基于LabVIEW 8.0测定纯液体蒸发焓的虚拟仪器

将常规的数字温度仪与压力计通过串行口和计算机连接,构建基于LabVIEW 8.0测定纯液体蒸发焓的虚拟仪器,实现实验数据的动态采集、实时显示、自动绘制p～T曲线并显示曲线方程、自动数据处理和写成结果报告等。应用结果表明:基于LabVIEW构建的测定纯液体蒸发焓的虚拟仪器,免除人工操作的繁琐和人为误差,测定乙醇和苯蒸发焓的相对误差分别为0.86%、-2.97%,提高了测量结果的准确度,且具有界面友好,操作简便和功能易扩展等诸多优点。     

拓扑距离特征指数对多氯联苯的QSPR研究

根据分子拓扑理论,提出1种新的多氯联苯(PCBs)化合物分子中,顶点原子(基团)点价值δ_i的计算方法,据以构建分子的结构参数——拓扑距离特征指数(TDEI);同时考虑分子体积以及氯原子在苯环上的位置和数目对PCBs性质的影响,引入分子体积参数(V_R)和基团定位指数(D),然后,采用多元线性回归法,分别建立了PCBs的正辛醇-水分配系数(lgK_(OW))和水溶解度(-lgS_w)的定量结构-性质相关模型,其相关系数分别达到0.9972和0.9730,均优于前人的工作。利用模型预测另外7个PCBs分子的lgK_(OW)和-lgS_W,预测值与实验值的一致性令人满意。     

PVA厂乙酸甲酯水解回收系统全流程模拟

本文应用HYSYS~等流程模拟软件模拟PVA厂乙酸甲酯(MeOAC)水解回收工序的全流程,分别模拟比较传统的固定床水解工艺、工业化的催化精馏水解工艺和本项目研究的萃取精馏与催化精馏耦合水解工艺。综合工艺流程的模拟,为了降低能耗,提出耦合水解工艺的中试方案:控制回流进料比约为3,进料水酯摩尔大于5,水解转化率大于90%mol,配合电渗析或连续萃取操作提浓乙酸,可望比原固定床工艺节能2成。通过中试反馈和进一步仿真,优化MeOAC水解耦合工艺流程体系,提出实现"深度水解"和"完全水解"的耦合水解工艺新方案,核算过程。结果表明耦合水解工艺方案可行,节能效果十分显著。     

米力农互变异构的理论计算

采用B3LYP/6-311G(d,P)法,分析米力农分子的酮式与烯醇式构型的构象.再采用相同方法,在气相和水相中计算并考察烯醇式与酬式结构互变时,质子迁移的两种可能途径:(a)分子内质子迁移,(b)水助质子迁移.结果表明,后者所需的活化能较小,氢键在降低反应活化能方面起重要作用,PCM溶剂化对分子几何结构参数和反应活化能的影响较小.     

萃取精馏的条件及选择萃取剂的研究

采用UNIFAC改进模型和MATLAB计算软件,对萃取精馏过程中萃取剂选择、萃取剂性能和萃取条件的影响进行了研究。通过对甲醇-丙酮、甲醇-乙酸甲酯和苯-环己烷等多个共沸体系在不同萃取剂和不同萃取条件下的相对挥发度的计算比较,确定了体系进行萃取精馏分离时合适的萃取剂分别为DMSO、水和DMF等,讨论了原料组成和溶剂比对相对挥发度的影响。结果表明:溶剂比是重要的影响因素之一,溶剂比越大,被分离体系的相对挥发度越大;原料组成对相对挥发度的影响较弱,随着重组分含量的增加,体系的相对挥发度稍有下降。本文还对文献数据进行了关联和比较,计算结果与文献符合良好,表明所开发的程序具有一定的可靠性和适用性。     

关于风险检测安全阀失效的分析系统

安全阀是保障石油化工设备正常运行的重要绀成部分,本文以目前广泛采用的AP1581标准为基础,建市安全阀的失效分析系统.该系统在计算机理和界向交互方式上改进原标准,可以根据工厂设备和安全阀管理数据,分析安伞阀的开启失效和泄漏频率,为提高石油化工牛产的安全性和可靠性提供重要的分析依据.     

DCS控制系统在蒸发工段的应用

分析了碱回收蒸发工段的工艺流程和控制要求,从硬件配置和软件设计等方面对碱回收蒸发工段进行了详细的叙述,同时采取软测量的方法测量黑液浓度,提出黑液浓度-压力串级控制方法;根据工艺要求设计出一种新型的DCS控制方法,它是由西门子公司的SIMATIC 400系列PLC连接ET200系列的分布式I/O系统及标准伺服控制系统组成的DP控制网络,通过相应的智能仪表及阀门对现场信号进行采集和控制;由此对碱回收蒸发工段实施集散控制的优化控制法案;工程实践表明,该系统在江苏甲乙粘胶公司生产线上投用,各种控制性指标能够很好地满足公司的要求。     

谷氨酸发酵菌体浓度的内模控制

针对由于缺乏可靠的生物传感器,谷氨酸发酵中重要的生物参数—菌体浓度不能在线测量,更不能对其实现控制的情况,提出了基于径向基神经网络的内模控制。既解决了由于谷氨酸发酵内部机理复杂而难以建立菌体浓度模型的难题,又实现了谷氨酸菌体浓度的内模控制;同时也解决了内模控制中逆模型建模的问题。仿真结果表明,该方法实现了谷氨酸菌体浓度的有效控制,鲁棒性强、抗干扰能力好,具有良好的实际应用和推广价值。     

Optimal sampling for the estimation of dispersion parameters in soil columns using an Iterative Genetic Algorithm

In groundwater modelling, an appropriate soil characterization is strongly recommended to evaluate both the fate and transport of solutes and the performance of groundwater remediation criterion, though parameter estimation techniques are often blocked by several inherent difficulties (i.e. ill-posedness and insufficient quantity and quality of observation data). In this paper, an iterative decision model is built and tested in order to locate the position of a fixed number of sample points in a soil column experiment to obtain optimal parameter estimation (OPE), minimizing the parametric uncertainty and the overall cost of the experimental campaign. Starting from an initial guess of chosen points (given by a fraction of the total disposable ones), an Iterative Genetic Algorithm (IGA) is capable of finding the best points able to minimize a first-order approximation of the parameter covariance matrix. The parameter estimates are updated under a Bayesian scheme, using exclusively the observations collected after the earlier run of minimization, and the iterative process stops when the imposed convergence criterion based on the parameter values is reached. An important contribution of this work is the development of an effective direct search algorithm (IGA) for solving the sampling network optimization problem at a laboratory scale.     

A software tool for translating deterministic model results into probabilistic assessments of water quality standard compliance

Most assessments of whether a water body will comply with pollutant standards after modification of land use, loading, or climate change are based on the results of deterministic simulation models. These models, including those used to support the United States Environmental Protection Agency (USEPA) total maximum daily load (TMDL) program, typically do not account for common sources of assessment uncertainty. Instead, model results are typically represented by a time series of predicted pollutant concentration values or the parameters of a frequency-based distribution of these values over a specified time period. The rate of exceedance of relevant pollutant limits is then assessed directly from this time series or distribution to determine standard compliance. In this way, sampling and analysis-based variability and model uncertainty are typically ignored, although they may substantially influence the probability of non-compliance. To help address this problem, we introduce ProVAsT (Probabilistic Water Quality Standard Violation Assessment Tool), a software tool encoded in the graphical model-based package Analytica^®. Here, we present a version of ProVAsT which translates model-predicted in situ fecal indicator bacteria (FIB) pollutant concentrations into the expected frequency of water quality standard violations and provides a Bayesian measure of the degree of confidence in this assessment. We call this version ProVAsT-FIB. Along with inputting their own simulation model results, users can specify the particular water quality analysis methods employed (e.g. the analytic procedure used and the number and volume of sample aliquots) as well as the numeric limits pertaining to local water quality standards. It is our hope that ProVAsT will encourage the rational consideration of uncertainty and variability in water quality assessments by reducing the burden of complex statistical calculations.