scholarly journals The Support Vector Regression with the parameter tuning assisted by a differential evolution technique: Study of the critical velocity of a slurry flow in a pipeline

2008 ◽  
Vol 14 (3) ◽  
pp. 191-203 ◽  
Author(s):  
S.K. Lahiri ◽  
K.C. Ghanta
Keyword(s):  
Differential Evolution ◽  
Support Vector Regression ◽  
Critical Velocity ◽  
Parameter Tuning ◽  
Operating Conditions ◽  
Superior Performance ◽  
Support Vector ◽  
Slurry Flow ◽  
Wide Range ◽  
Liquid Slurry

This paper describes a robust Support Vector regression (SVR) methodology, which can offer a superior performance for important process engineering problems. The method incorporates hybrid support vector regression and a differential evolution technique (SVR-DE) for the efficient tuning of SVR meta parameters. The algorithm has been applied for the prediction of critical velocity of the solid-liquid slurry flow. A comparison with selected correlations in the literature showed that the developed SVR correlation noticeably improved the prediction of critical velocity over a wide range of operating conditions, physical properties, and pipe diameters.

Hybrid Support Vector Regression and Genetic Algorithm Technique - A Novel Approach in Process Modeling

2009 ◽  
Vol 4 (1) ◽  
Author(s):  
Sandip K Lahiri ◽  
Kartik Chandra Ghanta
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Regression ◽  
Critical Velocity ◽  
Operating Conditions ◽  
Superior Performance ◽  
Support Vector ◽  
Novel Approach ◽  
Wide Range ◽  
Liquid Slurry ◽  
Solid Liquid

This paper describes a robust support vector regression (SVR) methodology, which can offer superior performance for important process engineering problems. The method incorporates hybrid support vector regression and genetic algorithm technique (SVR-GA) for efficient tuning of SVR meta parameters. The algorithm has been applied for prediction of critical velocity of solid liquid slurry flow. A comparison with selected correlations in the literature showed that the developed SVR correlation noticeably improved prediction of critical velocity over a wide range of operating conditions, physical properties, and pipe diameters.

scholarly journals Artificial neural network model with the parameter tuning assisted by a differential evolution technique: The study of the hold up of the slurry flow in a pipeline

2009 ◽  
Vol 15 (2) ◽  
pp. 103-117 ◽  
Author(s):  
S.K. Lahiri ◽  
K.C. Ghanta
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Differential Evolution ◽  
Parameter Tuning ◽  
Operating Conditions ◽  
Superior Performance ◽  
Slurry Flow ◽  
Wide Range ◽  
Artificial Neural ◽  
Hybrid Artificial Neural Network

This paper describes a robust hybrid artificial neural network (ANN) methodology which can offer a superior performance for the important process engineering problems. The method incorporates a hybrid artificial neural network and differential evolution technique (ANN-DE) for the efficient tuning of ANN meta parameters. The algorithm has been applied for the prediction of the hold up of the solid liquid slurry flow. A comparison with selected correlations in the literature showed that the developed ANN correlation noticeably improved the prediction of hold up over a wide range of operating conditions, physical properties, and pipe diameters.

scholarly journals SlurryNet: Predicting Critical Velocities and Frictional Pressure Drops in Oilfield Suspension Flows

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1263
Author(s):  
Alireza Sarraf Shirazi ◽  
Ian Frigaard
Keyword(s):  
Machine Learning ◽  
Critical Velocity ◽  
Flow Regimes ◽  
Operating Conditions ◽  
Support Vector ◽  
Slurry Flow ◽  
Pressure Drops ◽  
Integrated Method ◽  
Physical Knowledge ◽  
Wide Range

Improving the accuracy of the slurry flow predictions in different operating flow regimes remains a major focus for multiphase flow research, and it is especially targeted at industrial applications such as oil and gas. In this paper we develop a robust integrated method consisting of an artificial neural network (ANN) and support vector regression (SVR) to estimate the critical velocity, the slurry flow regime change, and ultimately, the frictional pressure drop for a solid–liquid slurry flow in a horizontal pipe, covering wide ranges of flow and geometrical parameters. Three distinct datasets were used to develop machine learning models with totals of 100, 325, and 125 data points for critical velocity, and frictional pressure drops for heterogeneous and bed-load regimes respectively. For each dataset, 80% of the data were used for training and the rest 20% for evaluating the out of sample performance. The K-fold technique was used for cross-validation. The prediction results of the developed integrated method showed that it significantly outperforms the widely used existing correlations and models in the literature. Additionally, the proposed integrated method with the average absolute relative error (AARE) of 0.084 outperformed the model developed without regime classification with the AARE of 0.155. The proposed integrated model not only offers reliable predictions over a wide range of operating conditions and different flow regimes for the first time, but also introduces a general framework of how to utilize prior physical knowledge to achieve more reliable performances from machine learning methods.

Artificial Neural Network Model with Parameter Tuning Assisted by Differential Evolution Technique: Study of Pressure Drop of Slurry Flow in Pipeline

2008 ◽  
Vol 3 (1) ◽  
Author(s):  
S.K. Lahiri ◽  
Kartik Chandra Ghanta
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Pressure Drop ◽  
Differential Evolution ◽  
Operating Conditions ◽  
Superior Performance ◽  
Slurry Flow ◽  
Wide Range ◽  
Artificial Neural ◽  
Hybrid Artificial Neural Network

This paper describes a robust hybrid artificial neural network (ANN) methodology, which can offer superior performance for important process engineering problems. The method incorporates hybrid artificial neural network and differential evolution technique (ANN-DE) for efficient tuning of ANN meta parameters. The algorithm has been applied for the prediction of pressure drop of solid liquid slurry flow. A comparison with selected correlations in the literature showed that the developed ANN correlation noticeably improved the prediction of pressure drops over a wide range of operating conditions, physical properties, and pipe diameters.

scholarly journals Data-Driven Prediction of Vessel Propulsion Power Using Support Vector Regression with Onboard Measurement and Ocean Data

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1588 ◽  
Author(s):  
Donghyun Kim ◽  
Sangbong Lee ◽  
Jihwan Lee
Keyword(s):  
Big Data ◽  
Support Vector Regression ◽  
Operating Conditions ◽  
Data Driven ◽  
Superior Performance ◽  
Support Vector ◽  
Power Prediction ◽  
Static Water ◽  
Data Driven Approach ◽  
Propulsion Power

The fluctuation of the oil price and the growing requirement to reduce greenhouse gas emissions have forced ship builders and shipping companies to improve the energy efficiency of the vessels. The accurate prediction of the required propulsion power at various operating condition is essential to evaluate the energy-saving potential of a vessel. Currently, a new ship is expected to use the ISO15016 method in estimating added resistance induced by external environmental factors in power prediction. However, since ISO15016 usually assumes static water conditions, it may result in low accuracy when it is applied to various operating conditions. Moreover, it is time consuming to apply the ISO15016 method because it is computationally expensive and requires many input data. To overcome this limitation, we propose a data-driven approach to predict the propulsion power of a vessel. In this study, support vector regression (SVR) is used to learn from big data obtained from onboard measurement and the National Oceanic and Atmospheric Administration (NOAA) database. As a result, we show that our data-driven approach shows superior performance compared to the ISO15016 method if the big data of the solid line are secured.

Machine Learning Approach to Predict Sand Transport in Horizontal and Inclined Flow

2021 ◽  
Author(s):  
R. E. Vieira ◽  
B. Xu ◽  
S. Karimi ◽  
S. A. Shirazi
Keyword(s):  
Machine Learning ◽  
Critical Velocity ◽  
Fluid Velocity ◽  
Operating Conditions ◽  
Solid Particles ◽  
Gradient Boosting ◽  
Support Vector ◽  
Mechanistic Models ◽  
Flow Conditions ◽  
Wide Range

Abstract Model predictions are routinely used to help in the decision-making process. For instance, in the oil and gas industry, the accumulation of solid particles, such as sand, and the formation of a bed of solids at the bottom of the pipe can be consequential. Such accumulation may decrease the efficiency of the pipeline due to the increase in the frictional pressure loss; increase the risk of pipeline damage due to erosion; or increase the possibility of pipeline corrosion damage under the bed of solids. In order to transport the solid particles in the pipe, the fluid velocity must exceed the critical velocity required for solid particle transport. Mechanistic models are used to provide a reasonable estimate for the critical velocity needed to transport the particles. However, those models are commonly applicable in their respective ranges of data fitting; and are limited by the applicability of the empirically based closure relations that are a part of such models. On the other hand, the accumulation of experimental data makes possible the application of data-driven methods for characterizing multiphase flow for a broader range of flow conditions. This paper presents a framework to predict the fluid velocity needed to transport solid particles in a pipeline via machine learning (ML) approach. In order to prepare a dataset for training ML models, the critical velocity data are collected from available sources in literature. With the purpose of decreasing the number of input parameters for ML algorithms and to make the model similar for different types of carrying fluids, a set of dimensionless variables has been used. To create the predictive models, three ML algorithms are applied: Random Forest, Support Vector Machine, and Gradient Boosting. The fine-tuned models are compared using statistical analysis to identify the ones that provide the most accurate velocity predictions for different operating conditions. Moreover, the predictive abilities of the models are further validated by comparing their performance with different mechanistic models. The proposed ML approach demonstrates high accuracy in predicting critical velocity across a wide range of flow conditions and inclination angles.

Process Modeling and Optimization Strategies Integrating Support Vector Regression and Differential Evolution: A Study of Industrial Ethylene Oxide Reactor

2008 ◽  
Vol 3 (1) ◽  
Author(s):  
Sandip Kumar Lahiri ◽  
Nadeem Khalfe
Keyword(s):  
Ethylene Oxide ◽  
Differential Evolution ◽  
Support Vector Regression ◽  
Process Modeling ◽  
Operating Conditions ◽  
Support Vector ◽  
Process Data ◽  
Support Vector Regression Model ◽  
Catalyst Selectivity ◽  
Modeling And Optimization

This paper presents artificial intelligence-based process modeling and optimization strategies, namely, support vector regression – differential evolution (SVR-DE) for modeling and optimization of catalytic industrial ethylene oxide (EO) reactor. In the SVR-DE approach, a support vector regression model is constructed for correlating process data comprising values of operating and performance variables. Next, model inputs describing process operating variables are optimized using Differential Evolution (DE) with a view to maximize the process performance. DE possesses certain unique advantages over the commonly used gradient-based deterministic optimization algorithms. The SVR-DE is a new strategy for chemical process modeling and optimization. The major advantage of the strategy is that modeling and optimization can be conducted exclusively from the historic process data wherein the detailed knowledge of process phenomenology (reaction mechanism, kinetics, etc.) is not required. Using SVR-DE strategy, a number of sets of optimized operating conditions leading to maximized EO production and catalyst selectivity were obtained. The optimized solutions, when verified in an actual plant, resulted in a significant improvement in the EO production rate and catalyst selectivity.

scholarly journals Deeply Learned Pose Invariant Image Analysis with Applications in 3D Face Recognition

2019 ◽  
Vol 2019 ◽  
pp. 1-21 ◽  
Author(s):  
Naeem Ratyal ◽  
Imtiaz Ahmad Taj ◽  
Muhammad Sajid ◽  
Anzar Mahmood ◽  
Sohail Razzaq ◽  
...  
Keyword(s):  
Face Recognition ◽  
Superior Performance ◽  
Support Vector ◽  
Complex Nature ◽  
Alignment Algorithm ◽  
3D Face Recognition ◽  
3D Face ◽  
Norm Minimization ◽  
Wide Range ◽  
The Face

Face recognition aims to establish the identity of a person based on facial characteristics and is a challenging problem due to complex nature of the facial manifold. A wide range of face recognition applications are based on classification techniques and a class label is assigned to the test image that belongs to the unknown class. In this paper, a pose invariant deeply learned multiview 3D face recognition approach is proposed and aims to address two problems: face alignment and face recognition through identification and verification setups. The proposed alignment algorithm is capable of handling frontal as well as profile face images. It employs a nose tip heuristic based pose learning approach to estimate acquisition pose of the face followed by coarse to fine nose tip alignment using L2 norm minimization. The whole face is then aligned through transformation using knowledge learned from nose tip alignment. Inspired by the intrinsic facial symmetry of the Left Half Face (LHF) and Right Half Face (RHF), Deeply learned (d) Multi-View Average Half Face (d-MVAHF) features are employed for face identification using deep convolutional neural network (dCNN). For face verification d-MVAHF-Support Vector Machine (d-MVAHF-SVM) approach is employed. The performance of the proposed methodology is demonstrated through extensive experiments performed on four databases: GavabDB, Bosphorus, UMB-DB, and FRGC v2.0. The results show that the proposed approach yields superior performance as compared to existing state-of-the-art methods.

scholarly journals HyP-ABC: A Novel Automated Hyper-Parameter Tuning Algorithm Using Evolutionary Optimization

2021 ◽  
Author(s):  
Leila Zahedi ◽  
Farid Ghareh Mohammadi ◽  
M. Hadi Amini
Keyword(s):  
Parameter Optimization ◽  
Real World ◽  
Optimization Problems ◽  
State Of The Art ◽  
Parameter Tuning ◽  
Gradient Boosting ◽  
Support Vector ◽  
Wide Range ◽  
Extreme Gradient Boosting ◽  
Art Techniques

Machine learning techniques lend themselves as promising decision-making and analytic tools in a wide range of applications. Different ML algorithms have various hyper-parameters. In order to tailor an ML model towards a specific application, a large number of hyper-parameters should be tuned. Tuning the hyper-parameters directly affects the performance (accuracy and run-time). However, for large-scale search spaces, efficiently exploring the ample number of combinations of hyper-parameters is computationally challenging. Existing automated hyper-parameter tuning techniques suffer from high time complexity. In this paper, we propose HyP-ABC, an automatic innovative hybrid hyper-parameter optimization algorithm using the modified artificial bee colony approach, to measure the classification accuracy of three ML algorithms, namely random forest, extreme gradient boosting, and support vector machine. Compared to the state-of-the-art techniques, HyP-ABC is more efficient and has a limited number of parameters to be tuned, making it worthwhile for real-world hyper-parameter optimization problems. We further compare our proposed HyP-ABC algorithm with state-of-the-art techniques. In order to ensure the robustness of the proposed method, the algorithm takes a wide range of feasible hyper-parameter values, and is tested using a real-world educational dataset.

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