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of 896
pro vyhledávání: '"A. M. Volin"'
Publikováno v:
Gornyi Zhurnal. :16-19
Autor:
Reichman, Henry
Publikováno v:
Slavic Review, 1984 Dec 01. 43(4), 676-677.
Externí odkaz:
https://www.jstor.org/stable/2499318
Autor:
Henry Reichman
Publikováno v:
Slavic Review. 43:676-677
Akademický článek
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Autor:
Yu. M. Volin, G. M. Ostrovskii
Publikováno v:
Automation and Remote Control. 68:523-538
The multicriteria optimization problem under uncertainty conditions is considered, which arises at the design stage of a technological process. The uncertainty stems from the inaccuracy of mathematical models and a certain uncertainty of the conditio
Publikováno v:
Optimization and Engineering. 7:249-276
The main issues in the design and optimization of a chemical process under uncertainty are the feasibility test, flexibility index and the two-step optimization problem. The formulations of these problems are based on the implicit supposition that at
Autor:
G. M. Ostrovsky, Yu. M. Volin
Publikováno v:
AIChE Journal. 52:3173-3188
The modern foundations of flexibility analysis were developed in the 1980s by Grossmann and co-workers. They formulated the approaches to solve the main problems of flexibility analysis, namely, the feasibility test, flexibility index, and two-stage
Autor:
Yu. M. Volin, G. M. Ostrovskii
Publikováno v:
Theoretical Foundations of Chemical Engineering. 40:281-290
Three phases in the development of computer simulation of chemical engineering systems are considered. In the first phase, the mass and heat balances are calculated using detailed mathematical models of apparatuses. In the second phase, the chemical
Publikováno v:
Chemical Engineering Science. 61:2379-2393
Inherent in chemical process models are parameters that have uncertainty associated with them. This paper addresses multicriteria optimization that accounts for model and process uncertainty at the design stage. Specifically the authors have develope
Autor:
Yu. M. Volin, G. M. Ostrovskii
Publikováno v:
Automation and Remote Control. 66:1195-1211
The existing methods of process optimization under uncertain initial information are based on the assumption that in the phase of service known are the exact values of all parameters, which in the phase of design were regarded as partially uncertain.