Performance and emissions testing of a small two stroke engine using mid-level ethanol blends

With the introduction of the mid-level ethanol blend gasoline fuel for commercial sale, the compatibility of different off-road engines is needed. This report details the test study of using one mid-level ethanol fuel in a two stroke hand held gasoline engine used to power line trimmers. The study sponsored by E3 is to test the effectiveness of an aftermarket spark plug from E3 Spark Plug when using a mid-level ethanol blend gasoline. A 15% ethanol by volume (E15) is the test mid-level ethanol used and the 10% ethanol by volume (E10) was used as the baseline fuel. The testing comprises running the engine at different load points and throttle positions to evaluate the cylinder head temperature, exhaust temperature and engine speed. Raw gas emissions were also measured to determine the impact of the performance spark plug. The low calorific value of the E15 fuel decreased the speed of the engine along with reduction in the fuel consumption and exhaust gas temperature. The HC emissions for E15 fuel and E3 spark plug increased when compared to the base line in most of the cases and NO formation was dependent on the cylinder head temperature. The E3 spark plug had a tendency to increase the temperature of the cylinder head irrespective of fuel type while reducing engine speed.

Analysis of spring break-up and its effects on a biomass feedstock supply chain in northern Michigan

Demand for bio-fuels is expected to increase, due to rising prices of fossil fuels and concerns over greenhouse gas emissions and energy security. The overall cost of biomass energy generation is primarily related to biomass harvesting activity, transportation, and storage. With a commercial-scale cellulosic ethanol processing facility in Kinross Township of Chippewa County, Michigan about to be built, models including a simulation model and an optimization model have been developed to provide decision support for the facility. Both models track cost, emissions and energy consumption. While the optimization model provides guidance for a long-term strategic plan, the simulation model aims to present detailed output for specified operational scenarios over an annual period. Most importantly, the simulation model considers the uncertainty of spring break-up timing, i.e., seasonal road restrictions. Spring break-up timing is important because it will impact the feasibility of harvesting activity and the time duration of transportation restrictions, which significantly changes the availability of feedstock for the processing facility. This thesis focuses on the statistical model of spring break-up used in the simulation model. Spring break-up timing depends on various factors, including temperature, road conditions and soil type, as well as individual decision making processes at the county level. The spring break-up model, based on the historical spring break-up data from 27 counties over the period of 2002-2010, starts by specifying the probability distribution of a particular county’s spring break-up start day and end day, and then relates the spring break-up timing of the other counties in the harvesting zone to the first county. In order to estimate the dependence relationship between counties, regression analyses, including standard linear regression and reduced major axis regression, are conducted. Using realizations (scenarios) of spring break-up generated by the statistical spring breakup model, the simulation model is able to probabilistically evaluate different harvesting and transportation plans to help the bio-fuel facility select the most effective strategy. For early spring break-up, which usually indicates a longer than average break-up period, more log storage is required, total cost increases, and the probability of plant closure increases. The risk of plant closure may be partially offset through increased use of rail transportation, which is not subject to spring break-up restrictions. However, rail availability and rail yard storage may then become limiting factors in the supply chain. Rail use will impact total cost, energy consumption, system-wide CO2 emissions, and the reliability of providing feedstock to the bio-fuel processing facility.