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Title:Dinamično optimiranje problemov kemijskega inženirstva z uporabo programskega okolja APMonitor : diplomsko delo univerzitetnega študijskega programa I. stopnje
Authors:ID Krajnc, Nika (Author)
ID Bogataj, Miloš (Mentor) More about this mentor... New window
ID Nemet, Andreja (Comentor)
Files:.pdf UN_Krajnc_Nika_2021.pdf (1,42 MB)
MD5: 8F0EA17E697E6C681D3C83C32EE78CCF
PID: 20.500.12556/dkum/dd226888-6997-49e5-9fe3-9a351a9e15c1
 
Language:Slovenian
Work type:Bachelor thesis/paper
Typology:2.11 - Undergraduate Thesis
Organization:FKKT - Faculty of Chemistry and Chemical Engineering
Abstract:Dinamično optimiranje je veja uporabne matematike, ki omogoča optimiranje matematičnih modelov, zapisanih z diferencialnimi in algebrskimi enačbami (DAE). Je orodje, ki omogoča sprejemanje odločitev na osnovi napovedi časovnega obnašanja sistemov. V diplomskem delu predstavljamo programsko okolje APMonitor oziroma njegov Pythonov modul GEKKO, ki je med drugim namenjeno reševanju prav takih optimizacijskih problemov. V delu poleg generičnih, ilustrativnih primerov dinamičnega optimiranja, ki so namenjeni predstavitvi ustreznih reformulacij in sintakse programa, predstavljamo tudi dva primera iz kemijskega inženirstva. Prvi izmed obeh je dinamična optimizacija temperaturnega profila v šaržnem reaktorju, drugi pa dinamična optimizacija prehoda med dvema stacionarnima stanjema v pretočnem mešalnem reaktorju. V obeh primerih smo izvedli občutljivostno analizo in opazovali vpliv omejevanja vrednosti manipulirnih in regulirnih veličin na spreminjanje optimalnih dinamičnih profilov. V prvem primeru smo se omejili na spreminjanje mej na koncentracijah reaktanta in produkta. V drugem primeru pa smo spreminjali vrednosti uteži v namenski funkciji. Rezultati dela nakazujejo, da je programsko okolje primerno orodje za izvajanje optimiranja dinamičnih sistemov. Temeljna prednost okolja je avtomatizirana pretvorbe DAE v sistem algebrskih enačb, ki jih nato rešujemo z integriranimi reševalniki za optimiranje nelinearnih problemov (npr. IPOPT). Od uporabnika tako zahteva le zapis modela v obliki DAE, ki je zaradi sintakse, ki temelji na sintaksi jezika Python, enostavno berljiva in se je lahko relativno hitro priučimo. Preostali koraki, ki vodijo do rezultatov so popolnoma avtomatizirani. Numerični rezultati pa poleg tega, da so bili omenjeni problemi rešljivi v manj kot 1 s procesorskega časa, nakazujejo, da lahko v odvisnosti od načina implementacije modela pridobimo različne rešitve, za katere lahko trdimo le, da so lokalno optimalne.
Keywords:dinamični sistemi, dinamično optimiranje, kemijsko inženirstvo, APMonitor, GEKKO
Place of publishing:Maribor
Place of performance:Maribor
Publisher:[N. Krajnc]
Year of publishing:2021
Number of pages:X, 34 str.
PID:20.500.12556/DKUM-79419 New window
UDC:66:004.383.4(043.2)
COBISS.SI-ID:69547267 New window
Publication date in DKUM:07.07.2021
Views:1436
Downloads:98
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FKKT
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:28.06.2021

Secondary language

Language:English
Title:Dynamic optimization of chemical engineering problems using APMonitor programming environment
Abstract:Dynamic optimization is a branch of applied mathematics that allows optimization of mathematical models described by differential and algebraic equations (DAE). It is a tool which enables decision-making based on predictions about the temporal behavior of systems. In our work we present APMonitor software environment, more precisely its Python module GEKKO, intended for solving such optimization problems. In addition to generic and illustrative examples of dynamic optimization intended to present the corresponding reformulation and syntax of the program, we also present two examples from chemical engineering. The first of the two chemical engineering examples is a dynamic optimization of the temperature profile in a batch reactor, and the other is the dynamic optimization of the transition between two steady states in a continuous stirred tank reactor. In both cases, we performed a sensitivity analysis and observed changes of the optimal dynamic profiles. In the first case we focused on changing the limits of the concentrations of reactant and product and in the second case we changed the values of the weights in the objective function. The results show that the software environment is a suitable tool for performing optimization of dynamic systems. A fundamental advantage of the environment is an automated transformation of DAE into a system of algebraic equations, which are then solved using integrated solvers for the optimization of nonlinear problems (e.g. IPOPT). The user only needs to write the model in the form DAE, which is easy to read and learn due to its syntax based on the Python language syntax. The other steps leading to the results are completely automated. The numerical results, in addition to the fact that these problems were solved in less than 1 s of CPU time, show that depending on the method of implementation of the model, we can obtain different solutions that are only locally optimal.
Keywords:dynamic systems, dynamic optimization, chemical engineering, APMonitor, GEKKO


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