Czech

3

doc. Ing. Radovan Šomplák, Ph.D.

Institute of Process Engineering

Before starting the course, students are able to:

• Load and perform basic processing of tabular data in Python and create visualisations.
• Calculate and interpret basic descriptive statistics.
• Prepare a simple material or energy balance and work with physical units.
• Understand basic technical documentation and structure the description of a technical solution.

• Collaborate on a specific task within a team and clearly present the results of their work.

The course is completed by a graded credit. The requirements are:

• Active participation in the teaching blocks, subject to the conditions specified below.
• Regular progress on the project, demonstrated through the deliverables required at the project milestones.
• Submission and defence of the final report and presentation, including the computational solution and an overview of the contributions of individual team members.

• A minimum of 50 points out of 100 overall and at least 15 points out of 30 for the individual oral defence.

Attendance and Make-up Requirements

Attendance at computer-assisted practical sessions is compulsory and is recorded by the instructor at each block. Absence is permitted for a maximum of two six-hour teaching blocks, corresponding to a total of 12 teaching hours. This allowance does not exempt students from completing the project deliverables and milestones.

If a student misses an interim presentation, they must document their contribution to the relevant milestone and discuss it with the instructor at an alternative time. Attendance at the final defence is compulsory for every student. If an absence is excused, a make-up date will be arranged.

The instructor assesses each milestone based on the completeness of the required deliverables and the evidence of progress. Any identified deficiencies must be addressed by the student or team within the specified deadline. Each student must document their individual contribution throughout the course in a brief record of completed activities.

Assessment is based on the total points awarded in three components:

• Project report (20%)
• Technical solution and code (50%)
• Oral defence (30%)

During the defence, each student must explain their individual contribution and the fundamental principles of the team's overall solution. Points awarded for team deliverables alone do not replace the requirement to pass the individual defence. Whole points are awarded for each assessment criterion.

If the requirements for the final deliverables are not met, one opportunity to revise and resubmit them is permitted. If the individual defence is unsuccessful, one resit is permitted. The instructor specifies the deficiencies and sets a deadline for the revision or resit to allow the course to be completed no later than the end of the examination period of the relevant semester. Any extension of this deadline is governed by Article 12 of the BUT Study and Examination Regulations. In the event of a resubmission or resit, only the relevant revised component is reassessed; all other assessment results remain unchanged.

The aim of the course is to develop project management, analytical, and presentation skills through solving a complex engineering problem.

Upon successful completion of the course, students will be able to:

• Refine an initial technical project brief in collaboration with the client and define objectives, required deliverables, and acceptance criteria.
• Develop a project plan covering stages, schedule, task allocation, workload estimates, and preliminary costs.
• Prepare project data, assess its quality, and select and justify appropriate statistical, analytical, or optimisation methods.
• Develop a computational model of the problem using Python or other suitable tools and validate its results.
• Compare alternative solutions and assess the impact of assumptions, constraints, and uncertainty in the input data.
• Interpret results in the context of process engineering and formulate recommendations for practical application.
• Document their individual contribution to the team solution, coordinate assigned tasks, and address feedback from the client and instructors.

• Prepare a technical report, present project results, and defend the chosen approaches.

Programme N-PRI-P: Process Engineering, Master's, compulsory

78 hours, compulsory

Computer-assisted practical sessions – 78 hours per semester, organised into 13 six-hour blocks. Teaching includes theory, demonstrations, practical exercises, consultations and guided teamwork.

Methods are practised on illustrative problems. Their use in individual projects depends on the assignment. Students justify their choice of methods and do not need to use all methods covered in one project.

Week 1 – Introduction and Project Definition
Students learn about the course structure, project examples and basic data analysis in Python. Teams select a project, brainstorm and prepare a draft brief and proposal. They define the technical problem, objectives, deliverables and team roles.

Week 2 – Project Proposal and Data Preparation
Teams prepare a project proposal and validate it with the client. They specify acceptance criteria, schedule, task allocation, workload and budget. They practise data cleaning and assess data completeness and quality.

Week 3 – Cluster Analysis and Stratification
Students practise clustering in Python using an illustrative problem. They assess its suitability for their projects, justify input variables and interpret the resulting groups.

Week 4 – Time Series and Forecasting
Students analyse time series, identify trends and deviations, and develop a simple forecast. They assess its accuracy and limitations and consider its use in their projects.

Week 5 – Location and Allocation Problems
Students define decision variables, an objective function and constraints for an optimisation model. They solve a transportation problem in Excel and Python and interpret the results. Teams develop a computational model suited to their project.

Week 6 – Multi-Criteria and Scenario-Based Optimisation
Students compare solutions using multiple criteria, evaluate scenarios and identify sources of uncertainty and risk. Teams test the sensitivity of results to input changes where relevant.

Week 7 – Network Optimisation and Model Development
Students formulate a network optimisation problem with capacity constraints and explain their impact on the solution. Teams validate and refine their models based on feedback.

Week 8 – Route Planning and Scheduling
Students formulate and solve illustrative Travelling Salesperson Problem (TSP) and Vehicle Routing Problem (VRP) instances using OR-Tools. They practise scheduling and assess the applicability of these methods to their projects.

Week 9 – First Project Presentation
Teams present their initial solutions and receive feedback. They report results, methods, individual contributions and progress against the schedule, and propose next steps.

Week 10 – Advanced Topic of Choice I
The topic is selected according to project needs in consultation with the instructor. Students apply the chosen method to a subproblem, justify their choice and discuss the results.

Week 11 – Second Project Presentation
Teams present their work to the client, demonstrate result validation, compare progress with objectives and address previous feedback. They identify the remaining tasks.

Week 12 – Advanced Topic of Choice II
Additional methods are introduced according to project needs. Teams complete their analyses, model validation, technical report and code documentation. They assess limitations and prepare practical recommendations.

Week 13 – Final Project Presentation and Defence
Teams present their final solutions to an evaluation panel and submit complete documentation. Each student defends their contribution, explains the team solution and answers questions. Assessment follows the course rules.