Modification and validation of particulate plug-I pressure drop model

Rawat, Anubhav; Kalman, Haim

doi:10.1016/j.powtec.2019.03.005

Cited by 13 publications

(3 citation statements)

References 39 publications

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“…Three datasets generated using Python code consisting of 50000, 250000 and 500000 samples were used. Datasets were generated by solving the modified pressure drop equation (Rawat & Kalman, 2019) and HR. Magnesium Oxide was taken as flow material and initial values were taken from the experiment conducted by Rawat and Kalman and the stress transmission coefficient was found using the relations given by Rabinovich.…”

Section: Dataset Generation and Pre-processingmentioning

confidence: 99%

“…The Hausner's Ratio (HR) and cohesion are the important parameters that can establish the existence of plug-1 and plug-2. As with Ar< 100 and HR> 1.25, plug-1 exists; otherwise, plug-2 is expected (Rawat & Kalman, 2019;Hausner, 1967). The multilayer perceptron (MLP) neural network (Offor & Alabi, 2016) for predicting friction factor in turbulent flow of water with two hidden layers having 30 neurons each had relative error up to a maximum of 0.004% when compared with the Colebrook equation.In order to generate the training set for the ANN model, they solved the Colebrook equation iteratively.Also, Sablani et al, 2003 was able to predict the pressure drop for bingham plastic fluids using two input parameters: Bingham Reynolds Number and Hedstrom Number.…”

Section: Introductionmentioning

confidence: 99%

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Artificial neural network models for wall parameters on plug-1 flow characteristics through pipelines

Sethi¹,

Rawat²,

Srivastava³

et al. 2022

JER is an international, peer-reviewed journal that publishes f

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Dense phase pneumatic conveying is a preferable method for transportation of a friable, fragile, abrasive, or agglomerated in nature material through pipeline with comparatively less wear and tear of the system and product as well. A fine particulate material moves as a single entity due to its cohesion in vertical or thin horizontal pipes in Plug-1 flow. A model based on mechanics was created and later modified using experiments for this type of flow. In this work Artificial Neural Networks (ANNs) models are used to study the effect of wall friction coefficient and coefficient of wall cohesion on the pressure drop. Three different datasets having 50000, 250000, and 500000 data points were used to test 19, 21, and 25 ANN architectures respectively. The best architecture was found to be t50-t40-r1 architecture with Adamax optimizer, with mean absolute percentage error (MAPE) being close to 0.00402% when tested on the 500000 samples dataset with 25000 test values, 0.0043% when tested on the dataset with 250000 samples and 25000 test values, and 0.0035% on the 50000 samples dataset with 10000 test values. The s20-s20-r1 architecture with Adam optimizer was quick and gave second best results with MAPE being close to 0.009% when tested on the 500000 samples dataset, 0.00988% when tested on the dataset with 250000 samples,and 0.00408% when tested on the 50000 samples dataset. The t40-t40-t40-t40-r1 with Adamax optimizer was slow but gave the third best results with MAPE being close to 0.0166% when tested on the 500000 samples dataset, 0.00496% when tested on the dataset with 250000 samples, and 0.00480% when tested on the 50000 samples dataset.

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Section: Dataset Generation and Pre-processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Artificial neural network models for wall parameters on plug-1 flow characteristics through pipelines

Sethi¹,

Rawat²,

Srivastava³

et al. 2022

JER is an international, peer-reviewed journal that publishes f

View full text Add to dashboard Cite

show abstract

“…The results showed that there were different initial pickup motions for large particles: by rolling and sliding for spherical and non-spherical particles, respectively. Recently, Kalman et al − tried to expand the applicability of the derived dimensional equations to other threshold velocity predictions, plug flow pressure-loss modeling, and flow regime chart building. Meanwhile, Dasani et al measured the pickup velocities of particles with diameters of less than 450 μm and developed a particle force balance equation for gas–solid systems.…”

Section: Introductionmentioning

confidence: 99%

Pickup Characteristics of Lump Non-spherical Particles in an Oscillating Airflow

Zhou

Shangguan

Gao

et al. 2020

Ind. Eng. Chem. Res.

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The pickup characteristics of lump non-spherical particles in an oscillating airflow were numerically studied on the basis of experimental verification. Five typical shapes of lump coal particles with diameters of 5−7 mm were numerically calculated. The impact of the oscillating amplitudes and the frequencies was discussed. The oscillating number was first defined to quantitatively examine the oscillating intensity. Two points are worth highlighting: (1) the increase in the particle pickup ratio caused by the strength interaction intensity mostly prioritized the particle pickup process in an oscillating airflow. Particles were liable to be entrained, but this process was weakened in the continuous pickup process in a high-frequency rectangular wave airflow. (2) A significant consistent regression relationship was observed in both the swirling airflow and oscillating airflow in which the particle pickup velocity increases at first and then decreases with both the oscillating number and swirling number.

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Flow regime chart for pneumatic conveying

Kalman

Rawat

2020

Chemical Engineering Science

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Modification and validation of particulate plug-I pressure drop model

Cited by 13 publications

References 39 publications

Artificial neural network models for wall parameters on plug-1 flow characteristics through pipelines

Artificial neural network models for wall parameters on plug-1 flow characteristics through pipelines

Pickup Characteristics of Lump Non-spherical Particles in an Oscillating Airflow

Flow regime chart for pneumatic conveying

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