Optimization of napthalene biodegradation by a genetic algorithm based response surface methodology

Abstract

-Naphthalene biodegradation was studied using the bacterial strain Pseudomonas putida S2. Three medium variables out of seven medium components were selected under Plakett-Burman (PB) design as having significant response on naphthalene biodegradation. These variables were citric acid (additional carbon sources), ammonium sulfate and sodium chloride. The levels of these three variables were optimized by the application of genetic algorithm (GA) based response surface methodology (RSM) in terms of maximum biodegradation efficiency. The maximum biodegradation efficiency of 55.51% was observed at concentrations of 1.0 g L -1 , 1.0 g L -1 , and 0.7g L -1 for citric acid, ammonium sulfate and sodium chloride, respectively. In addition, the interactive effects of significant medium variables were analyzed using three dimensional surface plots simulated by network output in terms of maximum fitness function.

Process optimization and comparative study on naphthalene biodegradation in anaerobic, anoxic, and aerobic moving bed bioreactors

“…RSM is a collection of mathematical and statistical tools for modeling and optimization of process parameters. The interaction effect among the process parameters can be analyzed through design of experiment with the help of RSM …”

“…The interaction effect among the process parameters can be analyzed through design of experiment with the help of RSM. 28,29 In this investigation, instead of using a pure culture mixed microbial consortium originated from cow dung slurry was used to serve as best microbial source for naphthalene removal in three different environments namely anaerobic, anoxic, and aerobic MBBRs. A comparative study has also been done between three reactors to perceive a suitable condition.…”

Valorization of Glycerol into Polyhydroxyalkanoates by Sludge Isolated Bacillus sp. RER002: Experimental and Modeling Studies

“…Moreover, the gradient methods often fail to converge to the global minima and stick to local minima [44]. GA is an effective stochastic global search algorithm which is inspired by the evolutionary features of biological systems [45][46][47].…”

Genetic Algorithm Development for Prediction of Modified Langmuir Isotherm Parameters of Asphaltene Adsorption onto Metal Surfaces: Using Novel Quartz Crystal Nanobalance

“…[19] Furthermore, recent studies show the multilayer formation on minerals depends on the type of asphaltene and their contact time with the surface. [20,22,29] In recent years, the concept of GAs has gained wide popularity in many areas of chemical and petroleum engineering such as reservoir studies, [33,34] optimization, [35,36] polymerization process, [37] and catalytic process. [38] Genetic algorithm (GA) is a search technique used in computing to find exact or approximate solutions to optimization and search problems.…”