Handheld FTIR outperforms total organic carbon swab in pharmaceutical cleaning validation.

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.

Soft-sensors application for automated feeding control in high-throughput mammalian cell cultures.

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 CO2 /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, CO2 /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.

Data-based dynamic compartment model: Modeling of E. coli fed-batch fermentation in a 600 m3 bubble column.

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.

Transforming data to information: A parallel hybrid model for real-time state estimation in lignocellulosic ethanol fermentation.

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.

Exceptionally rich keratinolytic enzyme profile found in the rare actinomycetes Amycolatopsis keratiniphila D2T.

The non-spore forming Gram-positive actinomycetes Amycolatopsis keratiniphila subsp. keratiniphila D2T (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 D2T, 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 D2T 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 D2T is predicted to encode 621 proteases. • This actinomycete efficiently converts bristle meal to a protein hydrolysate. • Proteome analysis identified 57 proteases in its secretome.

Assessment of alkaline stabilization processes in industrial waste streams using a model-based approach.

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.

Monitoring yeast fermentations by nonlinear infrared technology and chemometrics-understanding process correlations and indirect predictions.

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.

Two novel S1 peptidases from Amycolatopsis keratinophila subsp. keratinophila D2T degrading keratinous slaughterhouse by-products.

Two proteases, named C- and T-like proteases, respectively, were purified from the culture supernatant of Amycolatopsis keratinophila subsp. keratinophila D2T 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.

Towards smart biomanufacturing: a perspective on recent developments in industrial measurement and monitoring technologies for bio-based production processes.

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.

Dynamic investigation and modeling of the nitrogen cometabolism in Methylococcus capsulatus ( Bath).

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.

CFD predicted pH gradients in lactic acid bacteria cultivations.

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.

Sensors for biosensors: a novel tandem monitoring in a droplet towards efficient screening of robust design and optimal operating conditions.

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.

Model-based analysis and optimization of a full-scale industrial high-rate anaerobic bioreactor.

The objective of this paper is to present the model-based optimization results of an anaerobic granular sludge internal circulation reactor. The International Water Association Anaerobic Digestion Model No. 1 extended with phosphorus (P), sulfur (S), and ethanol is used to describe the main biological and physico-chemical processes. The high-rate conditions within the reactor are simulated using a flow + reactor model comprised of a series of continuous stirred tank reactors followed by an ideal total suspended solids separation unit. Following parameter estimation by least squares on the measured data, the model had a relative mean error of 13 and 15% for data set #1 and data set #2, respectively. Response surfaces show that the reactor performance index (a metric combining energy recovery in the form of heat and electricity, as well as chemicals needed for pH control) could be improved by 45% when reactor pH is reduced down to 6.8. Model-based results reveal that influent S does not impose sufficient negative impacts on energy recovery (+5.7%, in MWh/day,+0.20 M€/year when influent S is removed) to warrant the cost of its removal (3.58 M€/year). In fact, the process could handle even higher S loads (ensuring the same degree of conversion) as long as the pH is maintained above 6.8. Nevertheless, a higher S load substantially increases the amount of added NaOH to maintain the desired operational pH (>25%) due to the acidic behavior of HS - . CO 2 stripping decreases the buffer capacity of the system and hence use of chemicals for pH control. Finally, the paper discusses the possibilities and limitations of the proposed approach, and how the results of this study will be put into practice.

A fast and simple method to estimate relative, hyphal tensile-strength of filamentous fungi used to assess the effect of autophagy.

Fungal hyphal strength is an important phenotype which can have a profound impact on bioprocess behavior. Until now, there is not an efficient method which allows its characterization. Currently available methods are very time consuming, thus, compromising their applicability in strain selection and process development. To overcome this issue, a method for fast and easy, statistically verified quantification of relative hyphal tensile strength was developed. It involves off-line fragmentation in a high shear mixer followed by quantification of fragment size using laser diffraction. Particle size distribution (PSD) is determined, with analysis time on the order of minutes. Plots of PSD 90th percentile versus time allow estimation of the specific fragmentation rate. This novel method is demonstrated by estimating relative hyphal strength during growth in control conditions and rapamycin-induced autophagy for Aspergillus nidulans (parental strain) and a mutant strain (ΔAnatg8) lacking an important autophagy gene. Both strains were grown in shake flasks and relative hyphal tensile strength was compared. The mutant strain grown in control conditions appears to be weaker than the parental strain, suggesting that Anatg8 may play a role in other processes involving cell wall biosynthesis. Furthermore, rapamycin-induced autophagy resulted in apparently weaker cells even for the mutant strain. These findings confirm the utility of the developed method in strain selection and process development.

Mixing and mass transfer in a pilot scale U-loop bioreactor.

A system capable of handling a large volumetric gas fraction while providing a high gas to liquid mass transfer is a necessity if the metanotrophic bacterium Methylococcus capsulatus is to be used in single cell protein (SCP) production. In this study, mixing time and mass transfer coefficients were determined in a 0.15 m3 forced flow U-loop fermenter of a novel construction. The effect on the impeller drawn power when a gas was introduced into the system was also studied. Mixing time decreased and mass transfer increased with increasing volumetric liquid flow rate and specific power input. This happened also for a large volume fraction of the gas, which was shown to have only minor effect on the power drawn from the pump impeller. Very large mass transfer coefficients, considerably higher than those obtainable in an STR and previous tubular loop reactors, could be achieved in the U-loop fermenter equipped with static mixers at modest volumetric liquid and gas flow rates. Biotechnol. Bioeng. 2017;114: 344-354. © 2016 Wiley Periodicals, Inc.

Application of a mechanistic model as a tool for on-line monitoring of pilot scale filamentous fungal fermentation processes-The importance of evaporation effects.

A mechanistic model-based soft sensor is developed and validated for 550L filamentous fungus fermentations operated at Novozymes A/S. The soft sensor is comprised of a parameter estimation block based on a stoichiometric balance, coupled to a dynamic process model. The on-line parameter estimation block models the changing rates of formation of product, biomass, and water, and the rate of consumption of feed using standard, available on-line measurements. This parameter estimation block, is coupled to a mechanistic process model, which solves the current states of biomass, product, substrate, dissolved oxygen and mass, as well as other process parameters including kL a, viscosity and partial pressure of CO2 . State estimation at this scale requires a robust mass model including evaporation, which is a factor not often considered at smaller scales of operation. The model is developed using a historical data set of 11 batches from the fermentation pilot plant (550L) at Novozymes A/S. The model is then implemented on-line in 550L fermentation processes operated at Novozymes A/S in order to validate the state estimator model on 14 new batches utilizing a new strain. The product concentration in the validation batches was predicted with an average root mean sum of squared error (RMSSE) of 16.6%. In addition, calculation of the Janus coefficient for the validation batches shows a suitably calibrated model. The robustness of the model prediction is assessed with respect to the accuracy of the input data. Parameter estimation uncertainty is also carried out. The application of this on-line state estimator allows for on-line monitoring of pilot scale batches, including real-time estimates of multiple parameters which are not able to be monitored on-line. With successful application of a soft sensor at this scale, this allows for improved process monitoring, as well as opening up further possibilities for on-line control algorithms, utilizing these on-line model outputs. Biotechnol. Bioeng. 2017;114: 589-599. © 2016 Wiley Periodicals, Inc.

A novel model-based control strategy for aerobic filamentous fungal fed-batch fermentation processes.

A novel model-based control strategy has been developed for filamentous fungal fed-batch fermentation processes. The system of interest is a pilot scale (550 L) filamentous fungus process operating at Novozymes A/S. In such processes, it is desirable to maximize the total product achieved in a batch in a defined process time. In order to achieve this goal, it is important to maximize both the product concentration, and also the total final mass in the fed-batch system. To this end, we describe the development of a control strategy which aims to achieve maximum tank fill, while avoiding oxygen limited conditions. This requires a two stage approach: (i) calculation of the tank start fill; and (ii) on-line control in order to maximize fill subject to oxygen transfer limitations. First, a mechanistic model was applied off-line in order to determine the appropriate start fill for processes with four different sets of process operating conditions for the stirrer speed, headspace pressure, and aeration rate. The start fills were tested with eight pilot scale experiments using a reference process operation. An on-line control strategy was then developed, utilizing the mechanistic model which is recursively updated using on-line measurements. The model was applied in order to predict the current system states, including the biomass concentration, and to simulate the expected future trajectory of the system until a specified end time. In this way, the desired feed rate is updated along the progress of the batch taking into account the oxygen mass transfer conditions and the expected future trajectory of the mass. The final results show that the target fill was achieved to within 5% under the maximum fill when tested using eight pilot scale batches, and over filling was avoided. The results were reproducible, unlike the reference experiments which show over 10% variation in the final tank fill, and this also includes over filling. The variance of the final tank fill is reduced by over 74%, meaning that it is possible to target the final maximum fill reproducibly. The product concentration achieved at a given set of process conditions was unaffected by the control strategy. Biotechnol. Bioeng. 2017;114: 1459-1468. © 2017 Wiley Periodicals, Inc.

Development of in situ product removal strategies in biocatalysis applying scaled-down unit operations.

An experimental platform based on scaled-down unit operations combined in a plug-and-play manner enables easy and highly flexible testing of advanced biocatalytic process options such as in situ product removal (ISPR) process strategies. In such a platform, it is possible to compartmentalize different process steps while operating it as a combined system, giving the possibility to test and characterize the performance of novel process concepts and biocatalysts with minimal influence of inhibitory products. Here the capabilities of performing process development by applying scaled-down unit operations are highlighted through a case study investigating the asymmetric synthesis of 1-methyl-3-phenylpropylamine (MPPA) using ω-transaminase, an enzyme in the sub-family of amino transferases (ATAs). An on-line HPLC system was applied to avoid manual sample handling and to semi-automatically characterize ω-transaminases in a scaled-down packed-bed reactor (PBR) module, showing MPPA as a strong inhibitor. To overcome the inhibition, a two-step liquid-liquid extraction (LLE) ISPR concept was tested using scaled-down unit operations combined in a plug-and-play manner. Through the tested ISPR concept, it was possible to continuously feed the main substrate benzylacetone (BA) and extract the main product MPPA throughout the reaction, thereby overcoming the challenges of low substrate solubility and product inhibition. The tested ISPR concept achieved a product concentration of 26.5 gMPPA · L-1 , a purity up to 70% gMPPA · gtot-1 and a recovery in the range of 80% mol · mol-1 of MPPA in 20 h, with the possibility to increase the concentration, purity, and recovery further. Biotechnol. Bioeng. 2017;114: 600-609. © 2016 Wiley Periodicals, Inc.

Effects of influent fractionation, kinetics, stoichiometry and mass transfer on CH4, H2 and CO2 production for (plant-wide) modeling of anaerobic digesters.

This paper examines the importance of influent fractionation, kinetic, stoichiometric and mass transfer parameter uncertainties when modeling biogas production in wastewater treatment plants. The anaerobic digestion model no. 1 implemented in the plant-wide context provided by the benchmark simulation model no. 2 is used to quantify the generation of CH4, H2and CO2. A comprehensive global sensitivity analysis based on (i) standardized regression coefficients (SRC) and (ii) Morris' screening's (MS's) elementary effects reveals the set of parameters that influence the biogas production uncertainty the most. This analysis is repeated for (i) different temperature regimes and (ii) different solids retention times (SRTs) in the anaerobic digester. Results show that both SRC and MS are good measures of sensitivity unless the anaerobic digester is operating at low SRT and mesophilic conditions. In the latter situation, and due to the intrinsic nonlinearities of the system, SRC fails in decomposing the variance of the model predictions (R² < 0.7) making MS a more reliable method. At high SRT, influent fractionations are the most influential parameters for predictions of CH4and CO2emissions. Nevertheless, when the anaerobic digester volume is decreased (for the same load), the role of acetate degraders gains more importance under mesophilic conditions, while lipids and fatty acid metabolism is more influential under thermophilic conditions. The paper ends with a critical discussion of the results and their implications during model calibration and validation exercises.

Population balance models: a useful complementary modelling framework for future WWTP modelling.

Population balance models (PBMs) represent a powerful modelling framework for the description of the dynamics of properties that are characterised by distributions. This distribution of properties under transient conditions has been demonstrated in many chemical engineering applications. Modelling efforts of several current and future unit processes in wastewater treatment plants could potentially benefit from this framework, especially when distributed dynamics have a significant impact on the overall unit process performance. In these cases, current models that rely on average properties cannot sufficiently capture the true behaviour and even lead to completely wrong conclusions. Examples of distributed properties are bubble size, floc size, crystal size or granule size. In these cases, PBMs can be used to develop new knowledge that can be embedded in our current models to improve their predictive capability. Hence, PBMs should be regarded as a complementary modelling framework to biokinetic models. This paper provides an overview of current applications, future potential and limitations of PBMs in the field of wastewater treatment modelling, thereby looking over the fence to other scientific disciplines.