A catalyst selection method for hydrogen production through Water-Gas Shift Reaction using artificial neural networks

Cavalcanti, Fabio Machado; Schmal, Martín; Giudici, Reinaldo; Alves, Rita M.B.

doi:10.1016/j.jenvman.2019.02.092

Cited by 42 publications

(19 citation statements)

References 32 publications

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“…Neural networks have been successfully applied to catalysis to determine the relationship between the catalyst structure and its activity [8]. As heterogeneous catalysis has developed increasingly efficient experimentation techniques, the number of new data have increased exponentially [28], both from synthesis and from characterization and catalytic tests [29].…”

Section: Kinetics and Catalysismentioning

confidence: 99%

“…In the field of heterogeneous catalysis, ANNs can be used to select better possible catalysts -cheaper, less toxic, and composed of non-precious metals -for a given reaction, thus reducing the massive number of needed high-throughput experiments, peculiar conjuncture of combinatorial catalysis [39]. In this direction, Cavalcanti et al [40] used a three-layer feedforward neural network to predict the The first and last elements in topology represent the number of neurons in the input and in the output layer, respectively. Among them, the number of neurons in the hidden layer(s).…”

Section: Kinetics and Catalysismentioning

confidence: 99%

“…ANNs have been successfully applied to chemistry to correlate spectra of analytical methods and product properties [7]; in catalysis, to determine the relationships between the catalyst structure and its activity [8]; in process modeling, to predict product performance and operating conditions [9], and particularly in process control and fault diagnosis [10]. The main reasons for the growing popularity of the neural network approach are its lower computational cost compared to other methods and its ability to solve complex nonlinear problems [5].…”

Section: Introductionmentioning

confidence: 99%

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Application of Artificial Neural Networks to Chemical and Process Engineering

Cavalcanti¹,

Kozonoe²,

Pacheco³

et al. 2021

Deep Learning Applications

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The accelerated use of Artificial Neural Networks (ANNs) in Chemical and Process Engineering has drawn the attention of scientific and industrial communities, mainly due to the Big Data boom related to the analysis and interpretation of large data volumes required by Industry 4.0. ANNs are well-known nonlinear regression algorithms in the Machine Learning field for classification and prediction and are based on the human brain behavior, which learns tasks from experience through interconnected neurons. This empirical method can widely replace traditional complex phenomenological models based on nonlinear conservation equations, leading to a smaller computational effort – a very peculiar feature for its use in process optimization and control. Thereby, this chapter aims to exhibit several ANN modeling applications to different Chemical and Process Engineering areas, such as thermodynamics, kinetics and catalysis, process analysis and optimization, process safety and control, among others. This review study shows the increasing use of ANNs in the area, helping to understand and to explore process data aspects for future research.

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Section: Kinetics and Catalysismentioning

confidence: 99%

Section: Kinetics and Catalysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Application of Artificial Neural Networks to Chemical and Process Engineering

Cavalcanti¹,

Kozonoe²,

Pacheco³

et al. 2021

Deep Learning Applications

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“…A variety of catalysts combining noble or transition metals (Pt, Rh, Au, Pd, Fe, Cu, Ni, Co) with different oxides or mixed oxides can catalyze the WGS reaction, as described in many scientific reviews and numerous papers. The process parameters and recent advances in WGS catalysis, the design of novel and effective catalyst compositions, the preparation approaches, the nature of the support, the catalyst precursors and promoter additives (if there) as well as their impact on the performance in the WGS reaction have been debated and clarified [ 5 , 6 , 7 , 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

Improved Water–Gas Shift Performance of Au/NiAl LDHs Nanostructured Catalysts via CeO2 Addition

et al. 2021

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Supported gold on co-precipitated nanosized NiAl layered double hydroxides (LDHs) was studied as an effective catalyst for medium-temperature water–gas shift (WGS) reaction, an industrial catalytic process traditionally applied for the reduction in the amount of CO in the synthesis gas and production of pure hydrogen. The motivation of the present study was to improve the performance of the Au/NiAl catalyst via modification by CeO2. An innovative approach for the direct deposition of ceria (1, 3 or 5 wt.%) on NiAl-LDH, based on the precipitation of Ce3+ ions with 1M NaOH, was developed. The proposed method allows us to obtain the CeO2 phase and to preserve the NiAl layered structure by avoiding the calcination treatment. The synthesis of Au-containing samples was performed through the deposition–precipitation method. The as-prepared and WGS-tested samples were characterized by X-ray powder diffraction, N2-physisorption and X-ray photoelectron spectroscopy in order to clarify the effects of Au and CeO2 loading on the structure, phase composition, textural and electronic properties and activity of the catalysts. The reduction behavior of the studied samples was evaluated by temperature-programmed reduction. The WGS performance of Au/NiAl catalysts was significantly affected by the addition of CeO2. A favorable role of ceria was revealed by comparison of CO conversion degree at 220 °C reached by 3 wt.% CeO2-modified and ceria-free Au/NiAl samples (98.8 and 83.4%, respectively). It can be stated that tuning the properties of Au/NiAl LDH via CeO2 addition offers catalysts with possibilities for practical application owing to innovative synthesis and improved WGS performance.

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“…The growing interest in ANN is due to its great versatility and the continuous advancement of the training algorithms of the networks and of the hardware [3]. ANN is used to study and predict the behavior of a variety of industrial processes [4,5]. Also ANNs have distinguished themselves by owning a unique combination of features (e.g., data-driven, self-adaptive in nature, inherently nonlinear gained, and universally applicable approximates).…”

Section: Introductionmentioning

confidence: 99%

Post-combustion artificial neural network modeling of nickel-producing multiple hearth furnace

Góngora

Caneghem

Haeseldonckx

et al. 2020

International Journal of Chemical Reactor Engineering

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In a nickel-producing multiple hearth furnace, there is a problem associated to the automatic operation of the temperature control loops in two of the hearths, since the same flow of air is split into two branches. A neural model of the post-combustion sub-process is built and served to increase the process efficiency of the industrial furnace. Data was taken for a three-months operating time period to identify the main variables characterizing the process and a model of multilayer perceptron type is built. For the validation of this model, process data from a four-months operating time period in 2018 was used and prediction errors based on a measure of closeness in terms of a mean square error criterion measured through its weights for the temperature of two of the hearths (four and six) versus the air flow to these hearths. Based on a rigorous testing and analysis of the process, the model is capable of predicting the temperature of hearth four and six with errors of 0.6 and 0.3 °C, respectively. In addition, the emissions by high concentration of carbon monoxide in the exhaust gases are reduced, thus contributing to the health of the ecosystem.

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A catalyst selection method for hydrogen production through Water-Gas Shift Reaction using artificial neural networks

Cited by 42 publications

References 32 publications

Application of Artificial Neural Networks to Chemical and Process Engineering

Application of Artificial Neural Networks to Chemical and Process Engineering

Improved Water–Gas Shift Performance of Au/NiAl LDHs Nanostructured Catalysts via CeO2 Addition

Post-combustion artificial neural network modeling of nickel-producing multiple hearth furnace

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