office temperature monitoring system: a capstone project
TRANSCRIPT
Paper ID #21127
Office Temperature Monitoring System: A Capstone Project
Dr. Maher Shehadi, Purdue University
Dr. Shehadi is an Assistant Professor of MET in the School of Engineering Technology at Purdue Univer-sity. His academic experience have focused on learning and discovery in areas related to HVAC, indoor airquality, human thermal comfort, and energy conservation. While working in industry, he oversaw main-tenance and management programs for various facilities including industrial plants, high rise residentialand commercial buildings, energy audits and condition surveys for various mechanical and electrical andsystems. He has conducted several projects to reduce CO2 fingerprint of buildings by evaluating andimproving the energy practices through the integration of sustainable systems with existing systems. Pro-fessor Shehadi also has an interest in air pollution reduction and in providing healthier environment byanalyzing the various pollutants that are present in outdoor and indoor air. His current research focuseson sustainable and green buildings and energy conservation. He is currently investigating various ways toreduce energy consumption in office buildings.
c©American Society for Engineering Education, 2018
Office Temperature Monitoring System
Abstract
Energy consumption in residential and commercial buildings has increased significantly over the last decade contributing to 40% of the US primary energy usage. Heating, ventilation, and air-conditioning (HVAC) for these buildings contribute to more than half this amount. A reduction in the HVAC energy consumption load would reflect a significant reduction in the total energy consumed. Programmable thermostats are used to reduce energy consumption. However, how efficient the thermostats are in terms of representing the room temperature defines the level of comfort for the occupants inside the space. An increase in the variance between the thermostat value and the overall temperature distribution in a space would indicate inefficient representation and would increase occupants’ discomfort.
This capstone project was led by an MET student (Mechanical Engineering Technology) at Purdue Polytechnic Kokomo. The objective of the project was to investigate temperature disturbances across an office space that can help solve temperature non-uniformity that would ultimately help in saving HVAC energy consumption. The project built a temperature monitoring system by freely hanging temperature thermocouples in air throughout an office space. The temperatures at different locations were compared to a common reference point collected by a thermocouple located near the room’s current thermostat. The study investigated the instantaneous changes in temperature readings throughout the room during day and night times under normal conditions. The same conditions were repeated but when a warmer air stream was allowed to enter the office through the door.
The study showed that there exists 2-3 ˚F difference when considering all locations throughout the room under normal operating conditions, when the door was closed and no occupants were present. The system successfully detected changes in room temperatures as a result of door opening with some delay in response time that is a function of the thermocouples type used. Analysis of the results showed a potential 20% savings in energy consumption if proper calibration or adjustments are granted in future thermostats. The project was assessed against ABET learning outcomes and there was significant application and relevance between the students’ learning outcomes and the ABET rubrics.
Introduction
Energy consumption in buildings significantly increases on yearly basis due to the increased human comfort needs and services. Temperature control and air quality are top requirements in any modern house. The ventilation and air-conditioning power consumptions, needed to meet the
occupants’ needs for a good temperature level and acceptable quality of air delivered from these systems, can reach to approximately one fifth of the total energy bill of buildings [1]. In the United States alone, 19 billion US dollars were spent in 2014 to run the equipment for these systems [2].
The control system that runs and operates the air-conditioning system is as important as the efficiency of the equipment itself. Having a control system that is incapable of delivering the thermal needs of a building would cause negligence to the efficiency of the equipment used. Having an expensive air-conditioning system and a relatively cheaper control unit or cheaper thermostat would yield undesirable results and unsatisfied occupants. A wrongly selected equipment could increase the energy consumption of an HVAC system by 30% [3]. Research is being conducted to advance the technology of thermostats to better represent the spaces they are installed in. Research by [4], [5], [6] and others investigated different technologies to improve the efficiency of temperature control thermostats by looking into infrared (IR) technologies and by embedding CO2 concentration sensors in their systems. There have been many studies focusing on the impact of having a faulty unit such as by [3], [7] and [8], but none studied the accuracy of an installed thermostat in representing the desired temperature of the space it is placed in. A bad or inaccurate representation could mean higher occupant discomfort, less energy savings, and higher energy bills.
Temperature distribution inside any zone is highly distracted by adjacent room temperatures, window location and air-supply diffuser type and location. It is expected that regions close to windows are more vulnerable by outdoor changes than other locations. Also, spaces that are closer to heat sources such as computers and printers are expected to have more radiant heat and, thus, expected to experience higher temperature. Similarly, areas located under the air-supply diffusers are expected to have lower temperatures. HVAC controls that quickly detect changes in room temperature should allow optimization of air distribution and provide substantial energy savings [9].
For this reason, this capstone project was conducted. Its focus was on installing and monitoring a system in an office space to investigate air temperature distribution and variations inside the office at different heights and locations. The study looked into different scenarios such as normal conditions with no external heat sources and included changes due to the opening and closing of the room’s door. On the hand, this project would train the involved student/s within the Mechanical Engineering Technology program on how to save energy consumption, learn how to build and collect data, use various instruments such as thermometers, thermocouples, multi-meters, programming and conduct calibration. The students would gain significant knowledge in data analysis and statistics while comparing the results. A successful project would reflect good understanding for students in most core courses required by MET programs such as thermal
courses, control, instruments, and statistics. These learning outcomes were compared to the ABET outcome rubrics. Purdue Polytechnic has the main campus located in West Lafayette and nine other remote locations distributed across the state of Indiana. Purdue Polytechnic Kokomo started offering BS degree in MET recently which limited the number of senior students available at this time. For that reason, this project was done by one MET student but it is the department’s interest to start assigning teams for capstone projects in the coming semesters. This capstone project was part of a bigger project conducted at Purdue Polytechnic Kokomo to improve energy consumption in buildings.
Experimental Setup
An office having a 3×2 m2 floor area with 3.5m height was used to install and test the thermal monitoring system. Twelve thermocouples Type T were installed through the ceiling to measure and record the temperature at different locations in the room. The thermocouples were arranged at two different elevations (0.9 m and 1.8 m from ceiling) as shown in Figure1(a) and Figure 1(c). Figure 1(c) is a generic 3D model for the room showing the surrounding rooms, window and door locations, and the twelve thermocouples. The room was conditioned using the existing building HVAC system and the temperature was controlled by a thermostat located on the left side wall of the room. The hallway on the west side of the room was kept at higher temperature than the room temperature. The thermocouples were labelled for better analysis as
Figure 1. Office used for experimental data collection (a) actual office with hanging thermocouples, (b) actual
office with included equipment and furniture, (c) generic 3D view with surrounding environments
shown in Figure 2. T00 was attached to the wall next to the room’s thermostat to represent the temperature of the actual thermostat connected with the actual AC system for the room. To ensure experimental consistency, all thermocouples were 7.62 m (25 feet) long and were then calibrated against two known temperatures [boiling 100 ˚C (212 ˚F) and freezing 0 ˚C (32 ˚F)]. Distilled water was used in both calibration tests.
Figure 2. Plan view for the room with locations for window, door, thermocouples, air-supply and return ducts
All thermocouples were connected to an “Automation Direct” programmable logic controller (PLC) unit (model: H2DM1E). The thermocouple wires were run from the testing office through the suspended ceiling to the adjacent north side room where the PLC unit was housed to avoid any radiant heat by the PLC. The PLC had three thermal modules (Brand: Automation Direct; Model: F2-04-THM) which were used to connect the thermocouples into the PLC as shown in Figure 3(a). The thermocouples calibration was done after all wires were connected to the PLC. The PLC was programmed to embed the calibration equations for each thermocouple so that all tests were done with minimal errors. The PLC was programmed to allow temperature recordings at different time frames: every second, every minutes, every hour, and user defined. The display screen of the PLC was used for better visual inputs/outputs. All temperatures were displayed on the screen along with percent difference from T00 thermocouple reading (shown in Figure 3b).
Figure 3. (a) PLC unit with screen, thermal modules, and thermocouples connected and (b) GUI display screen
Methods
After calibrating all thermocouples, temperatures were collected through the 12 installed thermocouples under different cases as shown in Table 1. In both cases the air supply temperature was set at 65 ˚F (18.3 ˚C), lights were kept off, no occupants, and PC was turned on during daytime only. In the first case, the entrance door was kept shut all the time, whereas in case 2 the door was opened for a certain time, then closed, then opened, then closed as will be discussed in more details in the following section.
Table 1. Investigated cases variables
Variable Case 1 Case 2 Entrance Door Closed all time Opened and Closed Indoor Temperature 18.3 ˚C Lights Off Occupancy None PC ON during daytime
Results
Calibration procedures were done for each of the 12 thermocouples and the calibration equations were programmed into the PLC to accommodate for any differences coming from thermocouples connection or installation, differences in thermal modules reading, grounding issues, and other similar variables. Results of calibration for two thermocouples only (T00 and T01) are shown in
Figure 4. The other equations are similar to those shown in Figure 4 and are not shown to save space but are available upon request from the authors.
Figure 4. Thermocouple calibration results for (a) T00, (b) T01 (all temperatures are shown as programmed in ˚F)
The instantaneous temperature distribution collected every hour during various periods of time under normal operating conditions, presented under Case#1 in Table 1, are plotted in Figure 5 and Figure 6. The reference thermocouple (T00) is shown as solid line whereas all other 11 thermocouples’ readings are labeled as dotted points. It can be observed from figures 5 and 6 that all thermocouples follow the same trend as the temperature changes with time.
Figure 5. Temperature log data points collected every hour (Data collection set # 1) (door closed, lights off, no occupants, set temperature at 65 ˚F) – (x-axis is the actual 24-hour format daytime)
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Figure 6. Temperature data points collected every hour (Data collection set # 2) (door closed, lights off, no occupants, set temperature at ˚65 F) – (x-axis is the actual 24-hour format day time)
After completing the data collection with all parameters as given in Case#1, the system was turned ON to collect data for almost 40 hours with conditions similar to that in Case#1 before opening the door. These 40 hours included data overnight and during daytime. The door connects the office to the hallway that had relatively higher temperatures and was left open for 3½ hours before closing it for 2 hours, and then opening it and leaving it open for 10 hours. The open/close periods were completely random to represent real time data. The purpose behind the opening and closing of the door is to investigate the differences and similarities in the sensors transient behavior. The data was collected on hourly basis and is shown in Figure7.
Figure 7 showed that prior to the door opening all the thermocouples were following the same behavior. All thermocouples’ temperatures were shifted down overnight as the set temperature is automatically overridden and was reduced during night to reduce the building energy consumption. After that period, all curves continued with similar trends to that shown in figures 5 and 6. The thermocouples’ behavior could be categorized into three groups: the first one includes thermocouples that were similar to T00; the second group includes those that were slightly different than T00; and the third group includes thermocouples that experienced higher temperature differences compared to T00 readings. The following thermocouples T01, T03, T04, and T06 followed thermocouple T00. Other thermocouples including T02, T05, T07 and T09 has almost ½ °F difference from T00, and, lastly, T08, T10, and T11 had between 1-2 °F. When the door was opened all thermocouples reading started to increase due to the higher temperature air
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stream entering the room from the relatively warmer hallway. The temperature rise was delayed slightly after the door opening. This delay in response time for each thermocouple was even higher after the door was closed and then opened.
Figure 7. Instantaneous temperature distribution when door is opened and closed (x-axis is the actual 24-hour format day time – a new day is triggered every 24 hours)
Discussion
Results for the temperature distribution collected under Case#1 conditions revealed that the thermocouples can be divided into three categories according to their location and to the similarity of their behavior/trend with respect to T00. The first group, as discussed earlier, includes T01, T03, T04, and T06. All of these four thermocouples were at a distance of 1.8 m from the ceiling and had temperatures very close to that of T00. The second group included thermocouples with approximately ½ ˚F difference from T00 and it includes T02, T05, and T07 which are all at a distance of 0.9 m from the ceiling. The third group includes thermocouples with differences higher than ½ ˚F with respect to T00 and it includes T08, T10, and T11. T08 and T10 are both closer to the ceiling at a distance 0f 0.9 m. Thus, thermocouples with distances of 1.8 m, closer to office lower levels, were closer in behavior to T00. In other words, the higher region of the room experienced more variability in temperature with respect to T00. These results were consistent from time to time as shown in Figure 5, 6 and part of Figure 7 before opening the door. To investigate this observation in more details, Figure 8 plotted the average of the readings from thermocouples at a distance of 0.9 m as a group, against those at 1.8 m; T00
was plotted as well as a reference for comparisons. It is clear that the thermocouples closer to ground level (at 1.8m from ceiling) has similar or closer temperatures to T00 than those at higher levels in the room (located at 0.9m from ceiling).
Figure 8. Averages of 0.9 m and 1.8 m from ceiling thermocouples versus T00–(x-axis is the actual 24-hour format)
In Case#2, the entrance door was opened for a certain period of time, then closed, then opened to check the response behavior of the thermocouples due to abrupt changes in the air temperature. When the door was opened, the values for all thermocouples started rising after a slight delay as shown in Figure 7. After the delay, the rise in temperature for all thermocouples was much faster. Thermocouples T05 and T08 had the lowest rises compared to other thermocouples. Thermocouples at a distance of 1.8 m from the ceiling, including T01, T03, T04, T06 and T09, experienced the same behavior/trend or similar temperature rise rate as T00. This was very similar to the previous observations for Case#1 under normal operating conditions.
After the door was closed, temperatures recorded by T01, T03, T06 and T09 continued increasing for almost an hour at similar rate as before when the door was open. This was true for T00 as well. These thermocouples are all at a distance of 1.8 m from the ceiling. On the other hand, all other thermocouples closer to ceiling (0.9 m from ceiling) adjusted the rising rate after the door was closed. Examples of these thermocouples include T02, T05, T07, T08, T10, and T11. Thermocouples T02, T05, T07 and T10 were approximately 1 ˚F lower than T00 temperatures, whereas T08 and T11 were almost 2 ˚F lower than T00. Notice that both T08 and T11 are at the opposite corners of T00. T08 was closer to the return and T11 was all the way on the opposite corner of the room with respect to T00, beside the window.
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T00 0.9 meter - Average 1.8 meters - Average
The relative uncertainty for all thermocouples were checked by considering both the random relative uncertainty (ur) of all measurements and the bias uncertainty of each thermocouple. The random uncertainty was calculated using equation (1), where t95% is the 95% confidence t-value (t95%=1.72 for 24 samples representing 24 hours), S.D. is the standard deviation of the samples, n is the number of samples or measurements taken by each thermocouple, and is the average temperature. The bias uncertainty (ub) is the uncertainty of the thermocouple which was estimated by the manufacturer approximately ±0.75% of the average temperature measured. Therefore, ub is taken as ±0.75 since u is the relative uncertainty with respect to the average value. Finally, the PLC unit uncertainty (approximately ±3.4%) was added to the above two uncertainties to estimate the total relative uncertainty as shown in equation 2 using the “rms” method.
ur = %
. .√
100 (1)
ut = (2)
The standard error which is the numerator of equation (1) %. .
√ was very small,
approximately 0.05 ˚F due to small differences in values, thus yielding a very small relative uncertainty. Therefore, the dominant uncertainty was the bias and PLC uncertainties. The maximum relative uncertainty for all thermocouples was approximately ±3.5%.
Project assessment
By the end of this project, the student built and tested a thermal monitoring system, designed a testing procedures and collected data, and analyzed the data to better reach to a conclusion regarding his project. The student gained extensive experience in the field of HVAC, controls and measurements, data acquisition, statistical analysis, calibration, and factors that can affect temperature differences. Throughout this process, the student experienced many ABET outcomes as shown in Table 3. Performance assessment and feedback were done through the evaluation of biweekly submitted reports by the student. There were four main categories toward the final GPA of the student: biweekly reports (15%), draft report (10%), final report (50%), and presentation (25%).
1) Biweekly reports participated 15% of the final GPA. These reports summarized the work of the previous two weeks and allowed the instructor to provide continuous feedback. Each report was recorded on a log-book that included the following activities:
i. Agendas and minutes of meetings identifying decisions and action items taken.
ii. A weekly prioritized to-do list. iii. A weekly list summarizing goals achieved during the previous two weeks including
the time spent (in hours) working (how much of the to do list was completed?) iv. Notes from outside research. v. Notes of how to accomplish a task.
vi. Calculations, graphs (hand made and/or computer generated), drawings. vii. Test plans, collected data, analyses, and conclusions regarding testing. Each of the biweekly reports had a general theme as follows:
Report 1 Proposal (01/15/17) Report 2 Conceptual Design (02/02/17) Report 3 Preliminary Design (02/17/17) Report 4 Critical Design (03/02/17) Report 5 Proceed to Test (03/23/17)
Each report was evaluated based on rubrics given in Table 2.
Table 2. Rubrics used for evaluating biweekly reports
Points 4 3 2 1 0 Weekly notes from supervisor and other parties
Notes exceeded expectations
Appropriately relative to meeting content
Notes qty & quality were missing some meeting contents
Some evidence of notes
No evidence of notes
Legibility Exceeded expectations All entries clear & legible
75% or less clear & legible
50% or less clear & legible
25% or less clear & legible
Readability Exceeded expectations, cross-referenced
Well identified entries
< 75% are identified, erratic flow in places
50% are identified, erratic flow in most places
< 25% identified, erratic flow
Completeness Well documented, flow and content of entries demonstrated forethought, connections, and results, in and between process phases
75% of flow and content of entries demonstrated forethought, connection, and results
50% of flow and content of entries demonstrated forethought, connection, and results
Flow and content were spotty and unconnected
No evidence of forethought, connections, or results in and between process phases
Lab Notebook Guidelines (items i-viii above)
Followed all criteria
Criteria followed about 75% of the time
Criteria followed about 50% of the time
Criteria followed about 25% of the time
No evidence of following guidelines
2) Draft final report (10% of final GPA): submitted 1-week before project presentation to allow any final feedback from the supervisor.
3) Presentation (25% of final GPA): The student presented results of the project to interested MET, EET and ET faculty members and guests.
4) Final report (50% of final GPA): submitted by the end of the semester after getting feedback from the project supervisor, guests and other faculty members, who served as external evaluators, and then embedding their comments, suggestions and corrections in the final report.
Table 3 shows ABET learning outcomes and categories used to meet these expectations.
Table 3. ABET students learning outcomes used for project assessment and the respective means used to meet these outcomes
ABET ETAC Rubric Means used to meet the rubrics
(a) Apply knowledge, techniques and skills to engineering technology activities
Final Report and biweekly reports
(b) Apply knowledge of mathematics, science, engineering, and technology to engineering technology problems
Final report and biweekly reports
(c) Conduct tests, measurements, calibration and improve processes
Biweekly reports, draft report, and final report
(e) Problem Solving: ability to identify, formulate, and solve engineering problems
Project proposal and biweekly reports
(f) Effective Communication: ability to communicate effectively Presentation and biweekly reports
Conclusions
The capstone project built a temperature monitoring system that was able to accurately record and monitor the temperature inside the office room at different locations. Temperatures within the room varied by 2-3 ˚F due to forced convection resulting from the supplied air coming from the supply diffuser located in the middle of the ceiling.
Temperatures recorded by thermocouples located closer to the ground level were closer in value and trend to the reference thermocouple installed beside the room’s thermostat. This behavior was detected under normal operating conditions and when heat was added to the room by allowing a stream of warmer air to enter through the office door.
Despite the complexity of the issue, this system could accurately predict temperatures at different regions. It can be concluded that the location of the current thermostat in the room represents the temperature of the air in the lower half region of the room, a condition that is acceptable for an office room where most occupants would be seated. Thus, the temperature detected by the thermostat is more representative to what a seated person in an office experiences. However, to broaden this conclusion more testing is needed in similar offices, and in other commercial and residential spaces before a solution is deemed necessary.
Assessment rubrics reflected students expectations from ABET learning outcomes. Some outcomes were not met due to inapplicability such as team work. Team projects shall be assigned in the future to help the students meet more of the ABET learning outcomes.
Recommendations and Future Work
This capstone study was part of a bigger project conducted at Purdue Polytechnic Kokomo to improve energy consumption in buildings. This specific capstone targeted a model office that was representative of a regular office setup. Due to limitation in time and budget, it was not possible to conduct similar measurements in other spaces at the same time while conducting this project. It would be of interest to repeat such measurements in other office spaces, commercial buildings, and residential areas to check on any differences in the results.
Acknowledgment
Data collection and PLC programming was done by Andrew Bachman who was an MET senior student at Purdue Polytechnic Kokomo. The author would like to express his gratitude to the MET lab technician Mr. Dennis Carter for helping in setting up part of the experiments conducted in this study. Funding was made available by Purdue Polytechnic to support materials and equipment purchase needed for this project.
References
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