Biomass Leachate Treatment and Nutrient Recovery Using Reverse Osmosis: Experimental Study and Hybrid Artificial Neural Network Modeling
Bibliographic record
Abstract
The application of reverse osmosis (RO) to recover nutrients from biomass leachate, with special reference to permeate flux behavior during the filtration of the leachate, is investigated in this paper through a comprehensive laboratory and modeling approach. Various sources of biomass, including soybean straw, switchgrass, and miscanthus, were industrially leached using distilled water with agitation during the extraction experiments. The leachates were filtered using a RO flat sheet membrane module to recover the nutrients and water. On the basis of inductively coupled plasma (ICP) analytical results, calcium, magnesium, phosphorus, and silica for all types of biomass leachate samples had rejection efficiencies of >80%. Permeate flux decreased sharply at the beginning of the filtration, followed by a slight decline during the filtration process. A hybrid intelligent model based on a feed-forward artificial neural network (ANN) was also developed to estimate the permeate flux during the filtration in terms of the filtration time and total solid concentration in the leachate. A Levenberg–Marquardt optimization algorithm was chosen to perform the training phase for the network. An ANN with four neurons in one hidden layer was selected as the optimum structure, such that a maximum percent absolute error of 14% was attained while predicting the permeate flux. A reasonable agreement was observed between the ANN predictions and experimental data, which exhibits the potential usefulness of the hybrid ANN model to predict permeate flux during RO filtration of biomass leachate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".