Decrypting building data
I was on-site, commissioning a new chiller at the San Diego Marriott Hotel and Marina, when a frantic assistant chief engineer waved me down. After hearing what he had to say, my attention quickly switched from cooling to heating. An aging boiler serving the ancillary building was in dire need of replacement.
I was on-site, commissioning a new chiller at the San Diego Marriott Hotel and Marina, when a frantic assistant chief engineer waved me down. After hearing what he had to say, my attention quickly switched from cooling to heating.
An aging boiler serving the ancillary building was in dire need of replacement. It served the reheat system and was the original equipment from a 1986 installation. The boiler had begun to leak significantly and its safe operation was no longer a possibility. The loss of this system led to a loss of temperature control in the building’s meeting spaces. Guests were frustrated with the service; management had to dole out a number of refunds and were concerned about the potential loss of repeat business. Another complication arose when the vendor was contacted for the pricing on a new boiler. He proposed a boiler that was 40% smaller than its predecessor, reasoning being the original was oversized for the facility. Although the vendor had years of experience to support his recommendation for the boiler, the chief engineer and the director of engineering were understandably hesitant to endorse the decision; after all they would be the ones answering to management if the boiler’s performance fell below the required load. Compounding the complexity of the decision was the fact that the smaller boiler could be accommodated within the available emergency repair budget while an exact replacement would require financial approval from corporate headquarters and possibly extend the replacement time. The assistant chief engineer wanted my assessment of the required load that the boiler would need to deliver. When I asked when he needed an answer by, he said with a laugh, “by the end of the day.”
Coming up with a plan
Since it was already mid-afternoon, I thought the time limit would be a significant constraint on the depth of my analysis beyond an assessment of the load based on some combination of square foot parameters. But I remembered that the system, though not heavily monitored, did have data trended on 5-min intervals for some important fundamental parameters, including entering and leaving water temperatures and boiler gas consumption.
A system load extracted from actual operating information would take a lot of the guesswork out of a quick assessment. Assuming the spaces had remained under control, it would represent the response of the system to the actual conditions that existed inside and outside the facility. The automatic calculation and compensation for time lags and heat transfer rates provided by real-time building data greatly reduced the uncertainty as well as the number of assumptions required to provide a quick analysis.
From the building’s mouth
In order to have the building tell us what the load was, we logged into the control system and extracted several hours of data for one of the coldest days the facility had experienced in recent history. Initially, we thought that an assessment of the boiler gas consumption would give us the answer we were looking for. However, we also realized that it would be extremely difficult to ascertain an estimated load without knowing the efficiency of the boiler.
Ideally, boiler efficiency is determined by a number of variables, including the boiler design, burner adjustment, operating temperature, and the condition of the heat transfer surfaces. Given the age and degraded condition of the boiler, however, assuming its efficiency was anywhere near the 75% figure implied by its nameplate rating would be less than sound engineering judgment.
Measuring the efficiency in the field may have been possible if we had time and could get the boiler to run, but was struck as an option since the safe operating ability of the unit itself was at question.
Past experience and information available from various sources told us that the efficiency of the degraded boiler could be lower than 60%. Although the gas consumption pattern was an indicator of the actual load, we did not want to base our analysis on that solitary source of data.
With help from the building
Fortunately, we also knew the temperature rise across the boiler. Because the hot water system served by the boiler was a constant volume system, we decided to use the pump’s rated flow rate and the logged temperature rise across the boiler to give us a real-time measurement of the actual load on the system. Because we were measuring the output of the boiler instead of the input, the efficiency of the boiler would not be a factor. The challenge was to determine a realistic system flow rate, which would allow us to apply the given water side load equation.
Q = 500 x flow rate x (T in – T out)
Q = Load in Btuh
500 = Units conversion constant
flow rate = Flow in gpm
(T in – T out) = Temperature change across the boiler
Unfortunately, determining the rated flow capacity of the pump was not easy. The original drawing with the pump schedule was missing and the nameplate on the pump had become illegible over time. Undeterred, we decided we would base our assessment on a range of flows that would be accommodated by the line size associated with the system.
Specifically, the line size at the boiler and pump was 2.5 in. By using a pipe friction chart, and keeping in mind that designers tend to pick the next larger line size if friction rates are within 4 ft water column per 100 lineal ft of pipe, we determined that the flow rate in the system was probably 55 to 80 gpm.
Having established a flow range for the system, we downloaded trend data from the hot water system into a spreadsheet and then added a couple of columns to perform the load calculation using the logged temperatures and the flow rates we had established.
A concrete assessment
As illustrated by the graph in Figure 1, the required capacity from the boiler on the coldest day in San Diego seldom went over the capacity of the proposed replacement. This erased any doubts about whether the new boiler would provide sufficient heating for the building.
Some quick math based on the trend data revealed that if our assessment of flow was correct, the boiler efficiency was somewhere between 44% and 71%. While the efficiency of the existing boiler was probably better than 44%, it also was not likely to surpass 71%. Thus, the gas data reinforced our estimation of the load based on flow and water temperature rise across the boiler. Had the gas consumption been less than the capacity we thought we were getting from the boiler based on our estimated flow and logged temperature data, we probably would have had to reconsider our analysis. The fact that everything added up only served to strengthen our assessment of the load.
Learning from experience
Mining the data from your building can be a powerful technique for guiding ongoing O&M, and we had used it before on the facilities chilled water plant as shown in Figure 2.
The red line in Figure 2 represents the overall plant kW per ton at different load conditions when Marriott Retrocommissioning Commissioning (MRCx), Marriott’s own brand of retrocommissioning, was first started. The full load kW per ton was satisfactory but the part load performance was disappointing, especially when the team realized that its plant actually spent a lot of time operating at 250 to 400 tons. But the team identified and implemented an improvement that reduced the condenser and evaporator pump energy, shifting the plant’s profile to the green line. The opportunity was identified by testing the exiting pumps at full load and part load and allowing the test results guide the team to the optimization strategy.
In the course of the effort, it also became apparent that the redundancy provided by the existing plant was not commensurate with the caliber of the facility. As a result, the owner released more money to add capacity to the system for peaking and reliability. By virtue of the MRCx process, the commissioning team had done a lot of trending and realized that one approach for selecting the chiller was to use a technique similar to that described for the boiler. By pulling hourly trend data for a year and doing a little bit of math, the commissioning team and design team painted a picture of the plant’s load profile as illustrated in Figure 3.
This data allowed the project team to target chiller capacity and performance to optimize the new machine for the actual conditions it would see, as illustrated in Figure 4. The bottom line is that the new plant should deliver a kW per ton profile similar to what is shown by the blue line in Figure 2. Thus, the information gleaned from the ongoing operation of the building informed the design process to provide equipment that is closely tailored to its needs.
The new chiller was installed in a location that had once served one of the original chillers for the facility. The old machine had been removed when the central plant was upgraded, but its condenser pumps and related piping remained intact and the original plan was to overhaul the pumps and use them to serve the new chiller.
But as the project moved into construction, the MRCx team realized that the potential existed to pick up a 10% improvement in pumping efficiency by spending the overhaul dollars for one of the existing condenser pumps on a new pump selected to match the new machine’s flow rate. Rather than calculate the head, the team plans to perform a pump test to identify the exact requirements for the new point, allowing its operating point and peak efficiency to be matched to the new base load machine. This approach represents the true vision of Marriott’s MRCx process in particular and any retrocommissioning process in genera; that is, the evolution of retrocommissioning from a one-time event to the way a building is operated day in and day out. Marriott terms this process MCCx for Marriott Continual Commissioning.
The bottom line is that if the MRCx team is right in their assessment of the improvement potential described above, the proposed test will allow the new chiller and the condenser system supporting it to tell them what pump they need to achieve the plant kW per ton profile associated with the purple line in Figure 2 continuing the effort to improve plant efficiency.
The key to solving a problem like this is to have a good measure of the information available. The data available from the building can be a great resource if harnessed properly. Although it may seem that commissioning providers have sharper hearing when it comes to listening to the building, experience, a fundamental understanding of physics, and a passion for optimizing building efficiency are all that are really required.
Many commissioning providers started out as designers, contractors, or technicians themselves. It is that background that forms the foundation for their expertise. Make a point of meeting the commissioning provider working on your project. The experience will be helpful for both of you and may even push you to explore a career in commissioning.
|Sellers’ background includes more than 30 years of experience with commissioning, design engineering, facilities engineering, mechanical and control system contracting, and project engineering in a wide array of facilities. Sellers also provides technical training and develops technical guidelines on retro-commissioning and commissioning field techniques and engineering fundamentals in a number of venues. He is a member of Consulting-Specifying Engineer ‘s editorial advisory board.|
The technique illustrated in the preceding discussion can work well in many situations, but it is important to know its capabilities and limitations when applying it to an analysis.
Because we did not know the flow in the pipe and made some assumptions regarding what it might have been, our solution was approximate rather than exact. But had we known the flow, either via a pump test or measured data, the solution would have been as exact as our measurements, perhaps even more exact than a number we might have developed using standard load calculation techniques given the assumptions that come into play.
The accuracy of our analysis was directly linked to the accuracy of the temperature sensors we based it on and the credibility of our assumptions. Based on past experience with the facility, I knew the temperature sensors were accurate within a degree or two. In addition, they furnished readings were believable (the supply was hotter than the return) and in the ballpark of what we they should have been given the systems operating set points.
With regard to our assumptions about flow rates, given a bit more time, we could have firmed them up with a pump test, assuming we had a curve for the pump. Lacking that, our assumption implied that the pipe had been properly sized, which is a reasonable assumption, but not as accurate as the measured temperatures.
Bear in mind that all equipment will fully load under some operating condition; the key is to assess whether the fully loaded equipment keeps the load it serves under control if you are using the technique to assess capacity requirements. If the equipment is fully loaded but can not maintain the required conditions, then it is actually overloaded and using this technique to asses the real load on the facility will not provide the right answer.
If the load is going to change, then you need to supplement what the building is telling you with other information. The building can’t tell you about something it hasn’t experienced yet.
Qualifying the analysis
The following factors lent even more credibility to our work:
• The load was based on measured data from the building while in actual operation and was grounded in that reality.
• The existing boiler capacity was able to warm the building up and then perform below peak delivery to hold comfort conditions. In other words, the steady state load on the facility appeared to be significantly less than the peak capacity available from the proposed equipment.
• The gas consumption pattern, which represents the energy into the boiler, correlated with the load pattern and indicated that the boiler efficiency was somewhere in the range of 44% to 71%.