1 概论(Introduction) 1
1.1 多变量化学过程与化学计量学(Multi-variable Chemical Procedule and Chemometrics) 1
1.2 合成计量学与化学化工中的配方问题(Synthetometrics and Formula Problem in Chemistry and Chemical Engineering) 3
1.3 化学试验与化工过程优化(Optimization in Chemistry and Chemical Engineering Proceed) 5
1.4 本书各章内容简介(Outline for Book Contents) 6
2 化学化工试验条件研究(Stuty of Experimental Conditions for Chemistry and Chemical Engineering) 8
2.1 化学合成过程(Chemical Synthesis Procedure) 8
2.2 试验设计(Experiment Design) 15
2.3 试验变量、试验区间和响应值(Independent Variables,Experiment Domain and Responses) 17
24. 重要(有效)变量的筛选与优化(Screening of Important Variables and Optimization) 19
2.5 合成化学与定量模型(Synthetic Chemistry and Quantitative Models) 23
3 有机反应性相关分析与定量构效关系研究(Correlation Analysis of Organic Reactivity and Quantitative structure and activity relationship) 57
3.1 有机反应性相关分析及化学试剂选择(Correlation analysis of organic reactivity and selection of chemicals) 57
3.2 定量构效关系研究新近进展简介(Introduction of quantitative structure and activity relationship) 64
3.3 分子连接性法及化学反应活性(Molecular connectivity method and chemical reactional activity) 72
4 筛选试验(Screening Experiment) 91
4.1 筛选试验原理(Principls of Screening Experiment) 91
4.2 一步筛选法(One-step Screeing Method) 94
4.3 多步分组筛选法(Multi-step Screening in Groups) 97
5.1 因子设计与正交设计(Factorial design and orthogonal design) 107
5 试验设计的化学计量学方法(Chemometric Methods for Experimental Design) 107
5.2 均匀设计(Uniform design) 117
5.3 D-最优设计(D-optimal design) 120
6 配方问题的试验设计(Mathematical Modeling for Mixing Problem in Chemical Engineering) 128
6.1 一般配方问题(General Formula Problem) 128
6.2 无附加约束的配方问题(Formula Problem without Constraints) 132
6.3 有附加约束的配方问题(Formula Problem with Some Constraints) 142
6.4 特殊问题的配方模型(Other Special Formula Models) 153
7.1 单纯形优化法(Simplex Optimization Method) 170
7 化学化工试验优化方法(Experimental Optimization for Chemistry and Chemical Engineering) 170
7.2 化学模式识别方法(Chemical Pattern Recognition) 175
7.3 人工神经网络(Artificial Neural Network) 187
8 试验变量与量测响应的定量关系(Quantitative Relations between Observed Responses and Experimental variables) 199
8.1 多元线性回归(Multiple Linear Regression) 199
8.2 主成分分析与主成分回归(Principal Component Analysis and Principal Component Regression) 204
8.3 偏最小二乘方法(Partial Least Squares) 205
9 统计学和线性代数基础知识及一些常用算法(Necessary Knowledge on Statistics and Linear Algebra and Some Common Algorithms in Chemometrics) 217
9.1 必要线性代数基础知识(Necessary Knowledge on Linear Algebre) 217
9.2 必要的统计学基础知识(Necessary Knowledge on Statistic) 230