HVAC system with building infection control
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Solution Overview
Problem
Existing HVAC systems face challenges in maintaining occupant comfort and disinfection while minimizing expenses, as they often require manual adjustments that can inadvertently alter other environmental conditions, and current methods for disinfection, such as using UV lights and filters, can be costly and energy-intensive.
Innovation Solution
A controller-based HVAC system that employs predictive models, including a dynamic infectious quanta model and an energy model, to perform Pareto optimization, determining optimal control decision variables that balance infection risk and energy consumption, allowing for automated operation to maintain comfort and disinfection efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If building equipment is operated to change environmental conditions for occupant comfort and disinfection, then occupant comfort and disinfection are improved, but expenses increase
Solution Approach 1:
The system performs preliminary actions by predicting future environmental conditions and infection risks using predictive models before actually operating the HVAC equipment. This allows the system to pre-determine optimal control strategies that balance comfort, disinfection, and energy consumption, avoiding reactive adjustments that would waste energy and increase expenses.
Solution Approach 2:
The system enables self-service by implementing automated control that independently adjusts HVAC operations based on predictive modeling and real-time sensor data. The automated system optimizes environmental conditions and disinfection without requiring manual intervention, thereby reducing operational expenses while maintaining reliability.
2Ease of operation
If manual adjustments are made to environmental conditions, then occupant comfort is improved, but other environmental conditions are adversely affected
Solution Approach 1:
The system implements comprehensive feedback mechanisms by continuously monitoring multiple environmental parameters (temperature, humidity, CO2 levels, infection risk) and using this data to dynamically adjust HVAC operations. This closed-loop control ensures that adjustments for occupant comfort do not adversely affect other environmental conditions, as the system responds to actual measured states rather than isolated manual adjustments.
Solution Approach 2:
The HVAC control system performs multiple functions simultaneously - temperature control, humidity regulation, ventilation management, and infection risk mitigation - through a unified predictive control framework. This multi-functionality ensures that adjustments made for one environmental parameter automatically consider their impact on all other parameters, maintaining overall environmental stability while improving occupant comfort.
3Reliability
If UV lights and filters are used for disinfection, then infection control is improved, but energy consumption and costs increase
Solution Approach 1:
The system applies partial disinfection action by using predictive modeling to determine the minimum necessary disinfection effort required at any given time based on actual infection risk assessments. Rather than continuously operating UV lights and filters at full capacity, the system dynamically adjusts disinfection intensity to match predicted risk levels, thereby maintaining effective infection control while significantly reducing energy consumption compared to continuous full-capacity operation.
Data Source
AI summary
A heating, ventilation, or air conditioning (HVAC) system for one or more building zones includes airside HVAC equipment that provide air to the one or more building zones and a controller. The controller obtains predictive models to predict values of a first control objective and a second control objective for the one or more building zones as a function of control decision variables for the airside HVAC equipment. The controller performs a Pareto optimization for a time period using the one or more predictive models to determine multiple sets of Pareto optimal values of the control decision variables and corresponding sets of Pareto optimal values of the first control objective and the second control objective for the time period. The controller operates the airside HVAC equipment to provide the air to the one or more building zones in accordance with a selected set of the Pareto optimal values.