Integration of LSSVM technique with PSO to determine asphaltene deposition

Chamkalani, Ali; Zendehboudi, Sohrab; Bahadori, Alireza; Kharrat, Riyaz; Chamkalani, Reza; James, Lesley; Chatzis, Ioannis

doi:10.1016/j.petrol.2014.10.001

Cited by 63 publications

(25 citation statements)

References 38 publications

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“…Due to the simple concept, easy implementation and quick convergence, we used PSO combined with 10-fold cross validation procedure, by which these parameters were automatically tuned into the training phase. A more detailed review of the kernel function and the parameters selection for LSSVM can be found in [34]. Since there are plenty of studies on kernel function and parameters selection, construction of the training samples dataset is the main focus of this research.…”

Section: Implementation Of Lssvm Modelmentioning

confidence: 99%

Least Squares Support Vector Machine for Ranking Solutions of Multi-Objective Water Resources Allocation Optimization Models

Liu

Tong

2017

Water

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Abstract:There is an increasing trend in the use of multi-objective evolutionary algorithms (MOEAs) to solve multi-objective optimization problems of the allocation of water resources. However, typically the outcome is a set of Pareto optimal solutions which make up a trade-off surface between the objective functions. For decision makers to choose a satisfactory alternative from a set of Pareto-optimal solutions, this paper suggests a new method based on least squares support vector machine (LSSVM) and k-means clustering for ranking the optimal solutions for the multi-objective allocation of water resources. First, the k-means clustering method was adopted to reduce the large set of solutions to a few representative solutions. Then, to capture and represent the decision maker's preferences as well as to select the most desirable alternative, the LSSVM method was applied to obtain the utility value for each representative solution. According to the magnitude of the utility values, the final priority orders of the representative solutions were determined. Finally, this methodology was applied to rank the Pareto optimal solution set obtained from the multi-objective optimization problems of water resources allocation for the water-receiving areas of the South-to-North Water Transfer Project in Hebei Province, China. Moreover, the comparisons of the proposed method with the information entropy method and the artificial neural network (ANN) model were given. The results of the comparison indicate that the proposed method has the ability to rank the non-dominated solutions of the multi-objective operation optimization model and that it can be employed for decision-making on water allocation and management in a river basin.

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Section: Implementation Of Lssvm Modelmentioning

confidence: 99%

Least Squares Support Vector Machine for Ranking Solutions of Multi-Objective Water Resources Allocation Optimization Models

Liu

Tong

2017

Water

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“…Also, Kamari et al used least squares support vector machines (LSSVM) to develop the model for wax deposition and air specific heat ratios for prediction at elevated pressures. Another application of this type AI model can be found in the work of Chamkalani et al…”

Section: Introductionmentioning

confidence: 99%

“…Also, Kamari et al [20] used least squares support vector machines (LSSVM) to develop the model for wax deposition and air specific heat ratios for prediction at elevated pressures. Another application of this type AI model can be found in the work of Chamkalani et al [21] In our classification, modern approaches are those approaches incorporating data-driven models and AI in the decision-making process. Research has been conducted to develop data-based models (DBM) or EOR screening software.…”

Section: Introductionmentioning

confidence: 99%

Development of a data‐driven fuzzy screening model for enhanced oil recovery methods using an adaptive weighting system

2019

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Screening of enhanced oil recovery (EOR) methods is a main prerequisite for EOR planning and design. In this study, an integrated data‐driven screening model (DDSM) is developed to improve EOR screening using the combined capabilities of the fuzzy expert approach (FEA) and support vector regression (SVR) techniques. In this study, EOR field data from the past 40 years were reviewed to generate an updated and reliable EOR criteria table as a basis to construct a fuzzy screening model. The DDSM was evaluated to determine the quantitative screening and ranking of EOR methods using seven field datasets, including the fast forecasting of the nominated EOR methods. In order to improve screening performance, a fuzzy model was integrated using 4 SVR models to predict the adaptive weights of the screening parameter for decision making. The SVR models can predict the recovery factor (RF) of EOR methods including gas, chemical, steam, and combustion to calculate the adaptive effective weight of the screening parameters. The SVR models were trained with datasets generated from simulations of the EOR process. The absolute average error (AAE) of the SVR models from the simulation varied within the range of 0.078–0.095 for the prediction of the RF. The DDSM results were compatible to the data published in other literature. In addition, the developed model can provide comparable results to common screening software. The results showed improvements due to the adaptive weighting system on the EOR methods’ screening for the studied reservoirs relative to the fuzzy engine with constant weights. The presented integrated model can guide the screening process to select the efficient EOR method in practical applications.

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“…Afterwards, Hemmati‐Sarapardeh et al developed an LSSVM model for predicting asphaltene precipitation titration data. Chamkalani et al developed two LSSVM models optimized by coupled simulated annealing (CSA) and particle swarm optimization (PSO) for estimation of asphaltene precipitation titration data. Recently, Ameli et al used constrained multivariable search methods for predicting the onset of asphaltene precipitation conditions.…”

Section: Introductionmentioning

confidence: 99%

Modelling asphaltene precipitation titration data: A committee of machines and a group method of data handling

Hemmati‐Sarapardeh¹,

Dabir²,

Ahmadi³

et al. 2018

Can J Chem Eng

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Asphaltene precipitation causes different problems in the oil industry. In this study, a large data bank was used to model asphaltene precipitation titration data as a function of temperature, type of solvent, and solvent to crude oil dilution ratio. Three multilayer perceptron (MLP) neural networks and three radial basis function (RBF) neural networks were developed to estimate asphaltene precipitation data. The MLP models were optimized with scaled conjugate gradient (SCG), Levenberg‐Marquardt (LM), and Bayesian regularization (BR) algorithms and the RBF models were optimized with the particle swarm optimization (PSO) algorithm, imperialist competitive algorithm (ICA), and genetic algorithm (GA). All of the proposed models show an acceptable degree of accuracy and have an average absolute percent relative error (AAPRE) less than 4 %. Afterwards, three of the best models including MLP‐LM, MLP‐BR, and RBF‐GA were combined and a committee machine intelligent system (CMIS) was designed, which offers a higher accuracy compared to the other intelligent models. Aside from these intelligent models, the group method of data handling (GMDH) was used to develop an explicit and simple expression for estimating asphaltene precipitation. The proposed CMIS and GMDH models were compared to models developed based on scaling theory and the results show that the proposed models in this study outperform pre‐existing models. The validity and accuracy of the CMIS and GMDH models were proven by statistical and graphical techniques. Finally, a sensitivity analysis suggested that the solvent to crude oil dilution ratio has the largest effect on asphaltene precipitation.

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Integration of LSSVM technique with PSO to determine asphaltene deposition

Cited by 63 publications

References 38 publications

Least Squares Support Vector Machine for Ranking Solutions of Multi-Objective Water Resources Allocation Optimization Models

Least Squares Support Vector Machine for Ranking Solutions of Multi-Objective Water Resources Allocation Optimization Models

Development of a data‐driven fuzzy screening model for enhanced oil recovery methods using an adaptive weighting system

Modelling asphaltene precipitation titration data: A committee of machines and a group method of data handling

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