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Research On Technology Of CO2-RICH Tail Gas Capture In Natural Gas Purification Plant

Posted on:2021-02-14Degree:MasterType:Thesis
Country:ChinaCandidate:G X WuFull Text:PDF
GTID:2381330602485348Subject:Oil and gas engineering
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In recent years,CO2 emissions have been increasing,causing increasingly serious environmental problems.Controlling CO2 emissions from industrial processes is an important means of reducing and controlling carbon emissions.CO2 captured by industrial processes can also be used in the fields of carbon dioxide flooding and other fields,it is of great significance to study CO2 gas capture technology in purification plants.In this paper,for the CO2 capture project in Changqing Oilfield,the gas produced by the incineration plant of Jingbian Natural Gas Purification Plant was selected as the source of CO2 capture gas.Process analysis was performed,and simulation models of chemical absorption method,pressure swing adsorption method,and low temperature fractionation method were established using HYSYS software.After comparison,the chemical absorption method has lower energy consumption and the CO2 capture rate can reach 95%.The chemical absorption method was determined as the CO2 capture process for the acid gas incineration tail gas of the purification plant.Based on the chemical absorption method capture process model established by HYSYS,an in-depth study was performed.The simulation model used MDEA-PZ mixed amine solution.Sensitivity analysis of the main operating parameters of the absorption process,analysis process and overall cycle process in the capture CO2 process,to provide the best process parameters for the CO2 capture project,thereby achieving increased CO2 capture Rate,reduce energy consumption,and reduce the goal of capturing costs.During the simulation of absorption,the effects of various variables on the absorption rate of CO2 were studied,among which the number of plates,lean liquid flow,lean liquid concentration,lean liquid temperature,and absorption tower pressure had a large effect on the CO2 absorption rate.And the influence of the temperature of the feed gas is small,the CO2 load in the absorption tower under different conditions is compared,and the absorption pressure is 620 k Pa,the MDEA mass fraction is 20%,the lean liquid temperature is 50?,and the lean liquid flow is 225m3/h,the optimal process parameters for 16-layer trays.During the desorption process,the effects of reboiler load and reboiler pressure on the desorption rate of CO2 were analyzed,and the effect of the reboiler pressure on the lean liquid temperature was investigated.The increase of the reboiler pressure to a certain extent would make the The desorption rate of CO2 decreased,and the results showed that the optimal heat load of the reboiler was 15000 k W and the optimal pressure was 200 k Pa.The lean liquid after desorption was recycled back to the absorption tower,and the entire capture process was analyzed.Residual CO2 has a certain effect on the capture rate,and at the same time,it will cause an error in the calculation of the absorption rate during the absorption process.By changing the lean liquid flow and the heat load of the reboiler,the rationality of each parameter setting was proved.The CO2 capture rate of this project reaches 95%,and the annual CO2 capture volume can reach130,000 tons.This process scheme can adopt the optimal energy saving while ensuring the CO2 capture rate.The parameters have certain engineering guiding significance for CO2 capture.Finally,in order to reduce the energy consumption of the capture project and save operating costs,the analysis found that the energy consumption of the liquefied CO2 propane refrigeration process can be further optimized.A hybrid simulation platform of MATLAB and HYSYS software was built,and the SQP algorithm was used to optimize the energy consumption of the refrigeration process.The final optimization result reduced the energy consumption of the propane compressor by 13%.
Keywords/Search Tags:Carbon capture, Chemical absorption, Process modeling, process simulation, energy consumption optimization
PDF Full Text Request
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