色谱分析501抗氧防胶剂纯度及游离甲酚含量

采用毛细管气相色谱技术，使用二甲基聚硅氧烷毛细管色谱柱，利用程序升温、程序升压及分流进样法，通过实验选择最佳分析操作条件，较成功地测定出501抗氧防胶剂纯度及其游离甲酚含量。     

气相色谱法分析苯乙烯化酚中有效组分及杂质含量

采用气相色谱技术，使用OV－101毛细管色谱柱，利用程序升温及分流进样法，通过实验选择最佳分析操作条件，较成功地分析出苯乙烯化酚中组分及含量。     

20%氰戊菊酯·马拉硫磷乳油的气相色谱程序升温法同时分析

建立气相色谱程序升温法同时分析氰戊菊酯和马拉硫磷含量的方法。色谱柱为Rtx-1毛细管色谱柱,癸二酸二正丁酯为内标物,丙酮为溶剂。实验表明,氰戊菊酯和马拉硫磷的线性相关系数分别为0.996 4和0.998 4,变异系数分别为0.308 6和0.240 1,平均回收率分别为99.57%和99.78%。     

毛细管色谱法测定溶剂中碳氢化合物的含量

溶剂中N-甲酰吗啉和芳香烃的组成对萃取效果极为重要。采用填充色谱柱检测时程序升温基线难以稳定,毛细管色谱柱的稳定性较好。因为毛细管柱比填充柱有更高的分离效率,消除了填充柱中涡流扩散的问题,又减小了纵向扩散造成的谱带展宽。因此,我们开发了用毛细管气相色谱测定溶剂中碳氢化合物含量的方法,该法准确度高,操作简单,适用于生产控制。     

毛细管气相色谱在农药分析中的应用

本文介绍了毛细管气相色谱的特点,与填充柱气相色谱的不同,论述了毛细管气相色谱的三大优点以及促成这些优点的内外在因素。收集了毛细管气相色谱在农药分析中的一些应用实例,对18个CIPAC毛细管气相色谱的分析条件进行了汇总并作了简要概括。     

长白山原产地人参的气相色谱指纹图谱

根据人参成分组成的特点,研究了人参的预处理方法、色谱柱的选择、色谱仪柱温的设定程序对指纹图谱的影响。利用毛细管气相色谱法对人参指纹图谱研究最佳工作条件为:低沸点乙醚为提取溶剂,索氏提取器提取人参挥发油;选择聚二甲基硅氧烷毛细管色谱柱(30 m×0.25 mm×0.25μm);空气压力0.15 MPa,进样口150℃,分流比:1:40,一阶程序升温30℃/min,升时7 min(至250℃)。通过毛细管气相色谱法,对不同种类人参的指纹图谱提取对比。     

毛细管电色谱柱的研究进展

毛细管电色谱技术是一种结合了毛细管电泳和高效液相色谱双重优势的分离分析技术。该项技术具有高柱效、高选择性、分离快等优点。而色谱柱是毛细管电色谱技术的重要部分,所以色谱柱成为了国际上关注的热点。本文简单介绍了毛细管电色谱技术的基本原理、发展历史,又综述了近几年毛细管电色谱柱的研究进展,并对其进行了展望。     

间二氟甲氧基硝基苯的气相色谱分析

介绍了间二氟甲氧基硝基苯毛细管气相色谱测定方法，色谱条件为：程序升温，内标物为邻苯二甲酸二甲酯。间二氟甲氧基硝基苯的浓度在０．８～８．０ｍｇ／ｍＬ的范围内，样品有良好的线性关系，ｒ≥０．９９９７。     

航煤、柴油色谱模拟蒸馏的应用

介绍了采用气相色谱模拟蒸馏的原理快速测定航煤、柴油的馏程的方法。使用一条具有一定分离度的非极性毛细管色谱柱,将样品通过可程序升温进样口进入色谱柱,在线性程序升温条件下将样品按沸点次序分离,模拟经典实沸点蒸馏测定出馏程;并通过经典手工馏程分析数据,对每一类样品的结果进行修正,建立相对应的样品类型。试验表明,采用气相色谱模拟蒸馏可在8 min内准确、简便地测定出航煤和柴油的馏程。     

对二甲苯液相氧化体系分析方法进展

综述了运用于PX液相氧化体系反应物、产物及杂质的分析方法,主要有极谱法、气相色谱法、液相色谱法、毛细管电泳法、毛细管胶束电动色谱法以及气相色谱和液相色谱相结合、气相色谱和毛细管胶束电动色谱相结合的方法,讨论了各种方法的适用范围和优缺点。     

基于受控混杂Petri网的事件逻辑驱动的炼油厂动态调度建模方法

将受控混杂Petri网同事件逻辑网和数学规划相结合提出了一种适用于炼油等流程工业生产调度的动态建模方法.作为事件逻辑网和数学规划模型之间连接的桥梁,受控混杂Petri网将离散变量和连续变量统一在一个模型中.用受控混杂Petri网建立整个生产流程的仿真模型,事件逻辑网响应生产过程中的突发事件并结合经验规则做出逻辑判断,产生新的调度指令.生产过程重新调整后,上层数学规划模型进行相应更新,产生的优化结果作为设备新的运行参数.该模型既可以响应突发事件,又能及时进行优化,具有更好的动态优化特性.     

一种约束一般模型控制新方法

在引入近似预测模型的基础上 ,应用基于二次规划的滚动优化算法 ,处理被控量、操作量及其变化速率的线性约束 .将此优化算法与经典的一般模型控制 (GMC)方法相结合 ,给出了一种基于二次规划的约束一般模型控制新方法 .     

需求不确定下炼油企业生产计划优化混合规划模型

Abstract Production planning under uncertainty is considered as one of the most important problems in plant-wide optimization. In this article, first, a stochastic programming model with uniform distribution assumption is developed for refinery production planning under demand uncertainty, and then a hybrid programming model incorporating the linear programming model with the stochastic programming one by a weight factor is proposed. Subsequently, piecewise linear approximation functions are derived and applied to solve the hybrid programming model-under uniform distribution assumption. Case studies show that the linear approximation algorithm is effective to solve.the hybrid programming model, along with an error≤0.5% when the deviatiorgmean≤20%. The simulation results indicate that the hybrid programming model with an appropriate weight factor （0.1-0.2） can effectively improve the optimal operational strategies under demand uncertainty, achieving higher profit than the linear programming model and the stochastic programming one with about 1.3% and 0.4% enhancement, respectavely.     

Mixed integer polynomial programming

The mixed integer polynomial programming problem is reformulated as a multi-parametric programming problem by relaxing integer variables as continuous variables and then treating them as parameters. The optimality conditions for the resulting parametric programming problem are given by a set of simultaneous parametric polynomial equations which are solved analytically to give the parametric optimal solution as a function of the relaxed integer variables. Evaluation of the parametric optimal solution for integer variables fixed at their integer values followed by screening of the evaluated solutions gives the optimal solutions.     

Multiplicity of solutions in the optimization of a bifunctional catalyst blend in a tubular reactor

The reaction scheme for the isomerization and hydrogenation of methylcyclopentane using bifunctional catalyst is considered in a tubular reactor. The aim is to maximize the concentration of benzene at the outlet of the reactor by choosing the optimal blend of the catalyst along the reactor. When the problem was attempted by nonlinear programming employing sequential quadratic programming, twenty-five local optima were obtained from 100 random starting conditions. By using iterative dynamic programming in addition to the best two local optima obtained by nonlinear programming, the global optimum was also obtained.     

基于模糊预测型线性规划模型的矿山产能配置

将线性规划与模糊数学及灰色理论有机结合,构建了与产能分配实际情况较为接近的模糊预测型线性规划模型.利用灰色预测理论对各灰色系数进行白化,将模糊预测型线性规划模型转化为模糊线性规划模型,利用最优判决条件进一步转化,得到以最大隶属度为目标函数的一般线性规划模型,求解得到了矿山的最优产能分配,以实现矿山产能的科学配置和效益的最大化.     

Prediction of permeation flux decline during MF of oily wastewater using genetic programming

Genetic programming is an orderly method for getting computers to regularly solve a problem. The genetic programming creates a computer program from an obtained data and solves the problem. In this work, treatment of oily wastewaters with synthesized mullite ceramic microfiltration membranes was studied and a new approach for modeling of the membrane flux is presented. The model used input parameters for operating conditions (flux and filtration time) and feed oily wastewater quality (oil concentration, temperature, trans-membrane pressure and cross-flow velocity). The genetic programming utilized here delivers a mathematical function for the membrane flux as a function of the independent variables stated above. Parameters for controlling and termination criterion for a run are provided by the user. Result is provided as a tree of functions and terminals. The results thus obtained from the genetic programming model demonstrated good representation of the experimental data with an average error of less than 5%.