Energy & Environment

Energy and Environment Research

Personnel:

Raghava R. Kommalapati, PhD, PE, BCEE

Zhihua S. Liang, PhD, PE, Post Doctoral Researcher

Xinhua Shen, PhD, Post Doctoral Researcher

Introduction

The Energy and Environment subproject will investigate technologies for both assessing and reducing the environmental impacts of fossil and renewable energy cycles. The methodologies developed for environmental impact assessment of current technologies will be adapted to analyze biomass/biofuels and sustainable nuclear energy. The outcomes from this research will provide information to update technical and policy decisions to support environmentally responsible energy production and consumption. The conceptual framework for the E&E subproject is depicted in Figure 1.

The research foci for the E&E subproject are to identify methods for safe operation of the nuclear facilities and waste management, to conduct photochemical modeling of the various emission control scenarios that could be developed as part of our other Center sub-projects as well as other readily implementable technologies, and to study the life cycle analysis of green house gas (GHG) emissions for conventional (fossil fuels), nuclear and renewable (biofuels and wind) energy technologies.

Focus Area I: Sustainable Nuclear Energy (SNE)

Objectives and Tasks

  • Sustainable Nuclear Energy (SNE) through radiological safety, spent fuel management and advanced reactors
  • Identify back-end fuel cycle management strategies for safe operation of nuclear power plants and waste management
  • Main focus is on code simulations and risk assessment of nuclear power plants
  • Perform the analysis of a reactor core to determine the total number of radionuclides present and to estimate the total radiological dose for a spent fuel facility.
  • Perform optimization of nuclear fuel usage in the nuclear reactor to minimize the amount of spent fuel generated.
  • Perform probabilistic risk assessment of nuclear power plants

Work Completed/ In Progress

  • Find the chance of an undesired event such as reactor core damage, breach of containment, or release of radioactivity, and to determine potential causes.
  • Investigate accident sequences, or upset conditions in a process and how the sequence progression is mitigated or terminated through procedure guidance and operator intervention.
  • Determine the isotopic composition of the reactor core using reactor core analysis codes.
  • The research task is focused on energy sustainability and fuel cycles with minimized back-end HLW content.
  • The broader objective of this research is to investigate configuration variations to minimize nuclear waste and to achieve resource/waste management capability with minimum reprocessing, repository needs, and environmental impact.

Sample Work

The data obtained from a Swedish reactor notes that for a typical burn-up of 40 megawatt days per kilogram of uranium (MWd/kg U); only 4% of the uranium has been consumed; only 1% is converted to transuranic elements, and only 3% to fission products. The level of radioactivity after a thousand years is due mainly to the actinides- uranium, Pu-239 and Np-237. (A. Heiden, 1987). The nuclear burn-up gives the amount of energy released per metric ton of fuel material loaded into the reactor.

A detailed nuclear fuel burn-up; taking into account fuel burn-up constraints, will give an in-depth insight into very important relationships such as the variation of uranium fuel loaded into the reactor core with high fuel burn-up. The build-up of plutonium isotopes as a result of fuel burn-up will also be determined. The content of Pu isotopes in high burn up spent nuclear fuel samples (33.21–59.03 GWd/MTU) were determined by alpha spectrometry and mass spectrometry. (Kihsoo Joe et al, 2012)

The fuel burn-up calculations will be done using the MCNP code. Generally, an idea of the mechanism of fuel burn-up calculation is as below. The nuclear fission rate is calculated from the product of the macroscopic fission cross-section (∑f) and the neutron flux (Ý) as f= Ý∑f. The power density (p) is determined as p=fw, where w is the energy per fission. The energy produced in time t is W= pt. The number density of uranium is then determined as d=Numu, where Nu is the number density and mu is the mass of uranium atom. The burn-up is then calculated as B=W/d.

Focus Area II: Photochemical Modeling (PCM) Study

Objectives and Tasks

  • Photochemical Modeling (PCM) of Emissions Control Scenarios to investigate effects on air quality of the various energy technologies under study by CEES
  • Develop an emissions inventory for the area energy-related industries and on-road vehicle emissions inventory,
  • Identify processes from our other renewable energy sub-projects (biofuels and wind) that could be adopted for the Houston area, and estimate the potential emissions for these new processes,
  • Study and identify the various components of the CAMx model, the necessary emissions input data and the meteorological data to predict pollutant concentrations for the base years
  • Use the model to predict pollutant concentrations for various input emissions scenarios and control mechanisms (for example, replace a given percent of gasoline fuel with biofuels or take out one of the coal powered electricity and replace it with windmills, etc.)

Work Completed/ In Progress

  • Photochemical model simulations using CAMx model for the whole ozone season in Houston Galveston Brazoria Area.
  • Analysis of the sensitivity of ozone concentrations to NOx and VOC emissions
  • Develop input emissions data files which chemically and spatially allocated for the CAMx modeling episode with EPS3 model
  • Using WRF meteorological model to develop the meteorological inputs data for CAMx air quality modeling analyses.
  • Using GEOS–Chem model to develop the required specification of initial and boundary conditions input files for a given CAMx modeling episode.
  • Using the Global Biosphere Emissions and Interactions System (GloBEIS) biogenics emissions model to evaluate the biogenic emissions data specifically for Houston Galveston Brazoria Area.

Sample Work

Ozone Simulations using CAMx for Houston Galveston Brazoria Area

Ozone level is below NAAQS standard level for the 00:00:00 hours, 07:00:00 and 23:00:00 hours for the entire episode. However, Ozone level for the 15:00:00 hours are above the NAAQS level for the entire episode as shown in Figure 2.

The CAMx model prediction replicated the diurnal rise and fall ozone concentration quite well at all episode days. Figure 3 also presented that the ozone concentrations during the time period of 11:00 to 17:00 were usually exceed the 75 ppb limit. Both simulated and observed peak ozone concentrations occur at 12:00 to 15:00 when solar radiation and ambient temperature are highest during the daytime.

Figure 4 plot shows that this ratio is above the Sillman range of 0.30-0.60 indicating that VOC is plentiful and NOx is the limiting precursor for this day. The higher the values of H2O2, the higher the ratios of the concentration of H2O2 to the concentration of HNO3: The formation of Ozone is NOx limiting. Another observation is that lowest ratio of the concentration of H2O2 to the concentration of HNO3 is highest between the 12:00:00 hours and 21:00:00 hours this indicates an increase in the ratio as sunlight increases.

Table EPS3 Output File of Emissions by Profile Code, English Tons

Profile Input Speciated Input Speciated Input Speciated
  NOX NOX VOC VOC CO CO
 —————————————————————————————————————————————
0000 134.5367 134.5367 180.4469 163.0758 84.5828 84.5828
 —————————————————————————————————————————————
TOTAL 134.5367 134.5367 180.4469 163.0758 84.5828 84.5828

 

Focus Area III: Life Cycle Analysis of Green House Gas Emission

Objectives and Tasks

  • Life Cycle Analysis (LCA) of Greenhouse Gas Emissions from conventional (fossil fuels), nuclear, and renewable (biofuels, wind) energy technologies.
  • Determine the environmental impact of existing and advanced energy source utilization
  • Analyze the life cycle emissions for various methods of energy production, with particular attention to GHGs, based on the latest data published in reports and forums from various regions/countries.
  • The goals to identify the underlying mechanisms that affect these emissions for various technologies and in various geographic locations.

Work Completed/ In Progress

Develop Emissions Inventory & Life Cycle Analysis of Emissions

  • review literature to identify sources and databases to develop emissions inventory
  • Analyze the life cycle emissions for various methods of energy production, with particular attention to GHGs, based on the latest data published in reports and forums from various regions/countries.
  • Used GREET model to study the impact of Biofuels blends on mobile emissions

Sample Work

The Figure 5 shows the results of GREET model obtained on GHG Emissions in units of kg/day and g/mile for different bio-fuel blends and engines. The FFV with SI engine using E10, E15, E25 and E85 does not show a great deal of reduction in the emissions. Dedicated FFV with SI engines using E25 and E85 shows significant reductions.

The Figure 6 shows results of GREET model obtained on VOC Emissions in units of g/mile for different bio-fuel blends and engines, and then multiplied by the VMT data. The Dedicated FFV with SI engines using E25 and E85 show significant reductions of about 5% VOC.  The VOC emissions from FCV:E100 is the greatest reduction for all the targeted years of about 73%.

Figure 7 shows that fuel type did not contribute in reducing emissions as well. There is a significant change in the emissions reductions of years from 2011 to 2014 and 2017. However, the only and highest reduction of NOx emission is found for FCV using E100 of about 79-80%.

 

 

 

 

 

 

 

 

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