Self-adaptive firefly algorithm with neural network for design modelling and optimization of boiler plants

Savargave, Sangram Bhagwanrao; Lengare, M. J.

doi:10.1109/i-smac.2017.8058357

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…It needs input and output values for training the ANN. It presents higher accuracy than BP for nonlinear correlations Lengare, 2017).…”

Section: Firefly Algorithmmentioning

confidence: 98%

“…(55) Where the second term is formed due to the attraction between fireflies, and the third term is a random motion with α t ϵ t i representing the number selected in a random way using the uniform Gaussian distribution for some time t, y α t is the randomization parameter. When β 0 = 0, the firefly chooses the random motion, and if γ = 0 a minimum is obtained Lengare, 2017).…”

Section: Firefly Algorithmmentioning

confidence: 99%

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A review of the different boiler efficiency calculation and modeling methodologies

Mojica-Cabeza¹,

Sánchez²,

Silva-Rodríguez

et al. 2021

Inf. tec.

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A review of the different mathematical methodologies for calculating energy efficiency in boilers was carried out in this work, considering both the methods included in standards and the proposals and applications published in research works. The classification was delimited in analytical methods, mechanistic modeling, and empirical modeling; moreover, the main equations for each of the methodologies are presented, which allows building a compilation that is expected to be useful for a first approach to the subject. It is displayed that those mechanistic models are used to evaluate subsystems or specific cases that require a high level of detail, while analytical models are used to make a first approximation to the systems described, and empirical models stand out in terms of their use at the industrial level if there is access to a starting database to adjust them.

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“…It needs input and output values for training the ANN. It presents higher accuracy than BP for nonlinear correlations Lengare, 2017).…”

Section: Firefly Algorithmmentioning

confidence: 98%

Section: Firefly Algorithmmentioning

confidence: 99%

A review of the different boiler efficiency calculation and modeling methodologies

Mojica-Cabeza¹,

Sánchez²,

Silva-Rodríguez

et al. 2021

Inf. tec.

View full text Add to dashboard Cite

show abstract

A Comprehensive Review and Performance Analysis of Firefly Algorithm for Artificial Neural Networks

Nayak¹,

Naik

Pelusi

et al. 2019

Nature-Inspired Computation in Data Mining and Machine Learning

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Main Steam Temperature Control Based on Variable Universe Fuzzy Dynamic Matrix Control

Lian

2022

Therm. Eng.

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Self-adaptive firefly algorithm with neural network for design modelling and optimization of boiler plants

Cited by 3 publications

References 23 publications

A review of the different boiler efficiency calculation and modeling methodologies

A review of the different boiler efficiency calculation and modeling methodologies

A Comprehensive Review and Performance Analysis of Firefly Algorithm for Artificial Neural Networks

Main Steam Temperature Control Based on Variable Universe Fuzzy Dynamic Matrix Control

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