Multi-gene genetic programming based predictive models for municipal solid waste gasification in a fluidized bed gasifier

Pandey, Daya Shankar; Pan, Indranil; Das, Saptarshi; Leahy, James J.

doi:10.1016/j.biortech.2014.12.048

Cited by 60 publications

(19 citation statements)

References 26 publications

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“…In the past, different modelling approaches starting from black box modelling to thermodynamic equilibrium, kinetic rate, fluid-dynamics, neural network and genetic programming models (Pandey et al, 2015;Puig-Arnavat et al, 2010) and Gaussian process based Bayesian inference (Pan and Pandey, 2016) were applied for modelling gasification. These models were validated using pilot scale gasification data.…”

Section: As Per the Eu's New Directive Eachmentioning

confidence: 99%

Artificial neural network based modelling approach for municipal solid waste gasification in a fluidized bed reactor

et al. 2016

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In this paper, multi-layer feed forward neural networks are used to predict the lower heating value of gas (LHV), lower heating value of gasification products including tars and entrained char (LHVp) and syngas yield during gasification of municipal solid waste (MSW) during gasification in a fluidized bed reactor. These artificial neural networks (ANNs) with different architectures are trained using the Levenberg-Marquardt (LM) back-propagation algorithm and a cross validation is also performed to ensure that the results generalise to other unseen datasets. A rigorous study is carried out on optimally choosing the number of hidden layers, number of neurons in the hidden layer and activation function in a network using multiple Monte Carlo runs. Nine input and three output parameters are used to train and test various neural network architectures in both multiple output and single output prediction paradigms using the available experimental datasets. The model selection procedure is carried out to ascertain the best network architecture in terms of predictive accuracy. The simulation results show that the ANN based methodology is a viable alternative which can be used to predict the performance of a fluidized bed gasifier.2

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Section: As Per the Eu's New Directive Eachmentioning

confidence: 99%

Artificial neural network based modelling approach for municipal solid waste gasification in a fluidized bed reactor

et al. 2016

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“…MGGP is one of the most recent variants of GP, which is a popular evolutionary technique that has been extensively applied to developing data-driven nonlinear models. As a branch of GP techniques, MGGP can automatically evolve an explicit model using training data sets without the need for defining the model structure in advance [33]. This can not only facilitate the development of a mathematical model but can also avoid errors due to some subjective judgments, especially judgments regarding the model structure.…”

Section: The Mggp Methodsmentioning

confidence: 99%

Multigene Genetic-Programming-Based Models for Initial Dilution of Laterally Confined Vertical Buoyant Jets

Yan

Mohammadian

2019

JMSE

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A new approach based on the multigene genetic-programming (MGGP) technique is proposed to predict initial dilution of vertical buoyant jets subjected to lateral confinement. The models are trained and tested using experimental data, and the good matches demonstrate the generalization and predictive capabilities of the evolved MGGP-based models. The best Pareto-optimal MGGP-based model is also compared with the model evolved using a single-gene genetic-programming (SGGP) algorithm and an existing regression-based empirical equation. The comparisons reveal the superiority of the MGGP-based model. This study confirms that the MGGP technique is promising in evolving an explicit, accurate, and compact model, and the developed models can be employed to estimate effectively and efficiently the dilution properties of a laterally confined vertical buoyant jet.

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“…Multigene GP combines the power of classical linear regression with the ability to capture non-linear behavior without needing to pre-specify the structure of the non-linear model (Searson et al, 2010;Searson et al, 2007). The uniqueness of the multi-gene genetic programming based model is that it automatically evolves a mathematical expression in a symbolic form which can be analyzed further to find which variables impact the final prediction and in what fashion (Pandey et al, 2015).…”

Section: Genetic Programming (Gp)mentioning

confidence: 99%

“…The maximum allowable number of genes in an individual and the maximum tree depth directly influence the size of the search space and the number of solutions explored within the search space (Searson et al, 2007;Searson et al, 2010;Pandey et al, 2015). The allowable number of genes and tree depth were, respectively, set to optimal values as tradeoffs between the running time and the complexity of the evolved solutions (Searson et al, 2007;Searson et al, 2010;Pandey et al, 2015). The best MGGP models were chosen on the basis of providing the best fitness value on the training and testing data as well as the simplicity of the models (Bayazidi et al, 2014).…”

Section: Data and Model Developmentmentioning

confidence: 99%

A comparative study of concrete strength prediction using artificial neural network, multigene programming and model tree

Kulkarni¹,

Londhe²,

Dixit³

2019

CJSMEC

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In the current study 28 day strength of Recycled Aggregate Concrete (RAC) and Fly ash (class F) based concrete is predicted using Artificial Neural Network (ANN), Multigene Genetic Programming (MGGP) and Model Tree (MT). Four sets of models were designed for per cubic proportions of materials, Properties of materials and non-dimensional parameters as input parameters. The study shows that the predicted 28 day strength is in good agreement with the observed data and also generalize well to untrained data. ANN outperforms MGGP and MT in terms of model performance. Output of the developed models can be presented in terms of trained weights and biases in ANN, equations in MGGP and in the form of series of equations in MT. ANN, MGGP and MT can grasp the influence of input parameters which can be seen through Hinton diagrams in ANN, input frequency distribution in MGGP and coefficients of input parameters in MT. The study shows that these data driven techniques can be used for developing model/s to predict strength of concrete with an acceptable performance.

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Multi-gene genetic programming based predictive models for municipal solid waste gasification in a fluidized bed gasifier

Cited by 60 publications

References 26 publications

Artificial neural network based modelling approach for municipal solid waste gasification in a fluidized bed reactor

Artificial neural network based modelling approach for municipal solid waste gasification in a fluidized bed reactor

Multigene Genetic-Programming-Based Models for Initial Dilution of Laterally Confined Vertical Buoyant Jets

A comparative study of concrete strength prediction using artificial neural network, multigene programming and model tree

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