Slug Frequency Prediction Model for Fluid Flow in Flowline Bends

2021 ◽  
Author(s):  
Loveday Igbokwe ◽  
Michael Edwin
Keyword(s):  
Prediction Models ◽  
Model Development ◽  
Absolute Error ◽  
Phase Correlation ◽  
Petroleum Processing ◽  
Two Phase ◽  
Interface Motion ◽  
Absolute Deviation ◽  
New Correlation ◽  
Pi Theorem

Abstract The prediction of slug frequency for two-phase slug flow during multiphase transportation of oil reservoir productions is crucial in the design of slug controllers for petroleum processing installations. Mechanistic based slug prediction models have not had much successful application due to the difficulty in modelling the non-linear interface motion during slug development. The mechanism of slugging in offshore flowline-riser is complicated and requires rigorous experimental sampling and testing. This process can be time-consuming and costly. In this study, a new correlation is developed for the prediction of severe slugging frequency. The new model is developed based on the results of scaled experimental design. Dimensional analysis approach using the Buckingham pi-theorem is used in developing the two-phase correlation. The model development involves non-dimensional empirical correlations in terms of relevant dimensionless groups, which are obtained based on the design of the experiment. A broad range of experimental data from 10 varied choke opening size was used. The new correlation predicts 92.3% of the measurements within ±8% absolute error and the mean absolute deviation of the correlation is about 6.13%. The newly developed correlation can be applied for flow rates between 0.1 kg/s and 0.6 kg/s and choke openings between 10-98%.

scholarly journals Neural network based prediction models for evaporation

MAUSAM ◽  
2021 ◽  
Vol 67 (2) ◽  
pp. 389-396
Author(s):  
AMRENDER KUMAR ◽  
A. K. MISHRA ◽  
A. K. JAIN ◽  
C. CHATTOPADHYAY
Keyword(s):  
Relative Humidity ◽  
Prediction Models ◽  
Model Development ◽  
Meteorological Variables ◽  
Maximum Temperature ◽  
Percentage Error ◽  
Weather Variables ◽  
Two Phase ◽  
Sunshine Hours ◽  
The Mean

From statistical perspective, artificial neural networks (ANNs) are interesting because of their potential use in prediction. In this study, ANNs based approach has been used to assess the prediction of evaporation with meteorological variables, viz., maximum temperature (MaxT), minimum temperature (MinT), relative humidity in the morning (RHI), relative humidity in evening  (RHII), bright sunshine hours (BSH) and wind speed (WS) for different locations (Una, Karnal, Pantnagar, Raipur, Anantpur, Bangalore and Pattambi) in India. ANNs models were developed using Multilayer perceptron (MLP) architecture with two-phase algorithm of Backpropagation (BP) and Conjugate gradient descent (CGD) for prediction of evaporation as output and different combination of meteorological variables as input in different locations. Weekly predictions of evaporation have been obtained for subsequent years not included in model development. The performances of the developed models with different combination of weather variables compared based on mean absolute percentage error (MAPE). The sensitivity analysis indicated that the mean temperature and mean relative humidity are more sensitive to evaporation.   

Two-phase pressure drop due to friction in micro-channel

2013 ◽  
Vol 228 (5) ◽  
pp. 921-931 ◽  
Author(s):  
Tae-Woo Lim ◽  
Sam-Sang You ◽  
Jong-Su Kim ◽  
Serng-Bae Moon ◽  
Dong-Hoan Seo
Keyword(s):  
Pressure Drop ◽  
Flow Boiling ◽  
Homogeneous Model ◽  
Absolute Error ◽  
Heat Fluxes ◽  
Micro Channel ◽  
Two Phase ◽  
Pressure Drops ◽  
Frictional Pressure Drop ◽  
New Correlation

This paper deals with an experimental investigation to measure the frictional pressure drops for two-phase flow boiling in a micro-channel with a hydraulic diameter of 500 µm. First, the experimental study is performed under the test conditions: heat fluxes ranging from 100 to 400 kW/m2, vapor qualities from 0 to 0.2, and mass fluxes of 200, 400 and 600 kg/m2s. Then, the frictional pressure drop during flow boiling is estimated using two models: the homogeneous model and the separated flow model. The experimental results show that the two-phase multiplier decreases with the increase of mass flux. In addition, the measured pressure drops are compared with those from a few correlation models available for macro-scales and mini/micro-scales. Finally, the present paper proposes a new correlation for two-phase frictional pressure drops in mini/micro-scales. This correlation model is developed based on the Chisholm constant C as a function of two-phase Reynolds and Weber numbers. It is found that the new correlation satisfactorily predicts the experimental data within mean absolute error (MAE) of 3.9%.

Status of Advanced Two-Phase Flow Model Development for SRM Chamber Flow Field and Combustion Modeling

2004 ◽  
Author(s):  
Gary Luke ◽  
Mark Eagar ◽  
Michael Sears ◽  
Scott Felt ◽  
Bob Prozan
Keyword(s):  
Flow Field ◽  
Flow Model ◽  
Two Phase Flow ◽  
Model Development ◽  
Phase Flow ◽  
Two Phase ◽  
Combustion Modeling

scholarly journals Evaluation of Process Control Schemes for Sour Water Strippers in Petroleum Refining

Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 363
Author(s):  
Chii-Dong Ho ◽  
Yih-Hang Chen ◽  
Chao-Min Chang ◽  
Hsuan Chang
Keyword(s):  
Feedback Control ◽  
Prediction Models ◽  
Feedforward Control ◽  
Absolute Error ◽  
Dynamic Responses ◽  
Control Schemes ◽  
Online Measurements ◽  
Measurement Delay ◽  
Water Feed ◽  
Sour Water

For the sour water strippers in petroleum refinery plants, three prediction models were developed first, including the estimators of sour water feed concentrations using convenient online measurements, the minimum reboiler duty and the corresponding internal temperature at a specific location (Tstage,29). Feedforward control schemes were developed based on these prediction models. Four categories of control schemes, including feedforward, feedback, feedback with external reset, and feedforward-feedback, were proposed and evaluated by the rigorous dynamic simulation model of the sour water stripper for their dynamic responses to the sour water feed stream disturbances. The comparison of control performance, in terms of the settling time, integrated absolute error (IAE) of the NH3 concentration of the stripped sour water and IAE of the specific reboiler duty, reveals that FFT (feedforward control of Tstage,29) and FBA-DT3 (feedback control with 3 min concentration measurement delay) are the best control schemes. The second-best control scheme is FBAT (cascade feedback control of concentration with temperature).

scholarly journals Quantitative Longitudinal Predictions of Alzheimer’s Disease by Multi-Modal Predictive Learning

2021 ◽  
Vol 79 (4) ◽  
pp. 1533-1546
Author(s):  
Mithilesh Prakash ◽  
Mahmoud Abdelaziz ◽  
Linda Zhang ◽  
Bryan A. Strange ◽  
Jussi Tohka ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Prediction Models ◽  
Predictive Performance ◽  
Absolute Error ◽  
Disease Stage ◽  
Disease Assessment ◽  
Disease Category ◽  
Modal Data ◽  
Predictive Learning

Background: Quantitatively predicting the progression of Alzheimer’s disease (AD) in an individual on a continuous scale, such as the Alzheimer’s Disease Assessment Scale-cognitive (ADAS-cog) scores, is informative for a personalized approach as opposed to qualitatively classifying the individual into a broad disease category. Objective: To evaluate the hypothesis that the multi-modal data and predictive learning models can be employed for future predicting ADAS-cog scores. Methods: Unimodal and multi-modal regression models were trained on baseline data comprised of demographics, neuroimaging, and cerebrospinal fluid based markers, and genetic factors to predict future ADAS-cog scores for 12, 24, and 36 months. We subjected the prediction models to repeated cross-validation and assessed the resulting mean absolute error (MAE) and cross-validated correlation (ρ) of the model. Results: Prediction models trained on multi-modal data outperformed the models trained on single modal data in predicting future ADAS-cog scores (MAE12, 24 & 36 months= 4.1, 4.5, and 5.0, ρ12, 24 & 36 months= 0.88, 0.82, and 0.75). Including baseline ADAS-cog scores to prediction models improved predictive performance (MAE12, 24 & 36 months= 3.5, 3.7, and 4.6, ρ12, 24 & 36 months= 0.89, 0.87, and 0.80). Conclusion: Future ADAS-cog scores were predicted which could aid clinicians in identifying those at greater risk of decline and apply interventions at an earlier disease stage and inform likely future disease progression in individuals enrolled in AD clinical trials.

scholarly journals A Neural Network-Based Model Reference Control Architecture for Oscillation Damping in Interconnected Power System

Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3653
Author(s):  
Uddin ◽  
Zeb ◽  
Zeb ◽  
Ishfaq ◽  
Khan ◽  
...  
Keyword(s):  
Neural Network ◽  
Power Systems ◽  
Power System ◽  
Performance Indicator ◽  
Stable Condition ◽  
External Perturbation ◽  
Absolute Error ◽  
Unified Power Flow Controller ◽  
Two Phase ◽  
Model Reference

In this paper, a model reference controller (MRC) based on a neural network (NN) is proposed for damping oscillations in electric power systems. Variation in reactive load, internal or external perturbation/faults, and asynchronization of the connected machine cause oscillations in power systems. If the oscillation is not damped properly, it will lead to a complete collapse of the power system. An MRC base unified power flow controller (UPFC) is proposed to mitigate the oscillations in 2-area, 4-machine interconnected power systems. The MRC controller is using the NN for training, as well as for plant identification. The proposed NN-based MRC controller is capable of damping power oscillations; hence, the system acquires a stable condition. The response of the proposed MRC is compared with the traditionally used proportional integral (PI) controller to validate its performance. The key performance indicator integral square error (ISE) and integral absolute error (IAE) of both controllers is calculated for single phase, two phase, and three phase faults. MATLAB/Simulink is used to implement and simulate the 2-area, 4-machine power system.

Evaluation Analysis of Prediction Methods for Two-Phase Flow Pressure Drop in Mini-Channels

2008 ◽  
Author(s):  
Licheng Sun ◽  
Kaichiro Mishima
Keyword(s):  
Pressure Drop ◽  
Two Phase Flow ◽  
Phase Flow ◽  
Two Phase ◽  
Turbulent Region ◽  
Frictional Pressure Drop ◽  
New Correlation ◽  
Mini Channels ◽  
Flow Pressure ◽  
Better Than

2092 data of two-phase flow pressure drop were collected from 18 published papers of which the working fluids include R123, R134a, R22, R236ea, R245fa, R404a, R407C, R410a, R507, CO2, water and air. The hydraulic diameter ranges from 0.506 to 12mm; Relo from 10 to 37000, and Rego from 3 to 4×105. 11 correlations and models for calculating the two-phase frictional pressure drop were evaluated based upon these data. The results show that the accuracy of the Lockhart-Martinelli method, Mishima and Hibiki correlation, Zhang and Mishima correlation and Lee and Mudawar correalion in the laminar region is very close to each other, while the Muller-Steinhagen and Heck correlation is the best among the evaluated correlations in the turbulent region. A modified Chisholm correlation was proposed, which is better than all of the evaluated correlations in the turbulent region and its mean relative error is about 29%. For refrigerants only, the new correlation and Muller-Steinhagen and Heck correlation are very close to each other and give better agreement than the other evaluated correlations.

scholarly journals Prediction of Blended Yarn Evenness and Tensile Properties by Using Artificial Neural Network and Multiple Linear Regression

2016 ◽  
Vol 16 (2) ◽  
pp. 43-50 ◽  
Author(s):  
Samander Ali Malik ◽  
Assad Farooq ◽  
Thomas Gereke ◽  
Chokri Cherif
Keyword(s):  
Neural Networks ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Tensile Properties ◽  
Regression Models ◽  
Prediction Models ◽  
Research Work ◽  
Absolute Error ◽  
Knowledge Domain ◽  
Artificial Neural

Abstract The present research work was carried out to develop the prediction models for blended ring spun yarn evenness and tensile parameters using artificial neural networks (ANNs) and multiple linear regression (MLR). Polyester/cotton blend ratio, twist multiplier, back roller hardness and break draft ratio were used as input parameters to predict yarn evenness in terms of CVm% and yarn tensile properties in terms of tenacity and elongation. Feed forward neural networks with Bayesian regularisation support were successfully trained and tested using the available experimental data. The coefficients of determination of ANN and regression models indicate that there is a strong correlation between the measured and predicted yarn characteristics with an acceptable mean absolute error values. The comparative analysis of two modelling techniques shows that the ANNs perform better than the MLR models. The relative importance of input variables was determined using rank analysis through input saliency test on optimised ANN models and standardised coefficients of regression models. These models are suitable for yarn manufacturers and can be used within the investigated knowledge domain.

Comparative study for deriving stagedischarge– sediment concentration relationships using soft computing techniques

2021 ◽  
Vol 2 (104) ◽  
pp. 57-76
Author(s):  
P. Sihag ◽  
M.R. Sadikhani ◽  
V. Vambol ◽  
S. Vambol ◽  
A.K. Prabhakar ◽  
...  
Keyword(s):  
Random Forest ◽  
Soft Computing ◽  
Sediment Load ◽  
Prediction Models ◽  
Absolute Error ◽  
Load Prediction ◽  
Statistical Parameters ◽  
Hydro Power ◽  
Input Combination ◽  
Soft Computing Techniques

Purpose: Knowledge of sediment load carried by any river is essential for designing and planning of hydro power and irrigation projects. So the aim of this study is to develop and evaluating the best soft-computing-based model with M5P and Random Forest regressionbased techniques for computation of sediment using datasets of daily discharge, daily gauge and sediment load at the Champua gauging site of the Upper Baitarani river basin of India. Design/methodology/approach: Last few decades, the soft computing techniques based models have been successfully used in water resources modelling and estimation. In this study, the potential of tree based models are examined by developing and comparing sediment load prediction models, based on M5P tree and Random forest regression (RF). Several M5P and RF based models have been applied to a gauging site of the Baitarani River at Odisha, India. To evaluate the performance of the selected M5P and RF-based models, three most popular statistical parameters are selected such as coefficient of correlation, root mean square error and mean absolute error. Findings: A comparison of the results suggested that RF-based model could be applied successfully for the prediction of sediment load concentration with a relatively higher magnitude of prediction accuracy. In RF-based models Qt, Q(t-1), Q(t-2), S(t-1), S(t-2), Ht and H(t-1) combination based M10 model work superior than other combination based models. Another major outcome of this investigation is Qt, Q(t-1) and S(t-1) based model M4 works better than other input combination based models using M5P technique. The optimum input combination is Qt, Q(t-1) and S(t-1) for the prediction of sediment load concentration of the Baitarani River at Odisha, India. Research limitations/implications: The developed models were tested for Baitarani River at Odisha, India.

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