Closed-loop scheduling optimization strategy based on particle swarm optimization with niche technology and soft sensor method of attributes-applied to gasoline blending process

Gasoline blending scheduling optimization can bring significant economic and efficient benefits to refineries.However,the optimization model is complex and difficult to build,which is a typical mixed integer nonlinear programming(MINLP) problem.Considering the large scale of the MINLP model,in order to improve the efficiency of the solution,the mixed integer linear programming-nonlinear programming(MILP-NLP) strategy is used to solve the problem.This paper uses the linear blending rules plus the...     

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.     

Modeling and optimization for oil well production scheduling☆

In this paper, an oil wel production scheduling problem for the light load oil wel during petroleum field exploi-tation was studied. The oil well production scheduling was to determine the turn on/off status and oil flow rates of the wel s in a given oil reservoir, subject to a number of constraints such as minimum up/down time limits and well grouping. The problem was formulated as a mixed integer nonlinear programming model that minimized the total production operating cost and start-up cost. Due to the NP-hardness of the problem, an improved par-ticle swarm optimization (PSO) algorithm with a new velocity updating formula was developed to solve the problem approximately. Computational experiments on randomly generated instances were carried out to eval-uate the performance of the model and the algorithm's effectiveness. Compared with the commercial solver CPLEX, the improved PSO can obtain high-quality schedules within a much shorter running time for all the instances.     

基于差分进化和分布估计的改进混合算法在NLP及MINLP工程优化问题中的应用(英文)

In this paper, an improved hybrid differential evolution-estimation of distribution algorithm (IHDE-EDA) is proposed for nonlinear programming (NLP) and mixed integer nonlinear programming (MINLP) models in engineering optimization fields. In order to improve the global searching ability and convergence speed, IHDE-EDA takes full advantage of differential information and global statistical information extracted respectively from differential evolution algorithm and annealing mechanism-embedded estimation of distribution algorithm. Moreover, the feasibility rules are used to handle constraints, which do not require additional parameters and can guide the population to the feasible region quickly. The effectiveness of hybridization mechanism of IHDE-EDA is first discussed, and then simulation and comparison based on three benchmark problems demonstrate the efficiency, accuracy and robustness of IHDE-EDA. Finally, optimization on an industrial-size scheduling of two-pipeline crude oil blending problem shows the practical applicability of IHDE-EDA.     

一种求解阻塞型流水车间调度的离散人工蜂群算法(英文)

A discrete artificial bee colony algorithm is proposed for solving the blocking flow shop scheduling problem with total flow time criterion. Firstly, the solution in the algorithm is represented as job permutation. Secondly, an initialization scheme based on a variant of the NEH (Nawaz-Enscore-Ham) heuristic and a local search is designed to construct the initial population with both quality and diversity. Thirdly, based on the idea of iterated greedy algorithm, some newly designed schemes for employed bee, onlooker bee and scout bee are presented. The performance of the proposed algorithm is tested on the well-known Taillard benchmark set, and the computational results demonstrate the effectiveness of the discrete artificial bee colony algorithm. In addition, the best known solutions of the benchmark set are provided for the blocking flow shop scheduling problem with total flow time criterion.     

基于改进GSO的裂解炉原料切换策略研究(英文)

The scheduling process of cracking furnace feedstock is important in an ethylene plant. In this paper it is described as a constraint optimization problem. The constraints consist of the cycle of operation, maximum tube metal temperature, process time of each feedstock, and flow rate. A modified group search optimizer is pro-posed to deal with the optimization problem. Double fitness values are defined for every group. First, the factor of penalty function should be changed adaptively by the ratio of feasible and general solutions. Second, the“excel-lent”infeasible solution should be retained to guide the search. Some benchmark functions are used to evaluate the new algorithm. Final y, the proposed algorithm is used to optimize the scheduling process of cracking furnace feedstock. And the optimizing result is obtained.     

Logarithm-transform piecewise linearization method for the optimization of fasoline blending processes

Gasoline blending is a key process in a petroleum refinery, as it can yield 60%–70% of a typical refinery's total revenue. This process not only exhibits non-convex nonlinear blending behavior due to the complicated blend mechanism of various component feedstocks with different quality properties, but also involves global optimum searching among numerous blending recipes. Since blend products are required to meet a series of quality requirements and highly-sensitive to the proportion changes of blending feedstocks, global optimization methods for NLP problems are often difficult to be applied because of heavy computational burdens. Thus, piecewise linearization methods are naturally proposed to provide an approximate global optimum solution by adding binary variables into the models and converting the original NLP problems into MILP ones. In this paper, Logarithmtransform piecewise linearization(LTPL) method, an improved piecewise linearization, is proposed. In this method a logarithm transform is applied to convert multi-variable multi-degree constraints into a series of single-variable constraints. As a result, the number of 0–1 variables is greatly reduced. In the final part of this paper, an industrial case study is conducted to demonstrate the effectiveness of LTPL method. In principle, this method would be useful for blending problems with complicated empirical or theoretical models.     

不确定条件下炼化企业计划与调度整合策略

A strategy for the integration of production planning and scheduling in refineries is proposed.This strategy relies on rolling horizon strategy and a two-level decomposition strategy.This strategy involves an upper level multiperiod mixed integer linear programming(MILP) model and a lower level simulation system,which is extended from our previous framework for short-term scheduling problems [Luo,C.P.,Rong,G.,"Hierarchical approach for short-term scheduling in refineries",Ind.Eng.Chem.Res.,46,3656-3668(2007)].The main purpose of this extended framework is to reduce the number of variables and the size of the optimization model and,to quickly find the optimal solution for the integrated planning/scheduling problem in refineries.Uncertainties are also considered in this article.An integrated robust optimization approach is introduced to cope with uncertain parameters with both continuous and discrete probability distribution.     

改进的自适应免疫算法及其在对二甲苯氧化燃烧副反应优化中的应用(英文)

In recent years, immune genetic algorithm (IGA) is gaining popularity for finding the optimal solution for non-linear optimization problems in many engineering applications. However, IGA with deterministic mutation factor suffers from the problem of premature convergence. In this study, a modified self-adaptive immune genetic algorithm (MSIGA) with two memory bases, in which immune concepts are applied to determine the mutation parameters, is proposed to improve the searching ability of the algorithm and maintain population diversity. Performance comparisons with other well-known population-based iterative algorithms show that the proposed method converges quickly to the global optimum and overcomes premature problem. This algorithm is applied to optimize a feed forward neural network to measure the content of products in the combustion side reaction of p-xylene oxidation, and satisfactory results are obtained.     

应用群能量恒定粒子群优化算法的批次流加发酵过程批次间优化(英文)

An iterative optimization strategy for fed-batch fermentation process is presented by combining a run-to-run optimization with swarm energy conservation particle swarm optimization (SEC-PSO). SEC-PSO, which is designed with the concept of energy conservation, can solve the problem of premature convergence frequently appeared in standard PSO algorithm by partitioning its population into several sub-swarms according to the energy of the swarm and is used in the optimization strategy for parameter iden-tification and operation condition optimization. The run-to-run optimization exploits the repetitive nature of fed-batch processes in order to deal with the optimal problems of fed-batch fermentation process with inaccurate process model and unsteady process state. The kinetic model parameters, used in the operation condition optimization of the next run, are adjusted by calculating time-series data obtained from real fed-batch process in the run-to-run optimization. The simulation results show that the strategy can adjust its kinetic model dynamically and overcome the instability of fed-batch process effectively. Run-to-run strategy with SEC-PSO provides an effective method for optimization of fed-batch fermentation process.     

两群微粒群算法及其在油品调和优化中的应用

针对复杂的非线性约束优化问题,提出了一种含变异算子的两群微粒群算法。算法构造了两个粒子群,分别设置了不同的搜索速度上限,并设计了粒子群间的协调机制和变异算子,使算法的寻优能力得到增强。针对油品调和配方优化进行了实例仿真,研究结果表明所提出的算法能获得较理想的调和配方,在满足调和利润最大的同时能保证对调和指标的卡边,使调和成品油的指标富余量大大降低。     

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.     

Short-term scheduling and recipe optimization of blending processes

The main objective of this paper is to develop an integrated approach to coordinate short-term scheduling of multi-product blending facilities with nonlinear recipe optimization. The proposed strategy is based on a hierarchical concept consisting of three business levels: Long-range planning, short-term scheduling and process control. Long-range planning is accomplished by solving a large-scale nonlinear recipe optimization problem (multi-blend problem). Resulting blending recipes and production volumes are provided as goals for the scheduling level. The scheduling problem is formulated as a mixed-integer linear program derived from a resource-task network representation. The scheduling model permits recipe changeovers in order to utilize an additional degree of freedom for optimization. By interpreting the solution of the scheduling problem, new constraints can be imposed on the previous multi-blend problem. Thus bottlenecks arising during scheduling are considered already on the topmost long-range planning level. Based on the outlined approach a commercial software system has been designed to optimize the operation of in-line blending and batch blending processes. The application of the strategy and software is demonstrated by a detailed case study.     

基于序的原油调合调度问题

原油在线调合调度对于平稳混炼原油性质、保障先进控制的实施和优化炼油计划具有重要的意义。原油调合调度问题由于既包括顺序变量又包括连续变量,不存在明确的问题结构,复杂度高,传统的优化方法效率低,不能满足在线实时的要求。本文针对问题的两层结构提出了基于序的求解方案,利用实际原油性质数据的仿真结果表明,基于序的求解算法可以大幅度提高计算效率。     

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.     

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。     

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.     

清洁汽油优化调合系统

以汽油调合工段为研究对象,研究了汽油调合静态和动态的优化控制方案,并介绍了开发的油品调合软件（APC－Blend)的体系结构、设计思路和使用说明。