Models and algorithms for a multilevel control system of primary oil refinery installations

Algorithms and mathematical models for the technological process of primary oil refinery operating in the uncertain conditions are developed; the solution of the optimal control problem in the form of stochastic programming with probabilistic characteristics is presented. For solving the optimization problem, using the Lagrange method, the problem of development of decomposition algorithms is described and the method based on the transformation of the original problem according to the principle of deterministic analogue is proposed. The construction of the optimal control system created based on the developed models, optimization algorithm, and principles of automatic control of regime parameters of the primary oil refinery installation are considered.     

常减压蒸馏装置流程模拟及优化研究

在实际生产过程中,对流程模拟的研究关系到产品的质量和产量.根据石化炼油厂常减压蒸馏装置中初馏塔和常压塔的生产工艺和原油精馏原理,结合现场工艺数据,并基于Aspen Plus化工流程模拟软件,实现了初馏塔和常压塔的流程模拟.同时,通过灵敏度分析工具优化操作条件,指导操作人员对常压塔的操作条件进行有效调节,调节效果令人满意.     

炼油企业生产计划与重点装置工艺条件集成优化

传统的生产计划优化由于不考虑过程装置的操作优化,从而无法保证企业生产计划层与过程操作层的全局最优.为了在获得炼油企业最优生产计划的同时,确保计划优化中重点装置的操作条件可以实现,本文建立了集成装置工艺条件的炼油企业生产计划优化模型.该模型引入常减压装置侧线产品切割点温度、催化裂化装置转化率等过程工艺条件,基于物料质量平衡、产品质量指标约束等关系,进行厂级生产计划建模与求解,确定可达的装置操作条件.应用案例中重点通过与传统常减压装置侧线收率固定的生产计划方案比较,证明在满足可达的操作条件下,集成装置工艺条件操作范围的生产计划优化模型,可以实现更高的全厂利润与更优的装置收率分布,同时优化结果对炼厂实际生产更具有指导意义.     

Fuzzy possibilistic modeling and sensitivity analysis for optimal fuel gas scheduling in refinery

In refinery, fuel gas which is continuously generated during the production process is one of the most important energy sources. Optimal scheduling of fuel gas system helps the refinery to achieve energy cost reduction and cleaner production. However, imprecise natures in the system, such as prediction of production rate of fuel gas, prediction of energy demand of the equipments and cost coefficient in the objective function, make the deterministic optimization method which requires well-defined and precise data cannot be competent for the fuel gas scheduling problem. In this study, fuzzy possibilistic programming (FPP) method is proposed to deal with these imprecise natures by triangular possibility distributions. The fuzzy possibilistic model is transformed into usual mathematical model by definition of necessity measure and the α-level method. Although FPP models have been widely applied to modeling, few research works have been reported on the performance evaluation, namely sensitivity analysis, of these models. Marginal value analysis, which is always used to provide additional economic information, is proposed to give the sensitivity analysis in the paper. This method is demonstrated to be much more flexible than the simulation method. Particularly, the analytical method is adopted to examine how the imprecise natures in the fuel gas system affect the scheduling results.     

Process industry scheduling optimization using genetic algorithm and mathematical programming

This article addresses the problem of scheduling in oil refineries. The problem consists of a multi-product plant scheduling, with two serial machine stages—a mixer and a set of tanks—which have resource constraints and operate on a continuous flow basis. Two models were developed: the first using mixed-integer linear programming (MILP) and the second using genetic algorithms (GA). Their main objective was to meet the whole forecast demand, observing the operating constraints of the refinery and minimizing the number of operational changes. A real-life data-set related to the production of fuel oil and asphalt in a large refinery was used. The MILP and GA models proved to be good solutions for both primary objectives, but the GA model resulted in a smaller number of operational changes. The reason for this is that GA incorporates a multi-criteria approach, which is capable of adaptively updating the weights of the objective throughout the evolutionary process.     

A stochastic production planning problem with nonlinear cost

Most production planning models are deterministic and often assume a linear relation between production volume and production cost. In this paper, we investigate a production planning problem in a steel production process considering the energy consumption cost which is a nonlinear function of the production quantity. Due to the uncertain environment, the production demands are stochastic. Taking a scenario-based approach to express the stochastic demands according to the knowledge of planners on the demand distributions, we formulate the stochastic production planning problem as a mixed integer nonlinear programming (MINLP) model.Approximated with the piecewise linear functions, the MINLP model is transformed into a mixed integer linear programming model. The approximation error can be improved by adjusting the linearization ranges repeatedly. Based on the piecewise linearization, a stepwise Lagrangian relaxation (SLR) heuristic for the problem is proposed where variable splitting is introduced during Lagrangian relaxation (LR). After decomposition, one subproblem is solved by linear programming and the other is solved by an effective polynomial time algorithm. The SLR heuristic is tested on a large set of problem instances and the results show that the algorithm generates solutions very close to optimums in an acceptable time. The impact of demand uncertainty on the solution is studied by a computational discussion on scenario generation.     

Improving production planning by utilizing stochastic programming

The effects of fluctuating demand on production and inventory levels are important in manufacturing resource planning. Thus, the focus of this presentation is on aggregate production planning of manufacturing resources in order to satisfy stochastic demand for a family of products to minimize total costs that include production and inventory holding costs over a rolling horizon.

If it is assumed that, in a commercial setting, the demands are fixed, then the production plans generated by a mathematical programming procedure are not responsive to the actual fluctuations of stochastic demand in each time period.

The situation discussed here considers the case where demands are normally distributed with means and variances that are sequentially revised as new observations of demand are received over time. This assumption allows the probabilistic constraint to be converted to an equivalent linearly-constrained deterministic model. Extensions to the normality assumption are discussed. Also other ideas such as optimal control theory, learning and adaptive signal processing extensions are discussed as well.     

考虑石脑油两种用途的原油混输调度模型

针对炼厂石脑油用于催化重整和乙烯生产两种用途的原油混输调度问题建立数学模型，把重整过程和乙烯生产对石脑油芳烃潜含量的不同要求用线性规划的方法实现，以原油混输调度中对不同类的原油根据需要分别输送和储存，为常减压装置准备好“重整石脑油方案”和“乙烯石脑油方案”的原料，真正符合“宜芳则芳”、“宜烯则烯”的调度原则。建模中应用了MILP(混合整数线性规划)技术和多周期技术，并给出一个应用在6种不同的国外原油及6个生产调度周期的应用实例，考虑了原油从装置罐到厂区罐的一次混输调度，尽量保证管道和厂区罐调度周期末原油质量合格。     

A Petri Net-Based Heuristic Algorithm for Realizability of Target Refining Schedule for Oil Refinery

In discrete manufacturing, a just-in-time schedule is pursued so as to respond better to the market. It is also required in oil refinery. However, the existing techniques for short-term scheduling in oil refinery are based on the push production mode. This paper addresses the short-term scheduling problem for crude oil operations in a pull production way. A target refining schedule resulting from production planning is given as a constraint to make an executable schedule. The system is modeled by a timed hybrid Petri net. This model is structurally simple and can describe the dynamic behavior and all the constraints of the system without any difficulty. Based on the model, an efficient heuristic algorithm is proposed to test the realizability of a target refining schedule. If it is realizable, a feasible short-term schedule realizing it is created. A real-life industrial case study is presented to show the industrial application of the proposed method.     

A stochastic dynamic programming approach-based yield management with substitution and uncertainty in semiconductor manufacturing

Yield management is important and challengeable in semiconductor industry for the quality uncertainty of the final products. The total yield rate of the semiconductor manufacturing process is uncertain, each product is graded into one of several quality levels according to performance before being shipped. A product originally targeted to satisfy the demand of one product may be used to satisfy the demand of other products when it conforms to their specifications. At the same time, the products depreciate in allocation periods, which mainly results from technical progresses. This paper studies the semiconductor yield management issue of a make-to-stock system with single input, multi-products, multi-demand periods, upward substitution and periodic depreciation. The whole time horizon of the system operation process can be divided into two stages: the production stage and the allocation stage. At the first stage, the firm invests in raw materials before any actual demand is known and produces multiple types of products with random yield rates. At the second stage, products are classified into different classes by quality and allocated in numbers of periods. The production and allocation problem are modeled as a stochastic dynamic program in which the objective is to maximize the profit of the firm. We show that the PRA (parallel allocation first, then upgrade) allocation policy is the optimal allocation policy and the objective function is concave in production input. An iterative algorithm is designed to find the optimal production input and numerical experiments are used to illustrate its effectiveness.     

基于异步时间段的原油混输调度连续时间建模研究

采用严格的数学规划方法对沿海炼厂的原油混输调度问题进行了建模和求解,提出了一个基于异步时间段的连续时间混合整数非线性规划(MINLP)数学模型。该模型具有整型变量少,求解快的优点。采用求解一系列MILP来近似求解非凸的MINLP,避免了原油混输MILP模型产生的浓度不一致问题。文中采用提出的新模型和算法对文献中的9个实例进行了计算,相同计算条件的4个实例比离散时间模型效率提高86％-1011％。     

Models for robust tactical planning in multi-stage production systems with uncertain demands

We consider the problem of designing robust tactical production plans, in a multi-stage production system, when the periodic demands of the finished products are uncertain. First, we discuss the concept of robustness in tactical production planning and how we intend to approach it. We then present and discuss three models to generate robust tactical plans when the finished-product demands are stochastic with known distributions. In particular, we discuss plans produced, respectively, by a two-stage stochastic planning model, by a robust stochastic optimization planning model, and by an equivalent deterministic planning model which integrates the variability of the finished-product demands. The third model uses finished-product average demands as minimal requirements to satisfy, and seeks to offset the effect of demand variability through the use of planned capacity cushion levels at each stage of the production system. An experimental study is carried out to compare the performances of the plans produced by the three models to determine how each one achieves robustness. The main result is that the proposed robust deterministic model produces plans that achieve better trade-offs between minimum average cost and minimum cost variability. Moreover, the required computational time and space are by far less important in the proposed robust deterministic model compared to the two others.     

汽轮机速关油内漏故障分析与处理

对某厂重催装置汽压机组汽轮机控制油系统进行了阐述,分析速关油内漏故障引发原因,最后给出详细的故障排查和处理方法。     

Optimal production decision for a risk‐averse manufacturer faced with random yield and stochastic demand

Consider a risk‐averse manufacturer who produces a single product with a random yield to satisfy uncertain market demand. The manufacturer maximizes its expected profit subject to a chance constraint that requires the probability of the total profit below a target be less than a predetermined level. We show that due to the profit target constraint in the presence of random yield and stochastic demand, the manufacturer can neither produce too much nor too little irrespective of the predetermined probability level, depending solely on the profit target level. Further, the special case of uniform yield and uniform demand is examined and we obtain the manufacturer's optimal production quantity. In addition, two special cases of random yield rate and deterministic demand or deterministic yield rate and stochastic demand are considered. The opposite impacts of random yield and stochastic demand are revealed: the random yield induces a minimum production quantity that may cause the manufacturer to increase its production quantity, while the stochastic demand induces a maximum production quantity that may cause the manufacturer to decrease its production quantity. By comparing the solutions of the above‐mentioned special cases with the case of both random yield and stochastic demand, it is demonstrated that the existence of both random yield and stochastic demand results in a more constrained production requirement for the manufacturer (a larger minimum production quantity and a smaller maximum production quantity). That is, the opposite impacts of random yield and stochastic demand will not offset, but enhance each other.     

Mathematical modeling of steam-assisted gravity drainage

A mathematical model for gravity drainage in heavy-oil reservoirs and tar sands during steam injection in linear geometry is proposed. The mathematical model is based on experimental observations that the steam zone shape is an inverted triangle with the vertex fixed at the bottom production well. Both temperature and asphaltene content dependence of viscosity of the drained heavy oil and their impact on heavy oil production are considered. The developed model has been validated using experimental data presented in the literature. It is seen that the oil production rate is affected as the asphaltene content of the crude oil changes as a function of temperature. The oil production rate conforms well to previously published data covering a wide range of heavy oils and sands for gravity drainage.     

A continuous review inventory model with the controllable production rate of the manufacturer

Optimal operating policy in most deterministic and stochastic inventory models is based on the unrealistic assumption that lead‐time is a given parameter. In this article, we develop an inventory model where the replenishment lead‐time is assumed to be dependent on the lot size and the production rate of the manufacturer. At the time of contract with a manufacturer, the retailer can negotiate the lead‐time by considering the regular production rate of the manufacturer, who usually has the option of increasing his regular production rate up to the maximum (designed) production capacity. If the retailer intends to reduce the lead‐time, he has to pay an additional cost to accomplish the increased production rate. Under the assumption that the stochastic demand during lead‐time follows a Normal distribution, we study the lead‐time reduction by changing the regular production rate of the manufacturer at the risk of paying additional cost. We provide a solution procedure to obtain the efficient ordering strategy of the developed model. Numerical examples are presented to illustrate the solution procedure.     

石化企业成品油物流优化信息化建设探析

近年来,国际油价大幅攀升后在高位震荡,中国成品油需求快速增长,成品油价格从紧控制,日趋激烈的竞争环境促使石化企业从内部挖潜,向管理、向优化要效益。本文阐述了某石化企业的成品油物流优化信息化建设方案,并着重分析了物流优化信息化建设在优化资源配置、优化库存结构、优化运输调度等方面的作用,为石化行业成品油物流的创新发展提供了新的思路。     

铜精炼过程重油消耗泛函混沌优化器设计

基于铜精炼过程中保温阶段与氧化阶段的热工机理建立了铜精炼过程重油消耗真实目标泛函,采用泛函分析与混沌优化算法等理论相结合对其进行融合处理,得出了能计算铜精炼过程中铜液最优升温曲线、最优升温速度曲线以及能实现全局优化的重油消耗泛函混沌优化器.重油消耗泛函混沌优化器应用结果表明,重油耗量/t阳极铜降低了9.20%,铜精炼炉烟囱排烟林格曼黑度由3降低为1.     

Multistage System Planning for Hydrogen Production and Distribution

Hydrogen is an energy carrier that has the potential to improve the sustainability of transportation fuels and reduce oil dependence. This paper presents a stochastic dynamic programming model for sequentially building a hydrogen production and distribution system. The decision variables are the sequence and locations of the central production sites and the corresponding distribution systems from supply to demand sites. A case study based on the geographic setting of Northern California is included, in which the hydrogen is produced via coal gasification and transported from plant to city gates (demand sites) by cryogenic liquid hydrogen trucks. Future demands for hydrogen are modeled as uncertain parameters, with an assumption that hydrogen fuel cell vehicle (HFCV) market penetration rate increases from 1% to 25% over a 20-year period. This model provides multistage decision support for long term transportation energy planning at national and regional levels.     

Stochastic Programming For Order Allocation And Production Planning

Stochastic demand is an important factor that heavily affects production planning. It influences activities such as purchasing, manufacturing, and selling, and quick adaption is required. In production planning, for reasons such as reducing costs and obtaining supplier discounts, many decisions must be made in the initial stage when demand has not been realized. The effects of non-optimal decisions will propagate to later stages, which can lead to losses due to overstocks or out-of-stocks. To find the optimal solutions for the initial and later stage regarding demand realization, this study proposes a stochastic two-stage linear programming model for a multi-supplier, multi-material, and multi-product purchasing and production planning process. The objective function is the expected total cost after two stages, and the results include detailed plans for purchasing and production in each demand scenario. Small-scale problems are solved through a deterministic equivalent transformation technique. To solve the problems in the large scale, an algorithm combining metaheuristic and sample average approximation is suggested. This algorithm can be implemented in parallel to utilize the power of the solver. The algorithm based on the observation that if the remaining quantity of materials and number of units of products at the end of the initial stage are given, then the problems of the first and second stages can be decomposed.