A comprehensive analysis of hydrogen production through electrolysis of industrial wastewater: Prospects and challenges
Bibliographic record
Abstract
Sustainable hydrogen production is central to achieving global decarbonization and water stewardship goals. This review is the first to present an integrated techno-economic and environmental feasibility assessment of producing hydrogen from industrial wastewater in Bangladesh, directly linking high-strength effluent management with clean energy generation. Industrial wastewater, often untreated and rich in biodegradable organics, presents an underexploited feedstock that can simultaneously mitigate pollution, reduce freshwater consumption, and generate clean energy. However, such integrated analyses remain scarce, particularly in developing economies where industrial effluent discharge is a major sustainability challenge. This review assesses the feasibility of hydrogen generation from industrial effluents via dark fermentation (DF) and proton exchange membrane electrolysis (PEME), supported by advanced pretreatment strategies. DF achieves yields up to ∼3.5 L H 2 L −1 effluent (∼3 mol mol −1 glucose) with strong cost advantages for high-COD (>1.5 g L −1 ) streams, while PEME offers >75 % electrical efficiency and offsets 9–12 L kg −1 H 2 in freshwater demand when treated wastewater is used. Pretreatment methods physical, chemical, biological, and nanomaterial-ekgsnabled remove >90 % of inhibitory contaminants, enhancing system longevity. A Bangladesh case study illustrates the technology, cost, water-energy nexus, identifying DF as optimal for high-strength effluents and PEME as viable where low-cost renewable electricity and grid-service flexibility are prioritized. Addressing research gaps in pilot-scale validation, impurity-tolerant materials, and enabling policy frameworks can position wastewater valorization as a dual-benefit solution for SDGs 6 and 7, advancing both clean water and clean energy transitions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".