Patent Application
20130035794
This invention relates to energy conservation for buildings. U.S. patent application Ser. No. 12/888,277, filed Sep. 22, 2010, inventor Behzad Imani, is incorporated herein by reference in its entirety.
Existing buildings suffer from simultaneous cooling and heating, inefficient cooling, dead zones, simultaneous over cooling or under cooling in the system and in different zones, and uncontrolled and rapid deterioration of mechanical system components. Commercial buildings do not operate at their optimum energy state. Even if they do, buildings “drift” within 3 years from their optimum energy operations state. Moreover, even if operating in an optimum energy state, one or more or all of the HVAC components drift over time such that the faulty component will result in malfunction of the rest of the system. Thereby energy efficiency decreases substantially.
In the past 40 years, energy prices have elevated at an annual rate of about 7% per year in California. It has been found that 99% of installed HVAC systems in commercial building are operating sub-optimally. HVAC accounts for 55% of energy bills in a typical commercial building. HVAC optimization has the fastest pay back compared to other energy Efficiency retrofits.
There are five known efficiencies in a retrofitted building:
The present system in one embodiment includes an end-to-end software process with in depth monitoring and control solutions for commissioning of mechanical systems in large buildings such as commercial buildings. The present system “tunes up” (optimizes operation of) the HVAC (heating, ventilation, air conditioning) and/or hot water supply systems a building, aligns the components of the HVAC systems, then it applies in one embodiment remote wireless software as a service-based control to maintain the building systems at their optimum energy state. The present system also controls and monitors other energy systems within the building such as lighting, water usage, energy storage, and other renewable sources. The present system typical savings in cost of energy (electricity, gas, etc.) used for HVAC purposes over conventional systems is, e.g., 40% per year in commercial buildings. The key to success of the present system is efficiency by design and efficiency by monitoring and control. The present system uses standard and advanced fluid dynamic and advanced thermo-dynamic methods such as Multi Phase CFD, Compressible flow CFDs, and Refrigerants Flow Dynamics techniques for Energy Efficiency Building Information Modeling (EEBIM). The present system utilizes a new form of comprehensive Building Information Modeling, EEBIM where EE stands for Energy Efficiency, for dynamic energy modeling of building from an energy efficiency point of view. EEBIM is a linear and optimization model for the general energy flow equation linearized around an operating temperature range of, e.g., 69 to 73 degrees. Linearization means removal or reducing the effects of non-linear components such as dampers, and placement of bounds on their non linear behavior. The EEBIM s together with the placements of sensors in critical positions will enable the present system to better control the entire buildings including its mechanical and HVAC components, air cooling and or hot water system. The present system applies a matrix as explained below for precise control and full time commissioning of the entire HVAC system that includes the mechanical system, refrigeration flow, air delivery system and zone control.
The present system has detailed thermodynamic and fluid mechanic analysis and design plus approach as well as tight monitoring and control of the mechanical components within the building. Therefore the present system not only reduces the energy consumption curve of the building, but also makes daily operation of the building less problematic, and will increase the life expectancy of the components. Since the present system is e.g. wireless and may reside in a “cloud” computing environment, it can analyze the large volume of trend data in the buildings and compare them with ideal models. Thus control and monitoring of the system and its components will be easier because the present system monitor and control reach out to the components that are interdependent and otherwise hard to monitor. Yet, these independent components interact with each other and make the system highly interdependent. The fact is that the operation of mechanical systems and their interaction with the building is a very convoluted operation. The present system controls the component behavior in a synchronized fashion with respect to the entire system performance, such that the energy usage is kept at its most optimum level and the life expectancy of the system is increased. The present system is not restricted to optimization, monitor and control of HVAC systems. The present system optimization, monitor and control can be applied to any system and project that involves heat transfer in a medium. For example, the present system could be applied to energy storage devices or batteries that utilize compressed air as storage medium. Such batteries can be used in commercial buildings as storage components. The present system can also be applied to refineries, data centers and other high energy demand systems.
The present system optimizations, monitor, and control can be applied to HVAC systems, complex mechanical systems such as boilers, hot water generations, chillers, space cooling and space heating systems, geothermal systems, chill beams, radiant panels as well as solar hot water, solar co generations, and water treatments in commercial buildings. Moreover, the present system can be applied to massive cooling and heating systems in district cooling and district heating for down towns and joint commercial and apartment campuses.
The present system is ideal for high load infrastructures such as refineries, mass bio fuel systems, and mass bio fuels systems, and any other any conversion system. A goal of the present system is to analyze and model the heat flow characteristics within the systems with thermodynamic, fluid mechanics, and CFDs, comprehensible fluid and multi phased CFDs, establish the trend data at the critical junctions within the system, and then control the entire system. The present system is replacing the old enthalpy charts with detailed analytical techniques.
The present system is in the field of diagnostic, design, retrofit, control and optimization of HVAC systems for new and retrofit buildings. The present system produces energy savings in commercial buildings via auto commissioning, retro commissioning, and open commissioning of HVAC system using state of the art fluid dynamic techniques. An advantage of the present system is in detailed analysis of the HVAC system and establishing the critical trend data for observing and monitoring the HVAC system performance.
a, 3b, 3c and 3d show trend line plots.
a, 7b show simulations of VAV boxes.
The present system deals with the above mentioned HVAC efficiencies, control and monitoring efficiencies because they generally have the fastest payback time (e.g., less than 3 years) in a retrofitted building. Relative efficiencies achievable with the present system are shown in
These include:
The present system output includes retrofit, design, implementation, control and maintenance of an HVAC system, operating at the most optimum energy consumption and most comfortable condition. The present system produces maintenance schedules, operational alarms, and equipment tune up schedules. The present system uses a SaaS (software as a service) based EEBIM, monitoring and control software solution include: audit using the AutoCAD design and address of the building as input files to the present system; and bench marking prior to retrofit optimization. So the present system provider efficiency by design, retrofit, testing, commissioning, bench marking post retrofit optimization, sensor placements and calibrations, sensing, trending, and data stream monitoring, critical system alarm flags (when a component malfunctions), and efficiency by monitoring, control, and full time auto commissioning.
Many existing software packages establish the bench marking of a building based on the use (office, hospital, data center), occupancy, and the computers, hardware in the server rooms while talking to account the construction date of the building. The present system interfaces with such existing software.
The present system controls the actuators, damper positions, flow velocity, input powers to the individual components and fans, pressures, induction and suction pressures, mass rates, and all other controllable components within the system. The controllable components vary from system to system. Typically there are 3 to 4 layers of control between the present system and the individual components. The details of control functions and commands depend on the system under observation. But generally, dampers, actuators, temperature and pressure, input power, mass flow rate, input and indoor flow rates, refrigerant velocity, air pressure and velocity inside the duct system, and other such parameters constitute the control parameters.
Trending refers here to active sensing. Here more specifically, trending refers to observing behavior of the HVAC system and its interaction with the building to see how well the entire system is performing. The most important raw data are: temperature, carbon dioxide levels, air velocity in three directions, pressure in three direction, temperature drop, pressure drop, velocity, humidity, buoyancy, and air flow rate, mass flow rate, refrigerant flow rate, physical state of the refrigeration/heating medium in the system, and hydraulic torque. There are derivative trend points such as enthalpy, entropy, compression ratio, coefficient of performance and other thermodynamic values, output vs. input power in conventional VAV (variable air volume) boxes. The present system first establishes the trend points and then actively monitors their behavior over time. The present system is directed to the flow parameters such as velocity, pressure, buoyancy that are the output of the VAV boxes vs. input power. Similarly, the present system is directed to the placement of carbon dioxide monitors in return ducts for air quality measurements and for optimum filtrations and over all system occupancy loads.
The present system addresses in some embodiments: efficiency by design and efficiency by monitoring and control, and full time auto commissioning (continuous tune up of the system).
The system generates trend lines (plots) for e.g. a typical center fusion pump system and transmission valves in an HVAC system, the trend lines being determined using CFD techniques, see
By placing numerous pressure, temperature, velocity, mass flow rate, buoyancy, pressure drop, temperature gradient, humidity, enthalpy, linear and non linear transducers such as linear voltage differential transducers (LVDT), energy transducers, and other flow based sensors in the HVAC system, one monitors the performance of the system including the HVAC components, HVAC delivery systems including the VAV boxes and zone temperature. The system trend lines are based on flow parameters. The system trend lines are not necessarily a function of time. They may be a function of velocity magnitude, where time is an implicit variable. Note that in mechanical systems in general , and in VAV boxes in particular, the damper size affects the linearity of air flow. This is peculiar because it affects the critical position and placements of sensors for accurate monitoring of the flow trend data. The present system can establish flow based trend data that includes the non linear effects of component on the flow. The present system trend data are flow based variables, not necessarily electrical based. So the present system detail analysis allows for the static pressure drops to be resolved and reduced by 2 to 100 fold. For example, in a VAV box, if the pressure drop was at 0.1 inch of water, the present system reduces it to 0.03 inch of water, but accurately adjusting for the damper position for maximum flow with respect to the rest of the system. In centrifugal pumps, the hydraulic torque vs. velocity and the head diameter vs. flow velocity magnitude provide good trend lines. In valves, the velocity magnitude vs. inlet pressure provides good trend lines. The conclusion here is that the trend lines are not necessary a function of time. Trends could be a function of each other or a derivative of time.
Furthermore, since the present system analyzes and establishes critical trend data, one can place the sensors in exact positions where the flow is laminar. For instance, the present system places the pressure sensors inside the duct system at the most optimum laminar position instead of placing it at an arbitrarily selected location of ⅓ distance to the load.
The present system trend lines includes trend lines (see
The present system introduces piezo-electric, electro optic, acousto optic and, magneto optic or simple electro chemical and transducer components for measurements of the trend data. This may be a simple instrument for pressure measurements using either LVDTs and/or an optical light for accurate measurements of the pressure in a flow. All sensors are subject to frequent and periodic calibration by comparing their outputs to that of a calibration sensor coupled to observe a controlled flow parameter.
In the case of system fluids that undergo phase change such as refrigerants, or when the fluid undergoes extreme pressure, one uses simple spectrographic techniques such as white light sources, gratings and diode arrays to determine the state of the fluid medium. The state of fluid is gas, liquid or any combination of the two. Each state will present its own spectral response under constant measurement and observation. This is useful for determination of balance, or there lack of, in a refrigeration cycle where the refrigerant undergoes several phase changes.
Load Analysis: By performing detailed load calculations, solar radiation through windows and taking into account the R values of the building envelope, the present system provides a thermodynamic heat profile of the rooms as was illustrated in the above-cited patent application.
However, the present system goes further and uses spectroscopic spectral analysis for determining status or phases of flow inside mechanical systems such as refrigerant, compressed refrigerant and other fluid mediums. Moreover, the same spectral analysis reveals the carbon content, oxygen content, hydrogen and nitrogen contents or other elements within the HVAC pipes, ducts or refrigeration cycles. Spectral analysis is not confined to white light spectroscopy rather, one may use special spectroscopy involving X rays, ultra violet-visible-infra red, ultra sound, far infra red (FIR) laser spectroscopy and surface plasmons detectors for determining the status of mechanical system performance. For example, the ultra sound and surface plasmons will indicate leakages of fluid within the system.
The present system provides the spatial placements of the trend data for determining the flow parameters or flow variables for better control of the system.
Work flow of the present system includes:
The result of the simulation is called Energy (Or Efficiency) Building Information Modeling (EBIM) which is different from traditional EEBIM that only simulates the structure and static environmental of a building, (i.e. using the software called Design Studios which is commercially available from AutoDesk). The present EEBIM models the ratings of the HVAC components, air flow, and other parameters as well as the component and air delivery system and their interaction with the heat load of the building. This constitutes a working model or EEBIM of the building.
The largely conventional HVAC (in terms of components, delivery, etc.) is designed according to energy optimizations maximizing the system performance at the minimum energy cost. One designates the trend points on the components and zones of the system and observes the behavior of the system.
One models each existing component and constructs the performance EEBIM of the HVAC system. Then one commissions (tunes up) the building, then installs remote readout conventional sensors (temperature, pressure, velocity, LVDTs, and others) at critical points (trend points) on the components and zones of the system and start observing the trend data (system component performance vs. time) and issue commands for correcting the misbehaving parts. I.e., one finds out if the system is subcooling or superheating in the refrigeration cycle or how the zones are performing under normal load conditions.
Then using e.g., a cloud (software as a service) based control program, apply appropriate commands to correct for any deviation from the EEBIMs from the models.
In both new and retrofit buildings, the present system monitor and control software communicates with the building's largely conventional mechanical systems through a communications network such as the conventional BacNET. BacNET is a standard that exists for building management systems.
Some aspects of energy management of the building includes configuring one or more mechanical systems by modifying them to prevent them from over performing. The performance of some mechanical systems may be more than an optimum value with respect to the performance of the overall system. Such modification results not only in reducing energy consumption, but also in prolonging the life expectancy of the modified system.
These are the five main components (see
The present system performs multi-phase CFD analysis, and determines the refrigerant mass throughput rate, temperature, velocity, pressure, its state (liquid, gas, and liquid-gas combination) and determines critical pressure and temperature, Transducer (sensor) measurements are used to monitor the refrigeration cycle.
For example for the above five component refrigeration unit, for a typical 3 ton capacity air conditioning unit 38 to work properly, one must model and determine optimum operation of: moving 9,000 pounds per hour of outdoor air through the condensing coil 42; maintain 32.5 degree F. average temperature difference across the condensing coil maintain refrigerant mass flow rate as a function of condensing and evaporative temperatures; maintain 30 degree F. average temperature across the evaporative coil 48; and move 6000 pound per hour of indoor air through the evaporator 48.
Saturation point or the boiling point of the refrigerant is important in that 75% saturation (mixture of liquid and gas) occurs inside the condenser 42 and evaporator 48. One should have 12.5% gas and 12.5% liquid to have a perfect cooling cycle. So it is important to measure saturation temperature directly at the middle of both the indoor and outdoor coils (evaporator and condenser). Also one needs to measure the temperature in between the components.
If the system does not maintain the above, then undesirably either superheating (upper part of
Subcooling (lower part of
“Floodback” is slogging of the compressor, and allowing liquid refrigerant to flow into the compressor, that results in damaging the compressor bearing over time by washing oil from compressor, damaged compressor valves, reducing efficiency and cooling capacity. Causes of floodback are low airflow such as less than 350 to 500 CFM, small returns (thus the duct sizes have to be increased), high static filters (to be replaced with lower static filters or increased grill area), low load conditions (low load temperatures), bypassed ducts in zoned systems (so the cure is to remove the bypass, as entering air has to be at 70 degrees F.).
Overcharge is little or no superheat refrigerant to the compressor, where too much liquid in the condenser causes his super cooling.
Undercharge is a high level of super heat refrigerant entering into the compressor, so little or no liquid causes subcooling.
A system trending example is provided in the above-referenced patent application, which simulated the entire building EEBIM including the duct systems and the geothermal cooling, radiant panels and chilled beam system. The pressure, temperature, humidity, and velocity and other flow parameters (i.e., mass flow rate, entropy, enthalpy, buoyancy, and the like) were determined and could be used a critical trend points for controlling the entire system.
The present system simulates operation of a center fusion pump in a cooling system, for data trending at its optimum energy efficiency operation point.
The system also simulates operation of the transmission valve 46, for data trending at its optimum energy efficiency operation point.
Flow trending inside (at VAV boxes) and at the edge of the duct system is described in the above-referenced patent application and below.
In a conventional VAV box, the air comes from the air delivery system to the VAV box, and then VAV box moves the air into the proper building cooling zone while controlling the flow with a damper. The present system simulates operation of a conventional VAV box in
Moreover, in large commercial buildings often having over 100 VAV boxes, the functioning (or lack of functioning) of one VAV box will affect the functioning of the neighboring VAV boxes, and thus the temperature of the neighboring zone. The present system establishes trend lines based on the flow parameters (temperature, pressure, velocity, angle position) as a function of time while observing them, e.g. every minute. The goal is to apply the most optimum energy efficient control by balancing the VAV box with respect to the zone and with respect to the neighboring VAV boxes while avoiding non-linear swings. See
Zone trending is described in the above-referenced patent application, which shows the air velocity, temperature and throughput rate of the flow inside the rooms. The present system models the air flow produced by the mechanical components through the air delivery system and the duct systems, all the way through the zones and rooms while reducing the static pressure, non-linearity, and non laminar flows. The present system establishes the optimum flow, while modeling the room temperatures with respect to the system performance and the loads such as solar radiations through the windows, position and orientation of the rooms, number of occupants, and position of the furniture. Then the present system establishes accurate trend lines by placing relevant sensors for the entire system even through the duct systems and rooms while comparing them with the pre determined ideal models. This is an advantage of the present system over known systems that just place the sensors at random locations throughout the cooling zones and the rooms without determining what their optimum energy efficient behavior should be. The present system determines the flow parameters at every level and then compares the trend data with the ideal flow ones and issues control command accordingly.
The system performance S of a building can be defined algebraically as:
S(x)=S1(x)*S2(x)*S3(x) . . . *Sn(x)
where x is the state space variable, or state variable representation of the flow variables such as pressure, temperature, velocity, mass flow rate, buoyancy, pressure drop, temperature gradient and enthalpy, and where operation * stands for convolution, or multiplication and integration of sub-systems as functions of x.
S(x) represents the total system performance, and is a convolved function of its subsystems designated as S1(x) through Sn(x). For example, S1 represents the main refrigeration subsystem, S2 represents the geothermal cooling system, S3 represents the radiant panels, S4 represents the chill beams, S5 represents the air delivery subsystem through the ducts, and S6 represents the zone subsystem.
Other mechanical systems such as boilers, solar hot water, and solar hybrid gas energy systems could constitute the other system components of the above equation.
The optimization criteria are, for example, a set of control commands such that the energy cost of the system and the pressure drops are minimized.
So in the present system expressed mathematically as a matrix, each variable that can be observed via trend lines is categorized as a space state variable or in short as a state variable, i.e., the zone temperatures, the air flow velocity in, etc. The present system models the interactive process of the flow by a flowing mathematical matrix or a characterization matrix, expressed algebraically as:
x(k+1)=A x(k)+B u(k)
y(k)=C x(k)+D u(k)
where variable k is the discrete time measured, e.g., in minutes (or seconds), for example k=0 is 12:00 AM, k=1 is 12:01 AM and k=1439 is 11:59 p.m. Variable x is the state variable (for example the air flow in the VAV box, or temperature in a zone, or pressure in the refrigerant cycle), and y is the output variable (for example, the temperature in a room).
Expressed as matrixes for each of coefficients A, B, C and D:
where P, T, m, and V are air pressure, temperature, mass flow rate and velocity respectively and presented as state variables. The VAV box damper angle, global and local fan powers and pump torque are the inputs of the system as shown above. Furthermore, one can calculate the enthalpy H of each part of the HVAC system using the following equation:
H=U+pV.
where
Plots of the state variables are shown in
If v(x)=f(x)+g(x), where f(x) is the linear terms and g(x) represents the non-linear terms, then it is prudent to bound the non-linear terms such that the behavior of v(x) is bounded:
For example in a VAV box, the tilt angle of the damper (plus the size of damper) will introduce non-linearities in the air flow. So one bounds the cause of the non-linearity, namely the damper angle, by restricting it from going beyond certain angular positions that will otherwise dramatically create non-linear output velocity and pressure.
The present system establishes the system control based on the above system matrix as follows. For a simple two zoned building, with two VAV boxes, the temperature in each zone is a function of the air flow (determined by the air pressure, temperature, velocity, and damper tilt angle) in each VAV box.
Mechanical systems are inherently interactive. The air flow in a VAV box depends on velocity, temperature and pressure inside the VAV box. But the performance of one VAV box affects the neighboring VAV box. Similarly, the present system matrix allows the observation and control of both the HVAC and mechanical components on each other. The malfunctioning of a VAV box affects the other VAV boxes in the same zone.
For an exemplary building with 700 zones, and 1 different 20 different air handling units, the present system establishes the relevant parameters. So given the air delivery system parameters, then the present system defines the refrigeration cycle by defining the relevant parameters. There is interdependency between the refrigeration and cooling medium, and the air handling.
This description is illustrative and not limiting. Further modifications will be apparent to those skilled in the art in light of this disclosure, and are intended to fall within the scope of the appended claims.
