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常减压装置的模拟与优化的效果取决于原油的实沸点(TBP)数据,针对进料原油物性时常变化,且实沸点(TBP)数据(曲线)测定费时和成本高的情况,提出了一种校正原油TBP曲线的方法。该方法以各侧线产品干点的模拟值与工况值误差平方和最小为目标,将TBP曲线的校正转化为最优化问题来求解。采用该方法能使模拟的精度显著提高。在此基础上,以汽提蒸汽量、侧线采出量、中段回流量为操作变量,以常压塔的拔出率最大化和能耗最小化为优化目标,建立了常减压装置的多目标优化模型。应用带精英策略的非支配遗传算法(NSGA-Ⅱ)对常减压装置进行优化求解,得到了常压塔总收率-能耗的最优Pareto解集,为常减压装置的操作优化提供了依据。
The effectiveness of the simulation and optimization of the atmospheric and vacuum unit depends on the TBP data of the crude oil. In view of the fact that the physical properties of the feed crude oil vary constantly and the TBP data (curves) are time-consuming and cost-intensive, A method of calibrating crude oil TBP curve. The method takes the minimization of the sum of squared errors between the simulated value and the working condition value of each sideline product, and transforms the correction of the TBP curve into an optimization problem to solve. Using this method can significantly improve the simulation accuracy. On the basis of this, taking steam stripping amount, sideline amount and backflow amount as manipulated variables, taking the maximization of extraction rate and minimization of energy consumption as the optimization target, Goal optimization model. The non-dominated genetic algorithm with elitist strategy (NSGA-Ⅱ) was used to optimize the atmospheric and vacuum distillation unit, and the optimal Pareto solution set was obtained for the total recovery and energy consumption of the atmospheric tower. The basis.