Artificial intelligence-driven assessment of critical inputs for lead adsorption by agro-food wastes in wastewater treatment
Bibliographic record
Abstract
Due to environmental concerns and economic value, the adsorption process using agricultural wastes is one of the promising methods to remove lead (Pb) from contaminated water. The relationships between agricultural waste properties, adsorption conditions, and the maximum Pb adsorption capacity of selected adsorbents have not been adequately explored. A thorough understanding of these interactions is crucial for optimizing adsorption processes and enhancing the efficiency of agricultural wastes as sustainable adsorbents. To assess Pb adsorption by agricultural wastes and identify the key influencing factors, three artificial intelligence techniques, namely Extreme Learning Machine (ELM), Adaptive Nuro-Fuzzy Inference Systems (ANFIS), and Group Method of Data Handling (GMDH) have been employed in this study. Seven input variables, namely time, ratio, initial ion concentration, type of adsorbents from agricultural wastes, pH, temperature, and agitation speed, from 771 data points were used as inputs for model development, while the quantity of Pb adsorbed was chosen as target parameter. To identify the best input combinations with one to seven variables, 127 models were defined and analyzed using ELM integrated with the cross-validation technique. The results highlighted that the initial ion concentration is the most critical factor in enhancing heavy metal adsorption, and temperature is the least important factor. The top models, utilizing one to seven input variable(s), were then modeled with ANFIS and GMDH. Subsequently, all three models were compared. The GMDH model with four input variables (initial ion concentration, type of adsorbent, time, and agitation speed) demonstrated the highest performance in terms of accuracy and simplicity. • Development of promising technologies for environmental remediation and food waste management. • Pb adsorption efficiency by agricultural waste through ML models to pinpoint the key input factors. • All models demonstrated strong predictive performance and generalization capability. • IIC showed the most influential factor in Pb removal, while temperature had the least impact. • The GMDH model with four input variables showed the highest accuracy and simplicity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".