Chemical cyberneticsTopics

Topic learning outcomes

• 1. PLO a: fuzzy mathematics and system analysis (knowledge)outcome: the student has a deep understanding of the mathematical framework of fuzzy set theory, membership functions ($\mu_a(x)$) and fuzzy logic output systems (mamdani and sugeno models). it interprets the areas of application of the z-transformation method in signal processing at a scientific level.
• 2. PLO b: intelligent control and algorithm design (engineering application) result: the student designs fuzzy logic controllers. it programs and optimizes complex manufacturing processes, robotics systems or artificial intelligence algorithms with the principles of "word computing" and "soft computing".
• 3. PLO c: information security, ethics and scientific innovation (responsibility) conclusion: the student observes the standards of information security and ethics during the processing and management of big data. based on lutfi zadeh's legacy, he is responsible for leading the integration of modern artificial intelligence technologies into industry and managing scientific innovation.

Topic learning outcomes

• 1. PLO a: dynamics of transition processes (knowledge) result: the student knows non-stationary states of technological objects, dynamic errors and transition characteristics that occur during access to the regime. explains the mathematical basis of "bumpless transfer" algorithms between manual and automatic control modes.
• 2. PLO b: design of control circuits (engineering application) result: the student prepares functional automation schemes (p&id - piping and instrumentation diagram) of industrial facilities. establishes information communication (via modbus, profibus) between local control points (lnt) and the central control system (is) and programs the controllers.
• 3. PLO c: industrial safety and functional risk management (liability) result: the student evaluates critical industrial risks, especially at the stage of access to the regime, designs emergency shutdown (esd - emergency shutdown) systems. it bears the legal responsibility of industrial safety regulations to prevent accidents caused by the human factor (operator errors in manual control). 📌 brief final conclusion

Topic learning outcomes

• 1. PLO a: industrial automation evolutionary strategy (knowledge) conclusion: the student knows the technological differences of transition stages from pneumatic control systems to digital DCS and APC systems. it interprets hierarchical management levels applied in oil refineries (within the isa-95 standard) on a scientific level.
• 2. PLO b: design of distributed control systems (dcs) (engineering application) result: the student builds typical functional schemes between central control center (ccp) and subsystems (is) for oil refinery tp. designs integrated SCADA and PLC architectures, configures intersystem network protocols (industrial ethernet, profinet).
• 3. PLO c: process risk management and cyber-security (liability) conclusion: the student applies SIL (safety integrity level) standards for automatic protection of critical and explosive processes in the oil industry. responsible for cyber security regulations (iec 62443) and HSE industrial safety regulations in a central management environment. 📌 brief final conclusion

Topic learning outcomes

• 1. PLO a: measurement techniques and operating principles of sensors (knowledge) result: the student perfectly knows the methods of measuring technological parameters, the functional status of 3 main nodes of measuring devices (sensitive, intermediate, output). it interprets the physical working principles of sensors (capacitive, inductive, piezoelectric, optical) on a scientific level.
• 2. PLO b: integration of automation contours and measurement systems (engineering application) outcome: the student designs a functional control chain for industrial facilities. selects the correct transmitters according to the properties of the technological environment (aggressiveness, viscosity, explosion hazard), performs device calibration (zero and range adjustment) and integrates unified signal lines into PLC input-output ($i/o$) modules.
• 3. PLO c: metnological standards, calibration and quality assurance (responsibility) conclusion: the student calculates measurement uncertainty and organizes periodic metrological verification (calibration) of devices to maintain the integrity of measurements in industry. it bears legal responsibility for the honest operation of measuring devices within the framework of ISO 9001 (quality management) and industrial safety regulations.

Topic learning outcomes

• 1. PLO a: physical basis of industrial measurement technologies (knowledge) result: the student has a deep understanding of the physical and chemical principles of temperature, pressure, consumption, level and quality parameter sensors. scientifically interprets the effects and sources of error of various aggressive, foaming, high-pressure or viscous industrial environments on measurement accuracy.
• 2. PLO b: selection, calibration and integration of sensor systems (engineering application) result: the student selects optimal sensors and transmitters according to the requirements of the technological regulation. adjusts the zero sheet ($zero$) and span ($span$) of measuring devices, integrates unified analog ($4-20\text{ ma}$) and digital (hart, profibus) signals into central plc/dcs control systems.
• 3. PLO c: metrological standards, quality assurance and industrial risk management (liability) result: the student performs periodic metrological verification (calibration) of devices to ensure compliance of industrial measurements with international standards (iso/iec). the analyst has direct engineering and legal responsibility for preventing process accidents and environmental waste through honest measurement of quality and industrial parameters.

Topic learning outcomes

• Plo 1: structural analysis and modeling skills definition: the student is able to build functional and structural schemes of automatic control systems (ats) based on their principle schemes, analyze the interaction of elements and determine their mathematical models (transfer functions).
• Plo 2: selection and synthesis of regulatory laws definition: the student has the competence to select, justify and integrate the appropriate control law (p, pi, pid) into the structure of the system according to the dynamic characteristics of the technological process and quality requirements (speed, accuracy, stability).
• Plo 3: parametric tuning and optimization of switching processes definition: the student is able to tune the coefficients of the selected tuning laws (using modern engineering methods (for example, the ziegler-nichols method) and software (matlab/simulink, etc.) and optimize the system by analyzing the switching process curves.