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     

Continuous‐time modeling and global optimization approach for scheduling of crude oil operations

Scheduling of crude oil operations is a critical and complicated component of overall refinery operations, because crude oil costs account for about 80% of the refinery turnover. Moreover, blending with less expensive crudes can significantly increase profit margins. The mathematical modeling of blending different crudes in storage tanks results in many bilinear terms, which transforms the problem into a challenging, nonconvex, and mixed‐integer nonlinear programming (MINLP) optimization model. Two primary contributions have been made. First, the authors developed a novel unit‐specific event‐based continuous‐time MINLP formulation for this problem. Then they incorporated realistic operational features such as single buoy mooring (SBM), multiple jetties, multiparcel vessels, single‐parcel vessels, crude blending, brine settling, crude segregation, and multiple tanks feeding one crude distillation unit at one time and vice versa. In addition, 15 important volume‐based or weight‐based crude property indices are also considered. Second, they exploited recent advances in piecewise‐linear underestimation of bilinear terms within a branch‐and‐bound algorithm to globally optimize the MINLP problem. It is shown that the continuous‐time model results in substantially fewer bilinear terms. Several examples taken from the work of Li et al. are used to illustrate that (1) better solutions are obtained and (2) ε‐global optimality can be attained using the proposed branch‐and‐bound global optimization algorithm with piecewise‐linear underestimations of the bilinear terms. © 2011 American Institute of Chemical Engineers AIChE J, 2012     

改进生物地理学优化算法及其在汽油调合调度中的应用

汽油调合和调度优化问题中含有典型的非线性约束(NLP)问题。针对一般智能优化算法在解决此类优化问题中易陷于局部极值,提出了一种改进的生物地理学优化算法(HMBBO)。该算法设计了一种基于种群个体差异信息的启发式变异算子,弥补了Gauss变异、Cauchy变异算子缺乏启发式信息的不足,以解决原算法在局部搜索时易出现的早熟问题,提高算法的全局搜索能力,并且采用非线性物种迁移模型以适应不同的自然环境。采用4个测试函数进行仿真,结果表明:HMBBO算法与标准BBO算法、基于Gauss变异及基于Cauchy变异的BBO算法比较,其收敛速度和全局寻优能力有明显改善。汽油调合和调度优化实例表明,该算法能够快速有效地找到全局最优解。     

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。     

调和汽油的蒸发性能及其改善方法探讨

环保法规的日益严格对汽车尾气排放的要求也越来越高, 而汽车尾气排放与汽油的蒸气压和馏程分布密切相关。同时, 蒸气压和馏程分布还影响汽车的启动性能、加速性能以及气缸积炭等。文章简述了为满足环保要求和保证汽车驾驶性能, 汽油蒸气压和馏程分布应该达到的要求;介绍了几种能够改变汽油蒸气压和馏程分布的汽油改质方法, 以及目前组成商品汽油的各种调和组分, 比较了各种调和组分在蒸气压和馏程分布方面的性质特点, 及其对成品汽油蒸气压和馏程分布的影响。分析表明, 通过改变催化裂化汽油(FCC)过程工艺条件, 可改善FCC汽油蒸气压和馏程分布, 这是目前改善调和汽油蒸气压和馏程分布最为经济可行的方法;各调和组分中, 烷基化油在改善汽油蒸气压和馏程分布方面是最理想的调和组分。     

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 cost‐effective model for the gasoline blend optimization problem

Gasoline blending is a critical process with a significant impact on the total revenues of oil refineries. It consists of mixing several feedstocks coming from various upstream processes and small amounts of additives to make different blends with some specified quality properties. The major goal is to minimize operating costs by optimizing blend recipes, while meeting product demands on time and quality specifications. This work introduces a novel continuous‐time mixed‐integer linear programming (MILP) formulation based on floating time slots to simultaneously optimize blend recipes and the scheduling of blending and distribution operations. The model can handle non‐identical blenders, multipurpose product tanks, sequence‐dependent changeover costs, limited amounts of gasoline components, and multi‐period scenarios. Because it features an integrality gap close to zero, the proposed MILP approach is able to find optimal solutions at much lower computational cost than previous contributions when applied to large gasoline blend problems. © 2016 American Institute of Chemical Engineers AIChE J, 62: 3002–3019, 2016     

Global optimization of large‐scale mixed‐integer linear fractional programming problems: A reformulation‐linearization method and process scheduling applications

Mixed‐integer linear fractional program (MILFP) is a class of mixed‐integer nonlinear programs (MINLP) where the objective function is the ratio of two linear functions and all constraints are linear. Global optimization of large‐scale MILFPs can be computationally intractable due to the presence of discrete variables and the pseudoconvex/pseudoconcave objective function. We propose a novel and efficient reformulation–linearization method, which integrates Charnes–Cooper transformation and Glover's linearization scheme, to transform general MILFPs into their equivalent mixed‐integer linear programs (MILP), allowing MILFPs to be globally optimized effectively with MILP methods. Extensive computational studies are performed to demonstrate the efficiency of this method. To illustrate its applications, we consider two batch scheduling problems, which are modeled as MILFPs based on the continuous‐time formulations. Computational results show that the proposed approach requires significantly shorter CPU times than various general‐purpose MINLP methods and shows similar performance than the tailored parametric algorithm for solving large‐scale MILFP problems. Specifically, it performs with respect to the CPU time roughly a half of the parametric algorithm for the scheduling applications. © 2013 American Institute of Chemical Engineers AIChE J, 59: 4255–4272, 2013     

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。     

A general framework for process scheduling

Existing methods for process scheduling can be broadly classified as network‐based or sequential. The former are used to address problems where different batches of the same or different tasks are freely mixed or split, whereas the latter are used to address problems where batch mixing/splitting is not allowed. A framework is proposed that allows us to: (1) express scheduling problems in facilities that consist of network and sequential, as well as continuous subsystems, (2) formulate mixed‐integer programming (MIP) scheduling models for such problems, and (3) solve the resulting MIP formulations effectively. The proposed framework bridges the gap between network and sequential approaches, thereby addressing the major formulation challenge in the area of process scheduling, namely, the development of a framework that can be used to address a wide spectrum of problems. © 2010 American Institute of Chemical Engineers AIChE J, 57: 695–710, 2011     

Optimization of short-time gasoline blending scheduling problem with a DNA based hybrid genetic algorithm

Gasoline blending is a key process in the petroleum refinery industry posed as a nonlinear optimization problem with heavily nonlinear constraints. This paper presents a DNA based hybrid genetic algorithm (DNA-HGA) to optimize such nonlinear optimization problems. In the proposed algorithm, potential solutions are represented with nucleotide bases. Based on the complementary properties of nucleotide bases, operators inspired by DNA are applied to improve the global searching ability of GA for efficiently locating the feasible domains. After the feasible region is obtained, the sequential quadratic programming (SQP) is implemented to improve the solution. The hybrid approach is tested on a set of constrained nonlinear optimization problems taken from the literature and compared with other approaches. The computation results validate the effectiveness of the proposed algorithm. The recipes of a short-time gasoline blending problem are optimized by the hybrid algorithm, and the comparison results show that the profit of the products is largely improved while achieving more satisfactory quality indicators in both certainty and uncertainty environment.     

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     

考虑碳排放因素的汽油调合在线优化

针对国家“双碳”战略目标要求,以炼化企业汽油调合在线优化为研究对象,分析了国Ⅵ汽油新标准下被控属性更多、更严、调合效率要求更高等特点,以及由此带来的调合组分油调整导致调合成品汽油携带碳排放量的变化情况。考虑到传统的汽油调合在线优化一般只考虑调合成本、质量卡边等目标,首先建立了非线性的汽油调合辛烷值、蒸气压和馏程等软测量模型,然后构建了基于调合效应的汽油调合优化模型,优化目标中引入调合成品油二氧化碳排放最低化目标,开发了一种融合携带碳排放特征的汽油调合优化模型。为满足在线调合优化需求,优化模型中考虑了实际累积调合过程,将调合工艺过程中储罐汽油属性合格转化成调合头属性区间合格,利用调合头处优化的属性补偿已调合体积和罐底油的属性偏差。仿真结果表明,设计的考虑碳排放因素汽油累积调合优化技术能很好地满足汽油调合在线优化需求,为国Ⅵ标准和碳交易背景下汽油调合工艺设计及在线优化控制提供了技术支撑。     

改进的膜计算仿生优化算法及在汽油调和中的应用

为提高膜计算仿生优化算法在求解流程工业复杂优化问题的计算性能,提出一种改进的膜计算仿生优化算法。该算法采用一个新的不确定性提取规则取代改进前的提取规则。4个有约束标准测试函数被用于检验该算法的计算性能,计算结果及对比显示了改进算法在鲁棒性和效率等方面优于改进前算法。改进算法应用于汽油调和优化问题,更高利润的配方及算法的计算效率证实了改进算法的优越性和实用性。     

全局群搜索优化算法及其在汽油调合中的应用

汽油调合配比生产优化是一种非线性约束的多峰优化问题。针对一般群智能优化算法在解决此类优化中易陷于局部最优解,提出了一种改进的群搜索优化算法--全局群搜索优化算法（GGSO）。该算法采用混沌机制初始化粒子在解空间内均匀分布;在算法前期,保留GSO的追随者进化策略,以保证算法的收敛速度。在算法后期,对追随者引入速度更新和个体最优,以保证算法的收敛精度;在粒子陷入局部极值时,对追随者和游荡者引入一种新的交叉、变异机制和自适应混沌扰动机制,以保证粒子跳出局部极值,提高算法全局寻优性能。分别用4个标准测试函数对优化算法进行测试,结果表明：GGSO算法与标准GSO、线性递减惯性权重粒子群算法（LDWPSO）比较,收敛速度和全局寻优性能有明显优势。汽油在线调合优化实例应用表明：该算法有较快的收敛速度,能够较准确地寻得全局最优。     

Inventory pinch gasoline blend scheduling algorithm combining discrete- and continuous-time models

This work introduces multi-period inventory pinch-based algorithm to solve continuous-time scheduling models (MPIP-C algorithm), a three level method which combines discrete-time approximate scheduling with continuous-time detailed scheduling and with inventory pinch-based optimization of operating states. When applied to gasoline blending, the top level computes optimal recipes for aggregated blends over periods initially delineated by inventory pinch points. Discrete-time middle level uses fixed blend recipes to compute an approximate schedule, i.e. what, when, and how much to produce; it also allocates swing storage and associated product shipments with specific storage. Continuous-time model at the third level computes when exactly to start/stop an operation (blend, tank transfer, shipment). MPIP-C algorithm solves linear or nonlinear problems 2–3 orders of magnitude faster than full-space models.     

A bio-inspired algorithm based on membrane computing and its application to gasoline blending scheduling

For the purpose of applying membrane computing as a global optimization technique, a bio-inspired algorithm based on membrane computing (BIAMC) is proposed to solve both constrained and unconstrained problems. The membrane structure used in BIAMC is a network of membranes that is inspired by the Golgi apparatus. In the process of approaching to the optimum solution, the objects containing a tentative solution are evolved by the rewriting rule in the parallel identical membranes and synthesized by the novel rules of target indication, transition and abstraction in the membrane of quasi-Golgi. The information transfers according to directions defined by the communication rule. Eight well-known unconstrained and constrained functions are used for performance testing. Then we apply the proposed algorithm with two schemes to solving a typical nonlinear optimization of gasoline blending and scheduling problem. The results show that the proposed approach can find optimal or close-to-optimal solutions efficiently.     

A novel MINLP-based representation of the original complex model for predicting gasoline emissions

The Environmental Protection Agency (EPA) introduced Reformulated Gasoline (RFG) requirements as a measure to reduce emissions from gasoline-powered vehicles in certain geographic areas. As part of this effort, the EPA developed empirical models for predicting emissions as a function of gasoline properties and established statutory baseline emissions from a representative set of gasolines. All reformulated gasoline requires certification via this model, known as the Complex Model, and all refiners and importers calculate emissions performance reductions from the statutory baseline gasoline. The current representation of the Complex Model is extremely difficult to implement within refinery operations models or to use in combination with models for designer gasoline. RFG and boutique fuels are key driving forces in the North American refining industry.The RFG models introduce increasingly complex constraints with the major limitation that they are implicitly defined through a series of complicated disjunctions assembled by the EPA in the form of spreadsheets. This implicit and cumbersome representation of the emissions predictive models renders rigorous optimization and sensitivity analysis very difficult to address directly. In this paper, we discuss how the federal government requirements for reformulated gasoline can be restated as a set of mixed-integer nonlinear programming (MINLP) constraints with the aid of disjunctive programming techniques. We illustrate the use of this model with two simple example fuel blending problems.     

在线自适应波长选择方法及其在汽油调和过程中的应用

近红外光谱分析技术作为一种非侵入性的分析手段在工业上得到了广泛应用。然而,大多数近红外模型的波长选择方法是离线建立的,无法有效跟踪过程特性的变化。提出了一种新的在线自适应波长选择方法——在线自适应区间高斯过程回归波长选择方法（adaptive interval Gaussian process regression, AIGPR）,并用于汽油调和过程中的近红外模型的建立。该方法可以根据待测样本的特性对波长结构进行调整。为了降低在线应用的计算成本,该方法分为离线和在线两个部分,离线部分将光谱分割成若干个波长区间,并在每个波长区间上建立局部模型,为在线应用做准备;在线部分中根据划分规则将采样得到待测样本光谱进行分割并代入相应的局部模型中计算波长区间重要性指标,获得最优波长区间。在汽油辛烷值的光谱数据上证明了该方法的有效性。与重要变量投影法和改进的相关系数法相比,该方法具有更好的性能。