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Description
In an effort to stress the benefits of the application of renewable energy to the next generation of science, technology, engineering, arts, and mathematics (STEAM) professionals, instructional modules on energy and biogas were integrated into a summer camp curriculum that challenged students to apply STEAM concepts in the design and development of chain reaction machines. Each module comprised an interactive presentations and a hands-on component where students operated a manipulative relevant to the content. During summer 2013, this camp was implemented at two high schools in Arizona and one in Trinidad and Tobago. Assessments showed that the overall modules were effective in helping students learn and retain the information presented on energy and biogas production. To improve future implementations of these modules, specifically the module on biogas production, the anaerobic digester was redesigned. In addition, a designed experiment was conducted to determine how to optimize the influent and operational environment that is available in an average high school classroom to generate maximum biogas yield. Eight plug-flow anaerobic digesters made of PVC piping and fixtures were used in a 2x3 factorial design assessing: co-digestion (20mL or 50mL) used cooking oil, temperature (25°C or 40°C), and addition of inoculum (0mL or 200mL). Biogas production was captured at two intervals over a 30-day period, and the experiments were replicated three times. Results showed that temperature at 40°C significantly increased biogas production and should be used over 25°C when using anaerobic digesters. Other factors that may potentially increase biogas production are combination of temperature at 40°C and 50mL of used cooking oil. In the future, the improvements made in the design of the anaerobic digester, and the applications of the finding from the experimental design, are expected to lead to an improved manipulative for teaching students about biogas production.
ContributorsMcCall, Shakira Renee (Author) / Dalrymple, Odesma O (Thesis advisor) / Bradley, Rogers (Committee member) / Hristovski, Kiril (Committee member) / Arizona State University (Publisher)
Created2014
Description
This is a two part thesis:
Part – I
This part of the thesis involves automation of statistical risk analysis of photovoltaic (PV) power plants. Statistical risk analysis on the field observed defects/failures in the PV power plants is usually carried out using a combination of several manual methods which are often laborious, time consuming and prone to human errors. In order to mitigate these issues, an automated statistical risk analysis (FMECA) is necessary. The automation developed and presented in this project generates about 20 different reliability risk plots in about 3-4 minutes without the need of several manual labor hours traditionally spent for these analyses. The primary focus of this project is to automatically generate Risk Priority Number (RPN) for each defect/failure based on two Excel spreadsheets: Defect spreadsheet; Degradation rate spreadsheet. Automation involves two major programs – one to calculate Global RPN (Sum of Performance RPN and Safety RPN) and the other to find the correlation of defects with I-V parameters’ degradations. Based on the generated RPN and other reliability plots, warranty claims for material defect and degradation rate may be made by the system owners.
Part – II
This part of the thesis involves the evaluation of Module Level Power Electronics (MLPE) which are commercially available and used by the industry. Reliability evaluations of any product typically involve pre-characterizations, many different accelerated stress tests and post-characterizations. Due to time constraints, this part of the project was limited to only pre-characterizations of about 100 MLPE units commercially available from 5 different manufacturers. Pre-characterizations involve testing MLPE units for rated efficiency, CEC efficiency, power factor and Harmonics (Vthd (%) and Ithd (%)). The pre-characterization test results can be used to validate manufacturer claims and to evaluate the product for compliance certification test standards. Pre-characterization results were compared for all MLPE units individually for all tested parameters listed above. The accelerated stress tests are ongoing and are not presented in this thesis. Based on the pre-characterizations presented in this report and post-characterizations performed after the stress tests, the pass/fail and time-to-failure analyses can be carried out by future researchers.
Part – I
This part of the thesis involves automation of statistical risk analysis of photovoltaic (PV) power plants. Statistical risk analysis on the field observed defects/failures in the PV power plants is usually carried out using a combination of several manual methods which are often laborious, time consuming and prone to human errors. In order to mitigate these issues, an automated statistical risk analysis (FMECA) is necessary. The automation developed and presented in this project generates about 20 different reliability risk plots in about 3-4 minutes without the need of several manual labor hours traditionally spent for these analyses. The primary focus of this project is to automatically generate Risk Priority Number (RPN) for each defect/failure based on two Excel spreadsheets: Defect spreadsheet; Degradation rate spreadsheet. Automation involves two major programs – one to calculate Global RPN (Sum of Performance RPN and Safety RPN) and the other to find the correlation of defects with I-V parameters’ degradations. Based on the generated RPN and other reliability plots, warranty claims for material defect and degradation rate may be made by the system owners.
Part – II
This part of the thesis involves the evaluation of Module Level Power Electronics (MLPE) which are commercially available and used by the industry. Reliability evaluations of any product typically involve pre-characterizations, many different accelerated stress tests and post-characterizations. Due to time constraints, this part of the project was limited to only pre-characterizations of about 100 MLPE units commercially available from 5 different manufacturers. Pre-characterizations involve testing MLPE units for rated efficiency, CEC efficiency, power factor and Harmonics (Vthd (%) and Ithd (%)). The pre-characterization test results can be used to validate manufacturer claims and to evaluate the product for compliance certification test standards. Pre-characterization results were compared for all MLPE units individually for all tested parameters listed above. The accelerated stress tests are ongoing and are not presented in this thesis. Based on the pre-characterizations presented in this report and post-characterizations performed after the stress tests, the pass/fail and time-to-failure analyses can be carried out by future researchers.
ContributorsMoorthy, Mathan Kumar (Author) / Govindasamy, Tamizhmani (Thesis advisor) / Devarajan, Srinivasan (Committee member) / Bradley, Rogers (Committee member) / Arizona State University (Publisher)
Created2015