Computer Aided Formulation

Formulation chemists and engineers, along with their research managers, have long known the difficult nature of formulation problems. This book is a manual to help them to identify key issues, design efficient experiments, and develop math models to resolve conflicting objectives. Specific solved examples, co-authored by chemists, show how well these methods work on industrial-strength problems and document the power and efficiency of computer aided formulation. This book is the first one to combine techniques of operations researching decision science, and statistics to solve formulation problems. Readers will appreciate its chapters on experimental design, which are easy to read and enjoyable.

Author’s Note on Text Contributors Chapter 1 The Formulation Problem Introduction Basic Principles of Formulating Many Ingredients Measuring Product Quality Conflicting Goals Additional Challenges Identifying Experimental Variables Implicit Formula and Process Constraints Alternate Forms of Independent Variables Controlling the Effects of Background Noise Approaches to Formulation The Artsy Approach The Modeling-Graphical Approach The Math-Programming Approach Interactive Goal Programming Prioritizing Goals Math-Programming Format Computer Aided Formulation Expert Systems Other Expertise Needed Implementation Summary Definitions Bibliography Chapter 2 The Experimental Process Introduction Defining the Experimental System Independent Variables System Parameters Uncontrolled Variables System Constraints Summary Definitions Bibliography Chapter 3 Experimental Design Introduction—Experimental Models One-Factor-at-a-Time (OFAT) Designs Search Approaches to Experimental Design Two-Factor-at-a-Time (TFAT) Designs Factorial Designs Partial Factorial Designs Practical Considerations Box-Wilson Designs Simplex Centroid Designs Summary Definitions Bibliography Chapter 4 Fundamentals of Math Programming Introduction Math Programming Linear Programming Product Mix Blending Transportation Multiperiod Scheduling Portfolio Selection Covering Linear Programming Modeling Linear Programming Formulation The Graphical Solution Sensitivity Analysis Economic Impact of Changes to Right-Hand-Side Values Computation of Dual Prices Computation of Right-Hand-Side Ranges Reduced Costs Nonlinear Programming Multiple Objectives Models—Goal Programming Summary Definitions Bibliography Chapter 5 Multiple Goal Decision Methods Introduction Multiple Objective Scenarios Multiobjective Optimality Finding Efficient Solutions Decision Space versus Objective Space Utility Analysis Problems in Comparing Alternatives What Is Utility? Assessing Single Objective Utility Using Utility to Compare Alternatives Preference Structure Ranking and Weighting Utility Curves Multiobjective Decision Approaches The Weighting Method The Goal Approach Multiattribute Utility Analysis Summary Definitions Bibliography Chapter 6 Expert System Design Principles and Solved Example Developing Effective Regression Models Solved Example Background Experimental Objective Working Hypothesis Experimental System Testing Regression Modeling Measured Moistness versus Variables Listed Interactive Nonlinear Goal Programming Objective Function Constraints Goal Constraints Interactive Process Conclusion The Integrated Expert System Summary Definitions Bibliography Appendix 6.1 Appendix 6.2 Chapter 7 An Analytic Hierarchy Approach for Evaluating Product Formulations Introduction The Analytic Hierarchy Process An Example of the Process Extensions to the Analytic Hierarchy Process An Alternative Approach to Evaluate Product Formulations Summary Definitions Bibliography Appendix 7.1 Chapter 8 Plastics Compounding and Formulation Introduction Getting Started Overview of the Formulation Problem Selection of the Polymer A General Overview of Polymers Polymer Properties and Screening Tests Additives Used in Compounding Plastics Some Tips to Speed the Work Solved Example Summary Definitions Bibliography Chapter 9 Formulating Laundry Detergents Detergent Performance Bleaching and Brightening Freshening Fabric Softening Soils Fabrics Product Safety Product Stability Processing Formulating Fundamentals Particulate Soils Oily Soils Starchy Soils Fatty Soils Proteinaceous Soils Fabrics Cleaning Considerations Surfactants Water Hardness Alkalinity Application Method Solved Example Background Procedure Laboratory Confirmation Summary Definitions Bibliography Chapter 10 Case Study: Silica-Based Defoamers Introduction Definitions Foam Problems in Industry Mechanisms of Defoamers Side Effects Silica in Defoamers Forms of Silica Making Silica Hydrophobic Prior State of the Art Objectives Properties of the Perfect Antifoam Optimizing In-Situ Silica Formulations Experimental Design Dependent Variables Independent Variables Uncontrolled Variables System Parameters System Constraints Results Development of a Performance Index Choosing the Silicone Choosing the Silica Optimizing the Promoter Level Optimizing the Silicone Level Optimizing the Cost-Effectiveness Optimizing the Mineral Oil Level Optimizing the Dose Rate Optimizing the Performance to Cost Ratio Summary Definitions Bibliography Index