Energy‐efficient and cost‐effective separation model for solvent recovery from colloidal lignin particles dispersion
Bibliographic record
Abstract
Abstract Colloidal lignin particles (CLPs) are potentially one of the sustainable alternatives for petroleum‐based feedstock. CLPs address the heterogeneity of lignin by enhancing its homogeneous dispersion in aqueous phases. The main production steps are dissolving lignin in tetrahydrofuran, diluting the solution with ethanol, forming CLPs through self‐assembly after encountering water, recovering solvents, and finally drying CLPs. In this process, solvent recovery plays an important role in mitigating environmental problems. However, the formation of azeotropes makes the separation process energy‐intensive and costly. In this work, two separation methods, evaporation and distillation, are modelled in Aspen Plus® and compared based on their total annual costs (TACs). Sizing and cost estimations are conducted based on vendor quotations and using design and economic analyzer tools. Results show that distillation reduces costs by up to 37% compared to evaporation. Accordingly, as the main separation unit, distillation parameters are optimized based on the minimum TAC. For further assessment of the increase in the rate of costs by reaching nearly pure products, extractive distillation is simulated and examined. Results show that using an entrainer to enhance the tetrahydrofuran concentration from 88 to 99.5 wt.% substantially increases the TAC by over 50%. Finally, based on the results, the desired solvent recovery model is finalized by employing the rate‐based approach. Currently, studies with a focus on the techno‐economic assessment of pilot‐scale separation units are limited, and the developed model offers a good basis for designing optimal solvent recovery units, related to processes where lignin is dissolved, prior to commercialization.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".