用遗传算法辨识发酵动力学模型参数

提出发酵动力学模型参数辨识的遗传算法,并对典型模型进行了具体尝试。结果表明,遗传算法估计参数效果很好,优于麦夸脱方法。     

遗传算法在复杂反应动力学模型参数估算中的应用

用改进的遗传算法求解复杂反应动力学模型参数。算法中通过采用亲子竞争和遗忘变异策略,有效地提高了遗传算法的搜索性能,并避免了序贯优化方法有可能陷于局部极值的问题。该算法在苯热裂解脱氢反应体系的应用中取得了令人满意的计算结果。此外,对控制参数（变异遗传率）及遗传策略（选择策略,交叉策略）对算法性能的影响进行了探讨。     

运用改进的遗传算法估计发酵动力学模型参数

针对简单遗传算法（SGA）存在早熟，寻优效率偏低等缺点，提出了一种改进的遗传算法，并将它用于发酵动力学模型参数的估计，取得了满意的效果。     

混沌遗传算法估计反应动力学参数

为了准确地估计反应动力学参数 ,提出一种混沌遗传算法 (chaosgeneticalgorithm ,CGA) ,它基于混沌变量的遗传操作 ,将使子代个体均匀地分布于定义空间 ,从而可避免早熟 ,以较大的概率实现全局最优搜索 .与传统的遗传算法相比较 ,CGA的在线和离线性能都有较大的改进 .将CGA应用于 2 -氯苯酚在超临界水中氧化反应动力学参数的估算 ,获得了满意的结果     

废轮胎热解动力学多胶体优化模型

在废轮胎热解动力学多步模型和多胶体模型基础上建立废轮胎热解动力学多胶体优化模型,并以废轮胎胶粒热解试验数据作为热解动力学模型的计算样本数据进行模拟计算.结果表明,轮胎配方多胶体优化模型计算相对误差在30%以下,与多胶体模型相比明显减小,证明多胶体优化模型是有效的.     

加氢脱硫反应动力学研究进展

为了适应日益严格的环境法规,对加氢脱硫反应过程的研究十分重要.作者对模型化合物和实际油品中硫化物的加氢脱硫反应动力学研究进展分别进行评述,深入探讨了各硫化物的加氢反应动力学行为.通过本文可以提高对加氢脱硫反应过程的认识,更好地指导实际工业生产.     

基于PSO的丁二酸发酵动力学模型参数优化

丁二酸是一种重要的化工原料,对丁二酸发酵过程进行模型化研究可以为工艺放大提供必要的基础数据。根据丁二酸发酵过程的实验数据,在已有的丁二酸发酵动力学模型的基础上,采用粒子群优化算法进行模型参数优化研究,求得最优参数并利用其进行过程仿真。结果表明优化后的模型能够更好地模拟丁二酸分批发酵过程。和采用遗传算法进行的研究结果相比,粒子群算法提高了模型计算值与实验测量值的拟合程度,且算法简单,易于实现。     

用遗传算法求取乙基叔丁基醚合成反应动力学参数

采用多重交叉策略和多重变异策略的改进遗传算法 ,对以强酸性阳离子交换树脂S - 5 4为催化剂 ,用乙醇和叔丁醇为原料合成乙基叔丁基醚反应动力学方程组中的速度常数、水的阻碍系数等参数进行了求解计算 ,并就编码方式、遗传策略等对算法性能的影响进行了讨论 .该方法估算精度高 ,有效避免了传统算法可能陷于局部最优的问题 ,所得到的有关动力学参数可靠 ,模型计算结果与实验结果吻合良好     

柴油加氢脱硫的动力学模型

在对某炼厂加氢精制装置详细研究的基础上,建立了加氢脱硫动力学模型,并根据实际情况对模型进行了修改。通过麦夸特法（Levenberg-Marquardt）+全局优化法拟合出各参数,并进行分析说明。以该炼厂实际数据对模型进行了检验。结果表明,模型能够比较准确的预测产品硫含量。     

水处理絮凝反应动力学优化模型

根据絮凝动力学理论对絮凝反应器的设计进行了优化，提出了提高絮凝效率的技术方法。     

Use of Genetic Algorithm to Determine the Kinetic Model of Solid-State Reactions

Solid-state reactions take place by different rate-controlling heterogeneous processes. To find the appropriate kinetic model for a particular solid-state reaction, a genetic algorithm-based simulation technique was carried out using DTA data with a fitness function, and a computer program was developed for the same. The process was applied to the decomposition reactions of limestone and magnesite samples. It was observed that both the decomposition reactions mostly followed the Avrami–Erofeev kinetics model.     

Estimation of Kinetic Parameters for Hydrogenation Reactions Using a Genetic Algorithm

The kinetics of acetylene hydrogenation in a fixed‐bed reactor of a commercial Pd/Al₂O₃ catalyst has been studied. The hydrogenation reactor considered in this work is an essential part of a vinyl chloride monomer (VCM) plant. Three well‐known kinetic models were used to simulate the hydrogenation reactor under industrial operating conditions. Since none of the models provide appropriate prediction, the industrial data and calculated values were compared and optimum kinetic parameters were evaluated utilizing a genetic algorithm (GA) technique. The best kinetic parameters for the three models were determined under specified industrial operating conditions. The hydrogenation reactor was simulated using the estimated optimum kinetic parameters of the three models. Simulation results from the three models were compared to industrial data and the best kinetic model was found. This kinetic model with the evaluated optimum kinetic parameters can well predict the behavior of the industrial hydrogenation reactor to improve the performance of the process.     

A Hybrid Genetic Algorithm for the Estimation of Polyesterification Kinetic Parameters

A simple genetic algorithm (SGA) has been improved in this paper. The resulting hybrid genetic algorithm (HGA) was used to estimate the kinetic parameters of polyesterification between dimer fatty acid and ethylene glycol. The acid values of product predicted by the kinetic model match well with experimental data at different material proportions and conversion ratios. The kinetic model was proven to be effective. The hybrid genetic algorithm was compared with the simple genetic algorithm, and the result indicated that the improved genetic algorithm has higher efficiency, stronger ability on local searches, better precision, and a wider search range.     

Modeling of Rhamnolipid Biosurfactant Production: Estimation of Kinetic Parameters by Genetic Algorithm

Studies about kinetics and modeling of production parameters for biosurfactants are essential to the development of efficient processes from an economic point of view. In this sense, this work evaluated the performance of four nonstructured models to explain the experimental data for biomass growth, substrate consumption, and rhamnolipid production using glycerol as carbon source and a Pseudomonas aeruginosa strain. The kinetic parameters of each model were estimated using a global search method known as genetic algorithm and numerical discretization of differential equations by the Runge–Kutta 4th order method. The main result of this study showed that the Monod model best represented the experimental data, with μ_max values of 0.06 h⁻¹, K_S of 50.8 g L⁻¹, Y_X/S of 0.43 g g⁻¹, and Y_P/X equal to 0.017 g g⁻¹.     

基于Pareto遗传算法的聚合物黏度模型参数拟合

针对传统聚合物黏度模型参数拟合方法中存在的初值选取问题及权重分配问题,采用基于Pareto多目标遗传算法的拟合方法.根据实验测得PP和PMMA材料流变数据对常用的Cross-WLF 7参量黏度模型进行了拟合,拟合结果的复相关系数分别达到了0.999929和0.999036.研究结果为聚合物注射成型仿真提供了必需的材料流变数据,为塑料制品的模具设计、工艺参数优化和质量预测提供了理论依据.     

基于一种改进的自适应遗传算法估算环已烷自催化氧化反应动力学参数

A modified genetic algorithm of multiple selection strategies, crossover strategies and adaptive operator is constructed, and it is used to estimate the kinetic parameters in autocatalytic oxidation of cyclohexane. The influences of selection strategy, crossover strategy and mutation strategy on algorithm performance are discussed. This algorithm with a specially designed adaptive operator avoids the problem of local optimum usually associated with using standard genetic algorithm and simplex method. The kinetic parameters obtained from the modified genetic algorithm are credible and the calculation results using these parameters agree well with experimental data. Furthermore, a new kinetic model of cyclohexane autocatalytic oxidation is established and the kinetic parameters are estimated by using the modified genetic algorithm.     

遗传算法在动态黏弹性模型参数拟合中的应用研究

为了研究聚合物材料弹性变形对微结构成型制品变形的影响,采用仿真分析手段对环烯烃类共聚物(TOPAS-COC)材料的滚动热压成型进行了模拟计算,并对TOPAS-COC聚合物材料黏弹性模型参数进行了拟合。通过比较改进的遗传算法和最小二乘法拟合的结果,发现遗传算法在寻找动态黏弹性模型参数中具有很好的寻优功能。利用平移原理获得参考温度下主曲线,将拟合得到的模型参数输入非线性有限元软件Abaqus中进行仿真模拟,正交实验结果表明,适当增加温度、减小预压紧量以及减小滚动速度可以降低弹性回复对微结构弹性变形的影响。     

应用遗传算法优化露天矿设备

文章结合露天矿设备的型号和数量优化问题,阐述了遗传算法的原理和优化步骤,通过实例应用表明遗传算法能很好优化露天矿设备的型号和数量。     

气升式反应器中生物脱硫过程工艺因素的研究

利用已分离所得的具有高脱硫活性的微杆菌ZD-M2,以正十六烷为模拟油相,二苯并噻吩(DBT)为模型含硫化合物,在自行设计的气升式反应器中进行模拟燃料油生物脱硫的研究.通过与摇床的对比实验,确定不同休止细胞浓度下反应器中最佳比脱硫速率为0.283mmol·(kg DCW)-1·min-1,是摇床实验最佳比脱硫速率的2.26倍;进一步确定实验条件下的最适空塔气速为7.07 cm·s-1,并考察了不同生长介质和DBT浓度对生物脱硫的影响.结果显示过高气量下氧的传质速率反而降低,影响脱硫效果;ZD-M2休止细胞在生长介质(培养基)和非生长介质(磷酸缓冲溶液和生理盐水)中具有几乎相同的脱硫活性;较高的休止细胞浓度和DBT浓度都不利于传质的进行;推断ZD-M2降解正十六烷中DBT发生在有机相-水相界面.