Simultaneous optimization of heat-integrated crude oil distillation systems☆

Crude oil distil ation is important in refining industry. Operating variables of distil ation process have a critical ef-fect on product output value and energy consumption. However, the objectives of minimum energy consumption and maximum product output value do not coordinate with each other and do not lead to the maximum eco-nomic benefit of a refinery. In this paper, a systematic optimization approach is proposed for the maximum an-nual economic benefit of an existing crude oil distil ation system, considering product output value and energy consumption simultaneously. A shortcut model in Aspen Plus is used to describe the crude oil distillation and the pinch analysis is adopted to identify the target of energy recovery. The optimization is a nonlinear program-ming problem and solved by stochastic algorithm of particle warm optimization.     

炼厂生产计划优化模型

This article addresses a production planning optimization problem of overall refinery. The authors formulated the optimization problem as mixed integer linear programming. The model considers the main factors for optimizing the production plan of overall refinery related to the use of run-modes of processing units. The aim of this planning is to decide which run-mode to use in each processing unit in each period of a given horizon, to satisfy the demand, such as the total cost of production and inventory is minimized. The resulting model can be regarded as a generalized lot-sizing problem where a run-mode can produce and consume more than one product. The resulting optimization problem is large-sized and NP-hard. The authors have proposed a column generation-based algorithm called branch-and-price （BP） for solving the interested optimization problem. The model and implementation of the algorithm are described in detail in this article. The computational results verify the effectiveness of the proposed model and the solution method.     

基于线性时变扰动模型的间歇过程最优迭代学习控制

A batch-to-batch optimal iterative learning control (ILC) strategy for the tracking control of product quality in batch processes is presented. The linear time-varying perturbation (LTVP) model is built for product quality around the nominal trajectories. To address problems of model-plant mismatches, model prediction errors in the previous batch run are added to the model predictions for the current batch run. Then tracking error transition models can be built, and the ILC law with direct error feedback is explicitly obtained. A rigorous theorem is proposed, to prove the convergence of tracking error under ILC. The proposed methodology is illustrated on a typical batch reactor and the results show that the performance of trajectory tracking is gradually improved by the ILC.     

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.     

A planning model for multiple blending schemes☆

The key of production planning of refineries is to determine the production planning of units and blending schemes of blends in each period of the plan horizon, since they affect the effective utilization of components of refineries and hence profits. The optimization is difficult, because of many complicated product production–consumption relation-ships in production processes, which are closely related to the running modes of the units. Additional y, the blending products, such as gasoline and diesel, may use multiple blending schemes for their production that increase the complexity of the problem. This paper models the production planning problem as a mixed integer nonlinear programming. Computational experiments for a refinery show the effectiveness of the model. The optimal results give the effective utilization of the self-produced components and increase of the profit.     

Upgrading Siberian (Russia) crude oil by hydrodesulfurization: Kinetic parameter estimation in a trickle-bed reactor

Hydrodesulfurization(HDS) of sour crude oil is an effective way to address the corrosion problems in refineries and is an economic way to process sour crude oil in an existing refinery built for sweet oil.Siberian crude oil transported through the Russia-China pipeline could be greatly sweetened and could be refined directly in local refinery designed for Daqing crude oil after the effective HDS treatment.In this study,the HDS of Siberian crude oil was carried out in a continuous flow isothermal trickle-bed reactor over Ni-Mo/γ-Al_2 O_3.The effects of temperature,pressure and LHSV were investigated in the ranges of 320-360℃,3-5 MPa and 0.5-2 h~(-1),keeping constant hydrogen to oil ratio at 600 L·L~(-1).The HDS conversion could be up to 92.89% at the temperature of 360℃, pressure of 5 MPa,and LHSV of 0.5 h~(-1), which is sufficient for local refineries(84%).A three phase heterogeneous model was established to analyze the performance of the trickle-bed reactor based on the two-film theory using Langmuir-Hinshelwood mechanism.The order of sulfur component is estimated as 1.28,and the order of hydrogen is 0.39.By simulating the reactor using the established model,the concentration of H_2, H_2 S and sulfur along the catalyst bed is discussed.The model is significantly useful for industrial application with respect to reactor analysis,optimization and reactor design,and can provide further insight of the HDS of Siberian crude oil.     

Kinetic studies on extra heavy crude oil upgrading using nanocatalysts by applying CFD techniques

Regarding the growth of global energy consumption and the paucity of light crude oil,extracting and using heavy and extra heavy crude oil has received much more attention,but the application of this kind ofoil is complicated due to its very high molecular weight.High viscosity and low flowability complicate the transportation of heavy and extra heavy crude oil.Accordingly,it is essential to reduce the viscosity of heavy and extra heavy crude oil through in-situ operations or immediate actions after extraction to reduce costs.Numerical simulations are influential methods,because they reduce calculation time and costs.In this study,the cracking of extra heavy crude oil using computational fluid dynamics is simulated,and a unique kinetic model is proposed based on experimental procedures to predict the behavior of extra heavy crude oil cracking reaction.Moreover,the hydrodynamics and heat transfer of the system and influence of nanocatalysts and temperature on the upgrading of crude oil are studied.The geometry of a reactor is produced using commercial software,and some experiments are performed to examine the validity and accuracy of the numerical results.The findings reveal that there is a good agreement between the numerical and experimental results.Furthermore,to investigate the main factors affecting the process,sensitivity analysis is adopted.Results show that type of catalyst and concentration of catalyst are the parameters that influence the viscosity reduction of extra heavy crude oil the most.The findings further revealed that when using a 25 nm SiO2 nanocatalyst,a maximum viscosity reduction of 98.67％ is observed at 623 K.Also,a catalyst concentration of 2.28wt％ is best for upgrading extra heavy crude oil.The results obtained through sensitivity analysis,simulation model,and experiments represent effectual information for the design and development of high performance upgrading processes for energy applications.     

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.     

超声电脱盐联合预处理原油脱盐脱水

A technology of ultrasonic-electric united desalting and dewatering of crude oil is studied. The ultrasonic setup is designed to form a standing-wave field, which is more efficient for agglomeration of water particles. The desalting and dewatering results of the ultrasonic-electric united process are compared with those of the electric process. For high salt-contenting crude oil （40-70 mg·L ^-1）, the salt content is still above 10.0 mg·L^-1 after crude oil has been treated by two-stage electric desalting process in refinery, which cannot meet the need of refinery. Ultrasonic-electric united process is a novel technology for treating the high salt-contenting oil. On the optimal operating conditions of the ultrasonic-electric united process, the salt content of crude oil can be reduced from 67 5 mg·L^-1 to 3.97 mg·L ^-1 by one-stage ultrasonic-electric united process, and the water content falls below 0.3% （by volume）. The results show that the ultrasonic-electric united process is more effective than the electric process in high salt-contenting oil desalting. This technology should be useful in the refinery process.     

需求不确定下炼油企业生产计划优化混合规划模型

Abstract Production planning under uncertainty is considered as one of the most important problems in plant-wide optimization. In this article, first, a stochastic programming model with uniform distribution assumption is developed for refinery production planning under demand uncertainty, and then a hybrid programming model incorporating the linear programming model with the stochastic programming one by a weight factor is proposed. Subsequently, piecewise linear approximation functions are derived and applied to solve the hybrid programming model-under uniform distribution assumption. Case studies show that the linear approximation algorithm is effective to solve.the hybrid programming model, along with an error≤0.5% when the deviatiorgmean≤20%. The simulation results indicate that the hybrid programming model with an appropriate weight factor （0.1-0.2） can effectively improve the optimal operational strategies under demand uncertainty, achieving higher profit than the linear programming model and the stochastic programming one with about 1.3% and 0.4% enhancement, respectavely.     

A novel model for multi-plant mixed heavy crude oils refinery planning

In this paper, multi-refinery using the same heavy crude oils as raw materials is studied, while a new nonlinear model for mixed heavy crude distillation is proposed. In practical crude distillation operation, the distillate yield and product distribution of distillation units are different due to their various equipment and operating parameters, even the same ratio of raw materials is provided, so different process models for multi-refinery planning is therefore required. For process modeling, the relationships between total yields and mixing ratio of different refineries were determined, which is combined with process simulation using production data. Then, the yields and properties of crude distillation unit (CDU) fractions were calculated with the use of true boiling point (TBP) curves and property curves respectively when the initial cutting temperatures were given. Finally, in order to maximize the economic benefit of distillation, the optimal product distribution and the best mixing ratio of crude oil were calculated under the constraints of different properties of fractions. Comparing to previous models, the proposed model takes the influence of different refinery parameters on production process into account, while avoiding the complex process for determining the cutting points, which is considered more effi-cient and more accurate with respect to heavy crude refinery. Model was successfully verified by a case study, allowing a significant improvement of the refinery profit to be achieved.     

Optimization of crude distillation system using aspen plus: Effect of binary feed selection on grass-root design

With an objective to supplement guidelines available as general rules of thumb for the grass-root design of crude distillation unit (CDU) using binary crude mixtures, this work presents the optimization of crude distillation unit using commercial Aspen Plus software. The crude distillation unit constituted a pre-flash tower (PF), an atmospheric distillation unit (ADU) and a vacuum distillation unit (VDU). Optimization model constituted a rigorous simulation model supplemented with suitable objective functions with and without product flow rate constraints. Three different feed stocks namely Bombay crude, Araby crude and Nigeria crude were considered in this work along with various binary combinations of these crudes. The objective function considered was profit function (subjected to maximization) for cases without product flow rate constraints and raw-materials and energy cost (subjected to minimization) for cases with product flow rate constraints. Parametric study pertaining to feed selection and composition has been conducted in this work to further benefit refinery planning and scheduling. Simulation study inferred that the product flow rate constraints sensitively affect atmospheric distillation column diameter and crude feed flow rate calculations. Based on all simulation studies, a generalized inference confirms that it is difficult to judge upon the quality of the solutions obtained as far as their global optimality is concerned.     

Multiperiod Optimization for Production Planning of Petroleum Refineries

Production planning has been the focus of many articles in the mathematical programming literature. In particular, successful operation of oil companies depends on systematic decisions over complex logistics. The present article focuses on the decision-making process involved in petroleum production planning. The proposed model is based on a nonlinear programming formulation that was developed to plan production over a single period. First, the model incorporates multiple planning periods and the selection of different crude oil types. Uncertainty related to petroleum and product prices as well as demand is then included as a set of discrete probabilities. Finally, crude oil handling constraints are added. The resulting models are mixed integer nonlinear programs (MINLP), which were successfully applied to a real-world case at the Petrobras REVAP refinery. Problems of up to 19 time periods and up to five scenarios were solved to optimality.     

Multiperiod Optimization for Production Planning of Petroleum Refineries

Production planning has been the focus of many articles in the mathematical programming literature. In particular, successful operation of oil companies depends on systematic decisions over complex logistics. The present article focuses on the decision-making process involved in petroleum production planning. The proposed model is based on a nonlinear programming formulation that was developed to plan production over a single period. First, the model incorporates multiple planning periods and the selection of different crude oil types. Uncertainty related to petroleum and product prices as well as demand is then included as a set of discrete probabilities. Finally, crude oil handling constraints are added. The resulting models are mixed integer nonlinear programs (MINLP), which were successfully applied to a real-world case at the Petrobras REVAP refinery. Problems of up to 19 time periods and up to five scenarios were solved to optimality.     

炼油企业供应链管理中实沸点切割点的优化

20世纪90年代以来,国际石油和化工制造业面临新的挑战．企业要想提高竞争力,必须在产品成本、质量及新产品开发能力、生产灵活性以及用户的售后服务等方面取得优势．为此,应将企业整个生产过程放到供应链中去,使整个企业的原料供应→原料运输→仓储→生产制造→产品储存→产品运输→用户需求反馈等形成一个有机联系的系统（图1）．提高整个企业的生产和销售效率,就是提高供应链各环节的效率,对供应链进行动态优化,将所需数量和质量的产品,以最快的速度送到客户的手中．     

Product tri-section based crude distillation unit model for refinery production planning and refinery optimization

Accuracy of a crude distillation unit (CDU) model has a significant impact on refinery production planning. High accuracy is typically accomplished via nonlinear models which causes convergence difficulties when the entire refinery model is optimized. CDU model presented in this work is a mixed-integer linear model with a modest number of binary variables; its accuracy is on par with rigorous tray to tray CDU models. The model relies on the observation¹² that a line through the middle of the product true boiling point (TBP) curve depends on the crude feed properties and the yields of the adjacent products. Novelty of the product tri-section CDU model is that it does not require models of individual distillation towers comprising the CDU, thereby leading to a much simpler model structure. Significant reduction in the computational effort required for the optimization of nonlinear refinery models is illustrated by comparison with previous work.     

基于特征选择的常减压装置模型及在计划优化中的应用

常减压装置将原油切割为不同中间产品,其作为炼油工艺的龙头装置,对炼油过程生产计划排产与效益提升至关重要。通过建立一种高精度且具有良好求解效率的常减压模型,用以求解模型关键指标实沸点（TBP）曲线,即综合考虑切割产品的实沸点（TBP）与原油TBP、流量、温度等变量影响,构建非线性方程组模型来表征输入输出间的关系;利用特征选择方法遴选相关变量（包括进料性质、相邻TBP及其二次项等）,并采用鲸鱼优化算法优化方程组系数。仿真结果表明,该多输出相互关系模型与已有文献工作相比,在兼顾求解效率基础上,常减压装置各蒸馏切割产品TBP曲线预测上有更高的精度,将此模型应用到炼厂计划优化中,与传统的悬摆切割模型对比,优化结果优于传统悬摆切割模型。     

A multi-level simulation approach for the crude oil loading/unloading scheduling problem

An integrated approach for refinery production scheduling and unit operation optimization problems is presented. Each problem is at a different decision making layer and has an independent objective function and model. The objective function at the operational level is an on-line maximization of the difference between the product revenue and the energy and environmental costs of the main refinery units. It is modeled as an NLP and is constrained by ranges on the unit's operating condition as well as product quality constraints. The production scheduling layer is modeled as an MILP with the objective of minimizing the logistical costs of unloading the crude oil over a day-to-week time horizon. The objective function is a linear sum of the unloading, sea waiting, inventory, and setup costs. The nonlinear simulation model for the process units is used to find optimized refining costs and revenue for a blend of two crudes. Multiple linear regression of the individual crude oil flow rates within the crude oil percentage range allowed by the facility is then used to derive linear refining cost and revenue functions. Along with logistics costs, the refining costs or revenue are considered in the MILP scheduling objective function. Results show that this integrated approach can lead to a decrease of production and logistics costs or increased profit, provide a more intelligent crude schedule, and identify production level scheduling decisions which have a tradeoff benefit with the operational mode of the refinery.     

基于动态规划的炼厂多阶段生产调度模型的建立及求解

针对炼厂生产随时间变化的诸多因素,建立了市场需求不确定下的炼厂多阶段生产调度动态规划模型。以各阶段产品产量为决策变量,各阶段库存为状态变量,阶段收益为目标。由于模型中存在不等式约束,一般的动态规划求解方法很难求解,提出用动态规划-混合遗传算法求解,最后给出实例计算,其结果验证了该模型的适用性和算法的有效性。     

Decision support for integrated refinery supply chains: Part 1. Dynamic simulation

Supply chain studies are increasingly given top priority in enterprise-wide management. Present-day supply chains involve numerous, heterogeneous, geographically distributed entities with varying dynamics, uncertainties, and complexity. The performance of a supply chain relies on the quality of a multitude of design and operational decisions made by the various entities. In this two-part paper, we demonstrate that a dynamic model of an integrated supply chain can serve as a valuable quantitative tool that aids in such decision-making. In this Part 1, we present a dynamic model of an integrated refinery supply chain. The model explicitly considers the various supply chain activities such as crude oil supply and transportation, along with intra-refinery supply chain activities such as procurement planning, scheduling, and operations management. Discrete supply chain activities are integrated along with continuous production through bridging procurement, production, and demand management activities. Stochastic variations in transportation, yields, prices, and operational problems are considered in the proposed model. The economics of the refinery supply chain includes consideration of different crude slates, product prices, operation costs, transportation, etc. The proposed model has been implemented as a dynamic simulator, called Integrated Refinery In-Silico (IRIS). IRIS allows the user the flexibility to modify not only parameters, but also replace different policies and decision-making algorithms in a plug-and-play manner. It thus allows the user to simulate and analyze different policies, configurations, uncertainties, etc., through an easy-to-use graphical interface. The capabilities of IRIS for strategic and tactical decision support are illustrated using several case studies.