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。     

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。     

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.     

集成模糊安全评价的反应路径综合

为在过程早期获取本质安全性较好的反应路径,将模糊安全评价集成于反应路径综合形成有效方法。根据反应路径综合阶段信息选择指标,通过设定指标的隶属度函数,建立模糊推理系统,且应用层次分析法（analytic hierarchy process,AHP）求得指标的权重因子,形成了模糊评价方法。为消除中间变量的影响,分别建立单、双输入变量的模糊推理系统。将它集成于反应路径综合,通过原料筛选规则,模糊安全评价,建立以安全为目标的优化模型,求解得到优良的反应路径组合。应用于萘甲胺反应路径综合实例,定量得到了反应路径及其目标函数值,并对两种模糊系统的综合结果进行了比较。     

过程系统能量集成同步最优综合法

将换热网络超结构混合整数非线性规划多目标同步最优综合方法进一步扩展到与过程系统的联合优化,提出了改进的过程热集成同步综合方法,并以反应分离过程与换热网络能量集成为例,建立了同步优化超结构模型,采用混合整数非线性规划的遗传算法求解,可同时得到热集成系统最优的流程结构和操作条件。     

汽油调合调度优化

采用连续时间建模方法,建立了一种新的汽油非线性调合和调度集成优化的混合整数非线性规划（MINLP）模型,克服了当前在油品调合调度中采用线性调合模型或者将非线性调合过程和调度分开优化的缺陷。针对建立MINLP模型的特点,将原MINLP问题转化为求解一系列的混合整数线性规划（MILP）模型,避免了直接求解MINLP模型的复杂性。最后以某大型炼油企业为例,验证了模型和算法的实用性。     

基于高斯模型的聚乙烯过程设计与控制集成优化

聚乙烯反应过程中物流-能流剧烈交叠、反应-传递相互耦合,使得过程具有强非线性以及多重稳态。传统的顺序设计方法不能保证系统有足够的控制自由度,当存在扰动和过程参数不确定性时,仅依靠设计控制器很难提高产品质量。提出一种聚乙烯工艺稳态设计与运行控制的集成优化方案,创造性地引入Kriging高斯模型同时预测模型动态和模型不确定性。另一个重要的贡献是在聚乙烯工艺设计阶段,设计性能指标,定量描述过程稳态设计对闭环动态的影响。所提出的方法已经通过对气相聚乙烯工艺设计和运行控制的集成优化进行了验证,并在参数不确定性和扰动存在情况下仿真证实了集成优化设计方案的高效性。     

基于随机规划的炼厂氢网络改造设计

氢气是炼油企业重要的战略资源。越来越多的炼油企业开始改造其氢网络以应对日趋严格的环保标准和产品质量。炼厂的氢气产耗量受原油供应和产品需求的变化而随机波动,这使得传统的确定性模型无法有效描述氢网络设计问题。本文针对氢网络的改造设计问题,建立了同时考虑设计和调度问题的集成优化模型。采用基于场景的二阶段随机规划方法表达氢网络运行中的不确定因素,通过将决策变量分为一阶和二阶决策变量,优化计算使第一阶段决策成本与第二阶段决策的期望成本之和最小。算例表明了本文所提方法的有效性和与传统的确定性方法相比的优越性。     

基于FGCN的针铁矿沉铁过程建模

针铁矿沉铁过程是由多个连续反应器级联,并且包含氧化反应、还原反应以及中和反应等一系列复杂化学反应的复杂过程,具有强非线性、不确定性的特点,难以建立精确的数学模型。提出一种基于模糊灰色认知网络（fuzzy gray cognitive network,FGCN）的针铁矿沉铁过程的建模方法。根据专家经验和历史数据,建立针铁矿沉铁系统的模糊灰色认知网络模型,利用带终端约束的非线性Hebbian学习算法（nonlinear Hebbian learning,NHL）对权值进行学习。在不同程度的不确定性环境下对系统进行分析,结果表明模糊灰色认知网络能够在不确定性高的环境下对复杂工业系统进行有效模拟,收敛到一个灰度为零或者灰度很小的灰数平衡点,利用白化函数得到一个准确的控制输出。     

基于高斯模型的聚乙烯过程设计与控制集成优化

聚乙烯反应过程中物流-能流剧烈交叠、反应-传递相互耦合,使得过程具有强非线性以及多重稳态。传统的顺序设计方法不能保证系统有足够的控制自由度,当存在扰动和过程参数不确定性时,仅依靠设计控制器很难提高产品质量。提出一种聚乙烯工艺稳态设计与运行控制的集成优化方案,创造性地引入Kriging高斯模型同时预测模型动态和模型不确定性。另一个重要的贡献是在聚乙烯工艺设计阶段,设计性能指标,定量描述过程稳态设计对闭环动态的影响。所提出的方法已经通过对气相聚乙烯工艺设计和运行控制的集成优化进行了验证,并在参数不确定性和扰动存在情况下仿真证实了集成优化设计方案的高效性。     

Robust optimization and stochastic programming approaches for medium-term production scheduling of a large-scale steelmaking continuous casting process under demand uncertainty

Scheduling of steelmaking-continuous casting (SCC) processes is of major importance in iron and steel operations since it is often a bottleneck in iron and steel production. In practice, uncertainties are unavoidable and include demand fluctuations, processing time uncertainty, and equipment malfunction. In the presence of these uncertainties, an optimal schedule generated using nominal parameter values may often be suboptimal or even become infeasible. In this paper, we introduce robust optimization and stochastic programming approaches for addressing demand uncertainty in steelmaking continuous casting operations. In the robust optimization framework, a deterministic robust counterpart optimization model is introduced to guarantee that the production schedule remains feasible for the varying demands. Also, a two-stage scenario based stochastic programming framework is investigated for the scheduling of steelmaking and continuous operations under demand uncertainty. To make the resulting stochastic programming problem computationally tractable, a scenario reduction method has been applied to reduce the number of scenarios to a small set of representative realizations. Results from both the robust optimization and stochastic programming methods demonstrate robustness under demand uncertainty and that the robust optimization-based solution is of comparable quality to the two-stage stochastic programming based solution.     

基于马尔可夫链的蒸汽动力系统两阶段随机规划

现有的解决蒸汽动力系统蒸汽需求不确定性的优化方法有随机规划和鲁棒优化,但二者不能同时兼顾稳定性和经济性。本文提出一种基于马尔可夫链的两阶段随机规划去解决这个问题。第一阶段基于空间距离表达划分不确定变量,通过聚类算法划分成不同工况。第二阶段基于状态切换概率构建马尔可夫链,通过场景生成和削减的方法预测蒸汽的需求值。以某煤制气企业蒸汽动力系统为实例建立相应的优化模型,将预测的蒸汽值带入优化模型求解,得出的最优操作方案与随机规划和鲁棒优化法进行对比和分析。结果表明,本优化方法综合了随机规划经济性高和鲁棒优化稳定性高的优点,稳定性和经济性都介于随机规划和鲁棒优化的中间,为解决蒸汽动力系统的不确定优化问题提供了新思路。     

Optimization of a multiperiod refinery planning problem under uncertainty

Uncertainty in refinery planning presents a significant challenge in determining the day-to-day operations of an oil refinery. Deterministic modeling techniques often fail to account for this uncertainty, potentially resulting in reduced profit. The stochastic programming framework explicitly incorporates parameter uncertainty in the problem formulation, thus giving preference to robust solutions. In this work, a nonlinear, multiperiod, industrial refinery problem is extended to a two-stage stochastic problem, formulated as a mixed-integer nonlinear program. A crude-oil sequencing case study is developed with binary scheduling decisions in both stages of the stochastic programming problem. Solution via a decomposition strategy based on the generalized Benders decomposition (GBD) algorithm is proposed. The binary decisions are designated as complicating variables that, when fixed, reduce the full-space problem to a series of independent scenario subproblems. Through the application of the GBD algorithm, a feasible mixed-integer solution is obtained that is more robust to uncertainty than its deterministic counterpart.     

Medium-term maintenance turnaround planning under uncertainty for integrated chemical sites

Plant maintenance poses extended disruptions to production. Maintenance effects are amplified when the plant is part of an integrated chemical site, as production levels of adjacent plants in the site are also significantly influenced. A challenge in dealing with turnarounds is the difficulty in predicting their duration, due to discovery work and delays. This uncertainty in duration affects two major planning decisions: production levels and maintenance manpower allocation. The latter must be decided several months before the turnarounds occur. We address the scheduling of a set of plant turnarounds over a medium-term of several months using integer programming formulations. Due to the nature of uncertainty, production decisions are treated through stochastic programming ideas, while the manpower aspect is handled through a robust optimization framework. We propose combined robust optimization and stochastic programming formulations to address the problem and demonstrate, through an industrial case study, the potential for significant savings.     

A computational framework and solution algorithms for two‐stage adaptive robust scheduling of batch manufacturing processes under uncertainty

A novel two‐stage adaptive robust optimization (ARO) approach to production scheduling of batch processes under uncertainty is proposed. We first reformulate the deterministic mixed‐integer linear programming model of batch scheduling into a two‐stage optimization problem. Symmetric uncertainty sets are then introduced to confine the uncertain parameters, and budgets of uncertainty are used to adjust the degree of conservatism. We then apply both the Benders decomposition algorithm and the column‐and‐constraint generation (C&CG) algorithm to efficiently solve the resulting two‐stage ARO problem, which cannot be tackled directly by any existing optimization solvers. Two case studies are considered to demonstrate the applicability of the proposed modeling framework and solution algorithms. The results show that the C&CG algorithm is more computationally efficient than the Benders decomposition algorithm, and the proposed two‐stage ARO approach returns 9% higher profits than the conventional robust optimization approach for batch scheduling. © 2015 American Institute of Chemical Engineers AIChE J, 62: 687–703, 2016     

Approximate dynamic programming based approach to process control and scheduling

Multi-stage decision problems under uncertainty are abundant in process industries. Markov decision process (MDP) is a general mathematical formulation of such problems. Whereas stochastic programming and dynamic programming are the standard methods to solve MDPs, their unwieldy computational requirements limit their usefulness in real applications. Approximate dynamic programming (ADP) combines simulation and function approximation to alleviate the ‘curse-of-dimensionality’ associated with the traditional dynamic programming approach. In this paper, we present the ADP as a viable way to solve MDPs for process control and scheduling problems. We bring forth some key issues for its successful application in these types of problems, including the choice of function approximator and the use of a penalty function to guard against over-extending the value function approximation in the value iteration. Application studies involving a number of well-known control and scheduling problems, including dual control, multiple controller scheduling, and resource constrained project scheduling problems, point to the promising potentials of ADP.     

A hybrid evolutionary algorithm for solving two-stage stochastic integer programs in chemical batch scheduling

This contribution deals with the solution of two-stage stochastic integer programs with discrete scenarios (2-SIPs) that arise in chemical batch scheduling under uncertainty. Since the number of integer variables in the second-stage increases linearly with the number of scenarios considered, the real world applications usually give rise to large scale deterministic equivalent mixed-integer linear programs (MILPs) which cannot be solved easily without incorporating decomposition methods or problem specific knowledge.In this paper a new hybrid algorithm is proposed to solve 2-SIPs based on stage decomposition: an evolutionary algorithm performs the search on the first-stage variables while the second-stage subproblems are solved by mixed-integer programming. The algorithm is tested for a real-world scheduling problem with uncertainties in the demands and in the production capacity. Numerical experiments have shown, that the new algorithm is robust and superior to state-of-the-art solvers if good solutions are needed in short CPU-times.     

基于带补偿随机规划的蒸汽动力系统优化设计

在蒸汽动力系统优化设计中,考虑不确定因素的优化策略能避免基于确定性设计策略的保守设计,并能针对不确定因素的实现提出相应的调度调节策略.本研究分析了蒸汽动力系统设计包含的不确定因素的特性及其对蒸汽动力系统优化目标和约束条件的影响.不确定因素的表达分成两类:基于时间变化表达和基于发生概率表达.对基于时间变化表达的因素,转化为多周期问题进行处理;对外部工艺过程变化引起的汽电需求不确定波动等基于发生概率表达的因素,应用随机规划策略,补偿不确定参数的实现可能引起的约束背离.基于本研究建立的多周期带补偿的二阶段随机规划MILP模型,求解蒸汽动力系统结构,同时优化调度调节策略,用调节决策和惩罚不足应对汽电需求等不确定因素的实现,实现系统安全稳定运行和经济效益最优.