催化重整反应38集总动力学模型及其在连续催化重整中的应用

根据集总理论和催化重整的反应机理,基于工业连续重整装置,提出了一个包含38个集总组分、86个反应催化重整反应动力学模型。该模型将重整物料按碳原子数集总为C6～C11+组分,相同碳原子数的物料又划分为正构烷烃、异构烷烃、五元环烷烃、六元环烷烃和芳烃,裂化产物细分为C1～C5组分。通过合理简化,确定了86个待估模型参数,并在工业现场数据的基础上,利用分层策略与BFGS算法对其进行了估计。通过对某炼厂连续重整反应器的模拟计算对该模型进行了验证,计算值与实际值吻合较好,表明该模型具有较好的可靠性与准确性,达到了工业应用的要求。将模型用于芳烃收率的预测,在较大的时间跨度内,精度与趋势均令人满意。最后,利用该模型对芳烃收率进行了优化计算,经优化后芳烃收率提高0.17%,该结果可为连续重整装置的优化操作提供参考。     

基于差分进化算法和HYSYS机理模型的催化重整过程优化

选取催化重整18集总31反应集总动力学模型,以流程模拟软件HYSYS为工具,建立了催化重整机理模型。以最大化芳烃产率为优化目标,以4个反应器入口温度为决策变量,建立了过程优化模型。利用差分进化算法求解该优化问题,并利用可行性规则处理约束。仿真结果表明,芳烃产率有较大提高,证实了差分进化算法的有效性。     

催化重整集总动力学模型的建立及其在线应用

按照集总理论的指导原则,在原17集总反应网络的基础上,提出了一种包含20个集总组分、31个反应的催化重整动力学模型.该模型进一步细分了八碳芳烃组分,并考虑了烷烃组分的所有加氢裂化反应.在某工业连续重整装置上的验证结果表明,所建立的20集总模型可以精确地模拟包括八碳芳烃4个异构体在内的反应产物组成;与原17集总模型相比,裂化产物的预测精度明显提高.随后,采用特定的在线预测和校正策略,将该模型用于在线预测此连续重整过程的总芳烃收率、各芳烃组分收率以及重整油辛烷值,在进料和反应操作条件较大的变化范围内,在线预测趋势和预测精度均令人满意.总芳烃收率和重整油辛烷值的平均预测偏差仅分别为0.52 %和0.36,与离线模拟精度相当.     

催化重整精馏装置的模拟仿真与操作优化

为了优化精馏装置的操作参数,提高工业生产过程中芳烃的产品收率,提出了50万t/a催化重整装置的设计方案。首先对催化重整芳烃精馏过程进行了认知与分析,指出关键参数的选取与判断是设计过程中的难点。在此基础上运用流程模拟软件Aspen Plus建立模型并对芳烃精馏过程进行模拟,包括对苯塔、甲苯塔、二甲苯塔的模拟与分析,得出每个精馏塔的主要操作条件与参数。然后结合芳烃精馏模拟结果,分析塔板数、回流比、进料位置、操作压力对精馏的影响,确定最优操作参数。模拟结果表明,参数优化有效且可行,对工业生产过程中相关参数的设定有重要的指导意义。     

MTO烯烃分离过程的多目标操作优化

现代煤化工中,甲醇制烯烃(MTO)是一个非常重要的装置。其烯烃分离过程面临着原料变动大、烯烃产品损失以及较高的公用工程消耗等问题。这就需要在满足产品规格和需求的情况下,优化操作条件以实现最大效益。以Lummus前脱丙烷的烯烃分离工艺为研究对象,以增加乙烯与丙烯的总收率和降低总能耗为优化目标,对该工艺流程进行建模模拟与多目标优化。采用非支配排序遗传算法(NSGA-II)进行多目标优化的求解,实现了15个操作变量的同时优化。在维持产品收率不变的前提下,可通过降低脱丙烷塔、脱乙烷塔和1#丙烯精馏塔的回流比等优化措施找到了当前最优操作点。结果表明,该最优操作点与现有操作点相比可降低20 MW能耗。通过对决策变量的综合分析,确定了不同目标权衡下对应的各个操作变量的优化区间,发现精馏塔可以在多个最佳操作区间内运行。     

基于18集总动力学模型的连续重整反应装置建模与仿真

针对连续重整反应过程较为复杂、化学反应的耦合性强及参数估计难度较大的问题,提出了一种包含18个集总组分,27个反应的新型催化重整动力学模型。该模型在符合催化重整反应机理与集总理论划分原则的基础上,通过合理集总划分降低参数估计难度。新模型将8碳芳烃组分细分为二甲苯和乙苯2个集总,并将正烷烃、异烷烃分别集总,采用BFGS算法求解相应的模型参数。结果表明,该模型可以较为准确的预测出各芳烃产率,预测结果符合现代工业对模型的要求。     

芳烃抽提过程多目标优化

芳烃抽提是芳烃生产过程的重要环节,其生产调优对提高整个芳烃联合装置的效益具有重要意义。基于流程模拟及响应面分析方法,得到了芳烃抽提过程的产品纯度模型及能耗模型。建立了以产品纯度最大化及过程能耗最小化的多目标优化模型。提出了一种改进的自适应加权求和算法,并用于多目标优化模型的求解。求解结果表明新算法在Pareto最优解分布的均匀性上与原算法相当,但求解效率要高于原算法。给出了不同产品等级下的最佳操作参数,采用优化后的操作参数可有效地提高产品纯度并降低过程能耗。提出的多目标优化模型及求解算法用于芳烃抽提过程的操作调优,可有效地提高决策的准确性。     

乙醛生产过程数据挖掘

结合人工神经网络与遗传算法,对乙醛生产过程进行数据挖掘研究,从现有的生产数据中发现规律,实现了对乙醛生产过程模拟与优化。以乙醛收率为优化目标,建立人工网络模型并应用遗传算法对模型进行优化计算,获得生产过程最佳操作参数组合。实际应用表明,优化结果在高收率区。为化工生产过程优化提供一种新途径。     

基于多目标教学优化算法在二甲苯吸附分离过程优化中的应用

以C8芳烃混合物的吸附分离过程作为研究对象, 应用多目标教学优化算法(multi-objective teaching-learning-based optimization algorithm, MOTLBO)对模拟移动床多目标优化问题进行求解。采用TMB方法, 建立了模拟移动床模型, 并对两个典型的模拟移动床多目标操作优化问题进行了优化设计。通过与NSGA-Ⅱ算法的比较, 证明了多目标教学优化算法在求解模拟移动床多目标优化问题上的有效性和优势。此外, 还分析了抽出液流量、抽余液流量以及步进时间等对多目标优化非劣解的影响, 优化结果为模拟移动床分离过程的工艺设计和操作提供了依据。     

基于NSGA-Ⅱ算法的高炉生产配料多目标优化模型建立

高炉炼铁是一种典型的高能耗、高排放、高污染工业,合理的配料方案对高炉节能减排至关重要。基于高炉炼铁过程中的物质与能量守恒和高炉炉料结构理论,建立了以最小化生产成本和CO₂排放量为目标函数的高炉生产配料多目标优化模型,该模型采用非支配排序多目标遗传算法Ⅱ(NSGA-Ⅱ)进行求解,最终得到高炉生产配料多目标优化问题的Pareto最优解集。并将所得到的最优解与柳钢实际生产数据进行比较,结果表明建立的模型能使成本和CO₂排放量都有相应程度的降低,验证了该模型及NSGA-Ⅱ算法的正确性。炉长可以根据该多目标优化结果针对不同的需求选择相应的炉料配比,实现更精确的操作。     

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.     

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 mulfiobjective 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.     

Multiobjective Optimization of the Industrial Naphtha Catalytic Re-forming Process

1 INTRODUCTION Petroleum refining and petrochemical industries aim at maximizing one prime product while simulta-neously minimizing another accessory product to im-prove the quality of the prime product. Unfortunately, the two requirements are often conflicting or incon-sistent. It is necessary to determine the trade-off com-promises to balance the two objectives[1,2]. As the core of aromatics complex unit, catalytic reforming is a very important process for transforming naphtha into arom…     

催化重整装置产品质量软测量技术应用

采用机理方法、线性拟合方法和一阶TSK模糊神经网络算法分别对催化重整装置的主要产品质量指标——芳烃收率建立软测量模型,并在国内某大型工业级催化重整装置上进行在线应用。研究结果表明:进料负荷变化不大时3,种模型的在线预测趋势均能较好地跟踪芳烃收率的实际变化;当进料负荷变化较大时,3种模型的预测偏差分别为0.24wt%、0.67wt%和0.95wt%,且只有机理模型能如实地反映芳烃收率的变化。这说明机理模型具有预测精度高及外推性能好等优势,而且机理模型对样本数目要求最少,其预测精度基本不受样本数的影响。     

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.     

Multi-objective optimization for the periodic operation of the naphtha pyrolysis process using a new parallel hybrid algorithm combining NSGA-II with SQP

Nowadays naphtha pyrolysis is the most important process for ethylene production, which can bring along another important monomer, namely propylene. The demand of both the ethylene and propylene has recently increased dramatically and naphtha pyrolysis is indispensable to satisfy the demand of both crucial products simultaneously, resulting in a typical multi-objective optimization problem. The nondominated sorting genetic algorithm (NSGA-II), which has been successfully applied to many multi-objective optimization problems, cannot efficiently generate the Pareto set which spreads as widely as the true Pareto front in a limited time, meanwhile, its convergence process is rather slow and could not meet the speed requirement when used for the complicated industrial problem mentioned above. To efficiently solve the multi-objective optimization problem of the industrial complicated chemical processes, this paper first proposed a new parallel hybrid multi-objective optimization algorithm combing NSGA-II with SQP (Successive Quadratic Programming) used to improve the efficiency of the NSGA-II and the quality of the Pareto-optimal set. Then the multi-objective operation optimization model of naphtha pyrolysis was established, and at last the application of the proposed algorithm to improve the performance of an industrial naphtha pyrolysis process was presented and analyzed.     

误差校正新方法在重整装置芳烃收率在线软测量中的应用

针对软测量建模中含有误差会极大影响模型精度问题,提出一种将数据误差校正、模型参数校正和模型结构校正技术相结合的软测量误差三重校正方法.该方法可以有效地处理测量数据的过失误差和随机误差,从而提高软测量精度,并且该方法直接面向数据,实现方便.应用该方法对某芳烃厂的连续重整装置的芳烃收率软测量进行误差校正,取得了很好的效果.     

Multi-objective optimization of p-xylene oxidation process using an improved self-adaptive differential evolution algorithm

The rise in the use of global polyester fiber contributed to strong demand of the Terephthalic acid (TPA). The liquid-phase catalytic oxidation of p-xylene (PX) to TPA is regarded as a critical and efficient chemical process in industry [1]. PX oxidation reaction involves many complex side reactions, among which acetic acid combustion and PX combustion are the most important. As the target product of this oxidation process, the quality and yield of TPA are of great concern. However, the improvement of the qualified product yield can bring about the high energy consumption, which means that the economic objectives of this process cannot be achieved simulta-neously because the two objectives are in conflict with each other. In this paper, an improved self-adaptive multi-objective differential evolution algorithm was proposed to handle the multi-objective optimization prob-lems. The immune concept is introduced to the self-adaptive multi-objective differential evolution algorithm (SADE) to strengthen the local search ability and optimization accuracy. The proposed algorithm is successfully tested on several benchmark test problems, and the performance measures such as convergence and divergence metrics are calculated. Subsequently, the multi-objective optimization of an industrial PX oxidation process is carried out using the proposed immune self-adaptive multi-objective differential evolution algorithm (ISADE). Optimization results indicate that application of ISADE can greatly improve the yield of TPA with low combustion loss without degenerating TA quality.