# Presentation: 948335

## Corn stover—what do we know about what makes a good feedstock? 

**John Sheehan**
**Steve Thomas **
**Jim McMillan**
**Bonnie Hames**
**David Templeton**
**National Renewable Energy Laboratory**
**November  8, 2001**

**Notes:**
When Shahab asked us to talk about corn stover, he asked us to address feedstock quality needs and specifications.  I’ve changed the focus of the talk a little in way that more honestly describes what we know about minimum specifications--not all that much yet.

We are learning lots about corn stover composition and quality, and have ideas about how future corn stover processes might want to measure and pay for feedstock quality.

## Outline

- Key issue: $$$
- Key questions
- Corn stover and the Biofuels Program
- Corn stover processing
- Corn stover composition
- What we have observed
- Rapid biomass analysis
- Quality variables and their impacts
- Ash
- Moisture
- Carbohydrate and lignin

## Key Issue: $$$

- Feedstock composition influences process economics—possibly at several levels.
- Yield effects
- Equipment compatibility and operability
- Safety
- Though this may be obvious, it is important to recognize that the bottom line issue for corn stover specifications is economics.
- “It’s the economy, stupid”

## Key Questions

- What is the span of variability in corn stover composition?
- Near term: Corn Belt hybrid lines
- Other commercial hybrids, inbred lines, primitive species
- Are the factors that control stover composition mainly genetic or environmental?
- Can we control the variability
- Do harvesting and storage affect stover composition ?
- Does stover variability influence process strategy?
- Is there a “best” corn stover composition?

**Notes:**
These are questions we are still asking.  At best we have only preliminary answers.

## Corn stover and BiofuelsThe biorefinery based on corn stover

- The building blocks of a biorefinery
- The program is focused on the enabling pieces for sugars and for ethanol
- Industry will focus on the application of this technology for new products from sugars and lignin

## Processing corn stover

## Processing corn stover

- (22x micrographs)

## Processing corn stover: it’s not wood

**Notes:**
The complex composition of corn stover increases the potential sources of variance and presents new challenges to compositional analysis and process mass closure. Although the total sugar content of a stover may be higher than that of poplar, a greater percentage of the sugar is derived from hemicellulose.

## Processing corn stover

- AC	=	Acetyl/formyl

- Following components through the process

**Notes:**
Ash accumulation may be a significant problem in processing corn stover feedstocks.  Ash has two sources, soil and structural inorganics (silicates).
The structural inorganic content in stover ranges from 2-10%.  Samples of stover have arrived at NREL containing as much as 10% dirt (by weight).
With the soil contamination comes undesirable levels of  iron and manganese.  These transition metals may catalyze side reactions that affect process efficiency.  During compositional analysis using wet chemical methods, high ash content caused glucan and  xylan values to read 6-10% lower as sugars were converted to products not measured in the analysis.  
Some soils will neutralize the acids used in pretreatment and reduce process efficiency or increase cost since additional acid would be added to achieve the necessary pH for optimum processing
The abrasive nature of inorganic material may decrease the lifetime of some reactor parts.

## Corn stover composition: what do we know?

- Stover composition as a function of
- Location
- Soil types
- Irrigation/ drainage
- Weather
- Commercial variety
- Storage time and methods
- Harvesting and handling methods
- Harvest years

- Information obtained
- Average composition
- Heterogeneity within a feedstock
- Variance between feedstocks
- Sampling protocol
- Harvesting protocol
- Storage protocol
- Definition of ideal feedstock

- Research requiring thousands of samples is now possible

**Notes:**
In addition to significant savings in time and money, rapid analysis methods can be used to provide levels of information that were not previously available.  For example, approximately 200 samples can be analyzed from a bulk corn stover feedstock in one day at a cost of less than $2,000.  A similar compositional analysis using wet chemical methods would cost about $2,000 per sample.  The ability to analyze hundreds of samples provides new levels of information. The analysis of hundreds of samples would have been too costly to pursue without the savings in time and cost provided by NIR/PLS rapid analysis methods. 
This new information has been used to develop protocols for the representative sampling of fields, bales, totes and large boxes of feedstock. The ability to accurately sample a bulk feedstock and to analyze hundreds of samples for about $10 each provides a new tool that is being used to assess the compositional variability of corn stover as a function of variety, geographical location, harvest year, storage and collection method. Reliable information about average stover composition and ranges observed about that average may  require the analysis of thousands of samples. The effect of that variance on process efficiency and economics can then be assessed.  An ideal feedstock can then be defined.  Process can be evaluated on their ability to adjust to feedstock variance.
These bulk screening methods can also be used to identify samples with unusual compositions to be used for genetic studies

## Corn stover compositionon average

- Ranges (17 feedstocks):
- Glucan 	35.1-39.5
- Xylan	20.1-24.6
- Lignin 	11.0-19.1
- Ash		  2.2-15.9

**Notes:**
The current “best estimate” of an average corn stover chemical composition is shown including values for 11 constituents.   The variance seen in the major constituents is also listed to make the point that the chemical composition of corn stover is best reported as a range and not as a single value. Ash values vary significantly due to varying levels of soil contamination.

The compositional analysis of hundreds of corn stover samples is being used to determine a true average composition of commercially available corn stover.  These values are now reported as average compositional values with expected variance around that average number.  This type of reporting provides more realistic information for economic models of biomass conversion.  The observed ranges in the chemical composition of stover feedstocks may significantly effect the economics of the stover to ethanol conversion process.  

## Corn stover composition: heterogeneity within the feedstock

**Notes:**
Harvesting methods and storage conditions may significantly alter the relative proportions of these fractions.  

## Corn stover composition:Rapid biomass analysis

- Faster
- Minutes instead of days
- Minimal sample preparation
- “Real time” feedback in the field and the process
- Cheaper
- About $10 per sample
- Compared to $800-$1,000 for wet analysis
- Better
- Calibrated using best methods
- Less operator dependent
- Suitable for large volume screening

**Notes:**
Current methods for the chemical characterization of biomass feedstocks, process intermediates and residues are not applicable in a commercial setting because they are very expensive (labor intensive) and cannot provide the analysis information in a time frame useful for process control.  For example, a complete analysis using traditional wet chemical methods cost $800-$1,000 per sample and results are not available for days.  In contrast, Rapid Analysis methods can perform the same analysis for about $10 per sample.  A complete chemical characterization is available in a time frame relevant for process control, meaning that the information can be used to make the process adjustments necessary for steady-state production.  Rapid Analysis methods provide these advantages without any loss of precision or accuracy relative to the calibration methods. New techniques like rapid analysis are needed to provide analytical support for large-scale processes that convert biomass to fuels and chemicals. 

## NIR/PLS Rapid Method  forCorn Stover Feedstock Composition

**Notes:**
Our current Rapid Analysis model for corns stover compositional analysis is shown here.  The graph shows the correlation between composition by wet chemical analysis the rapid NIR/PLS method. This rapid method measures nine constituents providing a complete compositional analysis in minutes.  The wide calibration ranges for several major constituents are illustrated.  The NIR/PLS method provides significant savings in time and money without loss of precision or accuracy.  

The calibration set for this rapid method is limited and the wet chemical methods used to obtain the calibration data have been imporved.  The rapid methods will soon be improved and expanded with the addition of samples from many locations, varieties, harvesting methods and harvest years. 

## Rapid biomass analysisin the field

- Field analysis methods being developed
- Instrument manufacturers developing tools specific to biomass analysis
- Farm equipment manufacturers developing combine-mounted spectrometers for total harvest analysis.
- Feedstock prices could be based on quality instead of  weight

**Notes:**
The concept of NIR/PLS analysis of biomass is not novel. These techniques are common in industrial settings monitoring the composition of animal feed, cheese, beer and grain.  The application of this proven technology to biomass conversion and the development of calibrations specific for energy feedstocks is new.   For this reason, method development in this area has great potential for high impact with low risk. 
Field mobile instruments like the one shown that fits in a backpack and reduced wavelength versions that resemble digital cameras and run from palm pilots are now commercially available.  
Instrument manufacturers are also working with farm equipment companies to develop NIR spectrometers designed for use on farming and harvesting equipment.  With the proper calibrations, the spectroscopic data from NIR farming equipment could be converted to compositional  information or conversion yield estimates at the time of harvest.  Feedstock prices could be based on quality rather than weight.  

## Rapid biomass analysis

- Industrial applications

- Slide courtesy of B. Meglen

**Notes:**
Rapid, inexpensive biomass analysis can be useful at many stage of an industrial process.  The methods presented here involve the compositional analysis of biomass feedstocks and biomass-derived solids produced during ethanol production.  These tools can characterize the feedstock as it enters  the reactor.  If necessary, rapid analysis can be used to guide feedstock blending.  Chemical changes during the processing of biomass can provide feed-forward and feed-backwards information that can be used to ensure that the process maintains a steady state in spite of the feedstock variability. Finally, process residues and products can be evaluated to assess overall process economics.  As more samples are analyzed, information can be obtained about the composition of an “ideal feedstock”.  Field- mobile instruments can be used as purchasing tools.  Buyers can obtain compositional information about a biomass feedstock at the point of purchase.  Feedstocks can be assigned values based on quality.  Young plants and perhaps even seeds can be evaluated and selected for desirable characteristics and production potential.  

## Corn stover composition:  Summing up what we know

- Stover has a lower carbohydrate content relative to wood.
- Different anatomical parts of the plant have distinctly different chemical composition.
- Wide variation observed in ash content of as-received samples.

## Corn stover composition: Summing up what we know

- Whole stover composition is variable (NIR results).
- Component            Range (Wt %max-min)
- Glucose+Xylose:		 	 8.4
- Total sugars: 			10.0
- Lignin: 				 8.1

## Corn Stover Composition:  Summing up rapid biomass analysis

- NIR and wet chemical results are in pretty good agreement on average but both are being improved.
- The NIR calibration set is being expanded to cover a wider range of stover materials and composition.
- NIR and other rapid analysis techniques could bring a new level of sophistication to setting of biomass specifications

## Quality variables for corn stoverCauses of variability

- Seed variety - genetics
- Location, harvest year – environment
- Collection and storage methods
- Quantity of entrained dirt.
- May affect the ratio of leaves/stalks/cobs.
- May affect the amount of non-structural (extractable) carbohydrates present.
- Analytical method used and data reported
- Different test methods
- Extracted vs whole stover vs ash free stover

## Quality variables for corn stoverImpacts of ash content

**Two sources**
- Structural Inorganics – minerals in plant cells
- Entrained soil (when “soil” becomes “dirt”)
**Issues**
- Erosion damage to machinery and equipment
- Buffering capacity (more acid to reach target pH)
- Undesirable reactions catalyzed by inorganics (e.g., Fe, Mn) that could explain observed losses
**Targets**
- Structural Inorganics: 5% expected average
- Entrained Soil: 5% max.

## Quality variables for corn stoverImpacts of Moisture

**Two sources**
- In stover at time of collection
- Added (or lost) during handling and storage
**Issues**
- Storage stability
- Safety
- Ease of processing
- Decreased reactivity (too dry) vs. increased energy requirements (too wet)
**Targets **
- At collection: determined by grain harvest ?
- At delivery to conversion facility: 10%

## Quality variables for corn stoverImpacts of carbohydrates and lignin 

- Source
- In plant cells, carbohydrates are both structural (SC) and non-structural (NSC)
- Losses occur during storage, NSC are first to go.
- Issues
- Carbohydrate=sugar potential=product potential
- Lignin=energy and other product potential
- Targets
- Conventional wisdom is “more sugar, less lignin”
- Minimize losses during storage and processing

## Quality variables for corn stoverNREL’s B/MAP Stover Specifications

- Less than 10% ash.
- Washed stover but includes some dirt.
- “Structural inorganics” range is 5-10%.
- Less than 20% moisture.
- Aids in storage stability.
- Three particle sizes.
- Testing size effect on pretreatment yields.
- Maximum fiber length 0.5 to 3 inches.

## Concluding remarks

- Stover composition affects process economics and may influence process design.
- We have a very crude understanding of the range of variability in corn stover composition.
- Classical breeding and selection, and genetic engineering could be used to manipulate stover composition.
- Effects of environmental factors on composition is poorly understood.
- Harvesting, handling, and storage affect the ash and moisture content as well as organic composition of the stover.

## Next Steps

- Coordinate stover collection and conversion activities
- Understand system tradeoffs, variable impacts, capabilities, and overall optimization
- Suggestions ?