxidation, precipitation, aging, adsorption and co-precipitation. The persistence effect of accumulated iron-precipitates (HFO particulates) in the activated sludge seemed important for phosphorus removal, and therefore solids retention time plays a crucial role according to the model. The aerobic tank was deemed to be the most suitable dosing location for FeSO 4 addition, due to high dissolved oxygen levels and good mixing conditions. Finally, dynamic model-based analyses show the benefits of using automatic control when dosing chemicals., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Acetate as the major by-product in industrial-scale bioprocesses with Escherichia coli is found to decrease process efficiency as well as to be toxic to cells, which has several effects like a significant induction of cellular stress responses. However, the underlying phenomena are poorly explored. Therefore, we studied time-resolved population heterogeneity of the E. coli growth reporter strain MG1655/pGS20PrrnBGFPAAV expressing destabilized green fluorescent protein during batch growth on acetate and glucose as sole carbon sources. Additionally, we applied five fluorescent stains targeting different cellular properties (viability as well as metabolic and respiratory activity). Quantitative analysis of flow cytometry data verified that bacterial populations in the bioreactor are more heterogeneous in growth as well as stronger metabolically challenged during growth on acetate as sole carbon source, compared to growth on glucose or acetate after diauxic shift. Interestingly, with acetate as sole carbon source, significant subpopulations were found with some cells that seem to be more robust than the rest of the population. In conclusion, following batch cultures population heterogeneity was evident in all measured parameters. Our approach enabled a deeper study of heterogeneity during growth on the favored substrate glucose as well as on the toxic by-product acetate. Using a combination of activity fluorescent dyes proved to be an accurate and fast alternative as well as a supplement to the use of a reporter strain. However, the choice of combination of stains should be well considered depending on which population traits to aim for., (© 2019 American Institute of Chemical Engineers.)

In a conventional microorganism-mediated biological process for degradation of keratinous waste material the production of keratin-specific proteases (i.e., keratinases) and the hydrolysis of keratin-rich residual biomass both take place during the same stage of the bioprocess and, as a consequence, occur simultaneously under suboptimal conditions. In the present study the keratinolytic actinomycete Amycolatopsis keratiniphila D2 was successfully employed to biodegrade thermally pretreated porcine bristles at high solids loading (16% w/v) via a novel cultivation methodology. Indeed, the two-stage submerged fermentation process developed in this work enabled to efficiently recover, in a single unit operation, about 73% of the protein material contained in the keratinous biowaste structure, resulting in an overall accumulation of 89.3 g·L -1 protein-rich hydrolysate and a productivity of 427 mg crude soluble proteins per litre per hour. The obtained protein hydrolysate powder displayed a 2.2-fold increase in its in vitro pepsin digestibility (95%) with respect to the non-hydrolysed pretreated substrate (43%). In addition, the chromatogram obtained by size-exclusion chromatography analysis of the final product indicated that, among the identified fractions, those consisting of small peptides and free amino acids were the most abundantly present inside the analysed sample. Given these facts it is possible to conclude that the soluble proteins, peptides and free amino acids recovered through the newly designed two-stage bioextraction process could represent a viable alternative source of protein in animal feed formulation., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Understanding the biorecognition and transduction mechanisms is a key aspect in the development of robust sensing technologies. Therefore, the design of tools and analytical approaches that could allow gaining a deeper insight into the bio- and electrochemical processes would significantly accelerate the progress in the field of biosensors. Herein, we present a novel effective strategy for biosensor design screening based on tandem monitoring of individual system parameters in a droplet. The developed tandem approach couples the simultaneous chronoamperometric characterization of biosensors in the presence of an analyte (glucose) together with dissolved oxygen monitoring using a luminescence-based optical oxygen microsensor. Remarkably, an optical sensor was applied for the first time to analyse the amperometric biosensor response and kinetics. Two types of multi-layer glucose biosensors (first generation) were chosen as a case study and were evaluated at various operating conditions using multi-analytical techniques. Moreover, specific protocols were developed for the detection of oxygen conversion rates, iron and membrane elution inside the multi-layer glucose biosensor system. The presented tandem monitoring approach allows one to identify and build-up the correlations between the critical operation conditions and system parameters affecting the overall biosensor response, its sensitivity and lifetime. Thus, based on the obtained experimental results a more favorable composition of Nafion membrane films and enzyme loadings for glucose biosensors were identified in a time-efficient way and allowed to explain an improved stability (up to 3 months) and linear detection range of glucose concentrations (up to 5 mM). Furthermore, the presented tandem monitoring approach can be readily adapted to other oxygen dependent types of biosensors either for simultaneous multiple substrate detection or as an efficient tool for biosensor design and operating condition screening.

The formation of pH gradients in a 700 L batch fermentation of Streptococcus thermophilus was studied using multi-position pH measurements and computational fluid dynamics (CFD) modeling. To this end, a dynamic, kinetic model of S. thermophilus and a pH correlation were integrated into a validated one-phase CFD model, and a dynamic CFD simulation was performed. First, the fluid dynamics of the CFD model were validated with NaOH tracer pulse mixing experiments. Mixing experiments and simulations were performed whereas multiple pH sensors, which were placed vertically at different locations in the bioreactor, captured the response. A mixing time of about 46 s to reach 95% homogeneity was measured and predicted at an impeller speed of 242 rpm. The CFD simulation of the S. thermophilus fermentation captured the experimentally observed pH gradients between a pH of 5.9 and 6.3, which occurred during the exponential growth phase. A pH higher than 7 was predicted in the vicinity of the base solution inlet. Biomass growth, lactic acid production, and substrate consumption matched the experimental observations. Moreover, the biokinetic results obtained from the CFD simulation were similar to a single-compartment simulation, for which a homogeneous distribution of the pH was assumed. This indicates no influence of pH gradients on growth in the studied bioreactor. This study verified that the pH gradients during a fermentation in the pilot-scale bioreactor could be accurately predicted using a coupled simulation of a biokinetic and a CFD model. To support the understanding and optimization of industrial-scale processes, future biokinetic CFD studies need to assess multiple types of environmental gradients, like pH, substrate, and dissolved oxygen, especially at industrial scale., (© 2018 Wiley Periodicals, Inc.)

In this paper, a multi-scale model is used to assess the multiple mineral precipitation potential in a full-scale anaerobic granular sludge system. Reactor behaviour is analysed under different operational conditions (addition/no addition of reject water from dewatering of lime-stabilized biomass) and periods of time (short/long term). Model predictions suggest that a higher contribution of reject water promotes the risk of intra-granule CaCO 3 formation as a result of the increased quantity of calcium arriving with that stream combined with strong pH gradients within the biofilm. The distribution of these precipitates depends on: (i) reactor height; and (ii) granule size. The study also exposes the potential undesirable effects of the long-term addition of reject water (a decrease in energy recovery of 20% over a 100-day period), caused by loss in biomass activity (due to microbial displacement), and the reduced buffer capacity. This demonstrates how both short-term and long-term operational conditions may affect the formation of precipitates within anaerobic granules, and how it may influence methane production and consequently energy recovery.

The limited availability of metabolite-specific sensors for continuous sampling and monitoring is one of the main bottlenecks contributing to failures in bioprocess development. Furthermore, only a limited number of approaches exist to connect currently available measurement systems with high throughput reactor units. This is especially relevant in the biocatalyst screening and characterization stage of process development. In this work, a strategy for sensor integration in microfluidic platforms is demonstrated, to address the need for rapid, cost-effective and high-throughput screening in bioprocesses. This platform is compatible with different sensor formats by enabling their replacement and was built in order to be highly flexible and thus suitable for a wide range of applications. Moreover, this re-usable platform can easily be connected to analytical equipment, such as HPLC, laboratory scale reactors or other microfluidic chips through the use of standardized fittings. In addition, the developed platform includes a two-sensor system interspersed with a mixing channel, which allows the detection of samples that might be outside the first sensor's range of detection, through dilution of the sample solution up to 10 times. In order to highlight the features of the proposed platform, inline monitoring of glucose levels is presented and discussed. Glucose was chosen due to its importance in biotechnology as a relevant substrate. The platform demonstrated continuous measurement of substrate solutions for up to 12 h. Furthermore, the influence of the fluid velocity on substrate diffusion was observed, indicating the need for in-flow calibration to achieve a good quantitative output., (Copyright © 2018 Elsevier B.V. All rights reserved.)

The need for fast and comprehensive characterization of biocatalysts has pushed the development of new screening platforms based on microfluidics, capable of monitoring several parameters simultaneously, with new configurations of liquid handling, sample treatment and sensing. Modular microfluidics allows the integration of these newly developed approaches in a more flexible way towards increasing applicability of the microfluidic chips to different types of biocatalysts and reactions. A highly relevant operation in such a system is biocatalyst inactivation, which can enable the precise control of reaction time by avoiding the continuation of the reaction in another module or connecting tubes. Such control is important when different modules of reactors and/or sensing units are used and changed frequently. Here we describe the development, characterization and application of a module for rapid enzyme inactivation. The thermal inactivation platform developed is compared with a standard benchtop ThermoMixer in terms of inactivation efficiency for glucose oxidase and catalase. A higher activity loss was observed for enzyme inactivation under flow conditions (inactivation achieved at 120 s residence time at 338 K and 20 s residence time at 353 K) which indicated a high heat transfer to the fluid under dynamic conditions. Moreover, partial deactivation of the enzymes was observed for the continuous thermal inactivation module, when activity measurements were performed after 1 and 2 days following inactivation. The thermal inactivation unit presented can be easily integrated into modular microfluidic platforms and can be a useful addition for enzyme characterization and screening., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Background: Genome-scale metabolic models allow researchers to calculate yields, to predict consumption and production rates, and to study the effect of genetic modifications in silico , without running resource-intensive experiments. While these models have become an invaluable tool for optimizing industrial production hosts like Escherichia coli and S. cerevisiae , few such models exist for one-carbon (C1) metabolizers. Results: Here, we present a genome-scale metabolic model for Methylococcus capsulatus (Bath), a well-studied obligate methanotroph, which has been used as a production strain of single cell protein (SCP). The model was manually curated, and spans a total of 879 metabolites connected via 913 reactions. The inclusion of 730 genes and comprehensive annotations, make this model not only a useful tool for modeling metabolic physiology, but also a centralized knowledge base for M. capsulatus (Bath). With it, we determined that oxidation of methane by the particulate methane monooxygenase could be driven both through direct coupling or uphill electron transfer, both operating at reduced efficiency, as either scenario matches well with experimental data and observations from literature. Conclusion: The metabolic model will serve the ongoing fundamental research of C1 metabolism, and pave the way for rational strain design strategies toward improved SCP production processes in M. capsulatus .

Over the last two decades, the rapid development and widespread application of nanomaterials has significantly influenced research in various fields, including analytical chemistry and biosensing technologies. In particular, the simple functionalization and tuning of noble metal nanoparticle (NP) surface chemistry resulted in the development of a series of novel biosensing platforms with quick read-out and enhanced capabilities towards specific analyte detection. Moreover, noble metal NPs possess a number of unique properties, viz. high surface-to-volume ratio and excellent spectral, optical, thermal, electrical and catalytic characteristics. This manuscript provides an elaborate review on galvanic noble metal NPs deposited onto semiconductor surfaces, from the preparation stage towards their application in biosensors and gas sensing. Two types of deposition approaches, viz. galvanic displacement/electroless and conventional electroplating, are introduced and compared. Furthermore, the analytical merit of hybrid nanomaterials towards the improvement of sensing abilities is highlighted. Finally, some limitations and challenges related to progress in the development and application of analytical devices based on electroless and electroplated noble metal NPs-semiconductor hybrids (NMNPsHs) in biochemical and environmental sensing are discussed.

The application of conventional organic solvents has been essential in several steps of bioprocesses in order to achieve sufficient economic efficiency. The use of organic solvents is frequently used either to partly or fully replace water in the reaction medium or as a process aid for downstream separation. Nowadays, manufacturers are increasingly requested to avoid and substitute solvents with hazardous potential. Therefore, the solvent selection must account for potential environmental hazards, health and safety problems, in addition to fulfilling the ideal characteristics for application in a process. For the first time, criteria including Environment, Health and Safety (EHS), as well as the technical requirements for reaction and separation have been reviewed, collected and integrated in a single organic solvent screening strategy to be used as a guideline for narrowing down the list of solvents to test experimentally. Additionally, we have also included a solvent selection guide based on the methodology developed in the Innovative Medicines Initiative CHEM21 (IMI CHEM21) project and applied specifically to water-immiscible solvents commonly used in bioprocesses., (Copyright © 2018 Elsevier Inc. All rights reserved.)