14 research outputs found

    Engineering, nutrient removal, and feedstock conversion evaluations of four corn stover harvest scenarios

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    Crop residue has been identified as a near-term source of biomass for renewable fuel, heat, power, chemicals and other bio-materials. A prototype one-pass harvest system was used to collect residue samples from a corn (Zea mays L.) field near Ames, IA. Four harvest scenarios (low cut, high-cut top, high-cut bottom, and normal cut) were evaluated and are expressed as collected stover harvest indices (CSHI). High-cut top and high-cut bottom samples were obtained from the same plot in separate operations. Chemical composition, dilute acid pretreatment response, ethanol conversion yield and efficiency, and thermochemical conversion for each scenario were determined. Mean grain yield in this study (10.1 Mg ha−1 dry weight) was representative of the average yield (10.0 Mg ha−1) for the area (Story County, IA) and year (2005). The four harvest scenarios removed 6.7, 4.9, 1.7, and 5.1 Mg ha−1 of dry matter, respectively, or 0.60 for low cut, 0.66 for normal cut, and 0.61 for the total high-cut (top+bottom) scenarios when expressed as CSHI values. The macro-nutrient replacement value for the normal harvest scenario was 57.36ha−1or57.36 ha−1 or 11.27 Mg−1. Harvesting stalk bottoms increased stover water content, risk of combine damage, estimated transportation costs, and left insufficient soil cover, while also producing a problematic feedstock. These preliminary results indicate harvesting stover (including the cobs) at a height of approximately 40 cm would be best for farmers and ethanol producers because of faster harvest speed and higher quality ethanol feedstock

    Engineering High-Fidelity Residue Separations for Selective Harvest

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    Composition and pretreatment studies of corn stover and wheat stover anatomical fractions clearly show that some corn and wheat stover anatomical fractions are of higher value than others as a biofeedstock. This premise, along with soil sustainability and erosion control concerns, provides the motivation for the selective harvest concept for separating and collecting the higher value residue fractions in a combine during grain harvest. This study recognizes the analysis of anatomical fractions as theoretical feedstock quality targets, but not as practical targets for developing selective harvest technologies. Rather, practical quality targets were established that identified the residue separation requirements of a selective harvest combine. Data are presented that shows that a current grain combine is not capable of achieving the fidelity of residue fractionation established by the performance targets. However, using a virtual engineering approach, based on an understanding of the fluid dynamics of the air stream separation, the separation fidelity can be significantly improved without significant changes to the harvester design. A virtual engineering model of a grain combine was developed and used to perform simulations of the residue separator performance. The engineered residue separator was then built into a selective harvest test combine, and tests performed to evaluate the separation fidelity. Field tests were run both with and without the residue separator installed in the test combine, and the chaff and straw residue streams were collected during harvest of Challis soft white spring wheat. The separation fidelity accomplished both with and without the residue separator was quantified by laboratory screening analysis. The screening results showed that the engineered baffle separator did a remarkable job of effecting high-fidelity separation of the straw and chaff residue streams, improving the chaff stream purity and increasing the straw stream yield

    A Single Pass Multi-component Harvester for Small Grains

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    Abstract. In order to meet the U. S. government's goal of supplementing the energy available from petroleum by increasing the production of energy from renewable resources, increased production of bioenergy has become one of the new goals of th

    From Prediction to Prescription: Intelligent Decision Support for Variable Rate Fertilization

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    We describe the use of machine learning methods in the analysis of spatial soil fertility, soil physical characteristics, and yield data, with a particular objective of determining local (field- to farm-scale) crop response patterns. For effective prescriptive use, the output of these tools is augmented with economic data and operational constraints, and recast as a rulebased decision support tool to maximize economic return in variable rate fertilization systems. We describe some of the practical issues addressed in development of one such system, including data preparation, adaptation of regression tree output for use in a rule-based expert system, and incorporation of real-world limits on system recommendations. Results from various field trials of this system are summarized
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