Artificial neural network simulating microbial fuel cells with different membrane materials and electrode configurations

© 2019 Elsevier B.V. Microbial fuel cells (MFCs) are gaining interest due to higher power production achieved by deep analysis of their characteristics and their effect on the overall efficiency. To date, investigations on MFC efficiency, can only be based on laboratory experiments or mathematical modelling. However, there is only a handful of rule-based mathematical modelling due to the difficulties imposed by the high sensitivity of the MFC system to environmental parameters and the highly complex bacterial consortia that dictate its behavior. Thus, an application of an artificial neural network (ANN) is proposed to simulate the polarisation of cylindrical MFCs with different materials as the separation membranes. ANNs are ideal candidates for investigating these systems, as there is no need for explicit knowledge of the detailed rules that govern the system. The ANN developed here is a feed-forward back-propagation network with a topology of 4-10-1 neurons that approximates the voltage of each MFC at a given state. Two different membrane materials with two different electrode configurations were assembled and utilized in laboratory experiments to produce the data set on which the ANN was trained upon. For the whole data set the correlation coefficient (R) between real values and outputs of the network was 0.99662

Biotechnology and Bioengineering

Biotechnology and Bioengineering presents the most up-to-date research on biobased technologies. It is designed to help scientists and researchers deepen their knowledge in this critical knowledge field. This solid resource brings together multidisciplinary research, development, and innovation for a wide study of Biotechnology and Bioengineering

Quantification and Modelling of Fugitive Greenhouse Gas Emissions from Urban Water Systems

With increased commitment from the international community to reduce greenhouse gas (GHG) emissions from all sectors in accordance with the Paris Agreement, the water sector has never felt the pressure it is now under to transition to a low-carbon water management model. This requires reducing GHG emissions from grid-energy consumption (Scope 2 emissions), which is straightforward; however, it also requires reducing Scope 1 emissions, which include nitrous oxide and methane emissions, predominantly from wastewater handling and treatment. The pathways and factors leading to biological nitrous oxide and methane formation and emissions from wastewater are highly complex and site-specific. Good emission factors for estimating the Scope 1 emissions are lacking, water utilities have little experience in directly measuring these emissions, and the mathematical modelling of these emissions is challenging. Therefore, this book aims to help the water sector address the Scope 1 emissions by breaking down their pathways and influencing factors, and providing guidance on both the use of emission factors, and performing direct measurements of nitrous oxide and methane emissions from sewers and wastewater treatment plants. The book also dives into the mathematical modelling for predicting these emissions and provides guidance on the use of different mathematical models based upon your conditions, as well as an introduction to alternative modelling methods, including metabolic, data-driven, and AI methods. Finally, the book includes guidance on using the modelling tools for assessing different operating strategies and identifying promising mitigation actions. A must-have book for anyone needing to understand, account for, and reduce water utility Scope 1 emissions

Data-driven modelling for resource recovery: Data volume, variability, and visualisation for an industrial bioprocess

Advances in industrial digital technologies have led to an increasing volume of data generated from industrial bioprocesses, which can be utilised within data-driven models (DDM). However, data volume and variability complications make developing models that captures the underlying biological nature of the bioprocesses challenging. In this study, a framework for developing data-driven models of bioprocesses is proposed and evaluated by modelling an industrial bioprocess, which treats industrial or agrifood wastewaters whilst simultaneously generating bioenergy. Six models were developed to predict the reduction in chemical oxygen demand from the wastewater by the bioprocess and statistically evaluated using both testing data (randomly partitioned data from the model development) and unseen data (new data not used during the model development). The statistical error metrics employed were the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). The stacked neural network model was best able to model the bioprocess, having the highest accuracy on the testing data (R2: 0.98; RMSE: 1.29; MAE: 2.27; MAPE: 4.08) and the unseen data (R2: 0.82; RMSE: 2.57; MAE: 1.75; MAPE: 3.68). Data visualisation is used to observe (or confirm) whether new data points are within the model boundaries, helping to increase confidence in the model’s predictions on future data

Sewage Treatment Plants

Sewage Treatment Plants: Economic Evaluation of Innovative Technologies for Energy Efficiency aims to show how cost saving can be achieved in sewage treatment plants through implementation of novel, energy efficient technologies or modification of the conventional, energy demanding treatment facilities towards the concept of energy streamlining. The book brings together knowledge from Engineering, Economics, Utility Management and Practice and helps to provide a better understanding of the real economic value with methodologies and practices about innovative energy technologies and policies in sewage treatment plants

Complete mechanical model of a very large submerged membrane bioreactor

Membrane bioreactors (MBRs) are successfully being adopted in super-large-scale (>100,000 m3 .d-1 ) applications due to several advantages, mainly superior and consistent effluent quality. Moreover, the significant reduction in the membrane and operating costs has contributed to its wider acceptance. Despite their considerable evolution in the recent past and large-scale applications in municipal wastewater treatment, fouling and the cost associated with its mitigation are still hot topics and need the attention of researchers and academia to optimize and reduce the expense of MBR in the range of the conventional activated sludge process (CASP). Mathematical modeling is a great tool to explore the model-based optimization of operating costs associated with fouling mitigation strategies. For this, a comprehensive and integrated process model must be adapted, calibrated, and validated at a super-large-scale facility. MBR involves complex interactions between biology and filtration, and its modeling is challenging without considering these interactions. In the recent past, integrated models have been developed and applied to MBRs, ranging from bench to pilot scales and rarely for full-scale facilities of capacity up to 15,000 m3 .d-1 . In this work, a superlarge-scale MBR plant with a design capacity of 348,000 m3 .d-1 is dynamically modeled to simulate the depollution and filtration-fouling processes. The integrated model combines biochemical (ASM3-SMP-EPS-Bio-P, aeration, chemical precipitation), resistance in series (RIS) fouling, and energy sub-models. The comprehensive, integrated model is capable of simulating a) biological processes to describe the stoichio-kinetic activity of the biomass for carbon oxidation and nutrient removal (i.e., Nitrogen and Phosphorus) coupled with EPS-SMP production and degradation processes; b) the role of biological process aeration in carbon oxidation and nitrification under the influence of MLSS; c) the numerical balance of the volumes of the influent, effluent, sludge and all internal and external recirculation; d) coagulant addition inducing chemically enhanced phosphorus removal (CEPR) in addition to enhanced biological phosphorus removal (EBPR); e) fouling dynamics associated with synchronized filtration-relaxation, intermittent air-scouring and backwashing under the influence of transmembrane pressure (TMP), temperature, MLSS, and bound EPSs concentration, and f) specific energy consumption. The model was calibrated using one-week data collected during the first experimental campaign and was validated against 92 days of data from the plant with and without the addition of FeCl3. The calibrated integrated model provided an acceptable correspondence for pollutants (COD, NOx, NH4, PO4 3- , MLSS, EPSs, and SMPs) removal and prediction of the TMP, a direct indicator for fouling development. The model also successfully produced acceptable datasets not available from routine measurements, e.g., the evolution of the biomass and transformation of the pollutants in each reactor in series. Moreover, the model can provide detailed insights into reversible and irreversible fouling dynamics under the synchronized influence of multiple fouling abatement controls, including filtration-relaxation, intermittent air-scouring, and backwashing. In order to be used to develop model-based controls and intelligent decision-making tools to optimize the functioning of the full-scale MBRs, particularly the air-scouring and activation and de-activation of the chemical washes to save energy and chemicals, this model would have to be validated in fouling conditions. Since it was not possible to test the limits of the model, the sensitivity analysis approach was investigated

Environmental controls on marine methane oxidation : from deep-sea brines to shallow coastal systems

Methane is the most abundant greenhouse gas after carbon dioxide and accounts for ~25% of atmospheric warming since the onset of industrialization. Large amounts of methane are stored in the ocean seafloor as solid gas hydrates, gaseous reservoirs or dissolved in pore water. At cold seeps, various physical, chemical, and geological processes force subsurface methane to ascend along pathways of structural weakness to the sea floor. Additionally, methane can be produced in situ within organic-rich sediments. Increasing evidence suggests that ocean bottom water warming is leading to enhanced methane fluxes into the water column, for instance by dissociation of gas hydrates or by enhanced methane production in coastal ecosystems. Previous investigations showed that a large portion (~80%) of ascending methane in ocean sediments is utilised by anaerobic and aerobic methanotrophic microbes, but future elevated methane fluxes might not be counterbalanced by this sedimentary methane filter. Today, about 0.02 Gt/yr (3-3.5% of the atmospheric budget) of methane bypasses the benthic filter system on a global scale, and subsequently escapes into ocean bottom waters. In the water column, it can be oxidised aerobically (aerobic oxidation of methane - MOx), or less commonly where the water column is anoxic, anaerobically. Water column MOx is the final sink for methane before its release to the atmosphere. However, little is known on the efficiency of this pelagic microbial filter and its ability to adjust to a (rapidly) changing environment. In order to predict future changes, it is thus crucial to understand the efficiency of current water-column MOx, to identify the key organisms mediating MOx, and - most importantly - to determine environmental parameters controlling MOx. In this dissertation, I investigated the pelagic MOx filter in contrasting ocean environments using a multidisciplinary approach. Systems studied included a deep-sea brine, two gas seep systems, and a shallow, organic-rich coastal environment. The main goals were to determine hot spots of MOx, identify bacteria mediating this process, and to estimate the efficiency of the pelagic methane filter. Furthermore, an important aim was to identify environmental factors controlling the activity and distribution of MOx, which could help in predicting changes of MOx in a future (warmer) ocean. My investigations revealed the following: 1. In the water column above methane gas seeps at the West Spitsbergen margin, MOx rate measurements together with CARD-FISH analysis of the methanotrophic community revealed rapid changes in the abundance of methanotrophs. Simultaneous measurements of physico-chemical water mass properties showed that the change in methanotrophic abundance correlated with changes in the water mass present above the seep system. This water mass exchange was caused by short-term variations in the position (i.e., offshore or nearshore) of the warm-water core of the West Spitsbergen Current: In its offshore mode, methanotroph-rich bottom waters above the methane seeps showed a high MOx capacity. A shift of the warm-water core towards the shelf break during the nearshore mode of the current displaced this cold bottom water with warm surface water containing a much smaller standing stock of methanotrophs, and led to a drop in MOx capacity of ~60%. This water mass exchange, caused by short-term variations of the West Spitsbergen Current, thus constitutes an oceanographic switch severely reducing methanotrophic activity in the water column. Since fluctuating currents are widespread oceanographic features common at many methane seep systems, it follows that the variability of physical water mass transport is a globally important control on the distribution and abundance of methanotrophs and, as a consequence, on the efficiency of methane oxidation above point sources. 2. At a Blowout in the North Sea resulting from an accident during industrial drilling activities, vigorous bubble emanation from the seafloor and strongly elevated methane concentrations in the water column (up to 42 μM) indicated that a substantial fraction of methane bypassed the highly active (up to ~2920 nmol/cm3/d) AOM zone in sediments. In the water column, we measured MOx rates that were among the highest ever measured in a marine environment (up to 498 nM/d) and, under stratified conditions, have the potential to remove a significant part of the released methane prior to evasion to the atmosphere. We speculate, however, that the MOx filter is intermittently inhibited when the water column is fully mixed, so that the Blowout is a source of methane to the atmosphere. An unusual dominance of the water-column methanotrophic community by Type II methanotrophs is partially supported by recruitment of sedimentary methanotrophs, which are entrained together with sediment particles in the methane bubble plume. Hence, our study demonstrates that gas ebullition not only provides ample methane substrate to fuel MOx in the water column, it also serves as an important transport vector for sediment-borne microbial inocula that aid in the establishment/proliferation of a water-column methanotrophic community at high-flux colds seeps. 3. We investigated MOx in the water column above gassy coastal sediments on quarterly basis over a time-period of two years. At the Boknis Eck study site, which is located in the coastal inlet Eckernförde Bay in the southwestern Baltic Sea, the water column is seasonally stratified with bottom waters becoming hypoxic over the course of the stratification period. We found that MOx rates exhibited a seasonal pattern with maximum rates (up to 11.6 nmol/l/d) during the summer months when oxygen concentrations were lowest and bottom water temperatures highest. Overall, MOx consumed between 70 – 95% of methane under stratified conditions, but only 40 – 60% under mixed conditions. Additional laboratory-based experiments with adjusted oxygen concentrations in the range of 0.2 – 220 μmol/l confirmed a sub-micromolar MOx oxygen optimum. In contrast, the percentage of methane-carbon incorporation into biomass was reduced at submicromolar oxygen concentrations, suggesting a different partitioning of catabolic and anabolic processes at saturated and sub-micromolar oxygen concentrations. Additional laboratory experiments verified the above-described mesophilic behaviour of the MOx communities of both surface and bottom waters. Our results highlight the importance of MOx in mitigating methane emission from coastal waters and indicate the existence of an adaptation to hypoxic conditions on the organismic level of the water column methanotrophs. 4. Life in the deep-sea brine basin Kryos in the Eastern Mediterranean Sea faces extreme challenges since the brine is almost saturated in bischofite (MgCl2 - 3.9 mol/kg). Due to the strong density difference between the anoxic brine and the overlying Mediterranean seawater, mixing is impeded and a shallow (<3 m) interface has formed. Our ex situ measurements of microbial activity revealed highly active MOx (up to 60 nmol/kg/d) at micro-oxic conditions within the interface. In line with elevated MOx rates, the residual methane within the interface was 13C-enriched when compared to the brine, and we found diagnostic, 13C-depleted lipid biomarkers (e.g., diplopterol, -46.6‰), which can be attributed to aerobic methanotrophs. Additionally, we detected relatively δ13C-enriched fatty acids (up to -18‰) in the lower interface and in the brine, which are an indication for a different carbon fixation pathway than the Calvin Benson Cycle, such as the reverse tri-carboxylic acid carbon-fixation pathway found in sulfur-oxidizing Epsilonproteobacteria. Within the brine, we could not find evidence for AOM, despite of thermodynamically favorable conditions for this process. In contrast, we measured high rates of sulfate reduction within the brine (up to 430 μmol/kg/d) providing evidence that sulfate reducers are active under nearly Mg2+-saturated concentrations. Our results emphasize the adaptation of microbial life to the extremely harsh conditions below the chaotropicity limits of life in MgCl2-rich environments

Competition in nitrate-reducing microbial communities

The biogeochemical nitrogen cycle, including nitrate reduction processes, is highly affected by human activity such as fertilization and ammonia deposition caused by fossil fuel burning. Consequently, gaining a better understanding about the ecophysiology of nitrate-reducing microbial communities is crucial for inferring the impact of anthropogenic nitrogen input. Different nitrate-reducing pathways compete with each other for the electron acceptor nitrate: Denitrifiers reduce nitrate to dinitrogen and nitrous oxide while dissimilatory nitrate reducers reduce nitrate to ammonium. The outcome of this competition has important environmental consequences: denitrification removes fixed nitrogen from the ecosystem, while dissimilatory nitrite reduction to ammonium (DNRA) keeps fixed nitrogen bioavailable. Although a lot of studies have been performed on this topic, no conclusive factors responsible for the dominance of one or the other process could be identified so far. In this thesis, the competition between nitrate reduction pathways was addressed by combining continuous culture incubations of natural microbial communities with stable isotope labeling and metagenomics, complemented with metatranscriptomics and metaproteomics in order to gain insight into the identity, function and interaction of the enriched microbial populations. To be able to make the best use of the obtained metagenomic data a new metagenomic binning procedure was developed. Before the competition between two different nitrate reduction pathways was studied, the relationship between functional and compositional stability over time within one nitrate reduction pathway was investigated: In a heterotrophic denitrifying microbial community, enriched from a marine intertidal flat, strong community dynamics were occurring under constant conditions and during stable conversion of substrates. A stable metabolic interaction between the denitrifying populations and co-enriched fermenting microbes persisted throughout the experiment unaffected by the ongoing population dynamics. This indicated that functional stability was independent of the community composition. Apparently, only the persistence of the overall metabolic potential was important to maintain functional stability. This suggested that stochastic as well as deterministic processes are responsible for the observed community composition. Once the functional stability of denitrification was confirmed and interactions with other microbial guilds were known the competition between DNRA and denitrification was addressed. Several parallel continuous culture incubations that differed in one condition but were otherwise constant led to the identification of the generation time as most important control on the competition between DNRA and denitrification. The organic carbon to nitrate ratio and the kind of electron acceptor supplied (nitrate or nitrite) were identified as further controlling factors that together with the generation time discriminated between the two pathways. The metabolic interaction between nitrate- reducing and fermenting populations was stable under both pathways. One quarter of the nitrate reduction was coupled to the oxidation of sulfide, which was produced in the enrichment culture by microbial sulfate reduction, constituting a strong link between the nitrogen and sulfur cycle. All in all, this thesis provides new insights into the ecophysiology of microbial nitrate reducers by unraveling the driving forces of the competition between different nitrate reduction pathways and by revealing important metabolic interactions with other microbial guilds

Generation of (synthetic) influent data for performing wastewater treatment modelling studies

The success of many modelling studies strongly depends on the availability of sufficiently long influent time series - the main disturbance of a typical wastewater treatment plant (WWTP) - representing the inherent natural variability at the plant inlet as accurately as possible. This is an important point since most modelling projects suffer from a lack of realistic data representing the influent wastewater dynamics. The objective of this paper is to show the advantages of creating synthetic data when performing modelling studies for WWTPs. This study reviews the different principles that influent generators can be based on, in order to create realistic influent time series. In addition, the paper summarizes the variables that those models can describe: influent flow rate, temperature and traditional/emerging pollution compounds, weather conditions (dry/wet) as well as their temporal resolution (from minutes to years). The importance of calibration/validation is addressed and the authors critically analyse the pros and cons of manual versus automatic and frequentistic vs Bayesian methods. The presentation will focus on potential engineering applications of influent generators, illustrating the different model concepts with case studies. The authors have significant experience using these types of tools and have worked on interesting case studies that they will share with the audience. Discussion with experts at the WWTmod seminar shall facilitate identifying critical knowledge gaps in current WWTP influent disturbance models. Finally, the outcome of these discussions will be used to define specific tasks that should be tackled in the near future to achieve more general acceptance and use of WWTP influent generators