Algorithmic Planning of Ventilation Systems: Optimising for Life-Cycle Costs and Acoustic Comfort
The European Union's climate targets challenge the building sector to reduce energy use while ensuring comfort. Ventilation systems play an important role in achieving these goals. During system planning, the primary focus tends to lie on reducing life-cycle costs, including energy and investment expenses. Acoustic considerations which contribute significantly to occupant comfort, are either addressed as an afterthought or overlooked. This can result in suboptimal designs, where silencers are added indiscriminately without properly assessing their necessity. This paper introduces a novel method for optimising life-cycle costs through mathematical optimisation while adhering to predefined noise limits. We propose new model equations with reduce non-linearity better suited for integration into the optimisation framework. Further, they present a comprehensive approach to optimising ventilation systems under multiple load scenarios. Our method surpasses the traditional sequential approach by enabling simultaneous consideration of airflow and acoustics in a single, holistic optimisation step. A case study demonstrates the method's practical application, showing that optimal solutions can be computed efficiently. The results reveal that, with appropriate fan selection, many silencers can be eliminated. Additionally, the method supports decision-making by transparently illustrating the trade-offs between life-cycle costs and noise limits. Notably, while optimal solutions from the sequential and holistic approaches align for most noise limits, the holistic method achieves a 12 % reduction in costs under specific noise constraints. These results demonstrate the benefits of integrating airflow and acoustic design while underscoring the need for further application on more diverse building types and more complex ventilation system configurations.
Code (0)
등록된 구현이 없습니다.
Tasks
Decision MakingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Predicting Mortality Risk in Viral and Unspecified Pneumonia to Assist Clinicians with COVID-19 ECMO Planning
Respiratory complications due to coronavirus disease COVID-19 have claimed tens of thousands of lives in 2020. Many cases of COVID-19 escalate from Severe Acute Respiratory Syndrome (SARS-CoV-2) to viral pneumonia to acu…
DecompensationMethodology for Interpretable Reinforcement Learning for Optimizing Mechanical Ventilation
Mechanical ventilation is a critical life support intervention that delivers controlled air and oxygen to a patient's lungs, assisting or replacing spontaneous breathing. While several data-driven approaches have been pr…
Off-policy evaluationreinforcement-learningReinforcement LearningReinforcement Learning (RL)Simulating Battery-Powered TinyML Systems Optimised using Reinforcement Learning in Image-Based Anomaly Detection
Advances in Tiny Machine Learning (TinyML) have bolstered the creation of smart industry solutions, including smart agriculture, healthcare and smart cities. Whilst related research contributes to enabling TinyML solutio…
Anomaly DetectionReinforcement Learning (RL)Machine Learning for Mechanical Ventilation Control (Extended Abstract)
Mechanical ventilation is one of the most widely used therapies in the ICU. However, despite broad application from anaesthesia to COVID-related life support, many injurious challenges remain. We frame these as a control…
BIG-bench Machine LearningReinforcement Learning (RL)Reset-Free Lifelong Learning with Skill-Space Planning
The objective of lifelong reinforcement learning (RL) is to optimize agents which can continuously adapt and interact in changing environments. However, current RL approaches fail drastically when environments are non-st…
Lifelong learningMuJoCoReinforcement Learning (RL)