Application of fuzzy logic in modern landfills
Bibliographic record
Abstract
Landfill is by far the dominant and most economical method for the disposal of solid waste worldwide. The landfill ecosystem involves several physical, chemical, and biological processes that take place simultaneously. The complexity of the landfill processes as well as the uncertainty of solid waste characteristics have led to the implementation of unconventional techniques in modeling the system. In fact, no conventional model could be successfully developed for such a nonlinear ill-defined system because it is practically impossible to isolate the individual effect of its variables and satisfactorily identify its behaviour. Recently, knowledge-based techniques, such as fuzzy logic, became widely used to model complex systems based on qualitative knowledge about their behaviour. This paper presents an implementation of fuzzy logic to solve a serious operational problem in modern landfills. A typical sanitary landfill evolves through consecutive operational phases which are hard to distinguish and characterize. The identification of these phases is vital because each phase has different requirements that have to be met in order to assure safe and smooth transition from one phase to another. A fuzzy logic controller was developed to identify the operational phase of a landfill at a given time based on certain quantitative characteristics of the leachate generated and biogas produced.
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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) | 0.001 | 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 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".