Energy conservation and techno-environmental analysis in natural gas liquefaction with single and dual-mixed refrigerants: A comparison
Bibliographic record
Abstract
The need to meet energy demand and produce cleaner energy has propped up the drive to explore natural gas options and, more recently, liquefied natural gas. The thermal efficiency of a liquefaction process is very important when considering the energy requirement. The liquefaction technology by the refrigerant is one of the most common technologies becoming popular among researchers for its flexible features. This study compares the energy performance of the dual mixed refrigerant (PRICO DMR) and single mixed refrigerant (PRICO SMR) propane pre-cool liquefaction technology using a process simulator by carrying out sensitivity analysis on the process parameters to see its impact on the refrigerant flow rate, power consumption, and the specific power. The Honeywell UNISIM R451, software process simulator, was used to simulate the PRICO SMR and DMR processes with natural gas supplied at 55 °C, 61 bar, and 150 MMSCFD as the base condition. From the simulation, the ‘UA’ for the Heat Exchangers used with mixed refrigerant, the SMR was 36470 kW/°C, and the DMR was 5172 and 4612 kW/°C. The result shows that at the base condition, the refrigerant flow rate, power consumption, and specific power for the DMR and SMR was 404.9 MMSCFD, 96.8 MW, 0.409 kWhr/kg-LNG, and 507 MMSCFD, 144.5 MW, and 1.261 kWhr/kg-LNG, respectively. With sensitivity analysis, the simulation results showed that for both the SMR and DMR processes at any temperature, increasing pressure increases the refrigerant flow rate and power consumption but decreases the specific power. In addition, the DMR provided a potential CO 2 emissions potentials saving by an average factor of 2.953 and a better savings in energy demand with the specific power reaching 67.7 % and refrigerant consumption than the SMR process.
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".