Mathematical methods in chemical engineering

1967 ◽  
Vol 3 (8) ◽  
pp. 574-578
Author(s):  
V. V. Popov
Author(s):  
Gulnara Abitova

In this work is consider study and analysis of dynamic system for simulation of the technological process under uncertainty and complexity. To study and simulate a complicated technology process we carry out for consideration the technology of the process of roasting in fluidized bed furnaces of polymetallic sulphide ores. The choice is justified by the fact that, operation line producing of polymetallic sulphide ores represents a complex process, is characterized by a big number of transient processes, presence of process variables and deviations from technical regimes. To study process characteristics of any system functioning by means of mathematical methods the process should be formalized. This means, that adequate mathematical model needs to be developed. The choice of mathematical model depends a lot upon the features of the object and its controllability as well as of technological scheme and complexity of processes. Chemical engineering processes are complicated physical and chemical systems. Substance flows, which are part of these systems, are, as a rule, multicomponent. Therefore, for the purpose of study and qualitative control over chemical-engineering processes it is essential to apply the method of mathematic simulation, based on system analysis strategy, analysis of its structure, mathematical formulation development and evaluation of unknown parameters. Controllability means that such system attribute as having control actions, which make it possible to transfer the system from a pre-set initial state to the required condition during finite quantum of time. Therefore, the developed mathematical model of the process or control object should be controllable and stability.


2015 ◽  
Vol 1087 ◽  
pp. 424-428
Author(s):  
Azhani Mohd Razali ◽  
Jaafar Abdullah

Expectation Maximization Algorithm and the Exact Inversion Formula are two mathematical methods that have been developed for various computational applications, such as in medical imaging, nuclear industries, econometric and sociological studies, as well as chemical engineering industries. These image reconstruction methods are usually used to create the SPECT scan images. However, most of the improvement and development of the images are made by using a medical phantom, such as the human brain phantom. Here, in this paper the reconstruction of images by both algorithms are made by using a numerical phantom of laboratory scale bubble columns due to its wide application in the chemical reaction engineering studies. The results for both algorithms are compared, evaluated and discussed.


Author(s):  
G Abitova

In this work is consider study and analysis of dynamic system for simulation of the technological process under uncertainty and complexity. To study and simulate a complicated technology process we carry out for consideration the technology of the process of roasting in fluidized bed furnaces of polymetallic sulphide ores. The choice is justified by the fact that, operation line producing of polymetallic sulphide ores represents a complex process, is characterized by a big number of transient processes, presence of process variables and deviations from technical regimes. To study process characteristics of any system functioning by means of mathematical methods the process should be formalized. This means, that adequate mathematical model needs to be developed. The choice of mathematical model depends a lot upon the features of the object and its controllability as well as of technological scheme and complexity of processes. Chemical engineering processes are complicated physical and chemical systems. Substance flows, which are part of these systems, are, as a rule, multicomponent. Therefore, for the purpose of study and qualitative control over chemical-engineering processes it is essential to apply the method of mathematic simulation, based on system analysis strategy, analysis of its structure, mathematical formulation development and evaluation of unknown parameters. Controllability means that such system attribute as having control actions, which make it possible to transfer the system from a pre-set initial state to the required condition during finite quantum of time. Therefore, the developed mathematical model of the process or control object should be controllable and stability.


Sign in / Sign up

Export Citation Format

Share Document