D. S. Shrestha

THE ENERGY BALANCE OF SOYBEAN OIL BIODIESEL PRODUCTION : A REVIEW OF PAST STUDIES

Although several studies have found biodiesel to be a renewable source of energy, there has been a claim that it is not. This article investigates models used to calculate the net energy ratio (NER) of biodiesel production to point out the reasons for the contradictory results, compares their strengths and weaknesses, and proposes a uniform model for interpretation of the final result. Four commonly referenced models were compared for their assumptions and results. The analysis revealed that the most significant factors in altering the results were the proportions of energy allocated between biodiesel and its coproducts. The lack of consistency in defining system boundaries has apparently led to very different results. The definitions of NER used among the models were also found to be different. A unified model is proposed for biodiesel energy analysis to answer the renewability question. Using the unified boundary, a range of probable NERs was calculated using bootstrapping. The mean NER on a mass basis was 2.55 with a standard deviation of 0.38. The economic sustainability ratio (ESR) is defined as the monetary value ratio of biodiesel to biodiesels share of the energy inputs. The average ESR was found to be 4.43 with a standard deviation of 0.6.

Energy Life-Cycle Assessment of Soybean Biodiesel Revisited

The first comprehensive life-cycle assessment (LCA) for soybean biodiesel produced in the U.S. was completed by the National Renewable Energy Laboratory (NREL) in 1998, and the energy inventory for this analysis was updated in 2009 using 2002 data. The continual adoption of new technologies in farming, soybean processing, and for biodiesel conversion affects the life-cycle energy use over time, requiring that LCA practitioners update their models as often as possible. This study uses the most recently available data to update the energy life-cycle of soybean biodiesel and makes comparisons with the two past studies. The updated analysis showed that the fossil energy ratio (FER) of soybean biodiesel was 5.54 using 2006 agricultural data. This is a major improvement over the FER of 3.2 reported in the 1998 NREL study that used 1990 agricultural data and significantly better than the FER of 4.56 reported using 2002 data. The improvements are primarily due to improved soybean yields and more energy-efficient soybean crushing and conversion facilities. The energy input in soybean agriculture was reduced by 52%, in soybean crushing by 58% and in transesterification by 33% per unit volume of biodiesel produced. Overall, the energy input reduction was 42% for the same amount of biodiesel produced. The addition of secondary inputs, such as farm machinery and building materials, did not have a significant effect on the FER. The FER of soybean biodiesel is likely to continue to improve over time because of increases in soybean yields and the development of increasingly energy-efficient technologies.

Biodiesel Blend Level Detection Using Ultraviolet Absorption Spectra

Biodiesel is often blended with regular U.S. No. 2 diesel. The blending level influences engine performance, emissions, and fuel cold-flow properties. In this article, ultraviolet (UV) absorption spectroscopy is presented as a reliable and affordable technology for blend level detection based on the absorbance patterns of the aromatic compounds in the proposed spectrum. Blends of biodiesel from six different feedstocks and U.S. No. 2 diesels from five different sources were used to test the robustness of the method. Since the absorbance of undiluted samples was too high to measure reliably, the samples were diluted with n-heptane. It was found that the feedstock and alcohol used (methyl or ethyl) did not make a significant difference in the absorbance of diluted biodiesel in the 245 to 305 nm range, while absorbance from 254 to 281 nm was correlated with blend level with R2 > 0.99. It was also observed that if the absorbance of the diesel source was known, then a single wavelength could be used to detect the biodiesel blend level. However, a single wavelength was inadequate when the diesel source was unknown because of variation in the level of aromatics in diesel fuel. Absorbances at 265, 273, and 280 nm were used to calculate the absorbance index, which was found to be independent of the diesel fuel used. Using three wavelengths captured the shape information of the absorbance curve and eliminated the variation from the aromatics content. The root mean square error in determining blend level with this method was estimated to be 2.88%, and the R2 for the linear model was 0.99. The method worked well with biodiesel from the different feedstocks tested in this research and was independent of the diesel fuel used.

Automatic Corn Plant Population Measurement Using Machine Vision

From yield monitoring data, it is well known that yield variability exists within a field. Plant population variation is a major cause of this yield variability. Automated corn plant population measurement has potential for assessing in-field variation of plant emergence and also for assessing planter performance. Machine vision algorithms for automated corn plant counting were developed to analyze digital video streams. Video streams were captured along 6.1 m long cornrow sections at early stages of plant growth and various natural daylight conditions. A sequential image correspondence algorithm was used to determine overlapped image portions. Plants were segmented from the background using an ellipsoidal decision surface, and spatial analysis was used to identify individual crop plants. Performance of this automated method was evaluated by comparing its results with manual stand counts. Sixty experimental units were evaluated for counting results with corn population varying from 14 to 48 plants per 6.1 cornrow length. The results showed that in low weed field conditions, the system plants counts well correlated to manual counts (R 2 = 0.90). Standard error of population estimate was 1.8 plants over 34.3 manual plant count that corresponds to 5.4% of average error.

SHAPE AND SIZE ANALYSIS OF CORN PLANT CANOPIES FOR PLANT POPULATION AND SPACING SENSING

An effective corn plant population and spacing sensing system may provide a key layer of field variability information useful for crop management. An algorithm was developed to count corn plants and to estimate plant location and intra-row spacing in segmented images of 6.1-m (20-ft) long row sections. Images were scanned to detect and determine the boundaries of top projected corn plant canopy objects using a chain code methodology. Plant objects were fused together based on a multi-step process that took into account the spatial structure of the crop row. Position, roundness, and area of plant canopies were used to distinguish between corn plants and weeds. Estimates of plant counts in row sections were compared with manual counts across three growth stages, three populations, and three tillage treatments. Overall, the system estimated the number of plants with an RMSE of 1.49 plants per row section, which corresponds to 6.2% RMSE or 3210 plants/ha (1300 plants/acre). No evidence of significant differences in mean plant spacing estimates was detected although significant, albeit small, increases in spacing variance were detected. These results demonstrate the importance of canopy shape and size analysis in the implementation of a machine vision plant population and intra-row spacing sensing system.

Effectiveness of Cold Flow Additives on Various Biodiesels, Diesel, and Their Blends

One of the major reasons hindering the use of biodiesel is its filter plugging temperature, which is higher than that of No. 2 diesel. Cloud point (CP) and pour point (PP) temperatures have been shown to be well correlated with filter plugging point, which primarily determines the operability of a diesel engine in cold weather. Many biodiesel cold flow additives are available in the market that claim to reduce pour point. In this study, neat and blended biodiesel fuels from different feedstocks were tested for change in CP and PP with various cold flow additives at 100%, 200%, and 300% of the specified loading (application) rate. The additives in general worked better for ethyl esters than for methyl esters. Average reductions in CP and PP for neat mustard methyl esters were 0.3°C and 7.2°C, respectively, compared to 3°C and 19.4°C for mustard ethyl ester at the recommended loading rate. In general, mustard biodiesel responded to additives better than soybean or used vegetable oil biodiesel for reducing PP. The effect of additives on CP of diesel fuel was not statistically significant, but PP was reduced to < -36°C with all additives at recommended loading. This result is expected as additives are mainly targeted to inhibit the crystal growth not necessarily the onset of crystallization. The additives were found to be more effective in diesel than in biodiesel for reducing PP, and hence the higher the percentage of diesel in a blend, the better the effectiveness was. Most additives reduced the PP of B20 and lower blends to < -36°C at 100% loading, and all additives did that at 200% loading. No added benefit was observed at more than 200% loading.

Automatic corn plant population measurement using machine vision

A machine vision–based corn plant population sensing system was developed to measure early growth stage corn population. Video was acquired from a vehicle–mounted digital video camera at V3 to V4 stages under different daylight conditions. Algorithms were developed to sequence video frames and to segment, singulate, and count corn plants. Vegetation segmentation was accomplished using a truncated ellipsoidal decision surface. Two features were extracted from each pixel row of the segmented images: total number of plant pixels, and their median position. Adjacent rows of the same class were grouped together and iteratively refined for final plant counting. Performance of this system was evaluated by comparing its estimation of plant counts with manual stand counts in 60 experimental units of 6.1 m sections of corn rows. The number of corn plants in these experimental units ranged from 14 to 48, corresponding to a population of 30,000 to 103,000 plants /ha. In low–weed field conditions, the system plant count was well correlated to manual stand count (R2 = 0.90). Standard error of population estimate was 1.8 plants over 33.2 mean manual plant count, or 5.4% coefficient of variation.

Reassessment of Life Cycle Greenhouse Gas Emissions for Soybean Biodiesel

This study updates the life cycle greenhouse gas (GHG) emissions for soybean biodiesel with revised system boundaries and the inclusion of indirect land use change using the most current set of agricultural data. The updated results showed that life cycle GHG emission from biodiesel use was reduced by 81.2% compared to 2005 baseline diesel. When the impacts of lime application and soil N2O emissions were excluded for more direct comparison with prior results published by the National Renewable Energy Laboratory (NREL), the reduction was 85.4%. This is a significant improvement over the 78.5% GHG reduction reported in the NREL study. Agricultural lime accounted for 50.6% of GHG from all agricultural inputs. Soil N2O accounted for 18.0% of total agricultural emissions. The improvement in overall GHG reduction was primarily due to lower agricultural energy usage and improved soybean crushing facilities. This study found that soybean meal and oil price data from the past ten years had a significant positive correlation (R2 = 0.73); hence, it is argued that soybean meal and oil are both responsible for indirect land use change from increased soybean demand. It is concluded that when there is a strong price correlation among co-products, system boundary expansion without a proper co-product allocation for indirect land use change produces erroneous results. When the emissions associated with predicted indirect land use change were allocated and incorporated using U.S. EPA model data, the GHG reduction for biodiesel was 76.4% lower than 2005 baseline diesel.

Ultrasonic Sensing for Corn Plant Canopy Characterization

Non-destructive measurement of crop growth stage, canopy development, and height may be useful for more efficient crop management practices. In this study, ultrasonic sensing technology was investigated as one approach for corn plant canopy characterization. Ultrasonic echo signals from corn plant canopies were collected using a lab-based sensor platform. Echo signal peak features were extracted from multiple scans of plant canopies. These features included peak amplitude, scan number, and time of flight. Feature vectors with similarities were clustered together to identify individual leaves of the canopy. The mean height of the clustered data of individual leaves was estimated. The growth stage of each plant was estimated based on the number of leaves detected. Regression analysis was used to describe the relationship between manually measured leaf heights and ultrasonic estimates. A leaf-signal interaction model was developed to predict which

QUANTIFYING CORN DETERIORATION DUE TO FUNGAL GROWTH BY USE OF CO2-SENSITIVE GEL

Carbon dioxide (CO2) generation is a useful measure of aerobic respiration of the microbes that decompose organic materials. Woods End Labs markets the Solvita test kit to measure CO2 generated by samples of compost or soil. A procedure was developed to use the Solvita kit to quantify the storage condition of shelled corn. The ISU-Solvita Corn Testing Procedure uses shelled corn stored at 20C. After a 24-h incubation period, a CO2-sensitive gel coated paddle is sealed in a jar containing 100 g of corn. After 4 h, the CO2 level in the jar is indicated by the color of the gel. The ISU-Solvita Corn Testing Procedure was shown to be capable of quantifying the storage state of corn over a range of moistures and durations of incubation after re-wetting. A linear relation was observed between corn moisture and measured %CO2 for moistures between 18% and 22%. An exponential relation was observed between measured %CO2 and storage times of 20% moisture corn from 3 to 12 days at 27C. In other tests in which samples were rewetted to 20% moisture and incubated 24 h, there was no relationship between corn with visible mechanical damage or corn bulk density and measured CO2.