Biomass round bales infield aggregation logistics scenarios

Biomass bales often need to be aggregated (collected into groups and transported) to a field-edge stack or a temporary storage before utilization. Several logistics scenarios for aggregation involving equipment and aggregation strategies were modeled and evaluated. Cumulative Euclidean distance criteria evaluated the various aggregation scenarios. Application of a single-bale loader that aggregated bales individually was considered as the “control” scenario with which others were compared. A computer simulation program developed determined bale coordinates in ideal and random layouts that evaluated aggregation scenarios. Simulation results exhibited a “diamond pattern” of bales on ideal layout and a “random pattern” emerged when ≥ 10% variation was introduced. Statistical analysis revealed that the effect of field shape, swath width, biomass yield, and randomness on bale layout did not affect aggregation logistics, while area and number of bales handled had significant effects. Number of bales handled in the direct method significantly influenced the efficiency. Self-loading bale picker with minimum distance path (MDP, 80%) and parallel transport of loader and truck with MDP (78%) were ranked the highest, and single-bale central grouping the lowest (29%) among 19 methods studied. The MDP was found significantly more efficient (4%-16%) than the baler path. Simplistic methods, namely a direct triple-bale loader with MDP (64%-66%), or a loader and truck handling six bales running parallel with MDP (75%-82%) were highly efficient. Great savings on cumulative distances that directly influence time, fuel, and cost were realized when the number of bales handled was increased or additional equipment was utilized

A simple viability analysis for unicellular cyanobacteria using a new autofluorescence assay, automated microscopy, and ImageJ

<p>Abstract</p> <p>Background</p> <p>Currently established methods to identify viable and non-viable cells of cyanobacteria are either time-consuming (eg. plating) or preparation-intensive (eg. fluorescent staining). In this paper we present a new and fast viability assay for unicellular cyanobacteria, which uses red chlorophyll fluorescence and an unspecific green autofluorescence for the differentiation of viable and non-viable cells without the need of sample preparation.</p> <p>Results</p> <p>The viability assay for unicellular cyanobacteria using red and green autofluorescence was established and validated for the model organism <it>Synechocystis </it>sp. PCC 6803. Both autofluorescence signals could be observed simultaneously allowing a direct classification of viable and non-viable cells. The results were confirmed by plating/colony count, absorption spectra and chlorophyll measurements. The use of an automated fluorescence microscope and a novel ImageJ based image analysis plugin allow a semi-automated analysis.</p> <p>Conclusions</p> <p>The new method simplifies the process of viability analysis and allows a quick and accurate analysis. Furthermore results indicate that a combination of the new assay with absorption spectra or chlorophyll concentration measurements allows the estimation of the vitality of cells.</p

Effect of Torrefaction on Water Vapor Adsorption Properties and Resistance to Microbial Degradation of Corn Stover

The equilibrium moisture content (EMC) of biomass affects transportation, storage, downstream feedstock processing, and the overall economy of biorenewables production. Torrefaction is a thermochemical process conducted in the temperature regime between 200 and 300 °C under an inert atmosphere that, among other benefits, aims to reduce the innate hydrophilicity and susceptibility to microbial degradation of biomass. The objective of this study was to examine water sorption properties of torrefied corn stover. The EMC of raw corn stover, along with corn stover thermally pretreated at three temperatures, was measured using the static gravimetric method at equilibrium relative humidity (ERH) and temperatures ranging from 10 to 98% and from 10 to 40 °C, respectively. Five isotherms were fitted to the experimental data to obtain the prediction equation that best describes the relationship between the ERH and the EMC of lignocellulosic biomass. Microbial degradation of the samples was tested at 97% ERH and 30 °C. Fiber analyses were conducted on all samples. In general, torrefied biomass showed an EMC lower than that of raw biomass, which implied an increase in hydrophobicity. The modified Oswin model performed best in describing the correlation between ERH and EMC. Corn stover torrefied at 250 and 300 °C had negligible dry matter mass loss due to microbial degradation. Fiber analysis showed a significant decrease in hemicellulose content with the increase in pretreatment temperature, which might be the reason for the hydrophobic nature of the torrefied biomass. The outcomes of this work can be used for torrefaction process optimization, and decision-making regarding raw and torrefied biomass storage and downstream processing

Profile based image analysis for identification of chopped biomass stem nodes and internodes

Because of their significant variation in chemical composition, segregation of chopped biomass into nodes and internodes helps in efficient utilization of these feedstocks. Stem internodes having low ash content are a better feedstock for biofuel and bioenergy applications than nodes. However, separation of these components is challenging because their physical characteristics are similar. We applied an image processing technique to identify nodes and internodes of chopped biomass from scanned digital images. In this study, we utilized the object profile identified differences in the node and internode components and tested on chopped corn stalks and switchgrass stems. We considered four methods of image processing including rectangularity, solidity, width-, and slope-variation and developed an ImageJ plugin for the node-internode identification. Digital chopping of the ends of the objects was necessary for identification, especially dealing with projecting fibers and chipped rough ends, and an algorithm was developed for this. Among the methods tested, width-variation gave the best identification accuracy (97-98%), followed by rectangularity (93-96%), solidity (86-91%), and slope-variation (69-82%). Rectangularity - a relatively simpler method, and solidity - a standard ImageJ output, can be directly used to perform identification. The developed approach of node-internode identification can be easily applied to other chopped biomass and similar materials, and its application may lead to efficient biomass end use in biofuel and bioproduct industries

Biomass pyrolysis and combustion integral and differential reaction heats with temperatures using thermogravimetric analysis/differential scanning calorimetry

Integral reaction heats of switchgrass, big bluestem, and corn stalks were determined using thermogravimetric analysis/differential scanning calorimetry (TGA/DSC). Iso-conversion differential reaction heats using TGA/DSC pyrolysis and combustion of biomass were not available, despite reports available on heats required and released. A concept of iso-conversion differential reaction heats was used to determine the differential reaction heats of each thermal characteristics segment of these materials. Results showed that the integral reaction heats were endothermic from 30 to 700. °C for pyrolysis of switchgrass and big bluestem, but they were exothermic for corn stalks prior to 587. °C. However, the integral reaction heats for combustion of the materials followed an endothermic to exothermic transition. The differential reaction heats of switchgrass pyrolysis were predominantly endothermic in the fraction of mass loss (0.0536-0.975), and were exothermic for corn stalks (0.0885-0.850) and big bluestem (0.736-0.919). Study results provided better insight into biomass thermal mechanism

Novel front end processing method of industrial beet juice extraction for biofuels and bioproducts industries

Conventional raw beet juice extraction in food-grade crystal sugar production is a highly involved and energy intensive process, which includes beets washing, thawing of frozen beets, cossettes slicing, and high temperature denaturation and diffusion. Industrial beets, a new feedstock bred for non-food industrial use, processing for biofuel and bioproducts applications can use less stringent quality requirements and simplify the juice extraction process. A novel simplified front end processing (FEP), which is less expensive, energy efficient, and involved only common equipment (hammer mill and basket press), was developed and tested. The hammer mill pulverized the beets and basket press extracted the juice. Four beet conditions (fresh, frozen, thawed and fresh-frozen) and four presses with water addition were tested for juice extraction. The juice concentration had decreased with the increased number of presses, and the fitted exponential equations (R2≥0.97) determined the juice concentration as a function of number of presses. Frozen beets consistently produced significantly high concentration juice followed by fresh-frozen, thawed, and fresh beets. Freezing had a beneficial effect in increasing the cumulative approximate sugar extracted. Two presses for fresh (92%) and three for frozen (97%) beets extracted the most available sugars. Future research may focus on water temperature, beet particle size, juice for extraction, microbial stability, energy economics, and products utilization. This new FEP efficiently extracts industrial beet juice and has direct scope in industry deployment as well as enhances the potential of the fuel generated being recognized as an advanced biofuel by the renewable fuel standards

Rating Iron Deficiency in Soybean Using Image Processing and Decision-Tree Based Models

The most efficient way of soybean (Glycine max (L.) Merrill) iron deficiency chlorosis (IDC) management is to select a tolerant cultivar suitable for the specific growing condition. These cultivars are selected by field experts based on IDC visual ratings. However, this visual rating method is laborious, expensive, time-consuming, subjective, and impractical on larger scales. Therefore, a modern digital image-based method using tree-based machine learning classifier models for rating soybean IDC at plot-scale was developed. Data were collected from soybean IDC cultivar trial plots. Images were processed with MATLAB and corrected for light intensity by using a standard color board in the image. The three machine learning models used in this study were decision tree (DT), random forest (RF), and adaptive boosting (AdaBoost). Calculated indices from images, such as dark green color index (DGCI), canopy size, and pixel counts into DGCI ranges and IDC visual scoring were used as input and target variables to train these models. Metrics such as precision, recall, and f1-score were used to assess the performance of the classifier models. Among all three models, AdaBoost had the best performance (average f1-score = 0.75) followed by RF and DT the least. Therefore, a ready-to-use methodology of image processing with AdaBoost model for soybean IDC rating was recommended. The developed method can be easily adapted to smartphone applications or scaled-up using images from aerial platforms