Overview
For Methanivore to succeed industrially, we must not only consider the biological aspects of methane conversion, but also what upstream and downstream processes are required to support the genetically engineered solution. Our hardware team’s main mandate is to develop a process using chemical engineering principles to support the genetically engineered solution.
From discussions with our wet lab and dry lab teams, we have decided that methane in gaseous form as released from a landfill gas cannot be directly used as an input for the genetically engineered E. coli. It must be converted to - ideally - a related liquid, which, from our discussions with those teams, we decided was methanol, before it is fed into a bioreactor containing the engineered bacteria. A high-level visualisation of our suggested process scheme is as follows. For reasons that shall be elaborated on in detail in our sub-pages, this process scheme is by no means optimal, but represents a step forward towards developing a process as of yet unconsidered by industry.
With this process in mind, we designed each of the sub-processes with process simulation software using Aspen Plus and BioWin. We also attempted to prototype the separation step with the help of the Advanced Membranes Lab at the University of Toronto. Finally, to aid in visualising our solution and size our chemical engineering components, we created 3D models of the full process using Solidworks. Although each landfill has different conditions and will require different chemical engineering solutions, our work represents the initial stages of a typical plant design cycle and may be extended to other landfills in the future.
Methane-selective Gas Separation Modelling and Upscaling
Summary
The hardware team proposes the use of a hollow fibre membrane to purify the methane from landfill gas. Using MATLAB to model a system of differential equations [1] , the team was able to achieve a retentate molar stream composition of 95.37% methane and 4.63%, with a permeate molar stream composition of 5% methane and 95% carbon dioxide, all from a single pass. This is the first step in the methane conversion process, as the retentate stream is later provided to the oxidation process, allowing the team to convert methane to methanol.
Background
After landfill gas is collected, it must be separated into its constituents as effectively as possible.
The
hardware team chose to employ membrane separation technology due its low capital and operational cost
compared
to traditional gas phase separation techniques. It is critical to have a source of methane that is as
pure as
possible to ensure that the oxidation to methanol process is highly efficient.
A membrane separation makes use of the difference of physical size between various molecules. By
selecting a
membrane with a large enough pore size for one molecule to pass through but not the others, a gas phase
stream
can be separated into its components. After passing through the membrane, the inlet stream is split into
two
product streams: the permeate and retentate streams. The permeate stream is the stream that passes, or
permeates, through microscopic gaps in the membrane. In contrast, the retentate stream is the stream
that cannot
physically pass through the membrane.
Depending on the composition of the inlet stream, a membrane can serve as a very effective and efficient
separation process, in comparison to a process such as absorption. Membranes require little energy and
capital
to operate and are therefore very appealing to implement in a process when possible. For our application
(separating methane and carbon dioxide), a membrane is appropriate and commonly used in the industry.
After
first investigating the possibility of an absorption column, the team finally decided on choosing a
membrane for
the gas-phase separation stage of the project.
Methods
To approach this problem, the team began by using a cellulose acetate membrane with concurrent flow.
Using a
system of nonlinear differential equations
[1]
, this separation process can be modelled
using
any form of ordinary differential equation solving algorithm. The team experienced difficulties in
achieving
the desired outlet compositions, and after speaking with Professor Jay Werber at the University of
Toronto,
who conducts a large amount of membrane separation with his research group, the team was advised to
experiment
with an asymmetric hollow fibre membrane instead. Inlet compositions and flow rates were based on
London,
Ontario's W12A landfill's landfill gas emissions. Different landfills will have different
landfill gas
compositions and require potentially different reactor compositions.
Ultimately, the team chose to use MATLAB to model an asymmetric hollow fibre membrane for the
separation
process. The team based the model off a system of four ordinary differential equations
[2]
. Using the “bvp5c” function in MATLAB, which employs a collocation
method to
solve a system of boundary value problems (BVPs), the team successfully completed the membrane
separation
modelling process. The membrane modelled had an internal diameter of 389 micrometres, an
external diameter
of 735 micrometres, a length of 15 cm, and 100 fibres. Additionally, a permeate pressure of 1
atmosphere and
a feed side pressure of 7 atmospheres were also set. This was based on the parameters outlined
in the
research paper
[2]
. Finally, the team only considered a single pass
separation in
the MATLAB model, but decided to recycle the retentate stream after mixing it with fresh
landfill gas to
improve the separation prior to the oxidation process.
Several assumptions were made when setting up this model. First, the landfill gas was
treated as a binary
gas mixture, consisting of only carbon dioxide and methane. In reality, nitrogen has a
noticeable presence
in the gas mixture, with other gases such as oxygen and water vapour being present in trace
amounts.
Ultimately, the permeability of nitrogen was assumed to be the same as methane in the
membrane. This
assumption is not true in reality
[3]
, but it is an appropriate approximation for
the level of
accuracy required at this stage in the project. Another key assumption was that the
operating
temperature was assumed to be 25°C. With relatively minimal temperature changes, the
performance of the
membrane is not expected to change significantly, but this is an important assumption to
make
nonetheless. Finally, the team assumed that no pressure loss occurred during the
separation. Once again
this is not true, but it is a close and justifiable approximation that simplifies the
calculations.
The overarching goal was to size the membrane such that the retentate stream had an
outlet composition
of over 90% methane, to ensure a greater conversion to methanol during the oxidation
process. In the
case that this was unachievable with a single pass through the membrane (i.e. multiple
membranes or a
recycle stream was necessary), the team also devised an alternative plan to run membrane
separation
simulations in Aspen Plus. Despite this preparation, the team was able to achieve the
targeted outlet
composition with only a single pass through an appropriately-sized membrane, and
therefore did not have
to rely on Aspen Plus to run these tests.
Results
Running the MATLAB code and completing all computations resulted in a permeate stream composition of 95% carbon dioxide and 5% methane and a retentate stream composition of 4.63% carbon dioxide and 95.37% methane with a single pass. As previously mentioned, the goal was to have a retentate stream with over 90% methane, which the team successfully achieved with a single pass through one membrane separation unit. The inlet pressure required to achieve the target was 7 atm and the permeate pressure was 1 atm. This output stream was subsequently inputted to the oxidation process.
As seen in the graph above, the mole fraction of carbon dioxide in the feed/retentate stream starts at 50% at the membrane inlet, then achieves roughly a 5% mole fraction at the outlet. This graph allows us to visualise the permeation process as a fraction of the membrane that has been covered by the stream during the single pass.
By modelling we were able to assign dimensions to our membrane module. Our membrane module will be 30cm in diameter and 1m long, with 110 membrane fibres incorporated. Each membrane fibre is a straw-shaped cellulose acetate membrane unit that runs the length of the membrane (i.e. 1m), and has an external and internal diameter of 650 and 389 micrometres respectively.
Discussion
Achieving a retentate, or outlet, stream composition of over 95% methane certainly exceeded the team's
goal and
expectation of 90% methane. By implementing multiple membranes or recycle streams during this process,
it is
certainly possible to further improve the methane purity of the retentate stream, but the team concluded
that
this would be unnecessary as the increase in methanol conversion would be more or less the same.
Considering the single pass separation is very high, the fact that the current model does not have a
recycle
stream is not much of a limitation. As outlined in the oxidation section, the poor methanol yield is not
the
result of impurities present in the oxidation feed, but rather the process itself. Therefore, the
inclusion of
multiple membrane separations or recycle streams will most likely lead to unnecessary capital and
operational
costs, as a completely pure methane stream is not required for our proposed process.
Next Steps & Improvement
Moving forward, there are a number of improvements that can be made to this aspect of the model. First,
the
team would like to account for the influence of nitrogen in the separation process, and ideally all
gases,
including those present in trace amounts. The modelling aspect of a mixture with numerous components
would
require more sophisticated equations and computations, but this would give a more holistic overview of
the gas
phase separation required. Testing on a physical prototype may give more insight to how the complete gas
mixture
behaves, but due to safety reasons and logistical constraints the team could not test a membrane using
collected
landfill gas.
Another potential area for improvement would require the implementation of membrane modelling into Aspen
Plus.
Currently, AspenTech does not support membrane separation processes in Aspen Plus and the team had to
use a
custom separation unit operation instead. Having this feature available would likely improve the
accuracy of our
calculations, as the ideal separation process would recalculate the split fraction in every recycle
iteration,
and our current model cannot do this, so the team instead assumed a constant split fraction each time
the
recycle stream passed through the membrane.
Furthermore, the team would like to eventually use a non-ideal gas assumption model. This would
ultimately
determine the pressure loss in the retentate stream and would provide results that are closer to what
would be
observed in a practical application of the membrane separation. Doing so would require much more
sophisticated
differential equations but would result in a greater level of confidence on the actual performance of
our
separation unit prior to field use in an actual landfill.
Methane-selective Gas Separation Prototyping
Summary
A prototype to demonstrate the proof-of-concept for the gas phase separation was planned. The goal was to show that the membrane was selective towards methane over carbon dioxide. Due to the hazards of using methane in a lab, a mixture of nitrogen and carbon dioxide was to be fed into a system containing a polysulfone membrane acquired from the Advanced Membranes Lab run by Professor Jay Werber. The nitrogen would act as a proxy for methane, as a mixture of nitrogen and methane fed to a polysulfone membrane would behave similarly to a mixture of carbon dioxide and methane fed to a cellulose acetate membrane (which was our original design).
Introduction
The membrane model was designed after an existing single-gas membrane set-up, designed and assembled by a member of Professor Werber's lab:
We planned to implement the system above for a binary mixture of gases, in order to replicate the landfill
situation as closely as possible. However, to quantify the separation we would have needed a device to measure the
composition of either carbon dioxide or methane in addition to the flow rate. We did not have ready access to a
gas chromatography machine, and hence there was no feasible way to obtain composition data.
Thus, the working of our prototype had to be demonstrated by passing both gases individually, and then measuring
their inlet and outlet flow rates. The ratio of the flow rates would then signify the selectivity.
Should we acquire access to a gas chromatography machine, we will perform tests with both gases flowing through
the system. We will use a pressure regulator on the gas cylinders to ensure that both gas inlets are at the same
pressure. Flow rates will be controlled by valves and bubble flowmeters. The membrane cell will receive two gas
streams. There will be two membrane outlets; one for the permeate and one for the retentate. Once steady state
operation has been achieved, we will use a bubble flowmeter at the outlet and a gas chromatogram at the permeate
outlet to quantify membrane separation. See the P&ID below for context.
Prototype
A P&ID was developed for the prototype. Tubing and valves were to be purchased from McMaster-Carr, while the membrane-cell and flowmeter were to be obtained through Sigma-Aldrich. All pipes are 1.5cm in diameter.
Conclusion/Future Step
Due to logistical snags, the prototype was not constructed. In particular, the hardware team was unable to obtain a site for Our future steps are therefore to obtain the required materials and follow through with our initial plan.
Methane to Methanol Oxidation Reactor Modelling
Summary
The methane to methanol oxidation step is done with a packed bed reactor containing a copper chabazite (zeolite) catalyst [1] . The reactor has a single pass conversion of 0.07%. By using a recycle stream with a ~50 recycle ratio, the effective conversion is 7.20%. Phase separation is used to separate methanol from water, as a mixture of the two is normally miscible. Two different models were developed from [1] : one on Aspen and one with Excel; the two differ mainly in their phase separation techniques.
Background
The methane to methanol oxidation step is generally done with two ways: an energy intensive two-step reaction
involving combustion into CO2 and using a water gas shift reaction to reduce the CO2 into methanol using
hydrogen, and a single step reaction with catalytic chemistry. The two-step reaction is generally done at the
scale of large plants, as large amounts of fuel are needed to sustain the high temperatures and pressures
[2]
needed for the reaction. As our intended solution is to have a setup sited at
the landfill itself just like how a flare stack is situated within a landfill site, the scale of our solution
is incompatible with the two-step process, and thus we focus our attention on the catalytic process.
The conversion of methane to methanol has been called a “dream reaction” and the “holy grail of catalytic
chemistry”
[3]
. Despite at least two decades of research, no process has achieved single pass
conversions above 1%, and especially without requiring significant amounts of expensive catalyst. The focus
of research on this area is mostly on improving catalyst selectivity, instead of yields, on a per-atom basis
using tools such as X-ray diffraction spectrometry. However, a promising set of catalysts has advanced to
the point where some researchers have attempted incorporating it as part of a process in a reactor.
One of the most promising approaches for direct methane to methanol conversion involves the use of
electrocatalysts, particularly copper-based catalysts. Copper-exchanged zeolites, for example, have shown
promising results in converting methane to methanol
[3]
. The process typically involves
activating methane by catalysts on the anode, followed by the conversion to methanol through the attack of
electro-generated active oxygen species
[3]
. However, even state-of-the-art approaches
have difficulty achieving industrial yields and are difficult to upscale
[4]
.
Our particular process uses a copper chabazite
[1]
catalyst with a ~2%
yield and a 91% selectivity into methanol. The research incorporated a continuous flow packed bed
microreactor in their research, which may be scaled up to an industrial packed bed reactor already
used in petrochemical processes and hydrogen generation via steam reforming
[2]
. The low yield is due to an extreme methanol/oxygen molar ratio of
2450 entering the reactor, which means that the conversion of methane is choked by the extremely
small amounts of oxygen - the limiting reagent in this case - which is necessary to prevent
methane from being oxidised outright to carbon dioxide.
To improve yields, we are able to use a recycle stream, where some of the reactor product is
redirected back to the input to the reactor inlet. This will allow any unreacted methane to be
given a second pass at the reactor. The recycle stream also serves to artificially maintain the
large methane/oxygen ratio required to maintain the reactor's selectivity into methanol.
As methanol is miscible in water, we exploit their different boiling points to separate a mixture
of the two. Methanol has a lower boiling point than water
[5]
; therefore, a mixture
of methanol and water heated to a temperature that is between the boiling points of methanol and
water will be concentrated in methanol. This type of separation is normally achieved with a
distillation column; however, for simplicity and time we have used a heated flash drum to
achieve the separation.
In modelling the oxidation, we hope to demonstrate that methanol can be produced for the
bioreactor - however small - and to identify means of optimising methanol production. This
effort will also display the plant design process for one particular landfill - at least its
first stages - which may be extended towards developing a full bioprocess scheme for processing
methane to valuable downstream chemicals, and to other landfills with different conditions as
well.
Methods
Relevant reaction yield and conversion data was extracted from
[2]
by hand.
Modelling was mainly carried out in the Aspen Plus software package, and an Excel model was created to verify
results. For both models, no heat is assumed lost to the surroundings, and no pressure loss is assumed in the
reactor. For the Aspen Plus model, the UNIQUAC thermodynamics package was used, and compressors were assumed
to be isentropic. For the Excel model, gases are assumed ideal and complete gas/liquid separation is achieved
except for one component where UNIQUAC input is used from Aspen Plus. Inlet compositions and flow rates were
based on London, Ontario's W12A landfill's landfill gas emissions. Different landfills will have different
landfill gas compositions and require potentially different reactor compositions.
Aspen Plus simulates chemical engineering processes; that is, we are able to specify reactors, distillation
columns, material inflows and connections, etc. and Aspen Plus will calculate a steady state solution - the
amount of material flowing through and energy being exchanged in each reactor, distillation column, and
component so that a mass balance is achieved. When we worked with Aspen Plus, we first designed on paper a
process flow that may potentially work, evaluated it in Aspen Plus, and iterated based on the simulation
output. The final process flow we settled on is included in the results section.
Excel can be used to calculate steady state solutions as well, but is more involved as the calculations and
formulae have to be typed individually. Heat and energy calculations were done with the ideal gas assumption.
To improve the model's fidelity, vapour-liquid phase equilibria in heat exchangers were modelled with Aspen
Plus using UNIQUAC, and the results were copied to Excel. As phase equilibria cannot be represented as
explicit functions easily, ternary and binary search was used over multiple calculation iterations to identify
a steady state solution.
Results
Aspen model results
Our proposed process consists of a methane inlet stream which is derived from the membrane outlet, as well as an oxygen and water input stream. These input streams are mixed with recycle streams containing unreacted water and methane, and some methanol. The reactor (large rounded cylinder) converts some of the methane into methanol. Drum F1 separates the gaseous components from water and methanol. Drums F2 and F3 separate water from methanol via phase separation by heating and cooling. The gas exiting F1 is mainly unreacted methane, of which 99% is recycled back to the reactor and the remainder released. Most of the unreacted water exiting F2 in liquid form is recycled back to the reactor.
The single pass conversion is 0.0112% and the overall conversion is 0.1%. The concentration of methane at the outlet is ~4%.
Excel model results
Our proposed process consists of a methane inlet stream which is derived from the membrane outlet, as well as an oxygen and water input stream. These input streams are mixed with recycle streams containing unreacted water and methane, and some methanol. The reactor (large rounded cylinder) converts some of the methane into methanol. A condenser at 90C (blue circle with arrow pointing downwards) condenses most of the water from the gaseous reactor outlet stream. Another condenser at 60C (green circle with arrow pointing downwards) condenses most of the methanol from the resulting gas mixture. 99% of the remaining gas mixture is recycled while the remainder is released.
The single pass methane conversion is 0.0796% and the overall conversion is 7.20%.
Reactor dimensions
Our packed bed reactor will be 0.2m in diameter, 5m in height, with 2360 kg catalyst contained inside. 49 of these packed bed reactors will be required to handle the London, Ontario W12A landfill gas output.
Discussion
This process proves that production of methanol is possible from methane, but only at extremely low yields. The
main problem this process faces is poor catalyst performance, which displays extremely low methane conversions
(0.8 per mille) per reactor pass, despite being a recent development. To meaningfully upscale this process, we
would likely need a 100-fold increase in the single pass conversion. This is impossible to achieve with this
catalyst because oxygen is limiting; an extremely large ratio of methane to oxygen is needed to control the
reaction selectivity. As such, catalyst research towards developing a meaningful process in this field should be
directed at lessening this large methane/oxygen ratio, and we will keep an eye out for catalysts displaying this
improvement for the future.
To alleviate the poor catalyst performance, our team uses a large recycle stream, to ensure that the bulk of
unreacted methane gains a second pass through the reactor. However, a large recycle stream decreases stability,
and increases the time required to reach steady-state operational conditions. This process is therefore likely
difficult to manage in an actual plant setting.
Initially, Aspen Plus was selected to model this process. However, the process we designed required a specific
methane/oxygen ratio at the reactor inlet which Aspen Plus was not able to control easily. This is the main
rationale behind the creation of the Excel model, which we were able to customise in this way but lacks support
for phase equilibrium required in condenser and heater models. We adapted our Excel model with phase equilibrium
calculations from Aspen Plus, modelling the condensers and heaters on Aspen only while modelling the remainder
of the process on Excel.
As demonstrated by the Aspen model, the perfect phase separation that is assumed in Excel is unrealistic but
relatively accurate, as extremely small amounts of gas dissolved in the liquid phase and some methanol and water
ended up in the purge streams. The Excel model, however, displays better performance than the Aspen model,
because our team had more control over the model and this allowed us to configure the reactor so that more of
the limiting reagent reacted.
To customise the process several parameters may be changed in a chemical plant setting. The condenser
temperatures control the quality of the phase separation in the process. Methanol has a lower boiling point than
water. Increasing the condenser temperature increases the fraction of methanol that remains as a vapour - and
increases the amount of methanol exiting via the gas release streams - but increases the concentration of the
methanol exiting the process. This parameter may be varied to control the exit concentration of methanol.
The recycle split fraction determines how much of the gas exiting the reactor is recycled. This is controlled by
the various stream splitters in the process. Increasing the split fraction increases the amount of gas recycled.
This increases the overall yield and conversion of the process as less methane is wasted, but makes the system
more unstable and causes it to take longer to reach operational steady state. Ideally this parameter is kept as
low as possible, but it may be varied to modify how much methanol exits the process depending on requirements.
Noting the low yields of this process at its current state it is impractical for all landfill gas methane to be
processed this way. Some methane will still have to be burnt to ensure it is not released to the atmosphere. In
the absence of a better process, a possible implementation is to have the reactor and heaters be heated by the
combustion of methane. Once a stronger catalyst and a better process is found, hopefully no methane combustion
will not be required. Implementing this process will still be beneficial to the fight against climate change as
some methane is sunk into biomass instead of being emitted or turned into carbon dioxide.
Next Steps
Our team has demonstrated a proof of concept for a process, however inefficient, that can be utilised to
synthesise methanol from methane as an aqueous solution. Our results were demonstrated by both Aspen Plus and
Excel models; however, the Excel model suggests greater performance because it was easier to customise. The main
constraint this process encounters is the highly inefficient catalyst, and further research should be directed
at identifying catalysts that do not require such a constraining oxygen/methane ratio.
Two major points of improvement may be made. The first is that, industrially, phase separations are done with
distillation columns instead of flash drums. Our advisors suggested that we could size a single distillation
column instead of the three condensers and heaters we use currently; not only will this save work, but the
separation quality may be improved. The second is that Aspen Plus is not open source software, and many of the
process engineering routines done with that software package may be replicated in Python for open source use.
Bioreactor Design and Upscaling
Summary
The final step in our process design is the metabolism of methanol into biomass or other value-added chemicals. As such, we require a fermenter to house our engineered E. coli strain. Given the potentially harsh conditions near a landfill, and having identified potential regulatory issues by using a fermenter as part of a landfill gas treatment strategy, we have decided any such fermenter must be bespoke and custom-made. Furthermore, we intended to model our fermenter with the BioWin process simulation package; unfortunately, we were unable to carry this out as we lack requisite process kinetics data.
Introduction
Fermenters are containers used to grow bacteria and fungi in large amounts in a controlled environment to obtain the desired product. We will be using a custom stirred tank fermenter with a sparger system for our purposes.The sparged stirred tank fermenter offers the advantage of enhanced oxygen transfer (due to the increased gas-liquid interface area). Our main objective of implementing a fermentor is to convert methanol into biomass with the action of wet-lab and dry-lab's engineered bacteria. We intend to model the fermenter through a process simulation on BioWin to show dimensions, sensitivity analysis and then an overall view of the fermenter process with respect to our project to see the final output of our process.
Background
1. Main Components in a Fermenter
[1]
Fermenters, specifically sparged stirred tank fermenters, are instrumental in cultivating bacteria
and fungi
for
desired product generation. The enhanced oxygen transfer in these fermenters is achieved through a
sparger
system.
Key components include the fermenter vessel, heating/cooling apparatus, aeration system, sealing
assembly,
baffles, impeller, sparger, feed ports, foam control, valves, and controlling devices for
environmental
factors.
2. Landfill Implementation (Service Environment)
Our focus is on implementing fermenters in landfill sites. Hence understanding the service environment is important. Landfills, engineered for waste management, vary based on location, climate, regulations, and management practices:
- Location: Strategically placed away from water bodies and residential areas.
- Weather and Climate: Conditions influenced by regional weather, impacting decomposition rates and leachate production.
- Waste Disposal: Varied waste types compacted for space utilisation and odour control.
- Gas Generation and Control: Landfills produce methane and carbon dioxide, managed through gas collection systems.
- Environmental Protection: Adherence to regulations for liner systems, groundwater monitoring, air quality, and closure/post-closure care.
- Maintenance and Monitoring: Ongoing oversight for compliance, covering liners, leachate systems, groundwater monitoring, and vegetation.
In the context of landfill conditions and regulatory compliance, the decision to build a fermenter, rather than purchasing one commercially available, is grounded in its optimal suitability for unique requirements, regulatory compliance, cost-effectiveness, and simplified maintenance. Due to this decision of building a custom one instead of buying one from the market, we would have to model our fermenter using process simulation as mentioned in the introduction.
3. Buy vs. Building a Custom Fermenter
This wiki contains an abridged version of the following pdf, where considerations are made in more depth.
3.1 Customization for Unique Requirements:
Landfill sites have specific laws and regulations that define how the land around the site can be used. This includes specific size and space constraints and gas capture restrictions as well which . Buying a commercially available fermenter in the market may not follow the constraint of available usable area and the amount of feedstock we would like to process. By building a custom fermenter, we can ensure it precisely fits the available space and capacity required, utilising the landfill site to its full potential.
3.2 Regulatory Compliance:
Landfill operations must follow numerous environmental laws, especially those related to waste management and discharge. Building a fermenter provides the flexibility to consult with experts and ensure compliance with these laws. In contrast, commercial fermenters may not align perfectly with local regulations, potentially leading to compliance issues and additional purchases to meet requirements. Custom-built fermenters offer the needed flexibility to adapt to changing regulations (3 example regulations are listed below), allowing adjustments as laws evolve.
- Specifications and descriptions for the design of the landfill site must include an assessment of the potential for subsurface migration of landfill gas at the site and of any control system necessary for monitoring or controlling the migration of it [2] .
- The owner and the operator of a landfill site shall ensure that the waste fill area is completely surrounded by buffer area in accordance with this section [2] .
- To protect groundwater resources, landfills must be designed to meet stringent contaminant limits in accordance with the Ministry's Reasonable Use Guideline (1994) as described in Section 10 of Regulation 232/98 [2] .
Building a fermenter with the help of landfill site experts at each step of the process makes it much easier for the landfill site to pass the test of meeting the regulations and laws established.
3.3 Cost Effectiveness (Cost Analysis Table is found below)
Constructing a landfill-specific fermenter can be more cost-effective than purchasing one commercially. Commercial fermenters may come with unnecessary features, leading to higher costs. Building a fermenter with only required specifications avoids unnecessary expenses, ensuring a budget-friendly fermentation process at the landfill site. While the quality may be slightly lower than industrial-grade options, it meets our specific needs. Due to the significant price difference between buying and building a fermentor, we have flexibility. This allows us to invest in higher quality components for critical parts prone to wear and tear, while using slightly lower quality parts for those easily replaceable or less likely to fail. This ensures a strong and reliable fermentor within budget.
3.4 Maintenance Simplification
The contained facility (in which the fermenter is meant to be placed) in the landfill site may expose the fermenter to corrosive substances present in the waste. Therefore, when custom-building the fermenter, it is possible to select corrosion-resistant materials for critical components. This helps prevent premature deterioration of parts, minimising maintenance requirements, and reducing the risk of breakdowns.
4. Modelling Goals
As we intend to use a custom built fermenter, modelling will allow us to quantify fermenter performance against size, while identifying critical points in the design through sensitivity analysis. Through this process we intend to assign dimensions to our fermenter including its sub-components (metal jacket, impeller, sparger, inlet and outlet pipes, etc.) and determine its performance at steady-state operation. Finally, through this process we will be able to quantify the performance of our solution from end to end - landfill gas to value added chemical.
Methods
In sizing the fermenter, we intend to use the BioWin software package. BioWin is a software package that is geared towards modelling activated sludge reactors. Indeed, BioWin's main use case is in chemical and civil engineering applications for municipal and governmental water treatment solutions. However, the BioWin custom modeller allows creation of reactors involving custom bacteria, making it promising for modelling a bioreactor with a strain of bacteria whose use may not be as widespread in industry. Modelling a bioreactor in BioWin with custom bacteria takes two steps: modelling the bacteria's processes such as growth and decay, and modelling the reactor's steady state performance against reactor volume, inlet flows, and other overarching reactor parameters.
This is a depiction of BioWin's process simulation UI. The heart of the system is the bioreactor which is where our engineered bacteria will reside.
When modelling bacteria in BioWin, we must define the notion of a process. A process is any single change that the bacteria may undergo which may be expressed like a typical chemical reaction, for instance:
aA + bB → cC + dD
An example process that may be modelled this way is if a strain of bacteria consumes one gram of glucose and one gram of methane to produce one gram of biomass and two grams of carbon dioxide (in which case a, b, c, and d may be 1, 1, 1, and 2 respectively). BioWin requires the stoichiometry of a process (the coefficients a, b, c, and d) and the reaction rate (usually in terms of ) to model a process. Therefore, to model a bacterium, we must identify which processes are relevant and their stoichiometries and kinetics.
This is a depiction of BioWin's custom model builder. Processes are set in the box marked “processes”, and the stoichiometries are included in the columns beside it. BioWin can keep track of certain compounds - e.g. total Kjeldahl nitrogen, dissolved and bubbled oxygen, and metal ion concentrations - and incorporate them into the various processes we expect our engineered bacteria to undergo. Rate equations for each process are included at the bottom right side.
[3]
The process depicted in the picture is one of ammonia (NH3) removal; the stoichiometric coefficient for NH3 is set to -1.
We consulted our dry lab team to see if the necessary process data may be acquired. Our dry lab team conducted metabolic modelling using flux balance analysis (FBA), which calculates our E. coli strain's fluxes when it is undergoing a specific growth rate. However, this method is only applicable at the exponential growth phase and is less relevant at the log phase, which is the region of interest from a reactor engineering perspective as we are interested in reactor steady state inlet and outlet flow rates. However, other modelling methods such as dynamic FBA apply in the log phase and may produce flux vs growth rate data.
Our dry lab team has derived a relationship between the prior reaction rate against modelling-determined flux and reaction rate constants, provided that explicit processes with known stoichiometries have been determined prior. Unfortunately, iGEM Toronto does not possess enough time to complete metabolic modelling using other models, or to investigate the modelling data we have in depth to identify specific processes that may be fed as inputs to BioWin. If iGEM Toronto had more time, we would attempt to define discrete processes and determine their flow rates with dynamic FBA or other log growth phase modelling methods and communicate with wet lab to see how reaction rate constants may be derived experimentally for these processes.
Once these processes are defined, the team would then carry out a sensitivity analysis to determine which parameters have the greatest impact on the value added chemical. By varying parameters such as the sizes of the impeller, sparger, metal jacket, and pipes, among other critical sizing components, and then performing large iteration cycles, the size of the bioreactor can be optimised for performance. With an optimal simulation, the team would then consider capital and operational costs when making a decision on the size of fermenter to implement at a given landfill site.
Conclusion
Custom-built fermenters offer flexibility, tailored to landfill site needs for efficient waste processing and space utilisation. Compliance with regulations from the start minimises legal issues, streamlining operations. Building instead of buying ensures cost control by selecting site-specific materials, reducing expenses for a long-term, cost-effective solution. Moreover, a custom-built fermenter facilitates easier maintenance in challenging landfill conditions. Therefore, we have developed the process simulation of our fermenter to simulate the functionalities required and then the overall output which would be the biomass.
Fermenter Modelling
Motivation
The ultimate goal of any chemical engineering project is the design of a chemical plant. Currently, however, all we presented in our models are block diagrams, abstractions that take in and release chemicals. The goal of this visualisation project is to provide wiki readers with a visualisation of what, if implemented, our chemical engineering engineering process scheme will look like in real life. Should the project continue for longer, iGEM Toronto may also use the 3D models to size and place components, design palletization and modularization strategies for equipment, as well as design supports, braces, and fittings for the various pipes and equipment our chemical engineering process scheme will need to incorporate.
Methods
Sizing, which is the process of assigning dimensions to equipment, was part of the modelling process for our
processes. Our hardware team has sized the membrane module and the packed bed reactor, which are the most
important components for the separation and oxidation processes respectively. Unfortunately, due to a lack of
input data for the bioreactor model, our team was unable to size the fermenter we intend to use in our design.
These, and most of the pipings and heating/cooling elements in our 3D models, use placeholder dimensions instead.
Our 3D models are drawn using SolidWorks. Some components, such as the fermenter, are generic models obtained from
internet sources. Others, such as the packed bed reactor, are modelled from scratch. Positioning is arbitrary and
our models do not and are not intended to meet any size or footprint restrictions. No valves, measurement
devices such as pressure gauges, or structural supports are incorporated in the model for simplicity.
Hardware Part Description
Separation module
The following is a 3D CAD model of the separation module. The picture below contains details the module's components. Feel free to rotate and pan around the CAD model. The membrane module (see picture) has been sized; dimension details may be looked up in the "Gas Separation Model" subpage here.
Oxidation module
The following is a 3D CAD model of the oxidation module. The picture below contains details the module's components. The packed bed reactor module (see picture) has been sized; dimension and flow structure details may be looked up in the "Oxidation Reaction Model" subpage here.
Fermenter module
The following is a 3D CAD model of the fermenter module. The picture below contains details the module's components. Due to a lack of time and reaction kinetics information, we were not able to size the fermenter or the ultrafiltration unit. The following depiction is thus an approximate representative of what our system would look like on the field. Details are in the "Bioreactor Design" subpage here.
The fermenter and ultrafiltration models were acquired from [1] and [2] .