Optimization Of Peroxone Reaction Rate Using Metaheuristic Approach In The Dearomatization And Discoloration Process

Keywords

artificial neural network; cuckoo optimization algorithm; diazo dye; kinetic analysis; peroxone processes

Abstract

The main objective of this research focused on the optimization of apparent reaction rate constants (kapp) for degradation of a diazo compound, reactive yellow 42 (RY42) using peroxone reaction. Artificial Neural Network (ANN) was adopted to evaluate the effects of initial RY42 concentration, influent ozone mass flow rate, initial ozone (O3)/peroxide hydrogen ratio (H2O2), and pH on the discoloration rate constant. A proposed model by ANN was chosen after analysis of the topology based on R-square (R2) and mean squared error (MSE) criteria followed by coupling the obtained best net with a novel evolutionary algorithm named Cuckoo Optimization Algorithm (COA). Although the relative importance of pH and O3/H2O2 on kapp was at the same level (about 18%), initial dye concentration (34%), and influent ozone mass flow rate (30%) were the most important factors. A systematic ANN-COA approach indicated that the optimum values of pH and O3/H2O2 for 150 mg L−1 of RY42 were 7.5 and 1.44 L h−1, respectively, resulted in relatively different kapp for decolorization (0.18 min−1) and dearomatization (0.056 min−1). To confirm the validity of kinetic analysis, the predicted values were correlated with experimental values, which indicated the high degree of accuracy (R2= 0.98) for optimization of the peroxone process. This research with the experimental and modeling studies can be used as a guide for the design of full-scale treatment processes for various types of industrial wastewater containing aromatic compounds with diazo groups. © 2017 American Institute of Chemical Engineers Environ Prog, 37: 695–702, 2018.

Publication Date

3-1-2018

Publication Title

Environmental Progress and Sustainable Energy

Volume

37

Issue

2

Number of Pages

695-702

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1002/ep.12741

Socpus ID

85043503267 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85043503267

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