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 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.

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.

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.

scholarly journals Soft sensor development and optimization of the commercial petrochemical plant integrating support vector regression and genetic algorithm

2009 ◽  
Vol 15 (3) ◽  
pp. 175-187 ◽  
Author(s):  
S.K. Lahiri ◽  
Nadeem Khalfe
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Regression ◽  
Soft Sensor ◽  
Operating Conditions ◽  
Support Vector ◽  
Petrochemical Plant ◽  
Process Data ◽  
Soft Sensing ◽  
Modeling And Optimization ◽  
Sensor Modeling

Soft sensors have been widely used in the industrial process control to improve the quality of the product and assure safety in the production. The core of a soft sensor is to construct a soft sensing model. This paper introduces support vector regression (SVR), a new powerful machine learning method based on a statistical learning theory (SLT) into soft sensor modeling and proposes a new soft sensing modeling method based on SVR. This paper presents an artificial intelligence based hybrid soft sensormodeling and optimization strategies, namely support vector regression - genetic algorithm (SVR-GA) for modeling and optimization of mono ethylene glycol (MEG) quality variable in a commercial glycol plant. In the SVR-GA approach, a support vector regression model is constructed for correlating the process data comprising values of operating and performance variables. Next, model inputs describing the process operating variables are optimized using genetic algorithm with a view to maximize the process performance. The SVR-GA is a new strategy for soft sensor modeling and optimization. The major advantage of the strategies 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-GA strategy, a number of sets of optimized operating conditions were found. The optimized solutions, when verified in an actual plant, resulted in a significant improvement in the quality.

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.

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.

Development of a Hybrid Artificial Neural Network and Genetic Algorithm Model for Regime Identification of Slurry Transport in Pipelines

2009 ◽  
Vol 4 (1) ◽  
Author(s):  
Sandip K Lahiri ◽  
Kartik Chandra Ghanta
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Operating Conditions ◽  
Misclassification Error ◽  
Wide Range ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Pipeline Design ◽  
Hybrid Artificial Neural Network

Four distinct regimes were found existent (namely sliding bed, saltation, heterogeneous suspension and homogeneous suspension) in slurry flow in pipeline depending upon the average velocity of flow. In the literature, few numbers of correlations has been proposed for identification of these regimes in slurry pipelines. Regime identification is important for slurry pipeline design as they are the prerequisite to apply different pressure drop correlation in different regime. However, available correlations fail to predict the regime over a wide range of conditions. Based on a databank of around 800 measurements collected from the open literature, a method has been proposed to identify the regime using artificial neural network (ANN) modeling. The method incorporates hybrid artificial neural network and genetic algorithm technique (ANN-GA) for efficient tuning of ANN meta parameters. Statistical analysis showed that the proposed method has an average misclassification error of 0.03%. A comparison with selected correlations in the literature showed that the developed ANN-GA method noticeably improved prediction of regime over a wide range of operating conditions, physical properties, and pipe diameters.

Estimation of CO2 Diffusivity in Brine by Use of the Genetic Algorithm and Mixed Kernels-Based Support Vector Machine Model

2018 ◽  
Vol 141 (4) ◽  
Author(s):  
Qihong Feng ◽  
Ronghao Cui ◽  
Sen Wang ◽  
Jin Zhang ◽  
Zhe Jiang
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Diffusion Coefficient ◽  
Co2 Storage ◽  
Co2 Injection ◽  
Support Vector ◽  
Hybrid Technique ◽  
Machine Model ◽  
Proposed Model ◽  
Wide Range

Diffusion coefficient of carbon dioxide (CO2), a significant parameter describing the mass transfer process, exerts a profound influence on the safety of CO2 storage in depleted reservoirs, saline aquifers, and marine ecosystems. However, experimental determination of diffusion coefficient in CO2-brine system is time-consuming and complex because the procedure requires sophisticated laboratory equipment and reasonable interpretation methods. To facilitate the acquisition of more accurate values, an intelligent model, termed MKSVM-GA, is developed using a hybrid technique of support vector machine (SVM), mixed kernels (MK), and genetic algorithm (GA). Confirmed by the statistical evaluation indicators, our proposed model exhibits excellent performance with high accuracy and strong robustness in a wide range of temperatures (273–473.15 K), pressures (0.1–49.3 MPa), and viscosities (0.139–1.950 mPa·s). Our results show that the proposed model is more applicable than the artificial neural network (ANN) model at this sample size, which is superior to four commonly used traditional empirical correlations. The technique presented in this study can provide a fast and precise prediction of CO2 diffusivity in brine at reservoir conditions for the engineering design and the technical risk assessment during the process of CO2 injection.

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