10 research outputs found

    A compartmental model of anaerobic digester for improved description of the process performance

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    Understanding anaerobic digester (AD) performance relates to the complex interplay between hydrodynamics and kinetics. The latter is not straightforward and has been tackled by means of models. However, the computational burden to run such a model in a dynamic way is still too large. Here, a simplified compartmental model (CM) is derived from a CFD model (hydrodynamics). Compatibility of the CM and CFD model was tested by comparing the RTD curve of a virtual pulse tracer test. Subsequently, the CM was integrated with ADM1 in each compartment and the steady state performance was compared with that of a CSTR model with ADM1

    Linking CFD and kinetic models in anaerobic digestion using a compartmental model approach

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    Understanding mixing behavior and its impact on conversion processes is essential for the operational stability and conversion efficiency of anaerobic digestion (AD). Mathematical modelling is a powerful tool to achieve this. Direct linkage of Computational Fluid Dynamics (CFD) and the kinetic model is, however, computationally expensive, given the stiffness of the kinetic model. Therefore, this paper proposes a compartmental model (CM) approach, which is derived from a converged CFD solution to understand the performance of AD under non-ideal mixing conditions and with spatial variation of substrates, biomass, pH, and specific biogas and methane production. To quantify the effect of non-uniformity on the reactor performance, the CM implements the Anaerobic Digestion Model 1 (ADM1) in each compartment. It is demonstrated that the performance and spatial variation of the biochemical process in a CM are significantly different from a continuously stirred tank reactor (CSTR) assumption. Hence, the assumption of complete mixed conditions needs attention concerning the AD performance prediction and biochemical process non-uniformities

    Partial integration of ADM1 into CFD : understanding the impact of diffusion on anaerobic digestion mixing

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    Sufficient mixing is crucial for the proper performance of anaerobic digestion (AD), creating a homogeneous distribution of soluble substrates, biomass, pH, and temperature. The opaqueness of the sludge and mode of operation make it challenging to study AD mixing experimentally. Therefore, hydrodynamics modelling employing computational fluid dynamics (CFD) is often used to investigate this mixing. However, CFD models mostly do not include biochemical reactions and, hence, ignore the effect of diffusion-induced transport on AD heterogeneity. The novelty of this work is the partial integration of Anaerobic Digestion Model no. 1 (ADM1) into the CFD model. The aim is to better understand the effect of advection–diffusion transport on the homogenization of soluble substrates and biomass. Furthermore, AD homogeneity analysis in terms of concentration distribution is proposed rather than the traditional velocity distributions. The computed results indicate that including diffusion-induced transport affects the homogeneity of AD

    Integrating kinetic with hydrodynamic modelling to better understand the mixing behavior in anaerobic digesters

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    AD mixing is usually assumed to be uniform, and its kinetic process and performance predicted using simple models like continuously stirred tank reactor (CSTR), mostly ignoring the digester hydrodynamics. However, achieving uniform AD mixing is challenging because of the viscous nature of the sludge. Computational fluid dynamics (CFD) models are applied to study the non-ideal mixing of AD, but most often ignore the biokinetics. The limitations of both approaches are addressed in this work by integrating the kinetics and hydrodynamics of the CFD model and using a CFD based compartmental model (CM) approach. The latter was applied in this work as it is a promising non-ideal kinetics modelling method that is relatively fast compared to a CFD approach. In this dissertation, more attention was given to CFD modelling since a detailed understanding of the mixing process is a prerequisite for the accurate non-ideal kinetic modelling of the digester based on CFD-based CM. The work started from simple models and built step by step to more advanced models. The AD mixing hydrodynamics using axial flow impeller was studied, treating the sludge as a Newtonian fluid for TSS less than 4% and non-Newtonian using a Herschel-Bulkley rheology model for sludge TSS greater than 4% to understand the digester hydrodynamics first and then derive a compartmental model for non-ideal kinetics model. The non-uniformity of AD mixing based on velocity distribution was classified into high, medium, low, and stagnant velocity zones to compartmentalize the digester. The volumes of the compartments are calculated using cumulative volume distribution of velocity. The effects of the non-ideal mixing model on specific biogas production, biomass, pH, substrate distribution, and effectiveness digester volume are assessed and compared with CSTR digester implementing ADM1 into both CM and CSTR digester. CM showed that mixing affects the performance of AD, and biochemical process spatial variation differs from the CSTR kinetics model. The effect of diffusion transport on scalar variables like soluble substrates and biomass are modelled integrating part of ADM1 into the CFD model using a User-Defined Scalar (UDS). Scalar transport leads to understanding mixing uniformity under advection-diffusion and sole advection transport. Moreover, mixing non-uniformity description in terms of concentration of scalar variables distribution was proposed rather than velocity distribution. AD mixing uniformity description based on velocity and scalar variables' concentration distribution does not lead to the same conclusion. Hence it leads to further investigations implementing mixing time analysis and tracer residence time distribution (RTD). Mixing time modelling under advection-diffusion transport by injecting a tracer into the stagnant velocity zone is investigated for a detailed understanding of mixing behavior in addition to scalar variables distribution. The tracer mixing time distribution at different locations within low and stagnant velocity zones showed that uniform tracer concentration distribution is achieved irrespective of velocity magnitude. The validity of scalar concentration distribution and mixing time models uniformity against non-uniform velocity distribution was investigated, implementing the kinetic model into a full-scale CSTR AD and comparing with measured data. Specific biogas and methane production comparison of the measured data and CSTR AD model shows that the measured data is lower than the modelled results. This indicates the existence of other factors that affect AD mixing, which cannot be expressed using velocity distribution, concentration distribution, and mixing time. Short circuit flow and local recirculation effects are the factors that cannot be expressed using the variables discussed. The impact of short circuit flow and local recirculation effects on AD mixing are investigated, implementing a virtual tracer test under advection-diffusion transport in CFD modelling and compared with that of CSTR AD. The mixing non-uniformity analysis combining the velocity streamline, velocity vectors, and residence time distribution (RTD) of the tracer identify that a stagnant zone inside the digester is due to local recirculation flows. It was also understood that the stagnant volume is not only due to low-velocity zones, as described in the literature. Instead, it is mainly due to recirculation flow in all digester velocity zones. The combined study of the virtual tracer test, scalar variables, and velocity streamline/vector lead to the conclusion that AD mixing non-uniformity cannot be described based on one variable. The finding of this dissertation concludes that AD mixing is non-ideal, and non-uniformity of AD mixing can be described better based on the combination of two or more variables for an accurate representation of the CFD model. Kinetics modelling of AD integrating with CFD model is yet to be done and validated. Therefore, the non-ideal kinetic model using a CM, derived from combined velocity vector, scalar variables, and RTD of the tracer gives a detailed spatial and temporal variation of the AD kinetic process, which are highly important in digester optimum mixer design, mixing optimization and operation of the digester. It is recommended to include this detail in the system analysis and in a practical setting rather than ignoring it

    CFD Simulation and Optimization of Very Low Head Axial Flow Turbine Runner

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    The main objective of this work is Computational Fluid Dynamics (CFD) modelling, simulation and optimization of very low head axial flow turbine runner  to be used to drive  a centrifugal pump of turbine-driven pump. The ultimate goal of the optimization is to produce a power of 1kW at head less than 1m from flowing  river to drive centrifugal pump using mechanical coupling (speed multiplier gear) directly. Flow rate, blade numbers, turbine rotational speed, inlet angle are parameters used in CFD modeling,  simulation and design optimization of the turbine runner. The computed results show that power developed by a turbine runner increases with increasing flow rate. Pressure inside the turbine runner increases with flow rate but, runner efficiency increases for some flow rate and almost constant thereafter. Efficiency and power developed by a runner drops quickly if turbine speed increases due to higher pressure losses and conversion of pressure energy to kinetic energy inside the runner. Increasing blade number increases power developed but, efficiency does not increase always. Efficiency increases for some blade number and drops down due to the fact that  change in direction of the relative flow vector at the runner exit, which decreases the net rotational momentum and increases the axial flow velocity.</p
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