A virtual supply airflow rate meter for rooftop air-conditioning units

Abstract A proper amount of supply airflow is critical in all kinds of air-based HVAC systems to maintain desired control effectiveness, energy efficiency and indoor air quality (IAQ). Although knowledge of supply airflow rate (SCFM) is certainly very important, measuring and monitoring SCFM in rooftop air-conditioning units (RTUs) by using the conventional SCFM metering devices are very costly and more than often problematic. This paper proposes a low-cost but accurate virtual SCFM meter to solve the dilemma for RTUs. The SCFM values are indirectly derived from a first-principle model in combination with accurate measurements of low-cost virtual or virtually calibrated temperature sensors. Modeling, uncertainty analysis and experimental evaluation through a wide range of laboratory testing for both cooling- and heating-based approaches are performed respectively in the development. The study reveals that the heating-based method surpasses the other in terms of its simplicity, accuracy (uncertainty is ±6.9% vs ±13.8%) and reliability and is chosen to be the virtual SCFM meter in RTUs. This cost-effective application is promising with a number of merits, such as easy to implement, economical for use, and generic in RTUs with the same constructed gas furnaces. For applications, it could be applied as a permanently installed monitoring tool to indicate the SCFM and/or to automatically detect and diagnose improper quantity of SCFM for RTUs.

[1]  Abdelsalam Helal,et al.  Virtual sensors for service oriented intelligent environments , 2007 .

[2]  D. Meizel,et al.  Experimental validation of vehicle sideslip angle observers , 2004, IEEE Intelligent Vehicles Symposium, 2004.

[3]  Mingsheng Liu,et al.  Optimized pump speed control using pump water flow station for HVAC systems , 2007 .

[4]  Furong Gao,et al.  Advanced control of chemical processes : a proceedings volume from the 7th IFAC International Symposium (ADCHEM), Hong Kong, China, 11-14 January 2004 , 2004 .

[5]  J. Macgregor,et al.  Development of inferential process models using PLS , 1994 .

[6]  Heinz Unbehauen,et al.  Fault Detection and Diagnosis with the Help of Fuzzy-Logic and with Application to a Laboratory Turbogenerator , 1996 .

[7]  James E. Braun,et al.  Common faults and their impacts for rooftop air conditioners , 1998 .

[8]  Tao Gu,et al.  Toward an OSGi-based infrastructure for context-aware applications , 2004, IEEE Pervasive Computing.

[9]  Abdul Rahman Mohamed,et al.  Neural networks for the identification and control of blast furnace hot metal quality , 2000 .

[10]  Ali Charara,et al.  Virtual sensors, application to vehicle tire-road normal forces for road safety , 2009, 2009 American Control Conference.

[11]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[12]  C. Federspiel,et al.  Predicting the Frequency and Cost of Hot and Cold Complaints in Buildings , 2000 .

[13]  Guopeng Liu,et al.  Application of Fan Airflow Stations In Air-Handling Units , 2007 .

[14]  Haorong Li.  A decoupling-based unified fault detection and diagnosis approach for packaged air conditioners , 2004 .

[15]  J. E. Janssen,et al.  Ventilation for acceptable indoor air quality , 1989 .

[16]  M. Milanese,et al.  Direct Virtual Sensor (DVS) design in vehicle sideslip angle estimation , 2007, 2007 American Control Conference.

[17]  M Daszykowski,et al.  Dealing with missing values and outliers in principal component analysis. , 2007, Talanta.

[18]  Chris P. Underwood,et al.  HVAC Control Systems: Modelling, Analysis and Design , 1999 .

[19]  Ulf Holmberg,et al.  A Simple Virtual Sensor for Combustion Timing , 2003 .

[20]  H. Hotelling.  The Generalization of Student’s Ratio , 1931 .

[21]  James E. Braun,et al.  Evaluating the Performance of a Fault Detection and Diagnostic System for Vapor Compression Equipment , 1998 .

[22]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part I: Quantitative model-based methods , 2003, Comput. Chem. Eng..

[23]  Sven Serneels,et al.  Principal component analysis for data containing outliers and missing elements , 2008, Comput. Stat. Data Anal..

[24]  Haorong Li,et al.  A Generic Rating-Data-Based (GRDB)DX Coils Modeling Method , 2010 .

[25]  Stephen J. Citron,et al.  An On-Line Engine Roughness Measurement Technique , 1984 .

[26]  L.A.C Meleiro,et al.  A self-tuning adaptive control applied to an industrial large scale ethanol production , 2000 .

[27]  James E. Braun,et al.  A review of virtual sensing technology and application in building systems , 2011, HVAC&R Research.

[28]  Saffa Riffat,et al.  Turbulent flow in a duct: Measurement by a tracer gas technique , 1990 .

[29]  Theodora Kourti,et al.  Process analysis and abnormal situation detection: from theory to practice , 2002 .

[30]  Rubens Maciel Filho,et al.  Soft sensors development for on-line bioreactor state estimation , 2000 .

[31]  M. Dudzic,et al.  Industrial use of multivariate statistical analysis for process monitoring and control , 2002, Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301).

[32]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[33]  Casimir A. Kulikowski,et al.  Computer Systems That Learn: Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning and Expert Systems , 1990 .

[34]  James E. Braun,et al.  A Method for Modeling Adjustable Throat-Area Expansion Valves Using Manufacturers' Rating Data , 2008 .

[35]  M. Ouladsine,et al.  Sliding Mode Observers for the Estimation of Vehicle Parameters, Forces and States of the Center of Gravity , 2006, Proceedings of the 45th IEEE Conference on Decision and Control.

[36]  Luigi Fortuna,et al.  Soft Sensors for Monitoring and Control of Industrial Processes (Advances in Industrial Control) , 2006 .

[37]  Refrigerating.  2001 ASHRAE handbook : fundamentals , 2001 .

[38]  Max H. Sherman,et al.  Practical Diagnostics for Evaluating Residential Commissioning Metrics , 2002 .

[39]  Girijesh Prasad,et al.  Statistical and computational intelligence techniques for inferential model development: a comparative evaluation and a novel proposition for fusion , 2004, Eng. Appl. Artif. Intell..

[40]  Todd Michael Rossi.  Detection, diagnosis, and evaluation of faults in vapor compression equipment , 1995 .

[41]  A. Reyes,et al.  Probabilistic Virtual Sensor for On-line Viscosity Estimation , 2008, 2008 Seventh Mexican International Conference on Artificial Intelligence.

[42]  Dennis Johansson,et al.  Optimal supply air temperature with respect to energy use in a variable air volume system , 2004 .

[43]  Lorenzo Fagiano,et al.  A study on the use of virtual sensors in vehicle control , 2008, 2008 47th IEEE Conference on Decision and Control.

[44]  J. Friedman,et al.  A Statistical View of Some Chemometrics Regression Tools , 1993 .

[45]  M. Oosterom,et al.  Virtual sensor for fault detection and isolation in flight control systems - fuzzy modeling approach , 2000, Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187).

[46]  James E. Braun,et al.  A Smart Mixed-Air Temperature Sensor , 2009 .

[47]  James E. Braun,et al.  Development, Evaluation, and Demonstration of a Virtual Refrigerant Charge Sensor , 2009 .

[48]  T. Agami Reddy.  Formulation of a generic methodology for assessing FDD methods and its specific adoption to large chillers , 2007 .

[49]  Roozbeh Jafari,et al.  Implementation of virtual sensors in body sensor networks with the SPINE framework , 2009, 2009 IEEE International Symposium on Industrial Embedded Systems.

[50]  L. C. Zullo,et al.  Model predictive control of a slurry polymerisation reactor , 1996 .

[51]  H. Javaherian,et al.  Virtual air-fuel ratio sensors for engine control and diagnostics , 2006, 2006 American Control Conference.

[52]  Jose C. Principe,et al.  Neural and adaptive systems , 2000 .

[53]  Hani Hagras,et al.  Virtual appliances for pervasive computing: a deconstructionist, ontology based, programming-by-example approach , 2005 .

[54]  Nigel W. Hardy,et al.  De-Coupling for Re-Use in Design and Implementation Using Virtual Sensors , 1999, Auton. Robots.

[55]  James E. Braun,et al.  Virtual Refrigerant Pressure Sensors for Use in Monitoring and Fault Diagnosis of Vapor-Compression Equipment , 2009 .

[56]  D. W. Etheridge,et al.  Crack flow equations and scale effect , 1977 .

[57]  H. Lia,et al.  Decoupling features for diagnosis of reversing and check valve faults in heat pumps , 2009 .

[58]  James E. Braun,et al.  Decoupling features and virtual sensors for diagnosis of faults in vapor compression air conditioners , 2007 .

[59]  Fredrik Gustafsson,et al.  Estimation and change detection of tire-road friction using the wheel slip , 1996, Proceedings of Joint Conference on Control Applications Intelligent Control and Computer Aided Control System Design.

[60]  Chonghun Han,et al.  A nonlinear soft sensor based on multivariate smoothing procedure for quality estimation in distillation columns , 2000 .

[61]  Abdollah Homaifar,et al.  Creating virtual sensors using learning based super resolution and data fusion , 2009, 2009 IEEE Aerospace conference.

[62]  Todd M. Rossi,et al.  A Statistical, Rule-Based Fault Detection and Diagnostic Method for Vapor Compression Air Conditioners , 1997 .

[63]  Christine Julien,et al.  Virtual sensors: abstracting data from physical sensors , 2006, 2006 International Symposium on a World of Wireless, Mobile and Multimedia Networks(WoWMoM'06).

[64]  T. Agami Reddy,et al.  Development and evaluation of a simple model-based automated Fault Detection and Diagnosis (FDD) method suitable for process faults of large chillers , 2007 .

[65]  Ashutosh Kumar Singh,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2010 .

[66]  G.D. Gonzalez,et al.  Soft sensors for processing plants , 1999, Proceedings of the Second International Conference on Intelligent Processing and Manufacturing of Materials. IPMM'99 (Cat. No.99EX296).

[67]  Nigel W. Hardy,et al.  ViSIAr - A virtual sensor integration architecture , 1999, Robotica.

[68]  Ali Charara,et al.  Virtual sensor: application to vehicle sideslip angle and transversal forces , 2004, IEEE Transactions on Industrial Electronics.

[69]  MengChu Zhou,et al.  Virtual sensing techniques and their applications , 2009, 2009 International Conference on Networking, Sensing and Control.

[70]  Refrigerating.  ASHRAE handbook of fundamentals , 1967 .

[71]  Peter J. Gawthrop,et al.  Virtual Actuators with Virtual Sensors , 2005 .

[72]  Scott C. James,et al.  Comparative study of black-box and hybrid estimation methods in fed-batch fermentation , 2002 .

[73]  Chonghun Han,et al.  Intelligent integrated plant operation system for Six Sigma , 2002, Annu. Rev. Control..

[74]  D. Lewin,et al.  Ethylene compressor monitoring using model-based PCA , 2000 .

[75]  T. C. Ottney.  Particle management for HVAC systems , 1993 .

[76]  M. V. Velzen,et al.  Self-organizing maps , 2007 .

[77]  Guizhen Yu,et al.  Virtual sensors design in vehicle sideslip angle and velocity of the centre of gravity estimation , 2009, 2009 9th International Conference on Electronic Measurement & Instruments.

[78]  Saffa Riffat,et al.  Airflow rate through a heat-exchanger coil , 1991 .

[79]  J.P. Lauffenburger,et al.  Navigation as a Virtual Sensor for Enhanced Lighting Preview Control , 2007, 2007 IEEE Intelligent Vehicles Symposium.

[80]  J. E. Jackson,et al.  Control Procedures for Residuals Associated With Principal Component Analysis , 1979 .

[81]  Fredrik Gustafsson,et al.  Virtual Sensors of Tire Pressure and Road Friction , 2001 .

[82]  Gil Avery.  Do Averaging Sensors Average , 2002 .

[83]  Lorenzo Fagiano,et al.  On the design of linear virtual sensors for low cost vehicle stability control , 2008, 2008 IEEE International Conference on Control Applications.

[84]  Ali Charara,et al.  Evaluation of a sliding mode observer for vehicle sideslip angle , 2007 .

[85]  Marcel Rijckaert,et al.  Application of feedforward neural networks for soft sensors in the sugar industry , 2002, VII Brazilian Symposium on Neural Networks, 2002. SBRN 2002. Proceedings..

[86]  John F. MacGregor,et al.  Multivariate SPC charts for monitoring batch processes , 1995 .

[87]  H. J. Sauer,et al.  Field measurement of air velocity : pitot traverse or vane anemometer? , 1990 .

[88]  J. Friedman,et al.  [A Statistical View of Some Chemometrics Regression Tools]: Response , 1993 .

[89]  B. Bequette,et al.  Product property and production rate control of styrene polymerization , 2002 .

[90]  Tiina M. Komulainen,et al.  Fault detection and isolation of an on-line analyzer for an ethylene cracking process , 2008 .

[91]  Yuebin Yu,et al.  Virtual calibration of a supply air temperature sensor in rooftop air conditioning units , 2011 .

[92]  Desire L. Massart,et al.  ROBUST PRINCIPAL COMPONENTS REGRESSION AS A DETECTION TOOL FOR OUTLIERS , 1995 .

[93]  J. Braun,et al.  On-Line Models for Use in Automated Fault Detection and Diagnosis for HVAC & R Equipment , 2002 .

[94]  Student,et al.  THE PROBABLE ERROR OF A MEAN , 1908 .

[95]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[96]  Bogdan Gabrys,et al.  Data-driven Soft Sensors in the process industry , 2009, Comput. Chem. Eng..

[97]  J. Siegel,et al.  Modeling Filter Bypass: Impact on Filter Efficiency , 2004 .

[98]  Fredrik Gustafsson.  Slip-Based Estimation of Tire-Road Friction , 1995 .

[99]  Ashok N. Srivastava,et al.  Virtual sensors: using data mining techniques to efficiently estimate remote sensing spectra , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[100]  Fredrik Gustafsson,et al.  Slip-based tire-road friction estimation , 1997, Autom..

[101]  Mingsheng Liu,et al.  Development of a Pump Water Flow Station for HVAC Systems , 2007 .

[102]  Francis J. Doyle,et al.  Nonlinear inferential control for process applications , 1997 .

[103]  S. Wold,et al.  PLS-regression: a basic tool of chemometrics , 2001 .

[104]  Keith J. Burnham,et al.  Kalman filter as a virtual sensor: applied to automotive stability systems , 2007 .

[105]  R. Schulte,et al.  Continuous IAQ Monitoring , 2005 .

[106]  T Poggio,et al.  Regularization Algorithms for Learning That Are Equivalent to Multilayer Networks , 1990, Science.

[107]  R. H. Braun.  Problem and solution to plugging of a finned-tube cooling coil in an air handler , 1986 .

[108]  S.Joe Qin,et al.  Neural Networks for Intelligent Sensors and Control — Practical Issues and Some Solutions , 1997 .

[109]  Shengwei Wang,et al.  A Robust Fault Detection and Diagnosis Strategy for Centrifugal Chillers , 2006 .

[110]  Ana Casali,et al.  Particle size distribution soft-sensor for a grinding circuit , 1998 .

[111]  James E. Braun,et al.  A Methodology for Diagnosing Multiple Simultaneous Faults in Vapor-Compression Air Conditioners , 2007 .

[112]  Colin H. Hansen,et al.  Virtual Sensors in Active Noise Control , 2000 .

[113]  Srinivas Katipamula,et al.  Review Article: Methods for Fault Detection, Diagnostics, and Prognostics for Building Systems—A Review, Part I , 2005 .

[114]  C. S. George Lee,et al.  Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems , 1996 .

[115]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part III: Process history based methods , 2003, Comput. Chem. Eng..

[116]  Arnoud Visser,et al.  Evaluating automatic debiting systems by modelling and simulation of virtual sensors , 1998 .

[117]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part II: Qualitative models and search strategies , 2003, Comput. Chem. Eng..

[118]  Ashok N. Srivastava,et al.  Improvements in virtual sensors: using spatial information to estimate remote sensing spectra , 2005, Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05..

[119]  I. D. Walker,et al.  A novel approach to robotic climbing using continuum appendages in in-situ exploration , 2012, 2012 IEEE Aerospace Conference.

[120]  Charara Ali,et al.  Evaluation of sliding mode observer for vehicle sideslip angle , 2005 .

[121]  Max H. Sherman,et al.  A comparison of the power law to quadratic formulations for air infiltration calculations , 1998 .

[122]  Thomas J. McAvoy,et al.  Nonlinear PLS Modeling Using Neural Networks , 1992 .

[123]  Matthias Röckl,et al.  INTEGRATION OF CAR-2-CAR COMMUNICATION AS A VIRTUAL SENSOR IN AUTOMOTIVE SENSOR FUSION FOR ADVANCED DRIVER ASSISTANCE SYSTEMS , 2008 .

[124]  A. L. Dexter,et al.  A fuzzy sensor for measuring the mixed air temperature in air-handling units , 2005 .