Combining of intelligent models through committee machine for estimation of wax deposition

Gholami, Amin; Ansari, Hamid Reza; Ahmadi, Soleiman

doi:10.1002/jccs.201700329

Cited by 10 publications

(7 citation statements)

References 49 publications

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“…Therefore, when different predictive models are available for the estimation of the response variable, this method produces combined models reaping the benefits of individual models. Studies have indicated that the committee machine is more accurate than individual models (Gholami et al , 2018(Gholami et al , 2020.…”

Section: Committee Machinementioning

confidence: 99%

“…For longitudinal dispersion coefficient modeling considering interrelated influencing parameters, a proper selection of input parameters is important (Dehghani et al 2020). Since an intelligent model learns the relationship between input variables and the output variable, it can estimate the target value in unseen data (Gholami et al 2014b(Gholami et al , 2018 The performance in computing the longitudinal dispersion coefficient was judged by using two criteria as given by Equations ( 3)-( 6): where Y i obs is the measured value of sample i, Y i pred is the estimated value of sample i, Ŷobs is the average of real values, and n is the number of samples. When the values of MSE, MAE, and PB are close to zero and the value of R 2 is close to 1, a model with superior performance is achieved.…”

Section: Data Input/output Spacementioning

confidence: 99%

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Estimation of the longitudinal dispersion coefficient via a fusion of optimized models

Gholami

Singh

2022

Journal of Hydroinformatics

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Determination of the longitudinal dispersion coefficient (LDC) is fundamental to the development of strategies for environmental management of river systems. This paper presents an integrated model for an estimation of the longitudinal dispersion coefficient by a fusion of optimized intelligent models (optimized neural network (ONN), optimized fuzzy inference system (OFIS), and optimized support vector regression (OSVR)) via committee machine (CM), with optimization done by the Bat-inspired algorithm (BA). The optimization eliminates the associated loss of accuracy of the intelligent models, which is a direct consequence of an improper adjustment of parameters (weights and biases in the neural network, membership's functions in the fuzzy inference system, and user-defined parameters in support vector regression). Data gathered from literature are employed to validate the proposed integrated model. A comparison between the optimized models and a committee machine, based on statistical parameters, shows that the committee machine model can attain high accuracy. Sensitivity analysis (SA) shows the contribution of each optimized model to the committee machine and ranks the contribution of the optimized models in ascending order as optimized neural network, optimized fuzzy inference system, and optimized support vector regression, each significantly correlated with the accuracy of longitudinal dispersion coefficient prediction.

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Section: Committee Machinementioning

confidence: 99%

Section: Data Input/output Spacementioning

confidence: 99%

Estimation of the longitudinal dispersion coefficient via a fusion of optimized models

Gholami

Singh

2022

Journal of Hydroinformatics

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“…The Binbinzong formula (Ding Fig. 4 Changqing crude oil viscosity-temperature curve selected the pipeline flow, upstream outbound oil temperature, downstream-inlet oil temperature, and ground temperature along the line as the modeling parameters, and used the gray correlation method (Gholami et al 2018) and correlation formula analysis to determine the influence of the pipeline's friction factors. The predicted results are shown in Table 5 and indicate that the flow rate of the SCADA system data, the upstream-outlet oil temperature, and the downstream-inlet oil temperature are the main influencing factors of the pipeline friction, whereas the ground temperature correlation is small.…”

Section: Influencing Factor Selectionmentioning

confidence: 99%

“…In recent years, scholars have created pipeline wax prediction models based on data mining. Gholami et al (2018) and Xie and Xing (2017) used support vector regression (SVR) to estimate wax deposition, developed a human ANN, and constructed a mixed model of SVR and ANN. The analysis and acquisition of a hybrid model optimized using the genetic algorithm (GA) can effectively improve the prediction accuracy of the model.…”

Section: Introductionmentioning

confidence: 99%

Standard friction prediction model of long-distance hot oil pipelines

Yao

et al. 2020

Pet. Sci.

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We developed a predictive model for the pipeline friction in the 520-730 m 3 /h transmission range using the multi-layerperceptron-back-propagation (MLP-BP) method and analyzing the unit friction data after the pigging of a hot oil pipeline. In view of the shortcomings of the MLP-BP model, two optimization methods, the genetic algorithm (GA) and mind evolutionary algorithm (MEA), were used to optimize the MLP-BP model. The research results were applied to the standard friction prediction of three sections of a hot oil pipeline. After the GA and MEA optimizations, the average errors of the three sections were 0.0041 MPa for the GA and 0.0012 MPa for the MEA, and the mean-square errors were 0.083 and 0.067, respectively. The MEA-BP model prediction results were characterized by high precision and small dispersion. The MEA-BP prediction model was applied to the analysis of the wax formation 60 and 90 days after pigging. The analysis results showed that the model can effectively guide pipe pigging and optimization. There was little sample data for the individual transmission and oil temperature steps because the model was based on actual production data modeling and analysis, which may have affected the accuracy and adaptability of the model.

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“…After that, with the continuous precipitation of wax crystals, the solid particles will be gradually connected to form a spatial network structure, and the crude oil is wrapped in its pores, which results in a deterioration of flows and eventually causes the blockage of the pipeline. It is concluded that the wax deposition process is influenced by many factors, such as crude oil composition, pipe wall material, temperature, , flow velocity, and flow pattern. , For the wax deposition mechanism, the classical theories include molecular diffusion, ,− shear dispersion, , Brownian motion, , and gravity deposition. , Recently, with the further development of the wax-deposited research, the mechanisms of shear stripping, gelation (emulsified nucleation) − and aging was proposed. ,− Many scholars have performed research regarding those mechanisms. Hoffmann et al and Mehrotra et al found that the carbon number distribution on the surface of wax deposition layer is closer to that of the flowing oil when the flow rate is low and the water content is high.…”

Section: Introductionmentioning

confidence: 99%

Wax and Wax–Hydrate Deposition Characteristics in Single-, Two-, and Three-Phase Pipelines: A Review

Liu

Wang

et al. 2020

Energy Fuels

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In the oil pipeline transportation system, wax deposition will reduce the effective circulation area, weaken the overall pipeline transportation capacity, and even block the pipeline, which seriously threatens the safety of the pipeline flows. In the actual production of oil fields and the transportation of oil products, except for the single-phase waxy crude oil, under most conditions, operations are performed using multiphase mixed transportation, so it is necessary to determine the wax deposition characteristics in those pipeline systems. In this Review, three new wax deposition mechanisms proposed in recent years and several typical experimental devices were summarized, the wax deposition in oil–water and oil–gas flow systems and the wax–hydrate coupling deposition in oil–gas–water flow system flows were concluded; the mathematical models were also categorized based on the three flow systems. Meanwhile, it highlighted the fact that, in the two-phase flow, because of the complexity of the flow pattern and the presence of the second interface on the wax deposition process, independent theoretical research on the mechanism of wax deposition is particularly necessary; based on this, a more-accurate wax deposition prediction model can also be developed. For the coupled deposition of wax-hydrate in the three-phase flow system, experimental instruments such as FBRM, PVM and molecular dynamics simulations could be used to study the distribution characteristics and interaction rules of wax crystals and hydrate particles from a microscopic perspective. On the other hand, the mechanism of wax crystal and hydrate coupling deposition by additives such as antiwax agents, hydrate inhibitors, antipolymerization agents, and pipeline transportation conditions can be investigated experimentally to better control the deposition process during pipeline operation and ensure the safe flow in the transportation system.

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Combining of intelligent models through committee machine for estimation of wax deposition

Cited by 10 publications

References 49 publications

Estimation of the longitudinal dispersion coefficient via a fusion of optimized models

Estimation of the longitudinal dispersion coefficient via a fusion of optimized models

Standard friction prediction model of long-distance hot oil pipelines

Wax and Wax–Hydrate Deposition Characteristics in Single-, Two-, and Three-Phase Pipelines: A Review

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