Exergoeconomic evaluation and multi-objective optimization of a novel geothermal-driven zero-emission system for cooling, electricity, and hydrogen production: capable of working with low-temperature resources
Bibliographic record
Abstract
Abstract Geothermal energy is an abundant natural resource in many regions around the world. However, in some areas, the temperature of the geothermal energy resource is too low to be efficiently harvested. Organic Rankine cycles (ORCs) are known for recovering heat from low-temperature resources and generating electricity. Furthermore, half-effect absorption chillers (HEACs) are designed to produce cooling with low-temperature resources. This study proposes a novel configuration that utilizes an ORC for electricity generation, a HEAC for cooling production, and a PEM electrolysis system to produce hydrogen. The power section consists of two turbines, one driven by the vapor produced from the geothermal flow expansion, which powers the PEM section, while the other turbine in the ORC is used to drive pumps and electricity production. First, the system is thermoeconomically analyzed for an initial set of inputs. Then, various parameters are analyzed to determine their influences on system performance. The analyses reveal that the system can work with geothermal source temperatures as low as 80 °C, but the exergy and energy (thermal) efficiencies decrease to around 17% under the base settings. Furthermore, the system is capable of working with resource temperatures up to 170 °C. Ten parameters are found to affect the system’s efficiency and effectiveness. To optimize the system, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is implemented to find the optimum conditions. The objective functions are exergy efficiency and unit polygeneration cost (UPGC), which can conflict. The optimization shows that the exergy efficiency of the system can reach 48% in the optimal conditions (for a heat source temperature of 112 °C and a mass flow rate of geothermal fluid of 44 kg/s), with a hydrogen production rate of 1.1 kg/h.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".