Parameter estimation of activated sludge process based on an improved cuckoo search algorithm. (February 2018)

• Record Type: Journal Article
• Authors: Du, Xianjun; Wang, Junlu; Jegatheesan, Veeriah; Shi, Guohua
• Is Part Of: Bioresource technology. Volume 249(2018)
• Page Start: 447
• Page End: 456
• Publication Date: 2018-02
• Subjects: Activated sludge process -- ASM1 -- Cuckoo search algorithm -- Parameter estimation -- Wastewater treatment; Biomass -- Periodicals; Biomass energy -- Periodicals; Bioremediation -- Periodicals; Agricultural wastes -- Periodicals; Factory and trade waste -- Periodicals; Organic wastes -- Periodicals; Bioénergie -- Périodiques; Déchets agricoles -- Périodiques; Déchets industriels -- Périodiques; Déchets organiques -- Périodiques; Déchets (Combustible) -- Périodiques; 662.88
• DOI: 10.1016/j.biortech.2017.10.023
• Languages: English
• ISSNs: 0960-8524

Abstract

Graphical abstract: Highlights: Improved cuckoo search (ICS) algorithm is found to estimate parameters used in ASM1. The improved algorithm is tested to search global minima of six standard functions. 7 sensitive parameters in ASM1 are estimated using ICS for practical applications. Field data are used to validate prediction accuracy of ASM1 with estimated parameters. Predicted results of the model are closer to the actual data with adjusted parameters. Abstract: It is essential to use appropriate values for kinetic parameters in activated sludge model when the model is applied for wastewater treatment processes under different environments. An improved cuckoo search (ICS) algorithm was proposed in this paper for the estimation of kinetic parameters used in Activated Sludge Model No. 1 (ASM1). ICS is tested for its speed and accuracy in reaching solution by searching global minima of six standard functions. Cyclical adjustment strategy was employed into the detected probability to increase searching ability. Meanwhile, the searching step was adaptively adjusted based on the optimal nest of the last generation and the current iteration numbers. Subsequently, ICS is used to estimate 7 sensitive parameters in ASM1 for practical applications. Field data are used to validate prediction accuracy of ASM1 with estimated parameters. Predicted results of the model are closer to the actual data with adjusted parameters.