Financial viability of microalgal biodiesel production: a case study in Southern Ontario, Canada
Bibliographic record
Abstract
Biofuels are an alternative to fossil fuels with potential to help in the fight against climate change.With transportation accounting for 14% of global greenhouse gas emissions and most of the world's transportation energy coming from gasoline and diesel, biodiesel has the potential to replace fossil fuels, particularly in the road transportation sector.However not all biodiesel is created equal, due to the large number of potential feedstocks and conversion processes that may be followed to produce biodiesel.A suitable biodiesel must be environmentally and socially sustainable, and the manufacturing process must be financially cost-effective, before it can be considered as a replacement for conventional diesel fuel.This thesis evaluated the financial viability of a novel biodiesel source, produced from microalgae feedstock grown in wastewater with CO2 additions from flue gas.The case study was based on Canada-specific data.The optimal geographic location for producing microalgae feedstock in a municipal wastewater treatment facility was determined using climatic data on insolation and temperature effects, whilst also taking into account the location of existing wastewater treatment plants and biodiesel infrastructure in southern Ontario, Canada.Methods to grow the microalgae, whether in open ponds or photobioreactors, were determined from the literature review.Characteristics of the wastewater were derived from anonymous data provided by three wastewater treatment plants in Alberta, Canada.This was compared to microalgae growth requirements, to determine if sufficient nutrients and CO2 would be provided by the wastewater and flue gas to support microalgae growth in the case study, based on strains that were recommended for cold climates.Harvesting, dewatering and conversion pathways were determined from literature review and consultation with experts, and the most cost-effective options were retained for analysis.Financial metrics were estimated from ~ iii ~ literature, public and private data, and from consultation with industry professionals.The net present value (NPV), internal rate of return (IRR), breakeven date and investment multiple of the flue gas and wastewater co-utilization (FWC) system were then derived.A five case multi-scenario analysis was then conducted to identify the impact on the profitability of the proposed microalgae biodiesel production system in southern Ontario, Canada, with a ± 25% revenues or costs estimation error.Then, single-variable sensitivity analysis was conducted to assess the impact on the profitability of the system of a percentage change in key revenue or cost variables, notably: the percentage of nitrogen (N) and phosphorus (P) removal savings passed on to a private company, the price of diesel, the Ontario energy price, the quantity of biodiesel and methane produced, as well as capital expenses (CapEx) and operational expenses (OpEx).In conclusion, the FWC systems was found to be profitable with a base case NPV of roughly $256.5M, an IRR of 38.77%, a 3.8X investment multiple and a 3-year payback period.Given that these results are primarily dependent on the percentage of N and P removal savings passed on to a private company, this type of project was found not to be suited for private businesses and investors but rather governments and wastewater treatment plant operators who would incur 100% of wastewater treatment savings generated.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".