OPTIMIZATION OF BIO GAS RECIRCULATION VELOCITY IN BIOGAS MIXING ANAEROBIC DIGESTER WITH THE FEED OF 8% TDS USING CFD

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1 International Journal of Mechanical Engineering and Technology (IJMET) Volume 8, Issue 8, August 2017, pp , Article ID: IJMET_08_08_065 Available online at ISSN Print: and ISSN Online: IAEME Publication Scopus Indexed OPTIMIZATION OF BIO GAS RECIRCULATION VELOCITY IN BIOGAS MIXING ANAEROBIC DIGESTER WITH THE FEED OF 8% TDS USING CFD Jishu chandran, D. Yogaraj, K.Manikandan and P. Jeyaraman Assistant Professor, Department of Mechanical Engineering, Vel tech Dr. RR & Dr.SR University, Chennai, India ABSTRACT For the anaerobic conversion of plant biomass to biogas several different types of digester have been installed in practise in recent years. Digesters are often used when the feedstock treated has high solids content or high viscosity. This study evaluates the flow characteristics of a vertical digester by computational fluid dynamics (CFD) simulation. The simulation results showed that the dissolved content is under typical operation conditions to a considerable degree mixed in the vertical direction. However, there are number of problems involved in scaling up experimental anaerobic digestion plants to field level plants. One such problem associated with anaerobic digester is mixing, which is a vital to ensure adequate contact between bacteria and substrate in the digester. Such situations are well suited to Computational Fluid Dynamic (CFD) analysis. The biological and chemical parameters of the fermentation process have been primarily investigated in the past, so that a broad knowledge base exists. For these issues reliable mathematical models are available. There are also model applications available for describing the anaerobic degradation of plant biomass. However, in most cases the flow characteristics of the reactor are neglected, or only a very rough estimate is done. Knowledge about the rheological properties of the processed substrate is still limited. Computational Fluid Dynamics (CFD) has emerged as a common analysis tool for fluid engineering, and can be used for the design and optimization of bioreactors. Some examples of use exist for digesters, but no CFD simulation study has been published so far concerning the effect of biogas recirculation in digester.the aim in this work has been to further understand and enhance the use of biogas recirculation mixing approach to improve the performance of future bioreactors. A computational model has been developed to simulate the complex flows occurring in a digester, discusses CFD simulations of a lab scale Anaerobic Digester for evaluating biogas recirculation characteristics that provides understanding required for developing accurate simulations of mixing conditions in the large scale systems with the reactor contents. Keyword: Anaerobic digester, Biogas Recirculation, CFD editor@iaeme.com

2 Optimization of Bio Gas Recirculation Velocity in Biogas Mixing Anaerobic Digester with the Feed of 8% Tds using Cfd Cite this Article: Jishu chandran, D. Yogaraj, K. Manikandan and P. Jeyaraman, Optimization of Bio Gas Recirculation Velocity in Biogas Mixing Anaerobic Digester with the Feed of 8% Tds using Cfd, International Journal of Mechanical Engineering and Technology 8(8), 2017,pp INTRODUCTION Biomass is the plant material derived from the reaction between CO2 in the air, water and sunlight, via photosynthesis, to produce carbohydrates that form the building blocks of biomass. Typically photosynthesis converts less than 1% of the available sunlight to stored, chemical energy. The solar energy driving photosynthesis is stored in the chemical bonds of the structural components of biomass. If biomass is processed efficiently, either chemically or biologically, by extracting the energy stored in the chemical bonds and the subsequent energy product combined with oxygen, the carbon is oxidized to produce CO2 and water. The process is cyclical, as the CO2 is then available to produce new biomass Energy sources is broadly divided into non-renewable and renewable energy. Bioenergy is one of the renewable energy and there are various technologies to convert biomass into biofuels and bio based products. The primary biomass sources are energy crops and organic wastes from industry and agriculture. Although bio energy can be produced from a wide variety of processes employing a number of technologies, biochemical and thermo chemical technologies are suggested to be two basic pathways for biomass conversion. The biochemical conversion can be achieved by either anaerobes or photosynthetic microorganisms to produce gaseous and liquid fuels, while the thermo chemical conversion uses high temperatures to break the strong bonds of organic matter to produce synthesis gas and hydrocarbon fuels. Anaerobic digestion is the conversion of organic material directly to a gas, called biogas, a mixture of mainly methane and carbon dioxide with small quantities of other gases such as hydrogen sulphide. The biomass is converted by bacteria in an anaerobic environment, producing a gas with an energy content of about 20 40% of the lower heating value of the feedstock. AD is a commercially proven technology and is widely used for treating high moisture content organic wastes, i.e % moisture. Biogas can be used directly in s.i.g.e. and gas turbines and can be upgraded to higher quality i.e. natural gas quality, by the removal of CO2. Used as a fuel in s.i.g.e. to produce electricity. Biogas from anaerobic digestion is considered an important renewable energy source.unsafe and improper disposal of decomposable animal waste causes major environmental pollution problems, including surface and groundwater contamination, odors, dust, and GHG emissions. There is also a concern regarding methane emissions, which contribute to the greenhouse effect. Through anaerobic digestion, these large amounts of waste can be converted to methane rich gas, which is a renewable energy source. An intermediate degree of mixing appears to be optimal for substrate conversion. Mixing can be accomplished by mechanical mixers, biogas recirculation, and by slurry recirculation. Mechanical mixers are reported to be most efficient in terms of power consumed per gallon mixed. However, the internal fittings and equipment are not accessible for maintenance during digester operation, and long term reliability of operation is of paramount importance. In general, such reliability can be more readily attained with biogas or slurry recirculation systems, where there are no moving parts within the digester. Mixing becomes more critical with thicker manure slurry. Adequate mixing provides a uniform environment for anaerobic bacteria, which is one of the major factors in obtaining maximum digestion so as to give more biogas yield.such kind of mixing situations are well suited to Computational Fluid Dynamic (CFD) analysis, where editor@iaeme.com

3 Jishu chandran, D.Yogaraj, K.Manikandan and P.Jeyaraman models can be calibrated and validated using the pilot plant and can then be used to accurately simulate the performance of the large-scale reactors. CFD is a numerical method that solves fluid flow and heat transfer as well as other relevant physical and biochemical processes. Traditionally, CFD has been used in aerospace and mechanical engineering (e.g. simulating the forces that act on an airplane and the combustion process in an internal combustion engine). CFD emerged in the early 2000s as a tool to predict bio methane yield from covered anaerobic lagoons. Since then considerable research has been done on the various bioreactors for bio methane and bio hydrogen production in anaerobic lagoon, plug-flow digester, complete-mix digester, anaerobic bio hydrogen fermenter, anaerobic biofilm reactor, and photo bioreactor. In terms of the CFD model development, this review deals mainly with the simulation of mixingof a lab scale AD reactor.a computational model has been developed to simulate the complex flows occurring in a digester. This result give information for developing accurate simulations of mixing conditions in the large-scale systems with the reactor contents simulated. Computational fluid dynamics (CFD) has become a popular tool for reactor analysis, because it allows the investigation of local conditions in an arbitrary vessel size, geometry and operating conditions. CFD techniques are being increasingly used for experiments to obtain the detailed flow fields for a wide range of fluid types. The capability of CFD tools to forecast the mixing behavior in terms of mixing time, power consumption, flow pattern and velocity profiles is considered as a successful achievement of these methods and acceptable results have been obtained in many applications. Computational Fluid Dynamics (CFD) offers an approach to understanding the complex phenomena that occur between the gas phase and the particles. With the increased computational capabilities, computational fluid dynamics (CFD) has become an important tool for understanding the complex phenomena that occur between the gas phase and the particles in sludge. 2. MODELLING DIGESTER Flow characteristics inside the digester are complex to determine analytically due to a lot of turbulence, and mixing pattern of liquid solid and gas recirculation. In order to take these reactions in account, numerical methods in the form of computational fluid dynamics have to be introduced. In numerical methods, approximated constants determined from experiments are introduced in order to reduce the number of unknown factors in the full Navier-Stokes equation. These approximations lead to solvable, but somewhat uncertain set of equations to describe the flow pattern. The schematic diagram of the proposed an aerobic digester is shown in fig 2.1 Figure 2.1 Schematic DiagramOf the Biogas Recirculation Lab Scale Reactor editor@iaeme.com

4 Optimization of Bio Gas Recirculation Velocity in Biogas Mixing Anaerobic Digester with the Feed of 8% Tds using Cfd 2.1. Modelling in the computational domain In this present project work, the three dimensional analysis was done mx0.23m rectangular duct is selected as the domain of interest. The computational domain was modelled in the Ansys design modeller. Figure 2.2 Model of Lab Scale Reactor 3. BOUNDARY CONDITIONS The bottom, diffuser and the side walls are defined with non-slip conditions, at the top of the fluid (sludge) it is assumed as atmospheric pressure, in the simulation it is modeled as a wall with slip conditions.to account for biogas release from diffuser it is defined to allow Lagrangian particles (injected biogas bubbles) to escape through it. In the experimental reactor this gas produced would be collected by an external type tube in order for it to be reused for mixing purposes as well as being used for energy generation (converted into electricity). It is assumed that enough gas has been generated to provide continuous recirculation through the fluid for the purposes of mixing. In addition, the reactor produces gas during the ongoing anaerobic process that could additionally contribute to the mixing. This is not included in this model. In the transient simulations, the Eularian time step of 0.5s was used in all simulations. 4. SOLUTION METHODOLOGY The flow inside the domain is a multiphase flow. Currently there are two approaches for the numerical calculations of multiphase flow; The Euler-Lagrange approach and Euler-Euler approach. In the Euler-Euler approach, the different phases are treated mathematically as interpenetrating continua. Since the volume of phase cannot be occupied by the other phases, the concept of phasic volume fraction is used. These volume fractions are assumed to be continuous functions of space and time and their sum equal to one. Conservation equations for each phase are developed and these equations are closed by providing constitutive relations that are obtained from the application of kinetic theory in the case of granular flows. In the fluent three different Euler-Euler multiphase models are available; The Volume of Fluid (VOF) model, the mixture model, and Eulerian Model. The volume of fluid model is used for two or more immiscible fluids where the position of the interface between the fluids is of interest. Application of VOF model include stratified flows, free surface flows, filling, sloshing, the motion of large bubbles in a liquid, the motion of liquid after a dam break. In editor@iaeme.com

5 Jishu chandran, D.Yogaraj, K.Manikandan and P.Jeyaraman VOF model a single set of momentum equation is shared by the fluids and the volume fraction of each of fluid in each computational cell is tracked throughout the domain. The mixture model is used for two or more phases (fluid or particulate). The mixture model solves for the mixture momentum equation and prescribes relative velocity to describe the dispersed phases. Application of the mixture model includes particle-laden flows with low loading, bubbly flows, and sedimentation and cyclone separator. The Eulerian model solves the n-momentum and continuity equations for each phase. Coupling is achieved through the pressure and interphase exchange coefficients. The manner in which this coupling is handled depends upon the type of phases involved. For granular flows, the properties are obtained from application of kinetic theory. Applications of the Eulerian model include bubble column, risers, particle suspension and fluidized beds. Based on the above clarification for this analysis the Eulerian model is selected. Solver Selection There are two kinds of solvers available in Fluent. 1. Pressure based solver 2. Density based coupled solver (DBCS) The pressure based solver take momentum and pressure (or Pressure correction) as the primary variables. Pressure velocity coupling algorithms are derived by reformatting the continuity equation. The pressure based solvers are implicit. Two algorithms are available with pressure-based solvers, which are segregated solver solves for pressure correction and momentum sequentially and coupled solver (PBCS) solves pressure and momentum simultaneously. Density based couple solver equations for continuity, momentum, energy and species is required are solved in vector form. Pressure is obtained through the equation of state. Additional scalar equations are solved in a segregated fashion. The density based solver can use either an implicit or explicit solution approach. Implicit uses a point implicit Gauss- Seidal / Symmetric block Gauss- Seidel / ILU method to solve for variables. Explicit uses a multi-step RungeKutta explicit time integration method. The Density Based Coupled Solver is applicable when there is a strong coupling, or interdependence between density, energy, momentum, and/or species. Examples: High speed compressible flow with combustion, hypersonic flows, shock interactions. Based on the above explanations pressure based unsteady solver is used. Pre-processing Grid Check the grid check to ensure the grids are imported from Gambit in proper fashion and grid interface are correct or not. The grid check will run and inform the detail of grid related errors. Scaling the Mesh when Fluent reads a mesh all physical dimensions are assumed to be in units of meters. The model is built in meters so that it can be used directly without scaling. Multiphase Model the Eulerian multiphase model is selected and enable eulerian discrete phase model in eulerian parameters box. Discrete phase model defined by giving number of continues phase iteration per DPM iteration as 100, tracking parameters as number of steps I given 500then step length factor 5.Injection properties are set by giving no of particles 20 and also given the location of injection in X, Y coordinates. The number of phases is set to 2. Phase-1 is sludge and secondary dispersed phase-2 is methane gas editor@iaeme.com

6 Optimization of Bio Gas Recirculation Velocity in Biogas Mixing Anaerobic Digester with the Feed of 8% Tds using Cfd Model Selection pressure coupled solver selected with unsteady flow conditions, Green Gauss cell based gradient option and 2 nd order implicit. A laminar condition is selected in the viscous model. Operating Conditions the normal operating conditions taken as default one and the gravity is enabled since the particle flow against the gravity. In the gravity acceleration, the y directional acceleration is set to m/s 2. Setting material properties the material properties can be set and modified in Fluent for fluids, solids, and mixtures and particles. It provides a standard database of materials and he ability to create a customized user defined database. sludge properties are taken by fluent data base with respective sludge properties obtained from various journals. Methane properties have taken from the fluent data base. Phases in the phases panel, Phase-I is set as sludge and Phase-II is set as methane and in the Phase Material drop down list solids is selected Boundary Conditions In the case of multiphase flow, at each boundary the status of different phase to be specified. It involves by identifying the location of the boundaries, and supplying values (data) at the boundaries. These data were required at any boundary, depends upon the boundary condition type and physical model employed. In this analysis some of the following boundary conditions are used at various sections. Velocity Inlet, Pressure Outlet and Wall, Velocity Inlet Here, velocity to be specified normal to boundary components. Pressure Outlet Gauge pressure (Static) interpreted as static pressure of environment into which flow exhausts. Wall state of wall need to be specified. Stationary wall- no slip wall condition is applied here to avoid the flow outside the digester.. The interaction of each phase with wall need to be specified in the panel. Solution Control SIMPLE algorithm used for pressure velocity coupling. Under relaxation factor is mentioned for pressure, momentum, volume fraction and first order up wind scheme is used as discretization scheme. Convergence Convergence criteria will satisfy the following conservation equations (for example momentum, continuity, etc.,) are obeyed in all cells to a specified tolerance or the solution no longer changes with subsequent iterations. Monitoring Convergence using Residual History the unsteady analysis is done. So the solution changes with time interval. In this convergence made to the three order level. Initialization iterative procedure requires that all solution variables are available before calculating a solution. Realistic guesses improves solution stability and accelerates convergence. In this analysis the initial conditions are feed as closest to the actual operating conditions to get the better convergence. Iteration the time step size, number of time steps to be specified starts the iterations. In the current work time step size is and number of time steps is 10000, so results are taken at 10 sec. 5. RESULTS AND DISCUSSIONS SIMULATION RESULT FOR INJECTED VELOCITY OF 2M/S The gas injections are modeled as coupled Lagrangian particles injected over a circular (diameter 2mm) area. Eight separated streams are used in each case and the total mass flow editor@iaeme.com

7 Jishu chandran, D.Yogaraj, K.Manikandan and P.Jeyaraman rate of the biogas at this velocity is 4.32x10-05 kg/s. In the below simulation result, the biogas injected at a velocity 2 m/s through diffuser is shown Figure 5.1 Simulated Results for Mixing Behaviour of Sludge Inside The Fabricated Reactor, While Biogas Injected At The Velocity Of 2 m/s Biogas is injected through the diffuser at the velocity of 2 m/s by external means. Injected biogas started to coming through diffuser by the low velocity profile of 0.4 to 0.6 m/s shown in fig 5.1(a). By the end of 3 seconds biogas started to spread with the velocity of 0.8 to 1 m/s editor@iaeme.com

8 Optimization of Bio Gas Recirculation Velocity in Biogas Mixing Anaerobic Digester with the Feed of 8% Tds using Cfd which has shown Figure 5.1 (b).at the end of 5seconds more circulating gas coming through diffuser but still the propagation is low due to low velocity profile of 1.2 m/s, which is shown Figure 5.1 (c). Biogas started to move upward towards the outlet region of the digester with velocity of 1.5m/s at 8 seconds of injected biogas, At the same time after 8 seconds mixing pattern does not completed due to its poor flow rate of injected velocity as shown in Figure 5.1 (e f). In this injected profile velocity the digester have more dead zones identified, which has been shown in that figure 5.1(f). SIMULATION RESULT FOR INJECTED VELOCITY OF 4M/S Biogas injected at the velocity of 4m/s are modeled as coupled Lagrangian particles injected over a circular (diameter 2mm) area through diffuser at eight different locations. Eight separated streams are coming through it with the total mass flow rate of the biogas at this velocity is 8.64x10-05 kg/s. Figure 5.2 Simulated Results For Mixing Behaviour Of Sludge Inside The Fabricated Reactor, While Biogas Injected At The Velocity Of 4 m/s Biogas is injected through diffuser at the velocity of 4 m/s by external supply. Injected biogas started to coming through diffuser by the very low velocity nearly 0.4 to 0.8 m/s as shown in fig 5.2(a). At 3 seconds biogas started to spread more than the previous case with the velocity of 1.2 to 1.6 m/s. As time reaches at 5 seconds from fig 5.2(c) it has understood that due to more velocity of injected biogas, velocity profile inside digester attained 2 m/s. At 8 seconds velocity profile reaches saturated level after this time the level of mixing is over, at 10 and 12 seconds there is no more mixing ie mixing is completed at this velocity of injection as shown in fig 5.2 (e-f). And also it has been understood that there is lot of dead spaces which gives mixing is not sufficient at the velocity of 4 m/s also editor@iaeme.com

9 Jishu chandran, D.Yogaraj, K.Manikandan and P.Jeyaraman SIMULATION RESULT FOR INJECTED VELOCITY OF 5M/S Biogas injected at the velocity of 5m/s are modeled as coupled Lagrangian particles injected over a circular (diameter 2mm) area through diffuser at eight different locations. With the total mass flow rate of the biogas at this velocity is 1.08x10-04 kg/s. Figure 5.3 Simulated Results for Mixing Behaviour of Sludge inside The Fabricated Reactor, While Biogas Injected At The Velocity Of 5m/s editor@iaeme.com

10 Optimization of Bio Gas Recirculation Velocity in Biogas Mixing Anaerobic Digester with the Feed of 8% Tds using Cfd Biogas is injected through diffuser at the velocity of 5 m/s by external means. Injected biogas is started to spread nearby places at the opening in diffuser. At the initial stages injected biogas is entering through diffuser which has the velocity of nearly 1.5 m/s to 2.0 m/s at above the diffuser as shown in fig 5.3 (a). At 5 sec injected gas started to spread more spaces in digester also when 7sec dead spaces became low i.e. Mixing becoming more uniform throughout the reactor as show in fig 5.3 (d) When time reaches 10 seconds sludge mixed well by more biogas injection, at 12 sec dead spaces became too low, also mixing started to became saturated level fig 5.3 (f-h) These simulation results show the instantaneous flow field developing for the transient simulation following initiation of the injection of biogas being injected. The flow fields are plotted on a plane passing through the eight different locations at the diffuser of the lab scale reactor. Due to the symmetry of the reactor, the flow is axisymmetric throughout the vessel, it is reasonable to consider that the flow on this plane is representative of the whole reactor. As would be expected there is a high velocity zone entering through the injected point and moving vertically up to the center of the vessel. The developing flow field obtained from the transient simulation allows for the initiation of mixing to be observed. The flow has reached steady-state conditions after some time. The steady state is achieved for 2m/s and 4 m/s after approximately 8 to 10 seconds. The flow in the central region is at low velocity and much of it is significantly below optimum value indicating that there will be relatively poor mixing in this region. But for 5 m/s steady state has reached at 15 to 18 seconds. So flow through the diffuser of the reactor at 5m/s is adequate for mixing the contents of a reactor efficiently in this reactor of this size. 6. CONCLUSION Transient CFD simulations are performed in order to observe the progression of flow over time. The three dimensional lab scale anaerobic digester model s mixing simulation has been obtained from fluent solver. The mixing inside the digester was achieved by biogas recirculation through eight different locations through the diffuser. With different flow rate of velocity, the mixing behaviour has been analysed and the result are presented velocity contours. In case of 2m/s velocity of biogas injection, it was observed that the reactor takes between 8-10 seconds to reach a steady state condition. The other case i.e. in 4m/s the fully mixed condition (steady state behaviour) is reached after 7-10 seconds of time steps. But either of these two cases, mixing was not adequate, and lots of dead zones were observed in the simulation results.hence it is construed from the present simulation analysis, that for the feeding rate of 8% TS (MSW), the flow injected with a velocity of 5 m/s makes the mixing process more efficient. REFERENCES [1] Binxin Wu et al., (2013), Advances in the use of CFD to characterize, design and optimize bioenergy systems, Computers and Electronics in Agriculture, Vol. 93 PP [2] Bridgeman J et al., (2011), Computational fluid dynamics modeling of sewage sludge mixing in an anaerobic digester, Advances in engineering software, Vol. 44 PP [3] Buwa et al., (2002) Effects of mixing on methane production during thermophiles anaerobic digestion of manure: Lab-scale and pilot-scale studies, bio source Energy., Vol96 PP [4] Cumiskey et al, (2003) Multiphase modeling of settling and suspension in anaerobic digester, Applied Energy, Vol. 111 PP [5] Delnoij J and Lane K., (2009) CFD simulation of mixing in anaerobic digester), Bio resource Technology, Vol. 100 PP editor@iaeme.com

11 Jishu chandran, D.Yogaraj, K.Manikandan and P.Jeyaraman [6] Fluent user guide modelling of Eularian multiphase flow, Fluent. Inc, Usa [7] HilkiahIgoni A., Ayotamuno M.J., Eze CL., Ogaji S.O.T., and Probert S.D., (2008), Designs of anaerobic digesters for producing biogas from municipal solid-waste, Applied Energy, Vol. 85 PP [8] Lübken M, Koch K, Klauke I, Gehring T and Wichern M, CFD investigation of the flow characteristics of a plug flow anaerobic digester for lignocelluloses biomass mechanization [9] Suresh R and Vivekanandan S, Two Stage Anaerobic Digestion Over Single Stage on Biogas Yields from Edible and Non-Edible De-Oiled Cakes Under the Effect of Single and Co-Digestion System. International Journal of Civil Engineering and Technology, 8(3), 2017, pp [10] S. Sugumar, R. Shanmuga Priyan and S. Dinesh, A Review on Performance Study of Anaerobic Digestion to Enhance the Biogas Production. International Journal of Civil Engineering and Technology, 7(6), 2016, pp [11] Gangarapu Hareesh, Sija Arun and Dr. P. Shanmugam, Comparative Analysis of Multiple Effect Evaporators and Anaerobic Digester (UASB) for an Effective Management of RO Reject from Tannery. International Journal of Civil Engineering and Technology, 8(4), 2017, pp editor@iaeme.com