Application of artificial neural networks (ANN) for modeling of industrial hydrogen plant

Zamaniyan, Akbar; Joda, Fatemeh; Behroozsarand, Alireza; Ebrahimi, Hadi

doi:10.1016/j.ijhydene.2013.02.136

Cited by 86 publications

(27 citation statements)

References 23 publications

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“…The accuracy of the ANN to predict the rate of CO and H 2 production were measured using parameters such as the mean square error (MSE) and the correlation coefficient (R) [27]. The MSE defined in Equation (3) was used to measure the average squared difference between the predicted rate of CO and H 2 production and the actual values.…”

Section: Evaluation Of the Ann Performancementioning

confidence: 99%

“…Nasr et al [26] reported the use of ANN for the predictive modeling of biohydrogen production using a back-propagation configuration and concluded that the experimental and the predicted biohydrogen production were strongly correlated. Zamaniyan et al [27] employed a three-layer back-propagation feed-forward ANN for modeling industrial plant hydrogen. The study revealed that the ANN accurately predicted the temperature, pressure, and mole fraction of the hydrogen production in the plant.…”

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confidence: 99%

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Artificial Intelligence Modelling Approach for the Prediction of CO-Rich Hydrogen Production Rate from Methane Dry Reforming

et al. 2019

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This study investigates the applicability of the Leven-Marquardt algorithm, Bayesian regularization, and a scaled conjugate gradient algorithm as training algorithms for an artificial neural network (ANN) predictively modeling the rate of CO and H 2 production by methane dry reforming over a Co/Pr 2 O 3 catalyst. The dataset employed for the ANN modeling was obtained using a central composite experimental design. The input parameters consisted of CH 4 partial pressure, CO 2 partial pressure, and reaction temperature, while the target parameters included the rate of CO and H 2 production. A neural network architecture of 3 13 2, 3 15 2, and 3 15 2 representing the input layer, hidden neuron layer, and target (output) layer were employed for the Leven-Marquardt, Bayesian regularization, and scaled conjugate gradient training algorithms, respectively. The ANN training with each of the algorithms resulted in an accurate prediction of the rate of CO and H 2 production. The best prediction was, however, obtained using the Bayesian regularization algorithm with the lowest standard error of estimates (SEE). The high values of coefficient of determination (R 2 > 0.9) obtained from the parity plots are an indication that the predicted rates of CO and H 2 production were strongly correlated with the observed values.To overcome these challenges, several supported metal-based catalysts have been developed and tested. An extensive review by Abdullah et al. [10] revealed that supported nickel (Ni) catalysts have been mostly investigated for methane dry reforming due to its high catalytic performance. Nevertheless, the Ni-based catalysts are very prone to sintering and carbon deposition [11]. On the other hand, cobalt (Co)-based catalysts which have a comparative activity to Ni have been reported to show superior stability compare to Ni under the same process condition [12,13]. In our previous studies, the use of rare-earth metal oxide-supported Co catalysts for CO-rich hydrogen production showed considerable activity and stability [14][15][16]. However, one major challenge is understanding the kinetics of the methane dry reforming in terms of the rate of H 2 and CO production due to variations in the chemical composition of the various catalysts [17]. This challenge can be overcome by employing an artificial intelligence modeling approach for a better understanding of the process parameters [18,19]. Processes with non-linear and complex relationships between the input and the output parameters are often encountered in real life processes. The better understanding of the non-linear relationship between the input and the output parameters of the process can further be utilized to optimize the process operation and create the basis for the theoretical framework, process automation, and upscaling [20].An artificial intelligence modeling approach using an artificial neural network (ANN) has been widely employed for different catalytic processes, such as hydrodesulfurization [20], methanol steam reforming, glycerol steam reforming [2...

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Section: Evaluation Of the Ann Performancementioning

confidence: 99%

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confidence: 99%

Artificial Intelligence Modelling Approach for the Prediction of CO-Rich Hydrogen Production Rate from Methane Dry Reforming

et al. 2019

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“…Dynamic viscosities and densities of the binary mixture water-EG at several temperatures are well known [11][12][13][14][15][16] . To our knowledge, experimental ternary dynamic viscosities and densities as function of temperature are only published in a previous study for a limited range of compositions and temperatures [17][18][19][20] in spite of the importance of EG in thermal separations. This limitation could not give a good representation of these properties in an extractive distillation column.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Prediction of the Density and Viscosity of the Ternary System Water-Ethanol-Ethylene Glycol Using Support Vector Machine

Kianfar

2020

Fine Chemical Engineering

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Ethylene glycol is an organic solvent used in extractive distillation to separate water-ethanol mixtures. An appropriate process description requires accurate physical property data. In this paper, experimental liquid densities and dynamic viscosities of pure ethylene glycol as well as the ternary system water-ethanol-ethylene glycol are presented over a wide temperature range (298.15 to 328.15 K) at atmospheric pressure. Density and viscosity data of the ternary system of water-ethanol-ethylene glycol solutions were obtained from the literature and a support vector machine model was used to predict the density and viscosity of this system. The determination coefficients for density and viscosity of the ternary system of water-ethanol-ethylene glycol are 0.9854 and 0.9892, respectively. The mean square errors of density and viscosity are obtained 4.6572×10-4 and 3.4920×10-4, respectively. The results confirmed that the proposed method can predict the density and viscosity of the ternary system of water-ethanol-ethylene glycol as a function of temperature, using a support vector machine.

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“…Each of the mentioned methods has been used widely but individually. For example, CDD was investigated in building and optimizing [10][11][12][13][14][15], FEM was used in various engineering fields [9,[16][17][18], ANN was employed for thermal or other engineering disciplines [19][20][21][22][23], and MOGA was utilized for optimizing multiple responses [24][25][26]. Although these methods were well known, hybridization of them has not been developed for optimization of the LCM.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Hybrid Approach of Finite Element Method, Artificial Neural Network-Based Multiobjective Genetic Algorithm for Computational Optimization of a Linear Compliant Mechanism of Nanoindentation Tester

Chau

Dao

Nguyễn

2018

Mathematical Problems in Engineering

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This paper proposes a new evolutionary multiobjective optimization technique for a linear compliant mechanism of nanoindentation tester. The mechanism design is inspired by the elastic deformation of flexure hinge. To improve overall static performances, a multiobjective optimization design was carried out. An efficient hybrid optimization approach of central composite design (CDD), finite element method (FEM), artificial neural network (ANN), and multiobjective genetic algorithm (MOGA) is developed to solve the optimization problem. In this procedure, the CDD is used to lay out the experimental data. The FEM is developed to retrieve the quality performances. And then, the ANN is developed as black box to call the pseudo-objective functions. Unlike previous studies on multiobjective evolutionary algorithms, most of which generating only one Pareto-optimal solution, this proposed approach can generate more than three Pareto-optimal solutions. Based on the user’s real-work problem, one of the best optimal solutions is chosen. The results showed that the optimal results were found at the displacement of 330.68 μm, stress of 140.65 MPa, and safety factor of 3.6. The statistical analysis is conducted to investigate the behavior of the MOGA. The sensitivity analysis was carried out to determine the significant contribution of each factor. The results revealed that the lengths and thickness almost significantly affect both responses. It confirms that the proposed hybrid optimization approach gains high robustness and effectiveness with flexible decision maker rules to solve complex optimization engineering problems.

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Application of artificial neural networks (ANN) for modeling of industrial hydrogen plant

Cited by 86 publications

References 23 publications

Artificial Intelligence Modelling Approach for the Prediction of CO-Rich Hydrogen Production Rate from Methane Dry Reforming

Artificial Intelligence Modelling Approach for the Prediction of CO-Rich Hydrogen Production Rate from Methane Dry Reforming

Simultaneous Prediction of the Density and Viscosity of the Ternary System Water-Ethanol-Ethylene Glycol Using Support Vector Machine

An Efficient Hybrid Approach of Finite Element Method, Artificial Neural Network-Based Multiobjective Genetic Algorithm for Computational Optimization of a Linear Compliant Mechanism of Nanoindentation Tester

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