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.     

Multiperiod Optimization for Production Planning of Petroleum Refineries

Production planning has been the focus of many articles in the mathematical programming literature. In particular, successful operation of oil companies depends on systematic decisions over complex logistics. The present article focuses on the decision-making process involved in petroleum production planning. The proposed model is based on a nonlinear programming formulation that was developed to plan production over a single period. First, the model incorporates multiple planning periods and the selection of different crude oil types. Uncertainty related to petroleum and product prices as well as demand is then included as a set of discrete probabilities. Finally, crude oil handling constraints are added. The resulting models are mixed integer nonlinear programs (MINLP), which were successfully applied to a real-world case at the Petrobras REVAP refinery. Problems of up to 19 time periods and up to five scenarios were solved to optimality.     

Multiperiod Optimization for Production Planning of Petroleum Refineries

Production planning has been the focus of many articles in the mathematical programming literature. In particular, successful operation of oil companies depends on systematic decisions over complex logistics. The present article focuses on the decision-making process involved in petroleum production planning. The proposed model is based on a nonlinear programming formulation that was developed to plan production over a single period. First, the model incorporates multiple planning periods and the selection of different crude oil types. Uncertainty related to petroleum and product prices as well as demand is then included as a set of discrete probabilities. Finally, crude oil handling constraints are added. The resulting models are mixed integer nonlinear programs (MINLP), which were successfully applied to a real-world case at the Petrobras REVAP refinery. Problems of up to 19 time periods and up to five scenarios were solved to optimality.     

间歇过程不确定性短期调度综述

根据处理不确定性调度问题的方法,将各种不确定条件下的优化调度方法分成两大类：随机调度和反应调度。随机调度又可细分为基于求助的随机规划、概率规划、模糊规划和随机动态规划。通过讨论和对比它们的优、缺点和局限性,提出通用、有效的反应调度工具,启发式学习方法和模糊集合理论的新型随机调度方法,不确定性调度与计划集成、设计与调度集成及调度与控制集成将是进一步研究的方向。     

一种基于仿真的流程工业生产调度闭环优化方法

针对流程工业实际调度过程中存在的调度执行滞后、装置不完全利用以及生产资源浪费等问题,提出一种基于仿真的闭环调度优化方法,该方法具有多层次建模、分层调度优化及闭环校正等特点.     

夹点技术在炼油企业生产计划中的运用

本文通过分析炼油企业产品传递或配送过程中的质和量的约束关系,即时间和产品加工量的约束关系,提出将能量综合集成技术夹点技术运用于炼油企业生产计划。使企业能快速响应市场变化,降低生产成本,指导企业生产经营过程。文章最后以某炼油企业为例建立一个实例模型,表明了该模型的有效性和实用性。     

一种针对原油调合过程的新型调度方法(英文)

For those refineries which have to deal with different types of crude oil, blending is an attractive solution to obtain a quality feedstock. In this paper, a novel scheduling strategy is proposed for a practical crude oil blending process. The objective is to keep the property of feedstock, mainly described by the true boiling point (TBP) data, consistent and suitable. Firstly, the mathematical model is established. Then, a heuristically initialized hybrid iterative (HIHI) algorithm based on a two-level optimization structure, in which tabu search (TS) and differential evolution (DE) are used for upper-level and lower-level optimization, respectively, is proposed to get the model solution. Finally, the effectiveness and efficiency of the scheduling strategy is validated via real data from a certain refinery.     

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     

An MILP Model for Optimal Scheduling of the Lubricant Production Plant

In this article we are developing a model that can be used for determining the optimal production schedule in a lubricant production plant. The model includes all the main stages in the lubricant production process, contains both continuous and binary variables, and results in the formulation of a mixed integer linear programming (MILP) problem that is solved using standard optimization techniques. The model can be easily adapted to any lube production facility, thus providing a valuable tool to refineries in their effort to automate the production scheduling process. The proposed tool can save valuable time and resources by eliminating the time-consuming search for a feasible production plan that production engineers go through in order to meet production demands.     

A Graph-based Ant Colony Optimization Approach for Integrated Process Planning and Scheduling

This paper considers an ant colony optimization algorithm based on AND/OR graph for integrated process planning and scheduling (IPPS). Generally, the process planning and scheduling are studied separately. Due to the complexity of manufacturing system, IPPS combining both process planning and scheduling can depict the real situation of a manufacturing system. The IPPS is represented on AND/OR graph consisting of nodes, and undirected and directed arcs. The nodes denote operations of jobs, and undirected/directed arcs denote possible visiting path among the nodes. Ant colony goes through the necessary nodes on the graph fromthe starting node to the end node to obtain the optimal solution with the objective of minimizing makespan. In order to avoid local convergence and lowconvergence, some improved strategy is incorporated in the standard ant colony optimization algorithm. Extensive computational experiments are carried out to study the influence of various parameters on the system performance.