Modeling and optimization of processes in metallurgy

Description: Modern methods and algorithms for thermodynamic analysis of phase and chemical equilibria of complex systems, multielement heterogeneous media, methods of thermodynamic modeling, including the assessment of the reliability of the results, analysis of the features and results of thermodynamic modeling in relation to metallurgical processes, description and properties, as well as the capabilities of the HSC Chemistry software packages and Metsim.

Amount of credits: 5

Пререквизиты:

• Theory of metallurgical processes

Course Workload:

Component: University component

Cycle: Base disciplines

Goal

• Acquisition of knowledge of the theoretical foundations and practical skills of computer modeling of technological processes and other objects in metallurgy

Objective

• Mastering the methods and practical techniques of modeling the most important processes for the study of these processes and for further optimal management of them

Learning outcome: knowledge and understanding

• The essence of the metallurgical experiment; basic statistical criteria and their evaluation; basic concepts of modeling and techniques for constructing models of the n-factor experiment; methods for obtaining mathematical equations describing the experiment; methods of mathematical analysis of the production activity of a metallurgical enterprise.

Learning outcome: applying knowledge and understanding

• Correctly set the task, determine solutions and predict the result; calculate linear and nonlinear regression models based on the results of the experiment, check their adequacy and make informed decisions about the choice of models; plan a factor experiment to find the optimum, analyze the results obtained

Learning outcome: formation of judgments

• Possess computer modeling that makes it possible to process large amounts of information as part of mathematical models of complex metallurgical objects

Learning outcome: communicative abilities

• Effectively work individually, as a team member on interdisciplinary topics, as well as lead a team.

Learning outcome: learning skills or learning abilities

• The ability to present research results in the forms of reports, abstracts, publications and public discussions, the ability to formulate practical recommendations on the use of research results.

Teaching methods

Discussion, teamwork, individual training

Assessment of the student's knowledge

Teacher oversees various tasks related to ongoing assessment and determines students' current performance twice during each academic period. Ratings 1 and 2 are formulated based on the outcomes of this ongoing assessment. The student's learning achievements are assessed using a 100-point scale, and the final grades P1 and P2 are calculated as the average of their ongoing performance evaluations. The teacher evaluates the student's work throughout the academic period in alignment with the assignment submission schedule for the discipline. The assessment system may incorporate a mix of written and oral, group and individual formats.

The evaluating policy of learning outcomes by work type

Evaluation form

The student's final grade in the course is calculated on a 100 point grading scale, it includes:

• 40% of the examination result;
• 60% of current control result.

The final grade is calculated by the formula:

 

Where Midterm 1, Midterm 2are digital equivalents of the grades of Midterm 1 and 2;

E is a digital equivalent of the exam grade.

Final alphabetical grade and its equivalent in points:

The letter grading system for students' academic achievements, corresponding to the numerical equivalent on a four-point scale:

Topics of lectures

• Types of models and modeling, classification of models
• Modeling of metallurgical processes
• System analysis
• Mathematical methods of optimization of technological systems
• Similarity theory and dimension analysis in the construction of mathematical models
• Statistical methods in the construction of mathematical models
• Approximation of sample distributions
• Modeling of material and thermal balances of technological processes
• Visualization concepts in modeling, visualization methods
• Examples of modeling metallurgical processes

Key reading

• Ageev, N.G. Modelirovanie processov i ob"ektov v metallurgii. — Ekaterinburg: Izd-vo Ural. un-ta, 2016. — 108 s.
• Bykova, P.O. Modelirovanie ob"ektov i processov v metallurgii. – Perm': Izd-vo Perm. gos. tekhn. un-ta, 2010. – 132 s.
• B. M. Gorenskij, L. A. Lapina, A. SH. Lyubanova, A. V. Parshakov, R. A. SHigapov, S. V. Kapustina. Modelirovanie processov i ob"ektov v metallurgii. – Krasnoyarsk: IPK SFU, 2008. – 145 s.

Further reading

• Sovetov B.YA. Modelirovanie sistem: praktikum / B.YA. Sovetov, S.F. YAkovlev.M.: Vyssh. shk., 1999.
• Cymbal, V. P. Matematicheskoe modelirovanie metallurgicheskih processov. - M.: Metallurgiya, 1986. - 240 s.