optimization of bio gas recirculation …...optimization of bio gas recirculation velocity in biogas...

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International Journal of Mechanical Engineering and Technology (IJMET) Volume 8, Issue 8, August 2017, pp. 596–606, Article ID: IJMET_08_08_065

Available online at http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=8&IType=8

ISSN Print: 0976-6340 and ISSN Online: 0976-6359

© 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

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. 596–606.

http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=8&IType=8

1. 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. 80– 90% 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

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


of 8% Tds using Cfd


2.1. Modelling in the computational domain

In this present project work, the three dimensional analysis was done. 0.156mx0.23m

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



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.


of 8% Tds using Cfd


Model Selection – pressure coupled solver selected with unsteady flow conditions, Green

Gauss cell based gradient option and 2nd

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 -9.81 m/s2.

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 0.001 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



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


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).


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.




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


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.

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optimization of bio gas recirculation …...optimization of bio gas recirculation velocity in biogas...

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