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Strategy Of Energy Saving And Simulation For Trimethylbenzene Distillation Process

Posted on:2008-03-23Degree:MasterType:Thesis
Country:ChinaCandidate:J XinFull Text:PDF
GTID:2121360245493350Subject:Chemical Engineering
Abstract/Summary:PDF Full Text Request
Taking trimethylbenzene precise distillation for example,this paper presents three-column distillation process and strategy of saving energy in precise distillation. Trimethylbenzene distillation as a high difficult separation , has several characteristic: high theoretical numbers of plates, high reflux ratio, the investment and running cost are high. The main work of this paper can be summarized into several points as follows:At normal pressures, trimethylbenzene three-column processes in parallel flow type and series flow type have been established and simulated by applying PRO/II Sensitivity analysis about reflux ratio, total theoretical number of plates and the location of feed is dealed with in separation process. Comparing with the primary project, the two projects have better yield and purity, but the heat load is some bigger.Compared with the operating under normal pressure, the new process under differential pressure can make good use of heat integration.The energy saving for the distillation system will be carried out by the new process , with the heat load as the objective and the pressure as the optimal parameter. The research results show that the theoretical energy of heat integration technology becomes below 40% of the energy of former process ,and it is an effective energy-saving one.The operational performance and the energy saving effects of the thermally coupled distillation was simulated and analyzed with the trimethylbenzene separation unit. Applying the thermally coupled distillation, the extent of energy saving is above 15%,comparing with the conventional distillation process.The mathematical modeling and prediction of energy consume is established using improved BP artificial neural network.With this modeling, energy consume can be fast predicted under various yield and various purity . Results show that the estimation based on the model was accurate and helpful for engineering design .
Keywords/Search Tags:precise distillation, heat integration, thermally coupled distillation, artificial neural network, PRO/II
PDF Full Text Request
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