Abstract
A precise estimation of isotherm model parameters and selection of isotherms from the measured data are essential for the fate and transport of toxic contaminants in the environment. Nonlinear least-square techniques are widely used for fitting the isotherm model on the experimental data. However, such conventional techniques pose several limitations in the parameter estimation and the choice of appropriate isotherm model as shown in this paper. It is demonstrated in the present work that the classical deterministic techniques are sensitive to the initial guess and thus the performance is impeded by the presence of local optima. A novel solver based on modified artificial bee-colony (MABC) algorithm is proposed in this work for the selection and configuration of appropriate sorption isotherms. The performance of the proposed solver is compared with the other three solvers based on swarm intelligence for model parameter estimation using measured data from 21 soils. Performance comparison of developed solvers on the measured data reveals that the proposed solver demonstrates excellent convergence capabilities due to the superior exploration-exploitation abilities. The estimated solutions by the proposed solver are almost identical to the mean fitness values obtained over 20 independent runs. The advantages of the proposed solver are presented.
Highlights
The fate and transport of heavy metals in the soils are a serious concern due to their potential impact on the environment
The results showed that the performance in terms of mean fitness value by PCPSO and artificial bee colony (ABC) algorithms was improved slightly compared to the particle swarm optimization (PSO) algorithm
The results indicated that the modified artificial bee-colony (MABC) algorithm outperformed all the other solvers for finding the accurate model parameters
Summary
The fate and transport of heavy metals in the soils are a serious concern due to their potential impact on the environment. Particle swarm optimization (PSO) [9] and artificial bee colony (ABC) [10] are commonly used SI techniques These algorithms overcome several limitations of conventional, deterministic techniques. These global search techniques are widely applied in geotechnical engineering for several optimization problems [11,12,13,14,15,16,17]. A hybrid method based on particle swarm optimization and gradientbased algorithm is often used for configuration of sorption isotherms [19]. An inverse model based on artificial beecolony (ABC) optimization method is proposed in this work for selection and configuration of appropriate sorption isotherms using the experimental sorption data. The proposed technique is highly robust; it accurately estimates the sorption model parameters and appropriate isotherms to the experimental data
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