Mathematical model for process design and simulation of dephlegmators (partial condensers) for binary mixtures

1987 ◽  
Vol 65 (4) ◽  
pp. 559-564 ◽  
Author(s):  
Sergio Di Cave ◽  
Barbara Mazzarotta ◽  
Enzo Sebastiani
Keyword(s):  
Mathematical Model ◽  
Binary Mixtures ◽  
Process Design

Classification of mathematical models of technological design

2021 ◽  
pp. 68-71
Author(s):  
Keyword(s):  
Mathematical Model ◽  
Mathematical Models ◽  
Technological Process ◽  
Process Design ◽  
Geometric Parameters ◽  
Technological Design ◽  
Technological Processes ◽  
Technological Preparation

The use of mathematical models is of great importance for the automation of the design of technological processes. Representation of the geometric parameters of the part in the form of mathematical models allows automating the development of the structure and calculation of the parameters of the technological process, which is important for the complete digitalization of the technological preparation of production. Keywords: technological process, design, mathematical model, digitalization. [email protected]

Model development for enzymatic reactive crystallization of β-lactam antibiotics: a reaction–diffusion-crystallization approach

2020 ◽  
Vol 5 (11) ◽  
pp. 2064-2080 ◽  
Author(s):  
Hossein Salami ◽  
Colton E. Lagerman ◽  
Patrick R. Harris ◽  
Matthew A. McDonald ◽  
Andreas S. Bommarius ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Process Design ◽  
Model Development ◽  
Reaction Diffusion ◽  
Reactive Crystallization

A mathematical model for production of β-lactam antibiotics via enzymatic reactive crystallization is developed, and its application for catalyst and process design is discussed.

scholarly journals Austenite grain growth calculation of 0.028% Nb steel

2011 ◽  
Vol 47 (2) ◽  
pp. 199-209 ◽  
Author(s):  
D. Priadi ◽  
R.A.M. Napitupulu ◽  
E.S. Siradj
Keyword(s):  
Mathematical Model ◽  
Grain Growth ◽  
Process Design ◽  
Microalloyed Steel ◽  
Model Fit ◽  
Growth Data ◽  
Austenite Grain Size ◽  
Quantitative Calculation ◽  
Austenite Grain ◽  
Austenite Grain Growth

Modeling of microstructural evolution has become a powerful tool for materials and process design by providing quantitative relationships for microstructure, composition and processing. Insufficient attention has been paid to predicting the austenite grain growth of microalloyed steel and the effect of undissolved microalloys. In this research, we attempted to calculate a mathematical model for austenite grain growth of 0.028% Nb steel, which can account for abnormal grain growth. The quantitative calculation of austenite grain growth generated from this model fit well with the experimental grain growth data obtained during reheating of niobium steels. The results of this study showed that increasing the temperature increases the austenite grain size, with a sharp gradient observed at higher temperatures.

scholarly journals Pleasantness of Binary Odor Mixtures: Rules and Prediction

2020 ◽  
Vol 45 (4) ◽  
pp. 303-311
Author(s):  
Yue Ma ◽  
Ke Tang ◽  
Thierry Thomas-Danguin ◽  
Yan Xu
Keyword(s):  
Mathematical Model ◽  
Binary Mixtures ◽  
Mean Squared Error ◽  
Odor Intensity ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
Major Dimension ◽  
Odor Mixtures

Abstract Pleasantness is a major dimension of odor percepts. While naturally encountered odors rely on mixtures of odorants, few studies have investigated the rules underlying the perceived pleasantness of odor mixtures. To address this issue, a set of 222 binary mixtures based on a set of 72 odorants were rated by a panel of 30 participants for odor intensity and pleasantness. In most cases, the pleasantness of the binary mixtures was driven by the pleasantness and intensity of its components. Nevertheless, a significant pleasantness partial addition was observed in 6 binary mixtures consisting of 2 components with similar pleasantness ratings. A mathematical model, involving the pleasantness of the components as well as τ-values reflecting components’ odor intensity, was applied to predict mixture pleasantness. Using this model, the pleasantness of mixtures including 2 components with contrasted intensity and pleasantness could be efficiently predicted at the panel level (R2 > 0.80, Root Mean Squared Error < 0.67).

Optimum Process Design for a Single-Stage Multifunctional Production System

1981 ◽  
Vol 103 (2) ◽  
pp. 218-223 ◽  
Author(s):  
K. Hitomi ◽  
K. Ohashi
Keyword(s):  
Mathematical Model ◽  
Process Design ◽  
Production System ◽  
Computational Algorithm ◽  
Single Stage ◽  
Cutting Conditions ◽  
Optimum Process ◽  
Numerical Example ◽  
Optimum Cutting ◽  
Optimum Sequence

A mathematical model is constructed for analysis of optimum process design of a multifunctional production system, such as machining centers, under the minimum-time criterion. Proposed computational algorithm consists of determining the optimum sequence of operations and the optimum cutting conditions. A numerical example is given to show the practical applicability of this algorithm.

scholarly journals A THEORETICAL AND EXPERIMENTAL STUDY ON DESIGN CREATIVITY

2011 ◽  
Author(s):  
Yong Zeng ◽  
Baiquan Yan ◽  
Bo Chen ◽  
Shengji Yao
Keyword(s):  
Mathematical Model ◽  
Design Process ◽  
Governing Equation ◽  
Process Design ◽  
Process Model ◽  
Creative Design ◽  
Axiomatic Theory ◽  
Design Creativity ◽  
Design Process Model

This paper presents a mathematical model for understanding the factors that lead to a creative design, using the axiomatic theory of design modeling. This mathematical model provides the dynamic mechanism underlying the creative design process: design governing equation. It is pointed out that there may exist a chaotic motion behind the design process because of the nonlinearity of the design governing equation. Based on the recursive-logic based design process model for solving this equation, three routes to creative design are given. A case study from a patent and a software experiment are used to verify the presented mathematical theory.

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