Note : Les descriptions sont présentées dans la langue officielle dans laquelle elles ont été soumises.
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Systems and Methods for Waste Collection, Processing, and Optimization,
Biomass Fuel Generation, and Gasification
Field of the Invention
The present invention relates generally to generating energy from biomass
feedstocks and, in particular, to optimizing the processes for engineering
fuel and
generating syngas from a waste stream. The invention has particular advantages
for effectively and fully processing waste streams that vary in content and
volume
over time.
Background
Sources of fossil fuels are becoming increasingly scarce and costly, causing
the
energy and petrochemical industries to actively search for cost effective
engineered fuel feedstock alternatives to fossil fuels. Today, engineered
biomass
feedstocks are increasingly supplementing and/or replacing fossil fuels for
use in
combustion processes for the production of energy and gasification processes
for
generating syngas used in the downstream production of chemicals and liquid
fuels. Syngas generation has been quite successful in environments where the
feedstock is highly consistent in volume and composition, e.g., woodchips or
sewage processing. However, many challenges remain with respect to
environments where the feedstock is highly variable in composition (e.g., a
heterogeneous waste stream) and where volumes and compositions vary with
respect to time of day or seasonally.
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Summary
The present invention involves a system and methodology (a "utility") that may
be used to convert waste to energy. More
specifically, the inventors have
recognized the need for a utility for analyzing heterogeneous waste streams in
a
manner that informs the production of a predictable and stable biomass
feedstock for
use in a number of downstream processes such as, for example, a gasification
process. In this regard, the inventors have developed tools for use in
examining,
optimizing, and controlling inputs to waste streams so as to engineer fully or
substantially utilizable waste streams that may be blended to generate a
feedstock
having desired material properties (e.g., composition, thermodynamic
properties,
moisture content, density, etc.). These
tools allow a viable feedstock to be
produced on a year-round basis despite a number of fluctuating variables
relevant to
the waste streams themselves (e.g., the amount of waste collected, the types
of
waste collected) as well as environmental variables that impact the downstream
gasification of the feedstock (e.g., temperature, humidity).
Beyond producing an optimized feedstock from varying and heterogeneous
waste streams, the inventors have identified a number of design parameters
that are
relevant to one or more gasification reactors and/or technologies that combine
gasification with fast pyrolysis for use in generating syngas from the
engineered
feedstock. These
parameters are key to the development of waste-to-energy
systems that can meet the needs of both large and small
institutions/facilities in
consistently producing energy from on-site waste on a year-round basis.
Notably, while the disclosure below describes several implementations of the
waste-to-energy utility described above in the context of a zoo, animal
management,
or animal containment environment, it should be understood that the
contemplated
waste-to-energy utility is not limited to zoological or animal management
environments and may be used to convert waste to energy within any appropriate
environment, including any facility that generates waste and collects it in
multiple
locations, communities in which homes develop independent and unique waste
streams, facilities in which waste is managed by staff/contractors, facilities
interested
in minimizing waste handling costs, and/or facilities with waste collection,
disposal,
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and/or energy operational concerns. For instance, embodiments of the waste-to-
energy utility described below may be implemented within or in relation to
theme or
amusement parks, convention centers, outdoor arenas used for sporting and
other
events, concert venues, and much larger environments such as
municipalities/communities.
In the particular implementation described below, the processing can be
optimized with respect to variations in the waste stream so as to provide a
holistically
optimized solution. This may involve analyzing the waste stream, influencing
the
materials that are introduced into the waste stream, supplementing the waste
stream
with outside waste or additives, mixing waste stream components to generate a
desirable fuel stock, optimizing processing of waste materials to generate the
fuel
stock, optimizing reactor design and operation for generating syngas,
utilizing
multiple reactors to address fuel stock variation and scalability, and
reconfiguring
reactors and processing to optimize performance, efficiency and effluent/ash
control.
In this manner, a highly flexible process is enabled for handling varying
volumes
(including smaller facilities) and compositions including, for example,
combinations of
refuse derived solid (RDF) waste and animal waste.
Brief Description Of The Drawings
For a more complete understanding of the present invention and further
advantages thereof reference is now made to the following Detailed
Description,
taken in conjunction with the drawings in which:
FIG. 1 is a chart showing data and methodology for tracking and identifying
peak waste delivery times;
FIG. 2 is a chart showing standard deviation of carts within a timeframe;
FIG. 3 is a graph showing a delivery time distribution;
FIG. 4 is a graph showing the different types of waste generated by a
facility;
FIG. 5 shows an exemplary screenshot of a waste analysis data table;
FIG. 6 is a chart showing a simulation of a gasification process in accordance
with the present invention; and
FIG. 7 illustrates a waste stream collection, processing and gasification
system in accordance with the present invention.
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Detailed Description
In the following description, the invention is set forth in the exemplary
environment of processing all or substantially all of the waste from a zoo.
This is a
particularly challenging environment and serves to illustrate important
aspects of the
invention. However, it will be understood that the invention is not limited to
this
environment, but rather extends to the full scope of the claims set forth
below.
A zoo generates a waste stream that is highly variable in volume and in
composition. The waste stream includes, among other things, concessions solid
waste, office waste and animal waste. The concessions waste and office waste
may
include plastic, paper, metals, and a wide variety of other materials. The
animal
waste also varies in important ways. For example, the waste from herbivores
may
contain a more consistent and readily know biomass for downstream processing.
The waste stream arrives from multiple sources, typically in transportable
carts, and
varies depending on source location, time of day, season, attendance, special
events, and other factors. This environment is thus very different from many
conventional syngas environments, but much like many other waste management
environments (though perhaps an extreme example) where gasification has
generally not been attempted.
A fundamental recognition of the present invention is that the system is best
optimized not by reference to static assumptions but, rather, based on
understanding
of variations of the waste stream over time and through rigorous data
collection and
analyses. Through this understanding of existing processes fuel can be
generated
from the waste stream, and produce syngas that can be optimized with respect
to
such variations. This involves 1) monitoring and modeling the waste stream
volume
and composition over time, 2) selective waste processing/assessments to obtain
stock from which to produce fuel 3) selective processing of the stock,
including
drying and pelletizing, to produce an engineered fuel, and 4) controlling
gasification
and generator operation for optimal results. Each of these aspects is
discussed in
turn below. It will be appreciated that the utility of the invention lies
largely in
defining an approach that allows for optimizing processing for a variety of
waste
stream volumes and compositions, not in identifying the optimal parameter
values for
any specific, momentary waste stream volume and composition.
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Waste Data Collection:
A methodology has been developed to track the zoo's waste stream. Key
features of this are:
= Every building/exhibit/area has a waste collection bin that is labeled
and the
waste identified as being from that location.
= Weight is tracked daily from each building/exhibit/area.
= Time of delivery is tracked to identify statistical time of day.
= Weights of most typical materials are stored in a database and are used
in a
combination of visual assessments of waste containers to approximate
weights.
= Data is stored and sorted in a database to develop a statistical
assessment of
year's worth of data.
Using this data and the developed methodology for tracking, we can provide
graphically the disposal time and weight variance. FIG. 1 is a graph of total
material
delivery during each month. This data provides information about how the
material
weights delivered vary over time during each month to assist in assessing the
consistency of material quantities delivered yearly. This chart identifies
peak
delivery times during the day for materials and shows that the majority of
materials
are delivered in the morning and that the morning also contains the most
inconsistency. Further evaluation shows seasonal shifting due to weather
events.
Evaluating this data further on a waste cart location basis can define a
delivery schedule and weight value associated with a cart to statistically
develop a
model for waste carts based upon historical data. In other words, developing
theoretical delivery times and weights for carts moving forward. FIG. 2 is a
chart
showing that a concession cart will most likely show up at 7:30 am, but can
show up
between 7am and 8am.
Expanding upon the delivery time further, we may view every waste cart
delivery vs. time and see which carts can vary and which ones are easily
determined. The standard deviation of these carts within a timeframe (maximum,
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minimum, and average) can be seen in the graph of FIG. 3. This data is then
used
to define the statistical delivery parameters for the simulation model.
It can be seen from this graph that the 100 numbered cart (concessions,
carnivore, and operations) waste is typically delivered before 11 am, which
assisted
us in developing a run rule for the simulation whereby material in the morning
will be
stored or mixed separately/selectively from the 200 carts, which are the
herbivores
and contain a more consistent biomass material for downstream processing.
Because an institution's waste generation profile/weight may fluctuate with
attendance, an evaluation of the effects of attendance on waste generated and
the
types of waste generated is helpful. By expressing large data sets into a
graph, it
can be shown how types of waste may fluctuate and to what degree they are
expressed in the waste stream.
FIG. 4 is a graph of waste generated: for reference the different types of
waste are separately shown as segments of each bar: bottom= concessions waste;
middle = Animal, Concessions, and Operations; top = Herbivore/Compost. It has
been discovered that attendance did affect the type of waste generated, but
because
of material residence time in waste containers, only when attendance was high
consistently (8000+) for more than three days would it affect the waste
profile
significantly. This is somewhat predictable because only after two days of
high
attendance would the material mixture have to be evaluated closely or
adjusted.
By statistically sampling the data moving forward into a simulation,
continuous
seasonal profiling can be predictive and assist with making future adjustments
to
material processing and energy conversion.
The use of this data collection methodology may be placed anywhere that has
any of the following:
= A facility that generates waste and collects it in multiple locations.
= Community waste streams where homes develop independent unique
waste streams.
= Waste that is managed by staff or outside contractors.
= An interest in tracking their waste stream more closely.
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= A site that currently has waste collection, disposal, or energy
operational
concerns/issues.
= A facility who would like to minimize waste handling costs.
= An environmental leader who wants to tackle waste
sorting/handling/disposal effectiveness.
Waste data collection may be expanded to collect:
= More detailed information per each waste container including weight of
material, moisture content, and % weight of each constituent making up
each individual waste container.
= Combining any waste collection with the existing statistical model,
approximations may be derived for future operations for many facilities.
= Specific known heavy use Products (such as event based cups,
containers, etc.) may be tracked based upon sales to assist in balancing
out the waste stream from facility "cradle" to "fuel source".
o This may also be used to test how reusable products are treated by
guests/visitors/members.
= Waste containers may each be fabricated with a built-in means to collect
material weight and even photograph contents to remotely communicate
data to a "hub" for statistical modeling.
o This may be expanded to allow for staff to identify items of interest,
such as: batteries, electronic waste, metals, large quantities of
glass, etc.
o This may also relay information to waste management crew to pick-
up containers that may be filling quickly do to demand.
o This data may also be used to "test" the best areas, signage,
designs, sorting, etc. for proper waste handling and to eliminate
contamination and pollution.
o Data may be used to accurately create a facility waste-stream
environmental footprint.
o Informative signage may be developed that have statements like:
"Most (88.5%) of our guests have chosen to recycle cans" and
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"detailed data collected from each waste container to reduce OUR
impact". etc...
= Pick-up routes may be modeled for fuel, energy, and time to provide waste
management ¨ some modeling has been done for this already.
= Re-evaluation of current methodology for delivery of waste to a secondary
location (typical of offices, buildings with floors, etc.) may be performed
and data used to compare alternative or more efficient or appropriate
options.
Waste Processing:
Upon the arrival of material, we have developed a simulation model that is
capable of tracking this material as it changes state/form in processing
equipment
and mixes. Data tracked/collected may include the following:
= Calculations for mixing and development of formula for mixing based upon
stratification scientific journals and stated equipment operations from
manufacturer.
= Separation and storage of waste streams through tracking material as it
is
processed and storing material in one of three feed hoppers.
= Using dumpster surveys previously performed to track and appropriately
mix
materials throughout the processing. Source component materials are
tracked and may include: office paper, paperboard, cardboard, HDPE/LDPE,
PETE, and PLA plastics, woodchips, yard trimmings, animal waste, rice straw,
alfalfa stems, glass, aluminum, metals, and potential heavy metals. Additional
components can be added/removed when necessary for different waste
streams. In addition, fill of dumpsters and moisture content approximations
may be made. FIG. 5 shows an exemplary screenshot of the resulting data
table after data is collected, sorted and a max, min, and mode are identified:
= Each one of the source materials has specific laboratory data associated
with
it including but not limited to: btu/lb (LHV or HHV), ash analyses, chemical
make-up (typically ultimate and/or proximate analysis performed) which allows
for the capability to "dial" in the simulation to ANY waste stream containing
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these or other available component data as well as any laboratory data that
we have collected.
= Changing material properties during processing: density, volume, moisture
content, BTU value, chemical constituents that could cause problems like:
heavy metals, materials containing chlorides, and materials that give off
VOC's at lower temperatures.
= Tracking the separation of liquids or particulate matter for materials
during
processing. This can be valuable to assign liquid properties to incoming
materials and how material type affects losses/filtration/particulates
generated/volatiles created at temperatures/key liquid properties such as:
BOD, COD, TSS, Alkalinity, and PH.
= Track all energy use based upon duration of operation, kW load from
equipment, federates, and/or difficulty to process materials.
= Material is tracked per cubic foot (or whatever the user denotes as being
necessary) and the simulation model may track each "chunk" of material even
as it is split and shared between multiple chunks along the way.
= Using an AutoCAD file as a background, two-dimensional equipment may be
displayed with theoretical material in final locations and even transport the
material proper: distances, heights, depths, etc.
= A process flow chart is the backbone of this operation and the simulation
may
be run in this form to see the logic behind what is going on in the actual
material: mixing, delivery, errors, machine failures, etc.
= The simulation model may easily accept outside materials from additional
vendors and describe quantities and delivery schedules with them.
= Simulation may replace any piece of equipment at anytime via a
description/set-up and place it anywhere in the line and easily process
material to test the following:
o Throughput.
o Reduction.
o Moisture removal.
o Maintenance schedule.
o Particulate matter generation.
o Electrical usage.
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o Thermal usage.
o Feedrate.
o Holding time.
This simulation is a powerful tool that when used in conjunction with
operational data can provide a means to test equipment operation, performance,
energy balances, mixing, breakdowns, schedule maintenance, etc to any
facility.
Examples of how this simulation may assist plant operations include:
= Data tracking and comparing equipment stated parameters (energy usage,
throughput, mixing, moisture reduction, material make-up) with actual
operational parameters.
= Predicting equipment performance prior to material being processed.
= Developing "run-time" rules for unanticipated breakdowns, jams, bridging,
etc.
that provide the operator with a procedure to follow to get the system
operational more rapidly.
= Modeling equipment for other facilities to provide them with a cost,
mixing,
and operational assessment for a potential future facility.
= Adding more detailed information that may pertain to emissions, waste,
etc.
parameters to be able to provide real-time along with future anticipated
solid,
air, and water emissions to environmental agencies.
= Use simulation to test potential replacement/new equipment prior to
installation to verify how it could affect future operations.
= Use simulation in conjunction with feedback from equipment controls
system
and actual performance data to predict and improve future performance.
Simulation of Drying:
We have realized many options that are available to remove moisture from
material for further processing. The downstream technology that thermally
converts
the solid biomass defines the moisture content necessary for efficient and
effective
conversion of the biomass. Using
existing drying curves based upon basic
temperature, air introduced, moisture removed, fluid used, etc., we have
recognized
that different component materials will respond differently. Approximating
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response is important to understand associated energy balances and includes
the
following:
= Capability to develop drying curves and collect data based upon many
materials for a complex mixture.
= Ability to adjust incoming and outgoing moisture content and see the
estimated energy usage response.
= Ability to add/adjust energy introduced to test moisture change over
drying
time.
= Use of specific heat of materials and water to approximate energy usage
and
create multi-material curves.
= Capability to attach the dryer VBA module to the simulation model and
feed
data into the dryer material components as received. This allows for a more
realistic operation for the dryer.
= Ability to provide drying constants in model using moisture content and
time in
dryer to adapt curves for actual performance data.
= Ability to tune the dryer performance (throughput, feed-rate, moisture
content
etc.) to improve downstream equipment processes such as a specific
pelletizing technology.
1. Additional areas that may be optimized include:
= Ability to more accurately predict Volatile, Particulate Matter, and
other
problematic emissions (both liquid and air).
= Modeling mechanical and thermal drying technologies at the same time.
= Improve the quality of gases leaving dryer.
Simulation of Gasification:
The inventive system was a software platform constructed in VBA language
so that it may be tested and adjusted via spreadsheets and data can be
manipulated
in a material properties program language file. This model may be used to
approximate the following thermal conversions in a reactor (FIG. 6 is a
screenshot of
the spreadsheet side of the model):
Gasifier Model (performs pyrolysis and gasification reactions together).
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= Material Properties are entered into a .txt file. These properties
include:
o Proximate and Ultimate Analysis information (HHV, btu/lb, Carbon,
Nitrogen, etc., wt %/lb, ash wt %).
o Ash analysis (components such as: Si02, K20, CaO, etc.).
o Specific heat, moisture content, drying constants.
= Input Parameters.
o Temperature of material entering reactor ¨ This could also allow for
multiple thermo chemical biomass conversion units to be set in front of
one another (given a new input material ¨(example - pyrolyzed waste
paper)).
o Quantity of inlet air per lb of material entering. The amount of oxygen
will depend on elevation and is described below:
= Amount of oxygen (%) introduced into the gasifier (this will
define the level of thermal conversion/combustion in the reactor)
¨ a specific amount is necessary to retain the temperatures in
the reactor.
= Quantities, types, densities, and wt% for moisture. These
parameters can be added for an unlimited number of materials,
but the current data set is 16.
= Material Molar representative wt% elemental analysis for each
individual material based upon elemental analyses.
= Product Gas temperatures on exit.
= Ratio of inlet air to pounds of dry biomass.
= Using the material properties and the underlying Molar base conversions
that
are based upon the mole fraction of an air based gasifier and the oxygen
equivalence ratio graphs, an output is calculated.
= Output ¨
o Combustible Gasses.
= Carbon Monoxide (CO).
= Hydrogen gas (H2).
= Methane (CH4).
= Other hydrocarbons (such as C2H6, C3H8...etc.).
o Nitrogen Gases.
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= Ammonia (NH3).
= Nitric oxide (NO).
= Nitrous oxide (N20).
= Nitrous (HNO2).
o Sulfur Gasses.
= Hydrogen sulfide (H25).
= Sulfur dioxide (S02).
= Sulfer trioxide (S03).
o Chloride Gasses.
o Ash including the following examples:
= Silica.
= Alumina.
= Titanium oxide.
= Potassium oxide.
= Heavy metals.
= Chlorides.
= Additional areas that may be optimized include: Updating cost/design
analysis portion to sizing a reactor.
= Expand upon the fast pyroloysis reactions that can occur (no oxygen with
external heat) and the "staged" approach for the gasifier. This would allow
for
more detailed chemical equations and the ability to monitor temperature and
pressure locations. This would allow for more detailed analysis/assessment
of future reactor and allow for better tracking of temperatures and how they
affect the system.
= Integrate data collection system with model, allowing model to better
approximate actual operation.
= Energy balance equations for reactor
= Graphics added for reference
= Detailed model related specifically toward technology that is chosen.
Basically, a standard gasification "model" and a technology specific model
that is adapted toward the chosen vendor technology/custom design.
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= A model that may operate within the PLC for the reactor to show a direct
comparison of expectations vs. actual operation to approximate gasses
through historical material data collection combined with temperatures,
pressures, and flow characteristic variables.
= Model that may be easily scaled and integrated to future PLC's for
gasification/pyrolysis units for KNOWN materials being processed. Once
adequate material processing development occurs and then material testing
performed, the Programmable Logic Controller (PLC) will have a model for
syngas prediction and "run-rules" to operate continuously without the need of
expensive gas monitoring equipment.
Downdraft Gasification Ideation Design Parameters:
From our research, we are implementing a downdraft gasifier that may
perform with the following parameters:
= Preheat material using exhaust from engine and/or exiting syngas from the
reactor.
= Penetrate the reactor bed from center and tube outward on or using:
o Multiple planes.
o Feeding air/gases multiple directions.
o A designed manifold to distribute gases evenly through reactor bed.
o A mechanism that spins around the center access to both stir and
deliver gas evenly.
= Tar recycling
o Using/pulling syngas back through the reactor could be used to heat
and also openings in the bed could be used to reticulate syngas
through the bed an further "crack" or process tars in the outgoing gas.
This would eliminate waste in the form of tars that occur within the
gasification process. This is similar to a cyclonic action that separates
heavier particulate/tar matter.
= Pressurize reactor
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o The reactor will be capable of being pressurized, which may be used
as a control mechanism to maintain syngas quality and quantity given
small material characteristic fluctions.
o Two reactors may be operating at different pressures and temperatures
to produce a mixed syngas that captures a larger variation of
hydrocarbon gasses.
= Multiple reactors acting simultaneously
o Multiple reactors would be designed similarly and could be tuned
differently while being fed identical fuel to best describe how minor
adjustments in: temperature profile, air temperature, pressure, nozzle:
angle, height, or zone, restriction cone size, ash collection/filtration,
densified material size, moisture content, etc. affect the outgoing gas
quality.
o Be operated on different fuel streams depending upon fluctuation in
feed material.
o Allow for waste processing system to remain "active" while
adjustments/maintenance is performed on another unit.
o Allow for real-time comparisons of the operation of multiple.
o Allow for one reactor to be utilized to test feedstock from another
future
client without affecting operations significantly.
o Allow for growth to larger output by just "adding" a reactor to the line
for
processing more fuel "power-plant built for growth".
= Steam injection in multiple zones and in ash.
o It is known that a water-gas shift reaction can occur with the char
leftover after gasification, experimentation with this may be important to
control gas quality.
o Adding steam in zones may be a control mechanism for zones in
reactor by controlling temperature and minimizing bed temperature
fluctuations.
o This may also be used to increase hydrogen production in bursts to
control syngas combustion value "flame temperatures and flash
points".
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= Mechanically or easily accessible/adjusted and rotating reduction zone in
reactor.
o Adjusting the restriction zone shape, size, height, and texture are all
important aspects to the reduction zone.
o This adjustment could also reduce known "clinker" issues associated
with the gasification of difficult solid fuels by dropping out any solids
that may be impeding the flow and operation of the reactor.
o The rotation of the reduction zone keeps the flow of the
producer/syngas inside the reactor consistent by moving any
obstructive materials around and grinding them against the sides or
against and added purposeful scraping blade.
o The vertical movement of the reduction zone may be pulsed on a
programmed time duration to replace the "shaking" devices used to
assist in moving materials downward and reduce known bridging
issues with materials as they are pyrolyzed and may adhere to each
other.
o The reduction zone may also be attached to a grate that will rotate and
assist with the moving of ash downward, which is a known build-up
issue that can affect the performance of the reactor. The use of
scrapper blades may assist in eliminating this blockage as well.
= Temperature controlled reduction zone.
o Coiled high temperature heater added to ensure that temperatures are
being met for sufficient gasification even during feedstock fluctuations.
o Temperature coil could be used for start-up of reactor.
= Reintroduction of ash into the feedstock.
o would reduce moisture content.
o allow for ash to be more fully processed.
o increase steam introduction variable for testing.
o potential greater hydrogen production and reduced carbon monoxide.
= Feed syngas through long ash auger system for further tar clean-up.
o This would eliminate the need for some additional packed bed filtration
and keep the cyclone in the system from getting filled with particulate
matter.
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o Ash would then collect the tars for further use/introduction into waste
stream
= Introduction of a medium that could better control the federate of ash
out of
the bottom of the reactor.
o This may be multiple layers of perforated plate rotating together to only
allow smaller particulate matter and gasses to pass through.
o May introduce a media of lava rock, ceramic material, or high temp
alloys that hold the ash bed height and prevent "drop-out" of material in
the reduction zone, which causes temperature and gas issues.
o Spacing of rotating cylinders that hold the bed height and loosely
"grind" ash to a fine powder at a specific height below the reduction
zone.
o Added char-bed that is held at a higher temperature and will thermally
decompose/transition, but does allow for added syngas clean-up ¨
could be used and replaced when needed.
= Exterior of reactor is vacuum that is monitored to ensure no leaks in
reactor
and used for insulation.
Other Technology Parameters:
We have also identified and evaluated technologies that combine gasification
with fast pyrolysis. The major difference between these technologies is the
amount
of air/oxygen introduced and the source of attaining high temperatures to
thermo
chemically alter the material to a combustible gas In this regard, pyrolysis
can be
implemented as an option for gasification and stand-alone. The associated
design
options include:
= Using a pyrloysis auger prior to gasification to bring the feedstock to a
charred
state and dry it simultaneously to homogenize the fuel, improve efficiency,
and crack hydrocarbons and volatiles contained in the gas stream.
= Cooling the syngas by passing it through the feedstock/pyrolysis unit to
transfer heat to incoming material.
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= Using waste exhaust heat from the combustion technology to heat feedstock
material and assist with pyrolysis to improve efficiency of entire system.
= Using pyrolysis to improve the gas quality of syngas and then controlling
the
air addition downstream in a gasification zone to crack tars and volatiles
still
left after pyrolysis.
= Feed system for either gasification or pyrolysis is difficult and having
an air
lock and/or a means of extracting air via vacuum will affect the performance.
There is believed to be a way to do this by just compacting (improving the
density of material) either hydraulically or mechanically and minimizing air
introduction in the process.
= Another means of controlling air being introduced with material is to
accommodate and calculate quantities it in a feed hopper above the reactor
and just ensure that the feed hopper is always full and the feed system to
that
hopper could then be a batch airlock feed mechanism.
= The introduction of a thermally conductive media such as: high
temperature
metallic alloy or ceramic may be introduced with the feedstock to assist in
applying a more evenly distributed thermal profile in the reactor or pyrolysis
unit.
o This would mean that these conductive materials will be separated
from the ash stream and reintroduced into the process with or without
the ash.
= A grate system may allow ash to fall through, but not allow these
conductive materials to pass and then they could be collected a
redistributed into the feedstock in some percentage as to not
affect the overall performance.
= Keeping these conductive materials at a high temperature may
improve the overall efficiency of the gasification/pyrolysis
system.
= Allowing these conductive materials to cool with the ash could
improve the gas clean-up that is occurring when syngas is
passing through the ash by holding a tar cracking temperatures
in the ash for a longer duration and thus influence end gas.
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= Introducing or recirculation syngas through the feedstock in a pyrolysis
unit
may assist in balancing pressures in the reactor out while at the same time
allowing for heat transfer to occur between the syngas and the feedstock and
maybe improve efficiency. Many
scientific journals have identified the
potential of reintroducing syngas into a gasifier or pyrolysis unit.
= Heat source ¨ Most companies use external burners as a source of heat for
pyrolysis to occur. This would require propane or natural gas for start-up and
shut-down of the unit which may be problematic for a utility provider who does
not view any use of fossil fuels to violate the "sustainable", "green", or
"renewable" energy provider status. This may be avoided by:
o Storing syngas from previous runs and using the stored syngas to
start-up system (which would mean continuing to run reactor without
generation/combustion for some time to collect and store gas).
o Use an external renewable/sustainable electrical or heat providing
energy source such as (but not limited to): Solar Photovoltaics to
provide the boost necessary to start the system (with the use of local
power source (batteries) of course).
o Using gasification/introduction of air/steam/oxygen to get pyrolysis unit
up to temperature and then switching over to gas when system
temperatures meet operational parameters.
System Architecture
FIG. 7 illustrates a waste stream collection, processing and gasification
system 700 in accordance with the present invention. The illustrated system
obtains
waste from a variety of sources 702. For example, these sources may include
concession waste bins, office waste bins, public waste receptacles, and
various
animal waste collection tanks. Waste from the various sources 702 is
transported
via carts 704 to a collection station thereby defining a waste stream 712.
At the collection station, various measurements and observations may be
made on a cart-by-cart basis, as generally indicated at box 706. For example,
these
measurements and observations may include a weight of the waste, a moisture
content of the waste, a density of the waste, and a composition of the waste.
These
measurements and observations may be recorded on a processor 708 such as one
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or more computers. This information may be entered into the processor 708 by
one
or more users or automatically transferred to the processor 708 by measurement
instruments. In some cases, previously obtained information may be used in
analyzing waste from a cart. For example, statistical information may be
available
based on cart number, source location or categories of waste. Thus, for
example, a
technician may estimate the proportions of different waste components in a
load
based on visual inspection or otherwise, and use statistically derived
information to
determine information regarding weight, density, moisture content, fuel BTU
value,
ash content, and the like.
This information is used by the processor 708, executing appropriate logic
attached to the stream processing equipment 714, to develop statistical
information
regarding the waste stream and how the waste stream affected the operation of
the
stream processing 714 or a waste stream model 710. The processor will also
receive information from the Stream Processing 714 equipment that will
describe
how material is flowing through the system and the utilization properties of
the
equipment. This will allow the processor 708 to define operating rules that
may
relate to scheduled and unscheduled maintenance occurrences as well as allow
for
communication between components contained with the Stream Processing 714 and
also from 718,720, and 726. This may be conceptualized as involving a start-up
phase and an operating phase, though the functionality of these phases will
largely
overlap. In the start-up phase, which may be conducted at least in-part prior
to
operation of the gasification process, waste materials may be analyzed over
time to
develop a model, as described above, defining characteristics of the waste
stream
as a function of location, time of day, season of the year, and the like. It
may be
useful to develop this model over a substantial length of time prior to
implementing
the gasification process. The use of the stream data/model collection 710 will
be
compared with the actual equipment operation and the simulation model will be
updated with actual operational data to better improve performance of
equipment.
However, it will typically be useful to continue to develop and modify the
waste
stream model even after the gasification process has been implemented.
As noted above, it may be useful to compile separate waste stream
components and to separately process waste stream components using multiple
reactors. For example, this may be useful in order to tune processing
parameters for
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different waste stream components, to allow for scalability of the process,
and/or to
provide redundancy to accommodate repairs, maintenance and experimentation.
Such
parallel processing is generally indicated in FIG. 7 by schematic replication
of elements
712, 714, 716, 718, 720 and 726. This is dependent upon material types and
necessary
processing and modification of the feedstock throughout this process and not
all
processing may need to be considered parallel for all elements 712,
714,716,718,726.
The waste from the carts 704 defines a waste stream 712. The waste stream 712
may be physically separated into separate components or processed in an
orderly
fashion using statistical arrival data to establish an order, for example,
including one or
more animal waste components, one or more concession waste components, and one
or more paper stock components processed in that order or separated then
processed.
These components are then processed (714) to provide one or more fuel stocks
(716).
For example, such processing may involve mixing waste stream components in
desired
portions, drying the resulting fuel stock, compressing the fuel stock,
shredding and/or
pulverizing the material and forming the fuel stock into desired sizes, shapes
or textures
of fuel.
The fuel may then be further processed (718) and introduced into one or more
reactors (720). For example, the fuel processing may involve further
compressing or
dimensioning/texturing of the fuel, treating the fuel with additive agents or
the like,
heating the fuel, etc. The fuel may then be processed at one or more reactors.
For
example, a hydrolysis reactor and/or a multi-stage downdraft gasification
reactor may be
employed individually or in series. The processor 708 may control a number of
processing parameters depending, for example, on the characteristics of the
waste
stream or resulting fuel. For example, the processor may control: a pre-heat
temperature
of the fuel; the amount of air/oxygen introduced into the reactor per pound of
fuel; or the
location, number, direction, and output of air injection nozzles 722, and the
operating
parameters of any other reactor components 724 such as reactor bed agitation
components. The processor may also control the timing and operation of motors
that
control the reduction zone and the speed at which the ash is processed/moved
through
the reactor. The processor may also control the height of the material inside
of the
gasifier feed the reactor to retain the material height that best retains
temperature,
pressure, and throughput for the reactor.
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The processor 708 may also receive feedback regarding any output properties
(726) of the reactor 720, the Fuel Processing 718, and Stream Processing 714.
For
example, the processor 708 may receive information regarding: any volatiles or
particulates in an output stream; the volume, composition, temperature or the
like of
syngas produced; the amount and composition ash remaining; the energy produced
by
the process or efficiency in relation to energy required; or any other useful
feedback. The
Stream Processing 714 may receive information related to motor amp loads,
material fill
sensors, motor speeds (thus conveyor speeds), temperatures, and pressure drops
for
filtration components, The Fuel Processing 718 portion may relay information
related to
motor speeds, particulate and material fill sensors, temperatures of
equipment. The
gasifier 708 may communicate information related to fill to Fuel Processing
718
conveyances that may determine where and the quantities of fuel necessary to
be
delivered to continue operation. This information may be used to analyze and
optimize
the overall process over time.
The foregoing description of the present invention has been presented for
purposes of illustration and description. Furthermore, the description is not
intended to
limit the invention to the form disclosed herein. Consequently, variations and
modifications commensurate with the above teachings, and skill and knowledge
of the
relevant art, are within the scope of the present invention. The embodiments
described
hereinabove are further intended to explain best modes known of practicing the
invention and to enable others skilled in the art to utilize the invention in
such or other
embodiments and with various modifications required by the particular
application(s) or
use(s) of the present invention. It is intended that the appended claims be
construed to
include alternative embodiments to the extent permitted by the prior art.
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