Design and optimization of integrated membrane separation for natural gas decarbonization and light hydrocarbon recovery from LNG
Bibliographic record
Abstract
ObjectiveThe use of two-stage membrane separation process for decarbonization treatment of natural gas has good decarbonization effect and economy. However, the membrane separators necessitate a considerable amount of cooling equipment and result in increased energy consumption. Additionally, the volume fraction of light hydrocarbons in natural gas increases after decarbonization. Recovering light hydrocarbons from the decarbonized natural gas can help reduce energy waste and minimize the need for cooling equipment. MethodsThis paper proposes an approach that integrates membrane separation for natural gas decarbonization and light hydrocarbon recovery from LNG. HYSYS software was utilized to simulate both the single processes and the integrated processes, revealing the advantage of lower energy consumption in the integrated scenario compared to the single-process scenarios. Subsequent process optimization was conducted based on the simulation results, involving a comparative analysis of key parameters affecting process integration, such as comprehensive energy consumption, the volume fraction of CO2 in the retentate gas, the volume fraction of methane, and the C2+ recovery rate. With the objective of minimizing comprehensive energy consumption, the Box-Behnken Design (BBD) response surface method was employed to establish a regression equation. A genetic algorithm was then used to solve this regression equation, ultimately yielding the optimized parameters.ResultsCompared to the single-process scenarios, the integration of the processes resulted in lower energy consumption, specifically reducing comprehensive energy consumption by 2 363.97 kW. The volume fraction of CO2 in the retentate gas decreased to 1.48％, while the volume fraction and output of methane reached 98.55％ and 5 422 kmol/h, respectively. Additionally, key parameters such as the primary membrane area, secondary membrane inlet temperature, secondary membrane area, and Separator 2 inlet temperature were identified as influential factors affecting the comprehensive energy consumption, decarbonization efficiency, and light hydrocarbon recovery of the integrated processes. The final optimization results from solving the model are as follows: a primary membrane area of 11 200 m2, a secondary membrane area of 11 200 m2, and inlet temperatures of 40 °C for the secondary membrane and −103 °C for the Separator 2.ConclusionThe design of integrating membrane separation for natural gas decarbonization and light hydrocarbon recovery fully leverages the LNG cooling capacity at LNG terminals. By reducing process energy consumption, this approach facilitates the simultaneous implementation of the natural gas purification process through decarbonization and the recovery of light hydrocarbons, maximizing the utilization of equipment and resources. The study outcomes offer insights into potential solutions for industrial applications focused on green and sustainable development.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".