Semi-autogenous mill power model development using gene expression programming

Hoseinian, Fatemeh Sadat; Faradonbeh, Roohollah Shirani; Abdollahzadeh, Ali; Rezai, Bahram; Soltani-Mohammadi, Saeed

doi:10.1016/j.powtec.2016.11.045

Cited by 48 publications

(18 citation statements)

References 19 publications

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“…To overcome the disadvantageous of GA and GP algorithms, a new populationbased evolutionary algorithm, named gene expression programming (GEP), was introduced by Ferreira [11][12][13]. Inherent ability of GEP to illustrate an equation by consideration of independent 3 practical parameters as input to estimate the predefined output with acceptance accuracy distinguish GEP respect to the other modeling approaches [14][15][16][17]. The main component of GEP are terminal set, termination condition, fitness function, control parameters and function set [10].…”

Section: Gene Expression Programming (Gep)mentioning

confidence: 99%

“…In this approach, every gene has two elements as head and tail. The former composed of terminal and function while, the later only contain of terminal symbols [17,25]. GEP designer defined the head length (h) while the length of tail (t) is determined as Eq.…”

Section: Gene Expression Programming (Gep)mentioning

confidence: 99%

“…5. Determination of genetic operators as [10,28]: [17,30] This operator has the highest efficient operator within the length of chromosome with intrinsic modification power. Mutation is able to change the terminal or function each other in the head and convert the terminal to each other in the tail.…”

Section: Selection Of the Linking Function;mentioning

confidence: 99%

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Prediction of the size of silver nanoparticles prepared via green synthesis: A gene expression programming approach

Sattari

Khayati

2020

Scientia Iranica

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This study presents a new prediction model for estimating the size of silver nanoparticle (AgNPs) prepared by green synthesis via gene expression programming (GEP). Firstly, 30 different experiments were used to construct the GEP models. Plant extract, reaction temperature, concentration of silver nitrate (AgNO 3) and stirring time parameters were considered as input variables and the size of AgNPs parameter selected as output variable. Collected experimentally data randomly divided into 8 testing sets and 22 training sets for further analysis. By consideration of correlation coefficient (R 2), mean absolute error (MAE), root relative square error (RRSE) as criteria, the performance of proposed models by GEP were compared each other. Finally, the best model (i.e., GEP-1) with R 2 =0.9961, MAE=0.2545 and RRSE=0.0668 proposed as a new model with simplified mathematical expressions to estimate the size of AgNPs. The results of sensitivity analysis showed that the amount of plant extract, the concentration of AgNO 3 , stirring time and reaction temperature are the most effective parameters on the size of AgNPs, respectively. Proposed model can be extended for a wide range of applications and provides the possibility of minimum materials consumption for preparation of the lowest size of AgNPs by consideration of practical or economic constraints.

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Section: Gene Expression Programming (Gep)mentioning

confidence: 99%

Section: Gene Expression Programming (Gep)mentioning

confidence: 99%

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Prediction of the size of silver nanoparticles prepared via green synthesis: A gene expression programming approach

Sattari

Khayati

2020

Scientia Iranica

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“…GEP is a system of transcoding the language of chromosomes which consists of the random combination of terminals and functions to the language of the expression trees as nonlinear entities with various shape and sizes. The genes are specified as sub‐expression trees (Hoseinian, Faradonbeh, Abdollahzadeh, Rezai, & Soltani‐Mohammadi, ; Kaboli, Fallahpour, Selvaraj, & Rahim, ; Mirabbasi, Kisi, Sanikhani, & Gajbhiye Meshram, ). The GEP algorithm has been used by some researchers in various sciences; such as estimation of the porosity effect on predicting compressive and flexural strength (Emamian & Eskandari‐Naddaf, ), prediction of energy and exergy analysis (Şencan Şahin, Kovacı, & Dikmen, ), estimation of soil temperature in a semiarid region (Behmanesh & Mehdizadeh, ), estimation of daily global solar radiation (Sharifi, Rezaverdinejad, & Nourani, ).…”

Section: Introductionmentioning

confidence: 99%

Mathematical model for mint drying kinetics prediction by freeze‐drying process: Gene expression programming

Kovacı

Dikmen

Şahin

2020

J Food Process Engineering

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The drying ratio (DR) and the moisture ratio (MR) are the main drying characteristics properties which play an important role in the drying conditions. Therefore, it has been crucial for the accurate estimation of the selection of drying conditions needed for improving optimal drying process. In this study, DR and MR values have been experimentally determined, and these characteristics have been predicted as mathematical expressions by using gene expression programming (GEP). The outcomes of present study have been pointed out that the developed GEP models have been estimating target values with an acceptable accuracy guaranteed by using different statistical techniques. The R2 values have been denoted 0.973 and 0.972, whereas the RMSE values have been determined as 0.032324 and 0.013146 for developed models of DR and MR, respectively. The χ2values have been specified 0.001306 and 0.000194 for developed models of DR and MR, respectively. The developed nonlinear equations could be evaluated in areas you need to accurate and quick estimation of drying characteristics such as drying rate and moisture rate of mint. Practical Applications Mint drying kinetics (DR and MR) at different pressure of 30, 50, and 80 kPa and temperature of 40, 50, and 60°C have been experimentally studied. GEP model has been developed to predict this drying kinetics in the freeze‐drying process. Developed model can be evaluated in areas where you need to accurate and quick estimation of drying characteristics such as drying rate and moisture rate of mint.

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“…Grinding is known to be an energy-intensive operation process, since it consumes approximately two-thirds of the total energy of the mineral processing plants [3]. The literature reported that the power estimation of tumbling mills plays an important role in determining the grinding efficiency and mill performance [4,5]. To this end, establishing power equations of tumbling mill has attracted much attention in recent years.…”

Section: Introductionmentioning

confidence: 99%

Effect of the Lifting Velocity and Container Shape on Angle of Repose of Iron Ore Particles

Peng

Zhu

et al. 2017

Advances in Materials Science and Engineering

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To investigate the impact of lifting velocity and container shape on angle of repose, the fixed-base cylinder method was performed using three types of container shape. The container shape was lifted a series of lifting velocities. Six size fractions of iron ore particles ranging from coarser to fine particles were used as the test materials. And the sand-pile calibration method was then used to calibrate the contact parameters of iron ore particles. Results show angle of repose decreased exponentially with the lifting velocity, while it appeared approximately to be invariant to particle shape, for all size fractions. The sand pile highly depends on the container shape at a low lifting velocity but appears to be invariant to particle size for a high lifting velocity. And then a predictive equation is established and a very close agreement between the predicted and measured angle of repose is attained. Finally, a series of DEM simulations considering the irregular particle shape are conducted by means of sphere clump method to calibrate the contact parameters and are in good visual agreement with the experimental results, indicating the "tuned" contact parameters as well as the applicability of the predicted equation.

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Semi-autogenous mill power model development using gene expression programming

Cited by 48 publications

References 19 publications

Prediction of the size of silver nanoparticles prepared via green synthesis: A gene expression programming approach

Prediction of the size of silver nanoparticles prepared via green synthesis: A gene expression programming approach

Mathematical model for mint drying kinetics prediction by freeze‐drying process: Gene expression programming

Effect of the Lifting Velocity and Container Shape on Angle of Repose of Iron Ore Particles

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