Multi-objective Optimization of a Novel Hybrid Structure for Co-generation of Ammonium Bicarbonate, Formic Acid, and Methanol with Net-Zero Carbon Emissions
Bibliographic record
Abstract
Chemical storage of hydrogen, originated from renewable energy, is one of the most efficient and reliable methods to absorb carbon dioxide (CO 2 ) and easily transport large-scale energy to remote areas. In this study, a novel integration of an electro-thermochemical process with industrial flue gas thermal energy and wind turbines is proposed to absorb CO 2 and store chemically hydrogen in the form of methanol, formic acid, and ammonium bicarbonate. The proposed hybrid structure includes a post-combustion CO 2 capture technology, a copper–chlorine thermochemical process, and several ammonium bicarbonate, formic acid, and methanol production cycles. The proposed configuration produces 2597 kg/h methanol, 5270 kg/h ammonium bicarbonate, and 833.3 kg/h formic acid. The energy and exergy efficiencies of the proposed layout are computed at 63.68 and 66.82%, respectively. The exergy analysis depicts that three processes of electro-thermochemical, methanol production, and post-combustion CO 2 capture have the greatest destructed exergy contributions among other subsections to the amounts of 40.85, 30.16, and 7.97%, respectively. The verification, validation, and sensitivity analyses, along with a multi-objective optimization protocol (i.e., a hybrid neural network and a genetic algorithm), are also used to evaluate the proposed system. The objective functions, decision variables, and constraints for the optimization phase are determined through sensitivity analysis. Several multi-criteria decision evaluation methods are employed to prioritize and choose the optimal point from the Pareto set. The electrical power supplied from the wind turbines as well as the auxiliary power supply, and the energy and exergy efficiencies calculated based on the TOPSIS/LINAMP techniques are 9.90 MW, 89.23, and 67.27%, respectively. In addition, the power consumption, energy, and exergy efficiencies at the optimal operating condition, calculated using the Bellman–Zadeh approach, are 9.673 MW, 83.26, and 67.19%, respectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".