In this study, the feasibility of spatial filter velocimetry (SFV) as process analytical technology tool for the in-line monitoring of the particle size distribution during top spray fluidized bed granulation was examined. The influence of several process (inlet air temperature during spraying and drying) and formulation variables (HPMC and Tween 20 concentration) upon the particle size distrib...

To solve problems that exist in optimal design such as falling into local optimal solution easily and low efficiency in multi-objective optimization, a new approach based on design of experiments (DOE) and gradient optimization (GO) was proposed. The new optimization method is called DPG (DOE Plus GO) which used DOE for preliminary analysis of the function model, and took the optimal values obt...

For designs involving computer intensive systems analyses, approximation techniques are commonly used to create a simplified approach to evaluating the system behavior. These techniques help in reducing the product development time and in finding the optimal solutions. Two important types of approximation techniques are the Design of Experiments (DOE) and the Artificial Neural Networks (ANN). W...

Order picking is the process of retrieving products from storage in response to a specific customer order. Although this process’ design has significant impact on warehouse global performance, a practical-oriented design procedure is still lacking in literature. This paper presents a novel order picking system design methodology based on Design for Six Sigma approach, that aims to assist design...

The Taguchi design of experiments (DOE) approach is adopted here to evaluate the impact ofeffective factors such as nanoparticles type, nanoparticles to model solution mass ratio, asphaltenestructure, and temperature on asphaltene adsorption equilibrium. Herein, the toluene-asphaltenesolution model is applied. Three commercially nanoparticles (SiO2, Al2O3, and TiO2) are used.<...

Research on practical design verification techniques has long been impeded by the lack of published and yet detailed error data. Over the last few years we have systematically collected design error data from a number of academic microprocessor design projects. We present an analysis of this data and report on the lessons learned in the collection effort.