食品中亚硝酸盐含量测定实验的绿色化学探讨

研究了在稀HCl介质中，亚硝酸盐与碱性品红发生重氮反应，用8-羟基喹啉作偶联剂，在弱碱性条件下，生成茶红色偶氮染料，其最大吸收峰位于490nm波长处，亚硝酸盐在0．2～30μg／25mL范围内服从比尔定律，表观摩尔吸收系数ε=2．30×10^4L／mol．cm。通过在食品中亚硝酸盐含量测定实验中渗透绿色化学思想，使学生拥有一定设计绿色化学实验的能力，并应用绿色化学理念，拟定了一种测定食品中亚硝酸盐含量的新方案。     

Overview of the Importance of Intelligent Approaches on Machinery Faults Diagnosis and Prediction Based on Prognostic and Health Management/Condition-Based Maintenance

Condition monitoring is increasingly used to anticipate and detect failures of industrial machines.Failures of machines can cause high maintenance or replacement costs.If neglected,it may result in catastrophic accidents leading to production shrinkage.The potential failure would negatively affect the profitability of the company,including production shut down,cost of spare parts,cost of labor,damage of reputation,risk of injury to people and the environment.In recent years,condition-based maintenance( CBM) and prognostic and health management( PHM) are developed and formed a strong connection among science,engineering,computer,reliability,communication,management,etc.Computerized maintenance management systems( CMMS) store a lot of data regarding the fault diagnosis and life prediction of the machinery equipment.It's too necessary to uncover useful knowledge from the huge amount of data.It's vital to find the ways to obtain useful and concise information from these data.This information can be of great influence in the decision making of managers.This article is a review of intelligent approaches in machinery faults diagnosis and prediction based on PHM and CBM.