Note: Descriptions are shown in the official language in which they were submitted.
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SIMULATED FERMENTATION PROCESS
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] None.
STATEMENT REGARDING FEDERALLY SPONSORED
RESEARCH OR DEVELOPMENT
[0002] Not applicable.
REFERENCE TO A MICROFICHE APPENDIX
[0003] Not applicable.
BACKGROUND
[0004] Beer and other brewed beverages are generally produced in a brewery
within a
given geographic location to limit the transportation costs and the amount of
time required
to deliver the beverages to a consumer. As a result, the brewing industry
relies on many
breweries throughout the world to produce a consistent product from one
location to the
next. Each brewery may use traditional techniques and local ingredients that
can affect the
final product. Traditional beer brewing techniques involve the use of a
variety of feed
components including a variety of grains, each of which can produce
differences in the wort
introduced to the fermentation process. Local supply differences throughout
the world may
further contribute to a variability in the feed composition used for each
batch of beer
brewed. Brewing has developed as an art, at least partly in response to the
myriad feed
components and varieties of beers produced. Current quality control checks are
performed
by trained brew masters who are experts in using tastings and sample
fermentations to
control the brewing program to achieve a desired result. However, some
variability may
occur from brewery to brewery and batch to batch. In some circumstances, a
problem
batch may be identified without any specific information on how the brewing
program may
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be modified to achieve the desired final product. In addition, some
variability may exist in
the final products due to the differences between brew masters within a
brewery or from
brewery to brewery. All of these differences may contribute to the variability
of a desired
product throughout a particular region and/or the world.
SUMMARY
[0005] In one aspect there is provided, a method of modeling a fermentation
process
comprising providing a first principles model of a fermentation process;
determining the
concentration of at least one substrate in a fermentation composition at a
first time; and
predicting the concentration of at least one component of the fermentation
composition at a
second time using the first principles model, wherein the second time is after
the first time.
[0006] In a preferred embodiment, the method may further comprise
determining a
second concentration of the at least one substrate at a third time between the
first time and
the second time; and tuning the first principles model based on the second
concentration
determination.
[0007] In another preferred embodiment, the method may further comprise
regressing
at least one parameter of the first principles model using the second
concentration
determined at the third time; and predicting the concentration of the at least
one
component of the fermentation composition at the second time using the first
principles
model with the at least one parameter regressed from the second concentration
determined at the third time.
[0008] In another preferred embodiment, the method may further comprise
adjusting an
operating parameter of the fermentation process based on the predicted
concentration at
the second time; and predicting the concentration of the at least one
component of the
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fermentation composition at a fourth time using the first principles model
with the adjusted
operating parameter.
[0009] In another preferred embodiment, the method may further comprise an
operating
parameter comprising at least one parameter selected from the group consisting
of: the
dissolved oxygen content in an initial wort feed, a fermentation time, a
fermentation
temperature, and a fermentation pressure.
[0010] In another preferred embodiment, the method may further comprise a
fermentation process comprising; preparing a feed composition comprising the
at least one
substrate; introducing a biologic agent to the feed composition to form the
fermentation
composition; and fermenting the fermentation composition to convert at least a
portion of
the substrate into at least one product.
[0011] In another preferred embodiment, the method may further comprise a
fermentation process comprising a beer fermentation process, a wine
fermentation
process, a yogurt fermentation process, or a pharmaceuticals fermentation
process.
[0012] In another preferred embodiment, the method may further comprise
predicting a
final value of a parameter at the end of the fermentation process based at
least in part on
the first principles model, wherein the parameter comprises at least one
parameter
selected from the group consisting of: a sugar concentration, a density, a
color, a pH, an
alcohol concentration, a real extract value, an apparent extract value, a real
degree of
fermentation value, and any combination thereof.
[0013] In another preferred embodiment, the method may further comprise a
first
principles model including a plurality of sub-models comprising: a growth
model to account
for the growth of the biologic agent, a substrate model to account for the
decrease in the
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concentration of the substrate, and a product model to account for the
increase in the
concentration of the at least one product.
[0014] In another preferred embodiment, the method may further comprise a
substrate
comprising a sugar, a polysaccharide, a protein, an inorganic compound, or any
combination thereof.
[0015] In another preferred embodiment, the method may further comprise a
component comprising a biologic agent, the substrate, an additional substrate,
or a
fermentation product.
[0016] In another aspect there is provided, a method of modeling a
fermentation
process comprising providing a first principles model of a fermentation
process;
determining the concentration of at least one substrate in a fermentation
composition at a
first time; predicting the concentration of at least one component of the
fermentation
composition at a second time using the first principles model, wherein the
second time is
after the first time; adjusting, by a control system, an operating parameter
of the
fermentation process in response to the predicted concentration varying from a
target
concentration by more than a threshold amount.
[0017] In a preferred embodiment, the method may further comprise being
repeated at
periodic intervals during the fermentation process.
[0018] In another preferred embodiment, the method may further comprise
determining
a second concentration of the at least one substrate at a third time between
the first time
and the second time; tuning the first principles model based on the second
concentration
determination; and re-predicting the concentration of the component of the
fermentation
composition at the second time using the first principles model.
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[0019] In another preferred embodiment, the method may further comprise a
fermentation process comprising: preparing a feed composition comprising the
at least one
substrate; introducing a biologic agent to the feed composition to form the
fermentation
composition; and fermenting the fermentation composition to convert at least a
portion of
the substrate into at least one product.
[0020] In another preferred embodiment, the method may further comprise
determining
the concentration of the at least one substrate at the first time comprising
determining the
concentration of a plurality of substrates at the first time.
[0021] In another aspect there is provided, a computer implemented system
for
implementing a fermentation simulation tool comprises at least one processor;
a user
interface; a memory comprising a non-transitory computer readable medium
storing a first
principles fermentation simulation tool, wherein the first principles
fermentation simulation
tool, when executed by the processor, configures the processor to: receive the
concentration of at least one substrate in a fermentation composition at a
first time; predict
the concentration of at least one component of the fermentation composition at
a second
time using a first principles model of a fermentation process, wherein the
second time is
after the first time; and display the predicted concentration via the user
interface.
[0022] In a preferred embodiment, the system may further comprise the first
principles
fermentation simulation tool further configuring the processor to: receive a
second
concentration of the at least one substrate at a third time between the first
time and the
second time; and tune at least one parameter used by the first principles
model based on
the second concentration.
[0023] In another preferred embodiment, the system may further comprise the
first
principles fermentation simulation tool further configuring the processor to:
receive an
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adjusted operating parameter input for the fermentation process via the user
interface;
and predict the concentration of the at least one component of the
fermentation
composition at a fourth time using the first principles model with the
adjusted operating
parameter.
[0024] In another preferred embodiment, the system may further comprise a
control system interface configured to adjust one or more operating parameters
of the
fermentation process, and wherein the first principles fermentation simulation
tool
further configures the processor to: adjust at least one operating parameter
of the
fermentation process when the predicted concentration of the at least one
component
of the fermentation composition varies from a target concentration by more
than a
predetermined threshold.
[0024a] In an aspect, there is provided a system for adjusting a
fermentation
process, said system comprising: at least one processor; a memory comprising a
non-
transitory computer readable medium storing instructions for a fermentation
simulation
tool defining a model of said fermentation process, wherein instructions for
the model
configure the processor to: receive an initial concentration of at least one
substrate in
a fermentation composition at a first time; predict a first concentration of
at least one
component of the fermentation composition at a second time using the model of
the
fermentation process with the received initial concentration of the at least
one
substrate and, wherein the second time is after the first time; adjust an
operating
parameter of the fermentation process based on the predicted first
concentration at the
second time; and predict an additional concentration of the at least one
component of
the fermentation composition at an additional time after the second time using
the
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model with the adjusted operating parameter which is based on the predicted
first
concentration; and a control system responsive to said at least one processor
to adjust
said fermentation process.
[0024b] In another aspect, there is provided a system for use in adjusting
a
fermentation process comprising: at least one processor; a memory comprising a
non-
transitory computer readable medium storing a fermentation simulation tool
defining a
model for said fermentation process, wherein the fermentation simulation tool
configures the processor to: receive an initial concentration of at least one
substrate in
a fermentation composition at a first time; predict a first concentration of
at least one
component of the fermentation composition at a second time using the model of
the
fermentation process with the received initial concentration of the at least
one
substrate, wherein the second time is after the first time; predict an
additional
concentration of at least one component of the fermentation composition at a
time
after the second time using the model with the predicted first concentration
of the at
least one component; display on a user interface the predicted additional
concentration; and adjust an operating parameter of the fermentation process
in
response to input provided by an user via the user interface.
[0024c] In a further aspect, there is provided a system for use in
adjusting a
fermentation process comprising: at least one processor; a user interface; a
memory
comprising a non-transitory computer readable medium storing a fermentation
simulation tool defining a model for a fermentation process, wherein the
fermentation
simulation tool configures the processor to: receive an initial concentration
of at least
one substrate in a fermentation composition at a first time; predict a first
concentration
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of at least one component of the fermentation composition at a second time
using the
model of the fermentation process with the received initial concentration of
the at least
one substrate, wherein the second time is after the first time; receive an
adjusted
operating parameter input for the fermentation process via the user interface;
predict a
new concentration of the at least one component of the fermentation
composition at a
time after the second time using the model with the adjusted operating
parameters;
display on the user interface the predicted new concentration; and adjust an
operating
parameter of the fermentation process in response to input provided by an user
via the
user interface.
[0025] These and other features will be more clearly understood from the
following detailed description taken in conjunction with the accompanying
drawings
and claims.
BRIEF DESCRIPTION OF THE DRAWINGS
[0026] For a more complete understanding of the present disclosure,
reference
is now made to the following brief description, taken in connection with the
accompanying drawings and detailed description, wherein like reference
numerals
represent like parts.
[0027] FIG. 1 is an illustration of a flow chart of an embodiment of a
fermentation process.
[0028] FIG. 2 is an illustration of a flow chart of an embodiment of a
beer
fermentation process.
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[0029] FIG. 3 is an illustration of a flow chart of an embodiment of a
method for
simulating a fermentation process.
[0030] FIG. 4 is an illustration of a flow chart of another embodiment of a
method for simulating a fermentation process.
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[0031] FIG. 5 is an illustration of a flow chart of still another
embodiment of a method for
simulating a fermentation process.
[0032] FIG. 6 is an illustrative example of a computer.
DETAILED DESCRIPTION
[0033] It should be understood at the outset that although illustrative
implementations of
one or more embodiments are illustrated below, the disclosed systems and
methods may
be implemented using any number of techniques, whether currently known or not
yet in
existence. The disclosure should in no way be limited to the illustrative
implementations,
drawings, and techniques illustrated below, but may be modified within the
scope of the
appended claims along with their full scope of equivalents.
[0034] The present disclosure provides a method of simulating a
fermentation process
using a first principles approach. Fermentation is a complex biochemical
process involving
one or more biologic agents and a variety of feed components that may be
consumed by
the various biologic agents at different rates. The feed components (e.g., the
amount of
sugar, protein, etc.) may be derived from naturally occurring sources (e.g.,
agricultural
products) resulting in variability of the feed composition, such as when
various grains are
used in a beer fermentation process. For example, a feed composition
comprising an
agricultural product may vary from harvest to harvest due to non-uniformity in
agricultural
growing conditions such as the amount of water, sun, and growing temperatures,
all of
which can affect the feed composition. The variability is compounded when the
feed
comprises a combination of two or more agricultural products, each with
individual
variabilities in the amount of each component. Multiple side reactions, both
desirable and
undesirable, can occur during a fermentation process. Further, all of the
components
including the biologic agent, the feed composition, and the produced products
can affect
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the various chemical processes occurring in the fermentation. All of these
events may
make it difficult to achieve a consistent and/or predictable product from the
fermentation
process.
[0035] A first principles approach may be used to model a desired fermentation
process. While the reactions are complex, the processes can be modeled using
parameters that can be fitted using experimentally obtained data. The models
may be
applied outside the range for which data is available since the fundamental
mechanisms,
such as the individual reactions, are being modeled. In addition, a feedback
mechanism
may be used to tune the first principles model during use to more accurately
predict the
results of the fermentation including the final fermentation composition. Once
the models
are provided and the parameters are determined using available data, a variety
of results
for the fermentation process can be predicted throughout the fermentation
process. The
results may be used to help identify when adjustments may be needed in the
process. The
models also may be used to verify the results of any proposed adjustments by
simulating
the effects of changes to the operating parameters. In addition, the
predictions may be tied
to a control system that may be used to implement a fully automated control
system for
automatically adjusting one or more parameters of the fermentation process.
The resulting
system may allow consistent results to be obtained more frequently from a
process as
complex as a fermentation process. In an embodiment, the results may be used
to test
and/or design additional fermentation process lines and/or entire fermentation
processing
facilities. These and other advantages will be discussed in more detail below.
[0036] As
shown in Figure 1, a fermentation process 100 generally begins with the
preparation of a feed composition in step 102. The feed composition may
comprise one or
more components capable of being converted to one or more products in the
fermentation
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process 100. As described in more detail herein, the feed composition may
comprise one
or more active components capable of being converted by a biologic agent such
as sugars,
polysaccharides, proteins, inorganic compounds (e.g., minerals), or other
components
useful during the life cycle of one or more biologic agents. The active
components may be
referred to as a substrate. The feed composition also may comprise one or more
inactive
components that may be useful in providing a suitable environment for the
biologic agents
or that may be present without effect on the overall system. For example, the
active
components used in a fermentation typically may be present in an aqueous
solution to
provide an appropriate environment for the biologic agents.
[0037] At step 104, a biologic agent may be introduced to the feed
composition to form
a fermentation composition. The biologic agent may be chosen to produce a
desired
product, and the feed composition may be prepared for the specific needs of
the desired
biologic agent. In an embodiment, suitable biologic agents may include, but
are not limited
to, a yeast, a bacteria, an algae, a genetically modified yeast, a genetically
modified
bacteria, a genetically modified algae, any other micro-organisms capable of
producing a
desired product, and any strain or strains thereof, and any combination
thereof.
[0038] At step 106, the fermentation composition comprising the feed
composition and
the biologic agent may be fermented to allow the biologic agent to convert at
least a portion
of a substrate into one or more products. Within the fermentation process,
several
reactions and/or biological processes may be occurring. The main processes may
comprise: 1) the growth of the biologic agent due to the consumption of one or
more
substrates in the feed composition, 2) the decrease in the concentration of
the substrate
based on the biological activity of the biologic agent, and 3) the increase in
the
concentration of one or more products produced by the biologic agent. Each
process may
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involve a plurality of individual processes based on one or more variables.
For example,
the growth of the biologic agent may involve the growth of a plurality of
biologic agents,
each of which may consume one or more of the substrate components at different
rates.
These processes may be interrelated and create a dynamic process with respect
to the
concentration of the biologic agent, the substrate, and/or the product at any
point during
the fermentation process. Further, the conditions under which the fermentation
is carried
out may vary, resulting in a complex combination of components throughout the
fermentation process. In an embodiment, the fermentation process may comprise
a batch
process, which comprises a process where substantially no mass crosses the
process
boundaries between the time the feed is charged and the time the product is
removed
and/or a semi batch process, which comprises a process where a limited amount
of one or
more components are allowed to cross the system boundaries between the time
the feed is
charged and the time the product is removed. For example, a beer fermentation
process
may be considered a semi batch process since at least a portion of the
fermentation gases
(e.g., carbon dioxide) are allowed to escape the system during the brewing
process.
[0039] At step 108, a portion of the product and/or the biologic agent may
be optionally
separated. In an embodiment, the product may be desired in purified form,
which may
require some separation from the fermentation composition. In an embodiment,
the
biologic agent may be considered a product and may be separated.
[0040] The fermentation process 100 as shown in Figure 1 may be used to
produce a
variety of products. In an embodiment, the fermentation process 100 may be
used to
produce products including, but not limited to, beer, wine, other fermented
beverages,
yogurt, other fermented food products, pharmaceuticals, and other products
that may serve
as intermediate components in the manufacturing of various commercial products
(e.g.,
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algae based hydrocarbons useful in the production of fuels, cosmetics,
plastics, etc.).
Additional steps and/or processes may be involved in different fermentation
processes as
would be known to one of ordinary skill in the art.
[0041] In an embodiment, the fermentation process may be used to produce
beer. A
fermentation process 200 for producing beer is shown in Figure 2. The beer
fermentation
process may begin with the preparation of a feed composition, which may be
referred to as
"wort" in the context of a beer fermentation process. The preparation of the
wort may begin
with "malting" one or more cereal grains such as corn, sorghum, maize, rice,
rye, barley,
and/or wheat in step 202. Malting may involve the germination of the grains by
steeping
and soaking in water to sprout the grains. During this process, several types
of enzymes
may be produced and/or released within the grains, including those that
catalyze the
conversion of starch into fermentable sugars.
[0042] The germinated grains may pass through a process referred to as
"kilning" in
step 204 in which the sprouted grains are dried and roasted to kill the
sprouts and to
provide the grain with roasted grain flavors and color. After kilning, the
grains may be
referred to as malted grains or simply "malt." The malt may be milled to a
desired size to
allow the components of the malted grains to be better extracted in water
during the
subsequent processes. The milled malt may be referred to as "grist."
[0043] The grist may then pass through a mashing process in step 206. Mashing
may
involve the mixing of the grist with water, to obtain a mixture referred to as
a "mash." In
order to activate the enzymes within the malt and/or any added enzymes, the
mash may
be heated. Mashing may be carried out at temperatures ranging from about 45 C
to about
75 C. During mashing, a variety of sugars such as oligosaccharides,
disaccharides and
monosaccharides can be generated by the enzymatic breakdown of complex
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carbohydrates, including mainly starch. Specific sugars may include, but are
not limited to,
dextrin, maltose, maltotriose, glucose, sucrose, fructose, and other
polysaccharides.
These sugars generally form at least a portion of the substrate for a beer
brewing process
and may provide the carbon and energy source for the biological activity of
the biologic
agent during the fermentation process.
[0044] The
insoluble components remaining in the mixture, which may comprise the
spent grains, may be removed in a process referred to as "lautering" in step
208. The
remaining liquid and soluble components may be referred as the "wort." The
lautering
process may be carried out at a temperature of about 75 C to about 100 C.
[0045] The wort may then be heated in step 210 and a variety of components may
be
added before or during the heating process to alter and/or enhance the flavor
of the beer.
The wort may be heated to a temperature sufficient to boil the composition and
may be
held at this temperature for a period of time. The boiling of the wort may aid
in the
pasteurization of the wort to eliminate competition for the added biologic
agents during
fermentation. During the boiling of the wort, any proteins or other solids
that may affect the
final quality of the beer may coagulate and be precipitated for removal from
the wort. Once
the bitter, aromatic, and flavoring compounds from herbs, such as hops, or
herb extracts
have been extracted, the remaining solids may be removed from the wort through
filtering
or other separation means.
[0046] A biologic agent then may be introduced into the wort in step 212. The
wort may
be cooled after the heating process to a temperature that is optimal for the
biologic agents.
The biologic agents useful for beer fermentation generally comprise a brewer's
yeast, for
example saccharomyces cerevisiae. The biologic agents may be added to the wort
in a
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process referred to as "pitching." In an embodiment, the biologic agents may
be added to
the wort by spontaneous inoculation.
[0047] The combination of the biologic agent and the wort may then be
fermented in a
fermentation vessel to allow the biologic agents to convert at least a portion
of the
substrate (e.g., the sugars contained in the wort) into a product in step 214.
Fermentation
in the context of producing beer generally involves the incubation of the wort
inoculated
with the biologic agent. During fermentation, the sugars in the wort may be
converted by
the biologic agents into products including, but not limited to, carbon
dioxide, ethanol, flavor
components, and numerous other by-products. The fermentation process may
proceed for
a period of time ranging from days to weeks, depending on the type of beer
being
produced. In an embodiment, the fermentation process may proceed for about 9
to about
12 days before a product ready for further processing is produced.
[0048] During the beer fermentation process, a variety of parameters may be
varied.
The common parameters used to control the fermentation process include the
dissolved
oxygen content in the initial wort fed to the fermentation vessel, and the
fermentation time,
temperature, and pressure. The amount of dissolved oxygen in the initial wort
can be
controlled to some degree by the amount of air, which may be sterilized,
sparged in the
wort between the heating process and the pitching of the biologic agent. The
remaining
parameters may vary depending on the brewing process and the brewing program
selected for each batch of wort to be fermented. For example, a typical
brewing process
may maintain the wort inoculated with the biologic agent at a first
temperature for a first
amount of time. At some point in the fermentation process, the temperature may
be varied
and again held at a second temperature for a second amount of time. This
process may
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be repeated a desired number of times. The resulting "brewing program" or
"brewing
recipe" may be used to control the final content of the brewed composition.
[0049] The
final brewed composition may comprise the products of the fermentation
process. For beer fermentation, a variety of parameters may be used to
characterize the
brewed composition during and after the fermentation process including, but
not limited to,
sugar content, density, color, pH, alcohol content, real extract value,
apparent extract
value, diacetyl content, real degree of fermentation value, and any
combination thereof.
The sugar content in the initial wort may result, at least in part, from
grains used to form the
malt, the degree of malting, and the addition of any enzymes during mashing.
The density
of the fermentation solution is a product of the sugar concentration and the
alcohol
concentration, which varies throughout the fermentation process as sugars are
converted
to alcohol by the biologic agent. The
color may result, at least in part, from the
concentration of melanoidin compounds in the solution. The color may be varied
by the
selection of the malt composition, degree of kilning, and the duration of the
wort heating
process. The pH may be affected by acidification of the mash, products
produced by the
biologic agent, the choice of the biologic agent, and any additives used to
control the pH of
the water used in the process. Varying the pitching temperature, varying the
fermentation
temperature, and using additives (e.g., CaSO4) may control the pH of the final
brewed
composition. The alcohol content may be affected by the choice of the biologic
agent, the
brewing recipe, and the length of the fermentation process.
[0050]
Referring again to Figure 2, once the fermentation process has reached a
desired state, a variety of post fermentation processing may be carried out on
the resulting
liquid in step 216. Depending on the type of beer and the fermentation method
used, the
post-fermentation processes may include, but are not limited to, maturing the
beer to
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further develop desirable flavors and aromas and/or reduce the levels of
undesirable
flavors and aromas; filtering the beer to remove the residual yeast and other
turbidity-
causing materials; treating the beer with an absorbent to remove specific
compounds such
as hydrophilic proteins or polyphenols; subsequently fermenting the beer (with
or without
addition of an extra carbon source); adding additional components to the beer
such as
herbs or herb extracts, and/or fruits or fruit extracts; carbonating the beer
to increase the
bubbly aspect of beer; pasteurizing and/or microfiltering the beer to enhance
stability; and
packaging the beer using a variety of processes including bottling, canning,
and/or
kegging.
[0051] In
order to improve upon the traditional process of using tastings and brewing
test batches, a first principles model may be used to simulate the
fermentation process to
allow for a prediction of the final brewed composition. A first principles
model comprises
one or more established or fundamental rules or laws of mathematical or
scientific theory,
which may be expressed as one or more mathematical equations. In an
embodiment, a
first principles model for a fermentation process comprises one or more rules
or laws of
scientific theory related to the chemical reactions occurring within the
fermentation process.
For example, the first principles model may comprise a set of equations that
model the
various reactions and/or processes occurring within a fermentation process.
For example,
the first principles model may comprise a set of equations that are based, at
least in part,
on the law of conservation of mass and/or on the law of conservation of
energy.
[0052] The equations may comprise steady state and/or dynamic rate equations,
depending on the types of simulations desired, the type of fermentation being
modeled,
and the type of data available for analysis. In an embodiment, a steady state
model may
be used to establish the final fermentation conditions, and a dynamic model
may be used
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to analyze potential changes in the fermentation conditions. In an embodiment,
the same
model can be used for both steady-state and dynamic simulation. One or more
constants
(e.g., rate constants) may be used within the first principles equations and
may be
regressed using available data. Once the equations and constants are
determined, the
first principles model may be used to predict the state of the fermentation
process having a
set of operating conditions without further input, including at points that
are beyond the
data set used to determine the constants. The use of a first principles model
may be
distinguished from a purely empirical model in that the processes occurring
within the
fermentation process are being modeled, rather than merely establishing a
correlation
between the input parameters and the expected output parameters. In an
embodiment,
the use of a first principles model may allow for greater accuracy than an
empirical model
when simulating conditions beyond those for which data is available (i.e.,
when
extrapolating results).
[0053]
Simulation may involve the modeling of a system by representing the processes
occurring within the system using one or more mathematical models and/or
computations
to project and/or to reveal the behavior of these systems as relevant
parameters vary. For
example, a simulation may use a first principles mathematical model of a
fermentation
process to project the behavior of the fermentation system as the
concentrations of the
various components in the fermentation composition vary over time, for example
as feed
products are consumed and/or transformed by biologic agents. In an embodiment,
the
mathematical computations may be implemented on a computer, as described in
more
detail below. The simulation may be carried out by calculating a sequence of
states using
the mathematical models, where the results for each state are used as an input
into the
calculation of the subsequent state at a desired incremental time period
later. The
16
CA 02770702 2012-03-05
simulation may be carried out faster than real-time and thus may be used to
calculate the
future state of a process at a desired time in the future. For example, the
simulation may
be used to predict the state of a process an hour, a day, or even weeks from
the present.
[0054] In an embodiment, a first principles model for a general
fermentation process
may comprise equations and/or sets of equations grouped into sub-models to
simulate the
main reactions occurring during the fermentation process. The equations may
comprise:
1) a growth model to account for the growth of the biologic agent due to the
consumption of
one or more substrates in the feed composition, 2) a substrate model to
account for the
decrease in the concentration of the substrate based on the biological
activity of the
biologic agent, and 3) a product model to account for the increase in the
concentration of
one or more products produced by the biologic agent. Each model may comprise
one or
more equations depending on the number of components being simulated in each
sub-
model. For example, if two biologically active components were present in a
fermentation
process, at least two equations may be used to simulate the growth of each
biologic agent,
which may occur at different rates depending on the substrate used in the feed
composition. In an embodiment, rate based equations may be used along with a
suitable
rate constant or constants for each equation. The rate constant or rate
constants may be
regressed from available data, which may be obtained through preliminary
testing using
laboratory or production scale testing.
[0055] In an embodiment, the first principles model for a fermentation
process may be
tuned through the use of actual data obtained during the on-going fermentation
process.
During a fermentation process, a variety of conditions may result in the rate
constants
having a time dependency that may be difficult to model. For example, the
biologic agent
may pass through multiple generations during the fermentation process. Due to
the
17
CA 02770702 2012-03-05
complex nature of the reproductive process of microorganisms (e.g., due to
mutations
and/or adaptations), the activity of the biologic agent with respect to the
various substrates
may change during the fermentation process and/or over several fermentation
processes
when the biologic agent is reused. During the fermentation process, a
plurality of actual
samples may be taken at periodic intervals and the resulting concentrations
and/or values
compared to the concentrations and/or values predicted by the first principles
model. Any
discrepancies may be accounted for by adjusting the equations and/or the rate
constants
used in the equations. Thus, tuning the model through the use of feedback,
which may be
provided on a periodic basis and/or an aperiodic basis, may allow for improved
accuracy of
the model. In an embodiment, the time dependency of the rate constants may be
mathematically modeled and included in the model equations to provide for an
increased
accuracy.
[0056] In
order to demonstrate how a first principles model may be applied to a
fermentation process, a description of a model for simulating the fermentation
of beer is
provided. However, it is expressly intended that similar models can be
developed and
implemented for other fermentation processes. In an embodiment, a first
principles model
for a beer fermentation process may comprise sub-models to simulate the main
reactions
occurring during the beer fermentation process. The equations may comprise: 1)
a growth
and bioaccumulation model to account for the growth and/or accumulation of the
yeast, 2)
a substrate model to account for the decrease in the concentration of the
various
substrates (e.g., one or more of the sugars), and 3) a product model to
account for the
change in the concentration of the products (e.g., alcohol, intermediate
byproducts, etc.) in
the composition. In order to aid in the understanding of the description of
the models,
Table 1 contains a list of the nomenclature used in the equations presented
below.
18
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Table 1
Nomenclature for Beer Fermentation Models
Symbol Description
Alc (v/v) Ethanol (Alcohol) volume percentage (%)
Ethanol concentration (gmol/m3)
Eo Initial ethanol concentration (gmol/m3)
Eki Arrhenius activation energy for Ki (cal/gmol)
Erni Arrhenius activation energy for mi (cal/gmol)
Ekd Arrhenius activitation energy for Kd (cal/gmol)
Glycerol concentration (gmol/nri3)
Go Initial glycerol concentration (gmol/m3)
I; Inhibition term for the ith sugar
Kd Specific biomass death rate (hi)
Ki Michaelis constant for the ith sugar (gmol/m3)
Arrhenius frequency factor for K, (gmol/m3)
Yeast growth inhibition constant (gmol/m3)"
Inhibition constant of sugar i due to sugar j
mi Maximum velocity for the ith sugar (hi)
mio Arrhenius frequency factor for mi, (hr-1)
ni Number of sugars that inhibits the sugar consumption rate
ns Number of sugars
Gas constant
Si The ith sugar concentration (gmol/m3)
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Table 1
Nomenclature for Beer Fermentation Models
Symbol Description
So The initial ith sugar concentration (gmol/m3)
Time (hr)
Temperature (K)
X Biomass concentration (gmol/m3)
X0 Initial biomass concentration (gmol/nn3)
XD Dead biomass concentration (gmol/m3)
YES i Yield coefficient (mols Ethanol / mol of the ith sugar)
YGSi Yield coefficient (mols glycerol / mol of the ith sugar)
Yxsi Yield coefficient (mols biomass / mol of the ith sugar)
Specific ith sugar uptake rate (hil)
LL Specific latent formation rate (h(1)
Specific biomass growth rate(h1)
[0067] While the equations presented herein are expressed in standard
mathematical
forms, one of ordinary skill in the art would appreciate that some adjustments
may be used
to solve the equations using one or more mathematical methods and/or computer
based
numerical methods. For example, while the equations herein are articulated in
terms of
continuous time functions, in combination with the present disclosure, one
skilled in the art
would readily be able to adapt these continuous time equations to
corresponding discrete
time equations that may be more amenable to a computerized solution.
Additionally, the
equations may be adapted by multiplying through by one or more coefficients
and/or
CA 02770702 2012-03-05
additive offsets. In an embodiment, the growth model may be expressed as a
rate based
equation comprising rates for both the production of the biologic agent and
the
accumulation of biomass due to the dying biologic agent. The biologic agent
production
rate may be expressed as:
dX
¨ = kictx ¨ KdPC+ Latent term
dt (Eq. 1)
where,
ns
Px = v.x _________ E Yxs. m
x + - X )11
0 (Eq. 2)
The latent term may be represented by:
dA'
___________________________ = dt PLX L (Eq. 3)
where,
E kL
PL = PLoe- RT
(Eq. 4)
The dead biologic agent may be described as a dead biomass with a production
rate
expressed as:
dA'D _______________________ = KdX
di'
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where,
Ekd
Kd KdOe RT
(Eq. 6)
[0058] In an embodiment, the substrate model may be expressed as a set of
rate based
equations comprising rates for the decrease of each sugar being modeled. The
substrate
model accounting for the consumption of the ith sugar may be expressed as:
dS,
dt =- ,u,X (Eq. 7)
where the ith sugar can be dextrin, maltotriose, maltose, glucose, fructose,
or sucrose. In
an embodiment, additional sugars, or a subset of these sugars may be used to
model the
sugar consumption in the fermentation composition. The specific growth rates
Piz can be
expressed as:
MiSi
pi = _______________________________ I Ki + Si i (Eq. 8)
where the temperature dependency on the specific growth rates are expressed
as:
Emi
= Mi0e RT
(Eq. 9)
E
_
Ki = Ki0e RT
(Eq. 10)
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The inhibition term (I) of sugar i due to existence of other sugars may be
expressed as:
ni
In . = y
j=1 S (Eq. 11)
where ni is number of sugars that impact sugar i.
[0059] The product model to account for the increase in the concentration
of alcohol in
the composition may be expressed as:
n.,
E = E0 E Y(s 1 - Si) (Eq. 12)
Glycerol also may be produced as a reaction product during the beer
fermentation process.
The production of glycerol may be expressed as:
ns
G = Go +EYGs; (S10 -s1)
(Eq. 13)
[0060] The initial values for the parameters Xo, So, Eo, and Go may be
obtained through
the use of experimental data, for example using laboratory scale data. In an
embodiment,
the parameters Xo, So, Eo, and Go may be obtained through various measurement
techniques. In an embodiment, the various concentrations may be obtained using
a liquid
chromatography techniques, and in some embodiments, the sugar concentrations
may be
measured using high performance liquid chromatography (HPLC). The remaining
parameters including Kx, mo, Ko, Emil Eki, Ekd, 1<d, KO and yield
coefficients, YESil YXSil YGSi,
may be determined based on regression analysis (e.g., least squares fitting)
of data for the
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CA 02770702 2012-03-05
concentrations of the sugars being modeled, glycerol, ethanol, and the
temperature profile.
For example, the following least squares criterion may be used to regress the
remaining
parameters:
2
Nruns Npo int s Nsugarr
E E
E vos,,k (51 i,j,k,plant ¨ = = t,j,k,calc)]
min 0
j=i i=1 k=1
[Ns,jj (E=5 =
',plant ¨ E + [gjj Gi,j, plant ¨ G,,j,calc
(Eq. 14)
where co are weighting factors. The differential equations with algebraic
constraints (DAEs)
(Eq. 1 ¨ Eq. 13) can be integrated using any known DAE solvers. For example,
an Euler
Explicit method may be used to solve the DAEs. When the problem to be solved
is a non-
linear regression with DAE constraints, commercial regression solvers are
available. For
example the optimization problem in equation 14 may be solved using the
commercially
available solver in Excel 2007 available from Microsoft Corporation of
Redmond, WA.
[0061] In an embodiment, the model may use regressed constants to predict
the
concentration of one or more of the sugars, biomass, ethanol, glycerol, and
diacetyl, and/or
the value of various parameters such as the color and the pH, at a desired
point during the
fermentation process. The predicted values may be used to calculate or derive
additional
parameters including the density (e.g., the specific gravity) and the
inferential indices,
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CA 02770702 2012-03-05
including the apparent extract (AE), real extract (RE), and the real degree of
fermentation
(RDF).
100621 In
an embodiment, the density of the fermentation solution in the form of the
specific gravity (SG) may be determined using the Redlich-Kister excess volume
model.
The equations may be expressed as:
1
V ¨ Videal + v E
(Eq. 15)
(ncomp-1)ncomp
vE= E
(Eq. 16)
i=1 j=i+1
ncomp
V t=deal -= EX=V=
(Eq. 17)
i=1
PvE
________________________________________________________________________ = x x
[a + b . ¨ x + c ¨ x +d. ¨ x + e ¨ x,]
J i
RT
(Eq. 18)
where ncomp is the number of components, v is the molar volume of the mixture,
v
ideal .S
the ideal molar volume of the mixture, vE is the excess molar volume of the
mixture, vE1 is
the excess molar volume of the i,j binary pair, P is the absolute pressure, R
is the gas
constant, T is the absolute temperature, vi is the pure component molar
volume, x; is the
mole fraction of component i, and bib
cii,d, and e4 are the binary interaction parameters
regressed from experimental data. Equations 15 through 18 may be used to
derive the
specific gravity of the solution. The additional parameters then may be
derived from the
CA 02770702 2012-03-05
calculated ethanol concentration and the density. The following equations may
be used to
calculate the AE, the RE, and the RDF:
AE (w/w) = -616.868 + 1111.14(SG) ¨630.272(SG)2 + 135.997(SG)3 (Eq. 19)
RE = 0.385 (Alc(v/v)) + 0.915(AE) (Eq. 20)
RDF (%) = 100(RE0-RE)/(RE0) (Eq. 21)
[0063] The coefficients of a third-order polynomial, relating AE in degrees
Plato to
specific gravity are obtained by linear regression of the tabulation of
degrees Plato and
specific gravities as described in the book by J. de Clerck, A TEXTBOOK OF
BREWING, Vol. 1
(Chapman Hall, 1957).
[0064] The concentrations of the various components may be measured using
laboratory techniques and equipment such as an HPLC at periodic intervals
during the
fermentation. The model then may be tuned by recalculating the constants using
a new
regression analysis based on the newly obtained data, alone or in combination
with
previously measured data. The tuning of the model may increase the accuracy of
the
simulation if any of the constants vary with fermentation time. In an
embodiment, the
tuning of the parameters may be a periodic process in which the model is tuned
followed
by predicting new values at a later time. The newly predicted results may be
compared to
another sample and retuned as necessary. In an embodiment, retuning the model
may be
conditioned on a predicted value varying from the measured value by a
threshold amount.
[0065] In an embodiment, the first principles model may be used to predict
various
compositions throughout the fermentation process to achieve a fermentation
composition
during fermentation and/or at the completion of fermentation that satisfies
certain
specifications. The specifications may comprise concentrations of the various
substrates
and products, the amount of biomass produced, values of various parameters of
the
26
CA 02770702 2012-03-05
fermentation composition, and/or limitations on various by-products and/or
contaminants.
For a brewing process, the specifications may comprise concentrations of the
various
substrates (e.g., the sugars such as dextrin, maltotriose, maltose, glucose,
fructose, or
sucrose, proteins, inorganic compounds, etc.) and products (e.g., alcohol),
the amount of
biomass produced (e.g., the amount of yeast), values of various parameters of
the
fermentation composition (e.g., the density, the color, the pH, the real
extract value, the
apparent extract value, the real degree of fermentation value), and/or
limitations on various
by-products and/or contaminants (e.g., the diacetyl concentration, the glycol
concentration,
etc.). In an embodiment, the various specifications may be based on a desired
product
specification, contractual obligations, and/or various governmental
regulations.
[0066] As
shown in Figure 3, a method 300 for simulating a fermentation process may
begin by providing a first principles model of a fermentation process at step
302. As
discussed in more detail above, the fermentation process may be a beer
fermentation
process, a wine fermentation process, a yogurt fermentation process, and/or a
pharmaceuticals fermentation process. In an embodiment, a fermentation process
for any
of these processes generally may comprise preparing a feed composition
comprising at
least one substrate, introducing a biologic agent to the feed composition to
form the
fermentation composition, and fermenting the fermentation composition to
convert at least
a portion of the substrate into at least one product. The corresponding first
principles
model of the fermentation process then may comprise one or more sub-models
and/or
equations to account for the individual processes occurring with the
fermentation process.
For example, the first principles model may include a growth model to account
for the
growth and/or accumulation of the biologic agent, a substrate model to account
for the
decrease in the concentration of the substrate, and/or a product model to
account for the
27
CA 02770702 2012-03-05
increase in the concentration of the at least one product. The first
principles model may
comprise a dynamic model and/or a steady-state model. In an embodiment, the
first
principles model may comprise one or more rate based equations with rate
constants or
other parameters derived from measured data, for example data obtained using
laboratory
scale and/or production scale testing.
[0067] In
step 304, the concentration of at least one substrate in the fermentation
composition may be determined at a first time. In an embodiment, the first
time is the initial
time at which the biologic agent is combined with the feed composition, for
example at or
near the time the yeast is pitched in a beer fermentation process. In an
embodiment, a
sample of the feed composition may be taken immediately prior to introducing
the biologic
agent to ensure that the initial concentrations of the substrate can be
measured without
being affected by any action of the biologic agent. For example in a beer
fermentation
process, a sample may be withdrawn as the wort is transferred from the heating
process to
the fermentation vessel. In this embodiment, the first time represents the
initial point in
time at which the biologic agent is combined with the feed composition even if
the sample
is taken at a time prior to the formation of the fermentation composition
since the
concentration of the substrate will not significantly change until the
biologic agent is
combined with the feed composition. In an embodiment, the first time may be
any other
time at which the concentration of at least one substrate is determined in the
fermentation
composition, which may depend on the number of times the fermentation
composition is
tested during the fermentation process. In an embodiment, the concentration of
the
substrate, or any other component of the fermentation composition, may be
periodically
tested every 12 hours, every day, every 2 days, every 3 days, and/or every 4
days. In an
embodiment, the concentration of the substrate, or any other component of the
28
CA 02770702 2012-03-05
fermentation composition, may be tested using a plurality of periodic testing
phases. For
example, the concentration of the substrate or any other component of the
fermentation
composition may be tested during days 1, 2, 3, and then tested every other day
for the
remainder of the fermentation process. In another embodiment, the
concentration of the
substrate, or any other component of the fermentation composition may be
tested
aperiodically. In an embodiment, the concentration of a plurality of substrate
components
may be determined at a first time. For example, two or more of the sugars
forming the
substrate may be measured and their concentrations determined at the first
time.
[0068] The
concentration of the substrate may be determined using a number of
techniques. In an embodiment, a sample of the fermentation composition may be
withdrawn from the fermentation composition and tested using standard
laboratory
techniques and equipment. For example an HPLC device may be used to determine
the
concentration of one or more sugars in the fermentation composition during a
beer
fermentation process. Additional suitable testing means may be used depending
on the
components being measured.
[0069] As shown in Figure 3, the concentration of at least one component of
the
fermentation composition may be predicted using the first principles model at
a second
time at step 306. The component may comprise a biologic agent, a substrate,
and/or a
product. The second time is any point after the first time, including at the
completion of
fermentation. In an embodiment, a beer fermentation process may take from
about 9 to
about 12 days to complete, and the second time may be any time period up to
the
completion of the beer fermentation process. In an embodiment, the second time
may be
at any time up to and including packaging. In an embodiment, the second time
may be any
time up to and including a consumption date of the fermentation composition.
Depending
29
CA 02770702 2012-03-05
on the specific first principles model and/or equations used, the
concentration of at least
one component in the fermentation composition may be periodically predicted
throughout
the fermentation process. For example, a rate based equation may be used with
a chosen
time period to predict the concentration of at least one component, such as
one or more
sugars, throughout the fermentation process. In an embodiment, the time period
may be
any time period sufficient to predict the concentration of at least one
component. In an
embodiment of a beer fermentation process, the first principles model may be
used to
predict the concentration of one or more sugars every 12-24 seconds, for
example every
18 seconds, throughout the fermentation process. In another embodiment of a
beer
fermentation process, the first principles model may be used to predict the
concentration of
one or more sugars at a different time step or time interval. In an
embodiment, the first
principles model may be used to predict a final concentration or value at the
end of the
fermentation process of a variety of parameters including, but not limited to,
a sugar
content, a density, a color, a pH, an alcohol content, a real extract value,
an apparent
extract value, a real degree of fermentation value, and any combination
thereof. In an
embodiment, the first principles model may be used to predict the
concentration of two or
more components at a second time. For example, all of the modeled sugars may
be
predicted along with a product concentration (e.g., the alcohol concentration)
at the second
time.
[0070] In
the method 300, an optional tuning process may be employed. At step 308 a
second concentration of the at least one substrate may be determined at a
third time
between the first time and the second time. In an embodiment, second
concentrations of a
plurality of substrates may be determined at the third time and used in the
tuning process.
At step 310, the first principles model may be tuned based on the second
concentration
CA 02770702 2012-03-05
determination. In an embodiment, tuning may comprise regressing at least one
parameter
of the first principles model using the second concentration determined at the
third time.
The concentration of the component of the fermentation composition then may be
predicted at the second time using the first principles model with the updated
parameter.
Tuning may allow for an increased accuracy of the parameters of the first
principles model.
For example, any changes in the activity of the biologic agent with respect to
the substrate
may be accounted for by regressing the parameters used in the first principles
model,
including any sub-models, using the second concentration, alone or in
combination with the
previous concentration data.
[0071] The
knowledge of the predicted values of at least one component of the
fermentation composition throughout the fermentation process and at the end of
the
fermentation process may allow for proper planning for the use of the
fermentation
composition and/or products at the end of fermentation. The use of the first
principles
model may allow for the concentrations of the various components to be
predicted without
any additional inputs other than the initial composition of at least one
component of the
fermentation composition. When a predicted value for a concentration of one or
more of
the components of the fermentation composition indicates a problem (e.g., a
final
fermentation composition that does not meet one or more desired thresholds)
with the
fermentation process, corrective action may be taken to produce a final
fermentation
composition that meets acceptable thresholds. In a beer fermentation
embodiment, the
use of a first principles model may allow faster and more accurate predictions
than
traditional ideal brewing tests, which may take several days. Further, the use
of the
prediction of the concentrations throughout the brewing process and at the end
of brewing
31
CA 02770702 2012-03-05
may provide a more consistent product than can be achieved through the use of
traditional
testing alone.
[0072] As shown in Figure 4, another method 400 for simulating a fermentation
process
may start with providing a first principles model of a fermentation process at
step 402. At
step 404, the concentration of at least one substrate in a fermentation
composition may be
determined at a first time. The concentration may be determined using any of
the methods
and/or techniques described herein. At step 406, the concentration of at least
one
component of the fermentation composition may be predicted at a second time,
which is
any time after the first time, using the first principles model. At step 408,
an operating
parameter of the fermentation process may be adjusted. In an embodiment, an
operator
may adjust one or more operating parameters based on the predicted
concentration at the
second time. The operating parameter may be any operating parameter that
affects the
fermentation process including, but not limited to, the dissolved oxygen
content in an initial
wort feed, a fermentation time, a fermentation temperature, and a fermentation
pressure.
At step 410, the concentration of the at least one component of the
fermentation
composition may be predicted at a fourth time using the first principles model
with the
adjusted operating parameter. In an embodiment, the fourth time may be the
same as or
different than the second time.
[0073] The
knowledge of the predicted values of at least one component of the
fermentation composition throughout the fermentation process and at the end of
the
fermentation process may allow an operator to test one or more adjustment
scenarios. For
example, an operator may plan on making an adjustment to a fermentation
program and
allow the first principles model to predict the results throughout the
fermentation process,
including the concentration of the various components in the final
fermentation
32
CA 02770702 2012-03-05
composition. This method may allow an operator to more accurately adjust the
process to
achieve a desired final fermentation composition. The ability to test the
various adjustment
scenarios also may allow a broader range of feed compositions to be used.
Should a non-
typical feed composition be used, the first principles model may allow an
operator to adjust
the fermentation program to achieve a desired final fermentation composition,
even if the
feed composition previously has not been used. Since most fermentation
processes may
incorporate a variety of components into the feed composition, this may allow
some
flexibility in formulating the feed composition without the need to run
laboratory
experiments to verify that the particular feed mixture will produce the
desired result. In an
embodiment, the ability to predict the concentration of at least one component
of the
fermentation composition may be used to test and/or develop additional
fermentation
processes. In an embodiment, the simulation may be used to test and/or design
new
fermentation process facilities, such as a new brewery in a location where
local ingredients
can be tested prior to design and/or construction of the facility.
[0074] As shown in Figure 5, another method 500 for simulating a fermentation
process
may start with providing a first principles model of a fermentation process at
step 502. At
step 504, the concentration of at least one substrate in a fermentation
composition may be
determined at a first time. The concentration may be determined using any of
the methods
and/or techniques described herein. At step 506, the concentration of at least
one
component of the fermentation composition may be predicted at a second time,
which is
any time after the first time, using the first principles model. At step 508,
a control system
may adjust an operating parameter of the fermentation process when the
predicted
concentration varies from a target concentration by more than a predetermined
threshold.
A control system may comprise components allowing one or more parameters of
the
33
CA 02770702 2012-03-05
fermentation process to be adjusted based on a desired setpoint. The operating
parameter
may be any operating parameter that affects the fermentation process
including, but not
limited to, the dissolved oxygen content in an initial wort feed, a
fermentation time, a
fermentation temperature, and a fermentation pressure. Steps 506 and 508 may
be
repeated at periodic intervals during the fermentation process to allow the
control system to
produce a desired final fermentation composition.
[0076] A tuning procedure may be carried out during the fermentation process
500. At
step 510, a second concentration of the at least one substrate may be
determined at a third
time between the first time and the second time. The first principles model
may be tuned
based on the second concentration determination at step 512. The concentration
of the
component of the fermentation composition then may be re-predicted using the
tuned first
principles model at step 514.
[0076] The use of a method comprising a control system may promote increased
automation of the fermentation process. This process may allow for the use of
a wide
variety of feeds used to form the feed composition while maintaining a
consistent output
within desired thresholds. The use of tuning may further increase the accuracy
of the
simulation and resulting final fermentation composition.
[0077] In an embodiment, the control system may receive measurements of
fermentation process variables via a network providing communications
throughout the
plant and/or process, for example from sensors coupled to various components
in the
process plant such as a fermentation vessel. Sensors may measure the various
process
variables and may include temperature sensors, pressure sensors, and the like.
Portions
of the network may be provided by wired connections and/or links while other
portions of
the network may be provided by wireless connections and/or links. Based on the
values
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CA 02770702 2012-03-05
determined from the first principles model in combination with the sensed
fermentation
process variables, the control system may determine control and/or command
values. The
control system may then transmit the control and/or command values via the
network to a
process controller, where the process controller may be coupled to one or more
components of the fermentation process. For example, a process controller may
vary the
amount of heat or cooling fluid supplied to the fermentation process to
control the
fermentation temperature. The process controller may control one or more
operating
parameters (e.g., operating temperature, operating pressure, etc.) based on
the control
and/or command values received from the control system. The sensors may be
used to
provide feedback to the control system to indicate if further adjustments are
needed.
Portions of the control system may be implemented by a computer system.
Computer
systems are discussed further hereinafter.
[0078] In
an embodiment, at least portions of the methods disclosed herein may be
performed by a computer program executing on a computer system. In an
embodiment, a
first principles fermentation simulation tool may be stored on a memory
comprising a non-
transitory computer readable medium. The first principles fermentation
simulation tool may
be executed by at least one processor to configure the processor to perform
the methods
as described above. A system also may comprise a user interface to provide
feedback to
a user, and in some embodiments, allow for the input of data from various
sources such as
the determination of the concentration of the substrate in the fermentation
composition.
[0079] The computer program may be used to configure one or more processors in
a
computer system to receive the concentration of at least one substrate in a
fermentation
composition at a first time; predict the concentration of at least one
component of the
fermentation composition at a second time using a first principles model of a
fermentation
CA 02770702 2012-03-05
process, wherein the second time is after the first time; and display the
predicted
concentration via the user interface. A tuning component also may be
implemented using
the simulation tool to configure the processor to receive a second
concentration of the at
least one substrate at a third time between the first time and the second
time; and tune at
least one parameter used by the first principles model based on the second
concentration.
In an embodiment, the simulation tool may be used to provide guidance on
adjustments to
the fermentation process by predicting the results of the adjustments. In this
embodiment,
the simulation tool may configure the processor to receive an adjusted
operating parameter
input for the fermentation process via the user interface; and predict the
concentration of
the at least one component of the fermentation composition at a fourth time
using the first
principles model with the adjusted operating parameter. In an embodiment, the
simulation
tool may be used in conjunction with a control system interface to automate
the
fermentation process. In this embodiment, the system also may comprise a
control system
interface, and the simulation tool may configure the processor to adjust at
least one
operating parameter of the fermentation process when the predicted
concentration of the at
least one component of the fermentation composition varies from a target
concentration by
more than a predetermined threshold.
[0080] The control system described above may be implemented on any computer
with
sufficient processing power, memory resources, and network throughput
capability to
handle the necessary workload placed upon it. FIG. 6 illustrates a typical,
computer
system suitable for implementing one or more embodiments disclosed herein. The
computer system 680 includes a processor 682 (which may be referred to as a
central
processor unit or CPU) that is in communication with memory devices including
secondary
storage 684, read only memory (ROM) 686, random access memory (RAM) 688,
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input/output (I/O) devices 690, and network connectivity devices 692. The
processor may
be implemented as one or more CPU chips.
[0081] It
is understood that by programming and/or loading executable instructions onto
the computer system 680, at least one of the CPU 682, the RAM 688, and the ROM
686
are changed, transforming the computer system 680 in part into a particular
machine or
apparatus having the novel functionality taught by the present disclosure. It
is fundamental
to the electrical engineering and software engineering arts that functionality
that can be
implemented by loading executable software into a computer can be converted to
a
hardware implementation by well known design rules. Decisions between
implementing a
concept in software versus hardware typically hinge on considerations of
stability of the
design and numbers of units to be produced rather than any issues involved in
translating
from the software domain to the hardware domain. Generally, a design that is
still subject
to frequent change may be preferred to be implemented in software, because re-
spinning a
hardware implementation is more expensive than re-spinning a software design.
Generally, a design that is stable that will be produced in large volume may
be preferred to
be implemented in hardware, for example in an application specific integrated
circuit
(ASIC), because for large production runs the hardware implementation may be
less
expensive than the software implementation. Often a design may be developed
and tested
in a software form and later transformed, by well known design rules, to an
equivalent
hardware implementation in an application specific integrated circuit that
hardwires the
instructions of the software. In the same manner as a machine controlled by a
new ASIC
is a particular machine or apparatus, likewise a computer that has been
programmed
and/or loaded with executable instructions may be viewed as a particular
machine or
apparatus.
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[0082] The secondary storage 684 is typically comprised of one or more disk
drives or
tape drives and is used for non-volatile storage of data and as an over-flow
data storage
device if RAM 688 is not large enough to hold all working data. Secondary
storage 684
may be used to store programs which are loaded into RAM 688 when such programs
are
selected for execution. The ROM 686 is used to store instructions and perhaps
data which
are read during program execution. ROM 686 is a non-volatile memory device
which
typically has a small memory capacity relative to the larger memory capacity
of secondary
storage 684. The RAM 688 is used to store volatile data and perhaps to store
instructions.
Access to both ROM 686 and RAM 688 is typically faster than to secondary
storage 684.
The secondary storage 684, the RAM 688, and/or the ROM 686 may be referred to
in
some contexts as computer readable storage media and/or non-transitory
computer
readable media.
[0083] I/O devices 690 may include printers, video monitors, liquid crystal
displays
(LCDs), touch screen displays, keyboards, keypads, switches, dials, mice,
track balls,
voice recognizers, card readers, paper tape readers, or other well-known input
devices.
[0084] The network connectivity devices 692 may take the form of modems, modem
banks, Ethernet cards, universal serial bus (USB) interface cards, serial
interfaces, token
ring cards, fiber distributed data interface (FDDI) cards, wireless local area
network
(WLAN) cards, radio transceiver cards such as code division multiple access
(CDMA),
global system for mobile communications (GSM), long-term evolution (LTE),
worldwide
interoperability for microwave access (WiMAX), and/or other air interface
protocol radio
transceiver cards, and other well-known network devices. These network
connectivity
devices 692 may enable the processor 682 to communicate with the Internet or
one or
more intranets. With such a network connection, it is contemplated that the
processor 682
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might receive information from the network, or might output information to the
network in
the course of performing the above-described method steps. Such information,
which is
often represented as a sequence of instructions to be executed using processor
682, may
be received from and outputted to the network, for example, in the form of a
computer data
signal embodied in a carrier wave.
[0085] Such information, which may include data or instructions to be
executed using
processor 682 for example, may be received from and outputted to the network,
for
example, in the form of a computer data baseband signal or signal embodied in
a carrier
wave. The baseband signal or signal embodied in the carrier wave generated by
the
network connectivity devices 692 may propagate in or on the surface of
electrical
conductors, in coaxial cables, in waveguides, in an optical conduit, for
example an optical
fiber, or in the air or free space. The information contained in the baseband
signal or signal
embedded in the carrier wave may be ordered according to different sequences,
as may
be desirable for either processing or generating the information or
transmitting or receiving
the information. The baseband signal or signal embedded in the carrier wave,
or other
types of signals currently used or hereafter developed, may be generated
according to
several methods well known to one skilled in the art. The baseband signal
and/or signal
embedded in the carrier wave may be referred to in some contexts as a
transitory signal.
[0086] The processor 682 executes instructions, codes, computer programs,
scripts
which it accesses from hard disk, floppy disk, optical disk (these various
disk based
systems all may be considered secondary storage 684), ROM 686, RAM 688, or the
network connectivity devices 692. While only one processor 682 is shown,
multiple
processors may be present. Thus, while instructions may be discussed as
executed by a
processor, the instructions may be executed simultaneously, serially, or
otherwise
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executed by one or multiple processors. Instructions, codes, computer
programs, scripts,
and/or data that may be accessed from the secondary storage 684, for example,
hard
drives, floppy disks, optical disks, and/or other device, the ROM 686, and/or
the RAM 688
may be referred to in some contexts as non-transitory instructions and/or non-
transitory
information.
[0087] In
an embodiment, the computer system 680 may comprise two or more
computers in communication with each other that collaborate to perform a task.
For
example, but not by way of limitation, an application may be partitioned in
such a way as to
permit concurrent and/or parallel processing of the instructions of the
application.
Alternatively, the data processed by the application may be partitioned in
such a way as to
permit concurrent and/or parallel processing of different portions of a data
set by the two or
more computers. In an embodiment, virtualization software may be employed by
the
computer system 680 to provide the functionality of a number of servers that
is not directly
bound to the number of computers in the computer system 680. For example,
virtualization software may provide twenty virtual servers on four physical
computers. In an
embodiment, the functionality disclosed above may be provided by executing the
application and/or applications in a cloud computing environment. Cloud
computing may
comprise providing computing services via a network connection using
dynamically
scalable computing resources. Cloud computing may be supported, at least in
part, by
virtualization software. A cloud computing environment may be established by
an
enterprise and/or may be hired on an as-needed basis from a third party
provider. Some
cloud computing environments may comprise cloud computing resources owned and
operated by the enterprise as well as cloud computing resources hired and/or
leased from
a third party provider.
CA 02770702 2012-03-05
[0088] In
an embodiment, some or all of the functionality disclosed above may be
provided as a computer program product. The computer program product may
comprise
one or more computer readable storage medium having computer usable program
code
embodied therein to implement the functionality disclosed above. The computer
program
product may comprise data structures, executable instructions, and other
computer usable
program code. The computer program product may be embodied in removable
computer
storage media and/or non-removable computer storage media. The removable
computer
readable storage medium may comprise, without limitation, a paper tape, a
magnetic tape,
magnetic disk, an optical disk, a solid state memory chip, for example analog
magnetic
tape, compact disk read only memory (CD-ROM) disks, floppy disks, jump drives,
digital
cards, multimedia cards, and others. The computer program product may be
suitable for
loading, by the computer system 680, at least portions of the contents of the
computer
program product to the secondary storage 684, to the ROM 686, to the RAM 688,
and/or to
other non-volatile memory and volatile memory of the computer system 680. The
processor 682 may process the executable instructions and/or data structures
in part by
directly accessing the computer program product, for example by reading from a
CD-ROM
disk inserted into a disk drive peripheral of the computer system 680.
Alternatively, the
processor 682 may process the executable instructions and/or data structures
by remotely
accessing the computer program product, for example by downloading the
executable
instructions and/or data structures from a remote server through the network
connectivity
devices 692. The computer program product may comprise instructions that
promote the
loading and/or copying of data, data structures, files, and/or executable
instructions to the
secondary storage 684, to the ROM 686, to the RAM 688, and/or to other non-
volatile
memory and volatile memory of the computer system 680.
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[0089] In some contexts, a baseband signal and/or a signal embodied in a
carrier
wave may be referred to as a transitory signal. In some contexts, the
secondary
storage 684, the ROM 686, and the RAM 688 may be referred to as a non-
transitory
computer readable medium or a computer readable storage media. A dynamic RAM
embodiment of the RAM 688, likewise, may be referred to as a non-transitory
computer readable medium in that while the dynamic RAM receives electrical
power
and is operated in accordance with its design, for example during a period of
time
during which the computer 680 is turned on and operational, the dynamic RAM
stores
information that is written to it. Similarly, the processor 682 may comprise
an internal
RAM, an internal ROM, a cache memory, and/or other internal non-transitory
storage
blocks, sections, or components that may be referred to in some contexts as
non-
transitory computer readable media or computer readable storage media.
[0090] While several embodiments have been provided in the present
disclosure,
it should be understood that the disclosed systems and methods may be embodied
in
many other specific forms. The present examples are to be considered as
illustrative
and not restrictive, and the intention is not to be limited to the details
given herein.
For example, the various elements or components may be combined or integrated
in
another system or certain features may be omitted or not implemented.
[0091] Also, techniques, systems, subsystems, and methods described and
illustrated in the various embodiments as discrete or separate may be combined
or
integrated with other systems, modules, techniques, or methods. Other items
shown
or discussed as directly coupled or communicating with each other may be
indirectly
coupled or communicating through some interface, device, or intermediate
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component, whether electrically, mechanically, or otherwise. Other examples of
changes, substitutions, and alterations are ascertainable by one skilled in
the art and
could be made.
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