Building management system with clean air and infection reduction features
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Solution Overview
Problem
Existing building management systems fail to effectively balance the risk of infectious disease spread and energy consumption while optimizing indoor air quality and HVAC control.
Innovation Solution
A building management system (BMS) that utilizes sensors to gather data on CO2, humidity, and volatile organic compounds, estimates occupancy and ventilation rates, and employs a multi-objective optimization model to control HVAC equipment, balancing infection risk, energy cost, and indoor air quality through real-time adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If HVAC equipment operates at high capacity to improve indoor air quality and reduce infection risk, then air cleanliness and occupant safety are improved, but energy consumption increases
Solution Approach 1:
The system dynamically adjusts HVAC equipment operation based on real-time IAQ data, occupancy estimates, and predictive infection risk models. Control decision variables such as ventilation rates, recirculation ratios, and equipment runtime are continuously optimized to provide adequate infection protection only when and where needed, rather than operating at constant high capacity
Solution Approach 2:
The optimization model adjusts multiple operational parameters including ventilation rates, recirculation ratios, equipment runtime schedules, and temperature setpoints to achieve the minimum effective level of infection protection. This allows the system to operate at lower energy consumption levels while maintaining adequate IAQ and infection risk reduction
2Measurement precision
If multiple environment species are monitored and modeled to improve IAQ analysis accuracy, then prediction precision is improved, but system complexity increases
Solution Approach 1:
The system segments the IAQ analysis into separate single-species concentration models for different environment species (CO2, VOCs, particulate matter, humidity). Each species is modeled independently using its own sensors and parameters, which simplifies the overall computational complexity while maintaining comprehensive multi-species monitoring capability
Solution Approach 2:
The optimization model framework is designed to handle multiple environment species universally using the same mathematical structure and solution methods. This allows accurate multi-species analysis without proportionally increasing system complexity, as the same algorithms and computational approaches apply to each species
3Productivity
If real-time optimization modeling is performed to balance infection risk and energy cost, then control effectiveness is improved, but computational requirements and system complexity increase
Solution Approach 1:
The system performs preliminary actions by pre-calculating optimization results for different scenarios and using predictive models to forecast future IAQ conditions and infection risk. This allows the real-time control system to make informed decisions without performing computationally intensive optimization calculations at every control cycle, reducing instantaneous computational requirements
Data Source
AI summary
Systems and methods for executing an IAQ analysis of a building. One system includes a controller including memory and one or more processors configured to obtain IAQ data from one or more sensors within the building, wherein the IAQ data is associated with at least one of a plurality of environment species, obtain BAS data, identify one or more unknown parameters from the IAQ data and BAS data of two or more of the plurality of environment species, estimate the one or more unknown parameters based on inputting the IAQ data and the BAS data into an optimization model, and wherein the optimization model analyzes predicted concentrations of the plurality of environment species subject to the two or more of the plurality of environment species evolving according to a single-species concentration model, and provide the estimated one or more unknown parameters to one or more predictive models.