ECONOMIC AND TECHNICAL ANALYSIS OF ETHANOL DRY MILLING: MODEL DESCRIPTION

Ethanol, the common name for ethyl alcohol, is fuel grade alcohol that is predominately produced through the fermentation of simple carbohydrates by yeasts. In the United States, the carbohydrate feedstock most commonly used in the commercial production of ethanol is yellow dent corn (YDC). The use of ethanol in combustion engines emits less greenhouse gasses than its petroleum equivalent, and it is widely hoped that the increased substitution of petroleum by ethanol will reduce US dependence on imported oil and decrease greenhouse gas emissions. Production of ethanol within the United States is expected to double, from 3.4 billion gallons in 2004, to about seven billion gallons in the next five years. Two processes currently being utilized to produce ethanol from YDC are dry milling and wet milling. The wet mill process is more versatile than the dry mill process in that it produces a greater variety of products; starch, corn syrup, ethanol, Splenda, etc., which allows for the wet mill to better react to market conditions. However, the costs of construction and operation of a wet mill are much greater than those of a dry mill. If ethanol is the target product, then it can be produced at a lower cost and more efficiently in a dry mill plant than in a wet mill plant, under current economic conditions. Of the more than 70 US ethanol plants currently in production, only a few are of the wet mill variety. A descriptive engineering spreadsheet model (DM model) was developed to model the dry mill ethanol production process. This model was created to better understand the economics of the ethanol dry mill production process and how the profitability of dry mill plants is affected under different conditions. It was also developed to determine the economic and environmental costs and benefits of utilizing new and different technologies in the dry mill process. Specifically, this model was constructed to conduct an economic analysis for novel processes of obtaining greater alcohol yields in the dry mill process by conducting a secondary fermentation of sugars converted from lignocellulosics found in the dry mill co-product, distiller’s grains. This research is being conducted at Purdue University, Michigan State, Iowa State, USDA, and NCAUR under a grant from the US Department of Energy. The DM model is more technically precise, and more transparent, than other models of the dry mill process that have been constructed for similar purposes. The Tiffany and Eidman model (TE model) uses broad generalities of the dry mill process, based on the current state of production, to approximate the sensitivities of the process to changes in variables. The TE model parameters were well researched, but the model suffers from several drawbacks. The main limitations of this model are that the results are very sensitive to the input values chosen by the user. Unlike the DM model, complex manipulations, such as determining the effect of new technologies would require accurate parameter estimates using the TE model. The McAloon model [11].uses highly technical engineering software (ASPEN) that acts essentially as a “black box” in the dry mill production process. This very exact model does not allow for a more general examination of the dry mill process. Changes in the production process would necessitate precise engineering plans. Similar to the TE and McAloon models, the DM model is a spreadsheet model, but unlike the McAloon model it is completely self-contained. The DM model is a feed backward model, input requirements (corn, enzymes, chemicals, utilities, etc) are calculated based on the user entered values for annual production and process parameters. The mass flow rates, in pounds per hour were then calculated and used in estimating the size, in dimension or power, of each major piece of equipment. The cost associated with each piece of major equipment was then calculated as an exponential function of its corresponding size. Total capital costs associated with a dry mill plant were then estimated using the percentage of equipment costs method [13]. It was found that the DM model estimates of the total capital costs associated with medium to large dry mill plants (those with the capacity to produce between 10 and 100 million gallons of ethanol a year) were within 5% of total fixed costs estimated by BBI [2]. Operating costs were compared with the 2002 USDA survey results and also found to be very close [15]. A companion document, “Economic and Technical Analysis of Dry Milling: Model User’s Manual,” staff paper no 06-05, explains how the model is used to conduct analysis of dry milling alternatives.Ethanol, DDGS, Dry Milling, Biochemical Process Engineering, Economic Modeling, Financing, Fermentation Process Modeling

Economic and Technical Analysis of Ethanol Dry Milling: MOdel User's Manual

Using the DM model is not complex: the user changes input values of interest (plant size, conversion rates, etc.) and examines the effect of these changes on output values (annual profits, feed stock requirements, etc.). There are nine worksheets in four modules in the excel workbook- assumptions, process, economics, and technology assessment. All user inputs are entered in the assumptions module of the model, which consists of three worksheets denoted with bright yellow tabs: process assumptions, economic assumptions and physical assumptions. The values that are entered on this page are then used in each of the subsequent modules to calculate hourly flow rates, equipment size and cost, total costs, loan terms, and annual profits. At the top of each page is a title bar which describes the page, the color coding of the cells, and pertinent information from the other pages. Before each of the pages is discussed, an explanation of the different types of cells in the model is in order.model user's manual

Small neutrino masses due to R-symmetry breaking for a small cosmological constant

We describe a class of supersymmetric models in which neutrinos are kept light by an R-symmetry. In supergravity, R-symmetry must be broken to allow for a small cosmological constant after supersymmetry breaking. In the class of models described here, this R-symmetry breaking results in the generation of Dirac neutrino masses, connecting the tuning of the cosmological constant to the puzzle of neutrino masses. Surprisingly, under the assumption of low-scale supersymmetry breaking and superpartner masses close to a TeV, these masses are independent of the fundamental supersymmetry-breaking scale, and accommodate the correct magnitude. This offers a novel explanation for the vastly different scales of neutrino and charged fermion masses. These models require that R-symmetric supersymmetry exists at the TeV scale, and predict that neutrino masses are purely Dirac, implying the absence of neutrino-less double beta-decay. Interesting collider signals can arise due to charged scalars which decay leptonically, with branching ratios determined by the neutrino mixing matrix.Comment: 6 pages, 2 figures. v2 matches published versio

Pulsed-source luminescence measurements using a computer-controlled spectrometer

Traditionally fluorescence and phosphorescence measurements are made on fluorescence spectrometers using a DC source such as a 150 Watt xenon lamp. Differentiation between the fast decaying fluorescence signal and the relatively long lived phosphorescence is temporally obtained using a mechanical phosphoroscope. By periodically interrupting the exciting light measurements are made at the instant of excitation (fluorescence) or during the periods of darkness (phosphorescence). [Continues.

3. A Ceylon Embassy to Egypt

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Transcriptional Regulation of Dendritic Cell Diversity

Dendritic cells (DCs) are specialized antigen presenting cells that are exquisitely adapted to sense pathogens and induce the development of adaptive immune responses. They form a complex network of phenotypically and functionally distinct subsets. Within this network, individual DC subsets display highly specific roles in local immunosurveillance, migration, and antigen presentation. This division of labor amongst DCs offers great potential to tune the immune response by harnessing subset-specific attributes of DCs in the clinical setting. Until recently, our understanding of DC subsets has been limited and paralleled by poor clinical translation and efficacy. We have now begun to unravel how different DC subsets develop within a complex multilayered system. These findings open up exciting possibilities for targeted manipulation of DC subsets. Furthermore, ground-breaking developments overcoming a major translational obstacle – identification of similar DC populations in mouse and man – now sets the stage for significant advances in the field. Here we explore the determinants that underpin cellular and transcriptional heterogeneity within the DC network, how these influence DC distribution and localization at steady-state, and the capacity of DCs to present antigens via direct or cross-presentation during pathogen infection

REDBACK: Open-source software for efficient noise-reduction in plate kinematic reconstructions

Knowledge of past plate motions derived from ocean-floor finite rotations is an important asset of the Earth Sciences, because it allows linking a variety of shallow-rooted and deep-rooted geological processes. Efforts have recently been taken toward inferring finite rotations at the unprecedented temporal resolution of 1 Myr or less, and more data are anticipated in the near future. These reconstructions, like any data set, feature a degree of noise that compromises significantly our ability to make geodynamical inferences. Bayesian Inference has been recently shown to be effective in reducing the impact of noise on plate kinematics inferred from high-temporal-resolution finite-rotation data sets. We describe REDBACK, an open-source software that implements transdimensional hierarchical Bayesian Inference for efficient noise-reduction in plate kinematic reconstructions. Algorithm details are described and illustrated by means of a synthetic test

On the road again: assessing driving ability in patients with neurological conditions

Clinicians may not be aware of the specialised methods and adaptations that are used to help people with disabilities to drive a car. We describe a driving assessment process as carried out by one of the UK’s flagship assessment centres, including an overview of the available assessments, adaptations and relevant legislation to guide practitioners about how best to signpost and counsel their patients appropriately about driving

Wolbachia endosymbiont of the horn fly Haematobia irritans irritans: a supergroup A strain with multiple horizontally acquired cytoplasmic incompatibility genes

The horn fly, Haematobia irritans irritans, is a hematophagous parasite of livestock distributed throughout Europe, Africa, Asia, and the Americas. Welfare losses on livestock due to horn fly infestation are estimated to cost between USD 1-2.5 billion annually in North America and Brazil. The endosymbiotic bacterium Wolbachia pipientis is a maternally inherited manipulator of reproductive biology in arthropods and naturally infects laboratory colonies of horn flies from Kerrville, USA and Alberta, Canada, but has also been identified in wild-caught samples from Canada, USA, Mexico and Hungary. Re-assembly of PacBio long-read and Illumina genomic DNA libraries from the Kerrville H. i. irritans genome project allowed for a complete and circularised 1.3 Mb Wolbachia genome (wIrr). Annotation of wIrr yielded 1249 coding genes, 34 tRNAs, three rRNAs, and five prophage regions. Comparative genomics and whole genome Bayesian evolutionary analysis of wIrr compared to published Wolbachia genomes suggests that wIrr is most closely related to and diverged from Wolbachia supergroup A strains known to infect Drosophila spp. Whole-genome synteny analyses between wIrr and closely related genomes indicates that wIrr has undergone significant genome rearrangements while maintaining high nucleotide identity. Comparative analysis of the cytoplasmic incompatibility (CI) genes of wIrr suggests two phylogenetically distinct CI loci and acquisition of another CifB homolog from phylogenetically distant supergroup A Wolbachia strains suggesting horizontal acquisition of these loci. The wIrr genome provides a resource for future examination of the impact Wolbachia may have in both biocontrol and potential insecticide resistance of horn flies

Pushing the Pareto front of band gap and permittivity: ML-guided search for dielectric materials

Materials with high-dielectric constant easily polarize under external electric fields, allowing them to perform essential functions in many modern electronic devices. Their practical utility is determined by two conflicting properties: high dielectric constants tend to occur in materials with narrow band gaps, limiting the operating voltage before dielectric breakdown. We present a high-throughput workflow that combines element substitution, ML pre-screening, ab initio simulation and human expert intuition to efficiently explore the vast space of unknown materials for potential dielectrics, leading to the synthesis and characterization of two novel dielectric materials, CsTaTeO6 and Bi2Zr2O7. Our key idea is to deploy ML in a multi-objective optimization setting with concave Pareto front. While usually considered more challenging than single-objective optimization, we argue and show preliminary evidence that the $1/x$-correlation between band gap and permittivity in fact makes the task more amenable to ML methods by allowing separate models for band gap and permittivity to each operate in regions of good training support while still predicting materials of exceptional merit. To our knowledge, this is the first instance of successful ML-guided multi-objective materials optimization achieving experimental synthesis and characterization. CsTaTeO6 is a structure generated via element substitution not present in our reference data sources, thus exemplifying successful de-novo materials design. Meanwhile, we report the first high-purity synthesis and dielectric characterization of Bi2Zr2O7 with a band gap of 2.27 eV and a permittivity of 20.5, meeting all target metrics of our multi-objective search.Comment: 27 pages, 11 figures, 5 author