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。     

基于差分进化粒子群混合优化算法的软测量建模

针对乙烯生产过程中,用传统方法难以直接完成对乙烯收率的在线测量的问题,提出了一种新型差分进化粒子群混合优化算法,建立了乙烯收率软测量建模。改进算法将优化过程分成两阶段,两分群分别采用粒子群算法和差分进化算法同时进行。迭代过程中引入进化速度因子进行算法局部收敛性判断,通过两个群体间的信息交流阻止算法陷入局部最优。对高维复杂函数寻优测试表明,算法的整体优化性能均强于基本粒子群算法和差分进化算法。应用结果表明,基于改进算法的软测量模型具有测量精度较高、泛化性能较好等优点。     

免疫文化算法及其在乙烯裂解炉故障诊断中的应用

引言迄今为止,科研人员已经根据生物进化的机理提出很多用以解决复杂优化问题的方法,如遗传算法、蚁群优化算法、粒子群优化算法等。然而这些传统的进化算法只提供有限的或者隐性关于种群个体经验的知识表示和保存机制,这就让研究人员开始寻找一种利用显性机制来获取并保存种群进化求解知识和经验。在人类学的角度上,文化通常被看成是保存信     

基于多群竞争PSO-RBFNN的乙烯裂解深度智能优化控制

提出一种基于K均值聚类的多群竞争粒子群优化算法(MSCPSO),该算法避免陷入局部最优,提高了算法的全局搜索能力。同时利用MSCPSO训练RBF神经网络并建立裂解产物的在线预测模型,研究一种集成MSCPSO-RBFNN过程建模的裂解深度智能优化控制方法。该方法以实现乙烯和丙烯收率之和最大化为目标函数,把满足优化目标的裂解深度作为深度控制器的输入,并与裂解炉出口温度先进控制系统集成,实现裂解深度的平稳控制。实际应用效果表明,提高了乙烯和丙烯的收率,裂解深度控制更加稳定,该方法具有良好的适应性、稳定性。     

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.     

全局群搜索优化算法及其在汽油调合中的应用

汽油调合配比生产优化是一种非线性约束的多峰优化问题。针对一般群智能优化算法在解决此类优化中易陷于局部最优解,提出了一种改进的群搜索优化算法--全局群搜索优化算法（GGSO）。该算法采用混沌机制初始化粒子在解空间内均匀分布;在算法前期,保留GSO的追随者进化策略,以保证算法的收敛速度。在算法后期,对追随者引入速度更新和个体最优,以保证算法的收敛精度;在粒子陷入局部极值时,对追随者和游荡者引入一种新的交叉、变异机制和自适应混沌扰动机制,以保证粒子跳出局部极值,提高算法全局寻优性能。分别用4个标准测试函数对优化算法进行测试,结果表明：GGSO算法与标准GSO、线性递减惯性权重粒子群算法（LDWPSO）比较,收敛速度和全局寻优性能有明显优势。汽油在线调合优化实例应用表明：该算法有较快的收敛速度,能够较准确地寻得全局最优。     

基于改进GSO的裂解炉原料切换策略研究(英文)

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 operation, maximum tube metal temperature, process time of each feedstock, and flow rate. A modified group search optimizer is pro-posed to deal with the optimization problem. Double fitness values are defined for every group. First, the factor of penalty function should be changed adaptively by the ratio of feasible and general solutions. Second, the“excel-lent”infeasible solution should be retained to guide the search. Some benchmark functions are used to evaluate the new algorithm. Final y, the proposed algorithm is used to optimize the scheduling process of cracking furnace feedstock. And the optimizing result is obtained.     

多策略果蝇优化算法及其应用

针对果蝇算法容易陷入局部极值、收敛速度慢和收敛精度低的问题,基于粒子群优化算法中社会认知因子和差分演化算法的变异算子,提出了一种多策略果蝇优化算法(SFOA)。对于味道浓度值劣于平均味道浓度的个体,采用社会认知变异因子产生下一代个体,加快收敛速度。对于味道浓度值优于平均味道浓度的个体,引入差分向量,提高算法跳出局部极值的能力。经过8个测试函数的仿真实验对比,SFOA具有更好的全局搜索能力、更快的收敛速度和更高的收敛精度。最后,将改进后的果蝇算法运用到GE气化炉操作优化中,以有效合成气产率最大化为优化目标,氧煤比和水煤浆浓度为决策变量,结果表明,SFOA能够快速找到最优值,证明了多策略果蝇优化算法的有效性。     

乙烯裂解炉的优化操作标定

介绍了乙烯裂解炉产物含量标定的目的、意义及方法,以某100kt／a乙烯裂解炉操作优化为实例,对该裂解炉在不同工艺条件下的含量进行标定,根据标定结果对操作条件进行了优化。结果表明一,将操作条件调整后,裂解温度从815℃降低至805℃,裂解炉双烯的质量分数提高0．84个百分点。     

基于混合微分进化算法的生化过程动态参数估计(英文)

Determination of the optimal model parameters for biochemical systems is a time consuming iterative process. In this study, a novel hybrid differential evolution (DE) algorithm based on the differential evolution technique and a local search strategy is developed for solving kinetic parameter estimation problems. By combining the merits of DE with Gauss-Newton method, the proposed hybrid approach employs a DE algorithm for identifying promising regions of the solution space followed by use of Gauss-Newton method to determine the optimum in the identified regions. Some well-known benchmark estimation problems are utilized to test the efficiency and the robustness of the proposed algorithm compared to other methods in literature. The comparison indicates that the present hybrid algorithm outperforms other estimation techniques in terms of the global searching ability and the convergence speed. Additionally, the estimation of kinetic model parameters for a feed batch fermentor is carried out to test the applicability of the proposed algorithm. The result suggests that the method can be used to estimate suitable values of model parameters for a complex mathematical model.     

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。     

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.     

差分演化算法中变异策略的改进与算法的优化

自然界生物体进化现象可以形式化成一些优化算法,如差分演化算法、粒子群算法等。其中,差分演化算法在数值函数优化方面的性能要优于其它的优化算法。通过对差分演化算法的变异策略改进,使优化后的差分演化算法在函数优化方面性能得到进一步提高。通过十个基准函数的仿真测试可以验证这一结果。     

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。     

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...     

改进鲸鱼优化算法及其在渣油加氢参数优化的应用

针对智能优化算法在处理非线性优化问题中存在的容易陷入局部最优和收敛精度差等问题,提出了一种基于结合差分进化和精英反向学习的改进鲸鱼算法（DEOBWOA）。该算法引入对立搜索初始化、精英反向学习,并结合差分进化进行变异修正,显著有效地提高WOA算法的收敛精度和收敛速度,提高其跳出局部最优的能力。之后采用8个标准测试函数进行仿真实验,结果表明：DEOBWOA算法与标准WOA、HCLPSO、DE算法相比,全局搜索能力和收敛速度都有较大提升。最后建立了渣油加氢动力学模型,考虑到渣油加氢过程中存在诸多典型的非线性约束问题,以某炼化厂渣油加氢装置为例,应用DEOBWOA对渣油加氢反应动力学模型参数进行优化,结果表明该算法能较好地处理实际工程优化问题。     

基于差分进化算法的控制变量参数化方法及其在化工过程动态优化中的应用(英文)

Two general approaches are adopted in solving dynamic optimization problems in chemical processes, namely, the analytical and numerical methods. The numerical method, which is based on heuristic algorithms, has been widely used. An approach that combines differential evolution (DE) algorithm and control vector parameterization (CVP) is proposed in this paper. In the proposed CVP, control variables are approximated with polynomials based on state variables and time in the entire time interval. Region reduction strategy is used in DE to reduce the width of the search region, which improves the computing efficiency. The results of the case studies demonstrate the feasibility and efficiency of the proposed methods.     

基于免疫系统的自适应差分进化算法及在水银氧化动力学参数估计中的应用

A new version of differential evolution (DE) algorithm, in which immune concepts and methods are applied to determine the parameter setting, named immune self-adaptive differential evolution (ISDE), is proposed to improve the performance of the DE algorithm. During the actual operation, ISDE seeks the optimal parameters arising from the evolutionary process, which enable ISDE to alter the algorithm for different optimization problems and improve the performance of ISDE by the control parameters’ self-adaptation. The performance of the proposed method is studied with the use of nine benchmark problems and compared with original DE algorithm and other well-known self-adaptive DE algorithms. The experiments conducted show that the ISDE clearly outperforms the other DE algorithms in all benchmark functions. Furthermore, ISDE is applied to develop the kinetic model for homogeneous mercury (Hg) oxidation in flue gas, and satisfactory results are obtained.     

带有精英保留机制的混合差分化学反应算法

传统智能算法在求解复杂的带有多峰特点的优化问题时, 由于其计算量和变异方式的限制很容易陷入局部最优, 并且不具备跳出局部最优进行二次搜索等能力。针对这一问题, 本文提出了混合差分的化学反应算法, 在利用化学反应算法(CRO)良好的全局搜索能力的同时, 使用差分变异策略来加强算法的计算精度。对于优秀分子可能在反应中被消耗掉的现象, 有针对性地加入了精英保留机制来保持种群的优良。本文选取了CEC2005中的测试函数, 特别是几个带有多峰特点的复杂测试函数来分析改进算法的各项性能, 并与几个改进的智能算法进行了对比实验。最终验证改进算法在提高计算精度和全局搜索能力两方面具有良好的效果。     

一种改进的差分进化算法及其在补料分批式生化反应器动态优化中的应用

动态优化是生物化工过程中的重要课题,求解动态优化问题通常有两种方法:解析法和数值法。基于智能进化算法的数值方法在动态优化中的应用越来越广泛,但是这些方法局部寻优能力不强,容易陷入局部最优,并且求解速度相对较慢。针对这些方法的不足,提出了一种改进的差分进化算法,设计了新的局部寻优算子来增强算法的局部寻优能力,并且采用一种新的控制策略表示方法来求解动态优化问题。通过求解补料分批式生化反应器的动态优化实例,证明了算法的有效性和鲁棒性。通过与其他几种方法进行对比,实验结果表明,所提出的方法在优化结果和计算代价方面都有优势。