In the preliminary phase, we evaluate measurement data to create digital twins of the system components. When the controller is integrated into the system, measurement data is analysed in real-time and stored for later evaluation. The controller continuously compares data with the predictions of the implemented models—both to improve the accuracy of these models (through machine learning) and to detect unusual behaviour in individual components. If any anomalies are detected, a warning is sent to the operator so that maintenance can be scheduled before a component malfunctions and potentially disrupts the overall system (predictive maintenance).
Stored measurement data is evaluated at regular intervals, and automated reports are generated to provide the operator with an overview of the cooling system's performance.
A digital twin is a virtual model of a component or system. . It accurately reflects all key technical characteristics and allows for the optimization of interaction with all other components—just like a good conductor in an orchestra. Hence, it is able to predict component and system behavior in all operating conditions. We use both physical models, which are continuously adjusted during operation to maintain accuracy. The physical digital twins uniquely enable the detection of deviations and their translation into predictive maintenance insights.
The efficiency of cold generation depends heavily on the load case, which is definded not only by the cooling demand, but alsoby ambient temperature, and supply temperatures. Hence, operating a system at rated load, but different ambient temperature can also be seen as partload as much as different load at rated ambient temperature and/or varying cooling or heating temperatures.
Conventional system controls for central cooling plants typically rely on sequencing circuits for chillers (e.g., dividing them into base and peak load coverage) and a series of independent minor control loops for peripheral components. These controls are generally set up once and only adjusted if modifications are made to the installation. Many system parameters and setpoints are fixed according to design conditions or data sheets and do not adapt to real-time load variations. However, in most systems, these design conditions occur rarely—if at all—meaning the control system often operates under non-optimal conditions. Additionally, design points are determined separately for each component (e.g., compression chiller, pump, and re-cooling plant). While each component may function efficiently on its own, the overall system often runs suboptimally.
With the F4 system controller, we take a holistic approach. Our Model-Based System Control understands each component's role within the overall system. Instead of statically assigning chillers to base and peak loads, the system dynamically selects them based on efficiency at the required load. All system parameters and setpoints are continuously calculated based on real-time operating conditions to ensure not just efficient, but maximally efficient, operation. By digitally mapping the entire cooling system, the F4 system controller also factors in variable electricity and heat rates when selecting chillers and managing cold storage. It can instantly switch between cost-based and CO2 emission-based operation while detecting anomalies to prevent system failures through predictive maintenance.
A Building Management System (BMS) operates as a higher level control (HLC). The HLC is indicating that most components (pumps, fans, chillers, etc.) do have their own control algorithms and recieve freedom to operate by HLC. HLC ensures stable operation of the system. It often provides visualization of key metrics. In most cases we do find well operating BMS / HLC systems in terms of reliable, safe operation.
The real game-changer is the software behind your system. The F4S system manager includes a physical model of your system’s components and utilizes advanced algorithms, not only to operate safe and reliable but moreover to optimize the entire system based on your chosen target metric—whether it's cost, primary energy consumption, or CO2 emissions.
BMS / HLC typically use software with basic sequence control or operate based on single parameters, such as outdoor temperature. These systems do not actively optimize efficiency, meaning the operating point where your system runs may not be the most efficient.Our controller enhances your cooling plant's operation by optimizing all components simultaneously, ensuring maximum system efficiency while increasing component longevity and sustainability.
Our controller enhances your cooling plant's operation by optimizing all components simultaneously, ensuring maximum system efficiency while increasing component longevity and sustainability.
With our F4Ssystem management, you define the optimization strategy, wether it is primary energy efficiency, CO2 efficiency, electric efficiency, cost efficiency or any other efficiency. You can freely choose and you can switch between optimization targets at any time.
Generally, improving energy efficiency also leads to lower energy consumption, which reduces CO2 emissions, costs, etc.—even if the system is not primarily optimized for CO2 reduction.
Installation specific and sometimes also timely varying figures for the value of operating media (electricity, heat, water demand, etc.) lead to the optimization target resulting in different outcomes in terms of savings for the associated operating media.
No training period is required after commissioning to directly operate the system management. Our models are based on physics-based algorithms and can be parameterized using manufacturer data sheets and/or historical operational data. Nevertheless, to get best results on the long run, AI and ML is used to discover eldering, fouling effects and predict maintenance needs and maintain highest model accuracy.
Yes, this is one of our core USPs. The F4S system management is designed to operate onsite and offline on industrial-standard PLCs, ensuring 24/7 availability and reliability.
Yes. Together with our high-end partners in secure and robust VPN services, we offer remote access and first-level support for various system issues. If you prefer, we can also integrate with your existing VPN system.
Absolutely. We provide all necessary data, including visualizations, diagrams, and tables, to support your CSRD reporting requirements.
- The system can easily adapt to changes in your cooling infrastructure within a few hours.
- It can help you assess which refurbishments are truly necessary to improve system performance and efficiency.
By implementing F4S system management before refurbishment, you can make more informed decisions and ensure your new setup is fully optimized from the start.