Too many choices. Too many iterations.
Conversations with practicing design engineers have revealed that the overarching process for selecting equipment often means making sizing decisions, selecting from numerous options. Because the design approach for an HVAC project is often iterative, design professionals may be required to select and reselect equipment three to four times.
For a design firm, this represents inefficient use of time, and a loss of profits. For building owners and operators, the consequences are much greater and longer lasting. Faced with requirement to size pumps for varying performance levels, designers will often accept a selection that serves all requirements reasonably well but serves none optimally.
When selecting heat pump systems, for example, Engineers must find a way to serve three separate operating modes: Heating, Heat recovery and Cooling. Many designers will size the pumps on the basis of the cooling load, as that is the most energy intensive, and will optimize for those flow and pressure requirements. But that leaves the pumping system working to serve heating and heat recovery modes, using pump sizes and performance settings that may not be optimal for the load.
What seems like a wide range of options, becomes a burden to designers, owners and operators. The consequences are largest for owners and operators, as suboptimal selections that result from variation in performance requirements and/or repeated design changes can affect the efficiency of an HVAC system for a decade or more.
A New Tool and a New Approach
What if there were a new tool that, on the basis of just a few questions, could present a ranked list of equipment choices in seconds. What if that same tool could automate the process of comparing sizes of HVAC components, investigate every possible combination, and deliver the optimized selection of pump sizes and redundancy based on all available information?
More importantly, what if there were an entirely different approach, particularly for HVAC systems in geographies requiring both heating and cooling? How can designers adjust their practices to reduce equipment costs and improve long-term efficiencies? The answer lies in systems and the way systems are selected and optimized.
Connected workflows
- Selection using the Integrated Designer in ADEPT produces clear performance maps and efficiency comparisons for informed pump selection.
- Commissioning through the Wrench App streamlines setup, configuration, and asset management with a single mobile interface.
- Performance tracking using Envelope Core gathers real-time data from Armstrong equipment, enabling predictive analytics, fault detection, and fine-tuned system performance.
- Optimization – Envelope Advisor benchmarks performance, analyzes trends, and delivers actionable recommendations — all in compliance with ASHRAE Guideline 36 and NABERS reporting.
A better way to approach the three separate operating modes of Heating, Heat recovery and Cooling is to rely on parallel pumping, demand-based control and efficiency-based pump staging. Recently, engineers have begun to select and specify multiple (smaller) pumps in parallel, planning on using all 3 pumps in parallel for cooling and only 1 pump for heating. Advanced variable speed technology, combined with Sensorless pump control mean that, no matter how many pumps operate, output will be optimized for demand-based flow and pressure, with a minimum expenditure of energy.
The latest update to the ADEPT selection and project management tool is the Integrated Designer. This feature relies on detailed performance maps to make every component selection the best of all possible choices. The selection time is reduced to just a few seconds. More importantly, both the pump selection, and the overall HVAC system are made much more efficient for the life of the building.
Wikipedia contributors. (2025, July 31). Maximization (psychology). In Wikipedia, The Free Encyclopedia. Retrieved 16:33, September 8, 2025, from https://en.wikipedia.org/w/index.php?title=Maximization_(psychology)&oldid=1303557470
Pilat D., & Sekoul K. (2021). Choice Overload Bias. The Decision Lab. Retrieved September 8, 2025, from https://thedecisionlab.com/biases/choice-overload-bias
A better way to approach the three
