Decarbonization of coal-based chemical industry: the integral role of renewable energy
Bibliographic record
Abstract
Abstract Coal-based chemical industries remain vital for energy security and economic stability in regions lacking oil and gas resources, yet face increasing pressure from net-zero targets and low-carbon competition. At the same time, global renewable energy deployment is constrained by grid limitations. This study introduces renewablization—a transformative framework that repurposes renewable electricity, heat, and hydrogen as the core of a multivector energy system for coal-based chemical plants. Adopting the philosophy of EnergyPLAN model, we demonstrate strong operational and physical synergies between renewable energy supply and energy system demands of coal-based process, enabling large-scale integration without grid dependency. Unlike fragmented approaches such as carbon capture, utilization, and storage, which remain fossil-based, or isolated green hydrogen applications lacking systemic impact, renewablization offers a unified, scalable pathway. It repositions renewable energy as the dominant energy and feedstock source, with coal retained only as a carbon input. The strategy unfolds across system-wide, unit-level, and equipment-level layers. While the concept is operationally and economically viable, it calls for the future development of dedicated simulation tools to support its complex, integrated scenarios. Renewablization thus presents a compelling alternative to coal phase-out—aligning legacy industries with climate goals while maintaining their strategic value.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".