Abstract
Recently, global energy consumption has increased due to industrial development, resulting in increasing demand for various energy sources. Aside from the increased demand for renewable energy resources, the demand for fossil fuels is also on the rise. Accordingly, the demand for resource development in the deep sea is also increasing. Various systems are required to efficiently develop resources in the deep sea. A study on an in-line type oil–water separator is needed to compensate for the disadvantages of a gravity separator that separates traditional water and oil. In this paper, the separation performance of the axial-flow oil–water separator for five design variables (conical diameter, conical length, number of vanes, angle of vane, and thickness of vane) was analyzed. Numerical calculations for multiphase fluid were performed using the mixture model, one of the Euler–Euler approaches. Additionally, the Reynolds stress model was used to describe the swirling flow. As a result, it was found that the effect on the separation performance was large in the order of angle of vane, conical diameter, number of vanes, the thickness of vane, and conical length. A neural network model for predicting separation performance was developed using numerical calculation results. To predict the oil–water separation performance, five design parameters were considered, and the evaluation of the separation performance prediction model was compared with the multilinear regression (MLR) model. As a result, it was found that the R square was improved by about 74.0% in the neural network model, compared with the MLR model.
Highlights
Due to the increase in worldwide energy consumption and the decrease in the production of fossil fuels on land and offshore, the demand for resource development in the deep sea is increasing
As a result of the neural network model prediction, the model performance was improved by about 79.2%, 71.9% and 74.0%, respectively, in Mean absolute error (MAE), mean absolute percentage error (MAPE) and R2 compared to the multilinear regression (MLR) model
A neural network model was developed to analyze the effect of various design variables onMthLeRsMepoadraetlion perforNmeaunracleNofetawnoarkxiMal-ofdloewl oil–watIemr sperpovareamtoenr tand to pMreAdEict the separatio0n.0p12e9rformance
Summary
Due to the increase in worldwide energy consumption and the decrease in the production of fossil fuels (oil, gas, etc.) on land and offshore, the demand for resource development in the deep sea is increasing. Due to the relatively large volume, there are limitations on the installation area and the need for a robust design to withstand high pressure To improve on these shortcomings, research on in-line-type separators capable of efficiently separating in the deep sea is being actively conducted. The performance of the cyclone separator was analyzed through numerical calculations and experiments using a Reynolds stress model, which is excellent for anisotropic flow prediction [18,19]. Machine learning, which has the advantages of efficient calculation, is used in the field of predicting cyclone separator performance based on swirl flow. In this study, a machine learning algorithm was applied to the development of a predictive model for liquid–liquid separation performance using swirling flow. Teoriaccacloruesnut lftosrucsoinmgptlhexe Rfloeywnso, ltdhse scthroesicsemoof dtuerlb(uRlSeMnc)ewmitohdtehleis eixmpperoirmtaenntt.aYladnagtae.tAals. [a40re] scuolmt,pthareeidr ftihnedninugms erreivcaelalretshualttsthuesitnugrbthueleRnecyenmoloddsesltruessesdmwodasel a(gRrSeeMa)blwe.itChatiheet aelx.p[4e1ri]mcoemntpaal rdeadtath. eAnsuamreersiuclatl, rtehseuirltfis nodf iEnqgusarteiovnea(l2)thmaot dtheel (truerabliuzlaebnlcee km–εo,dReNl uGsekd–wεa, sanagdreke–aωbleS.SCTa)i aent dal.E[q4u1]atcioomn p(7a)remdotdheeln(uRmSMer)icaanl drescuonltfsiromf Eedqutahtaitonth(e2) RmSModmelo(rdeeallipzraebdleickt–sεt,hReNswGikrl–iεn,ganfldowk–wωeSlSl Tth)raonudgEhqeuxapteiorinm(7e)nmts.oTdheler(RefSoMre),athned RcoSnMfirwmaesd atphpaltiethdeinRStMhismsotuddeyl .pNreudmictesritchael scwalicrulilnagtioflnoswwwereell ptherrofourgmheedxpuesrinimg eAnNts.SYThSeFrelufoernet,. tAhe wRoSrMkstwataiosnapeqpuliiepdpeind twhiisthstaundIyn.teNl uXmeoenriCcaPlUcaplcruoclaetsison(@s 2w.0e0reGpHezrf,o1r4mceodreu, sXi2ngprAoNceSsYs)S aFnldue2n0t0.GABwRoArMkstwataiosnuseeqduifpopr ecdalwcuiltahtiaonnsIn. tel Xeon CPU process (@ 2.00 GHz, 14 core, X2 process) and 200GB RAM was used for calculations
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