2013
DOI: 10.1089/ees.2012.0158
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Evaluation of Anaerobic Biofilm Reactor Kinetic Parameters Using Ant Colony Optimization

Abstract: Fixed bed reactors with naturally attached biofilms are increasingly used for anaerobic treatment of industry wastewaters due their effective treatment performance. The complex nature of biological reactions in biofilm processes often poses difficulty in analyzing them experimentally, and mathematical models could be very useful for their design and analysis. However, effective application of biofilm reactor models to practical problems suffers due to the lack of knowledge of accurate kinetic models and uncert… Show more

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Cited by 13 publications
(6 citation statements)
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“…Until now, several researchers have developed various metaheuristics, which find their source of inspiration in nature. Some of them are as follows: genetic algorithm (GA) (first described by John Henry Holland in the 1960s) is based on the natural selection [2][3][4], ant colony optimization (ACO) (initially proposed by Marco Dorigo in 1992) finds its inspiration from ant colony behavior [5][6][7], particle swarm optimization (PSO) (developed by James Kennedy and Russell C. Eberhart in 1995) is based on social flocking behavior of birds [8][9][10], artificial bee colony (ABC) (invented by Dervis Karaboga in 2005) is inspired by intelligent foraging behavior of honey bee swarm [11,12], magnetic charged system search [13], charged system search [14], firefly algorithm (FA) (created by Xin-She Yang in 2008) is inspired by the flashing light pattern of fireflies [15][16][17][18], biogeography-based optimization (BBO) (introduced by Dan Simon in 2008) is based on the equilibrium theory of island biogeography [19][20][21], bat algorithm (BA) (proposed by Xin-She Yang in 2010) is a metaheuristic algorithm, which is inspired by the echolocation behavior of microbats [22,23], and more recently, butterfly optimization algorithm (BOA) (developed by Arora and Singh in 2015) which finds its source of inspiration in food foraging behavior of butterflies [24].…”
Section: List Of Symbols Bmentioning
confidence: 99%
“…Until now, several researchers have developed various metaheuristics, which find their source of inspiration in nature. Some of them are as follows: genetic algorithm (GA) (first described by John Henry Holland in the 1960s) is based on the natural selection [2][3][4], ant colony optimization (ACO) (initially proposed by Marco Dorigo in 1992) finds its inspiration from ant colony behavior [5][6][7], particle swarm optimization (PSO) (developed by James Kennedy and Russell C. Eberhart in 1995) is based on social flocking behavior of birds [8][9][10], artificial bee colony (ABC) (invented by Dervis Karaboga in 2005) is inspired by intelligent foraging behavior of honey bee swarm [11,12], magnetic charged system search [13], charged system search [14], firefly algorithm (FA) (created by Xin-She Yang in 2008) is inspired by the flashing light pattern of fireflies [15][16][17][18], biogeography-based optimization (BBO) (introduced by Dan Simon in 2008) is based on the equilibrium theory of island biogeography [19][20][21], bat algorithm (BA) (proposed by Xin-She Yang in 2010) is a metaheuristic algorithm, which is inspired by the echolocation behavior of microbats [22,23], and more recently, butterfly optimization algorithm (BOA) (developed by Arora and Singh in 2015) which finds its source of inspiration in food foraging behavior of butterflies [24].…”
Section: List Of Symbols Bmentioning
confidence: 99%
“…Once this learning is complete, ANN can be used for prediction. There are several variations of ANN, such as feed-forward, backpropagation, recurrent, counter-propagation, radial basis function, pulsed, and fuzzy-based [24][25][26][27] and, as it is a well-researched field, ANN has applications in almost all engineering fields.…”
Section: Artificial Neural Network (Ann)mentioning
confidence: 99%
“…The non-linear application of ACO was utilized in the research conducted by Satya and Venkateswarlu [25] to estimate the kinetic and film thickness model parameters that give the best performance of an anaerobic biofilm reactor. An optimal substrate composition was achieved by Verdaguer et al [68] for co-digestion of sewage sludge and agrifood wastes by ACO for combinatorial or continuous optimization, and the simulation resulted in maximized volume of waste and biogas.…”
Section: Ant Colony Optimization (Aco)mentioning
confidence: 99%
“…Cascaded anaerobic ponds are the most commonly used process for the treatment of wastewaters to withstand high organic loading rates, such as for palm oil mill effluent (Fulazzaky, 2013). High-rate anaerobic treatment of pharmaceutical wastewaters in a packedbed biofilm reactor with the various types of supporting materials possesses a basic understanding of fixed-film biological reactor processes (Gullicks et al, 2011;Satya and Venkateswarlu, 2013). The materials used to retain active biomass in the reactor can be arranged in various confirmations made out of different materials, such as plastics, granular activated carbon, sand reticulated foam polymers, granite, quartz and stones, and can be loosely or modularly packed.…”
Section: Introductionmentioning
confidence: 99%