{"id":12287,"date":"2023-05-04T20:43:00","date_gmt":"2023-05-04T18:43:00","guid":{"rendered":"https:\/\/clients.triloogia.ee\/proekspert\/wp-new\/?post_type=case-study&p=12287"},"modified":"2025-01-07T11:29:57","modified_gmt":"2025-01-07T09:29:57","slug":"hvac-optimization","status":"publish","type":"case-study","link":"https:\/\/clients.triloogia.ee\/proekspert\/wp-new\/case-studies\/hvac-optimization\/","title":{"rendered":"HVAC Optimization: Reduce heating, ventilation and air conditioning power consumption by 10%"},"content":{"rendered":"\n
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Significant Energy Savings for Rheinhold & Mahla<\/h2>\n\n\n\n

Approximately 80% of HVAC (heating, ventilation and air conditioning) systems use proportional-integral-derivative controllers (PID) for modulated control. PID control logic is easy to implement. However, experience shows that in most cases, the technology lacks efficiency in its control method. This is because responsive decisions are made reactively.<\/p>\n\n\n\n

Commercial buildings consume significant energy, making HVAC optimization crucial for energy savings and improved building efficiency.<\/p>\n\n\n\n

The rapid development of IoT technologies<\/a> allows for easy data collection using the HVAC system. This enables building predictive models that help proactively control the system, lowering energy bills. System reliability is critical to HVAC optimization, ensuring consistent air quality and comfort while reducing energy consumption and costs.<\/p>\n\n\n\n

In cooperation with Rheinhold & Mahla, a marine industry powerhouse, Proekspert<\/a> devised an HVAC control solution that uses model predictive control to enhance system performance through hvac system optimization.<\/p>\n\n\n\n

Our HVAC Equipment: Tools of Wizardry<\/h2>\n\n\n\n

The HVAC systems<\/a> were analyzed in several stages. Exploratory Data Analysis (EDA) was initially implemented to identify the available data and determine its quality. The importance of air quality in HVAC optimization was also considered, as it plays a critical role in maintaining healthy indoor environments.<\/p>\n\n\n\n

The analysis also included evaluating the heating system alongside the cooling system to ensure precise temperature control in sensitive environments. When the EDA was performed, crew behavior was noted, such as changing the chillers\u2019 setpoints. This resulted in an increase in power consumption. Moreover, problems with the system design, like low delta T syndrome, were found throughout the analysis.<\/p>\n\n\n\n

After the EDA was complete, the second stage of the analysis was performed. The second stage focused on the work process, efficiency, and potential improvement of the HVAC system. The role of air source heat<\/a> pumps in enhancing energy efficiency was also examined. To increase the efficiency and the effectiveness of the analysis, Proekspert executed interactive visualizations that offered new viewpoints for the existing designed system, which for the first time made visible how the system lives and breathes on operational conditions. Monitoring HVAC system performance<\/a> is crucial for predictive maintenance<\/a>, leading to cost savings and improved energy efficiency.<\/p>\n\n\n\n

Correlation Analysis was performed in the next phase to understand how the system reacted to changes and how different parts, including the cooling system, affected each other. Proekspert created a model predictive control method based on the collected knowledge and observations. This method used grey and black box system identification techniques to acquire a model for simulating the system\u2019s future states. The simulation models enabled optimization problem formulation, which resulted in HVAC savings. Energy monitoring was also highlighted as a key benefit, providing centralized control and data analytics<\/a> to enhance performance.<\/p>\n\n\n\n

Proekspert worked on a pilot on two RoPax class ferries. Using R&M\u2019s domain knowledge and Proekspert\u2019s technical experience, a solution was devised that gathers data remotely from the vessels, uses trained models to simulate different control decisions, and sends the optimal decision to a local PLC communicating the command to a device.<\/p>\n\n\n\n