Optimal real-time outcome-based ventilation rate control for buildings
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Solution Overview
Problem
Current ventilation systems in commercial buildings often fail to optimize ventilation rates based on factors like outdoor pollution levels, energy prices, and occupancy patterns, leading to suboptimal indoor air quality and energy consumption, which can impact health and productivity.
Innovation Solution
An outcome-based ventilation system that uses sensors, data analytics, and optimization algorithms to dynamically control ventilation rates, considering multiple factors such as work performance, health risks, and energy consumption, to minimize losses and maximize benefits for building operators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If ventilation rates are increased to improve indoor air quality and health outcomes, then indoor air quality and health benefits are improved, but energy consumption increases
Solution Approach 1:
The patent implements dynamic ventilation rate adjustment based on real-time outdoor pollution levels, occupancy patterns, and energy prices. The system transitions from static minimum ventilation rates to dynamically optimized rates that adapt to changing conditions, resolving the contradiction by providing high ventilation only when and where needed.
Solution Approach 2:
The system changes the ventilation rate parameter in response to varying outdoor air quality conditions, occupancy levels, and energy pricing. By adjusting this key parameter dynamically rather than maintaining a fixed rate, the system achieves both improved indoor air quality and reduced energy consumption.
2Reliability
If ventilation rates are adjusted based on outdoor pollution cycles and occupancy patterns, then indoor air quality outcomes are improved, but system complexity increases
Solution Approach 1:
The system incorporates feedback loops that continuously monitor outdoor pollution levels, occupancy patterns, and energy prices, then adjust ventilation rates accordingly. This feedback mechanism enables the system to respond automatically to changing conditions without requiring complex manual control, resolving the contradiction between improved air quality and system complexity.
Solution Approach 2:
The ventilation system performs self-adjustment based on sensor inputs and pre-programmed optimization algorithms. The system serves itself by automatically determining optimal ventilation rates without requiring constant human intervention or complex control infrastructure, thereby improving indoor air quality while limiting complexity growth.
3Reliability
If higher ventilation rates are implemented to reduce health risks and improve productivity, then health and productivity outcomes are improved, but operational costs increase
Solution Approach 1:
The system implements periodic assessment of ventilation needs based on cyclical patterns in occupancy, outdoor air quality, and energy pricing. By aligning high ventilation rates with periods of low outdoor pollution and high occupancy, and reducing rates during periods of poor outdoor air quality or low occupancy, the system improves health outcomes while minimizing energy waste and operational costs.
Data Source
AI summary
The framework herein may be called outcome-based ventilation (OBV) for evaluating ventilation rates (VR) in commercial buildings and using a system to make informed control decisions based on the resultant indoor air quality (IAQ) and energy consumption outcomes. A ventilation control system includes a ventilation system that provides air circulation to a building and a controller that controls the ventilation system based on input that include a current ventilation rate. The system also includes an optimization system that drives the controller based on factors like building and pollution transport models, scientific estimates of ventilation impacts on productivity, sick leave, and health, user preference parameters, and weather, pollution, and price forecasts.