Control of the Fenton process for textile wastewater treatment using artificial neural networks
2010
Yu, Ruey-Fang | Chen, Ho-Wen | Liu, Guangyu | Cheng, Wen-Po | Hsieh, Peng-Han
BACKGROUND: The Fenton process is a popular advanced oxidation process (AOP) for treating textile wastewater. However, high consumption of chemical reagents and high production of sludge are typical problems when using this process and in addition, textile wastewater has wide-ranging characteristics. Therefore, dynamically regulating the Fenton process is critical to reducing operation costs and enhancing process performance. The artificial neural network (ANN) model has been adopted extensively to optimize wastewater treatment. This study presents a novel Fenton process control strategy using ANN models and oxygen reduction potential (ORP) monitoring to treat two synthetic textile wastewaters containing two common dyes.RESULTS: Experimental results indicated that the ANN models can predict precisely the colour and chemical oxygen demand (COD) removal efficiencies for synthetic textile wastewaters with correlation coefficients (R²) of 0.91-0.99. The proposed control strategy based on these ANN models effectively controls the Fenton process for various effluent colour targets. For treating the RB49 synthetic wastewater to meet the effluent colour targets of 550 and 1500 ADMI units, the required Fe⁺² doses were 13.0-84.3 and 5.5-34.6 mg L⁻¹ (Fe⁺²/H₂O₂ = 3.0), resulting in average effluent colour values of 520 and 1494 units. On the other hand, an effluent colour target of 550 ADMI units was achieved for RBB synthetic wastewater. The required Fe⁺² doses were 14.6-128.0 mg L⁻¹; the average effluent colour values were 520 units.CONCLUSION: The Fenton process for textile wastewater treatment was effectively controlled using a control strategy applying the ANN models and ORP monitoring, giving the benefit of chemical cost savings. Copyright
Afficher plus [+] Moins [-]Mots clés AGROVOC
Informations bibliographiques
Cette notice bibliographique a été fournie par National Agricultural Library
Découvrez la collection de ce fournisseur de données dans AGRIS