Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

Biosorption Parameter Estimation with Genetic Algorithm

In biosorption research, a fairly broad range of mathematical models are used to correlate discrete data points obtained from batch equilibrium, batch kinetic or fixed bed breakthrough experiments. Most of these models are inherently nonlinear in their parameters. Some of the models have enjoyed widespread use, largely because they can be linearized to allow the estimation of parameters by least-squares linear regression. Selecting a model for data correlation appears to be dictated by the ease with which it can be linearized and not by other more important criteria such as parameter accuracy or theoretical relevance. As a result, models that cannot be linearized have enjoyed far less recognition because it is necessary to use a search algorithm for parameter estimation. In this study a real-coded genetic algorithm is applied as the search method to estimate equilibrium isotherm and kinetic parameters for batch biosorption as well as breakthrough parameters for fixed bed biosorption. The genetic algorithm is found to be a useful optimization tool, capable of accurately finding optimal parameter estimates. Its performance is compared with that of nonlinear and linear regression method

Using Parallel Genetic Algorithms for Estimating Model Parameters in Complex Reactive Transport Problems

In this study, we present the details of an optimization method for parameter estimation of one-dimensional groundwater reactive transport problems using a parallel genetic algorithm (PGA). The performance of the PGA was tested with two problems that had published analytical solutions and two problems with published numerical solutions. The optimization model was provided with the published experimental results and reasonable bounds for the unknown kinetic reaction parameters as inputs. Benchmarking results indicate that the PGA estimated parameters that are close to the published parameters and it also predicted the observed trends well for all four problems. Also, OpenMP FORTRAN parallel constructs were used to demonstrate the speedup of the code on an Intel quad-core desktop computer. The parallel code showed a linear speedup with an increasing number of processors. Furthermore, the performance of the underlying optimization algorithm was tested to evaluate its sensitivity to the various genetic algorithm (GA) parameters, including initial population size, number of generations, and parameter bounds. The PGA used in this study is generic and can be easily scaled to higher-order water quality modeling problems involving real-world application

Transport of Enterococcus faecalis JH2-2 through sandy sediments: A combined experimental and modelling approach

The agricultural sector is one of the largest consumers of fresh water. With the ever-increasing problem of water scarcity, urbanization, over-population, and climate change, fresh water resources used by agriculture could be put to better use by redirecting it for drinking water purposes. In this context, many countries reuse treated urban waste water for irrigation, to overcome this problem. While this is a sustainable practice, the reuse of urban wastewater could facilitate the spread of pathogenic bacteria (or antibiotic resistant bacteria) in the subsoil region and consequently the groundwater. Since groundwater is one of the main sources of drinking water, the contaminants could pose a risk to human health. Furthermore, obtaining scientific data for emerging contaminants during water reuse is the need of the hour. The objective of this work is to build a mechanistic model that can aid in the development of large-scale risk assessment models; thus facilitating the setup of water reuse regulations for the relevant pathogenic organisms. In the present study, process based models were developed and evaluated using lab scale results. Then, the relative time scales of the processes are compared, and the relative importance of the various process studies are assessed. When assessing time scales of the processes, it is kept in mind that processes with relatively fast time scales can be approximated using equilibrium models, relatively slow processes can be neglected, and only the rate limiting processes can neither be neglected or further simplified in further model development. Therefore, an idea of the rate limiting processes assessed in lab scale can serve as important tools facilitating model simplification when evaluating larger scale models. A combined experimental and modelling approach has been used to study relevant transport and reactive processes during bacteria transport through sandy sediments. The mechanistic model contained transport processes which were implemented using the advective dispersive equation. An additional straining process was added using non-linear rate law. The biological processes of decay, respiration, attachment, and growth were expressed using linear rate laws. This mechanistic model was verified using data from fully water saturated, sediment packed lab-scale column experiments. Continuous injection of tracer, microspheres, and Enterococci (in water environments with and without dissolved oxygen and nutrients) was performed. The experiment was verified for three flow velocities (0.13, 0.08 and 0.02 cm/min), and the parameter values were compared for these flow velocities using dimensionless numbers. The linear rate coefficients were converted to a dimensionless form (Peclet and Damkoehler numbers respectively) to facilitate the comparison of processes across the various flow velocities. The results indicate that the processes of attachment and growth are flow dependent. Furthermore, in the presence of dissolved oxygen, attachment of bacteria to sediment was the most influential process. Sensitivity analysis showed that the parameters representing growth and respiration were influential, and care must be taken when using the results for field-scale experiments or models. These processes and parameters add new knowledge on the impact of urban wastewater reuse on the spread of pathogenic bacteria (especially resilient species like Enterococci), and emphasizes the importance of research in this area. Future work could focus on obtaining data from culture independent methods and extension of the model framework, and include (where necessary) non-linear rate laws. This will provide a critical pathway to developing a decision support framework for use by regulatory frameworks, policy makers, stakeholders, local and global environmental agencies, World Health Organization, or the United Nations.:List of Figures vii List of Tables xi List of Abbreviations xiii List of Symbols xv Summary xvii Zussamenfassung xix 1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 Broad Scope. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Hypotheses and Research objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3 Outline of the work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2 Concepts, terminologies, and methodology 7 2.1 Concepts and terminologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.1.2 The vadose zone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.1.3 Porosity and pore models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.1.4 Darcy’s law . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.2 Bacteria strain used and Processes Studied . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.2.1 Enterococcus faecalis JH2-2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.2.2 Advection and Dispersion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.2.3 Straining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.2.4 Microbial Decay and Respiration . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.2.5 Microbial Attachment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.2.6 Microbial Growth. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.2.7 Dimensionless numbers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.3 Experimental design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.4 Model setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3 Reactive-transport modelling of Enterococcus faecalis JH2-2 passage through water saturated sediment columns. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 3.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3.2 Materials and methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 3.2.1 Experimental study. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 3.2.2 Modeling and data analysis procedure. . . . . . . . . . . . . . . . . . . . . . . . 40 3.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 3.3.1 Determination of hydraulic and non-reactive transport parameters (experiments E1 and E2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 3.3.2 Determination of parameters related to the bacteria transport (E3 series) . . . 45 3.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 3.4.1 Physical processes (E1 and E2) . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 3.4.2 Biological Processes (E3 series) . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 3.5 Conclusions and Outlook. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 3.6 Supplementary material . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 4 Determining the impact of flow velocities on reactive processes associated with Enterococcus faecalis JH2-2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 4.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 4.2 Materials and methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 4.2.1 Experimental setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 4.2.2 Model Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 4.3 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 4.3.1 Tracer and microsphere experiments. . . . . . . . . . . . . . . . . . . . . . . . . 74 4.3.2 Bacteria experiments - comparison of processes. . . . . . . . . . . . . . . . . . . 75 4.4 Conclusions and Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 4.5 Supplementary material 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 4.6 Supplementary Material 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 5 Synthesis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 5.1 Discussion and conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 5.2 Critical review, pathways towards future work . . . . . . . . . . . . . . . . . . . . . . . 91 Bibliography. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 Note on the commencement of the doctoral procedure. . . . . . . . . . . . . . . . . . . . 107 Übereinstimmungserklärung. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 List of Publications and conference presentations. . . . . . . . . . . . . . . . . . . . . . . . 111 Acknowledgements. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115Der Agrarsektor ist einer der größten Verbraucher von Süßwasser. Angesichts der zunehmenden Wasserknappheit, der Verstädterung, der Überbevölkerung und des Klimawandels könnten die von der Landwirtschaft genutzten Süßwasserressourcen besser genutzt werden, indem sie für Trinkwasserzwecke umgewidmet werden. In diesem Zusammenhang verwenden viele Länder aufbereitetes kommunales Abwasser für die Bewässerung, um dieses Problem zu lösen. Dies ist zwar eine nachhaltige Praxis, aber die Wiederverwendung von kommunalem Abwasser könnte die Ausbreitung pathogener Bakterien (oder antibiotikaresistenter Bakterien) im Untergrund und damit im Grundwasser fördern. Da das Grundwasser eine der Hauptquellen für Trinkwasser ist, könnten diese Schadstoffe eine Gefahr für die menschliche Gesundheit darstellen. Darüber hinaus ist es ein Gebot der Stunde, wissenschaftliche Daten über neu auftretende Verunreinigungen bei der Wasserwiederverwendung zu gewinnen. Ziel dieser Arbeit ist es, ein mechanistisches Modell zu erstellen, das bei der Entwicklung groß angelegter Risikobewertungsmodelle behilflich sein kann und somit die Aufstellung von Vorschriften für die Wiederverwendung von Wasser für die relevanten pathogenen Organismen erleichtert. In der vorliegenden Studie wurden prozessbasierte Modelle entwickelt und anhand von Ergebnissen im Labormaßstab bewertet. Anschließend werden die relativen Zeitskalen der Prozesse verglichen und die relative Bedeutung der verschiedenen Prozessstudien bewertet. Bei der Bewertung der Zeitskalen der Prozesse wird berücksichtigt, dass Prozesse mit relativ schnellen Zeitskalen durch Gleichgewichtsmodelle angenähert werden können, relativ langsame Prozesse können vernachlässigt werden, und nur die ratenbegrenzenden Prozesse dürfen in der weiteren Modellentwicklung weder vernachlässigt noch vereinfacht werden. Daher kann eine Vorstellung von den ratenbegrenzenden Prozessen, die im Labormaßstab bewertet werden, als wichtiges Instrument zur Vereinfachung des Modells bei der Bewertung von Modellen in größerem Maßstab dienen. Ein kombinierter experimenteller und modellierender Ansatz wurde verwendet, um relevante Transport- und reaktive Prozesse während des Bakterientransports durch sandige Sedimente zu untersuchen. Das mechanistische Modell enthielt Transportprozesse, die mit Hilfe der Advektions-Dispersions-Gleichung implementiert wurden. Ein zusätzlicher Filtrationsprozess ('straining') wurde mit Hilfe nichtlinearer Ratengesetze hinzugefügt. Die biologischen Prozesse des Zerfalls, der Atmung, der Anhaftung und des Wachstums wurden durch lineare Ratengesetze ausgedrückt. Dieses mechanistische Modell wurde anhand von Daten aus vollständig wassergesättigten, sedimentgefüllten Säulenexperimenten im Labormaßstab verifiziert. Kontinuierliche Injektion von Tracer, Mikrosphären und Enterokokken (in Wasserumgebungen mit und ohne gelösten Sauerstoff und Nährstoffe) wurde durchgeführt. Das Experiment wurde für drei Strömungsgeschwindigkeiten (0,13, 0,08 und 0,02 cm/min) verifiziert, und die Parameterwerte wurden für diese Strömungsgeschwindigkeiten anhand dimensionsloser Zahlen verglichen. Die linearen Ratengesetze wurden in eine dimensionslose Form umgewandelt (Peclet- bzw. Damköhler-Zahlen), um den Vergleich der Prozesse bei den verschiedenen Strömungsgeschwindigkeiten zu erleichtern. Die Konzentrationen wurden in regelmäßigen Abständen sowohl am Einlass als auch am Auslass der Kolonnen gemessen. Die überprüften Prozesse waren Advektion, Dispersion, Filtration, Zerfall, Atmung, Wachstum und Anhaftung. Der Versuch wurde für drei Strömungsgeschwindigkeiten (0,13, 0,08 und 0,02 cm/min) wiederholt, und die verifizierten Parameterwerte wurden für diese Strömungsgeschwindigkeiten verglichen. Die Ergebnisse zeigen, dass die Prozesse der Anhaftung und des Wachstums strömungsabhängig sind. Darüber hinaus war bei Vorhandensein von gelöstem Sauerstoff die Anhaftung der Bakterien an das Sediment der einflussreichste Prozess. Die Sensitivitätsanalyse zeigte, dass die Parameter, die das Wachstum und die Atmung repräsentieren, einflussreich sind, so dass bei der Verwendung der Ergebnisse für Experimente oder Modelle im Feldmaßstab Vorsicht geboten ist. Diese Prozesse und Parameter liefern neue Erkenntnisse über die Auswirkungen der Wiederverwendung von kommunalem Abwasser auf die Ausbreitung pathogener Bakterien (insbesondere widerstandsfähiger Arten wie Enterokokken) und unterstreichen die Bedeutung der Forschung in diesem Bereich. Zukünftige Arbeiten könnten sich auf die Gewinnung von Daten aus kulturunabhängigen Methoden und die Erweiterung des Modellrahmens konzentrieren und (wo nötig) nichtlineare Parameter einbeziehen. Dies wird einen entscheidenden Weg zur Entwicklung eines Rahmens für die Entscheidungsfindung darstellen, der von Regulierungsbehörden, politischen Entscheidungsträgern, Interessengruppen sowie lokalen und globalen Umweltbehörden, der Weltgesundheitsorganisation oder den Vereinten Nationen genutzt werden kann.:List of Figures vii List of Tables xi List of Abbreviations xiii List of Symbols xv Summary xvii Zussamenfassung xix 1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 Broad Scope. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Hypotheses and Research objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3 Outline of the work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2 Concepts, terminologies, and methodology 7 2.1 Concepts and terminologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.1.2 The vadose zone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.1.3 Porosity and pore models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.1.4 Darcy’s law . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.2 Bacteria strain used and Processes Studied . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.2.1 Enterococcus faecalis JH2-2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.2.2 Advection and Dispersion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.2.3 Straining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.2.4 Microbial Decay and Respiration . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.2.5 Microbial Attachment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.2.6 Microbial Growth. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.2.7 Dimensionless numbers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.3 Experimental design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.4 Model setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3 Reactive-transport modelling of Enterococcus faecalis JH2-2 passage through water saturated sediment columns. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 3.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3.2 Materials and methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 3.2.1 Experimental study. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 3.2.2 Modeling and data analysis procedure. . . . . . . . . . . . . . . . . . . . . . . . 40 3.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 3.3.1 Determination of hydraulic and non-reactive transport parameters (experiments E1 and E2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 3.3.2 Determination of parameters related to the bacteria transport (E3 series) . . . 45 3.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 3.4.1 Physical processes (E1 and E2) . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 3.4.2 Biological Processes (E3 series) . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 3.5 Conclusions and Outlook. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 3.6 Supplementary material . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 4 Determining the impact of flow velocities on reactive processes associated with Enterococcus faecalis JH2-2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 4.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 4.2 Materials and methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 4.2.1 Experimental setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 4.2.2 Model Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 4.3 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 4.3.1 Tracer and microsphere experiments. . . . . . . . . . . . . . . . . . . . . . . . . 74 4.3.2 Bacteria experiments - comparison of processes. . . . . . . . . . . . . . . . . . . 75 4.4 Conclusions and Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 4.5 Supplementary material 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 4.6 Supplementary Material 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 5 Synthesis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 5.1 Discussion and conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 5.2 Critical review, pathways towards future work . . . . . . . . . . . . . . . . . . . . . . . 91 Bibliography. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 Note on the commencement of the doctoral procedure. . . . . . . . . . . . . . . . . . . . 107 Übereinstimmungserklärung. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 List of Publications and conference presentations. . . . . . . . . . . . . . . . . . . . . . . . 111 Acknowledgements. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

State And Parameter Estimation With A Sequential Monte Carlo Method In A Three Dimensional Transport Model

Due to the inherent randomness and heterogeneity of the transport process, macrodispersion, non-fickian motion, and ergodicity, general assumptions of linearity and Gaussian distribution do not hold for the real field. Therefore, a state-space transport model for the non-linear and non-Gaussian system is proposed in this study. In this study, the state variable (concentration vector) and parameter (first-order decay) are updated with the available measurements. The probabilistic state-space formulation and updating of information on receipt of new measurements is formulated in the Bayesian framework. particle filter, a sequential Monte Carlo method, provides a rigorous general framework for dynamic state estimation problems in the Bayesian scheme. Here the reactive contaminant transport in subsurface is treated as a dynamic state and parameter estimation problem. A type of particle filter, commonly called Sequential Importance Resampling (SIR) is used for this subsurface transport problem. The model estimation is compared with a reference true random field. A promising improvement of the estimation accuracy is attained with the SIR particle filter while compared with a traditional deterministic approach. The standard deviations of the residuals were calculated for the comparison purpose. The particle filter data assimilation scheme reduces the prediction error by 48% in estimation accuracy. In case of having fixed parameters in the model, a standard technique to perform parameter estimation consists of extending the state with the parameter to transform the problem into optimal filtering problem. This approach requires the use of special particle filtering techniques which suffer from several drawbacks. An alternative statistical approach was adopted here to combine parameter estimation with the particle filter scheme. The concept of Euclidian norm was introduced in order to address the sequential weight assignment to the parameter estimation. The SIR particle filter scheme successfully estimated the parameter (first-order decay). With the use of the updated parameter in the state prediction, prediction error of the SIR particle filter data assimilation scheme became 78% smaller than the error from the deterministic model

Posterior Assessment of Parameters in a Time Domain Random Walk Model of Partitioning Tracer Tests in Two‐Phase Flow Scenarios

We provide a reliable and efficient methodological framework for the interpretation of laboratory-scale partitioning tracer test data under uncertainty. The proposed approach rests on a Time domain random walk (TDRW) particle tracking methodology. The range of applicability of the latter is extended to include transport of partitioning tracers upon considering retardation and trapping mechanisms. A classical maximum likelihood (ML) approach is applied considering the extensive set of experimental observations of Dwarakanath et al. (1999, ). This yields best estimates of model parameters, including residual immobile phase saturation, the partition coefficient and the parameters of the memory function employed to simulate the impact of solute trapping. Experimental observations of the partition coefficient are included in the objective function upon relying on a regularization term. We show that considering these types of information, which are typically obtained through batch experiments, is important to attain joint estimates of the partition coefficient and of residual immobile phase saturation. Sample probability distributions of model parameters conditional on available data are then assessed through a stochastic inverse modeling approach. This step poses a signi?cant challenge in terms of computational effort and is performed through a reduced order surrogate model. Our results show that the TDRW-based approach can effectively capture the key features of the observed breakthrough curves of the various partitioning tracers analyzed and provide satisfactory estimates of residual immobile phase saturation

Hydrogeochemical controls on reactive and nonreactive solute transport in heterogenous porous media

This work examines how physical and chemical heterogeneity can affect reactive and non-reactive transport in porous media. The effect of heterogeneity of the porous media is investigated both on dissolution rate of magnesite and attenuation time of nonreactive contaminants in non-reactive media. Various spatial distribution were created using statistical parameters in PETREL.A total of 6793 transport modeling simulations were run using CrunchFlow. Lasso regression was used to select most significant features and those features are then used in linear regression and deep learning models. The magnesite dissolution simulations were performed under different permeability ratios (magnesite /sand permeability) and inlet pH. The variables used for building different realizations of porous media are mineral abundance, major direction anisotropy and minor direction anisotropy. Overall, permeability ratio had the most significant impact on dissolution rate. Deep learning captured 89.0 % of the variance in the data while linear regression only captured 73.2%. The bromide transport simulations were conducted under various flow rates and transverse dispersivity values. Different spatial distributions were created with different permeability standard deviations and major and minor direction anisotropies. Standard deviation proved to have the most significant impact on attenuation time, followed by major and minor direction anisotropies A more heterogeneous and anisotropic distribution resulted in a slower concentration reduction. The effect of anisotropies were trivial in a relatively homogenous distributions. The linear model can describe 70.83 % of the variance in the data. --Abstract, page iv

A Comprehensive Study Of Esterification Of Free Fatty Acid To Biodiesel In a Simulated Moving Bed System

Simulated Moving Bed (SMB) systems are used for separations that are difficult using traditional separation techniques. Due to the advantage of adsorption-based chromatographic separation, SMB has shown promising application in petrochemical and sugar industries, and of late, for chiral drug separations. In recent years, the concept of integration of reaction and in-situ separation in a single unit has achieved considerable attention. The simulated moving bed reactor (SMBR) couples both these unit operations bringing down the operation costs while improving the process performance, particularly for products that require mild operating conditions. However, its application has been limited due to complexity of the SMBR process. Hence, to successfully implement a reaction in SMB, a detailed understanding of the design and operating conditions of the SMBR corresponding to that particular reaction process is necessary. Biodiesel has emerged has a viable alternative to petroleum-based diesel as a renewable energy source in recent years. Biodiesel can be produced by esterification of free fatty acids (present in large amounts in waste oil) with alcohol. The reaction is equilibrium-limited, and hence, to achieve high purity, additional purification steps increases the production cost. Therefore, combining reaction and separation in SMBR to produce high purity biodiesel is quite promising in terms of bringing down the production cost. In this work, the reversible esterification reaction of oleic acid with methanol catalyzed by Amberlyst 15 resin to form methyl oleate (biodiesel) in SMBR has been investigated both theoretically and experimentally. First, the adsorption and kinetic constants were determined for the biodiesel synthesis reaction by performing experiments in a single column packed with Amberlyst 15, which acts as both adsorbent and catalyst. Thereafter, a rigorous model was used to describe the dynamic behaviour of multi-column SMBR followed by experimental verification of the mathematical model. Sensitivity analysis is done to determine robustness of the model. Finally, a few simple multi-objective optimization problems were solved that included both existing and design-stage SMBRs using non-dominated sorting genetic algorithm (NSGA). Pareto-optimal solutions were obtained in both cases, and moreover, it was found that the performance of the SMBR could be improved significantly under optimal operating conditions