Performance evaluation of microbial fuel cell by artificial intelligence methods

Garg, Akhil; Vijayaraghavan, Vineeth; Mahapatra, Siba Sankar; Tai, Kang; Wong, Chee How

doi:10.1016/j.eswa.2013.08.038

Cited by 91 publications

(34 citation statements)

References 39 publications

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“…For more details, please refer to [138]. GP mimics the evolution process, and uses crossover and mutation operations for learning [138]. In GP, the solutions to a problem are represented by tree structures, and an initial structure is generated randomly.…”

Section: Data Mining Methodsmentioning

confidence: 99%

“…The optimal structure is learned via heuristic searching over all structures created by crossover and mutation operations until some termination criterion is met. For more details, please refer to [138]. GP mimics the evolution process, and uses crossover and mutation operations for learning [138].…”

Section: Data Mining Methodsmentioning

confidence: 99%

“…Most models used for MFC modeling are based on linear regression (LR), and these models usually have good interpretability and simple model structures [125][126][127][129][130][131][132][133]. In recent years, data mining methods were also introduced for modeling the MFC performance such as artificial neural network (ANN), genetic programming (GP), support vector machine (SVM) and relevance vector machine (RVM) [128,137,138]. These data mining methods can yield good prediction performance.…”

Section: Overviewmentioning

confidence: 99%

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A Review of Modeling Bioelectrochemical Systems: Engineering and Statistical Aspects

et al. 2016

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Bioelectrochemical systems (BES) are promising technologies to convert organic compounds in wastewater to electrical energy through a series of complex physical-chemical, biological and electrochemical processes. Representative BES such as microbial fuel cells (MFCs) have been studied and advanced for energy recovery. Substantial experimental and modeling efforts have been made for investigating the processes involved in electricity generation toward the improvement of the BES performance for practical applications. However, there are many parameters that will potentially affect these processes, thereby making the optimization of system performance hard to be achieved. Mathematical models, including engineering models and statistical models, are powerful tools to help understand the interactions among the parameters in BES and perform optimization of BES configuration/operation. This review paper aims to introduce and discuss the recent developments of BES modeling from engineering and statistical aspects, including analysis on the model structure, description of application cases and sensitivity analysis of various parameters. It is expected to serves as a compass for integrating the engineering and statistical modeling strategies to improve model accuracy for BES development.

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Section: Data Mining Methodsmentioning

confidence: 99%

Section: Data Mining Methodsmentioning

confidence: 99%

Section: Overviewmentioning

confidence: 99%

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A Review of Modeling Bioelectrochemical Systems: Engineering and Statistical Aspects

et al. 2016

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“…Although both GP and GA share the same working principle but there exists difference between them. The GP represents the problem solution as a tree in a symbolic form (Poli, Langdon, & McPhee, 2008;Raja, 2008) whereas GA evolves solutions represented by strings (binary or real number) of fixed length (Garg, Vijayaraghavan, Mahapatra, Tai, & Wong, 2014). Figure 2 below illustrates the main parts of GP approach.…”

Section: Multi-gene Genetic Programmingmentioning

confidence: 99%

Optimization of Windspeed Prediction Using an Artificial Neural Network Compared With a Genetic Programming Model

Deo

Ghimire

Downs

et al. 2018

Advances in Computational Intelligence and Robotics

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“…O 2 detectors are also used in order to detect oxygen in some chemical manufacturing processes. So far, a few O 2 sensors based on nanotubes have been developed [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

A computational study on the interaction between O$_{2}$ and pristine and Ge-doped aluminum phosphide nanotubes

Rezaei–Sameti¹,

Kazemi²

2015

Turk J Phys

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Abstract:In this research the interaction between oxygen molecules and the outside and inside surfaces of pristine and Ge-doped (4,4) armchair and (6,0) zigzag models of aluminum phosphide nanotubes (AlPNTs) was systematically investigated using density function theory. Structural parameters, adsorption energy, quantum parameters, HOMO/LUMO orbitals, and nuclear quadrupole resonance (NQR) parameters were calculated for all models of AlPNTs. The aim of this work was to investigate the effects of Ge doping and O 2 adsorption on the electrical and structural parameters of (4,4) armchair and (8,0) zigzag models of AlPNTs. The results revealed that adsorption energies for all models were negative with exothermic chemical bonding. By doping Ge in spite of the B52 site of (4,4) armchair and (6,0) zigzag models the adsorption energy increased significantly from pristine values and therefore Ge doping increased the reactivity of the nanotubes to O 2 adsorption. The NQR results showed that in AlPNTs Al atoms at the edges of nanotubes played a significant role in determining the electronic behaviors of AlPNTs and the average values of C Q ( 27 Al) and ηQ for the O 2 attached on (4,4) armchair and (6,0) zigzag AlPNTs were higher than those of the pristine model. Analysis of the electronic properties indicated that adsorption of O 2 reduced the energy gap of AlPNTs. Quantum molecular resultsshowed that the global hardness ( η) of Ge-doped models was smaller than that of other models.

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Performance evaluation of microbial fuel cell by artificial intelligence methods

Cited by 91 publications

References 39 publications

A Review of Modeling Bioelectrochemical Systems: Engineering and Statistical Aspects

A Review of Modeling Bioelectrochemical Systems: Engineering and Statistical Aspects

Optimization of Windspeed Prediction Using an Artificial Neural Network Compared With a Genetic Programming Model

A computational study on the interaction between O$_{2}$ and pristine and Ge-doped aluminum phosphide nanotubes

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