A New Integrated Ocean Thermal Energy Conversion-Based Trigeneration System for Sustainable Communities
Bibliographic record
Abstract
Abstract One of the main solutions to climate change is to harness energy from renewable and clean resources. A novel ocean thermal energy conversion (OTEC) system is proposed for the production of methanol; cooling and power is developed and energetically analyzed. In this proposed trigeneration system, a two-stage Rankine cycle that operates on the inherent temperature difference along the depth of the ocean is used for power production, along with an electrolytic cation exchange membrane (ECEM) reactor for carbon dioxide and hydrogen production to feed the methanol production system. The carbon dioxide is sourced from the deep cold seawater, where the concentrations are found to be the highest. The proposed system performance is modeled and simulated on the Aspen Plus, where the performance of the proposed system is assessed under various operating conditions. The results of this study shows that the maximum net power output of the cycle is found to be 51.5 GW, with a fixed rate of district cooling of 69.0 GW. The maximum methanol production rate was found to be 1.36 kg/s at the power input of 51.5 GW. The system is tested under three different operation cases, to fully assess its viability. It should be noted that in all three cases district cooling is included as a product of the system. Case 1: ECEM reactor operates at its current efficiency with fuel production, Case 2: ECEM reactor operates at proton exchange membrane (PEM) efficiency, and Case 3: Only power was produced with no fuel. The maximum overall energy efficiency of the cycle was found to be 8.0, 8.6, and 7.3% for Cases 1, 2, and 3, respectively.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".