Summer 2012 Research Projects

 

Exergetic Life Cycle Analysis of Biofuels
Mentors:  Professors Sabrina Spatari and Mira Olson

Description:  A diverse set of biofuel technologies is under development in an effort to “de-carbonize” the U.S. energy sector. Next generation biofuels include fuels derived from biomass sources that typically do not compete with food for agricultural land such as biomass grown on degraded lands or in winter rotations when food is not grown. A promising thermochemical technique for processing biomass is fast pyrolysis (rapid decomposition without oxygen at high temperatures). The objective of this work is to perform an exergy based life cycle analysis of bio oil produced by the pyrolysis process. Exergy is an expression of the maximum theoretical available work from a substance if it were to achieve equilibrium with the environment. Recently it has been a useful tool in life cycle analysis in estimating depletion of renewable resources. 

What you will do: The student will be responsible for constructing an exergetic life cycle model for bio oil production from the pyrolysis processes using commercial LCA software and potentially Aspen chemical plant design software. The student will also assist with ongoing laboratory experiments designed to measure process parameters and oil extraction techniques.

Prerequisites:  A fundamental understanding of thermodynamics is required. Undergraduate study in chemical or mechanical engineering or physics and math are helpful. Some experience or background knowledge of life cycle analysis would be helpful, but is not necessary; good working knowledge of MS Excel is helpful. 

 

Schuylkill River Water Quality
Mentors:  Professors Paul Block and Charles Haas

Description:  Prior analysis of pathogen levels in the Schuylkill River has led to a daily forecast of water quality (denoted as red, yellow, or green flag days.)   The forecast, available to the public online through the Philadelphia Water Department, is predominantly based on the local precipitation forecast.   The goal of this project is to explore the feasibility of extending the forecast to the seasonal scale by associating large-scale climate drivers with local precipitation and pathogen levels.  Additionally, evidence of historic land use changes along the river and concurrent water quality changes will be evaluated.

What you will do: Together, we will undertake a computational, computer-based study on the relationship between water quality and large-scale climate, including building a predictive model.  Datasets from water quality, streamflow, and climate sources will be used.

Prerequisites: Courses in hydrology or climate science would be beneficial, but not required.  Basic statistics knowledge and programming skills (e.g. Matlab) needed. 

 

Green Infrastructure Monitoring and Modeling
Mentor:  Professor Franco Montalto

Description:  Cities are increasingly looking to green infrastructure (GI) as a means of reducing urban runoff, and associated problems, while at the same time promoting urban sustainability. GI technologies such as green roofs, porous pavements, bioretention facilities help to promote decentralized infiltration, evapotranspiration, thereby reducing overland flow discharges to sewers and receiving water bodies. Now that big cities like Philadelphia and New York City have committed to a green approach to urban stormwater management, there is a need to better document how individual technologies work under different climatic, land use, and other conditions

What you will do:  Over the 10 week period, you will join other researchers at the Sustainable Water Resource Engineering lab in monitoring GI facility performance in the field and under our laboratory rainfall simulator. You will work with various sensors (pressure transducers, climate stations, soil moisture sensors, flumes, and other flow measurement devices), and will analyze the data using various commercial software (e.g. MS Excel, Matlab, etc). In some cases you will also engage in modeling activities, during which you attempt to simulate the observations by developing conceptual descriptions of the hydrologic flow processes ongoing in these facilities.

Prerequisites:  Courses related to hydrology and water resources. Ability/interest to work both outdoors and in the lab. Computer programming and other analytical skills a plus, but not necessary.

 

Lateral Stress Measurements During One Dimensional Consolidation of Soft Sediments
Mentor:  Professor Kurt Sjoblom

Description: The coefficient of lateral stress, K0, of a soil is not routinely measured in the laboratory due to the need for specialized testing equipment. The parameter is therefore estimated in practice from published correlations, usually from strength data. While this approach can provide reasonable estimates of K0 is some cases, it can also lead to large errors for soils that exhibit changes in consolidation state such as over-consolidated clays that transition into normally consolidated clays. The goal of this project is to instrument an oedometer cell used to determine consolidation characteristics with strain gauges in order to measure lateral stresses during vertical consolidation.

What you will do: This project will require you to apply strain gauges to an oedometer cell, attach appropriate connectors/leads to interface a data acquisition system and evaluate the setup. Initially the instrumented oedometer will be tested in a dry environment, but it is expected that the system will be used submerged in a water bath and therefore the final goal of the project will be to water proof the setup.

Prerequisites: Ideally the candidate will have taken an undergraduate soil mechanics class and have familiarity with data acquisition systems, but either is not required.

 

Community Gardens and Community Health
Mentor: Professors Christian Hunold and Yvonne Michael

Project Description: Community gardens may contribute to improving individual and community health. But this contention has not been demonstrated by a great deal of empirical evidence. This multi-disciplinary, mixed-methods research project seeks to advance our knowledge of how community-based urban agriculture in low-wealth communities addresses health disparities. We hypothesize that community gardens influence community health through environmental changes (food and physical activity resources, social action) and social processes (perceptions and attitudes). Mixed methods allow us to incorporate stakeholder perspectives into our analysis. Including community perspectives promotes understanding of the meaning of health generally and community causes specifically. Community members and other key stakeholders elaborate on researchers’ perspectives, assist in developing culturally sensitive interpretations, and improve the robustness of findings. We suggest it is crucial to understand community perspectives of factors that support and limit the health benefits of community gardens in order to make policy recommendations to foster urban agricultural activity.

What you will do: Surveys and semi-structured interviews with gardeners in West Philadelphia’s Powelton Village and Mantua neighborhoods will identify policies and community factors related to successful creation of community gardens that enhance health among participants and community members. Structured observations will test for increased leisure-time outdoors and improved neighborhood physical and social environment.

Prerequisites: We are looking for a student with an interest in sustainable community development. Social science training (political science, public policy, sociology, economics) is desirable but not required. Depending on the student’s skills and aptitude, research activities may include a mix of observational study and interviews.

 

Measurements of Particulate Matter Impacting Philadelphia Air Quality
Mentor: Professor Peter DeCarlo

Description: Particulate matter in the atmosphere is of interest due to its impacts on human health, climate, and the environment.  Current air quality regulations only monitor particulate mass to determine compliance, which is insufficient to determine sources, and most impacts of particulate matter. Research instrumentation at Drexel allows for much more detailed analysis of particulate matter, with the addition of both size and chemical measurements.  These additional measurements can provide important information about the sources and chemical composition of particulate matter in the atmosphere, which in turn helps researchers understand the air quality and climate impacts of these pollutants.

What you will do:  This position will include the use of research grade mass spectrometry instrumentation to measure the size and composition of particulate matter in both the laboratory and outdoor environment.  Measurement principles and data analysis of the measurements will also be part of the research project.

Prerequisites:  Coursework in chemistry required.  Laboratory and data analysis experience are a plus, but are not required.

 

Decision Support for Energy Efficiency Investments in Commercial Buildings
Mentors: Patrick Gurian and Jin Wen

Project: The Department of Energy-sponsored Greater Philadelphia Innovation Cluster (GPIC) has a goal of achieving 50% reductions in the energy use of existing commercial buildings. While technologies exist to achieve these reductions, there are financial risks in making the upfront capital investments required. In fact the entire retrofit process can be viewed as a decision under uncertainty problem in which different building characteristics may be used to predict the performance of future retrofit options. The research center has invested considerable effort in both identifying the existing building stock and simulating energy efficiency improvements associated with different retrofits. This creates an opportunity to organize this information from the decision maker’s point of view, and identify which building characteristics are most likely to result in favorable retrofits, and which observations are most diagnostic as to the retrofit potential of the building.

What you will do: You will develop a Bayesian Belief Network which will allow information on building characteristics to be integrated and used to develop probabilities of different payoffs from various potential retrofits. You will then consider alternative information collection strategies and evaluate how effective they are at diagnosing the potential of a building for a retrofit.

Prerequisites: One term of statistics. Interest in learning a Bayesian Belief Network software package.