Production of 2-propanol, butanol and ethanol using Clostridium beijerinckii optonii

With an unpredictable market for gasoline and increased concerns with the pollution created by burning fossil fuels, there is a push for developing suitable replacements for gasoline. While corn-based ethanol production is the most common renewable biofuel produced in the United States, ethanol is not an ideal solution to gasoline replacement due to low energy density, hygroscopic and corrosive properties and inability to purify by distillation alone. Higher alcohols such as butanol do not have the same problems with energy density, purification and hygroscopic and corrosive properties. The fermentation of butanol by using solventogenic Clostridium species, creating acetone, butanol and ethanol (known as ABE fermentation) is one of the world’s oldest industrial fermentations. Since butanol is toxic to Clostridium species at a concentration of only 13 g/L, traditional batch fermentation of butanol with steam stripping distillation is currently not as economical as fermentation and distillation of ethanol. Fermentation using glucose produced higher solvent outputs, rates of productivity and yields than fermentations using sugarcane products as substrates. Butanol and total solvent production using glucose as a substrate averaged 7.2 (+/- 0.7) g/L and 11.2 (+/- 0.9) g/L, respectively. Fermentation using sugarcane molasses and sugarcane juice as substrates produced 6.5 g/L butanol and 9.7 g/L total solvents and 3.1 g/L butanol and 4.0 g/L total solvents, respectively. Production of butanol was increased to 9.1 g/L in a fermentation of glucose when soy oil was used as a coextractant. Fermentations in which the pH dropped below 4.80 showed decreased solvent production and the pH was unable to rise in the same manner as other fermentations. The acid crash was exhibited in several batch fermentations as well as continuous fermentation using an immobilized culture of C. beijerinckii optonii. The acid crash resulted in lowered solvent production, low pH and physiological differences in the cells in the culture. Fermentation using immobilized culture produced a maximum 5.4 g/L butanol and 6.8 g/L total solvents at a dilution rate of 0.18 hr-1 and 25 g/L initial glucose. Higher glucose levels and different dilution rates gave lower butanol and total solvent productions

Road Weather Severity Based on Environmental Energy

Effective and efficient removal of snow and ice from public roadways is a key outcome for winter road maintenance operations. This outcome depends on the severity of the wintry weather as well as the quality and quantity of resources used to treat the roadways. Wintry weather conditions vary substantially from hour-to-hour, storm-to-storm, and season-to-season. Many different transportation departments have used empirical statistical models and machine learning methods based upon weather parameters to develop indices to estimate the severity of winter weather. Many of these previous studies used summary statistics, such as the number of days with certain events (snowfall, freezing rain, frost), to provide a seasonal index of winter severity. While summarizing the winter severity for the entire season is quite useful, providing information over shorter time periods will allow for more precise evaluation of maintenance performance during a winter season. A winter weather severity index has been developed that can be used to evaluate the performance of winter weather maintenance. This project involves the development of a physically-based analysis of winter severity, using estimates of the hourly rate of deposition of new snow/ice and the energy required melt it. The “Road Weather Severity Based on Environmental Energy” (RWSBEE) index can be considered an accumulation of energy, beyond that which is available from the environment, needed to melt snow/ice that has been deposited on the road surface on an hourly basis. The energy not provided by the environment that would be required to melt new snow can be thought of as a measure of the work required to remove the new snow from the road surface. We expect that RWSBEE will provide a clearer understanding of the severity of the weather, allowing INDOT to better evaluate their performance, assist with after-action review of recent storms, and improve the reaction to future weather events. Measurable improvements in the winter maintenance decision-making process are expected as a result. Winter weather conditions that occur across different regions vary substantially from hour-to-hour, storm-to-storm, and season-to-season. The methods of road maintenance for fighting snow and ice can also vary between different maintenance units. It is important for organizations that perform road maintenance to be able to quantify the severity of the winter weather conditions, for purposes of monitoring, planning, and evaluating their performance. The Indiana Department of Transportation (INDOT) currently uses estimates of winter weather hours to quantify the severity of winter weather. The definition of a “weather hour” is fairly straightforward: any hour when wintry precipitation (snow, ice pellets, freezing rain) is falling with air temperatures below 35 °F. While this definition is reasonable, it does not take into account numerous factors that can strongly affect road conditions and subsequent efforts needed for road treatment, such as: precipitation rate, wind speed, and availability of sunshine. Consequently, INDOT has determined that the information provided by the weather hour estimates result in wide variations in roadway treatment expenses across Indiana. In order to more accurately and effectively evaluate the performance of winter maintenance, it is important to have detailed data related to winter weather conditions that provide useful information regarding the impact of winter weather on road conditions. State-of-the-art weather information can provide a clearer understanding of the severity of the weather, allowing INDOT to better evaluate their performance, assist with after-action review of recent storms, and improve the reaction to future weather events

Indiana’s Past & Future Climate: A Report from the Indiana Climate Change Impacts Assessment

Indiana’s climate is changing. Temperatures are rising, more precipitation is falling and the last spring frost of the year has been getting steadily earlier. This report from the Indiana Climate Change Impacts Assessment (IN CCIA) describes historical climate trends from more than a century of data, and future projections that detail the ways in which our climate will continue to change

Climate change and hazardous convective weather in the United States: Insights from high-resolution dynamical downscaling

Global climate model (GCM) projections increasingly suggest that large-scale environmental conditions favorable for hazardous convective weather (HCW) may increase in frequency in the future due to anthropogenic climate change. However, this storm environment-based approach is undoubtedly limited by the assumption that convective-scale phenomena will be realized within these environments. The spatial resolution of GCMs remains much too coarse to adequately represent the scales at which severe convective storms occur, including processes that may lead to storm initiation. With the advancement of computing resources, however, it has now become feasible to explicitly represent deep convective storms within a high-resolution regional climate model. This research utilized the Weather Research and Forecasting (WRF) model to produce high-resolution, dynamically downscaled simulations for the continental United States under historical (1971--2000) and future (2071--2100) climate periods using GCM data provided by the Geophysical Fluid Dynamic Laboratory Climate Model version 3 (GFDL-CM3). Model proxies were used to provide an objective estimate of the occurrence of simulated severe weather and how their spatiotemporal distribution may change in the future under an aggressive climate change scenario. Results demonstrated that severe storms may increase in both their frequency and intensity in the future. In comparison to the projected changes in HCW favorable environments from the GCM, the dynamically downscaled largely agree in terms of the seasonal timing and spatial patterns of greatest potential change in activity by the end of the 21st century. Likewise, each approach supports the notion that severe weather activity may begin earlier within the annual cycle and also later within the calendar year, such that the severe weather season is lengthened. However, by all indications, the environment-event frequency relationship has been altered in future climate, such that the uptick in the number of days with simulated HCW events does not increase proportionally to the rise in days with HCW favorable environments. Such an outcome supports the motivation for continued use of dynamical downscaling to overcome the limitations of the GCM-based environmental analysis

A proposed method for objectively identifying and characterizing frontal zones

Presented here is a method to objectively identify and characterize thermal frontal zones from gridded datasets. The detection scheme identifies frontal zones based upon a thermal definition: two thermal parameters are utilized, virtual potential temperature and equivalent potential temperature. Minimum strength and size constraints are implemented to exclude frontal zones that are thermally weak and too small in size to be considered synoptic frontal zones. The resulting near-surface frontal zones are then characterized according to type, and additional frontal attributes may be obtained. Additionally, the extension of the procedure to multiple levels in the vertical demonstrates the unique examination of the frontal zones in three-dimensions. An evaluation of the overall technique indicates that a reasonable agreement between objective and subjective analyses may be obtained, though the minimum strength criterion is tuneable and can vary between synoptic situation and seasons. Further, the applicability of the objective frontal zone detection method as a forecast tool for convective mode prediction is investigated. The premise of the implementation utilizes the relationship between the orientation of the deep-layer shear vector—perpendicular, oblique, or parallel—with respect to the initiating boundary (e.g. cold front or dryline) to evaluate the likely convective evolution of storms within a short time after initiation. To this end, a procedure was developed to quantify the angle between the linear boundary and the deep-layer shear vector. Results from several cases of severe weather during 2011 have suggested that reasonable approximations of the deep-layer shear angle across the initiation boundary are obtainable, and that the overall relationship may provide useful insight to forecasters regarding short-term convective mode evolution

Assessment and Recommendations for Using High-Resolution Weather Information to Improve Winter Maintenance Operations

A variety of methods for obtaining detailed analyses regarding the timing and duration of winter weather across the state of Indiana for multiple seasons were compared and evaluated during this project. Meteorological information from sources such as surface reporting stations, National Weather Service radars, and three-dimensional weather analysis and prediction systems were utilized in this work. In addition, daily weather forecasts were provided by students at Purdue University during the 2012-13 winter season. These forecasts supplemented the weather forecast information already in use at INDOT. Purdue weather forecasts were systematically evaluated during this project. The results from these assessments are provided in this report, along with a set of recommendations for sources of detailed weather information to be utilized by INDOT in future winter seasons