Computational Intelligence Techniques for Control and Optimization of Wastewater Treatment Plants

The development of novel, practice-oriented and reliable instrumentation and control strategies for wastewater treatment plants in order to improve energy efficiency, while guaranteeing process stability and maintenance of high cleaning capacity, has become a priority for WWTP operators due to increasing treatment costs. To achieve these ambitious and even contradictory objectives, this thesis investigates a combination of online measurement systems, computational intelligence and machine learning methods as well as dynamic simulation models. Introducing the state-of-the-art in the fields of WWTP operation, process monitoring and control, three novel computational intelligence enabled instrumentation, control and automation (ICA) methods are developed and presented. Furthermore, their potential for practical implementation is assessed. The methods are, on the one hand, the automated calibration of a simulation model for the Rospe WWTP that provides a basis for the development and evaluation of the subsequent methods, and on the other hand, the development of soft sensors for the WWTP inflow which estimate the crucial process variables COD and NH4-N, and the estimation of WWTP operating states using Self- Organising Maps (SOM) that are used to determine the optimal control parameters for each state. These collectively, provide the basis for achieving comprehensive WWTP optimization. Results show that energy consumption and cleaning capacity can be improved by more than 50%

Development of an intelligent dynamic modelling system for the diagnosis of wastewater treatment processes

In the 21st Century, water is already a limited and valuable resource, in particular the limited availability of fresh water sources. The projected increase in global population from 6 billion people in 2010 to 9 billion in 2050 will only increase the need for additional water sources to be identified and used. This situation is common in many countries and is frequently exacerbated by drought conditions. Water management planning requires both the efficient use of water sources and, increasingly, the re-use of domestic and industrial wastewaters. A large body of published research spanning several decades is available, and this research study looks specifically at ways of improving the operation of wastewater treatment processes.Process fault diagnosis is a major challenge for the chemical and process industries, and is also important for wastewater treatment processes. Significant economic and environmental losses can be attributed to inappropriate Abnormal Event Management (AEM) in a chemical/processing operation, and this has been the focus of many researchers. Many researchers are now focusing on the application of several fault diagnosis techniques simultaneously in order to improve and overcome the limitations experienced by the individual techniques. This approach requires resolution of the conflicts ascribed to the individual methods, and incurs additional costs and resources when employing more than one technique. The research study presented in this thesis details a new method of using the available techniques. The proposal is to use different techniques in different roles within the diagnostic approach based upon their inherent individual strengths. The techniques that are excellent for the detection of a fault should be employed in the fault detection, and those best applied to diagnosis are used in the diagnosis section of a diagnostic system.Two different techniques are used here, namely a mathematical model and data mining are used for detection and diagnosis respectively. A mathematical model is used which is based upon the principal of analytical redundancy in order to establish the presence of a fault in a process (the fault detection), and data mining is used to produce production rules derived from the historical data for the diagnosis. A dataset from an industrial wastewater treatment facility is used in this study.A diagnostic algorithm has been developed that employs the techniques identified above. An application in Java was constructed which allows the algorithm to be applied, eventually producing an intelligent modelling agent. Thus the focus of this research work was to develop an intelligent dynamic modelling system (using components such as mathematical model, data mining, diagnostic algorithm, and the dataset) for simulation of, and diagnosis of faults in, a wastewater treatment process where different techniques will be assigned different roles in the diagnostic system.Results presented in Chapter 5 (section 5.5) show that the application of this combined technique yields better results for detection and diagnosis of faults in a process. Furthermore, the dynamic update of the set value for any process variable (presented in Chapter 5, section 5.2.1) makes possible the detection of any process disturbance for the algorithm, thereby mitigating the issue of false alarms. The successful embedding of both a detection and a diagnostic technique in a single algorithm is a key achievement of this work, thus reducing the time taken to detect and diagnose a fault. In addition, the implementation of the algorithm in the purposebuilt software platform proved its practical application and potential to be used in the chemical and processing industries

Multivariate Analysis in Management, Engineering and the Sciences

Recently statistical knowledge has become an important requirement and occupies a prominent position in the exercise of various professions. In the real world, the processes have a large volume of data and are naturally multivariate and as such, require a proper treatment. For these conditions it is difficult or practically impossible to use methods of univariate statistics. The wide application of multivariate techniques and the need to spread them more fully in the academic and the business justify the creation of this book. The objective is to demonstrate interdisciplinary applications to identify patterns, trends, association sand dependencies, in the areas of Management, Engineering and Sciences. The book is addressed to both practicing professionals and researchers in the field

Modelling activated sludge wastewater treatment plants using artificial intelligence techniques (fuzzy logic and neural networks)

Activated sludge process (ASP) is the most commonly used biological wastewater treatment system. Mathematical modelling of this process is important for improving its treatment efficiency and thus the quality of the effluent released into the receiving water body. This is because the models can help the operator to predict the performance of the plant in order to take cost-effective and timely remedial actions that would ensure consistent treatment efficiency and meeting discharge consents. However, due to the highly complex and non-linear characteristics of this biological system, traditional mathematical modelling of this treatment process has remained a challenge. This thesis presents the applications of Artificial Intelligence (AI) techniques for modelling the ASP. These include the Kohonen Self Organising Map (KSOM), backpropagation artificial neural networks (BPANN), and adaptive network based fuzzy inference system (ANFIS). A comparison between these techniques has been made and the possibility of the hybrids between them was also investigated and tested. The study demonstrated that AI techniques offer viable, flexible and effective modelling methodology alternative for the activated sludge system. The KSOM was found to be an attractive tool for data preparation because it can easily accommodate missing data and outliers and because of its power in extracting salient features from raw data. As a consequence of the latter, the KSOM offers an excellent tool for the visualisation of high dimensional data. In addition, the KSOM was used to develop a software sensor to predict biological oxygen demand. This soft-sensor represents a significant advance in real-time BOD operational control by offering a very fast estimation of this important wastewater parameter when compared to the traditional 5-days bio-essay BOD test procedure. Furthermore, hybrids of KSOM-ANN and KSOM-ANFIS were shown to result much more improved model performance than using the respective modelling paradigms on their own.Damascus Universit

Multi-tier framework for the inferential measurement and data-driven modeling

A framework for the inferential measurement and data-driven modeling has been proposed and assessed in several real-world application domains. The architecture of the framework has been structured in multiple tiers to facilitate extensibility and the integration of new components. Each of the proposed four tiers has been assessed in an uncoupled way to verify their suitability. The first tier, dealing with exploratory data analysis, has been assessed with the characterization of the chemical space related to the biodegradation of organic chemicals. This analysis has established relationships between physicochemical variables and biodegradation rates that have been used for model development. At the preprocessing level, a novel method for feature selection based on dissimilarity measures between Self-Organizing maps (SOM) has been developed and assessed. The proposed method selected more features than others published in literature but leads to models with improved predictive power. Single and multiple data imputation techniques based on the SOM have also been used to recover missing data in a Waste Water Treatment Plant benchmark. A new dynamic method to adjust the centers and widths of in Radial basis Function networks has been proposed to predict water quality. The proposed method outperformed other neural networks. The proposed modeling components have also been assessed in the development of prediction and classification models for biodegradation rates in different media. The results obtained proved the suitability of this approach to develop data-driven models when the complex dynamics of the process prevents the formulation of mechanistic models. The use of rule generation algorithms and Bayesian dependency models has been preliminary screened to provide the framework with interpretation capabilities. Preliminary results obtained from the classification of Modes of Toxic Action (MOA) indicate that this could be a promising approach to use MOAs as proxy indicators of human health effects of chemicals.Finally, the complete framework has been applied to three different modeling scenarios. A virtual sensor system, capable of inferring product quality indices from primary process variables has been developed and assessed. The system was integrated with the control system in a real chemical plant outperforming multi-linear correlation models usually adopted by chemical manufacturers. A model to predict carcinogenicity from molecular structure for a set of aromatic compounds has been developed and tested. Results obtained after the application of the SOM-dissimilarity feature selection method yielded better results than models published in the literature. Finally, the framework has been used to facilitate a new approach for environmental modeling and risk management within geographical information systems (GIS). The SOM has been successfully used to characterize exposure scenarios and to provide estimations of missing data through geographic interpolation. The combination of SOM and Gaussian Mixture models facilitated the formulation of a new probabilistic risk assessment approach.Aquesta tesi proposa i avalua en diverses aplicacions reals, un marc general de treball per al desenvolupament de sistemes de mesurament inferencial i de modelat basats en dades. L'arquitectura d'aquest marc de treball s'organitza en diverses capes que faciliten la seva extensibilitat així com la integració de nous components. Cadascun dels quatre nivells en que s'estructura la proposta de marc de treball ha estat avaluat de forma independent per a verificar la seva funcionalitat. El primer que nivell s'ocupa de l'anàlisi exploratòria de dades ha esta avaluat a partir de la caracterització de l'espai químic corresponent a la biodegradació de certs compostos orgànics. Fruit d'aquest anàlisi s'han establert relacions entre diverses variables físico-químiques que han estat emprades posteriorment per al desenvolupament de models de biodegradació. A nivell del preprocés de les dades s'ha desenvolupat i avaluat una nova metodologia per a la selecció de variables basada en l'ús del Mapes Autoorganitzats (SOM). Tot i que el mètode proposat selecciona, en general, un major nombre de variables que altres mètodes proposats a la literatura, els models resultants mostren una millor capacitat predictiva. S'han avaluat també tot un conjunt de tècniques d'imputació de dades basades en el SOM amb un conjunt de dades estàndard corresponent als paràmetres d'operació d'una planta de tractament d'aigües residuals. Es proposa i avalua en un problema de predicció de qualitat en aigua un nou model dinàmic per a ajustar el centre i la dispersió en xarxes de funcions de base radial. El mètode proposat millora els resultats obtinguts amb altres arquitectures neuronals. Els components de modelat proposat s'han aplicat també al desenvolupament de models predictius i de classificació de les velocitats de biodegradació de compostos orgànics en diferents medis. Els resultats obtinguts demostren la viabilitat d'aquesta aproximació per a desenvolupar models basats en dades en aquells casos en els que la complexitat de dinàmica del procés impedeix formular models mecanicistes. S'ha dut a terme un estudi preliminar de l'ús de algorismes de generació de regles i de grafs de dependència bayesiana per a introduir una nova capa que faciliti la interpretació dels models. Els resultats preliminars obtinguts a partir de la classificació dels Modes d'acció Tòxica (MOA) apunten a que l'ús dels MOA com a indicadors intermediaris dels efectes dels compostos químics en la salut és una aproximació factible.Finalment, el marc de treball proposat s'ha aplicat en tres escenaris de modelat diferents. En primer lloc, s'ha desenvolupat i avaluat un sensor virtual capaç d'inferir índexs de qualitat a partir de variables primàries de procés. El sensor resultant ha estat implementat en una planta química real millorant els resultats de les correlacions multilineals emprades habitualment. S'ha desenvolupat i avaluat un model per a predir els efectes carcinògens d'un grup de compostos aromàtics a partir de la seva estructura molecular. Els resultats obtinguts desprès d'aplicar el mètode de selecció de variables basat en el SOM milloren els resultats prèviament publicats. Aquest marc de treball s'ha usat també per a proporcionar una nova aproximació al modelat ambiental i l'anàlisi de risc amb sistemes d'informació geogràfica (GIS). S'ha usat el SOM per a caracteritzar escenaris d'exposició i per a desenvolupar un nou mètode d'interpolació geogràfica. La combinació del SOM amb els models de mescla de gaussianes dona una nova formulació al problema de l'anàlisi de risc des d'un punt de vista probabilístic

Digestat fra biogassproduksjon som substrat og vektor for introduksjon av N2O-respirerende bakterier til landbuksjord

Anthropogenic nitrous oxide (N2O) emissions are largely driven by the input of N-based fertilizers in agriculture. N2O emissions from agricultural soils in Europe are estimated to 0.51 Tg annually (Fig. I), which sums to 48 % of total European N2O emissions and 35 % of the climate forcing from European agriculture. Yet, N2O emission mitigation from agriculture is still hampered by a lack of implemented abatement options. Whilst several biogeochemical reactions may release N2O (Fig. I) the enzyme nitrous oxide reductase (Nos) is the only known enzyme to reduce nitrous oxide. Nos is expressed in denitrifying and non-denitrifying prokaryotes and catalyzes the reduction of N2O to N2. The complete denitrification pathway is the stepwise reduction NO3- → NO2- → NO → N2O → N2, catalyzed by the enzymes Nar/Nap, Nir, Nor, and Nos that are encoded by the genes nar/nap, nirK/nirS, nor, and nosZ, respectively (Fig. I). A significant proportion of the denitrifying community in soils have truncated denitrification pathways, i.e. lacking one to three of the genes encoding the enzymes in the stepwise reduction of NO3- to N2. The consequence of such modularity is that organisms lacking nosZ are net N2O emitters, while organisms with nosZ only are net sinks for N2O. However, organisms equipped with a complete denitrification pathway can also be strong sinks or sources of N2O depending on their regulatory biology. N2O emissions from soils make up a substantial fraction of the climate forcing from food production and mitigation beyond that achieved by “good management practices” are needed if we are to limit global warming by 2 °C, as set in the Paris Agreement. One approach for reducing N2O emissions is to modify the soil microbiome, increasing the proportion of N2O-respiring bacteria (NRB) resulting in reduced N2O emissions. This would, however, be costly and impractical as a standalone operation. As an element towards a low-carbon circular economy, the volume of organic wastes channeled through AD is expected to increase in the coming decades. This presents a unique possibility for mitigation of N2O emissions as the residues of biogas production, digestates, destined as bio-fertilizers in agriculture, could be enriched with N2O-respiring bacteria before soil fertilization. Thus, providing a cost-efficient N2O mitigation measure (Fig. I). Here we demonstrate the use of biogas digestates from anaerobic digestion (AD) as a widely available, low-cost vector for NRB to agricultural soils. A primary task was to search for suitable organisms that 1) could grow to high cell densities in digestate and 2) would act as net N2O sinks in soil. To achieve this, enrichment culturing under anaerobic conditions with N2O as the sole electron acceptor was used. The enrichment cultures were monitored both by measuring the gas kinetics and by inspecting the composition of the microbiota by genomics and proteomics. Based on genomic information and targeted isolation, we obtained axenic cultures of the organisms that became dominant in the enrichment cultures. As a first approach, we enriched indigenous N2O-respiring bacteria in anaerobically digested sewage sludge (digestate) by anoxic incubation with N2O. The gas kinetics predicted that N2O-respiring organisms grew to high cell densities, which was confirmed by metagenomic and metaproteomic (omics-) analyses of the enriched digestate. The omics demonstrated dominance of organisms equipped with the nosZ clade II (coding for N2O-reductase), but also with the genes for the preceding steps of the denitrification pathway. Three digestate-derived N2O-reducing bacteria were isolated, of which one (Azonexus sp.) matched the recovered Metagenome-Assembled Genome (MAG) of the dominant N2O reducer with an average nucleotide identity (ANI) of 98.2%. This MAG also demonstrated a high complement of Nos in the enrichment as quantified by metaproteomics. Gas kinetics and meta-omics indicated that the anaerobic consortium of the digestate remained active during anaerobic incubation with N2O and that N2O-respiring bacteria grew by harvesting fermentation intermediates. The latter was supported by screening carbon catabolism profiles of the isolated organisms. The isolated Azonexus sp. demonstrated regulatory traits that would predict the organism to be a strong N2O sink, and it reduced immediate N2O emissions from digestate-amended soils. However, the Azonexus sp. was probably not an ideal N2O-respiring inoculant in soil because it was equipped with a full-fledged denitrification pathway and because its capacity to utilize soil carbon was limited. The importance of an active methanogenic community throughout the enrichments, providing fermentation intermediates as a carbon source for the N2O-respiring organisms, would predict a selective advantage for organisms with a streamlined (narrow) catabolic capacity, which was the case for the Azonexus sp.. It was evident that we needed to refine our search, to find organisms with a broader catabolic repertoire. A new procedure to obtain more ideal isolates was designed, involving a deliberate enrichment of N2O-respiring organisms with the characteristics of strong growth both in digestate and soil. We thought this could be achieved by “dual enrichment culturing”, i.e. a sequence of enrichment cultures where a fraction of a batch enrichment was passaged to the next batch, alternating between sterile soil and sterile digestate as substrate. Our point of departure was to model this approach, using a simple logistic model for the competition for a common substrate, between three distinctive groups; 1: Organisms with a competitive advantage in digestate (digestate specialists), 2: Organisms with a competitive advantage in soil (soil specialists), and 3: organisms capable of sustaining growth in both environments (generalists). The modelling revealed that generalists could indeed become dominant within a limited number of batch cultures, depending on their competitive edge vis a vis the specialists. Based on this we realized a dual enrichment experiment, using the microbiota of wastewater digestate and soil as initial inocula, sterile digestate and sterile soil as substrate, and monitored the gas kinetics and the community composition (by 16S rDNA amplicon sequencing) throughout seven consecutive enrichment cultures. The gas kinetics corroborated the model’s prediction of a gradual enrichment of organisms that grew both in soil and digestate, and the generalists that became dominant were identified as a limited number of Operational Taxonomic Units (OTUs, based on 16S rDNA sequencing). OTUs that became dominant circumscribed isolates obtained from the enrichment cultures. These OTUs also portrayed the targeted generalist as predicted by the modelling. Most isolates obtained had traits of strong N2O sinks, of which a dominating Cloacibacterium sp., carrying Nos (Clade II) as the sole N-reductase, significantly reduced N2O emissions in digestate amended soils of both neutral and acidic pH. A full-fledged denitrifying Pseudomonas sp. was able to persist in the soil for at least one month whereby significant N2O emissions reduction was obtained upon a fertilization event. Genome analysis of the isolated organisms shed some light as to why these organisms had a competitive advantage in both soil and digestate. Although the ideal isolate is yet to be found, we’ve opened an avenue to a concept that, within the expected expansion of AD, could be scaled to secure a substantial reduction in N2O emissions.Menneskeskapte utslipp av drivhusgassen lystgass (N2O) skyldes i stor grad tilførsel av nitrogenholdig gjødsel til landbruksjord. N2O-utslipp fra landbruksjord i Europa er estimert til 0,51 Tg årlig (Fig. I), som utgjør om lag 48% av de totale utslippene av N2O, som igjen representerer 35 % av det totale klimagassfotavtrykket fra europeisk landbruk. Begrensning av disse utslippene har vært utfordrende grunnet mangel på implementerte metoder og teknologier som effektivt reduserer lystgassutslippet fra landbruksjord. Flere biogeokjemiske reaksjoner kan frigjøre N2O (Fig. I), men enzymet lystgassreduktase (Nos) er det eneste kjente enzymet som reduserer N2O til N2. Nos uttrykkes av denitrifiserende prokaryoter og katalyserer reduksjonen av N2O til N2. Denitrifiserende prokaryoter katalyserer den trinnvise reduksjon av NO3- → NO2- → NO → N2O → N2, som katalyseres av enzymene Nar/Nap, Nir, Nor og Nos som er kodet av genene nar/nap, nir, nor og nosZ (Fig. I). Men, en betydelig andel av det denitrifiserende mikrobesamfunnet i jord er trunkert, dvs. en andel av denitrifikantene mangler ett til tre av genene som koder enzymene involvert i reduksjonen av NO3- til N2. En organisme som kun mangler nosZ vil produsere N2O. I motsatt tilfelle vil en organisme som kun er utstyrt med nosZ bare evne å redusere N2O. Organismer utstyrt med et komplett sett av gener for en fullstendig denitrifikasjon kan være både sterke og svake N2O-reduktanter. Dette bestemmes av deres regulatoriske biologi. N2O-utslipp fra jord utgjør en betydelig mengde av det totale klimafotavtrykket fra matproduksjon og en reduksjon av dette utslippet er nødvendig om vi skal nå de målene som er satt i Parisavtalen og begrense global oppvarming til 2 °C. En mulighet for å redusere N2O-utslipp er å modifisere jordmikrobiomet ved å øke andelen N2O-respirerende bakterier (NRB) – noe som vil redusere utslippene av N2O. Men, som ett frittstående tiltak vil en storskala modifisering av mikrobiologien i jordsmonnet være svært ressurskrevende. Som et ledd i overgangen til en lav-karbon sirkulærøkonomi forventes anaerob utråtning (AD) å øke i omfang og rekkevidde de neste årene. Denne utviklingen skaper en unik mulighet for å redusere N2O-utslipp dersom digestater, restproduktet fra AD, som brukes som organisk gjødsel i landbruket, kan anrikes med N2O-reduserende bakterier før disse digestatene benyttes som gjødsel (Fig. I). Her demonstrerer vi at lett tilgjengelige digestater kan benyttes som vekstsubstrat og en vektor for å overføre NRB til jord. En slik modifikasjon være et svært kostnadseffektivt N2O-reduserende tiltak. Det primære målet i denne avhandlingen var å lete etter egnede organismer som 1) kan gro til høy celletetthet i digestater, og 2) redusere N2O-utslipp fra jord. For å oppnå dette ble anrikninger av slike organismer ved bruk av N2O som eneste elektronakseptor gjennomført. Anrikningskulturene ble monitorert ved å måle gasskinetikk og ved overvåking av samfunnsprofiler og bakteriell populasjonsdynamikk ved bruk av DNA- og proteomanalyser. Med basis i den genetiske informasjonen var målet å isolere dominerende organismer fra anrikningskulturene. Som en første tilnærming anriket vi N2O-reduserende bakterier som er naturlig tilstedeværende i digestat i anoksiske inkubasjoner hvor N2O ble tilsatt som eneste elektronakseptor. Gasskinetikk predikerte at NRB vokste til høye celletettheter under inkubasjonen, som ble bekreftet av metagenom- og metaproteomanalyser av det anrikede digestatet. Meta-omikk analysene viste at organismer utstyrt med nosZ Type II (genet for N2O-reduktase), men også med de øvrige genene for et komplett denitrifiseringsspor, dominerte anrikningen. Tre N2O-reduserende bakterier ble isolert hvorav det ene isolatet, en Azonexus sp., samsvarte med et gjenvunnet Dechloromonas-beslektet metagenom som dominerte anrikningen med en aminosyreidentitet på 98,2% delt med det dominerende metagenomet. Metaproteomikk viste at dette metagenomet utrykte brorparten av Nos under anrikningen. Gasskinetikk og meta-omikk avslørte videre at det metanogene konsortiet i digestatet forblir aktivt også under den anaerobe inkubasjonen med N2O, og at dominerende bakterier med en anaerob respiratorisk metabolisme sannsynligvis vokste ved å høste fermenteringsmellomprodukter fra det metanogene samfunnet. Det sistnevnte ble støttet ved karbonkatabolismeprofiler for de isolerte organismene. Den isolerte Azonexus sp. demonstrerte regulatoriske egenskaper som ville forutsi at organismen var en sterk N2O-reduktant, og den reduserte N2O-utslipp fra jord gjødslet med Azonexus anriket digestat. Likevel så var anrikningsvinneren sannsynligvis ikke en ideell N2O-reduserende inokulant i jord fordi dens evne til å overleve i jord-miljøet sannsynligvis var begrenset. Betydningen av et aktivt metanogent bakteriesamfunn, som produsenter av karbonkilder for NRB igjennom anrikningene, gav sannsynligvis en selektiv fordel for organismer med en strømlinjeformet (smal) katabolsk kapasitet, som var tilfelle for Azonexus sp.. Det var tydelig at vi trengte å videreforedle anrikningsprosedyrene våre for å anrike kompetente organismer en bredere metabolsk fleksibilitet. En ny tilnærming for å oppnå mer ideelle isolater som evner å vokse i både jord og i digestat ble designet med utgangspunkt i å selektivt anrike organismer med disse egenskapene. Vi antok at slike organismer kunne anrikes ved en «dobbelt-anrikning»-prosedyre der miljøet ble vekslet mellom jord og digestat. Mao: En sekvens av batch-anrikningskulturer hvor en overfører en fraksjon av anrikningen til en ny batch og vekslet mellom jord og digestat som vekstsubstrat. Med dette utgangspunktet ble logistisk vekst, kun med konkurranse om tilgjengelig karbon, modellert for tre ulike bakteriegrupper; 1) Organismer med konkurransefortrinn i digestat (digestat-spesialister), 2) Organismer med konkurransefortrinn i jord (jordspesialister), og 3) organismer som er i stand til å opprettholde vekst/aktivitet i begge miljøer (generalister). Modelleringen avslørte at generalister teoretisk sett kunne anrikes ved å passere fraksjoner av disse anrikningene mellom digestat og jord, avhengig av generalistenes konkurransefortrinn relativt til spesialistene. Basert på denne modelleringen realiserte vi et nytt anrikningseksperiment med bruk av digestat og jord som initielt inokulum og sterilt digestat og jord som vekstsubstrat og lot populasjonene konkurrere om tilgjengelig karbon med tilsats av N2O. Monitorering av gasskinetikk og populasjonsdynamikk (ved 16S amplikonsekvensering) igjennom syv sammenhengende anrikninger viste en populasjonsutvikling slik predikert fra modelleringen: Gasskinetikken støttet modellprediksjonen om en gradvis ankrikning av organismer som vokste i jord og digestat, og 16S-analysen vist at et fåtall operasjonelle taksonomiske enheter (OTUer) dominerte anrikningen. Isolatene fra disse anrikningskulturene var omsluttet av en dominerende gruppe OTUer som portretterte vekstegenskaper igjennom hele anrikningsserien som representerte de ønskede generalistvinnerne. Ett av isolatene, en Cloacibacterium sp., hvis genom kun kodet for genet for Nos, dominerte anrikningene, og denne reduserte også N2O-utslipp i jord med lav pH. Et annet isolat, en Pseudomonas sp., demonstrert en mer langvarig N2O reduserende aktivitet i jord da aktiviteten var fremtredende selv 30 dager etter gjødsling. Genomanalyse av isolerte organismer kastet noe lys kring hvorfor disse organismer kunne ha et konkurransefortrinn i anrikningene. Selv om det ideelle isolatet ennå ikke er funnet, har vi åpnet en vei for et konsept som, i kontekst av den forventede utviklingen av AD, kan skaleres for å sikre betydelig reduksjon i N2O-utslipp.Vestfjorden Avløpsselskap (VEAS

Biological investigation and predictive modelling of foaming in anaerobic digester

Anaerobic digestion (AD) of waste has been identified as a leading technology for greener renewable energy generation as an alternative to fossil fuel. AD will reduce waste through biochemical processes, converting it to biogas which could be used as a source of renewable energy and the residue bio-solids utilised in enriching the soil. A problem with AD though is with its foaming and the associated biogas loss. Tackling this problem effectively requires identifying and effectively controlling factors that trigger and promote foaming. In this research, laboratory experiments were initially carried out to differentiate foaming causal and exacerbating factors. Then the impact of the identified causal factors (organic loading rate-OLR and volatile fatty acid-VFA) on foaming occurrence were monitored and recorded. Further analysis of foaming and nonfoaming sludge samples by metabolomics techniques confirmed that the OLR and VFA are the prime causes of foaming occurrence in AD. In addition, the metagenomics analysis showed that the phylum bacteroidetes and proteobacteria were found to be predominant with a higher relative abundance of 30% and 29% respectively while the phylum actinobacteria representing the most prominent filamentous foam causing bacteria such as Norcadia amarae and Microthrix Parvicella had a very low and consistent relative abundance of 0.9% indicating that the foaming occurrence in the AD studied was not triggered by the presence of filamentous bacteria. Consequently, data driven models to predict foam formation were developed based on experimental data with inputs (OLR and VFA in the feed) and output (foaming occurrence). The models were extensively validated and assessed based on the mean squared error (MSE), root mean squared error (RMSE), R2 and mean absolute error (MAE). Levenberg Marquadt neural network model proved to be the best model for foaming prediction in AD, with RMSE = 5.49, MSE = 30.19 and R2 = 0.9435. The significance of this study is the development of a parsimonious and effective modelling tool that enable AD operators to proactively avert foaming occurrence, as the two model input variables (OLR and VFA) can be easily adjustable through simple programmable logic controller