ENVIRONMENTAL GROUP The Secondary Impact of System Optimisation on Building Equipment; Maintenance and Life Expectancy Dr. Houman Tamaddon JUNE 2015
INTRODUCTION The increased pace of global energy consumption in recent years leads to significant environmental and socioeconomic issues: (i) carbon emissions, from the burning of fossil fuels for energy, contribute to global warming, and (ii) increased energy expenditures lead to reduced standard of living. Efficient use of energy, through energy conservation measures, is an important step toward mitigating these effects. Residential and commercial buildings represent a prime target for energy conservation, comprising over 20% of global energy consumption and 40% of the total energy consumption in the industrial countries. Thus, reducing building energy consumption can have a significant impact on the global energy footprint and, consequently, global energy expenditures. In order to identify target areas for improvement, an understanding of the building energy end-uses (eg. lighting, lifts, etc.) is necessary. On average, 50% of the building energy is consumed in building systems that cater to occupant comfort, namely, heating, ventilation, air conditioning (HVAC). Buildings are expected to become the largest consumer of global energy by 2025, according to the National Science and Technology Council. Thus, improvements in the building HVAC systems can result in significant energy and cost savings. BACKGROUND CIM Environmental Group offers optimisation solutions to building owners who are looking to reduce their energy footprint and, consequently, energy expenditures. Their Automated Commissioning for Energy (ACE) Platform implements state of the art fault detection and rectification algorithms to detect and pinpoint faults in building systems and the building envelope as and when they happen. Reducing energy wastage due to faulty systems or off-design control strategies represents a low-cost, high-impact approach to energy conservation which could easily save up to 15-20% of the HVAC energy consumption. LIFECYCLE COSTS While the immediate impact of building optimisation on the cost of utilities is usually the main incentive for building owners, there are additional longer-term benefits as well. In the optimised systems, plant equipment loads are moderated and used more efficiently compared to non-optimised systems. This results in the extension of the effective life of individual equipment and reduces the life cycle costs of the system. To understand and quantify the benefits of HVAC optimisation on life cycle costs of the system, it is necessary to understand the different approaches used to manage facility maintenance. Maintenance constitutes a significant percentage of expenses in most facilities. Based on International Facility Management Association (IFMA) research report on operations and maintenance benchmarks, maintenance costs could consume nearly as much of a typical facility s operating budget as utility costs and amount to more than one-third of the total operating expenses.
TRADITIONAL MAINTENANCE Traditional maintenance practice either is a function of a somewhat arbitrary rule-of-thumb e.g. perform maintenance every 90 days or 1,000 hours of operation, which is called preventive; or it is reactive (i.e., performed when the equipment breaks). In fact, referring to reactive maintenance as maintenance is a misnomer; it should really be called repair. By waiting until actual failure, these building operators ensure that repair costs will be at a maximum and that there will be interruptions in service while the repairs are made. Performing scheduled maintenance based on engineering judgment or mean-time-betweenfailure (MTBF) statistics in the preventive approach is an attempt to reduce this problem. However, this practice typically results in equipment being serviced or replaced before doing so is necessary. A more efficient way to achieve minimal costs and maximum reliability is to implement service plans that use predictive maintenance based on the actual history of equipment rather than a predetermined schedule. With this approach, equipment is maintained at a continuously high level of performance rather than waiting for something to fail. In addition, a predictive approach can be used to prioritize repairs and maintenance so that the most important systems are repaired first, ensuring the most effective return on investment. In this approach, the operational history of equipment could be analysed based on reliability and maintenance models which are able to identify critical failure modes and causes of unreliability. So this approach provides an effective tool for predicting equipment behaviour and selecting appropriate logistics measures to ensure minimal failure and satisfactory performance. ADVANCED MAINTENANCE MODELLING Developing a model to predict the remaining life of the equipment is complicated. Failure rates of mechanical components cannot usually be described by a constant failure rate distribution because of wear, fatigue and other stress related failure mechanisms resulting in equipment degradation and the mechanical equipment fatigue rate is sensitive to loading, operating mode and utilization rate. Many life-limiting failure modes such as corrosion, erosion, creep and fatigue operate on the component in parallel and all contribute to reducing the life cycle of the equipment. The current predictive models are mainly developed based on identified failure modes and their causes. These models incorporate simplified equations for each failure mode which are originally derived from design information plus experimental data. These equations incorporate those variables affecting reliability and failure rate. Modification factors are then compiled for each variable to reflect its quantitative impact on the failure rate of an individual component part. The total failure rate for individual equipment is the sum of the failure rates for the component parts.
CASE STUDY Quantifying the benefit that system optimisation has on a large multi-variant HVAC system requires extensive experimental and analytical resources. Therefore we have developed a case study on an actual piece of equipment that explains the practical application of the reliability and fatigue models. The case study is about a 7.5 kw chilled water pump which is subjected to load-based optimisation as part of a chilled water plant. Figure 1 shows the pump speed profile in a typical working day before and after CIM s optimisation solution. The optimised pump not only operates at lower average speed, but also benefits from less daily operational hours. Figure 1: Pump Usage Profile Before and After Optimisation A quick estimate based on current utility prices in Australia and five duty days per week identifies $2,746 immediate annual savings due to optimising this particular pump.
However, as discussed earlier, the total impact of optimisation is more than immediate savings in utility costs. To investigate the amount of savings as a result of having lower maintenance costs and extended equipment life we need to take a look into the failure model for pump assembly. Pump assemblies are comprised of many component parts including seals, shaft, bearings, casing, and fluid driver. In order to properly determine total pump reliability, failure rate models should be studied for each pump component.
The total pump failure rate is a combination of the failure rates of the individual component parts. The general failure model for centrifugal pumps can be expressed using equation below: Where: Each of these failure factors can be expressed as a combination of some empirical equations which are specifically developed for that particular component. As an example, the failure rate of a centrifugal pump fluid driver can be estimated from the following equation: Where: Each of these multiplying factors will be calculated based on a specific function. For example, operating speed affects the failure rate multiplying factor, caused by the increased RPM leading to accelerated rubbing wear of the shaft and mechanical seal faces, increased bearing friction and lubricant degradation. Increased operating speed also increases the energy level of the pump which can lead to cavitation damage. The effects of wear on these components are almost linear as a function of RPM. The following equation provides a multiplying factor for operating speed based on actual and design RPM:
Where: The complete description of different failure models for various components is far beyond the scope of this report. Performing all the calculations for this case study resulted in reducing the failure rate from 2269 to 1342 failures per million hours which is just over 41% increase in the life expectancy of this particular pump assembly -or 41% less failures- as a result of system optimisation. Performing the same analysis on different equipment will results in different results. However, as a rule of thumb, as the degree of complexity of equipment increases, its sensitivity to load and operational condition will increase. It can be concluded that the effect of optimisation on life expectancy of complex equipment such as chillers would be even more significant. The unique design of ACE platform provides all necessary data to monitor and analyse the fatigue and failure modes in building equipment. Key operating parameters of equipment are checked and monitored automatically by the software. The readings are then analysed and used to evaluate the condition of the equipment and predict the future performance or likelihood of failure. Maintenance is then prioritized based on potential cost and comfort impact. CONCLUSION It has been proven that predictive maintenance not only reduces regular maintenance costs, but also extends the lifetime of HVAC plant and equipment by several years. However, as long as the building system operates at its optimal point, whether building owners choose to implement predictive maintenance scheme or not, the advantages of optimisation are clear. Other benefits include increased safety from properly maintained equipment and greater comfort and productivity for occupants. The optimisation process also ensures that environmental ratings are easier to achieve and maintain.