过程工业不确定条件下的计划与调度优化

过程工业的计划与调度优化是提高企业综合效益的有效途径,然而各种不确定因素的存在导致在确定性条件假设下得到的“最优”生产计划或调度次优或者不可实施。本文对当前过程工业不确定条件下计划与调度优化的研究进展进行了综述,并对系统建模和优化技术面临的挑战和下一步的发展趋势进行了分析。     

需求不确定下炼油企业生产计划优化混合规划模型

Abstract Production planning under uncertainty is considered as one of the most important problems in plant-wide optimization. In this article, first, a stochastic programming model with uniform distribution assumption is developed for refinery production planning under demand uncertainty, and then a hybrid programming model incorporating the linear programming model with the stochastic programming one by a weight factor is proposed. Subsequently, piecewise linear approximation functions are derived and applied to solve the hybrid programming model-under uniform distribution assumption. Case studies show that the linear approximation algorithm is effective to solve.the hybrid programming model, along with an error≤0.5% when the deviatiorgmean≤20%. The simulation results indicate that the hybrid programming model with an appropriate weight factor （0.1-0.2） can effectively improve the optimal operational strategies under demand uncertainty, achieving higher profit than the linear programming model and the stochastic programming one with about 1.3% and 0.4% enhancement, respectavely.     

不确定条件下石化企业供应链计划优化

针对石化企业供应链计划层的不确定性,提出了一种多周期、多产品的计划优化模型。基于离散时间建模方法,利用模糊可能性方法将不确定性问题转化为确定性问题,建立了混合整数线性规划模型。同时采用模型预测控制思想,利用模型的动态属性为决策者提供了不同满意度下的决策序列。以某炼油企业供应链为实例,证明了本方法的有效性。     

油轮到达时间不确定条件下的原油调度优化

原油调度是炼油企业生产的第一个环节,它直接影响后续生产过程的稳定性和经济性.文中采用连续时间建模方法.建立了油轮到达时间不确定条件下的原油从到港、卸载、储存、调合到进料全过程的随机规划机会约束调度优化模型,模型的优化目标是最小化给定调度时界内的总操作费用.采用直方图法对油轮迟到时间进行回归,得到油轮迟到时间的概率密度函数和分布函数,并引入置信水平,将模型中的不确定性约束转化为确定性约束,使得油轮到达时间不确定条件下的随机规划机会约束模型转变为可以求解的确定性混合整数非线性规划模型.针对原油调度模型的特点,采用广义Benders分解算法将原模型分解为两个混合整数线性规划问题和一个非线性规划问题进行迭代求解.避免了直接求解混合整数非线性规划问题的复杂性.最后,将建立的模型和算法应用于背景企业的原油调度过程,结果表明模型和算法都有良好的实用性.     

多因素不确定条件下的间歇生产调度优化

不确定条件下的间歇生产调度优化是生产调度问题研究中具有挑战性的课题。提出了一种基于混合整数线性规划(MILP)的鲁棒优化模型;来优化不确定条件下的生产调度决策。考虑到生产过程中的操作成本和原料成本;建立了以净利润最大为调度目标的确定性数学模型。然后考虑需求、处理时间、市场价格三种不确定因素;建立可调整保守程度的鲁棒优化模型并转换成鲁棒对应模型。实例结果表明;鲁棒优化的间歇生产调度模型较确定性模型利润减少;但生产任务数量增加;设备空闲时间缩短;从而增强了调度方案的可靠性;实现了不确定条件下生产操作性和经济性的综合优化。     

不确定条件下中间存储时间有限多产品间歇生产过程调度

针对产品处理时间不确定条件下中间存储时间有限多产品间歇生产过程调度问题, 采用三角模糊数描述处理时间的不确定性, 通过一种模糊排序的方法建立了以最小化模糊最大完工时间的值以及不确定度作为调度目标的数学模型, 提出一种基于改进粒子群和分布估计的混合算法(IPSO-EDA)。IPSO-EDA算法在粒子群更新公式中引入基于所有粒子自身最优位置的优质个体分布信息, 提高了算法的全局搜索能力, 同时采用NEH初始化获得理想的初始解, 采用NEH局部搜索提高算法的局部搜索能力。通过正交实验设计对算法的参数进行调节, 仿真结果表明了所提出算法的有效性和优越性。     

一种新的整定方法用于参数不确定条件下二自由度内部模型控制(英文)

Internal model control （IMC） yields very good performance for set point tracking, but gives sluggish response for disturbance rejection problem. A two-degree-of-freedom IMC （2DOF-IMC） has been developed to overcome the weakness. However, the setting of parameter becomes a complicated matter if there is an uncertainty model. The present study proposes a new tuning method for the controller. The proposed tuning method consists of three steps. Firstly, the worst case of the model uncertainty is determined. Secondly, the parameter of set point con- troller using maximum peak （Mp） criteria is specified, and finally, the parameter of the disturbance rejection con- troller using gain margin （GM） criteria is obtained. The proposed method is denoted as Mp-GM tuning method. The effectiveness of Mp-GM tuning method has evaluated and compared with IMC-controller tuning program （IMCTUNE） as bench mark. The evaluation and comparison have been done through the simulation on a number of first order plus dead time （FOPDT） and higher order processes. The FOPDT process tested includes processes with controllability ratio in the range 0.7 to 2.5. The higher processes include second order with underdarnped and third order with nonminimum phase processes. Although the two of higher order processes are considered as difficult processes, the proposed Mp-GM tuning method are able to obtain the good controller parameter even under process uncertainties.     

Hybrid model for optimization of crude oil distillation units

Planning, scheduling, and real time optimization are currently implemented using different types of models, which causes discrepancies between their results. This work presents a single model of a crude distillation unit (preflash, atmospheric, and vacuum towers) suitable for all of these applications, thereby eliminating discrepancies between models used in these decision processes. Product true boiling point (TBP) curves are predicted via partial least squares model from the feed TBP curve and operating conditions (flows, pumparound heat duties, furnace coil outlet temperatures). Combined with volumetric and energy balances, this enables prediction of crude distillation on par with a rigorous distillation model, with 0.5% root mean square error (RMSE) over a wide range of conditions. Associated properties (e.g., gravity, sulfur) are computed for each product based on its distillation curve and corresponding property distribution in the feed. Model structure makes it particularly amenable for development from plant data. © 2015 American Institute of Chemical Engineers AIChE J, 62: 1065–1078, 2016     

炼油企业全厂调度优化系统的设计与开发

生产调度优化对于炼油企业提高经济效益、增强市场响应速度有着重要的作用。本文设计并开发出一款图形化炼厂生产调度优化软件,采用MVC架构技术,实现了调度优化建模的图形化,可以方便组态炼油厂调度模型;将开源优化代码Coin-OR移植为调度优化软件的求解器,实现了求解器与图形化建模接口的连接;丰富的报表输出,让现场操作工可以各种形式获得调度优化结果;完善的内部模型数据管理和丰富的外部数据接口,包括原油性质指标库和原油评价数据库接口、成品油性质指标库等。仿真结果表明,该软件能够优化出符合炼厂生产实际的调度优化排产方案。     

面向对象的炼油厂全厂调度优化模型及程序框架

基于考虑炼油装置优化操作模式切换过程的总体思想,构建了一套炼油厂全厂调度优化离散时间模型结构,并形成配套的程序框架。采用面向对象的建模方式,引入模态指示矩阵等表达,为炼油厂生产调度建模提供了较为清晰的参考思路。通过GAMS和MATLAB的数据交互,实现二者优势互补,为进一步研究炼油生产调度模型提供便利、奠定基础。案例研究验证了所提模型结构及程序框架的有效性。     

炼厂生产计划优化模型

This article addresses a production planning optimization problem of overall refinery. The authors formulated the optimization problem as mixed integer linear programming. The model considers the main factors for optimizing the production plan of overall refinery related to the use of run-modes of processing units. The aim of this planning is to decide which run-mode to use in each processing unit in each period of a given horizon, to satisfy the demand, such as the total cost of production and inventory is minimized. The resulting model can be regarded as a generalized lot-sizing problem where a run-mode can produce and consume more than one product. The resulting optimization problem is large-sized and NP-hard. The authors have proposed a column generation-based algorithm called branch-and-price （BP） for solving the interested optimization problem. The model and implementation of the algorithm are described in detail in this article. The computational results verify the effectiveness of the proposed model and the solution method.     

灵活应用LP模型提高炼油厂设计水平

炼油厂设计水平的高低首先取决于总加工流程的选择是否优化合理。本文利用PIMS软件提供的模块化和开放性结构的建模环境,结合炼油工艺特点,根据设计需要灵活地构建了各种LP模型,用于炼油厂实际总加工流程的优化设计中。同时重点介绍了其在原油配置、重油加工路线选择和新工艺评估方面的应用成果,结果表明其对提高炼油厂的整体设计水平具有重要指导意义。     

考虑需求不确定性的化工生产计划与调度集成

生产计划与调度是化工供应链优化中两个重要的决策问题。为了提高生产决策的效率,不仅要对计划与调度进行集成,而且要考虑不确定性的影响。对于多周期生产计划与调度问题,首先在每个生产周期内,分别建立计划与调度的确定性模型,通过产量关联对二者进行集成。然后考虑需求不确定性,使用有限数量的场景表达决策变量,建立二阶段随机规划模型。最后运用滚动时域求解策略,使计划与调度结果在迭代过程中达到一致。实例结果表明,在考虑需求不确定性时,与传统方法相比,随机规划方法可以降低总费用,结合计划与调度的分层集成策略,实现了生产操作性和经济性的综合优化。     

Data‐driven mathematical modeling and global optimization framework for entire petrochemical planning operations

In this work we develop a novel modeling and global optimization‐based planning formulation, which predicts product yields and properties for all of the production units within a highly integrated refinery‐petrochemical complex. Distillation is modeled using swing‐cut theory, while data‐based nonlinear models are developed for other processing units. The parameters of the postulated models are globally optimized based on a large data set of daily production. Property indices in blending units are linearly additive and they are calculated on a weight or volume basis. Binary variables are introduced to denote unit and operation modes selection. The planning model is a large‐scale non‐convex mixed integer nonlinear optimization model, which is solved to ε‐global optimality. Computational results for multiple case studies indicate that we achieve a significant profit increase (37–65%) using the proposed data‐driven global optimization framework. Finally, a user‐friendly interface is presented which enables automated updating of demand, specification, and cost parameters. © 2016 American Institute of Chemical Engineers AIChE J, 62: 3020–3040, 2016     

炼油污水A/O处理工艺模拟与优化

针对我国某炼油厂含油系列污水处理生化段缺氧/好氧(A/O)工艺,利用BioWin 5.0模拟软件,进行了模拟和优化。结果表明,校正后的模型能够较好地反映目前A/O工艺的运行状况。然后调节工艺参数对校正后的模型进行优化,结果表明:在优化后的运行条件下,出水COD几乎没有变化,TN去除率提高2.9%,曝气能耗下降了17.8%,节约了运行费用。     

从催化干气中回收C2的工艺模拟与优化

乙烯是一种重要的化工原料,从催化干气中回收乙烯,能有效地降低我国炼油厂的生成成本,提高其经济效益。采用水合物-吸收耦合新工艺,对催化干气中C2组分进行回收。应用Aspen Custom Modeler模拟软件,对该工艺流程进行了设计和模拟优化。通过对乳液用量、理论板数、水含量、操作温度等参数进行优化,得到了不同乙烯回收率下的最优操作条件。