2025
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Tomažič, Simon; Škrjanc, Igor: Advanced Model Predictive Control Strategies for Energy-Efficient HVAC Systems in Pharmaceutical Facilities. V: Energy and Buildings, str. 116348, 2025, ISSN: 0378-7788 . @article{TOMAZIC2025116348,
title = {Advanced Model Predictive Control Strategies for Energy-Efficient HVAC Systems in Pharmaceutical Facilities},
author = {Simon Toma\v{z}i\v{c} and Igor \v{S}krjanc},
url = {https://www.sciencedirect.com/science/article/pii/S0378778825010783?utm_source=chatgpt.com},
doi = {10.1016/j.enbuild.2025.116348},
issn = {0378-7788 },
year = {2025},
date = {2025-08-28},
urldate = {2025-08-28},
journal = {Energy and Buildings},
pages = {116348},
abstract = {The paper presents a comprehensive study of advanced Model Predictive Control (MPC) strategies for pharmaceutical HVAC systems, subject to stringent regulatory requirements and substantial energy demands. Three control approaches\textendashPredictive Functional Control (PFC), nonlinear MPC with Particle Swarm Optimisation (PSO) and energy-efficient MPC (EMPC)\textendashare examined. Their ability to maintain strict temperature and humidity setpoints, reduce energy consumption and deal with system nonlinearities is evaluated. Data recorded over 445 days of HVAC operation, capturing variations in external temperature and humidity, underpins the assessment. The results demonstrate that EMPC can reduce total energy consumption by up to 20%, while PFC offers a simpler implementation well-suited for industrial control systems. The results highlight the key trade-offs between control accuracy, computational complexity, and energy savings and provide a practical framework for the adoption of MPC-based solutions in pharmaceutical HVAC environments.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The paper presents a comprehensive study of advanced Model Predictive Control (MPC) strategies for pharmaceutical HVAC systems, subject to stringent regulatory requirements and substantial energy demands. Three control approaches–Predictive Functional Control (PFC), nonlinear MPC with Particle Swarm Optimisation (PSO) and energy-efficient MPC (EMPC)–are examined. Their ability to maintain strict temperature and humidity setpoints, reduce energy consumption and deal with system nonlinearities is evaluated. Data recorded over 445 days of HVAC operation, capturing variations in external temperature and humidity, underpins the assessment. The results demonstrate that EMPC can reduce total energy consumption by up to 20%, while PFC offers a simpler implementation well-suited for industrial control systems. The results highlight the key trade-offs between control accuracy, computational complexity, and energy savings and provide a practical framework for the adoption of MPC-based solutions in pharmaceutical HVAC environments. |
2024
|
Tomažič, Simon; Škrjanc, Igor; Andonovski, Goran; Logar, Vito: The Development of Simulation and Optimisation Tools with an Intuitive User Interface to Improve the Operation of Electric Arc Furnaces. V: Machines, vol. 12, iss. 8, no. 508, str. 508, 2024, ISSN: 2075-1702. @article{machines12080508,
title = {The Development of Simulation and Optimisation Tools with an Intuitive User Interface to Improve the Operation of Electric Arc Furnaces},
author = {Simon Toma\v{z}i\v{c} and Igor \v{S}krjanc and Goran Andonovski and Vito Logar},
url = {https://www.mdpi.com/2075-1702/12/8/508?utm_source=chatgpt.com},
doi = {10.3390/machines12080508},
issn = {2075-1702},
year = {2024},
date = {2024-08-05},
urldate = {2024-08-05},
journal = {Machines},
volume = {12},
number = {508},
issue = {8},
pages = {508},
abstract = {The paper presents a novel decision support system designed to improve the efficiency and effectiveness of decision-making for electric arc furnace (EAF) operators. The system integrates two primary tools: the EAF Simulator, which is based on advanced mechanistic models, and the EAF Optimiser, which uses data-driven models trained on historical data. These tools enable the simulation and optimisation of furnace settings in real time and provide operators with important insights. A key objective was to develop a user-friendly interface with the Siemens Insights Hub Cloud Service and Node-RED that enables interactive management and support. The interface allows operators to analyse and compare past and simulated batches by adjusting the input data and parameters, resulting in improved optimisation and reduced costs. In addition, the system focuses on the collection and pre-processing of input data for the simulator and optimiser and uses Message Queuing Telemetry Transport (MQTT) communication between the user interfaces and models to ensure seamless data exchange. The EAF Simulator uses a comprehensive mathematical model to simulate the complex dynamics of heat and mass transfer, while the EAF Optimiser uses a fuzzy logic-based approach to predict optimal energy consumption. The integration with Siemens Edge Streaming Analytics ensures robust data collection and real-time responsiveness. The dual-interface design improves user accessibility and operational flexibility. This system has significant potential to reduce energy consumption by up to 10% and melting times by up to 15%, improving the efficiency and sustainability of the entire process.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The paper presents a novel decision support system designed to improve the efficiency and effectiveness of decision-making for electric arc furnace (EAF) operators. The system integrates two primary tools: the EAF Simulator, which is based on advanced mechanistic models, and the EAF Optimiser, which uses data-driven models trained on historical data. These tools enable the simulation and optimisation of furnace settings in real time and provide operators with important insights. A key objective was to develop a user-friendly interface with the Siemens Insights Hub Cloud Service and Node-RED that enables interactive management and support. The interface allows operators to analyse and compare past and simulated batches by adjusting the input data and parameters, resulting in improved optimisation and reduced costs. In addition, the system focuses on the collection and pre-processing of input data for the simulator and optimiser and uses Message Queuing Telemetry Transport (MQTT) communication between the user interfaces and models to ensure seamless data exchange. The EAF Simulator uses a comprehensive mathematical model to simulate the complex dynamics of heat and mass transfer, while the EAF Optimiser uses a fuzzy logic-based approach to predict optimal energy consumption. The integration with Siemens Edge Streaming Analytics ensures robust data collection and real-time responsiveness. The dual-interface design improves user accessibility and operational flexibility. This system has significant potential to reduce energy consumption by up to 10% and melting times by up to 15%, improving the efficiency and sustainability of the entire process. |
2022
|
Tomažič, Simon; Andonovski, Goran; Škrjanc, Igor; Logar, Vito: Data-Driven Modelling and Optimization of Energy Consumption in EAF. V: Metals, vol. 12, iss. 816, no. 5, 2022, ISSN: 2075-4701. @article{met12050816,
title = {Data-Driven Modelling and Optimization of Energy Consumption in EAF},
author = {Simon Toma\v{z}i\v{c} and Goran Andonovski and Igor \v{S}krjanc and Vito Logar},
url = {https://www.mdpi.com/2075-4701/12/5/816},
doi = {10.3390/met12050816},
issn = {2075-4701},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {Metals},
volume = {12},
number = {5},
issue = {816},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
2021
|
Tomažič, Simon; Škrjanc, Igor: An Automated Indoor Localization System for Online Bluetooth Signal Strength Modeling Using Visual-Inertial SLAM. V: Sensors, vol. 21, no. 8, 2021. @article{s21082857,
title = {An Automated Indoor Localization System for Online Bluetooth Signal Strength Modeling Using Visual-Inertial SLAM},
author = {Simon Toma\v{z}i\v{c} and Igor \v{S}krjanc},
doi = {10.3390/s21082857},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {Sensors},
volume = {21},
number = {8},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
2019
|
Tomažič, Simon; Dovžan, Dejan; Škrjanc, Igor: Confidence-Interval-Fuzzy-Model-Based Indoor Localization. V: IEEE Transactions on Industrial Electronics, vol. 66, no. 3, str. 2015-2024, 2019. @article{8370834,
title = {Confidence-Interval-Fuzzy-Model-Based Indoor Localization},
author = {Simon Toma\v{z}i\v{c} and Dejan Dov\v{z}an and Igor \v{S}krjanc},
doi = {10.1109/TIE.2018.2840525},
year = {2019},
date = {2019-01-01},
journal = {IEEE Transactions on Industrial Electronics},
volume = {66},
number = {3},
pages = {2015-2024},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
2015
|
Tomažič, Simon; Škrjanc, Igor: Fusion of visual odometry and inertial navigation system on a smartphone. V: Computers in Industry, vol. 74, str. 119–134, 2015, ISSN: 01663615. @article{Tomazic2015a,
title = {Fusion of visual odometry and inertial navigation system on a smartphone},
author = { Simon Toma\v{z}i\v{c} and Igor \v{S}krjanc},
doi = {10.1016/j.compind.2015.05.003},
issn = {01663615},
year = {2015},
date = {2015-01-01},
journal = {Computers in Industry},
volume = {74},
pages = {119--134},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
2013
|
Tomažič, Simon; Logar, Vito; Kristl, Živa; Krainer, Aleš; Škrjanc, Igor; Košir, Mitja: Indoor-environment simulator for control design purposes. V: Building and Environment, vol. 70, str. 60–72, 2013, ISSN: 03601323. @article{Tomazic2013a,
title = {Indoor-environment simulator for control design purposes},
author = { Simon Toma\v{z}i\v{c} and Vito Logar and \v{Z}iva Kristl and Ale\v{s} Krainer and Igor \v{S}krjanc and Mitja Ko\v{s}ir},
doi = {10.1016/j.buildenv.2013.08.026},
issn = {03601323},
year = {2013},
date = {2013-01-01},
journal = {Building and Environment},
volume = {70},
pages = {60--72},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
