Matching Items (4)
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- All Subjects: Renewable Energy
- Creators: Phelan, Patrick
- Member of: ASU Electronic Theses and Dissertations
Description
An eco-industrial park (EIP) is an industrial ecosystem in which a group of co-located firms are involved in collective resource optimization with each other and with the local community through physical exchanges of energy, water, materials, byproducts and services - referenced in the industrial ecology literature as "industrial symbiosis". EIPs, when compared with standard industrial resource sharing networks, prove to be of greater public advantage as they offer improved environmental and economic benefits, and higher operational efficiencies both upstream and downstream in their supply chain.
Although there have been many attempts to adapt EIP methodology to existing industrial sharing networks, most of them have failed for various factors: geographic restrictions by governmental organizations on use of technology, cost of technology, the inability of industries to effectively communicate their upstream and downstream resource usage, and to diminishing natural resources such as water, land and non-renewable energy (NRE) sources for energy production.
This paper presents a feasibility study conducted to evaluate the comparative environmental, economic, and geographic impacts arising from the use of renewable energy (RE) and NRE to power EIPs. Life Cycle Assessment (LCA) methodology, which is used in a variety of sectors to evaluate the environmental merits and demerits of different kinds of products and processes, was employed for comparison between these two energy production methods based on factors such as greenhouse gas emission, acidification potential, eutrophication potential, human toxicity potential, fresh water usage and land usage. To complement the environmental LCA analysis, levelized cost of electricity was used to evaluate the economic impact. This model was analyzed for two different geographic locations; United States and Europe, for 12 different energy production technologies.
The outcome of this study points out the environmental, economic and geographic superiority of one energy source over the other, including the total carbon dioxide equivalent emissions, which can then be related to the total number of carbon credits that can be earned or used to mitigate the overall carbon emission and move closer towards a net zero carbon footprint goal thus making the EIPs truly sustainable.
Although there have been many attempts to adapt EIP methodology to existing industrial sharing networks, most of them have failed for various factors: geographic restrictions by governmental organizations on use of technology, cost of technology, the inability of industries to effectively communicate their upstream and downstream resource usage, and to diminishing natural resources such as water, land and non-renewable energy (NRE) sources for energy production.
This paper presents a feasibility study conducted to evaluate the comparative environmental, economic, and geographic impacts arising from the use of renewable energy (RE) and NRE to power EIPs. Life Cycle Assessment (LCA) methodology, which is used in a variety of sectors to evaluate the environmental merits and demerits of different kinds of products and processes, was employed for comparison between these two energy production methods based on factors such as greenhouse gas emission, acidification potential, eutrophication potential, human toxicity potential, fresh water usage and land usage. To complement the environmental LCA analysis, levelized cost of electricity was used to evaluate the economic impact. This model was analyzed for two different geographic locations; United States and Europe, for 12 different energy production technologies.
The outcome of this study points out the environmental, economic and geographic superiority of one energy source over the other, including the total carbon dioxide equivalent emissions, which can then be related to the total number of carbon credits that can be earned or used to mitigate the overall carbon emission and move closer towards a net zero carbon footprint goal thus making the EIPs truly sustainable.
ContributorsGupta, Vaibhav (Author) / Calhoun, Ronald J (Thesis advisor) / Dooley, Kevin (Committee member) / Phelan, Patrick (Committee member) / Arizona State University (Publisher)
Created2014
Description
Many expect renewable energy technologies to play a leading role in a sustainable energy supply system and to aid the shift away from an over-reliance on traditional hydrocarbon resources in the next few decades. This dissertation develops environmental, policy and social models to help understand various aspects of photovoltaic (PV) technologies. The first part of this dissertation advances the life cycle assessment (LCA) of PV systems by expanding the boundary of included processes using hybrid LCA and accounting for the technology-driven dynamics of environmental impacts. Hybrid LCA extends the traditional method combining bottom-up process-sum and top-down economic input-output (EIO) approaches. The embodied energy and carbon of multi-crystalline silicon photovoltaic systems are assessed using hybrid LCA. From 2001 to 2010, the embodied energy and carbon fell substantially, indicating that technological progress is realizing reductions in environmental impacts in addition to lower module price. A variety of policies support renewable energy adoption, and it is critical to make them function cooperatively. To reveal the interrelationships among these policies, the second part of this dissertation proposes three tiers of policy architecture. This study develops a model to determine the specific subsidies required to support a Renewable Portfolio Standard (RPS) goal. The financial requirements are calculated (in two scenarios) and compared with predictable funds from public sources. A main result is that the expected investments to achieve the RPS goal far exceed the economic allocation for subsidy of distributed PV. Even with subsidies there are often challenges with social acceptance. The third part of this dissertation originally develops a fuzzy logic inference model to relate consumers' attitudes about the technology such as perceived cost, maintenance, and environmental concern to their adoption intention. Fuzzy logic inference model is a type of soft computing models. It has the advantage of dealing with imprecise and insufficient information and mimicking reasoning processes of human brains. This model is implemented in a case study of residential PV adoption using data through a survey of homeowners in Arizona. The output of this model is the purchasing probability of PV.
ContributorsZhai, Pei (Author) / Williams, Eric D. (Thesis advisor) / Allenby, Braden (Committee member) / Phelan, Patrick (Committee member) / Arizona State University (Publisher)
Created2010
Description
Concentrating solar thermal power systems gained a wide interest for a long time to serve as a renewable and sustainable alternate source of energy. While the optimization and modification are ongoing, focused generally on solar power systems to provide solar-electrical energy or solar-thermal energy, the production process of Ordinary Portland Cement (OPC) has not changed over the past century. A linear refractive Fresnel lens application in cement production process is investigated in this research to provide the thermal power required to raise the temperature of lime up to 623 K (350C) with zero carbon emissions for stage two in a new proposed two-stage production process. The location is considered to be Phoenix, Arizona, with a linear refractive Fresnel lens facing south, tilted 33.45 equaling the location latitude, and concentrating solar beam radiation on an evacuated tube collector with tracking system continuously rotating about the north-south axis. The mathematical analysis showed promising results based on averaged monthly values representing an average hourly useful thermal power and receiver temperature during day-light hours for each month throughout the year. The maximum average hourly useful thermal power throughout the year was obtained for June as 33 kWth m-2 with a maximum receiver temperature achieved of 786 K (513C), and the minimum useful thermal power obtained during the month of December with 27 kWth m-2 and a minimum receiver temperature of 701 K (428C).
ContributorsAlkhuwaiteem, Mohammad (Author) / Phelan, Patrick (Thesis advisor) / Shuaib, Abdelrahman (Committee member) / Neithalath, Narayanan (Committee member) / Arizona State University (Publisher)
Created2021
Description
The operating temperature of photovoltaic (PV) modules has a strong impact on the expected performance of said modules in photovoltaic arrays. As the install capacity of PV arrays grows throughout the world, improved accuracy in modeling of the expected module temperature, particularly at finer time scales, requires improvements in the existing photovoltaic temperature models. This thesis work details the investigation, motivation, development, validation, and implementation of a transient photovoltaic module temperature model based on a weighted moving-average of steady-state temperature predictions.
This thesis work first details the literature review of steady-state and transient models that are commonly used by PV investigators in performance modeling. Attempts to develop models capable of accounting for the inherent transient thermal behavior of PV modules are shown to improve on the accuracy of the steady-state models while also significantly increasing the computational complexity and the number of input parameters needed to perform the model calculations.
The transient thermal model development presented in this thesis begins with an investigation of module thermal behavior performed through finite-element analysis (FEA) in a computer-aided design (CAD) software package. This FEA was used to discover trends in transient thermal behavior for a representative PV module in a timely manner. The FEA simulations were based on heat transfer principles and were validated against steady-state temperature model predictions. The dynamic thermal behavior of PV modules was determined to be exponential, with the shape of the exponential being dependent on the wind speed and mass per unit area of the module.
The results and subsequent discussion provided in this thesis link the thermal behavior observed in the FEA simulations to existing steady-state temperature models in order to create an exponential weighting function. This function can perform a weighted average of steady-state temperature predictions within 20 minutes of the time in question to generate a module temperature prediction that accounts for the inherent thermal mass of the module while requiring only simple input parameters. Validation of the modeling method presented here shows performance modeling accuracy improvement of 0.58%, or 1.45°C, over performance models relying on steady-state models at narrow data intervals.
This thesis work first details the literature review of steady-state and transient models that are commonly used by PV investigators in performance modeling. Attempts to develop models capable of accounting for the inherent transient thermal behavior of PV modules are shown to improve on the accuracy of the steady-state models while also significantly increasing the computational complexity and the number of input parameters needed to perform the model calculations.
The transient thermal model development presented in this thesis begins with an investigation of module thermal behavior performed through finite-element analysis (FEA) in a computer-aided design (CAD) software package. This FEA was used to discover trends in transient thermal behavior for a representative PV module in a timely manner. The FEA simulations were based on heat transfer principles and were validated against steady-state temperature model predictions. The dynamic thermal behavior of PV modules was determined to be exponential, with the shape of the exponential being dependent on the wind speed and mass per unit area of the module.
The results and subsequent discussion provided in this thesis link the thermal behavior observed in the FEA simulations to existing steady-state temperature models in order to create an exponential weighting function. This function can perform a weighted average of steady-state temperature predictions within 20 minutes of the time in question to generate a module temperature prediction that accounts for the inherent thermal mass of the module while requiring only simple input parameters. Validation of the modeling method presented here shows performance modeling accuracy improvement of 0.58%, or 1.45°C, over performance models relying on steady-state models at narrow data intervals.
ContributorsPrilliman, Matthew (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Phelan, Patrick (Thesis advisor) / Wang, Liping (Committee member) / Arizona State University (Publisher)
Created2020