WO2015095255A1

WO2015095255A1 - Method of monitoring and controlling a bioprocess using near- and mid-infrared spectroscopy

Info

Publication number: WO2015095255A1
Application number: PCT/US2014/070705
Authority: WO
Inventors: Glen Austin; Edo Johann Becker; Carl BECKSTROM; Gordana Djordjevic; Ian Dobson; Helen Mason
Original assignee: Bp Corporation North America Inc.
Priority date: 2013-12-20
Filing date: 2014-12-17
Publication date: 2015-06-25

Abstract

Methods of monitoring and controlling a bioprocess (100, 200, 300) may include using either a near-infrared or mid-infrared spectroscopic process analytic technology (102, 202, 302) to monitor at least one analyte (104, 204, 304) within the bioprocess, assessing an output (108, 208, 308) of the spectroscopic process analytic technology, and adjusting at least one parameter (110, 210, 310) of the bioprocess in response to the output of the spectroscopic process analytic technology.

Description

METHOD OF MONITORING AND CONTROLLING A BIOPROCESS USING NEAR- AND

MID-INFRARED SPECTROSCOPY

CROSS-REFERENCE TO RELATED APPLICATIONS This application claims benefit of U.S. provisional patent application Serial No. 61/919,250 filed December 20, 2013.

TECHNICAL FIELD

The invention relates to the use of near- and mid-infrared spectroscopic process analytical technologies to monitor and control fermentation and other biofueis processes,

BACKGROUND

Process analytical technologies have been applied to numerous manufacturing processes, such as pharmaceutical, chemical, petroleum, pulp and paper, and dry- grind/com ethanol manufacturing processes. These technologies have resulted in improvements in process control, safety and reliability, optimization of product consistency and production capacity, and overall asset utilization. Additional benefits include improvements in quality and consistency of raw materials, optimization of product inventory, minimization of production out of specification, more optimal energy consumption, and lesser environmental impact.

Bioprocess monitoring, on the other hand, has traditionally been dominated by basic physical measurements, such as temperature, pressure, weight, gas and liquid flow, conductivity, and mixing; and chemical measurements, such as pH, density, dissolved oxygen, and redox; while lacking true process control. Bioprocesses, such as fermentation and other processes used in the production of renewable materials, often involve complex mixtures and analytes that are present in low concentrations. Since most of the basic physical measurements only determine one physical parameter (e.g. density) to infer the concentration of an analyte (e.g. c(ethanol)), if other components interfere (e.g. solids, water, etc.) and cannot he accounted for accurately, the measurement will be inaccurate, inherently, infrared spectroscopy is more accurate as most components of interest absorb in the infrared region (apart from homonuclear molecules). Without process analytical technologies, bioprocesses Sack real-time reaction monitoring and control. Additionally, traditional analytical methods, such as high- performance liquid chromatography and gas chromatography, require high sampling frequency and entail difficult separation of complex mixtures, resulting in low throughput and accuracy. Furthermore, with the risk of repetitive task injuries, such traditional analytical methods potentially jeopardize workers' safety.

There is a need and a desire for methods that enable real-time monitoring and control of bioprocesses. There is a further need and desire for process analytical technologies for use with bioprocesses that can improve safety and efficiency of the processes.

SUMMARY

The invention relates to methods of monitoring and controlling bioprocesses in real time using near- and mid-infrared spectroscopic process analytical technologies, as well as methods for producing biofuels and other renewable materials using such process analytical technologies.

According to certain embodiments, a spectroscopic process analytic technology may be used to monitor one or more analytes simultaneously within a bioprocess. After assessing an output of the spectroscopic process analytic technology, one or more parameters of the bioprocess may be adjusted in response to the output of the spectroscopic process analytic technology. The spectroscopic process analytic technology may include near-infrared, mid-infrared, Fourier transform infrared, Raman spectroscopy, or mass spectrometry.

Bioprocesses to which the methods herein may apply include, but are not limited to, lignocellulose to ethanol or mixed alcohol processes, saccharification processes, liquefaction processes, sugar cane to ethanol processes, sugar to diesel processes, fermentation processes, simultaneous saccharification and fermentation processes, and hybrid hydrolysis fermentation processes. More particularly, the bioprocesses herein may include pretreatment, hydrolysis, saccharification, liquefaction, biocatalyst propagation, fermentation, distillation, or combinations of any of these processes.

Methods of producing a renewable material may include the bioprocesses described herein, as well as the monitoring and controlling of these bioprocesses. Examples of such renewable materials may include material suitable for use as biofuels, blendstocks, chemicals, intermediates, solvents, adhesives, polymers, and or lubricants. In certain embodiments, the renewable material may include one or more biofuel components. For example, the renewable material may include an alcohol, such as ethanol, butanol, or isobutanol, or lipids. Biofuels resulting from the methods herein may include gasoline, diesel, jet fuel, kerosene, or a combination of any of these biofuels.

According to some embodiments, monitoring and controlling the bioprocess may be performed on-line. According to other embodiments, monitoring and controlling the bioprocess may be performed at-line. In certain methods in which the monitoring and controlling is performed at-line and on-line, a single instrument may be used to measure multiple process streams.

In certain embodiments, either a diffuse reflection probe or a transflectance probe may be used in combination with near-infrared spectroscopic process analytic technology. In other embodiments, an attenuated total reflectance probe or an attenuated total reflectance optical flow cell may be used in combination with mid-infrared spectroscopic process analytic technology.

Analytes that may be monitored in the processes herein include sugars, sugar alcohols, fusel alcohols, solids, dissolved C0₂, organic acids, proteins, acetone, ethanol, butanol, acetoin, diols, butyrate, lipids, triacyl glycerides, fatty alcohols, esters, and combinations of any of these. More particularly, the analytes that may be monitored in the processes herein include glucose, xylose, arabinose, cellobiose, sucrose, fructose, lactose, galactose, glycerol, glucan, insoluble solids, lactic acid, acetic acid, succinic acid, fonnic acid, ethanol, xylitol, and combinations of any of these.

Parameters that may be adjusted in the processes herein include strain, media composition, enzyme concentration, temperature, pressure, inlet gas flow, inlet gas composition, dissolved oxygen, off-gas output, UV treatment of a fermentation recirculation loop, temperature increase or decrease, pH increase or decrease, feed composition such as adjusting the amount of glucose or xylose, feed rate, agitator speed, and combinations of any of these.

Certain embodiments herein may further include controlling a bio-burden of the bioprocess, such as by adding a bacteriostatic or bacteriocidal compound to the bioprocess or taking bacteriostatic actions such as temperature decrease, pH decrease, increasing flow (or starting) UV recycle to reduce undesirable micro-organism populations such as bacteria, wild yeasts, or the like.

According to certain embodiments, an output of the spectroscopic process analytic technology used herein may include fermentation organism performance or enzyme performance, for example. The output may be assessed by such techniques as analyzing a concentration of a compound produced by a contaminating organism, or when the concentration of the compound reaches at least a trigger level, or a ratio of a concentration of a compound to another reaches a trigger level, or a ratio of rate of change of one analyte to another reaches a trigger level, controlling addition of a bacteriostatic or bacteriocidal compound to the bioprocess or taking bacteriostatic actions such as temperature decrease, pH decrease, increasing flow (or starting) UV recycle to reduce undesirable micro-organism populations such as bacteria, wild yeasts, or the like. Compounds produced by a contaminating organism may include lactic acid, acetic acid, butyric acid, isobutyric acid, formic acid, succinic acid, or combinations or any of these.

In certain embodiments, for example, the output may be assessed by calculation of rates of production and, in response, decreasing temperature in the fermentor and or harvesting within 0 to 24 hours in response to the assessment of the output. In response to the output of the spectroscopic process analytic technology, the methods herein may further include altering, terminating, or extending the bioprocess following, for example, an increase or decrease of an analyte to a predetermined level or an increase or decrease of a ratio of rates of one analyte to another. Additionally, a product may be harvested, either immediately or up to 24 hours later, in response to the assessment of the output.

In response to the output of the spectroscopic process analytic technology, the methods herein may further include altering, terminating, or extending the bioprocess. Additionally, a product may be harvested in response to the assessment of the output. In certain embodiments, a product may be harvested in 0 to 24 hours in response to the assessment of the output.

According to certain embodiments, the methods may also include measuring a by- product to product ratio, reactant to product ratio, or ratio of rates of the bioprocess. In response, the methods may further include increasing an agitator speed, increasing a gas flowrate, and/or adding additional media components, for example, mineral salts, xylose, glucose, or enzyme, or other suitable media components, when the ratio reaches a trigger level. For example, in response to analyzing a ratio of xylose/glucose, additional glucose may be added when the ratio falls outside of about 2 to about 100. As a further example, when monitoring a ratio of rate of increase of monosaccharides/rate of decrease of oligosaccharides, an enzyme concentration may be increased when a rate of change falls outside of about 0.01 to about 100. Additionally or alternatively, in response to analyzing a ratio of xylose/glucose and a ratio of rate of increase of monosaccharides/rate of decrease of oligosaccharides, a fermentor or sacchanfication vessel may be harvested between 0 and about 24 hours once a ratio between about 0.01 and about 100 is achieved.

According to certain embodiments, the methods may also include analyzing a rate of change of product/available sugar monomer and in response to a trigger point of the rate of change, increasing a feed rate of sugar monomers, increasing enzyme concentration, or harvesting a fermentor.

The methods may be carried out at demonstration or commercial scale.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and, together with the description, serve to explain the features, advantages, and principles of the invention. In the drawings:

FIG. 1 is a process flow diagram illustrating one embodiment of a bioprocess using on-line near-infrared spectroscopic process analytical technologies (NIR).

FIG. 2 is a process flow diagram illustrating one embodiment of a bioprocess using on-line mid-infrared spectroscopic process analytical technologies (MIR).

FIG. 3 is a process flow diagram illustrating one embodiment of a bioprocess using at-line MIR.

FIG. 4 is a graphical process flow diagram illustrating a sugar cane to ethanol fermentation process.

FIGS. 5 and 6 are graphs of on-line MIR monitoring in the sugar cane to ethanol process.

FIG. 7 is a graph of on-line NIR monitoring in the sugar cane to ethanol process. FIGS. 8 and 9 are graphs of on-line MIR sucrose monitoring in the sugar cane to ethanol process.

FIGS. 10a and 10b are graphs showing on-line NIR sucrose and ethanol monitoring in the sugar cane to ethanol process.

FIG. 1 1 a is a graph of on-line MIR monitoring in the sugar cane to ethanol process.

FIG. l ib is a graph of on-line NIR monitoring in the sugar cane to ethanol process.

FIG. 12a and FIG. 12b are graphs comparing on-line MIR and at-line MIR data of the sugar cane to ethanol fermentation process.

FIG. 13 is a graphical process flow diagram illustrating a lignocellulose to ethanol sequential single tank process at a demonstration scale.

FIGS. 14 and 15 are graphs of MIR monitoring of the lignocellulose to ethanol sequential single tank process at a pilot scale.

FIG. 16 is a graph of NIR monitoring of the lignocellulose to ethanol sequential single tank process at a demonstration scale.

FIGS. 17a and 17b are graphs of two different runs of the lignocellulose to ethanol sequential single tank process at a demonstration scale, measuring insoluble solids continuously by NIR.

FIGS. 18a and. 18b are graphs of the two runs of the lignocellulose to ethanol sequential single tank process as shown in FIGS. 17a and 17b, but measuring glucan continuously by NIR.

FIG. 19 is a graph of bio-burden detection during NIR monitoring of the lignocellulose to ethanol sequential single tank process at a demonstration scale.

FIG. 20 is a graph of NIR monitoring of the lignocellulose to ethanol Hybrid

Hydrolysis Fermentation (HHF) process at a pilot scale.

FIGS. 21 , 21b, and 21c are graphs comparing at-line MIR and lab analysis of key components within an ABE fermentation during an aqueous phase.

FIGS. 22a and 22b are graphs comparing at-line MIR. and lab analysis of key components within an ABE fermentation during a solvent phase. FICFS. 23a and 23b are graphs comparing at-line MIR and lab analysis of total proteins during a Trichoderma reesei enzyme fermentation.

FIGS. 24a and 24b are graphs comparing at-line MIR and lab analysis of anhydrous ethanol during a distillation process.

FIGS. 25a and 25b are graphs comparing at-line MIR and lab analysis of mono- elhylene glycol during a distillation process.

DETAILED DESCRIPTION

The invention uses near-infrared and mid-infrared spectroscopic process analytical technologies to monitor and control bioprocesses. These real-time process analytic capabilities also improve throughput of laboratory analysis.

As used herein, the term "bioprocess" refers to a process that uses living cells or components of living cells, such as yeast, bacteria or enzymes, to obtain desired results, Lignocellulosic biomass is one type of material that may be used in a bioprocess.

The terms lignocellulosic and lignocellulose preferably broadly refer to materials containing cellulose, hemicellulose, lignin, juice, and/^'or the like, such as may be derived from plant material and/or the like. Lignocellulosic material may include any suitable material, such as energy cane, energy cane bagasse, sugarcane, sugarcane bagasse, bamboo, rice, rice straw, corn, corn stover, maize, maize stover, wheat, wheat straw, sorghum, sorghum stover, sweet sorghum, sweet sorg xm stover, arundo, cotton remnant, sugar beet, sugar beet pulp, soybean, rapeseed, jatropha, switchgrass, miscanthus, napier grass, other grasses, and hybrids of any of these material s.

Due to the versatility of the tools required for the methods described herein, these methods may apply to a wide variety of bioprocesses. More particularly, various bioprocesses that may be used with the spectroscopic process analytical technologies described herein include, but are not limited to, lignocellulose to ethanol processes, saceharification processes, liquefaction processes, sugar cane to ethanol processes, sugar to diesel processes, fermentation processes, simultaneous saceharification and fermentation (SSF) processes, hybrid hydrolysis fermentation (HHF) processes, and acetone ethanol biitanol (ABE) or other mixed alcohol fermentation processes, to name a few. The bioprocesses may also include one or more steps or phases within on overall process. For example, the bioprocesses may include pretreatment, hydrolysis, saccharification, liquefaction, biocataSyst propagation, fermentation, distillation, and combinations thereof of any of these steps or phases.

A simultaneous saccharification and fermentation (SSF) process is a process used in biofuel production in which hydrolysis of starch or glucan and fermentation of glucose occur simultaneously.

A hybrid hydrolysis fermentation (HHF) process is a process in which enzymatic hydrolysis is initially incubated at elevated temperatures and, after the enzymatic hydrolysis rates drop and the temperature is reduced, fermentation is carried out concurrently.

The spectroscopic process analytic technology used in the methods described herein may include any one or more of the following: near- infrared, mid-infrared, Fourier transform infrared, Raman spectroscopy, and mass spectrometry. Near-infrared spectroscopy uses the near-infrared region of the electromagnetic spectrum, from about 800 nm to about 2500 nm. An example of near-infrared spectroscopy equipment suitable for use in the methods herein is Matrix F spectrometer, available from Bruker Optics of Billerica, Massachusetts. Mid-infrared spectroscopy uses the mid-infrared region of the electromagnetic spectrum, from about 2500 nm to about 25,000 nm. An example of mid- infrared spectroscopy equipment suitable for use in the methods herein is Matrix MF spectrometer, also available from Bruker Optics.

Methods of monitoring and controlling a bioprocess using spectroscopic process analytic technology, as described herein, may be carried out either on-line, at-line, or offline. In any case, the spectroscopic process analytic technology is used to monitor one or more analytes within the bioprocess. Examples of suitable analytes include sugars, sugar alcohols, fusel alcohols, solids, dissolved C0₂, organic acids, proteins, acetone, ethanol, butanoi, acetoin, dio!s, butyrate, lipids, triacyi giycerides, fatty alcohols, esters, and combinations of any of these. More particularly, the analyte(s) may include glucose, xylose, arabinose, cellobiose, sucrose, fructose, lactose, galactose, glycerol, glucan, insoluble solids, lactic acid, acetic acid, succinic acid, formic acid, xylitol, and/or ethanol.

The phrase "assessing an output," as used herein, refers to analyzing data collected by methods of monitoring and controlling bioprocesses, as described herein. After assessing an output of the spectroscopic process analytic technology based on the monitored analyte(s), one or more parameters of the bioprocess can be adjusted in response to the output of the spectroscopic process analytic technology. The output may indicate, for example, fermentation organism performance or enzyme performance. As used herein, the term "fermentation organism" may refer to a lipid producer, an enzyme producer, or a yeast. As used herein, the term "enzyme" may refer either to a mixture of fungal proteins or a single defined protein. In response, the parameters that may be adjusted may include strain, media composition, enzyme concentration including, for example, the concentration of proteases, lignocellulosic enzymes, or starch saccharification enzymes, temperature, pressure, inlet gas flow, inlet gas composition, off-gas-output, UV treatment of a fermentation recirculation loop, temperature increase or decrease, addition of an antimicrobial compound, pH increase or decrease, feed composition such as adjusting the amount of glucose or xylose, feed rate, agitator speed, and combinations of any of these. Additionally or alternatively, the bioprocess may be altered, terminated, or extended in response to the output of the spectroscopic process analytic technology.

When performed on-line, continuous real-time measurements of an analyte are taken while the process is ongoing, and the results are used to immediately adjust one or more parameters. These adjustments can be made through an automated response to the analyte measurements. When performed at-line, numerous samples of an analyte are taken at various times during the process, and some delay (in some instances, up to an hour or more) may occur such as due to assessing the results before adjusting one or more parameters. When performed off-line, numerous samples of an analyte are taken at various times during the process, results are accumulated and subsequently assessed, and one or more parameters may be adjusted either during or in preparation for a subsequent run of the process. When data is taken at-line or off-line, parameter adjustments are generally carried out manually, which provides a source of variability.

Comparing near-infrared spectroscopic process analytic technology (NIR) and mid-infrared spectroscopic process analytic technology (MIR), NIR is more nigged/robust and therefore suitable for large-scale implementation, but lacks the precision and accuracy of MIR for analytes present at low concentration. MIR is more accurate, precise, and sensitive than NIR, with measurements made in terms of parts per million (ppm) rather than percentages. However, on-line MIR is often limited to a 3 meter cable length, whereas NIR has a 100 meter maximum cable length, which may lend on-line NIR to be a more practical solution to on-line analysis for commercial applications.

In one embodiment of the method, illustrated in FIG. 1 , NIR 102 is used to monitor on-line one or more analytes 104 within either a lignocellulose to ethanol fermentation process or a sugar cane to ethanol fermentation process 100. In this embodiment, the primary input 106 of the bioprocess is either sugar cane or other lignocellulosic material. The analyte(s) 104 may be ethanol, total sugars, sucrose, glucan, insoluble solids, and/or lactic acid. Either a diffuse reflection probe, such as Turbido ΓΝ264 available from Bruker Optics, or a transflectance probe, such as ΓΝ271Ρ- 02, also available from Bruker Optics, may be a suitable probe for monitoring the anal te(s) 104 in this near-infrared system. An output 108 of the spectroscopic process analytic technology may be, in this case, yeast performance or enzyme performance, in response to this output 108, a parameter 110, such as feed, feed rate, strain, temperature, or pH, is adjusted. As explained above, the process may also be altered, terminated, or extended in response to the output 108. When the process has finished running, the resulting ethanol 112 may be used to produce a renewable material 114. The renewable material 114 may be used as a biofuel, for example.

in another embodiment of the method, illustrated in FIG. 2, MIR 202 is used to monitor on-line one or more analytes 204 within either a lignocellulose to ethanol fermentation process or a sugar cane to ethanol fermentation process 200. hi this embodiment, as in the embodiment illustrated in FIG. 1, the primary input 206 of the bioprocess is either sugar cane or other lignocellulosic material. The analyte(s) 204 may be ethanol, total sugars, sucrose, glucose, fructose, xylose, glycerol, lactic acid, and/or acetic acid. An attenuated total reflectance probe, such as PIR900/100, available from Fibre Photonics, may be a suitable probe for monitoring the analyte(s) 204 in this mid- infrared system. An output 208 of the spectroscopic process analytic technology may be, in this case, yeast performance or enzyme performance. In response to this output 208, a parameter 210, such as feed, feed rate, strain, temperature, or pH, is adjusted. When the process has finished running, the resulting ethanol 212 may be used to produce a renewable material 214, such as a bio fuel.

In yet another embodiment of the method, illustrated in FIG. 3, MIR 302 is used to monitor at-line one or more analytes 304 within either a lignocellulose to ethanol fermentation process or a sugar cane to ethanol fermentation process 300. In this embodiment, as in the embodiments illustrated in FIGS. 1 and 2, the primary input 306 of the bioprocess is either sugar cane or other lignocellulosic material. The analyte(s) 304 may be ethanol, total sugars, sucrose, glucose, fructose, galactose, xylose, arabinose, glycerol, xylitol, lactic acid, and/or acetic acid. One economic benefit to using at-line analysis is that a single instrument, such as an attenuated total reflectance probe or an attenuated total reflectance optical flow cell, optionally in combination with a sample- spectrometer interfacing device such as a fiber-optic cable, may be used to measure multiple process streams. Two process streams are illustrated in FIG. 3. An output 308 of the spectroscopic process analytic technology may be, in this case, yeast performance or enzyme performance. In response to this output 308, a parameter 310, such as feed feed rate, strain, temperature, or H, is adjusted. When the process has finished running, the resulting ethanol 312 may be used to produce a renewable material 314, such as a biofuel.

According to certain embodiments, the bioprocesses may be used to produce a renewable material. As used herein, the term "renewable material" preferably refers to a substance and/or an item that has been at least partially derived from a source and/or a process capable of being replaced at least in part by natural ecological cycles and/or resources. Renewable materials may broadly include, for example, chemicals, chemical intermediates, solvents, adhesives, lubricants, monomers, oligomers, polymers, biofuels, biofuel intermediates, biogasoline, biogasoline blendstocks, biodiesel, green diesel, renewable diesel, biodiesel blend stocks, biodistillates, biochar, biocoke, biological oils, renewable building materials, and/or the like. As a more specif c example, the renewable material may include, without being limited to, any one or more of the following: methane, ethanol, n-butanol, isobutanol, 2-butanol, fatty alcohols, isobutene, isoprenoids, triglycerides, lipids, fatty acids, lactic acid, acetic acid, propanediol, butanediol. According to certain embodiments, the renewable material may include one or more biofuel components. For example, the renewable material may include an alcohol, such as ethanol, butanol, or isobutanol, or lipids. In certain embodiments, the renewable material can be derived from a living organism, such as algae, bacteria, fungi, and/or the like. According to certain embodiments, the renewable material is a lipid, such as fatty acids with a carbon chain length profile at least somewhat similar to rapeseed oil.

The term "biofuel" preferably refers to components and/or streams suitable for use as a fuel and/or a combustion source derived at least in part from renewable sources. The biofuel can be sustainably produced and/or have reduced and/or no net carbon emissions (total carbon lifecycle) to the atmosphere, such as when compared to fossil fuels. According to certain embodiments, renewable sources can exclude materials mined or drilled, such as from the underground. In certain embodiments, renewable sources can include single cell organisms, multi-cell organisms, plants, fungi, bacteria, algae, cultivated crops, non-cultivated crops, timber, and/or the like,

Biofuels can be suitable for use as transportation fuels, such as for use in land vehicles, marine vehicles, aviation vehicles, and/or the like. More particularly, the biofuels may include gasoline, diesel, jet fuel, kerosene, and/or the like. Biofuels can be suitable for use in power generation, such as raising steam, exchanging energy with a suitable heat transfer media, generating syngas, generating hydrogen, making electricity, and/or the like. According to certain embodiments, the biofuel is a blend of biodiesel and petroleum diesel.

Any of the aforementioned spectroscopic process analytical technologies can be used to analyze a composition of the starting material on an on-line basis as it progresses through the bioprocess. This on-line monitoring of the composition confers a control benefit to several aspects of the bioprocess, More particularly, ratios of components in the broth can be taken and used as control parameters in order to improve the process yield, reaction rate, or final concentration.

For instance, in order to control a bio-burden of the bioprocess, the composition may be analyzed on-line for concentration of lactic acid, or other compound produced by a contaminating organism such as acetic acid, or when the concentration of the compound reaches at least a trigger level, such as greater than 8 grams/liter of lactic acid in an anaerobic process, or in a ratio of a rate of production of a compound, such as rate of lactic acid production, to a rate of production of another compound, such as rate of production of a desired fermentation product, reaches a trigger level, such as lactic aeidrethanol where the ratio is greater than 0.5%. Other techniques for assessing the output may include analyzing a ratio of rate of change of one anal te to another reaches a trigger level, controlling addition of a bacteriostatic or bacteriocidal compound to the bioprocess or taking bacteriostatic actions such as temperature decrease, pH decrease, increasing flow (or starting) UV recycle to reduce undesirable micro-organism populations such as bacteria, wild yeasts, or the like. As used herein, the term "bio- burden" refers to microbial contamination. As used herein, the term "contaminating organism" refers to bacteria, wild yeast, fungi, algae, and any other undesired microorganism population. Examples of other compounds produced by a contaminating organism may include lactic acid, acetic acid, butyric acid, isobutyric acid, formic acid, succinic acid, and any combinations thereof. This data may be used to control addition of a bacteriostatic or bacteriocidal compound to the bioprocess within a defined period of time or taking bacteriostatic actions such as temperature decrease, pH decrease, increasing flow (or starting) UV recycle to reduce undesirable micro-organism populations such as bacteria, wild yeasts, or the like, within a defined period of time. As used herein, the term "bacteriostatic compound" refers to agents that restrict bacterial growth and activity without killing contaminating organisms, such as antibiotics, hop acid, chlorine dioxide, and the like. In contrast, the term "bacteriocidal compound" refers to agents that kill contaminating bacteria.

On-line monitoring of the composition may also be used to analyze the composition for a by-product to product ratio, reactant to product ratio, or ratio of rates of the bioprocess. For example, on-line monitoring of the composition may be used to analyze the composition for a ratio of glycerol to ethanol produced. This ratio is an indicator of redox stress to yeast ceils. When the ratio reaches a trigger level, such as at least 0.5 wt% per hour by wt% per hour, for example, temperature may be reduced to slow growth or relieve stress, the agitator speed or gas flowrate may be increased in order to increase oxygen transfer, and/or additional media components may be added, for example, mineral salts, xylose, glucose, or enzyme, or other suitable media components. A second defined value of the ratio, such as when the ratio reaches at least 1 , may define the fermentation end point, indicating that harvest should occur within 0 to about 24 hours, or within 0 to about 12 hours, or within 0 to about 8 hours, or within 0 to about 4 hours.

Another beneficial use for on-line monitoring of the composition is for assessing the output by calculating rates of production of, for example, a fermentation organism, an enzyme host organism, ethanol, or butanoi, and, in response, decreasing temperature in the femientor and/or harvesting within 0 to 24 hours in response to the assessment of the output. In response to the output of the spectroscopic process analytic technology, the methods herein may further include altering, terminating, or extending the bioprocess following, for example, an increase or decrease of an analyte to a predetermined level or an increase or decrease of a ratio of rates of one analyte to another. Additionally, a product may be harvested, either immediately or up to 24 hours later, in response to the assessment of the output. In certain embodiments, the output may be assessed by analyzing the composition for dissolved C0₂ in the broth and, in response, increasing agitator speed, reducing fermentor back-pressure, or flowing additional gas into the broth in order to nucleate the dissolved C<¾.

On-line monitoring of the composition may also be used to analyze a ratio of xylose/glucose and monosaccharides/oligosaccharides. For example, in response to analyzing a ratio of xylose/glucose, additional glucose may be added when the measured amount falls to a minimum level of about 0.01 or the ratio falls outside of about 0.01 to about 100. As a further example, when monitoring a ratio of rate of increase of monosaccharides/ rate of decrease of oligosaccharides, an enzyme concentration may be increased when a rate of change falls outside of about 0.01 to about 100. Additionally or alternatively, in response to analyzing a ratio of xylose/glucose and a ratio of rate of increase of monosaccharides/rate of decrease of oligosaccharides, a fermentor or saccharification vessel may be harvested between 0 and about 24 hours once a ratio between about 0.01 and about 100 is achieved.

Yet another beneficial use for on-line monitoring of the composition is for analyzing a rate of change of product/available sugar monomer and using this data to either increase feed rate of sugar monomers, increase enzyme concentration in the tank, or harvest the fermentor. Trigger points are process-specific. For example, trigger points will differ among a simultaneous saecharification and fermentation, fed-batch, and batch process.

On-line monitoring of the composition may also be used to analyze the composition for major broth components, taking a point carbon balance and using it in a ratio with current product concentration. This scenario is dependent on having an accurate measure of C(¾ evolved, or its rate of evolution, unless a stoichiometric estimation is made throughout the bioreaction. This ratio is an indicator of carbon losses to the reaction, either to contamination by-products, stress electron sinks, or gaseous C0₂, indicating a need to alter any one or more fermentation variables dependent upon the process. For example, early on in the fermentation process, this "fermentation health" metric could be used in a ratio with the aforementioned lactic acid ratio to provide a fermentation time independent measure of contaminant concentration, keeping in mind that the target biocatalyst may also produce small amounts of organic acids.

While on-line monitoring is beneficial for all of the reasons explained above, at- line MIR also provides precise, accurate, and instantaneous results for the key lignocelhilose to ethanol components, namely ethanol, glucose, xylose, arabinose, cellobiose, sucrose, fructose, lactose, galactose, glycerol, insoluble solids, giucan, acetic acid, succinic acid, formic acid, xylitol, and lactic acid. Previously, it would take more than 1 week to receive off-line results for these components, thus eliminating the ability to control the live process. At-line MIR is versatile because it can be used to measure many process streams or tanks. Because of the at-line results, yeast performance (etlianol production and sugar consumption rates), enzyme performance (sugar release rates), byproduct to product ratios (glycerol vs. ethanol), and bio-burden detection (production of acetic and lactic acid) can be quickly determined.

The various graphs accompanying the examples below display the benefits of online and at-line monitoring using NIR and MIR spectroscopic process analytical technologies for lignocelhilose to ethanol and sugar cane to ethanol bioprocesses.

EXAMPLES

Spectroscopic process analytic technology was used to monitor and control a sugar cane to ethanol fermentation process. The graph in FIG. 4 is a process flow diagram, showing the specifications of the process. The graphs in FIGS. 5-12 show the benefits of monitoring and controlling the sugar cane to ethanol fermentation process using MIR and NIR spectroscopic process analytic technology.

As shown in FIG. 4, the process in this example began by feeding yeast into a 550 m³ tank. The yeast was a commercial yeast purchased in Brazil, used as ethanoiogen, a strain selected at the beginning of each sugar cane crushing season, which varies from year to year and also mutates during the year. For example, a strain used as an inoculum after several months is different from a commercial strain used at the beginning of the season. The yeast feed took approximately 30 minutes, filling approximately 1/3 of the volume of the tank, with about 30-35% solids.

More particularly, the fermentation process was run in a batch mode but on a continuous cycle, not to be confused with continuous fermentation, running 24 hours a day, 7 days a week. A typical fermentation began with a sulfuric acid treated yeast being fed into the fermentor until a 1/3 tank volume was reached. The pH of the yeast during the initial feed was approximately 2.0 to 2.5. Once the target yeast volume was reached, the juice/molasses (must) feed continued for approximately 5 hours until a total tank volume of 550 m³ was achieved wherein 2/3 of the volume was the must. At the final volume, the pH of the tank was approximately 4.0 to 5.0 with a temperature of 33°C. The fermentation was not pH controlled, thus contributions to pH change came from the must feed, treated yeast, and the biological reactions within the tank. Following the must feed, fermentation continued until the Brix% remained constant (a measure in degrees (o) of the amount of dissolved solids in a liquid; loBrix = 1% w/w). Typically, the Brix% readings were around 10% during the must feed and dropped to about 2% at the end of fermentation. Once fermentation was empirically deemed complete, the tank was emptied and the fermented broth was sent to the continuous centrifuge. The centrifuge separated the yeast from the wine. The wine (7-1 1% EtOH) was transferred to the distillation columns. The yeast (initially about 60-70% solids with 7-11% EtOH after centrifugation) was diluted with water and treated with sulfuric acid and antibiotics to inactivate bacterial contaminants. After treatment, the yeast (about 30-35% solids with 3- 5% EtOH) was transferred back to a fermentor to restart the process.

FIG. 5 is a graph showing the benefits of on-line monitoring using MIR. If monitored and controlled on-line, the fermentation could have been terminated when completed, which the graph shows occurred after a 12-hour run time, in this example, the run continued for an additional 4 hours, which, in light of plant optimization, needlessly added operations cost and reduced throughput of the mill without resulting in additional production of ethanol.

FIG. 6 is a graph showing how on-line monitoring using MIR allows a better understanding of run kinetics, namely sugar consumption and ethanol production. More particularly, after the media feed has been completed, ethanol production rates can be calculated to determine when the production of ethanol levels off, and sugar consumption rates can be calculated to determine when the sugar consumption levels off as well. This transparency can be used to optimize process conditions, such as feed, feed rate, strain, temperature, pH, and the like, to improve productivity and reduce costs.

FIG. 7 is a graph showing the benefits of on-line monitoring using NIR, While precision is lost in comparison to on-line MIR, as shown in FIG, 6, operators using online NIR can still determine from data, such as that in FIG. 7, the appropriate time to terminate the fermentation.

FIG. 8 is a graph showing on-line sucrose monitoring in the sugar cane to ethanol process, using MIR. Continuous sucrose monitoring by on-line MIR can assess yeast performance. During an ideal sugar cane to ethanol fermentation, low sucrose throughout the process indicates strong yeast performance, which results in a faster fermentation with a higher ethanol titre, such as about 90 grams/liter. However, this titre is a function of both yeast performance and juice/molasses feed and varies depending on both factors.

Similar to FIG. 8, FIG. 9 is another graph showing on-line sucrose monitoring in the sugar cane to ethanol process, using MIR. Unlike the low sucrose level throughout the process in FIG. 8, the process in FIG. 9 exhibits a high sucrose accumulation, which is an indicator of poor yeast performance. The result is a slower fermentation and a lower ethanol yield. FIGS. 8 and 9 also show a prediction of ethanol, glucose, fructose, and total sugars. This prediction was derived from a model, which can be well served by the continuous information supplied by NIR/MIR.

FIG. 10a and FIG. 10b are graphs showing on-line sucrose monitoring in the sugar cane to ethanol process, using NIR. As explained above, on-line NIR is more practical than on-line MIR in commercial settings because of the considerably longer cable length of the NIR equipment. These examples show that sucrose can also be monitored on-line by NIR. The example illustrated in FIG. 10a shows that the production of ethanol had leveled off by the time operations terminated the fermentation. In contrast, the example illustrated in FIG. 10b shows that the ethanol production was continuing at the time operations terminated the fermentation. On-line monitoring could have extended the fermentation to increase the ethanol yield.

FIG. 11a and FIG. l ib are graphs demonstrating potential efficiency improvements in commercial sugar cane to ethanol fermentation processes, FIG. 1 1 a shows on-line MIR data, while FIG, 1 1 b shows on-line NIR data. The vertical line in each graph shows where the 550 nr commercial fermentation vessel terminated prematurely. The line depicting ethanol production continues to slope upwards to the right of the vertical line, while the line depicting sugar consumption continues to slope downwards to the right of the vertical line, indicating incomplete ethanol fermentation and unutilized sugars. If monitored and controlled on-line, fermentation can be terminated when completed. At a commercial scale, even 1% efficiency improvement could result in over a million dollars a year in cost savings. For example, 1% efficiency improvement in fermentation yield in a sugar cane mill that could be achieved by using process analytic technology to determine a true end point in a sugar cane to ethanol fermentation process would result in an additional revenue of about 0.53 USS/ton of sugar cane. With a crushing capacity of 2.5 million ton/year, this would result in a savings of about 1.3 million US$/year/mill.

FIG. 12a and FIG. 12b are graphs comparing on-line and at-line data of the sugar cane to ethanol fermentation process. Because of the relatively short 3 -meter maximum cable length of the MIR equipment in this example, the on-line MIR may not be practical in many commercial settings. However, other MIR technologies may be available without restrictive fiber-optic cables. Although sampling is required, the at-line MIR can serve as an alternative to on-line MIR analysis. Fast decision-making and process control, similar to what is attainable by on-line MIR, is still achievable with at-line analysis. As shown in FIG. 12a and FIG. 12b, the on-line and at-line data are very comparable. In the set of examples depicted in FIGS. 13-20, MIR and NIR spectroscopic process analytical technologies were used to monitor and control a lignocellulose to ethanol (LCE) sequential single tank process run at demonstration (FIGS, 13-19) or pilot scale (FIG, 20). The graph in FIG. 13 is a process flow diagram, showing the specifications of the process.

Prior to the fermentation run in FIG. 13, a 5% sodium hydroxide clean-in-place (CIP) was performed. The CIP solution was heated to 176°F (80°C) (1 psi). Following CIP, a steam-in-place (SIP) was completed at 220°F (104°C). The LCE sequential single tank process began with a 5-carbon (C5) sugar hydro lysate filled to 5600 gallons. The C5 feed took approximately 13 hours, with adjustments to the temperature and pH made at the end of the feed. The pH and temperature were adjusted (pH 6.0, temperature 33°C) followed by inoculation (yeast pitch of about 4 gDW/L depending on desired fermentation conditions). Following inoculation, a concentrated glucose media was fed to the tank to increase the total glucose concentration by about 15 g/L. The C5 fermentation continued for about 40 hours. At about the 40-hour time point, the temperature and pH were readjusted (pH 5.5, temperature 35°C) for the SSF. An enzyme cocktail (225 Cell T units (CTU)/g TR1 (including slurry) and 1.1 mL/L beta- glucosidase) was added to reach a 225 CTU/mL concentration, CTU is an assay method supplied by Megazyme for determination of endoglucanse from a commercially-available substrate called 'cellazyme T tablets,' and TR1 is an enzyme strain Trichoderma reesei strain 1. Following enzyme addition, a 6-carbon substrate (C6) feed of lignocellulosic cake was initiated. The feed continued for about 12 hours until a total tank volume of 21,000 gallons (about 10-12% solids) was reached. Fermentation continued for about 28 hours until it was terminated. Total fermentation time was about 80 hours,

FIG. 14 is a graph showing the MIR monitoring of the lignocellulose to ethanol sequential single tank process. As can be seen in the graph, process upsets or sudden changes can be quickly detected/measured. More particularly, the initiation of lignocellulose cake feed, marked by the arrow, resulted in ethanol dilution. Also, a ratio of by-products can be quickly calculated, such as with the glycerol and ethanol.

Similar to FIG. 14, FIG. 15 is another graph showing the MIR monitoring of the lignocellulose to ethanol sequential single tank process, hi FIG. 15 as well, a process upset can be quickly detected, in this case the upset was caused by glucose accumulation during fermentation due to enzymes converting cellulose to glucose and an inactive yeast not converting glucose to ethano!. If detected early, the fermentation can be quickly salvaged with the addition of viable yeast, or terminated to reduce operating costs.

FIG. 16 is a graph showing the NIR monitoring of the lignocellulose to ethanol sequential single tank process. Similar to what was demonstrated for the sugar cane to ethanol process, NIR can also continuously measure ethanol for the lignocellulose to ethanol process. As shown in this example, the diffuse reflection probe shows better precision in the presence of lignocellulose cake due to the presence of solids. Diffuse reflection probes need solids to reflect light. The transflectance probe serves as a better alternative for the C5 liquid fermentation. In a commercial process, continuous measurements inform operations when ethanol production has plateaued. Because it is difficult to assess glucose values during an SSF, ethanol production is an extremely important indicator to know if the yeast and enzymes are working in synergy.

FIG. 17a and FIG. 17b show two different runs of the lignocellulose to ethanol sequential single tank process, measuring insoluble solids continuously by NIR. Process upsets can be quickly detected, such as the feed interruption marked by the arrow in FIG. 17b. By understanding the insoluble solids degradation rate, the cake feed rates can be controlled to optimize enzyme performance. Improved enzyme performance would lead to faster fermentations with higher ethanol yields.

FIG. 18a and FIG. 18b show the same two runs of the lignocellulose to ethanol sequential single tank process as shown in FIGS. 17a and 17b, but measuring glucan continuously by NIR. Once again, process upsets can be quickly detected, such as the feed interruption in FIG. 18b. Enzyme performance can be quickly assessed. For example, glucan production or consumption kinetic rates are a reflection of the enzyme performance. As exemplified by the off-line data points circled in FIG. 18b, the off-line method is historically unreliable, particularly with a slow turn-around time, often 1-2 weeks. The entire lignocellulose to ethanol process can be optimized from hydrolysis to fermentation, such as by assessing the severity of hydrolysis and adjusting temperature/acid accordingly to optimize, and terminating fermentation when glucan degradation ceases and glucose is no longer being released. FIG. 19 is a graph showing use of bio-burden detection during NIR monitoring of the lignocellulose to ethanol sequential single tank process. More particularly, lactic acid measurements can be used to control addition of bacteriostatic or bacteriocidal compound, if applicable, or to terminate the process in order to save costs and increase reactor throughput in the plant.

FIG. 20 is a graph showing the NIR monitoring of the lignocellulose to ethanol Hybrid Hydrolysis Fermentation (HHF) process run at pilot scale. For the HHF process, a diffuse reflection NIR probe has displayed good precision during the fermentation, in comparison to sequential single tank, because of the presence of C6 cake throughout. During the HHF process, the saccharification step precedes fermentation. If monitored on-line, an operator could efficiently inoculate the batch when the total sugars value reaches a plateau. This would ensure maximum efficiency of the saccharification step.

As described in the above examples corresponding to FIGS. 4-20, many benefits can be derived from monitoring and controlling lignocellulose to ethanol and sugar cane to ethanol fermentations, as well as other bioprocesses, on-line and at-line. These benefits can improve process efficiency and reduce costs.

Using on-line NIR monitoring to monitor ethanol and total sugars in both lignocellulose to ethanol and sugar cane to ethanol processes allows an operator to understand the exact time to terminate fennentaiion in order to reduce operating costs and improve throughput. Furthermore, this on-line NIR monitoring allows an operator to understand run kinetics in order to improve process efficiency by adjusting process parameters, such as feed, feed rate, strain, temperature, pH, and the like.

On-line NIR monitoring can also be used to monitor sucrose in sugar cane to ethanol processes in order to assess yeast performance. More particularly, this monitoring enables an operator to notice such indicators as low sucrose, or slow sucrose uptake rates.

On-line NIR monitoring can also be used to monitor glucan and insoluble solids in lignocellulose to ethanol processes in order to assess feed rates and enzyme performance.

Additionally, on-line NIR monitoring can be used to monitor lactic acid in both lignocellulose to ethanol and sugar cane to ethanol processes in order to indicate bio- burden activity. This monitoring enables an operator to add bacteriostatic or bacteriocidal compounds to favor the ethanologen, or to terminate or rejuvenate a failing fermentation in order to reduce operating costs.

On-line MIR monitoring can be used to measure all of the key fermentation components, such as ethanol, total sugars, sucrose, glucose, fructose, xylose, arabinose, cellobiose, lactose, galactose, glycerol, glucan, insoluble solids, lactic acid, succinic acid, formic acid, xylitoi, and acetic acid, for both lignocellulose to ethanol and sugar cane to ethanol processes in real-time. Benefits of using on-line MIR monitoring include being able to understand the exact time to terminate a fermentation in order to reduce operating costs and improve plant throughput, as well as being able to understand run kinetics in order to improve process efficiency by adjusting parameters, such as feed, feed rate, strain, temperature, H, and the like. Also, upsets can quickly be detected and the process adjusted accordingly. On-line MIR monitoring also measures by-product versus product ratios, and enables the control of bio-burden.

As an alternative to on-line MIR, monitoring, at-line MIR monitoring provides nearly the same accuracy and precision for measuring all of the key fermentation components, such as ethanol, total sugars, sucrose, glucose, fructose, xylose, glycerol, total protein, lactic acid, and acetic acid. At-line MIR analysis provides versatility by allowing the same instrument to be used to measure many process streams.

At-line MIR technology can also be used in acetone ethanol butanol (ABE) fermentation processes. More particularly, at-line MIR technology can be used the measure such analytes as acetone, butanol, acetoin, and butyrate in ABE fermentation processes.

FIGS. 21a, 21b, and 21c are graphs comparing at-line MIR and lab analysis of key components within an ABE fermentation during an aqueous phase. These graphs are based on chemometric models. Samples were collected at various times over the course of a 72-hour ABE fermentation. The key components that were studied were acetate, acetoin, butyrate, lactate, glucose, acetone, butanol, and ethanol, FIG. 21a includes the data for acetate, acetoin, butyrate, and lactate. FIG, 21 b includes the data for glucose. FIG. 21c includes the data for acetone, n-butanol, and ethanol. FIGS, 22a and 22b are graphs comparing at-line MIR and lab analysis of key components within an ABE fermentation during a solvent phase. Like the graphs in FIGS. 21a-21c, these graphs are also based on chemometric models, with samples collected at various times over the course of a 72-hour ABE fermentation. The key components that were studied were acetone, ethanol, and butanol. FIG. 22a includes the data for acetone and ethanol. FIG. 22b includes the data for butanol.

Total proteins during a Trichoderma reesei enzyme fermentation can be measured at-line and on-line by MIR. In the example corresponding to the graphs in FIGS. 23a and 23b, proteins were measured by at-line MIR and then compared to a 120-hour sample sent for lab analysis. Due to the cost of outsourcing samples for protein measurements, only the 120-hour sample was selected for laboratory analysis.

FIGS. 24a and 24b are graphs comparing at-line MIR and lab analysis of anhydrous ethanol during a distillation process. The at-line MIR can be directly calibrated to measure anhydrous ethanol for the distillation process. A 50% test set (FIG. 24a) shows that a calibration (FIG. 24b) built using 92%, 94%, 96%, 98%, and 100% ethanol can accurately measure the 91%, 95%, 97%, and 99% samples.

FIGS. 25a and 25b are graphs comparing at-line MIR and lab analysis of mono- ethylene glycol (MEG) during a distillation process. The at-line MIR. can be directly calibrated to measure MEG for the distillation process. A 50% test set (FIG. 25a) shows that a calibration (FIG. 25b) built using 97.2%, 97.6%, 98%, 98.6%, 99.4%, and 99.8% MEG can accurately measure the 97%, 97.4%, 97.8%, 98.4%, 98.8%, 99.2%, and 99.6% samples.

As explained above, due to the economies of scale, even minor improvements have a material impact on the returns when the methods herein are used at a commercial scale. At laboratory scale, analysis throughput improvements can result in major cost savings due to more efficient use of resources.

It will be apparent to those skilled in the art that various modifications and variations can be made in the disclosed structures and methods without departing from the scope or spirit of the invention. Particularly, descriptions of any one embodiment can be freely combined with descriptions or other embodiments to result in combinations and/or variations of two or more elements or limitations. Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. It is intended that the specification and examples be considered exemplary only, with a true scope and spirit of the invention being indicated by the following claims.

Claims

What is claimed is:

1. A method of monitoring and controlling a bioprocess, comprising:

using a spectroscopic process analytic technology to monitor at least one analyte within the bioprocess;

assessing an output of the spectroscopic process analytic technology; and adjusting at least one parameter of the bioprocess in response to the output of the spectroscopic process analytic technology.

2. The method of claim 1, wherein the spectroscopic process analytic technology comprises at least one of the group consisting of near-infrared, mid-infrared, Fourier transform infrared, Raman spectroscopy, and mass spectrometry.

3. The method of claim 1 or 2, wherein the bioprocess comprises a lignocellulosic to ethanoi process or a sugar cane to ethanoi process.

4. The method of claim 1, 2, or 3, wherein the bioprocess comprises at least one of the group consisting of pretreatment, hydrolysis, saccharification, liquefaction, biocatalyst propagation, fermentation, distillation, and combinations thereof.

5. The method of any of the above claims, comprising monitoring and controlling the bioprocess on-line or the bioprocess at-line.

6. The method of any of the above claims, wherein the at least one analyte is selected from the group consisting of sugars, sugar alcohols, fusel alcohols, solids, dissolved CQ₂, organic acids, proteins, acetone, ethanoi, butanol, acetoin, diols, butyrate, and combinations thereof.

7. The method of any of the above claims, wherein the at least one analyte is selected from the group consisting of glucose, xylose, arabinose, cellobiose, sucrose. fructose, lactose, galactose, glycerol, glucan, insoluble solids, lactic acid, acetic acid, succinic acid, formic acid, xylitol, ethanol, and combinations thereof.

8. The method of any of the above claims, wherein the at least one parameter is selected from the group consisting of strain, media composition, temperature, pressure, gas flow, gas composition, off-gas-output, pH, feed composition and rate, agitator speed, and combinations thereof.

9. The method of any of the above claims, comprising altering, terminating, or extending the bioprocess in response to the output of the spectroscopic process analytic technology.

10. The method of any of the above claims, comprising measuring a by-product to product ratio or ratio of rates of the bioprocess or a reactant to product ratio or rado of rates of the bioprocess.

1 1. The method of any of the above claims, comprising analyzing a rate of change of product/available sugar monomer and in response to a trigger point of the rate of change, increasing a feed rate of sugar monomers, increasing enzyme concentration, or harvesting a fermentor.

12. The method of any of the above claims, comprising using an attenuated total reflectance probe or an attenuated total reflectance process flow cell with a sample- spectrometer interfacing device in combination with the spectroscopic process analytic technology.

13. The method of any of the above claims, comprising using a diffuse reflection probe in combination with the spectroscopic process analytic technology or a transfSectance probe in combination with the spectroscopic process analytic technology.

14. The product made by the bioprocess of any of the above claims.

15. A method of monitoring and controlling a bioprocess, comprising:

using a near-infrared spectroscopic process analytic technology to monitor on-line at least one analyte within the bioprocess, wherein the bioprocess is either a lignocellulose to ethanol process or a sugar cane to ethanol process;