Leachate Degradation Data from Solar Photo-Fenton Process Experiments

Experimental data from a study modeling leachate degradation via the solar photo-Fenton process. The dataset includes results from a fractional factorial design optimizing pH, solar radiation, H2O2, and Fe2+ concentrations, achieving an 88.7% chemical oxygen demand reduction. The research was conducted by Alessandro Sampaio Cavalcanti using leachate from Cachoeira Paulista-SP.

Use Cases

• Train neural network models for predicting chemical oxygen demand reduction based on input variable levels.
• Analyze the significance of Fe2+ concentration in advanced oxidation processes.
• Optimize wastewater treatment cost-efficiency based on experimental performance and cost data.
• Benchmark statistical modeling approaches against neural network performance for non-linear chemical processes.

Strengths

• Neural model achieved linear correlation coefficients above 0.9 for training, validation, and test sets.
• Best experiment achieved a specific 88.7% reduction in chemical oxygen demand.
• Experimental design used a fractional (24-1) factorial design in duplicate and triplicate.

Limitations

• Row count and dataset size are unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Data reflects a specific geographic source (Cachoeira Paulista-SP) and may not generalize to other leachate types.

Provenance

Source

Alessandro Sampaio Cavalcanti via paperswithcode.

Collection Method

Bench-scale experiments using an open reactor with constant volume and flow rate.

Geography

Cachoeira Paulista-SP, Brazil.