模糊多目标粒子群算法在乙烯裂解工业中的应用研究

文中研究了模糊多目标粒子群算法（MOPSO）在乙烯裂解工业中应用。算法在Pareto排序基础上引入子目标的最优操作条件来扩展属于非劣解集的操作条件范围,使非劣解集对于每个单目标而言都有较广的覆盖范围,确保非劣解集（操作条件）均匀分布,改进了非劣解集的质量,同时对非劣解引入工况实际要求,通过后验的模糊评价,来确定非劣解的满意操作条件,为决策者提供了明确的操作条件。将模糊多目标粒子群算法用于解决乙烯裂解过程中乙烯和丙烯收率多目标优化问题,较好地平衡了两种目标之闯的冲突,为流程工业多目标优化问题提供了理论指导。     

基于模糊规划的质量负荷不确定水网络的综合

针对单组分质量负荷不确定的工业用水网络综合,提出了修正的对称模糊规划（SFLP）模型,同时提出了用水网络设计的多目标混合整数对称模糊规划模型,以及求解这一问题的主目标设定法策略.对多个算例问题进行求解,结果表明,本文提出的模型以及求解策略对负荷不确定的工业用水网络的综合问题有良好的适用性,能有效避免退化解,从而获得优化流程设计.     

换热网络多目标综合优化算法研究进展

资源和能源的可持续发展使得换热网络综合不仅要考虑经济性,同时要满足柔性、可靠性、可操作性和环境影响度等指标的要求。目前,换热网络多目标综合的研究有了初步进展并引起了广泛关注。本文阐述了进行换热网络多目标综合的必要性并总结了相关研究。重点对常用的多目标优化算法作了总结和对比,综述了其在换热网络多目标优化设计中的应用进展。研究表明,传统多目标算法越来越无法满足复杂模型的求解,而多目标进化算法可以很好地求解换热网络综合多目标优化问题,其中NSGA-Ⅱ算法是目前应用最广的有效算法。提出尝试NSGA-Ⅱ等多目标进化算法,基于超结构建立包括经济性、柔性、可靠性、可操作性和环境影响度等在内的换热网络多目标综合模型,给出Pareto最优解集合供决策者选择是未来的研究方向。     

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。     

化工过程多目标优化适宜解的模糊确定

应用模糊优化原理求解化工多目标优化问题.基于相对优属度的多目标模糊优化法适合复杂系统的求解,而且在过程中充分运用了空间图形直现的特性,达到了问题求解的过程简约化.本文作者以合成氨凝液回收系统的多目标优化为例,说明化工过程多目标优化适宜解的模糊确定.     

基于非逆流传热的热交换网络系统的3E优化

考虑非逆流传热对换热设备传热温差、壳数和面积的影响，对包含非逆流换热设备的热交换网络系统进行优化设计。基于非等温混合分流分级超结构，采用能源、经济和环境（3E）综合评价指标，引入温差修正系数，建立了热交换网络多目标混合整数非线性规划（MO-MINLP）模型，并基于非支配排序遗传算法（NSGA-Ⅱ）提出了系统性的求解策略和求解方法。应用案例研究表明，涉及非逆流传热的热交换网络，其优化设计结果与基于纯逆流换热假设的设计结果有很大区别，且仅对基于纯逆流换热假设的设计结果进行修正并不能得到最优解，必须在建模中考虑温差修正效应的影响，从而保证设计结果的优化性、可靠性和实用性；3E评价反映了热交换网络系统在经济、能耗和环境影响之间的权衡和约束关系，使系统的设计更加实际，同时多目标的优化方法不但可以获得与单目标经济优化相当的最经济的结果，而且提供了多样性的优化解集供选择，提高了设计的灵活性，可以满足不同的设计需求。     

失效情景下气体探测器多目标布置优化

目前有关气体探测器布置优化研究较少考虑探测器的失效情景,本文以某柴油加氢装置的硫化氢气体探测器布置优化为例,提出一种失效情景下气体探测器多目标布置优化方法。首先对待检测区域的潜在泄漏源进行辨识,构建泄漏场景集并进行场景缩减,通过计算流体力学方法预测泄漏实时浓度场。其次,以时效性和鲁棒性作为评价指标,以考虑泄漏场景概率和探测器失效概率的检测时间最小化、探测器网络鲁棒性最大化作为优化目标函数,并结合逻辑约束条件建立数学模型。在此基础上,采用基于模拟退火的多目标粒子群算法对模型进行求解,得到Pareto非劣解集,采用理想点逼近法（TOPSIS）对Pareto解集进行排序得到最优方案,最后决策者可根据不同需求确定最终布置方案。     

基于非支配排序遗传算法的乙苯脱氢工艺条件优化

为提高现有乙苯脱氢制苯乙烯生产装置的生产率和节能水平,优化技术是一种有效的技术手段。基于改进的非支配排序遗传算法(NSGA-Ⅱ)研究了乙苯脱氢工艺条件的优化问题。把乙苯脱氢反应过程的转化率、选择性作为优化目标,动力学模型以及实际生产状况作为约束条件,构造乙苯脱氢过程的多目标优化问题。基于NSGA-Ⅱ算法求解得到的优化问题的Pareto最优集,分析了各个操作条件对乙苯脱氢生产过程转化率和选择性的影响,最后利用模糊综合评价法,为合理决策提供了有效的依据。结果表明NSGA-Ⅱ具有良好的全局优化性能,运用该算法可在不同的操作约束条件下,求解得到相应的满意解。     

考虑环境影响的间歇过程多目标最优化设计

同时考虑费用和环境影响的间歇化工过程多目标最优化设计问题的求解,通常的做法是使用权重系数法,将其转变成单一目标来优化。但大多数情况下,这种权重系数很难确知。因此,有必要提供多个解以便于设计者作出合理的最终选择。采用多目标遗传算法和线性规划相结合的方法求解出间歇化工过程优化设计模型的非劣解集,并与不同权重系数下的单目标算例进行了比较。结果表明,用多目标遗传算法求解间歇化工过程是有效的。这为设计者在间歇化工过程最优化研究考虑环境因素的决策提供了更多的选择。     

基于模糊HSE评价的多目标反应路径综合方法

为在过程早期获取HSE综合性质优良的反应路径,提出了基于模糊HSE评价的多目标反应路径综合方法。全面考虑HSE因素,形成了HSE指标结构,通过设定指标的隶属度函数,建立模糊推理系统,应用层次分析法（analytic hierarchy process,AHP）确定指标的权重因子,形成了模糊HSE评价方法。基于模糊HSE评价体系,建立以安全、环境和健康为目标函数的多目标优化模型,求解得到优良的反应路径组合。应用于萘甲胺反应路径综合实例,定量得到了反应路径及其HSE目标函数值,为过程早期的路径优选提供定量数据。     

Multi-objective optimal control of chemical processes using ACADO toolkit

Many practical chemical engineering problems involve the determination of optimal trajectories given multiple and conflicting objectives. These conflicting objectives typically give rise to a set of Pareto optimal solutions. To enhance real-time decision making efficient approaches are required for determining the Pareto set in a fast and accurate way. Hereto, the current paper illustrates the use of the freely available toolkit ACADO Multi-Objective (www.acadotoolkit.org) on several chemical examples. The rationale behind ACADO Multi-Objective is the integration of direct optimal control methods with scalarisation-based multi-objective methods enabling the exploitation of fast deterministic gradient-based optimisation routines.     

基于改进粒子群算法的乙苯脱氢过程多目标优化

设计了一种基于支配关系构造非支配解集的多目标粒子群算法(MOPSO),将当前找到的非支配解保存到一个外部集——最优解集,利用支配更新其最优解集,多次迭代后得到Pareto最优解集。把乙苯脱氢反应过程的收率和选择性作为优化目标,动力学模型和实际生产状况作为约束条件构造乙苯脱氢过程的多目标优化问题,利用改进的多目标粒子群算法进行优化求解。基于求得的Pareto最优解集研究了各个操作条件对乙苯脱氢生产过程收率和选择性的影响,为后续乙苯催化脱氢系统实施先进控制奠定了基础。     

Interactive NBI and (E)NNC methods for the progressive exploration of the criteria space in multi-objective optimization and optimal control

A wide range of problems arising from real world applications present multiple and conflicting objectives to be simultaneously optimized. However, this multi-objective nature is too often neglected. Multi-objective optimization proved to be a powerful tool to correctly describe the trade-offs among conflicting objectives in a set of optimal solutions known as the Pareto set. This paper introduces an interactive method to solve multi-objective problems based on geometric considerations. The method returns a wider Pareto set, at a negligible computational cost, when compared to existing methods. The interactivity also allows the decision-maker to explore only relevant parts of the Pareto set. The extreme solutions yield insightful considerations on the generation of the scalarization parameters for the Normal Boundary Intersection and the Enhanced Normalized Normal Constraints methods. The proposed method is applied to: (i) three scalar multi-objective problems and (ii) the multi-objective optimal control of a tubular and a fed-batch reactor.     

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…     

Using NSGA‐II and TOPSIS Methods for Interior Ballistic Optimization Based on One‐Dimensional Two‐Phase Flow Model

In order to meet the design demands of new gun systems or new types of projectiles, the interior ballistic charge design seems especially important. In this paper, a one‐dimensional two‐phase flow model is presented. The model describes the transient combustion of granular propellants in a gun, and pressure waves are considered as an objective. This study adopts a hybrid method to solve the problem. In the first stage, the non‐dominated sorting genetic algorithm (NSGA‐II) with “a “filter” is employed to approximate a set of Pareto‐optimal solutions. In the subsequent stage, a multi‐attribute decision‐making (MADM) approach is adopted to rank these solutions from the best to the worst. The ranking of Pareto‐optimal solutions is based on the technique ordered preference by similarity to ideal solution (TOPSIS) method. In TOPSIS method each objective needs a corresponding weight coefficient, and a practical problem is introduced. Both the entropy method and linear analysis method are adopted to get two sets of weights for the objectives, respectively. The two pairs of final, best compromise solutions are compared for satisfying the designer’s aim. For the analysis of the results, a two‐phase flow interior ballistic model for design optimization is established, and the hybrid approach could get a reasonable design scenario.     

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.     

化工过程总体裕量与控制性能的权衡优化

化工过程的总体裕量可以用操作优化的经济效益进行评价,根据稳态优化和动态优化的经济效益可进一步划分为服务于操作控制的控制裕量和表征过程可实现经济效益的工艺裕量,二者都与化工过程的控制性能有关。针对具有一定裕量的化工过程进行多目标动态优化,优化目标分别为操作点的经济效益与动态过程中的控制性能指标,采用0-1变量描述控制结构,将控制结构和控制器参数也作为优化变量进行混合整数动态优化,采用Constrained NSGA-Ⅱ算法求解非劣解集,根据非劣解集分析总体工艺裕量、总体控制裕量与控制性能指标的关系。通过催化裂化装置的实例分析发现,对于具有一定裕量的化工过程,控制性能越高,所需的总体控制裕量越多,表征操作优化可实现经济效益的总体工艺裕量越少,只有通过对总体控制裕量和总体工艺裕量进行权衡,才能找到兼顾工艺要求和控制性能的工艺操作点和控制设计方案。     

分批补料发酵过程多目标优化的分布式强化学习策略

发酵过程优化问题通常包含有互相冲突的多重优化目标,另外反应本身具有诸多复杂性。提出一种基于Pareto的分布式Q学习多目标策略,用以求解赖氨酸分批补料发酵过程流加速率轨迹的Pareto最优解。该策略中,Q学习算法和Pareto排序法将结合来产生非支配解集,并使之逼近真实的Pareto前沿,利用奖赏机制来描述多重目标之间的关系,并同时使用多组含有随机初始值的agent共同作用改善搜索能力。将所提出的方法应用于赖氨酸分批补料发酵过程的优化中,并与粒子群优化进行了对比,验证策略的性能。