Statistical Models in Chemical Engineering
Tanase G. Dobre, J. Sánchez · Chemical Engineering · 2007
This chapter contains sections titled: Basic Statistical Modelling Characteristics of the Statistical Selection The Distribution of Frequently Used Random Variables Intervals and Limits of Confidence A Particular Application of the Confidence Interval to a Mean Value An Actual Example of the Calculation of the Confidence Interval for the Variance Statistical Hypotheses and Their Checking Correlation Analysis Regression Analysis Linear Regression Application to the Relationship between the Reactant Conversion and the Input Concentration for a CSR Parabolic Regression Transcendental Regression Multiple Linear Regression Multiple Linear Regressions in Matrix Forms Multiple Regression with Monomial Functions Experimental Design Methods Experimental Design with Two Levels (2k Plan) Two-level Experiment Plan with Fractionary Reply Investigation of the Great Curvature Domain of the Response Surface: Sequential Experimental Planning Second Order Orthogonal Plan Second Order Orthogonal Plan, Example of the Nitration of an Aromatic Hydrocarbon Second Order Complete Plan Use of Simplex Regular Plan for Experimental Research SRP Investigation of a Liquid–Solid Extraction in Batch On-line Process Analysis by the EVOP Method EVOP Analysis of an Organic Synthesis Some Supplementary Observations Analysis of Variances and Interaction of Factors Analysis of the Variances for a Monofactor Process Analysis of the Variances for Two Factors Processes Interactions Between the Factors of a Process Interaction Analysis for a CFE 2n Plan Interaction Analysis Using a High Level Factorial Plan Analysis of the Effects of Systematic Influences Use of Neural Net Computing Statistical Modelling Short Review of Artificial Neural Networks Structure and Threshold Functions for Neural Networks Back-propagation Algorithm Application of ANNs in Chemical Engineering References