Optimization and exergoeconomic analyses of water-energy-carbon nexus in steel production: Integrating solar-biogas energy, wastewater treatment, and carbon capture
Bibliographic record
Abstract
The steel industry is one of the hard-to-abate sectors for decarbonization, and direct electrification is not possible or economically infeasible. This study investigates the application of the Water-Energy-Carbon nexus in the steel industry through a multi-generation plant, addressing the industry's demands for water, energy, and alternative fuel. The proposed multi-generation plant consists of parabolic trough solar collectors, an organic Rankine cycle, urban wastewater treatment, carbon capture, anaerobic digestion combined with heat and power, a proton exchange membrane electrolyzer, and a steelmaking plant. Thermodynamic and exergoeconomic analyses are conducted to investigate the system's efficiency and economic performance. A sensitivity analysis is conducted to identify the optimal links between nexus resources, followed by a multi-dimensional evaluation and multi-objective optimization. The results showed that under base conditions, the plant can annually produce 900 ktons of steel, 105 GWh of net power, 430.1 tons of water, and 517 tons of hydrogen, while preventing 34.7 ktons of CO 2 emissions. The exergy efficiency and unit exergy cost of the products are 46.1% and 57.8 $/GJ, respectively. Under optimized conditions, the plant achieves a maximum net annual power output of 160 GWh, an exergy efficiency of 46.9%, and a minimum unit exergy cost of 51.4 $/GJ. • Multi-generation plant for energy, water, and fuel for steel industry. • System integrates solar, wastewater treatment, and carbon capture. • Thermodynamic and exergoeconomic analyses assess efficiency and costs. • Optimization achieves 46.9% exergy efficiency and 51.4 $/GJ unit exergy cost. • Plant annually produces 900 kt steel, 105 GWh power, 430.1 t water, 517 t hydrogen.
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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) | 0.000 | 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".