一种改进的差分进化算法及其在化工过程优化中的应用

Differential evolution （DE） is an evolutionary optimization method, which has been successfully used in many practical cases. However, DE involves large computation time, especially, when used to optimize the compurationally expensive objective function. To overcome this .difficulty, the concept of immunity based on vaccination is used to help proliferate excellent schemata and to restrain the degenerate phenomenon. To improve the effective- ness of vaccines, a new vaccine autonomous obtaining method, and a method of deciding the probability of vacci- nation are proposed. In addition, a method for modifying the search space dynamically is proposed to enhance the possibility of converging to the true global optimum. Experiments showed that the improved DE performs better than the classical DE significantly.     

基于多策略自适应差分进化算法的污水处理过程多目标优化控制

针对污水处理过程中的能耗过高和出水水质不达标等问题,提出一种基于多策略自适应差分进化算法的污水处理过程多目标优化控制方法。首先,在常规跟踪控制结构的基础上,增加对第3、4单元溶解氧浓度的跟踪控制,扩大了能耗和出水水质的优化调节范围。然后,设计一种多策略自适应差分进化算法（MSADE）,该算法采用多策略融合变异和排序优选方法,选取合适的变异策略和较优的随机个体引导种群变异,并根据进化过程信息自适应地更新交叉率,以提升算法的收敛性和pareto解的多样性。最后,将MSADE算法与PID控制器相结合,并构建以能耗和出水水质为优化目标的多目标优化问题,实现对溶解氧和硝态氮浓度设定值的动态寻优和跟踪控制。基于国际基准仿真平台BSM1进行验证,结果表明所提的多目标优化控制方法能有效降低污水处理过程的能耗并提升出水水质。     

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。     

微分进化算法应用于换热网络全局最优化

前言换热网络是过程系统中实现能量回收和高效利用的重要环节,其性能直接关系到整个系统的能量利用水平。目前,换热网络优化设计的方法主要分为三类,分别是夹点法[1-2]、数学规划法[3-4]与启发式方法[5-7]。     

Design and control of a p-xylene oxidation process

The p-xylene (PX) oxidation process is of great industrial importance because of the strong demand of the global polyester fiber. A steady-state model of the PX oxidation has been studied by many researchers. In our previous work, a novel industrial p-xylene oxidation reactor model using the free radical mechanism based kinetics has been developed. However, the disturbances such as production rate change, feed composition variability and reactor temperature changes widely exist in the industry process. In this paper, dynamic simulation of the PX oxidation reactorwas designed by Aspen Dynamics and used to develop an effective plantwide control structure, which was capable of effectively handling the disturbances in the load and the temperature of the reactor. Step responses of the control structure to the disturbances were shown and served as the foundation of the smooth operation and advanced control strategy of this process in our future work.     

PX氧化反应段工艺条件的优化研究

根据实验测取的对二甲苯氧化反应动力学关系与数据,建立了Amoco工业氧化反应器和冷凝器的全混流模型,用以考察反应器特性。计算表明,进料m(AcOH)/m(PX)增加、催化剂用量增加、含水量减少有利于提高TA收率和反应转化率,降低4 CBA液相浓度,但副反应加剧。根据计算结果给出了优化了的Amoco工艺条件:m(AcOH)/m(PX)=3～3.5、进口催化剂w(Co)=(300～350)×10-6、w(H2O)=13%～15%。     

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.     

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.     

基于改进差分进化算法的质子交换膜燃料电池模型参数优化识别

质子交换膜燃料电池（proton exchange membrane fuel cell, PEMFC）模型中通常包含一些未知的难以确定的参数。为了有效确定这些参数,提出一种基于概率选择模型的改进差分进化算法。该概率选择模型为进化群体中的每个个体分配关于个体优劣性能的选择概率,然后基于该选择概率选择优秀的个体参与变异和交叉操作。为了验证改进算法的有效性,首先将其用于求解标准测试函数,实验结果表明,基于概率选择模型的变异、交叉操作能显著提高差分进化算法的收敛速度和求解精度。然后将改进算法用于质子交换膜燃料电池模型最优参数识别问题,得到的仿真结果和实验测试数据之间具有较高的拟合精度,表明本文改进算法是一种有效的求解系统模型参数优化识别的方法。     

一种自适应多策略差分进化算法及其应用

针对差分进化算法由于固定参数设置而易早熟或陷入局部最优的问题,提出了一种自适应多策略差分进化算法（SMDE）。该方法以基本差分进化为框架,首先引入一个变异策略候选集合,一个缩放因子候选集合和一个交叉参数候选集合,然后在搜索过程中,以过去的搜索信息为基础,自适应地为下一时刻进化群体中的每个个体从候选集合中选择一组合适的变异策略和控制参数,以便在不同的进化时刻设置合适的变异策略和控制参数。对10个常用的标准测试函数进行优化计算,并与其他算法的结果进行了比较,实验结果表明,SMDE具有较好的搜索精度和更快的收敛速度。将SMDE用于化工过程动态系统不确定参数估计问题,实验结果表明该算法能较好地处理实际工程优化问题。     

一种改进的知识进化算法及其在化工动态优化中的应用

智能优化算法在动态优化问题的求解中,一方面可以一定的概率收敛到全局最优,避免局部极值而得到了广泛应用;但另一方面,基于随机机制的仿生智能算法也面临收敛速度慢、寻优效率较低的瓶颈,限制了其工业实时应用的场合。为此,从提高智能优化算法在动态优化问题的求解效率出发,提出了一种改进的基于知识引导的进化算法结构,主要包括候选控制策略-时域与控制域的离散策略、知识库空间的进化、知识引导的种群进化。该算法分别在批式反应器等4个典型化工动态优化问题上进行了仿真验证,计算结果表明,该方法能够以较小的种群规模通过知识的引导,以较少的计算代价找到较好的全局解,有效提高了算法的收敛效率。     

使用博弈差分算法的电站锅炉高效低污染燃烧均衡优化

提高电站锅炉热效率,降低NO_x等污染物的排放量是电站节能减排必须解决的问题。经过采用经量子遗传算法）QGA）优化参数后的最小二乘支持向量机（LSSVM-QGA）建立燃烧优化模型,预测的锅炉热效率和NO_x排放量的平均相对误差分别达到了0.054%和1.229%,其预测精度及泛化能力均较优,有更强的适用性能。在此模型基础上,提出一种采用自适应缩放因子与交叉因子和共享函数机制的差分进化算法（DE）,通过其演化博弈论中的NASH均衡,实现锅炉燃烧的多目标优化,结果表明,基于NASH均衡的优化方法可以得到操作变量的最优解集,能够更好地改善运行工况,最终可以实现削峰填谷,使电站锅炉保持一个稳定均衡的燃烧状态。     

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。     

文化差分进化算法及其在化工过程建模中的应用

提出了一种新的文化差分进化算法,该算法将差分进化算法作为文化算法的种群空间,在文化算法的信念空间和影响函数设计中提出了基于多种知识源的设计方法,通过多种知识指导差分进化的变异操作和交叉操作,使知识的表达和指导种群进化的能力得到加强。函数测试结果表明,基于知识机制的引入使得文化差分进化算法在寻优性能上比差分进化算法有了较大的提高,而对参数的敏感性却相对较小。将文化差分进化算法用于训练补偿模糊神经网络,建立乙烯精馏塔产品质量软测量模型。通过训练与泛化能力的比较结果表明,基于文化差分进化算法的补偿模糊神经网络软测量模型在建模精度和泛化性能上均优于常规补偿模糊神经网络、模糊神经网络以及采用遗传算法优化的模型,具有更好的应用前景。     

Multi-objective optimization of fuel oil blending using the jumping gene adaptation of genetic algorithm

Production and marketing of heavy fuel oil (HFO) are an easy, effective and economical way to dispose off certain very heavy refinery streams such as short residue (SR, available from the bottom of vacuum distillation units) and clarified liquid oil (CLO, available from the bottom of the main fractionators of fluidized-bed catalytic crackers). Certain lighter streams such as heavy cycle oil (HCO), light cycle oil (LCO) and kerosene, are added to the heavy residual stock to improve its quality in terms of fluidity, combustibility, etc., to be marketed as fuel oil. The present study aims at optimization of the fuel oil blending process to maximize profit, minimize quality give-away, maximize production, minimize use of lighter products such as LCO and kerosene, and maximize the calorific value, etc. Several multi-objective optimization problems have been formulated comprising of two and three-objective functions and solved using the elitist non-dominated sorting genetic algorithm (NSGA-II). This evolutionary technique produces a set of non-dominating (equally good) Pareto optimal solutions from which the operator can choose the one that is most suitable (preferred point). Also, a fixed-length macro–macro mutation operator, inspired by jumping genes in natural genetics, has been used with NSGA-II to solve this problem. This modified algorithm leads to a significant reduction in the computational effort. Indeed, this adaptation can be of immense use in reducing the computational effort for other problems in chemical engineering.     

PX氧化反应过程多变量预测控制

精对苯二甲酸(purified terephthalic acid,PTA)是我国最重要的化工原料之一。对二甲苯(p-xylene,PX)氧化反应器是PTA装置的核心运行设备,PX氧化反应是高温高压下的固液相催化反应,其关系到整个装置的产量和消耗。目前,PX氧化反应过程的常规控制虽然可以满足工业装置的生产,但关键产品指标粗对苯二甲酸中对羧基苯甲醛(4-carboxybenzaldehyde,4-CBA)含量和尾气氧浓度波动范围较大,不利于工业装置的进一步优化操作。本文利用FRONT-Suite先进控制软件进行了工业PTA装置PX氧化反应过程的多变量预测控制,稳定优化了关键控制指标反应器尾氧浓度和第一结晶器尾氧浓度以及质量指标4-CBA含量的控制,取得了显著的控制效果。     

基于Kriging的差分进化算法及其在苯乙烯流程优化中的应用

自适应差分进化算法基于个体生成策略和控制参数自适应,无须人为设置参数,对问题有较好的适应性,但其收敛速度和精度有待提高。将具有较高预测精度的Kriging模型应用于自适应差分进化算法中,建立跟随种群变化的Kriging模型,通过模型极值点与种群最优个体竞争,对种群产生扰动,影响种群进化过程,改善算法的收敛速度和寻优性能。对10个典型测试函数的测试结果表明,该算法较标准和自适应差分进化算法收敛速度加快,收敛精度提高,且具有更好的稳定性。将基于Kriging的差分进化算法应用于苯乙烯装置的流程优化,操作运行费用显著降低。     

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。     

Successive approximate model based multi-objective optimization for an industrial straight grate iron ore induration process using evolutionary algorithm

Multi-objective optimization of any complex industrial process using first principle computationally expensive models often demands a substantially higher computation time for evolutionary algorithms making it less amenable for real time implementation. A combination of the above-mentioned first principle model and approximate models based on artificial neural network (ANN) successively learnt in due course of optimization using the data obtained from first principle models can be intelligently used for function evaluation and thereby reduce the aforementioned computational burden to a large extent. In this work, a multi-objective optimization task (simultaneous maximization of throughput and Tumble index) of an industrial iron ore induration process has been studied to improve the operation of the process using the above-mentioned metamodeling approach. Different pressure and temperature values at different points of the furnace bed, grate speed and bed height have been used as decision variables whereas the bounds on cold compression strength, abrasion index, maximum pellet temperature and burn-through point temperature have been treated as constraints. A popular evolutionary multi-objective algorithm, NSGA II, amalgamated with the first principle model of the induration process and its successively improving approximation model based on ANN, has been adopted to carry out the task. The optimization results show that as compared to the PO solutions obtained using only the first principle model, (i) similar or better quality PO solutions can be achieved by this metamodeling procedure with a close to 50% savings in function evaluation and thereby computation time and (ii) by keeping the total number of function evaluations same, better quality PO solutions can be obtained.