Strategy of changing cracking furnace feedstock based on improved group search optimization

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 opera...     

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.     

基于精细自由基反应机理的丙烷裂解炉炉管CFD模拟(英文)

In the radiant section of cracking furnace, the thermal cracking process is highly coupled with turbulent flow, heat transfer and mass transfer. In this paper, a three-dimensional simulation of propane pyrolysis reactor tube is performed based on a detailed kinetic radical cracking scheme, combined with a comprehensive rigorous computational fluid dynamics（CFD） model. The eddy-dissipation-concept（EDC） model is introduced to deal with turbulence-chemistry interaction of cracking gas, especially for the multi-step radical kinetics. Considering the high aspect ratio and severe gradient phenomenon, numerical strategies such as grid resolution and refinement, stepping method and relaxation technique at different levels are employed to accelerate convergence. Large scale of radial nonuniformity in the vicinity of the tube wall is investigated. Spatial distributions of each radical reaction rate are first studied, and made it possible to identify the dominant elementary reactions. Additionally, a series of operating conditions including the feedstock feed rate, wall temperature profile and heat flux profile towards the reactor tubes are investigated. The obtained results can be used as scientific guide for further technical retrofit and operation optimization aiming at high conversion and selectivity of pyrolysis process.     

基于微粒群优化算法的不确定性调和调度

Blending is an important unit operation in process industry. Blending scheduling is nonlinear optimization problem with constraints. It is difficult to obtain optimum solution by other general optimization methods. Particle swarm optimization （PSO） algorithm is developed for nonlinear optimization problems with both continuous and discrete variables. In order to obtain a global optimum solution quickly, PSO algorithm is applied to solve the problem of blending scheduling under uncertainty. The calculation results based on an example of gasoline blending agree satisfactory with the ideal values, which illustrates that the PSO algorithm is valid and effective in solving the blending scheduling problem.     

Genetic algorithm for short-term scheduling of make-and-pack batch production process

This paper considers a scheduling problem in industrial make-and-pack batch production process. This process equips with sequence-dependent changeover time, multipurpose storage units with limited capacity, storage time, batch splitting, partial equipment connectivity and transfer time. The objective is to make a production plan to satisfy al constraints while meeting demand requirement of packed products from various product fam-ilies. This problem is NP-hard and the problem size is exponentially large for a realistic-sized problem. Therefore, we propose a genetic algorithm to handle this problem. Solutions to the problems are represented by chromo-somes of product family sequences. These sequences are decoded to assign the resource for producing packed products according to forward assignment strategy and resource selection rules. These techniques greatly reduce unnecessary search space and improve search speed. In addition, design of experiment is carefully utilized to de-termine appropriate parameter settings. Ant colony optimization and Tabu search are also implemented for com-parison. At the end of each heuristics, local search is applied for the packed product sequence to improve makespan. In an experimental analysis, al heuristics show the capability to solve large instances within reason-able computational time. In al problem instances, genetic algorithm averagely outperforms ant colony optimiza-tion and Tabu search with slightly longer computational time.     

Synthesis and optimization of utility system using parameter adaptive differential evolution algorithm☆

Synthesis and optimization of utility system usual y involve grassroots design, retrofitting and operation optimi-zation, which should be considered in modeling process. This paper presents a general method for synthesis and optimization of a utility system. In this method, superstructure based mathematical model is established, in which different modeling methods are chosen based on the application. A binary code based parameter adaptive differential evolution algorithm is used to obtain the optimal configuration and operation conditions of the sys-tem. The evolution algorithm and models are interactively used in the calculation, which ensures the feasibility of configuration and improves computational efficiency. The capability and effectiveness of the proposed approach are demonstrated by three typical case studies.     

设计余量对运行优化和化学过程控制性能的影响简

Operation optimization is an effective method to explore potential economic benefits for existing plants. The m.aximum potential benefit from operationoptimization is determined by the distances between current operating point and process constraints, which is related to the margins of design variables. Because of various ciisturbances in chemical processes, some distances must be reserved for fluctuations of process variables and the optimum operating point is not on some process constraints. Thus the benefit of steady-state optimization can not be fully achied（ed while that of dynamic optimization can be really achieved. In this study, the steady-state optimizationand dynamic optimization are used, and the potential benefit-is divided into achievable benefit for profit and unachievable benefit for control. The fluid catalytic cracking unit （FCCU） is used for case study. With the analysis on how the margins of design variables influence the economic benefit and control performance, the bottlenecks of process design are found and appropriate control structure can be selected.     

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.     

Dual-radiation-chamber coordinated overall energy efficiency scheduling solution for ethylene cracking process regarding multi-parameter setting and multi-flow allocation

Ethylene cracking process is the core production process in ethylene industry, and is paid more attention to reduce high energy consumption. Because of the interdependent relationships between multi-flow allocation and multi-parameter setting in cracking process, it is difficult to find the overall energy efficiency scheduling for the purpose of saving energy. The traditional scheduling solutions with optimal economic benefit are not applicable for energy efficiency scheduling issue due to the neglecting of recycle and lost energy, as well as critical operation parameters as coil outlet pressure(COP) and dilution ratio. In addition, the scheduling solutions mostly regard each cracking furnace as an elementary unit, regardless of the coordinated operation of internal dual radiation chambers(DRC). Therefore, to improve energy utilization and production operation, a novel energy efficiency scheduling solution for ethylene cracking process is proposed in this paper. Specifically, steam heat recycle and exhaust heat loss are considered in cracking process based on 6 types of extreme learning machine(ELM) based cracking models incorporating DRC operation and three operation parameters as coil outlet temperature(COT), COP, and dilution ratio according to semi-mechanism analysis. Then to provide long-term decision-making basis for energy efficiency scheduling, overall energy efficiency indexes, including overall output per unit net energy input(OONE), output-input ratio per unit net energy input(ORNE), exhaust gas heat loss ratio(EGHL), are designed based on input–output analysis in terms of material and energy flows. Finally, a multiobjective evolutionary algorithm based on decomposition(MOEA/D) is employed to solve the formulated multi-objective mixed-integer nonlinear programming(MOMINLP) model. The validities of the proposed scheduling solution are illustrated through a case study. The scheduling results demonstrate that an optimal balance between multi-flow allocation, multi-parameter setting, and DRC coordinated operation is reached, which achieves 3.37% and 2.63% decreases in net energy input for same product output and conversion ratio, as well as the 1.56% decrease in energy loss ratio.     

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.     

Multi-objective modeling and optimization for scheduling of cracking furnace systems

Cracking furnace is the core device for ethylene production. In practice, multiple ethylene furnaces are usual y run in parallel. The scheduling of the entire cracking furnace system has great significance when multiple feeds are simultaneously processed in multiple cracking furnaces with the changing of operating cost and yield of product. In this paper, given the requirements of both profit and energy saving in actual production process, a multi-objective optimization model contains two objectives, maximizing the average benefits and minimizing the average coking amount was proposed. The model can be abstracted as a multi-objective mixed integer non-linear programming problem. Considering the mixed integer decision variables of this multi-objective problem, an improved hybrid encoding non-dominated sorting genetic algorithm with mixed discrete variables (MDNSGA-I ) is used to solve the Pareto optimal front of this model, the algorithm adopted crossover and muta-tion strategy with multi-operators, which overcomes the deficiency that normal genetic algorithm cannot handle the optimization problem with mixed variables. Finally, using an ethylene plant with multiple cracking furnaces as an example to illustrate the effectiveness of the scheduling results by comparing the optimization results of multi-objective and single objective model.     

考虑切料过程的乙烯裂解炉炉群调度建模与优化

乙烯裂解炉炉群通常由多台裂解炉并行运行,将烃类原料裂解成小分子烃类化合物。由于随着裂解炉连续运行不可避免地在炉管内壁产生结焦,结焦导致裂解炉运行效率下降,所以需要对裂解炉进行周期性的停炉清焦。对于不同价格参数的多种原料不同清焦费用的多台裂解炉来说,整个乙烯裂解炉炉群系统的循环调度应是求得最优解使得收益最大化。本文对此类裂解炉炉群循环调度问题提出了一个新的混合整数非线性（MINLP）模型,相比较以前的研究该模型能够得到更好的求解多原料多裂解炉的问题,同时解决了裂解过程中切料时机选择的问题。最后,以某乙烯厂为研究实例进行切料时机的优化,优化后裂解炉全周期的运行效益显著提高,为操作人员选择最佳切炉时机提供了理论依据,说明了此模型的有效性。     

A Hybrid Algorithm Based on Differential Evolution and Group Search Optimization and Its Application on Ethylene Cracking Furnace

To find the optimal operational condition when the properties of feedstock changes in the cracking furnace online, a hybrid algorithm named differential evolution group search optimization (DEGSO) is proposed, which is based on the differential evolution (DE) and the group search optimization (GSO). The DEGSO combines the advantages of the two algorithms: the high computing speed of DE and the good performance of the GSO for preventing the best particle from converging to local optimum. A cooperative method is also proposed for switching between these two algorithms. If the fitness value of one algorithm keeps invariant in several generations and less than the preset threshold, it is considered to fall into the local optimization and the other algorithm is chosen. Experiments on benchmark functions show that the hybrid algorithm outperforms GSO in accuracy, global searching ability and efficiency. The optimization of ethylene and propylene yields is illustrated as a case by DEGSO. After optimization, the yield of ethylene and propylene is increased remarkably, which provides the proper operational condition of the ethylene cracking furnace.     

基于P-graph的乙烯裂解原料调度建模与优化

乙烯工业不同的裂解装置间存在着设备、技术上的差别，每一种原料在乙烯工厂不同炉型或工艺的裂解装置的乙烯产品收率、能耗也存在着差别。随着新的乙烯工厂的投产，需要同时运行台数众多的差异化裂解装置，从而为通过优化调度乙烯裂解原料实现提高物效、降低能耗提供了空间。对于此类工厂间原料调度及能耗优化问题提出了一种基于P-graph的建模和优化方法(scheduling generation based on P-graph, SGBP算法)，该算法通过P-graph本身提取过程结构信息的能力，在加速求解的同时，保留了次优解集。之后以两个实际的乙烯厂为研究实例，采用提出的SGBP方法实现了原料调度的建模和优化，该方法与MINLP优化算法的对比分析验证了提出方法的优势：(1)可以同时提供较为丰富的最优解与次优解方案；(2)提出方法的最优结果与MINLP的优化效果相当；(3)优化后的整体能耗下降明显，为生产计划人员选择可采用灵活的原料调配方案提供了多种可选择的运行方案。     

基于改进NSGA-Ⅱ算法的乙烯裂解炉操作优化

现有的乙烯裂解炉优化通常只针对两个目标函数即产物乙烯和丙烯的收率，并且采用遗传算法的收敛效果一般，故提出一种基于改进NSGA-Ⅱ算法来研究一个多目标运行的解决方案，以此来解决乙烯裂解炉的固定周期操作优化问题，即在增大产物乙烯和丙烯收率的同时减少原料以及蒸汽流量来提高整体运行状况。把具体问题量化为数学模型，分析了原料气烃比、原料流量、出口温度对乙烯和丙烯收率的影响。实验结果表明，相较于原有操作条件，提出的优化方案具有良好的可行性。     

基于模糊核聚类的乙烯裂解深度DE-LSSVM多模型建模

乙烯裂解深度的建模与控制对于裂解炉的实时优化具有重要意义。针对石脑油原料组分复杂、油品特性波动大等状况,采用模糊核聚类对石脑油数据库进行最优划分,建立最小二乘支持向量机的多模型,对于最小二乘支持向量机中模型的参数选取,利用差分进化算法进行参数寻优,提高了模型的精度和泛化能力。通过对现场数据的建模实验,结果表明：基于模糊核聚类的乙烯裂解深度最小二乘支持向量机多模型跟踪性能良好,预测精度较高。     

采用支持向量机实现烯烃裂解料优化选择

采用支持向量机(SVM)、粒子群搜索最优算法实现烯烃裂解原料结构的优化选择,相比以往原料优选方法,该方法建模与维护便捷、计算精度高,达到根据市场价格变化及时调整生产运行过程中烯烃裂解原料结构的目的,在竞争日益激烈的市场环境下,能提升烯烃裂解生产过程的产出效益。