SQP-based optimization algorithm: A novel calculation analysis for improved energy-economic efficiency and CO2 purity in stripper segments of CCUS systems
Bibliographic record
Abstract
This study investigates the effect of stripper numerical segment and physical change configuration on CO 2 purity, reboiler energy consumption, and overall economic performance in a monoethanolamine (MEA)-based post-combustion carbon capture (PCC) process. The number of numerical segments controls the numerical resolution of internal temperature and concentration profiles and therefore affects the predicted desorption performance and associated metrics such as specific reboiler duty and CO 2 purity. The number of numerical segments in the stripper plays a critical role in accurately determining the driving force for desorption, solvent regeneration efficiency, and ultimately the purity of the captured CO 2 stream. In this work, a detailed rate-based rigorous model was developed using chemical simulation software, incorporating industrially relevant thermodynamic and hydraulic constraints. Segment numbers were systematically varied across nine cases: 10, 20, 30, 40, 50, 70, 80, 90, and 100. The Sequential Quadratic Programming (SQP) algorithm was implemented in Fortran and externally coupled with the chemical simulation software via a sequential iterative loop. It was applied to minimize reboiler duty and operational cost, subject to process constraints including absorber lean loading, solvent circulation rate, and product purity specifications. The simulation and optimization results revealed that refining the number of numerical segments improves numerical resolution and reduces discretization error, leading to more accurate predictions of CO 2 desorption performance. At the numerical resolution of 100 segments, the model achieved a capture efficiency of 99.87% and a rich solvent loading of 0.48 molCO 2 /molMEA. Higher segment counts lead to more accurate values for lean loading and capture rates, which in turn facilitates further process optimization with increased column height and accompanying capital requirements. SQP successfully identified optimal operating conditions, particularly for pressure, reboiler temperature, and lean solvent conditions that balance energy savings with cost-effectiveness. This work contributes a quantitative and systematic framework for optimizing stripper design using deterministic optimization methods and offers new insights into the trade-offs between mass transfer efficiency, energy consumption, and economic feasibility in large-scale PCC systems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".