Analysis of hydraulic performance in a structured packing column for air/water system: RSM and ANN modeling

Naderi, Kamyar; Foroughi, Amirsoheil; Ghaemi, Ahad

doi:10.1016/j.cep.2023.109521

Cited by 11 publications

(3 citation statements)

References 37 publications

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“…Experimental design is a scientific methodology that entails manipulating input variables to acquire knowledge about the process and establish a desired input–output correlation. This approach has multiple advantages, including the ability to identify the pivotal aspects that impact the process, decrease the process's expenses, construct a model of the process, and ascertain the correlation between input and output variables 23 . RSM is a statistical modeling technique that uses quantitative data from well-designed experiments to answer numerous regression equations simultaneously 24 .…”

Section: Methodsmentioning

confidence: 99%

“…The structure of these networks is determined by weight parameters and activation functions. Throughout the learning process, a dedicated training algorithm optimizes weights and bias values, aiming to minimize error values between real parameter values and the projected values by the ANN 23 , 30 . Neurons are the tiniest units that process data.…”

Section: Methodsmentioning

confidence: 99%

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Evaluation of hydrogen production via steam reforming and partial oxidation of dimethyl ether using response surface methodology and artificial neural network

Mansouri,

Bahmanzadegan,

Ghaemi

2024

Sci Rep

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This study aims to develop two models for thermodynamic data on hydrogen generation from the combined processes of dimethyl ether steam reforming and partial oxidation, applying artificial neural networks (ANN) and response surface methodology (RSM). Three factors are recognized as important determinants for the hydrogen and carbon monoxide mole fractions. The RSM used the quadratic model to formulate two correlations for the outcomes. The ANN modeling used two algorithms, namely multilayer perceptron (MLP) and radial basis function (RBF). The optimum configuration for the MLP, employing the Levenberg–Marquardt (trainlm) algorithm, consisted of three hidden layers with 15, 10, and 5 neurons, respectively. The ideal RBF configuration contained a total of 80 neurons. The optimum configuration of ANN achieved the best mean squared error (MSE) performance of 3.95e−05 for the hydrogen mole fraction and 4.88e−05 for the carbon monoxide mole fraction after nine epochs. Each of the ANN and RSM models produced accurate predictions of the actual data. The prediction performance of the ANN model was 0.9994, which is higher than the RSM model's 0.9771. The optimal condition was obtained at O/C of 0.4, S/C of 2.5, and temperature of 250 °C to achieve the highest H2 production with the lowest CO emission.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Evaluation of hydrogen production via steam reforming and partial oxidation of dimethyl ether using response surface methodology and artificial neural network

Mansouri,

Bahmanzadegan,

Ghaemi

2024

Sci Rep

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“…However, both papers follow a predomoinantly heuristic trial-and-errorbased approach for ANN design. The most effective and efficient design of ANNs is still an open research question across a variety of disciplines [59][60][61][62][63][64][65].…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Hyperparameter Optimization of Artificial Neural Networks for Optimal Feedforward Torque Control of Synchronous Machines

Monzen,

Stroebl,

Palm

et al. 2024

IEEE Open J. Ind. Electron. Soc.

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Multi-objective hyperparameter optimization (MO-HPO) is applied to find optimal artificial neural network (ANN) architectures used for optimal feedforward torque control (OFTC) of synchronous machines. The proposed framework allows to systematically identify Pareto optimal ANNs with respect to multiple (partly) contradictory objectives such as approximation accuracy and computational burden of the considered ANNs. The obtained Pareto optimal ANNs are trained and implemented on a realtime system and tested experimentally for a nonlinear reluctance synchronous machine (RSM) against non-Pareto optimal ANN designs and a state-of-the-art OFTC approach. Finally, based on the most recent results from ANN approximation theory, guidelines for Pareto optimal ANN-based OFTC design and implementation are provided.

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Integrating response surface methodology and machine learning for analyzing the unconventional machining properties of hybrid fiber‐reinforced composites

Vinoth,

Sathiyamurthy,

Saravanakumar

et al. 2024

Polymer Composites

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The aim of this investigation was to delve into the impact of abrasive water jet machining (AWJM) process variables on the surface roughness (Ra) and kerf angle (Ka) of hybrid fiber‐reinforced polyester composites. Utilizing both response surface methodology (RSM) and artificial neural network (ANN) prediction models, the study sought to optimize the input parameters for abrasive water jet machining, specifically in the context of paddy straw and PALF‐reinforced polyester hybrid composites. The process parameters targeted for optimization included the abrasive flow rate, traverse rate, and standoff distance during AWJM. The investigation identified an optimal combination of AWJM parameters that effectively meets the practical requirements for machining hybrid fiber‐reinforced polyester composites. According to the RSM, the suggested optimal values for the process parameters are an abrasive flow rate set at 300 g/min, traverse speed at 110 mm/min, and standoff distance at 1 mm. The ANN exhibited robust predictive capabilities, achieving high R2 scores of 0.932 and 0.962 for surface roughness and kerf angle, respectively. To enhance the performance of abrasive water jet machining and minimize surface roughness and kerf angle, the researchers conducted an optimization of the process parameters. Subsequently, confirmation experiments were executed to validate the predictive model and fine‐tune the set of process parameters for practical application.Highlights Investigated AWJM impact on Ra value and kerf angle of hybrid composites. Used RSM and ANN models for parameter optimization in biocomposite. Optimal AWJM parameters: AFR (300 g/min), TS (110 mm/min), and SOD (1 mm). ANN showed strong predictions: R2 scores 0.932 (Ra) and 0.962 (Ka). Confirmation experiments validated the predictive model for applications.

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Analysis of hydraulic performance in a structured packing column for air/water system: RSM and ANN modeling

Cited by 11 publications

References 37 publications

Evaluation of hydrogen production via steam reforming and partial oxidation of dimethyl ether using response surface methodology and artificial neural network

Evaluation of hydrogen production via steam reforming and partial oxidation of dimethyl ether using response surface methodology and artificial neural network

Multiobjective Hyperparameter Optimization of Artificial Neural Networks for Optimal Feedforward Torque Control of Synchronous Machines

Integrating response surface methodology and machine learning for analyzing the unconventional machining properties of hybrid fiber‐reinforced composites

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