Natural gas‐fueled multigeneration for reducing environmental effects of brine and increasing product diversity: Thermodynamic and economic analyses
Bibliographic record
Abstract
Abstract Water scarcity threatens human life and it is likely to be a main concern in the next century. In this work, a novel multigeneration system (MGS) is introduced and assessed with energy, exergy, and economic analyses. This MGS includes a gas cycle, multieffect distillation, an absorption refrigeration cycle, a heat recovery steam generator, and electrodialysis. Electrodialysis is integrated into this configuration to produce sodium hydroxide and hydrogen chloride from brine to prevent its release to the environment with harmful impacts. The other products are electricity, cooling, and demineralized water. For the evaluation of the proposed system, one computer code is provided in engineering equation solver software. For physical properties calculation, the library of this software is used. The MGS produces 614.7 GWh of electrical energy, 87.44 GWh of cooling, 12.47 million m3 of demineralized water, and 0.092 and 0.084 billion kg of sodium hydroxide and hydrogen chloride over a year. Energy and exergy evaluations demonstrate that the MGS energy and exergy efficiencies are 31.3% and 18.7%, respectively. The highest and lowest value of exergy destruction rate is associated with the combustion chamber and pump, respectively. The economic evaluation indicates that the net present value of this proposed system is 3.8 billion US$, while the internal rate of return and payback period, respectively, are 0.49 and 2.1 years.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".