What are the trends in the hot water circulatory pump market? Could you talk us through the stressors influencing the pumps sub-industry?
There is a great deal of innovation in the market with respect to fluid flow applications in the built community, and that can be seen in both new and existing building stock. What’s really interesting is the arrival of digitalization as a way to build digital twins for both optimization and condition-based maintenance. An incredible amount of technology can be integrated into fluid flow devices, even the smaller circulators. This means all types and sizes of these devices can have IP addresses and can support wi-fi communications, along with the traditional control and communications connections. This shift to digitalize and connect all aspects of a system has changed and will continue to change industry thinking. The performance and efficiency challenge for building operators is no longer about how to make any one mechanical piece of equipment marginally more efficient from a mechanical approach. Now, it’s about making the entire system and surrounding networks perform better.
In both comfort systems and process systems, information technology takes us light years ahead of traditional setpoint control. With respect to the challenges that every business is facing from the global COVID-19 experience, building operators are opting for more remote management capabilities and intelligent systems. That’s leading to demand in a different but related area. intelligent, connected systems with data-tracking functions can quickly accumulate staggering amounts of raw data. With all of the new information sources in a building, problems around data overload become apparent very quickly.
So, what we’re seeing recently is a marked increase in the number of building operators who are asking for informative insights that will help them with decision-making. You asked specifically about pumps. A good example of data versus insights would be the vibration data that can be obtained from a pump. The operator really doesn’t know what good or bad vibration data looks like. However, advanced systems with diagnostic capabilities can analyze mountains of vibration data and detect a trend more easily. The system can alert an operator if the vibration data is indicating a degradation of the motor bearings, a blocked strainer or pump cavitation. The more sophisticated systems can even indicate the degree of urgency, to tell an operator whether an issue needs attention today, or inspection within a month.
During COVID-19, resources have had restricted access to facilities, and reductions in the resources available to go to sites. The ability to know in advance if there is a health issue with a piece of equipment, before the process performance is at risk, makes managing larger portfolios of buildings by a small group of service staff a genuine possibility. And it’s all made possible with digitalization and smart diagnostics.
From a company perspective, what R&D-related updates would you like to share with readers of Climate Control Middle East?
Something that we have just completed is the development of a new machine learning technology. Specifically for buildings, this will be available for HVAC systems. The idealist view is that with machine learning, data models will inform operators on how to manage the process setpoints and the controllable process inputs to optimize water consumption, or energy consumption, or create the most stable environmental condition. In a static world, processing to determine optimization strategies is easy. However, buildings and the environments in which they operate are anything but static, so the inputs into an optimization decision are very dynamic.
Of course, with changes in weather, the operating environment changes. And with variations in building occupancy and tenant make up, the internally generated inputs and internal HVAC requirements change. But it’s worth noting that the state of equipment repair and efficiency can also vary dramatically during every part of the day, but the state of equipment repair and servicing can also vary dramatically over time.
The learning process becomes even more challenging as system characteristics change. For example, if a cooling tower has a blocked air inlet, this needs to be differentiated from a chronic change in equipment condition caused by the contamination of a condenser coil in a refrigerant circuit. There are very different types of changes that we know of. If the accumulation of valuable data and insights is left to a natural learning process, it will take years for a grass roots mathematical model to understand and learn to adapt.
The machine learning system that we have developed accelerates the learning process, and that fundamentally changes the decision-making process to the point that instead of relying on the data points available in the moment as the basis of a decision, operators come to trust the machine learning process of testing, modeling and predicting for decisions on operating settings. As we connect facilities to our cloud diagnostics server, the large population learnings allow us to predict events and suggest the maintenance calls.
Machine learning capabilities gives our new solution a new degree of persistence in performance management without adding heavy layers of routine maintenance and recommissioning. It’s economically practical and the potential for savings is really exciting.
What is your perspective on regulation? How can the government make the private sector in the Middle East more mindful of energy conservation? While there is a stick approach, what can the government do to introduce incentives? Could we apply triedand-tested models in the United States here in the region?
Early in my career, while working for a large electrical power utility in Canada, I had the opportunity to develop what was referred to as Demand Side Management initiatives, with the intent of retaining transient commercial and industrial customer segments on our grid. These initiatives were given high priority in response to environmental concerns. At the time, we saw three separate levers for managing behaviours around the energy conservation or ‘green’ agenda. Those three levers were rate structures and efficiency standards (the stick), incentives (the carrot), and information and knowledge programs (the mental and emotional motivations).
My observations from that time are that people take actions for emotional reasons and justify those decisions later by using technical and financial facts. The financial incentives were our flag to catch the market’s attention. Those incentives for energy improvements to their facilities quickly caught the attention of rate payers. Some saw the initiative as positive, and others saw it as a negative. Many early adapters invested in new technology, and saw payback periods of less than one year. The majority of people took their time and studied the information on the technology options to improve efficiency and learn what the economics looked like.
A key step in the change is new rate structures that offers lower rates based on the electrical efficiency of a building’s footprint. It doesn’t take long for owners to understand that if they are paying the higher rates, they are funding the efficiency upgrades of neighbouring buildings, perhaps even competitors. This is a big motivational element that can prompt owners to take action. But the building operators and owners can’t bring about change on their own. There must be a supporting delivery channel that is motivated towards driving the discussions one-on-one with the larger users. This includes manufacturers, contractors, service companies and others. These are the organizations that take energy upgrades as a serious part of their new business model, and financial organizations that provide funding to make these projects cash flow positive from day one. It is very difficult to suggest what might work in other markets.
Regulation on its own is often a long process that requires a lot of breathing room for businesses to survive during a transition. It also requires audits, red tape and heavy administration costs. Ideally, those resources can be turned towards creating a desire for Q&A change and nurturing a marketplace infrastructure to cater to that change. Real change can take root when the environmental factors are aligned to create a win-win-win scenario for building owners, channel participants and power providers.
Are we pursuing the exotic at the cost of ignoring the simple to realise 30% savings, say, in building energy consumption?
Fundamentally, the days of making changes on a one-by-one basis for 30% efficiency gains are gone. Today, in almost all spaces of a building, the key to increased efficiency is in the interactions within and between systems. With new intelligent technology we are seeing more than the 20-30% savings of higher equipment efficiency, and often seeing 50% or more.
A great example is an intelligent pump upgrade on a constant flow cooling tower application. It’s easy to see that as an upgrade project that might sound like an exotic solution for a mundane application. The intelligent self-regulating variable speed pump can be set to constant flow mode, and as blockages occur in the strainer it speeds up to maintain flow. The non-intelligent solution operates at an energy savings fixed speed with a drive on the wall.
Because the non-intelligent solution operates at a fixed speed, the flow to the chiller’s condenser would drop when there is strainer blockage. The efficiency of the chiller drops, leading to big increase in energy consumption. And that situation can remain in place, unnoticed, until there is a maintenance call to inspect the strainer. In contrast, an intelligent condenser pump will sense the change over time and might even sense the onset of cavitation. It will alert the operator that there is up-stream blockage that requires service.
The performance of the chiller is maintained for a small increase of pumping energy, and the service person can attend to the issue before there is a performance complication. These impacts are hard to quantify, but are a reality that we see regularly with connected intelligent devices. I am providing pumping as an example, as I am very close to it, but the same idea applies elsewhere, when intelligence is added to what was traditionally just a controlled device. There are new understandings and opportunities that arise from the experience.
Where are we headed in terms of cloud-based analytics and digital controls for HVACR systems? In what way does Armstrong Fluid Technology contribute to creating efficient technology?
Most operators, at first, are fearful of the term, ‘cloud’, as it feels like something that is in their hands today is being moved to a supercomputer at an unknown location. There is apprehension about confidentiality of information and a perceived loss of control. Interestingly, what we have seen is that human error is the biggest threat to systems maintaining their efficiency overtime. Through the observed experiences of large populations of building systems, these threats to building efficiency are already being identified and appropriate notifications and quantifications being sent to customers.
What we are doing at Armstrong is attempting to change the perceptions of owners and operators towards energy savings initiatives. Rather than seeing energy savings as a one-time upgrade ‘event’, we want to people to see the reality that energy savings is a continuous activity after a capital investment is made. Without this behaviour, the potential for success that led to the capital outlay will be quickly put at risk. The power of cloud services to sift through data can give building operators the daily insights they need to protect their investments and bring persistence to their energy performance. This is what we are doing to help customers with their on-going energy performance.