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.     

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.     

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...     

一种基于测量的双层动态优化策略及其在青霉素发酵过程中的应用(英文)

One measurement-based dynamic optimization scheme can achieve optimality under uncertainties by tracking the necessary condition of optimality (NCO-tracking), with a basic assumption that the solution model remains invariant in the presence of al kinds of uncertainties. This assumption is not satisfied in some cases and the stan-dard NCO-tracking scheme is infeasible. In this paper, a novel two-level NCO-tracking scheme is proposed to deal with this problem. A heuristic criterion is given for triggering outer level compensation procedure to update the solution model once any change is detected via online measurement and estimation. The standard NCO-tracking process is carried out at the inner level based on the updated solution model. The proposed approach is il ustrated via a bioreactor in penicil in fermentation process.     

工业流化床乙烯共聚合生产过程中牌号切换的最优化研究

A model of grade transition is presented for a commercialized fluidized bed gas-phase polyethylene production process.The quantity of off-specification product and the time of grade transition can be minimized by the optimization of operating variables,such as polymerization temperature,the ratio of hydrogen to ethylene,the ratio of co-monomer to ethylene,feed rate of catalyst,and bed level.A new performance index,the ratio of melt flow(MFR),is included in the objective function,for restraining the sharp adjustment of operation variables and narrowing the distribution of molecular weight of the resion.It is recommended that catalyst feed rate and bed level are decreased in order to reduce the grade transition time and the quantity of off-specification product.This optimization problem is solved by an algorithm of sequential quadratic programming (SQP) in MATLAB. There is considerable difference between the forward transition and reverse transition of grade with regard to the operating variables due to the non-linearity of the system.The grade transition model is extended to a high space time yield(STY)Process with the so-called condensed model operation.In the end,an optimization strategy for multi-product transition is proposed with two-level optimization of the objective function J(x,u) on the the basis of the optimal grade transition model.A sequential transition of six commercial polyethylene grades in illustrated for an optimal multi-product operation.     

基于ARX-NNPLS模型的优化策略及在聚合物牌号切换过程中的应用(英文)

Since it is often difficult to build differential algebraic equations (DAEs) for chemical processes, a new data-based modeling approach is proposed using ARX (AutoRegressive with eXogenous inputs) combined with neural network under partial least squares framework (ARX-NNPLS), in which less specific knowledge of the process is required but the input and output data. To represent the dynamic and nonlinear behavior of the process, the ARX combined with neural network is used in the partial least squares (PLS) inner model between input and output latent variables. In the proposed dynamic optimization strategy based on the ARX-NNPLS model, neither parameterization nor iterative solving process for DAEs is needed as the ARX-NNPLS model gives a proper representation for the dynamic behavior of the process, and the computing time is greatly reduced compared to conventional control vector parameterization method. To demonstrate the ARX-NNPLS model based optimization strategy, the polyethylene grade transition in gas phase fluidized-bed reactor is taken into account. The optimization results show that the final optimal trajectory of quality index determined by the new approach moves faster to the target values and the computing time is much less.     

混合分离过程的有序设计框架

The design of optimal separation flow sheets for multi-component mixtures is still not a solved problem This is especially the case when non-ideal or azeotropic mixtures or hybrid separation processes are considered. We review recent developments in this field and present a systematic framework for the design of separation flow sheets. This framework proposes a three-step approach. In the first step different flow sheets are generated. In the second step these alternative flow sheet structures are evaluated with shortcut methods. In the third step a rigorous mixed-integer nonlinear programming （MINLP） optimization of the entire flow sheet is executed to determine the best alternative. Since a number of alternative flow sheets have already been eliminated, only a few optimization runs are necessary in this final step. The whole framework thus allows the systematic generation and evaluation of separation processes and is illustrated with the case study of the separation of ethanol and water.     

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.     

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.     

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.     

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.     

Rolling horizon based planning and scheduling integration with production capacity consideration

The rolling horizon method has been proposed to address the integrated production planning and scheduling optimization problem. Since the method can generally result in small-scale optimization model and fast solution, it has been used in a number of applications in realistic industrial planning and scheduling problems. In this paper, it is first pointed out that the incorporation of valid production capacity information into the planning model can improve the solution quality in the rolling horizon solution framework. A novel method is then proposed to derive the production capacity information representing the detail scheduling model based on parametric programming technique. A heuristic process network decomposition strategy is further applied to reduce the computational effort needed for larger and more complex process networks. Several case studies have been studied, which illustrate the efficiency of the proposed methodology in improving the solution quality of rolling horizon method for integrated planning and scheduling optimization.     

Integrated production planning and scheduling using a decomposition framework

To ensure the consistency between planning and scheduling decisions, the integrated planning and scheduling problem should be addressed. Following the natural hierarchy of decision making, integrated planning and scheduling problem can be formulated as bilevel optimization problem with a single planning problem (upper level) and multiple scheduling subproblems (lower level). Equivalence between the proposed bilevel model and a single level formulation is proved considering the special structure of the problem. However, the resulting model is still computationally intractable because of the integrality restrictions and large size of the model. Thus a decomposition based solution algorithm is proposed in this paper. In the proposed method, the production feasibility requirement is modeled through penalty terms on the objective function of the scheduling subproblems, which is further proportional to the amount of unreachable production targets. To address the nonconvexity of the production cost function of the scheduling subproblems, a convex polyhedral underestimation of the production cost function is developed to improve the solution accuracy. The proposed decomposition framework is illustrated through examples which prove the effectiveness of the method.     

A decision tree based decomposition method for oil refinery scheduling

Refinery scheduling attracts increasing concerns in both academic and industrial communities in recent years. However, due to the complexity of refinery processes, little has been reported for success use in real world refineries. In academic studies, refinery scheduling is usually treated as an integrated, large-scale optimization problem, though such complex optimization problems are extremely difficult to solve. In this paper, we proposed a way to exploit the prior knowledge existing in refineries, and developed a decision making system to guide the scheduling process. For a real world fuel oil oriented refinery, ten adjusting process scales are predetermined. A C4.5 decision tree works based on the finished oil demand plan to classify the corresponding category (i.e. adjusting scale). Then, a specific sub-scheduling problem with respect to the determined adjusting scale is solved. The proposed strategy is demonstrated with a scheduling case originated from a real world refinery.     

A reactive optimization strategy for the simultaneous planning,scheduling and control of short-period continuous reactors

The efficient and economic operation of processing systems ideally requires a simultaneous planning, scheduling and control framework. Even when the optimal simultaneous solution of this problem can result in large scale optimization problems, such a solution can represent economic advantages making feasible its computation using optimization decomposition and/or few operating scenarios. After reducing the complexity of the optimal simultaneous deterministic solution, it becomes feasible to take into account the effect of model and process uncertainties on the quality of the solution. In this work we consider those changes in product demands that take place once the process is already under continuous operation. Therefore, a reactive strategy is proposed to meet the new product demands. Based on an optimization formulation for handling the simultaneous planning, scheduling, and control problem of continuous reactors, we propose a heuristic strategy for dealing with unexpected events that may appear during operation of a plant. Such a strategy consists of the rescheduling of the products that remain to be manufactured after the given disturbance hits the process. Such reactive strategy for dealing with planning, scheduling and control problems under unforeseen events is tested using two continuous chemical reaction systems.     

考虑需求不确定性的化工生产计划与调度集成

生产计划与调度是化工供应链优化中两个重要的决策问题。为了提高生产决策的效率,不仅要对计划与调度进行集成,而且要考虑不确定性的影响。对于多周期生产计划与调度问题,首先在每个生产周期内,分别建立计划与调度的确定性模型,通过产量关联对二者进行集成。然后考虑需求不确定性,使用有限数量的场景表达决策变量,建立二阶段随机规划模型。最后运用滚动时域求解策略,使计划与调度结果在迭代过程中达到一致。实例结果表明,在考虑需求不确定性时,与传统方法相比,随机规划方法可以降低总费用,结合计划与调度的分层集成策略,实现了生产操作性和经济性的综合优化。     

An integrated model of supply network and production planning for multiple fuel products of multi-site refineries

An integrated model of supply network and production planning is proposed for the collaboration among refineries manufacturing multiple fuel products at different locations. The simulation and optimization based on the model indicate the following. The distribution costs can be reduced by relocating distribution centers as well as by reconfiguring their linkages to various markets. Moreover, the multiple fuel products manufactured need to be segregated during storage and transportation to be able to satisfy the demands of the various markets. The production planning, therefore, should be an integral part of the supply-network planning, and vice versa. Specifically, the proposed integrated model is for the nationwide supply of multiple fuel products manufactured by the individual refineries. The efficacy and usefulness of the integrated model is illustrated with an example involving three refineries and four varieties of fuel products.     

Data‐driven adaptive nested robust optimization: General modeling framework and efficient computational algorithm for decision making under uncertainty

A novel data‐driven adaptive robust optimization framework that leverages big data in process industries is proposed. A Bayesian nonparametric model—the Dirichlet process mixture model—is adopted and combined with a variational inference algorithm to extract the information embedded within uncertainty data. Further a data‐driven approach for defining uncertainty set is proposed. This machine‐learning model is seamlessly integrated with adaptive robust optimization approach through a novel four‐level optimization framework. This framework explicitly accounts for the correlation, asymmetry and multimode of uncertainty data, so it generates less conservative solutions. Additionally, the proposed framework is robust not only to parameter variations, but also to anomalous measurements. Because the resulting multilevel optimization problem cannot be solved directly by any off‐the‐shelf solvers, an efficient column‐and‐constraint generation algorithm is proposed to address the computational challenge. Two industrial applications on batch process scheduling and on process network planning are presented to demonstrate the advantages of the proposed modeling framework and effectiveness of the solution algorithm. © 2017 American Institute of Chemical Engineers AIChE J, 63: 3790–3817, 2017     

The integration of production plan and operating schedule in a pharmaceutical pilot plant

In reality, planning and scheduling activities are loosely integrated. In the case of a complex supply chain, it might be too difficult to maintain both long-term and short-term information within one large model. However, in the case of a single facility and relative short time horizon this task becomes feasible. This is the case in a pharmaceutical pilot plant, where a 2- or 3-year planning horizon is the most one would expect due to frequent changes in the products and their demands. In earlier work, we decomposed the pilot plant planning and scheduling problem into long-term planning of resources and short-term scheduling of operations [Computer Aided Process Eng. 8 (2000) 1057]. The long-term plan is mainly concerned with allocation of human resource, such as operators, engineers, chemists, and analysts to projects that have uncertain timelines. The short-term schedule fixes the human resources and considers the scheduling of activities in the pilot plant itself. The algorithm resolves uncertainty in future projects by over-allocating resources so that, on average, resource constraints are met. In this paper, we continue this exploration. In particular, we explore techniques for combining the long- and short-term into one plan without creating undue burden on the solver and the planning group. The uncertainty is a daily life of a pharmaceutical pilot plant. We present a methodology to deal with it without compromising the accuracy of the operating schedule.