Development of an artificial neural network (ANN) for the prediction of a pilot scale mobile wastewater treatment plant performance
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View abstract on PubMed
Summary
This summary is machine-generated.This study optimized advanced oxidation processes for piggery wastewater treatment, achieving high removal rates for pollutants like Chemical Oxygen Demand and color using Fenton and solar photo Fenton methods. An artificial neural network was developed for predicting treatment efficiency.
Area Of Science
- Environmental Engineering
- Water Treatment Technologies
- Chemical Engineering
Background
- Piggery wastewater poses significant environmental challenges due to high organic loads.
- Existing treatment methods may be insufficient for complete pollutant removal.
- Advanced Oxidation Processes (AOPs) offer a promising solution for recalcitrant organic pollutants.
Purpose Of The Study
- To optimize Fenton and solar photo Fenton (SPF) processes for treating piggery wastewater.
- To identify optimal operating parameters (pH, time, H2O2, Fe2+) for pollutant degradation.
- To develop an artificial neural network (ANN) model for predicting treatment efficiency.
Main Methods
- Laboratory-scale experiments using a Taguchi L9 design for Fenton and SPF.
- Statistical analysis to determine optimal operating parameters.
- Development of a cascade forward neural network (ANN) to correlate operational variables with pollutant removal.
- Pilot-scale validation and projection for industrial application.
Main Results
- Optimal conditions identified: pH=3, time=60 min, H2O2/COD=1.5 mg/L, H2O2/Fe2+=2.5 mg/L.
- Fenton process achieved 91.44% COD, 47.14% TOC, and 97.89% color removal.
- SPF showed moderately increased degradation percentages.
- ANN model achieved a high correlation coefficient (0.99) for predicting pollutant removal.
Conclusions
- Fenton and SPF are effective AOPs for piggery wastewater treatment.
- The developed ANN model enables accurate prediction and optimization of treatment processes.
- The findings support the scalability of these AOPs for industrial wastewater management.
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