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A Reliable and Rapid Method for Simultaneous Determination of Furfural and Hydroxymethyl Furfural in Oil Refinery Wastewaters by Ant Colony/Partial Least-Squares Analysis

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Abstract

Ant colony optimization (ACO) is a population-based metaheuristic algorithm that can be used to find approximate solutions to difficult optimization problems. The solution construction process is stochastic and is biased by a pheromone model which is used to probabilistically sample the search space. ACO is a relatively novel technique for solving hard combinatorial optimization problems. The inspiring source of ACO is the foraging behavior of real ants. Since wavelength selection is a strategy used for improving the quality of calibration methods, we investigated simultaneous spectrophotometric determination of two hazardous materials, namely, furaldehydes 2-furaldehyde (F) and 5-hydroxymethyl-2-furaldehyde (HMF), using ant colony optimization-partial least squares (ACO−PLS) regression. Predictive abilities of ACO in wavelength selection process were examined for spectrophotometric determination of these pollutants. Furthermore, ACO in combination with PLS was compared with other regression methods, such as classical least squares regression, principle component regression, PLS, and genetic algorithm−PLS, for F and HMF simultaneous determination. ACO−PLS showed superiority over other methods regarding the prediction ability of the resulted model and providing useful information about the chemical system. The proposed method was successfully applied for the determination of such hazardous materials as furaldehydes in oil refining wastewaters.

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  1. Oil Refining Research Division, Research Institute of Petroleum Industry, Tehran, Iran

    Ali Akbar Miran Beigi

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  1. Ali Akbar Miran Beigi
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Correspondence to Ali Akbar Miran Beigi.

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Ali Akbar Miran Beigi A Reliable and Rapid Method for Simultaneous Determination of Furfural and Hydroxymethyl Furfural in Oil Refinery Wastewaters by Ant Colony/Partial Least-Squares Analysis. J Anal Chem 75, 1486–1496 (2020). https://doi.org/10.1134/S1061934820110039

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  • DOI: https://doi.org/10.1134/S1061934820110039

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