Simulation and optimization of Mixed Fluid Cascade process to produce Liquefied natural gas
Bibliographic record
Abstract
As the worldwide energy consumption continues to grow, natural gas and Especially LNG are expected to keep contributing significantly with this growth. Liquefied natural gas by cooling the natural gas is obtained. Due to the volume of liquefied natural gas is less than the volume of natural gas, leading to a substantial reduction in the cost of transport. The compressor is one of the most important equipment used in the liquefaction process. The overall objective of this thesis is to simulation an LNG liquefaction process, then describes optimized work compressor. The mixed fluid cascade (MFC) processes are used for this purpose. Optimization of the process was attempted using Genetic Algorithms by interfacing the Aspen Hysys with MATLAB software's. We must determine the objective function and constraints for optimization. The constraints are defined using the degrees of freedom, in this project we are working with 11 degrees of freedom and Compressor work, we consider the objective function. Optimization of both obtains good result. Optimization with genetic algorithms, decreased 13.29% energy consumption and too 11.7× 10 6 $ per year savings are obtained with genetic algorithm. Keywords: Liquefied natural gas, optimization, compressor work, genetic algorithm.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".