批处理过程优化调度研究综述--1.MILP调度方法的描述框架

综述化工批处理过程调度建模研究及其实际应用。计划／调度在企业生产管理中起着承上启下的作用，合理的计划调度不但能提高企业的服务水平、降低存储费用，而且还能提高企业的生产能力、加深对过程机制及关键数据的理解。整数规划方法应用于批处理过程计划调度，具有较好的适用性和扩展性，解的性质良好，在批处理过程调度研究得到广泛的应用。     

批处理过程优化调度研究综述——2.MILP调度

详细阐述MILP建模的关键技术和现有的有关参考文献,同时给出MILP调度实施框架和已经报道的成功应用。在此基础上,指出进一步的研究方向。     

间歇化工过程和模型优化调度

Chemical batch processes have become significant in chemical manufacturing.In these processes,large numbers of chemical products are produced to satisfy human demands in daily life.Recently,economy globalization has resulted in growing worldwide competitions in traditional chemical process industry.In order to keep competitive in the global marketplace,each company must optimize its production management and set up a reactive system for market fluctuation.Scheduling is the core of production management in chemical processes.The goal of this paper is to review the recent developments in this challenging area.Classifications of batch scheduling problems and optimization methods are introduced.A comparison of six typical models is shown in a general benchmark example from the literature.Finally,challenges and applications in future research are discussed.     

多产品间歇过程的分层次在线调度--一种数学规划与遗传算法的混合算法

In this contribution we present an online scheduling algorithm for a real world multiproduct batch plant. The overall mixed integer nonlinear programming (MINLP) problem is hierarchically structured into a mixed integer linear programming (MILP) problem first and then a reduced dimensional MINLP problem, which are optimized by mathematical programming (MP) and genetic algorithm (GA) respectively. The basis idea relies on combining MP with GA to exploit their complementary capacity. The key features of the hierarchical model are explained and illustrated with some real world cases from the multiproduct batch plants.     

Scheduling of crude oil operations under demand uncertainty: A robust optimization framework coupled with global optimization

Scheduling of crude oil operations is an important component of overall refinery operations, because crude oil costs account for about 80% of the refinery turnover. The mathematical modeling of blending different crudes in storage tanks results in many bilinear terms, which transform the problem into a challenging, nonconvex, mixed‐integer nonlinear programming (MINLP) optimization model. In practice, uncertainties are unavoidable and include demand fluctuations, ship arrival delays, equipment malfunction, and tank unavailability. In the presence of these uncertainties, an optimal schedule generated using nominal parameter values may often be suboptimal or even become infeasible. In this article, the robust optimization framework proposed by Lin et al. and Janak et al. is extended to develop a deterministic robust counterpart optimization model for demand uncertainty. The recently proposed branch and bound global optimization algorithm with piecewise‐linear underestimation of bilinear terms by Li et al. is also extended to solve the nonconvex MINLP deterministic robust counterpart optimization model and generate robust schedules. Two examples are used to illustrate the capability of the proposed robust optimization approach, and the extended branch and bound global optimization algorithm for demand uncertainty. The computational results demonstrate that the obtained schedules are robust in the presence of demand uncertainty. © 2011 American Institute of Chemical Engineers AIChE J, 58: 2373–2396, 2012     

A superstructure representation,generation, and modeling framework for chemical process synthesis

We present a framework for the efficient representation, generation, and modeling of superstructures for process synthesis. First, we develop a new representation based on three basic elements: units, ports, and conditioning streams. Second, we present four rules based on “minimal” and “feasible” component sets for the generation of simple superstructures containing all feasible embedded processes. Third, in terms of modeling, we develop a modular approach, and formulate models for each basic element. We also present a canonical form of element models using input/output variables and constrained/free variables. The proposed methods provide a coherent framework for superstructure‐based process synthesis, allowing efficient model generation and modification. © 2016 American Institute of Chemical Engineers AIChE J, 62: 3199–3214, 2016     

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。     

Sustainable design and synthesis of algae‐based biorefinery for simultaneous hydrocarbon biofuel production and carbon sequestration

The superstructure optimization of algae‐based hydrocarbon biorefinery with sequestration of CO₂ from power plant flue gas is proposed. The major processing steps include carbon capture, algae growth, dewatering, lipid extraction and power generation, and algal biorefinery. We propose a multiobjective mixed‐integer nonlinear programming (MINLP) model that simultaneously maximizes the net present value (NPV) and minimizes the global warming potential (GWP) subject to technology selection constraints, mass balance constraints, energy balance constraints, technoeconomic analysis constraints, and environmental impact constraints. The model simultaneously determines the optimal decisions that include production capacity, size of each processing unit, mass flow rates at each stage of the process, utility consumption, economic, and environmental performances. We propose a two‐stage heuristic solution algorithm to solve the nonconvex MINLP model. Finally, the bicriteria optimization problem is solved with ε‐constraint method, and the resulting Pareto‐optimal curve reveals the trade‐off between the economic and environmental criteria. The results show that for maximum NPV, the optimal process design uses direct flue gas, a tubular photobioreactor for algae growth, a filtration dewatering unit, and a hydroprocessing pathway leading to 47.1 MM gallons of green diesel production per year at $6.33/gal corresponding to GWP of 108.7 kg CO₂‐eq per gallon. © 2013 American Institute of Chemical Engineers AIChE J, 59: 1599–1621, 2013     

基于遗传算法的汽油调和生产优化研究

油品调和调度问题可归结为混合整数非线性规划 (MINLP)问题。针对汽油调和生产调度选取了预测汽油辛烷值和蒸汽压的合适方法,提出了基于逻辑的数学模型,采用改进的遗传算法进行求解。将其应用于生产实例,通过与文献结果比较验证了该方法的有效性。     

基于遗传算法的原油混合优化研究

对原油混合优化问题进行了研究,在模型建立过程中综合考虑了原油的储运调度约束和混合原油属性约束,并计入了装置加工的可变成本,使之更加符合生产实际,采用遗传算法来求解模型,通过实例分析,证明了模型和算法的有效性。     

A multi‐objective optimization approach to polygeneration energy systems design

Polygeneration, typically involving co‐production of methanol and electricity, is a promising energy conversion technology which provides opportunities for high energy utilization efficiency and low/zero emissions. The optimal design of such a complex, large‐scale and highly nonlinear process system poses significant challenges. In this article, we present a multiobjective optimization model for the optimal design of a methanol/electricity polygeneration plant. Economic and environmental criteria are simultaneously optimized over a superstructure capturing a number of possible combinations of technologies and types of equipment. Aggregated models are considered, including a detailed methanol synthesis step with chemical kinetics and phase equilibrium considerations. The resulting model is formulated as a non‐convex mixed‐integer nonlinear programming problem. Global optimization and parallel computation techniques are employed to generate an optimal Pareto frontier. © 2009 American Institute of Chemical Engineers AIChE J, 2010     

炼油厂氢气网络建模与多目标优化(英文)

The demand of hydrogen in oil refinery is increasing as market forces and environmental legislation, so hydrogen network management is becoming increasingly important in refineries. Most studies focused on single-objective optimization problem for the hydrogen network, but few account for the multi-objective optimization problem. This paper presents a novel approach for modeling and multi-objective optimization for hydrogen network in refineries. An improved multi-objective optimization model is proposed based on the concept of superstructure. The optimization includes minimization of operating cost and minimization of investment cost of equipment. The proposed methodology for the multi-objective optimization of hydrogen network takes into account flow rate constraints, pressure constraints, purity constraints, impurity constraints, payback period, etc. The method considers all the feasible connections and subjects this to mixed-integer nonlinear programming (MINLP). A deterministic optimization method is applied to solve this multi-objective optimization problem. Finally, a real case study is intro-duced to illustrate the applicability of the approach.     

Integrated gasoline blending and order delivery operations: Part I. short‐term scheduling and global optimization for single and multi‐period operations

Gasoline is one of the most valuable products in an oil refinery and can account for as much as 60–70% of total profit. Optimal integrated scheduling of gasoline blending and order delivery operations can significantly increase profit by avoiding ship demurrage, improving customer satisfaction, minimizing quality give‐aways, reducing costly transitions and slop generation, exploiting low‐quality cuts, and reducing inventory costs. In this article, we first introduce a new unit‐specific event‐based continuous‐time formulation for the integrated treatment of recipes, blending, and scheduling of gasoline blending and order delivery operations. Many operational features are included such as nonidentical parallel blenders, constant blending rate, minimum blend length and amount, blender transition times, multipurpose product tanks, changeovers, and piecewise constant profiles for blend component qualities and feed rates. To address the nonconvexities arising from forcing constant blending rates during a run, we propose a hybrid global optimization approach incorporating a schedule adjustment procedure, iteratively via a mixed‐integer programming and nonlinear programming scheme, and a rigorous deterministic global optimization approach. The computational results demonstrate that our proposed formulation does improve the mixed‐integer linear programming relaxation of Li and Karimi, Ind. Eng. Chem. Res., 2011, 50, 9156–9174. All examples are solved to be 1%‐global optimality with modest computational effort. © 2016 American Institute of Chemical Engineers AIChE J, 62: 2043–2070, 2016     

Optimization models for shale gas water management

There are four key aspects for water use in hydraulic fracturing, including source water acquisition, wastewater production, reuse and recycle, and subsequent transportation, storage, and disposal. Water use life cycle is optimized for wellpads through a discrete‐time two‐stage stochastic mixed‐integer linear programming model under uncertain availability of water. The objective is to minimize expected transportation, treatment, storage, and disposal cost while accounting for the revenue from gas production. Assuming freshwater sources, river withdrawal data, location of wellpads, and treatment facilities are given, the goal is to determine an optimal fracturing schedule in coordination with water transportation, and its treatment and reuse. The proposed models consider a long‐time horizon and multiple scenarios from historical data. Two examples representative of the Marcellus Shale play are presented to illustrate the effectiveness of the formulation, and to identify optimization opportunities that can improve both the environmental impact and economical use of water. © 2014 American Institute of Chemical Engineers AIChE J, 60: 3490–3501, 2014     

Using Dynamic Optimization Technique to Study the Operation of Batch Reactors

How to apply the global optimization technique, simulated annealing, and to explore the operation of batch reactors is addressed in this study. Based on the operating purposes and the imposed constraints, the batch reactor operations are first formulated as two optimal control problems: the maximal yield (or conversion) problem and the minimal operating time problem. The problems are then converted into non‐linear programming problems by the concept of control vector parameterization. The converted problems are solved by the algorithm derived from simulated annealing to determine the optimal operating policy and the performance index. These results are useful in assessing design and operation of batch reactors. In this article, the CSTR model is used to demonstrate the convenience and robustness of the proposed algorithm. Two typical reaction models are used to discuss the operations based on the optimal solutions.     

基于TPN模型的间歇过程GA优化调度方法研究

为了建立间歇工业过程的优化调度策略,研究了采用TPN(赋时Petri网)建立其生产调度模型的方法,并提出了基于GA(遗传算法)对此TPN调度模型进行优化的技术。论文引入了一个青霉素发酵间歇过程的仿真平台,以此为实例对象,讨论了TPN调度建模及GA优化的具体步骤,给出了满意的优化调度计算结果。     

ZW多产品间歇过程调度及在线调整

针对多产品间歇过程调度提出了分层递阶的Petri网建模方法，利用赋时Petri和Petri网的简化技术描述不同层次的生产问题，具有很强的模型描述能力。另外，在调度决策层可以方便地集成优化策略和在线调整算法，使问题求解更加灵活。     

抗生素发酵过程优化调度模型的研究

对抗生素多罐并行发酵过程进行了分析,将任务、设备和事件之间的分配关系表达为两类0-1变量,建立了一个基于连续时间的抗生素多罐并行发酵过程优化调度的M ILP(混合整数线性规划)模型。该模型整数变量少,求解速度快,并给出了最短生产时间和罐批最优生产序列。最后以头孢菌素发酵过程为例证明了此调度模型的可行性和有效性。     

Real‐time Dynamic Optimization of Non‐linear Batch Systems

In this paper, a methodology for designing and implementing a real time optimizing controller for non‐linear batch processes is discussed. The controller is used to optimize the system input and state trajectories according to a cost function. An interior point method with penalty function is used to incorporate constraints into a modified cost functional, and a Lyapunov‐based extremum seeking approach is used to compute the trajectory parameters. Smooth trajectories were generated with reduced computing time compared to many optimizations in literature. In this paper, the theory is applied to general non‐flat non‐linear systems in a true on‐line optimization.     

Proactive scheduling of batch processes by a combined robust optimization and multiparametric programming approach

We address short‐term batch process scheduling problems contaminated with uncertainty in the data. The mixed integer linear programming (MILP) scheduling model, based on the formulation of Ierapetritou and Floudas, Ind Eng Chem Res. 1998; 37(11):4341–4359, contains parameter dependencies at multiple locations, yielding a general multiparametric (mp) MILP problem. A proactive scheduling policy is obtained by solving the partially robust counterpart formulation. The counterpart model may remain a multiparametric problem, yet it is immunized against uncertainty in the entries of the constraint matrix and against all parameters whose values are not available at the time of decision making. We extend our previous work on the approximate solution of mp‐MILP problems by embedding different uncertainty sets (box, ellipsoidal and budget parameter regulated uncertainty), and by incorporating information about the availability of uncertain data in the construction of the partially robust scheduling model. For any parameter realization, the corresponding schedule is then obtained through function evaluation. © 2013 American Institute of Chemical Engineers AIChE J, 59: 4184–4211, 2013