基于软件集成的常减压蒸馏装置的全流程模拟与优化

根据炼油厂提供的数据,利用Aspen Plus Pinch和B-JAC等软件分别实现了常减压蒸馏车间的主装置模拟、换热网络优化和旧换热器的校核及新换热器的设计。并最终实现了整个车间的全流程模拟,为常减压蒸馏装置的改造奠定了基础。     

安庆石化Ⅰ套常减压装置流程模拟及优化

安庆石化应用ASPEN PLUS软件,建立Ⅰ套常减压装置模型,运用灵敏度分析工具,对装置进行模拟优化,将优化结果应用于生产,取得较好的应用效果.     

胜利原油常减压蒸馏装置的流程模拟优化与应用

采用Aspen Plus软件对常减压蒸馏装置进行流程模拟计算,获得了各产品的温度、流量等关键参数及产品的馏程曲线,并与实际数据对比,二者吻合良好。分析了温度沿塔板的分布,运用灵敏度分析工具对相关参数进行优化,并将优化结果应用于实际生产,获得良好的应用效果。     

C10常减压蒸馏装置过程模拟与优化

综述了国内外从重整C10中分离均四甲苯的技术和进展,并利用成熟的Aspen plus 软件,建立了常减压蒸馏分离C10混合重芳烃的稳态模型并进行稳态模拟,将模拟结果与现场生产数据进行比较,从而确定了模型的可靠程度。然后,对现有装置的工艺进行模拟并对工艺操作参数加以优化。为今后该工艺的进一步研究及优化工业化生产操作奠定了基础。     

常减压装置优化流程增产柴油实施措施

介绍在当前市场对柴油需求不断增长的前提下,通过对炼油厂常减压装置侧线产品的分析,提出常减压蒸馏装置流程的优化改造方案,将常四线油通过减二线中段回流并入减压塔的实施,在保证其他侧线产品质量合格的前提下,实现增产柴油的目的,同时通过后续工艺流程的优化调整,有助于提高常减压装置换热终温,降低装置加热炉燃料消耗,提高装置运行的...     

原油常减压蒸馏塔的流程模拟

常减压蒸馏装置是炼油加工的第一道工序,它的运行状况对后续装置乃至全厂都有着重要影响。针对初馏塔、常压蒸馏塔和减压蒸馏塔的稳态流程模拟,选取适合的蒸馏塔中段循环设定变量和减压塔模拟方法,最终流程模拟结果与实际工艺参数相似度极高,说明常减压蒸馏塔的流程模拟能够如实体现实际工艺状态,可以作为指导工艺生产、操作优化的有效方法。     

优化操作条件提高常减压装置轻质油收率

本文主要讲述如何通过优化操作 ,改进装置操作的工艺条件 ,来提高装置的轻质油收率     

基于特征选择的常减压装置模型及在计划优化中的应用

常减压装置将原油切割为不同中间产品,其作为炼油工艺的龙头装置,对炼油过程生产计划排产与效益提升至关重要。通过建立一种高精度且具有良好求解效率的常减压模型,用以求解模型关键指标实沸点（TBP）曲线,即综合考虑切割产品的实沸点（TBP）与原油TBP、流量、温度等变量影响,构建非线性方程组模型来表征输入输出间的关系;利用特征选择方法遴选相关变量（包括进料性质、相邻TBP及其二次项等）,并采用鲸鱼优化算法优化方程组系数。仿真结果表明,该多输出相互关系模型与已有文献工作相比,在兼顾求解效率基础上,常减压装置各蒸馏切割产品TBP曲线预测上有更高的精度,将此模型应用到炼厂计划优化中,与传统的悬摆切割模型对比,优化结果优于传统悬摆切割模型。     

原油常减压蒸馏装置的流程模拟及参数优化

根据某原油常减压蒸馏装置的工艺流程,采用Aspen Plus软件对装置的初馏塔、常压塔和减压塔进行流程模拟计算,获得了各产品的温度、流量等关键参数及减压塔侧线产品的馏程曲线,并与实际数据对比,二者吻合良好. 分析了塔板温度和气液相流量沿塔板的分布,运用灵敏度分析工具对相关参数进行优化,优化后常压塔柴油收率可增加0.22 t/h,常压塔热负荷降低6.3%,减压塔轻油收率上升0.18%,减压塔热负荷降低11.2%.     

流程模拟在尿素生产优化操作中的应用

用流程模拟软件对尿素生产的操作数据进行模拟计算，计算出所有物流的温度、压力、流量和组成，这些数据能够准确地反映工艺状况。通过对模拟结果进行分析，找出装置运行的瓶颈问题，并采取相应措施，解决问题，使生产操作达到增产、节能、降耗、优化的目的。     

Product tri-section based crude distillation unit model for refinery production planning and refinery optimization

Accuracy of a crude distillation unit (CDU) model has a significant impact on refinery production planning. High accuracy is typically accomplished via nonlinear models which causes convergence difficulties when the entire refinery model is optimized. CDU model presented in this work is a mixed-integer linear model with a modest number of binary variables; its accuracy is on par with rigorous tray to tray CDU models. The model relies on the observation¹² that a line through the middle of the product true boiling point (TBP) curve depends on the crude feed properties and the yields of the adjacent products. Novelty of the product tri-section CDU model is that it does not require models of individual distillation towers comprising the CDU, thereby leading to a much simpler model structure. Significant reduction in the computational effort required for the optimization of nonlinear refinery models is illustrated by comparison with previous work.     

一种新的生产计划与催化裂化装置过程操作集成的闭环策略

Production planning models generated by common modeling systems do not involve constraints for process operations, and a solution optimized by these models is called a quasi-optimal plan. The quasi-optimal plan cannot be executed in practice some time for no corresponding operating conditions. In order to determine a practi- cally feasible optimal plan and corresponding operating conditions of fluidized catalytic cracking unit （FCCU）, a novel close-loop integrated strategy, including determination of a quasi-optimal plan, search of operating conditions of FCCU and revision of the production planning model, was proposed in this article. In the strategy, a generalized genetic algorithm （GA） coupled with a sequential process simulator of FCCU was applied to search operating conditions implementing the quasi-optimal plan of FCCU and output the optimal individual in the GA search as a final genetic individual. When no corresponding operating conditions were found, the final genetic individual based correction （FGIC） method was presented to revise the production planning model, and then a new quasi-optimal production plan was determined. The above steps were repeated until a practically feasible optimal plan and corresponding operating conditions of FCCU were obtained. The close-loop integrated strategy was validated by two cases, and it was indicated that the strategy was efficient in determining a practically executed optimal plan and corresponding operating conditions of FCCU.     

Problem-oriented modules for process simulation. resolution strategies for simulation and optimization

A new resolution strategy is presented to overcome short-comings of the sequential modular process flowsheeting. Following a logic flow of information, different from the material flow of streams in the flowsheet, changes the mathematical structure of the system of equations that models the units. New programs have been generated, very different from traditional modules. Deciding an appropriate resolution strategy usually leads to a dramatic reduction in required CPU times and to an increased flexibility to handle different problems. The simulation convergence and process optimization can be approached from a different perspective.     

裂解C_4分离的过程集成优化控制

针对从裂解Ｃ４分离丁二烯的过程，论述了过程集成的方法和实现集成优化控制的方案。关于过程集成的方法基于已往的工作基础和开发的专家知识库提出了八步集成策略；关于集成优化控制提出了模型化的方法和系统结构及实现方法。实践已证明提出的集成方法及控制方案是可行的，已产生了重大的技术经济效益。     

集成炼油企业生产与能量系统的生产计划优化

炼油企业通常由物料生产系统与能量系统组成。传统的企业生产计划优化通常以生产系统物流优化为主, 能量系统基于物料生产优化结果进行产能优化以满足企业级能源供需平衡。此种优化方法不仅压缩了企业整体优化空间并且降低了燃料油与瓦斯等中间产品的利用率。基于装置能耗模型与能源供需以及中间产品的产耗质量平衡关系, 建立炼油企业生产系统与能源系统的集成优化模型, 通过求解MINLP模型进而实现企业生产物流与能流的集成优化。案例实践表明, 相较于传统分步优化方法, 集成优化方法不仅有效降低了全厂的生产成本, 并且实现了生产的节能减排。     

氯碱厂CPE生产工艺选择及优化

介绍氯碱厂ＣＰＥ生产工艺、供氯工艺及主要设备的选择及优化。     

基础油供应链生产-分销计划模型及其优化

随着市场全球化的飞速发展,供应链思想已渗透到各行各业,是一种新型的企业管理思想。然而在一些 产业结构不合理、急需产业升级的领域,几乎没有相关的供应链研究,譬如石油化工领域的量化研究少之又少。 本文分析了在独立决策下基础油供应链生产、分销系统中,信息不共享,生产商、分销商以及客户只追求各自 利益最大化,忽视甚至是损害了供应链整体利益的现象。针对上述现象,构建了生产-分销集成计划模型,以实 现供应链总成本和总反应时间最小,提出了一种基于粒子群算法寻找多目标供应链网络Pareto 最优解的方法。 仿真实例的结果表明了集成模型的可行性和优越性。     

CAE及其在化工过程模拟优化中的应用

介绍了CAE的组成、软件及其在化工过程模拟优化中的应用。重点介绍了ROM的构成及其应用。     

Multistage distributionally robust optimization for integrated production and maintenance scheduling

In chemical manufacturing processes, equipment degradation can have a significant impact on process performance or cause unit failures that result in considerable downtime. Hence, maintenance planning is an important consideration, and there have been increased efforts in scheduling production and maintenance operations jointly. In this context, one major challenge is the inherent uncertainty in predictive equipment health models. In particular, the probability distribution associated with the stochasticity in such models is often difficult to estimate and hence not known exactly. In this work, we apply a distributionally robust optimization (DRO) approach to address this problem. Specifically, the proposed formulation optimizes the worst-case expected outcome with respect to a Wasserstein ambiguity set, and we apply a decision rule approach that allows multistage mixed-integer recourse. Computational experiments, including a real-world industrial case study, are conducted, where the results demonstrate the significant benefits from binary recourse and DRO in terms of solution quality.