A design of higher-level control based genetic algorithms for wastewater treatment plants

Trung, Hai; Bach, Nam Van; Nguyen, Lanh Van; Tran, Hoang T.; Nguyen, Minh Tuan

doi:10.1016/j.jestch.2021.01.004

Cited by 17 publications

(11 citation statements)

References 21 publications

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“…Selected individuals undergo crossover and mutation operations in genetics to generate a new population composed of a new generation of individuals. Analogous to the evolution of natural organisms, through iterative evolution, the optimal solution of the entire population is obtained [30] .…”

Section: Application Of Improved Genetic Algorithm In Mine Water Sche...mentioning

confidence: 99%

Mine water cooperative optimal scheduling based on improved genetic algorithm

Liu,

Zhang,

Zhu

et al. 2024

Heliyon

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Section: Application Of Improved Genetic Algorithm In Mine Water Sche...mentioning

confidence: 99%

Mine water cooperative optimal scheduling based on improved genetic algorithm

Liu,

Zhang,

Zhu

et al. 2024

Heliyon

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“…Genetic algorithm (GA) [6] is a classical and advanced evolutionary algorithm, and it mainly involves the operators of tournament selection, crossover and mutation. Owning its good versatility, GA has been found in many fields such as assembly job shop scheduling [15], control of wastewater treatment plants [16], indoor space compositions [17] and optimal receive beamforming in spatial antenna diversity system [18]. Since CHPED has complex characteristics and is A modified genetic algorithm for combined heat and power economic dispatch different from these problems, it is necessary to improve GA to suit CHPED.…”

Section: Modified Genetic Algorithmmentioning

confidence: 99%

A modified genetic algorithm for combined heat and power economic dispatch

Yang

Zou

2023

Preprint

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Combined heat and power economic dispatch (CHPED) is an important issue in the energy field, since it has great potential in improving the utilization efficiency of power and heat energies. This paper proposes a modified genetic algorithm (MGA) to determine the power and heat outputs of three kinds of units for CHPED. First, MGA replaces the simulated binary crossover by a new one based on the uniform and guassian distributions, and its convergence can be enhanced. Second, MGA modifies the mutation operator by introducing a disturbance coefficient based on guassian distribution, which can decrease the risk of being trapped into local optima. Eight instances with or without prohibited operating zones are used to investigate the efficiencies of MGA and other four genetic algorithms for CHPED. MGA attains the smallest mean and standard deviation related to 50 cost values for every instance, thus it has desirable convergence and stability for CHPED in comparison with the other four genetic algorithms.

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“…If all three operations remain constant throughout the algorithm, it is referred to as a simple genetic algorithm [83]. GA begins with no knowledge of the optimal solution and relies entirely on environmental responses to find the best solution by utilizing evolutionary principles [97]. Application of GA starts from a population containing individual data structures that resemble chromosomes, which consists of genes that encode the individual's hereditary characters that can be reproduced when running the algorithm [98][99][100].…”

Section: Mathematical Formulation and Backgroundmentioning

confidence: 99%

Modeling Energy Gap of Doped Tin (II) Sulfide Metal Semiconductor Nanocatalyst Using Genetic Algorithm‐Based Support Vector Regression

Okoye

Azi

Owolabi

et al. 2022

Journal of Nanomaterials

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Tin (II) sulfide (SnS) is a metal chalcogenide semiconducting material with fascinating and admirable physical features for practical applications in solid-state batteries, photodetectors, gas sensors, optoelectronic devices, emission transistors, and photocatalysis among others. The energy gap of SnS semiconductor nanomaterial that facilitates its usefulness in many applications can be adjusted through dopant incorporation which results in crystal lattice distortion at various crystallite sizes of the semiconductor. This work employs lattice parameter descriptors to develop a hybrid genetic algorithm (GA) and support vector regression algorithm (SVR) intelligent model for determining the energy gap of doped SnS semiconductors. The predictive strength of the developed GA-SVR model is compared with the stepwise regression algorithm- (STRA-) based model using different performance evaluation parameters. The developed GA-SVR model performs better than STRA model based on root mean square error, mean absolute error, and correlation coefficient with performance improvement of 70.68%, 67.63%, and 20.98%, respectively, using the testing set of data. Influence of different dopants and experimental conditions on energy gap of SnS semiconductor were investigated using the developed model, while the obtained values for the energy gaps agree with the measured values. The developed models demonstrate high degree of potentials in terms of accuracy, precision, and ease of implementation that fosters their real-life applicability in estimating the energy gap of doped SnS semiconductor with experimental stress circumvention.

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A design of higher-level control based genetic algorithms for wastewater treatment plants

Cited by 17 publications

References 21 publications

Mine water cooperative optimal scheduling based on improved genetic algorithm

Mine water cooperative optimal scheduling based on improved genetic algorithm

A modified genetic algorithm for combined heat and power economic dispatch

Modeling Energy Gap of Doped Tin (II) Sulfide Metal Semiconductor Nanocatalyst Using Genetic Algorithm‐Based Support Vector Regression

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