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The Study On Approach Of C4 Hydrocarbons Separation System Synthesis Optimization

Posted on:2004-09-25Degree:MasterType:Thesis
Country:ChinaCandidate:L HeFull Text:PDF
GTID:2121360122997381Subject:Chemical Engineering
Abstract/Summary:PDF Full Text Request
Separation process synthesis is a manipulation to deal with mixtures to be separated at the low expense of energy consumption and capital cost. And it is often used in the pretreatment of raw material, the purification of the product, the disposal of scrap, etc.Due to the close boiling points, C4 hydrocarbons are difficult to be separated. In this paper, combining with the refit plan in a factory, the energy efficiency of the whole separation system is increased through synthesis and optimization of the separation sequence.The six points are summarized as follows:1. The draft is designed by means of the tool of heuristic rules and technique of energy integration.2. The C4 hydrocarbons separation system is simulated using the simulator ASPEN PLUS. For each unit, the thermodynamics function is confirmed.3. Based on the simulation results and energy analysis of the system, the plans are simulated with the established thermodynamics functions, and the operation parameters are optimized by the sensitivity analysis. Calculation shows that all of the proposed plans are applicable to effectively separated the mixtures at lower expense of energy consumption.4. The acetoniltrile recovery column is considered to increase the operation pressure. The energy integration plan is designed to further increase the energy efficiency of the whole system.5. Mathematical model of the thermally coupled distillation is established with artificial neural network. Calculation shows that the model can simulate the process rigorously.6. A modified genetic algorithm is used to optimize the model by artificial neural network. It shows that the separation process can be optimized with the established model and optimization algorithm.
Keywords/Search Tags:Separation sequence, Neural network, Genetic algorithm, Simulation, Optimization
PDF Full Text Request
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