Overview of Process Modeling Software: Utilizing Alternative Fuels in Cement Plant for Air Pollution Reduction
Bibliographic record
Abstract
The use of process systems engineering tools, such as process modeling software enable the alternative generation of more efficient and sustainable processes. This paper presents the simulation of cement process using alternative fuels to replace coal. The process modeling is performed using Aspen HYSYS. Simulation results revealed that the substitution of fuel oil, natural gas and palm kernel shell for coal had a significant contribution for emission reduction in cement industry. The emissions for the base case scenario found to be 40,317 kg/h CO 2 , 806 kg/h NO 2 and 146.8 kg/h SO 2 . Utilizing fuel oil mitigated 22% of CO 2 and 92% of NO 2 but increased 232% of SO 2 emissions. Altering coal to palm kernel shell resulted in 46.16% of CO 2 , 73% of NO 2 and 68% of SO 2 emission reduction. In the best case 45.64 % reduction of CO 2 emissions was achieved by replacing coal to natural gas and neither NO 2 nor SO 2 was generated. Key words : Cement plant; Process simulation; Aspen HYSYS; Alternative fuels; Air pollution reduction
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".