DEGRADATION OF SOFC AND SOEC AND ITS IMPACT ON SYSTEM OPERATION
Bibliographic record
Abstract
The key to a cleaner future is to alleviate reliance of energy sectors on non-renewable fossil fuel sources and reduce their environmental burdens. This can be achieved by using power generation units such as Solid Oxide Fuel Cell (SOFC) that use natural gas more efficiently compared to the existing fossil fuel-based technologies as well as energy storage units such as Solid Oxide Electrolysis Cell (SOEC) that enable increase in the amount of intermittent renewable power supply. This thesis describes the models constructed for conventional SOFC and SOEC degradation to show how performance of these cells degrade over long periods of time under their operating conditions. The goal of this thesis is to evaluate the economic and environmental performance of SOFC and SOEC under degradation and compare them to the existing technologies. In this thesis project, the economically optimal sizing, and trajectories of natural gas-SOFC and SOEC are found considering their degradation. Impacts of SOFC capital cost, price of natural gas, and CO2 tax on economic performance of SOFC and impacts of SOEC capital cost and price of electricity on economic performance of SOEC are quantified to help decision makers evaluate economic feasibility of these cells in different locations. Economical operation of an SOFC plant requires lowering its current density slowly over time which results in power output reduction. This can limit the application of SOFC for communities that need constant load of power all the time. To combat this issue, this thesis also focuses on scheduling start-up of optimally operated SOFC modules such that SOFC system as a whole has a constant power output. This thesis assesses environmental performances of SOFC and SOEC and compares them with the existing technologies. A detailed life cycle analysis of optimally operated natural gas-fueled SOFC plant under degradation is performed using ReCiPe 2016 and TRACI 2.1 US-Canada 2008 methods. Then, it compares the major environmental impact categories of this plant with those of two mature technologies that use natural gas for power generation. Moreover, life cycle greenhouse gases (GHG) emitted from manufacturing SOEC and its power source are quantified to assess environmental performance of SOEC. This is performed for SOECs with various power sources from low GHG emission sources to high GHG emission ones. The levelized cost of hydrogen and life cycle GHG emissions of SOECs with different power sources are then compared to those of other hydrogen generation units. SOFC can serve as an efficient, cost-effective, environmentally friendly option for power only or heat and power generation in many places even at high natural gas prices and carbon taxes. While SOEC, despite its high efficiency, might not be a very economical or environmentally responsible option for hydrogen production when the price of electricity is high and power supply has high GHG emissions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".