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.     

催化重整过程的多目标优化

In this article, a multiobjective optimization strategy for an industrial naphtha continuous catalytic reforming process that aims to obtain aromatic products is proposed. The process model is based on a 20-lumped kinetics reaction network and has been proved to be quite effective in terms of industrial application. The primary objectives include maximization of yield of the aromatics and minimization of the yield of heavy aromatics. Four reactor inlet temperatures, reaction pressure, and hydrogen-to-oil molar ratio are selected as the decision variables. A genetic algorithm, which is proposed by the authors and named as the neighborhood and archived genetic algorithm （NAGA）, is applied to solve this multiobjective optimization problem. The relations between each decision variable and the two objectives are also proposed and used for choosing a suitable solution from the obtained Pareto set.     

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.     

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.     

化工过程分离循环系统的多目标模糊优化研究

Separation-recycle system is an important part in chemical process, and its optimization is a multiobjective problem. In this paper the process optimization procedure is proposed. The fuzzy optimization algorithm with the concept of relative importance degree (RID) is utilized to transfer multi-objective optimization (MO-O) model into a single-objective optimization (SO-O) framework. The treatment of process condensate in synthesisa mmonia plant is taken as example to illustrate the optimization procedure, and the satisfactory result demonstrates feasibility and effectiveness of the suggested method.     

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.     

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.     

Simultaneous hybrid modeling of a nosiheptide fermentation process using particle swarm optimization

Hybrid modeling approaches have recently been investigated as an attractive alternative to model fermentation processes. Normally, these approaches require estimation data to train the empirical model part of a hybrid model. This may result in decreasing the generalization ability of the derived hybrid model. Therefore, a simulta-neous hybrid modeling approach is presented in this paper. It transforms the training of the empirical model part into a dynamic system parameter identification problem, and thus al ows training the empirical model part with only measured data. An adaptive escaping particle swarm optimization (AEPSO) algorithm with escaping and adaptive inertia weight adjustment strategies is constructed to solve the resulting parameter identification problem, and thereby accomplish the training of the empirical model part. The uniform design method is used to determine the empirical model structure. The proposed simultaneous hybrid modeling approach has been used in a lab-scale nosiheptide batch fermentation process. The results show that it is effective and leads to a more consistent model with better generalization ability when compared to existing ones. The performance of AEPSO is also demonstrated.     

一种新的生产计划与催化裂化装置过程操作集成的闭环策略

Production planning models generated by common modeling systems do not involve constraints for process operations, and a solution optimized by these models is called a quasi-optimal plan. The quasi-optimal plan cannot be executed in practice some time for no corresponding operating conditions. In order to determine a practi- cally feasible optimal plan and corresponding operating conditions of fluidized catalytic cracking unit （FCCU）, a novel close-loop integrated strategy, including determination of a quasi-optimal plan, search of operating conditions of FCCU and revision of the production planning model, was proposed in this article. In the strategy, a generalized genetic algorithm （GA） coupled with a sequential process simulator of FCCU was applied to search operating conditions implementing the quasi-optimal plan of FCCU and output the optimal individual in the GA search as a final genetic individual. When no corresponding operating conditions were found, the final genetic individual based correction （FGIC） method was presented to revise the production planning model, and then a new quasi-optimal production plan was determined. The above steps were repeated until a practically feasible optimal plan and corresponding operating conditions of FCCU were obtained. The close-loop integrated strategy was validated by two cases, and it was indicated that the strategy was efficient in determining a practically executed optimal plan and corresponding operating conditions of FCCU.     

A novel 3-layer mixed cultural evolutionary optimization framework for optimal operation of syngas production in a Texaco coal-water slurry gasifier☆

Optimizing operational parameters for syngas production of Texaco coal-water slurry gasifier studied in this paper is a complicated nonlinear constrained problem concerning 3 BP (Error Back Propagation) neural networks. To solve this model, a new 3-layer cultural evolving algorithm framework which has a population space, a medium space and a belief space is firstly conceived. Standard differential evolution algorithm (DE), genetic algorithm (GA), and parti-cle swarm optimization algorithm (PSO) are embedded in this framework to build 3-layer mixed cultural DE/GA/PSO (3LM-CDE, 3LM-CGA, and 3LM-CPSO) algorithms. The accuracy and efficiency of the proposed hybrid algo-rithms are firstly tested in 20 benchmark nonlinear constrained functions. Then, the operational optimization model for syngas production in a Texaco coal-water slurry gasifier of a real-world chemical plant is solved effective-ly. The simulation results are encouraging that the 3-layer cultural algorithm evolving framework suggests ways in which the performance of DE, GA, PSO and other population-based evolutionary algorithms (EAs) can be improved, and the optimal operational parameters based on 3LM-CDE algorithm of the syngas production in the Texaco coal-water slurry gasifier shows outstanding computing results than actual industry use and other algorithms.     

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.     

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。     

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 new Lagrangian decomposition approach applied to the integration of refinery planning and crude-oil scheduling

The aim of this paper is to introduce a methodology to solve a large-scale mixed-integer nonlinear program (MINLP) integrating the two main optimization problems appearing in the oil refining industry: refinery planning and crude-oil operations scheduling. The proposed approach consists of using Lagrangian decomposition to efficiently integrate both problems. The main advantage of this technique is to solve each problem separately. A new hybrid dual problem is introduced to update the Lagrange multipliers. It uses the classical concepts of cutting planes, subgradient, and boxstep. The proposed approach is compared to a basic sequential approach and to standard MINLP solvers. The results obtained on a case study and a larger refinery problem show that the new Lagrangian decomposition algorithm is more robust than the other approaches and produces better solutions in reasonable times.     

基于流程模拟的常减压装置过程操作与生产计划集成优化

传统的炼油企业生产计划优化与过程操作优化往往是分离的, 从而造成生产计划优化系统制定出的生产方案可能在实际的生产装置操作上无法实现的情况。为了确保石化企业生产计划制定的方案可行的同时实现过程装置操作优化, 基于流程模拟软件建立了常减压蒸馏装置生产计划与过程操作的集成优化策略, 并提出了该优化策略的有效寻优方法。该方法通过流程模拟软件验证生产计划的可达性, 不断修正生产计划关键变量的优化区间, 在求得生产计划最优解的同时确定装置的工艺操作条件。以某炼油厂常减压蒸馏装置炼油为例验证提出的集成优化方法, 案例证明该集成策略不仅确保生产计划在实际生产的可操作性, 还得到了生产计划与过程装置操作的同步优化。     

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。     

Optimal rate allocation for production and injection wells in an oil and gas field for enhanced profitability

An oil and gas field requires careful operational planning and management via production optimization for increased recovery and long-term project profitability. This article addresses the challenge of production optimization in a field undergoing secondary recovery by water flooding. The field operates with limited processing capacity at the surface separators, pipeline pressure constraints, and water injection constraints; an economic indicator (net present value, NPV) is used as the objective function. The formulated optimization framework adequately integrates slow-paced subsurface dynamics using reservoir simulation, and fast-paced surface dynamics using sophisticated multiphase flow simulation in the upstream facilities. Optimization of this holistic long-term model is made possible by developing accurate second-order polynomial proxy models at each time step. The resulting formulation is solved as a nonlinear program using commercially available solvers. A comparative analysis is performed using MATLAB's fmincon solver and the IPOPT solver for their robustness, speed, and convergence stability in solving the proposed problem. By implementing two synthetic case studies, our mathematical programming approach determines the optimal production and injection rates of all wells and further demonstrates considerable improvement to the NPV obtained by simultaneously applying the tools of streamline, reservoir, and surface facility simulation for well rate allocation via systematic NLP optimization.     

Optimization of a multiperiod refinery planning problem under uncertainty

Uncertainty in refinery planning presents a significant challenge in determining the day-to-day operations of an oil refinery. Deterministic modeling techniques often fail to account for this uncertainty, potentially resulting in reduced profit. The stochastic programming framework explicitly incorporates parameter uncertainty in the problem formulation, thus giving preference to robust solutions. In this work, a nonlinear, multiperiod, industrial refinery problem is extended to a two-stage stochastic problem, formulated as a mixed-integer nonlinear program. A crude-oil sequencing case study is developed with binary scheduling decisions in both stages of the stochastic programming problem. Solution via a decomposition strategy based on the generalized Benders decomposition (GBD) algorithm is proposed. The binary decisions are designated as complicating variables that, when fixed, reduce the full-space problem to a series of independent scenario subproblems. Through the application of the GBD algorithm, a feasible mixed-integer solution is obtained that is more robust to uncertainty than its deterministic counterpart.