Method, system, and apparatus for controlling a compressor system

Abstract

A method, system, and apparatus are provided for improving the control and efficiency of a compressor system (199). The compressor system includes a controller (106). The controller is configured to predict future demands on the compressor system, determine an initial sequence based on the predicted future demands, determine a set of initial switch times for the initial sequence, refine the initial switch sequence based on the set of switch times to form an improved switch sequence, iteratively determine a set of switch times for the improved switch sequence, refine the improved switch sequence based on the set of switch times, and control operation of a set of components based on the final switch sequence and final switch times until a final switch sequence and final switch times are obtained.

Description

[Technical Field]

[0001] The present disclosure relates to methods, systems, and apparatus for monitoring and controlling compressor systems, and more particularly to methods, systems, and apparatus for monitoring, controlling, and optimizing the efficiency of components of compressor systems for supplying compressed air or gas to consumers. [Background technology]

[0002] It is known that compressors are used to compress air or gas in one or more compression stages. The compressed air or gas is then supplied to one or more consumers. The distribution can be effected through a compressed air or gas system.

[0003] As the number of consumers can be huge and spatially distributed over a wide area, for example in an industrial plant or a hospital, a central hub is usually installed to supply compressed air or gas from there.

[0004] Typically, a central hub contains one or more compressor rooms, each containing one or more compressors, as well as auxiliary equipment such as valves, filters, dryers, vessels, sensors, control components, and / or other devices for managing and / or controlling the compressor rooms. Pipes or ducts then exit the compressor room or rooms to supply consumers. As the last part of the chain, the compressed air or gas is utilized by consumers for various uses.

[0005] Additionally, there may be another set of devices between the compressor and the consumer, such as safety valves, distribution valves, control sensors, or other devices for controlling and protecting the distribution of compressed air or gas.

[0006] The described configuration will be further identified as a compressor system, and thus, although a compressor system may comprise one compressor supplying one consumer, it will generally be considered to be broader, comprise multiple components, and comprise a complex system of elements interacting with one another.

[0007] To utilize a compressor system, its various parts need to be controlled. It is already known to control the compressors individually by independent local controllers, whereby the different controllers are set to pre-set pressure values, which sequentially switch the compressors on or off depending on the consumption of compressed air.

[0008] Furthermore, a method is known for controlling a compressor system by means of multiple communicating controllers for controlling components that are part of the compressor system, whereby the components are controlled such that no controller determines the operating state of any component controlled by another controller. WO 2008 / 009073 discloses such a method.

[0009] WO 2008 / 009072 discloses another method for controlling a compressed air unit consisting of multiple compressed air or gas networks with at least one common controllable component, whereby at least one common component is controlled by at least one controller based on measurement data of at least one of the compressed air or gas networks.

[0010] However, a drawback of these control methods identified by the inventors is that they operate solely based on the current state of the compressor system, i.e., they are unable to take into account any kind of prediction, which leads to suboptimal control and high energy costs.

[0011] The compressor system can be implemented as a switched dynamic system, which is a continuous-time nonlinear system defined by multiple subsystems and nonlinear switching rules. While switched dynamic systems offer great flexibility in modeling a wide range of real-world applications, achieving advantageous control of such systems has proven difficult due to the discrete nature of the switched dynamics.

[0012] The problem in control of switched dynamic systems identified by the inventors can take the following form: where x(t) is the state of the system and u(t) and v(t) are the inputs. The switching properties of the system are captured in the variable u(t), which can only take on values ​​from a finite, discrete set of options. This is known as a mixed-integer optimal control problem (MIOCP).

[0013] Known approaches for addressing MIOCP include mixed integer nonlinear programming (MINLP), relaxed solving, control parameterization techniques (CPET), and combined integral approximation (CIA). However, the inventors have determined that all currently known methods exhibit significant drawbacks. In particular, the inventors have determined that such methods and systems are highly complex, require significant computational power and time delays for the required calculations, and can be inaccurate. [Prior art documents] [Patent documents]

[0014] [Patent Document 1] International Publication No. 2008 / 009073 [Patent Document 2] International Publication No. 2008 / 009072 Summary of the Invention [Problem to be solved by the invention]

[0015] The inventors have found that known calculations and optimizations are, in practice, unnecessary and inefficient, requiring more time and processing power than necessary, and have discovered a robust and efficient method and system for accurately controlling and optimizing the performance efficiency of a compressor system. [Means for solving the problem]

[0016] A compressor system is provided that includes a set of components fluidly connected to a common compressed air distribution network and a controller, wherein the controller is configured to: predict future demands of the compressor system; determine an initial switchover sequence for operation of at least one component of the set of components that meets the predicted future demands; determine a set of switch times for the initial switchover sequence; refine the initial switchover sequence based on the set of switchover times to form an improved switchover sequence; iteratively determine a set of switchover times for the improved switchover sequence and refine the improved switchover sequence based on the set of switchover times until a final switchover sequence and a set of final switchover times are obtained; and control the operation of the set of components based on the final switchover sequence and the set of final switchover times.

[0017] In another embodiment, a controller for a compressor system is also provided. The controller is configured to operate a compressor system having a set of components fluidly connected to a common compressed air distribution network, the controller comprising a computer-readable storage medium and a processor, the processor is configured to: predict future demands of the compressor system; determine an initial switchover sequence for operation of at least one component of the set of components that satisfies the predicted future demands; determine a set of switch times for the initial switchover sequence; refine the initial switchover sequence based on the set of switch times to form an improved switchover sequence; iteratively determine a set of switch times for the improved switchover sequence and refine the improved switchover sequence based on the set of switch times until a final switchover sequence and a set of final switchover times are obtained; and control the operation of the set of components based on the final switchover sequence and the set of final switchover times.

[0018] In another embodiment, a computer-implemented method for controlling a compressor system including a set of components fluidly connected to a common compressed air distribution network to improve efficiency of the compressor system is also provided. The method includes the steps of: predicting future demands of the compressor system; determining an initial switchover sequence for operation of at least one component of the set of components that satisfies the predicted future demands; determining a set of switch times for the initial switchover sequence; refining the initial switchover sequence based on the set of switch times to form an improved switchover sequence; iteratively determining a set of switch times for the improved switchover sequence and refining the improved switchover sequence based on the set of switch times until a final switchover sequence and a set of final switchover times are obtained; and controlling operation of the set of components based on the final switchover sequence and the set of final switchover times.

[0019] A hardware storage device having computer-executable instructions stored thereon, which, when executed by one or more processors of a computing system, configures the computing system to perform a method for controlling a compressor system, including: predicting future demands of the compressor system; determining an initial switching sequence for operation of at least one component of the set of components that meets the predicted future demands; determining a set of switching times for the initial switching sequence; refining the initial switching sequence based on the set of switching times to form an improved switching sequence; iteratively determining a set of switching times for the improved switching sequence and refining the improved switching sequence based on the set of switching times until a final switching sequence and a final set of switching times are obtained; and controlling the operation of the set of components based on the final switching sequence and the final set of switching times. [Brief explanation of the drawings]

[0020] [Figure 1] 1 illustrates an embodiment of a compressor system. [Figure 2] 2 illustrates one embodiment of the controller from the embodiment of FIG. 1. [Figure 3] 3 illustrates an embodiment of model predictive control and further details from the embodiment of FIG. 2. [Figure 4] 4 illustrates an embodiment of a future prediction and further details from the embodiment of FIG. 3. [Figure 5] 1 illustrates one embodiment of a method for iterative switching time optimization. DETAILED DESCRIPTION OF THE INVENTION

[0021] The drawings are included to provide a better understanding of the components and are not intended to be limiting in scope but are intended to provide an exemplary explanation.

[0022] The inventive concept of the present disclosure will be described below with reference to embodiments and with reference to the drawings. However, the claimed invention is not limited thereto. The drawings described are merely schematic and are non-limiting in scope. In the drawings, the size of some elements may be exaggerated and not drawn to scale for ease of illustration. The dimensions and relative dimensions do not necessarily correspond to actual embodiments of the present invention.

[0023] Furthermore, terms such as first, second, third, etc. may be used to distinguish between similar elements and not necessarily to describe a sequential or chronological order. These terms are interchangeable under appropriate circumstances, and embodiments of the invention may be performed in orders other than those described or illustrated herein.

[0024] Furthermore, various embodiments, which may be described as "preferred embodiments," should be construed as merely illustrative of ways and modes of carrying out the invention, and do not limit the scope of the invention.

[0025] When used in the claims, the terms "comprising," "including," or "having" should not be interpreted as being limited to the means or steps listed thereafter. These terms should be interpreted as specifying the presence of the mentioned feature, element, step, or component, but not excluding the presence or addition of one or more other features, elements, steps, or components, or groups thereof. Thus, the scope of the expression "an apparatus or device comprising means A and B" should not be interpreted as being limited to an apparatus or device consisting only of components A and B. For purposes of this disclosure, although only elements A and B of the apparatus are specifically mentioned, it is intended that the claims should further be interpreted to include equivalents of these elements.

[0026] Generally, a compressor system of the present disclosure includes one or more compressors configured to supply compressed air or gas to a client network. As described herein, a compressor is provided as a compressed gas source, but the compressor system may include other compressed gas sources, such as a pre-compressed gas tank, reservoir, or supply pipes or lines. The compressor system may further include a container or tank for storing compressed air or gas and a valve connected to a client network, which may have one or more consumers. Additional devices may also be included, such as a dryer, filter, regulator, and / or lubricator.

[0027] FIG. 1 shows a compressor system 100 comprising three compressors 101, 101′, 101″ configured to supply compressed air or gas to a client network 105. The compressor system 100 further comprises a container or tank 103 for storing the compressed air or gas and a valve 104 connected to the client network 105. The client network 105 has one or more consumers. It should be further understood that the compressor system 100 may further include other devices such as dryers, filters, regulators, and / or lubricators, as mentioned above, however, in the remainder of this specification, embodiments will be described with reference to FIG. 1 as a setup of the compressor system 100. In FIG. 1, solid lines indicate fluid connections and dashed lines indicate data connections.

[0028] Each of the compressors 101, 101', 101" can be locally controlled by a respective controller 102, 102', 102". Furthermore, to efficiently control the compressor system 100, the controllers 102, 102', 102" can be controlled in a coordinated manner. In other words, it can be avoided that each of the controllers 102, 102', 102" controls the respective compressors 101, 101', 101". However, the controllers 102, 102', 102" may be instructed by the controller 106 such that the overall performance and efficiency of the compressor system 100 is improved.

[0029] The controllers 102, 102′, 102″, 106 may include a processor, such as a microprocessor, a memory storage device, an output interface, and an input interface. The controllers 102, 102′, 102″, 106 may be configured to receive input signals via the input interface, which may be received via wired or wireless means, and process received sensor signals obtained from components and associated sensors within the compressor system 100. Further, as described herein, the controllers 102, 102′, 102″, 106 may output control signals to components of the compressor system 100 via the output interface. As described in more detail below, based on iterative STO determinations of an optimal schedule for the compressor system 100, the controller 106 sends control signals to adjust operating parameters of the compressor system. In certain embodiments, the controller 100 is configured to send control signals to the controllers 102, 102', 102'' to adjust the operation of the compressors 101, 101', 101'' or to turn the compressors 101, 101', 101'' on or off according to the optimal schedule determined by the controller 106.

[0030] The controller 106 can be located near the controllers 102, 102′, 102″, but can also be located remotely relative to the other components of the compressor system 100. For example, the controller 106 does not necessarily have to be integrally formed with or coupled to the compressor system 100. The controller 106 can be located in close proximity to the compressor system 100, for example, within the same spatial volume or housing. Alternatively, the controller 106 can be remote from the compressor system and its components but still receive signals from and send signals to the components of the compressor system 100. Furthermore, the controller 106 can be communicatively connected to a remote computer system for, for example, remote monitoring, control, adjustment, and / or software updates, and the like, and data acquired by the controller or control unit 106, and operating parameters sent as control signals by the controller or control unit 106, can be sent to the remote computer system or data storage device for further analysis and / or processing.

[0031] As described in further detail below, the controller or control unit 106 may include or use a special-purpose or general-purpose computer system, or computing system, specifically within or alternatively in communication with the control unit or controller 106, including computer hardware such as a processor or two or more processors and system memory. The controller 106 may be relatively close to the compressor system 100 and may receive hardwired or wireless signals from and transmit hardwired or wireless signals to the other components of the compressor system 100. Alternatively, the controller 106 may be located remotely from the other components of the compressor system and may receive signals from and transmit signals to the other components of the compressor system, including signals from one or more sensors that provide data indicative of one or more operating characteristics within the compressor system, via a network, such as a local area network (LAN), a wide area network (WAN), the Internet, or some other network. Alternatively, one of the controllers 102, 102', 102'' can be configured to function as the controller 106 for controlling all of the compressors 100, 100', 100''.

[0032] The controller 106 may be used to manage the operation, switching, and idle costs of the compressor system 100, thereby reducing wear on various equipment components while also reducing or otherwise improving the energy consumption of the compressor system 100. To this end, the controller 106 may be configured to schedule the operation of the components of the compressor system 100 in an optimal manner, in accordance with various embodiments of the present disclosure.

[0033] In one aspect of the present disclosure, the controller 106 receives characteristic data 110 describing technical or functional characteristics of one or more elements of the compressor system 100. This characteristic data may be obtained from a database, a model, measurements made on one or more elements of the compressor system 100, or some other suitable means. Additionally, the controller 106 also receives forecast data 120 describing at least future projected airflow and / or pressure demands of the client network 105. Again, this forecast data may be obtained from a database, a model, measurements made on one or more elements of the compressor system 100 or the client network 105, or some other suitable means. Based on the characteristic data 110, the forecast data 120, and the action profile determined for the compressor system 100 in accordance with the present disclosure, the controller 106 may send configuration data 130 to the controllers 102, 102', 102'' to adjust control of the compressors 101, 101', 101''.

[0034] One example of how the controller 106 controls the compressor system 100 is shown in the flow diagram of Figure 2. In the illustrated embodiment, the controller 106 controls and communicates with the compressor system 100 via outputs 210 and, optionally, inputs 211. Various modules or elements of the controller 106 may be arranged to provide data to a model predictive control (MPC) block 205 to determine the outputs 210.

[0035] According to various embodiments, the controller 106 can include a database 200, a set of compressor models and / or a compressor system model 201, one or more estimators 202, a flow prediction block 203, and a sampling block 204 for providing an initial sequence to the MPC block 205. It should be noted that while these blocks 200, 201, 202, 203, 204, and 205 are shown as being part of a single controller 106, they may be physically or virtually distributed with respect to one another. For example, the database 200 may be located on a remote server and accessible via a custom data connection. Similarly, it should be noted that the controller 106 can include more or less than these blocks 200, 201, 202, 203, 204, and 205 and can be configured with various architectures for determining the output 210.

[0036] 2 , one or more estimators 202 can be configured to receive 220 measurements 211 of the compressor system 100. The one or more estimators 202 can receive additional inputs 221 from the database 200, such as using an existing set of compressor models 201 as another input 222. In various embodiments, the set of compressor models 201 can also be built 223 into the database 200 itself. The set of compressor models 201 can represent the compressor system 100. For example, the model can be a digital twin of the compressor system, include a set of differential equations that represent the compressor system, or include a black-box approach.

[0037] The estimator block 202 can estimate the current state of the compressor system 100 based on the received (220) measurements 211, and optionally based on the model 201. Additionally, previous estimates 221 can be uploaded from the database 200 to improve the accuracy of the estimation. The output of the estimation block 202 can be used as inputs 224, 227 of the flow prediction block 203 and / or the MPC block 205.

[0038] The prediction block 203 may be configured to predict one or more future process variables of the compressor system 100. The prediction 225 may be based on the output 224 of the estimator block 202 and data 226 stored in the database 200. The prediction block 203 may use the current process variables and agent state data of the compressor system 100 to calculate a desired state of the compressor system 100 for a suitable time horizon. For example, vessel pressure and flow demand may be represented in a future process variable profile as a predicted future demand of the compressor system 100.

[0039] According to the present disclosure, terms such as "prediction," "forecast," and "predicted" relate to estimating outcomes for unknown data, while forecasting is a subcategory of prediction, which is made with respect to time series data for future use. For example, the difference between prediction and forecasting is that the latter takes into account the time dimension. Thus, while the term "prediction" can also be interpreted as "forecast," the remainder of this specification will use the term "forecasting."

[0040] The prediction block 203 can be configured to consider (226) past process variable data through the database 200 and (224) current process variable data through the estimator block 202. Additionally, other input data can be considered, such as sensor data, past and future state agent data, production schedules, calendar data, holiday data, and / or weather forecast data.

[0041] The output 225 of the prediction block 203 includes a predicted process variable data profile given for a predefined time period that can be set by the user or by the MPC program, which will be described further. In the latter case, setting the time period can be automated.

[0042] The prediction block 203 is a prediction function block based on an input-output model having inputs, outputs, model parameters, and hyperparameters. As examples, four prediction paradigms suitable for the prediction block 203 will be described.

[0043] First, a multi-output forecasting strategy can be used, in which some function approximator is used to directly estimate or train a forecast function for a given fixed time horizon H. This approach is further known as a multi-step approach, in which a multivariate forecast function is directly trained taking into account current and past observations. Second, a recursive multi-step forecasting method is used, in which an appropriate (I) / O model is selected. From the trained parameters of the (I) / O model, a forecast function is constructed, and outputs are recursively simulated or predicted for a given time horizon H. Third, a direct multi-step forecasting strategy can be used, which involves building a separate forecaster for each forecast time step. As a fourth forecasting paradigm, a hybrid forecasting strategy can be used, combining two or more of the above paradigms. Of course, other forecasting strategies can also be used, as will be apparent to those skilled in the art from this disclosure.

[0044] In the sampling block 204, the output 225 can be sampled at a sampling frequency suitable for the MPC block 205. If desired, the sampling frequency can be reset or varied over time.

[0045] 3 illustrates a flow diagram of the MPC block 205 configured to control the compressor system 100 in accordance with various embodiments of the present disclosure. As shown in FIG. 3, the MPC block 205 operates based on one or more requirements 300, one or more constraints 301, and a future forecast 302 of demand for the compressor system 100.

[0046] The requirements 300 may include, for example, a constant pressure or a constant flow rate in the client network 105. Constraints may include, for example, demand constraints such as pressure limits or setpoint pressures, flow demands for a mixture or portion of a mixture, humidity limits, temperature limits, dust particle limits, or limits on other impurities such as oil in the effluent fluid or dissolved oxygen in the process. Constraints may further include system constraints such as, for example, maximum and / or minimum temperature limits, humidity limits, flow rate limits, impurity limits, speed limits, acceleration limits, jerk limits, valve limits and rate of change of valve position, vibration limits, current limits, sequencing between units in the system, and time constraints such as between starts, between stops, minimum time on state, maximum time on state, delayed second stop, etc.

[0047] System constraints can be included at any location or component of the system. For example, system constraints can include maximum and minimum limits on temperatures at the inlet of the air plant or booster, motor components such as windings or converters, compressor elements, water in a cooling system, oil in a compressor, outlet of a compressor in an energy recovery system, etc. Humidity limits, flow limits, impurity limits can be included at the inlet of the air plant, booster, etc.

[0048] Additionally, some of the critical settings and / or mentioned constraints can be tracked to improve the quality of the outflow air and, consequently, the quality of the final product. In a related aspect, the use of weights allows combinations of the above constraints to be created in the same framework without significant adjustments.

[0049] Considering the identified constraints, embodiments of the compressor system may include one or more sensors located at multiple pre-defined locations within the system. For example, the compressor system may include any or combination of temperature sensors, humidity sensors, flow sensors, speed sensors, acceleration sensors, image sensors, current sensors, vibration sensors, particle sensors, oxygen sensors, nitrogen sensors, position sensors, pressure sensors, pressure dew point sensors, rotational speed sensors, and related components.

[0050] As will be understood by those skilled in the art from this description, the compressor system can be configured to include any known sensors associated with the compressor system and / or the compressed gas. For example, the temperature sensors can include one or more thermocouples, liquid or gas thermometers, electrical thermometers, including, for example, electrical resistance thermometers, silicone diodes, bimetallic devices, valve-capillary sensors, sealed bellows, and / or radiation thermometer devices, or any other type of temperature sensing device. Additionally, one or more sensors of the compressor system can be remote from a wall or sidewall of a system component, such as a pressure vessel or line or pipe, while still obtaining respective sensor data based, for example, on a radiation thermometer or other remote sensing means.

[0051] Any of the above sensors may be provided with a means for communicating with the controller 106. The communication connection may be wireless or wired, although for purposes of clarity, the sensors and associated communicating sensors are not shown. Respective output signals or data from these sensors are transmitted to the controller 106, either via hardwiring or wireless communication, and may be further used by the controller 106 to adjust or modify inputs for determining an optimal schedule for the compressor system and / or to track operating characteristics of the system. The described embodiments additionally or optionally include writing the sensor and / or constraint data to a memory. The memory may be a component of the controller 106 or may be external to the controller 106.

[0052] In an embodiment of the present disclosure, the MPC block 205 can use iterative switch time optimization (STO) to define optimal sequences and switch timings for the compressor system 100 in the form of an action profile 320 or schedule for the compressor system. The action profile 320 can include instructions for improved operation of the compressor system 100 such that the energy requirements of the compressor system 100 and wear on its components are reduced. The iterative STO can include an STO module 310 and a refinement module 311 for determining the action profile 320, as described in more detail below.

[0053] According to the present disclosure, a controller is configured to determine a compressor system action profile using an MPC framework that takes future consumer demand into account. As shown in the plot of FIG. 4, setpoints 405, historical data 410, models, and predicted demand can be used to form the inputs for the MPC. The historical data 410 can include past setpoints 402 and their actual values ​​403, as well as actions 404 previously taken by the compressor system. In various aspects, the models can include static or dynamic mechanical models, static or dynamic air net models, and / or horizon flow demand versus instantaneous flow demand. Predictions of the controlled parameters 406 can be generated from a limited subset of conditions from the compressor system, such as compressor or other component states, generated flow rates and pressures for a forecast period 409 at each time step k up to k+n, and predictions of inlet air or atmospheric conditions based on weather information. These inputs can be provided as part of or along with the initial sequence for the iterative STO of the present disclosure and can be prepared using, for example, dynamic programming, analytical dynamic programming (ADP), artificial intelligence (AI), a heuristic, a branch and bound scheme, a linear program simplex solver, or similar methods.

[0054] Using this initial sequence 407, STO can be applied to determine optimal switch times for the compressor system's action profile, according to the method of FIG. 5. The initial sequence is provided to the STO module 310 in a first step 501 of the method 500. As described above, the initial sequence can be provided using dynamic programming, ADP, AI, heuristics, branch and bound schemes, linear program simplex solvers, or similar methods. In a second step 502, the STO module establishes the switch times as variables to be optimized for the compressor system based on the assumptions of the initial sequence. In a third step 503, the STO module optimizes costs, including the constraints and requirements of the compressor system, to determine time values ​​for each portion of the initial sequence 407.

[0055] The STO module can calculate a set of switching times based on the following formulas, constraints and requirements:

number

[0056] In this example, c k represents the capacity of the unit, and S on,k represents the operating state, and S lo,k represents the load state, and P k represents the power of unit k as a function of its capacity and state, p(t) represents the pressure in the system,

number

[0057] According to various embodiments, the STO module can calculate a set of switch times based on the following equations, constraints, and requirements, using at least some representation for the constants, variables, and parameters similar to those described above:

number

[0058] Further alternatives, modifications, and combinations are, of course, contemplated and are not excluded from this disclosure.

[0059] Based on the results of the STO, the refinement module 311 identifies any portions of the initial sequence 407 that indicate the STO should allocate zero time to that portion in a refinement step 504, and removes the identified portions to form a new sequence. In another step 505 of the method 500, the refinement module passes the new sequence to the STO module 310 for further iterations that are warm started from the results of the previous iteration. The method can be performed iteratively until an optimal sequence and optimal switch times are determined, which form an action profile or schedule for operation of the compressor system.

[0060] Advantageously, the iterative STO of the described embodiments allows for more precise start and stop timing in compressor systems, allows for a larger set of constraints to be included, and can address a wider range of air installations and objectives, compared to known methods.

[0061] The disclosed iterative approach recognizes two sub-problems to optimizing a compressor system schedule: (1) finding a switchover sequence, and (2) optimizing the switchover instances given a switchover sequence. Advantageously, any remaining problem can be posed as a continuous linear programming problem for which efficient numerical algorithms exist.

[0062] In an embodiment of the present disclosure, a sequence is an ordered set of states S={s0, s1, ..., s ns}. To specify the switching instances, we define a set W = {w0, w1, ..., w ns} indicates that the system is in state s i defined as the time spent in i Therefore, STO determines W given S.

[0063] Surprisingly, to find the optimal sequence according to the described embodiment, the method begins with an initial sequence S, which does not need to be optimal or based on a relaxed solution. Instead, the method advantageously only needs to include the optimal sequence as a set. Extraneous states that are not part of the optimal sequence are iteratively removed from the initial sequence by STO and refinement of the described method. In other words, given a sequence, STO is used to find the optimal switching time, and given the optimal switching time, the sequence can be refined. This is done iteratively, and after each removal, STO is solved again for the new sequence, from which other removal candidates are identified. Because the initial sequence is finite, the iterations end with the identification of the optimal sequence and the optimal switching time.

[0064] Without being bound by any particular theory, the efficiency of the disclosed iterative STO method is believed to stem from the fact that it transforms a mixed-integer problem into a continuous one, allowing many constraints to be entered into the problem in a natural and simple way, such as w>0.5 to add a minimum operating time as a constraint for a compressor in a compressor system. Since the maximum number of switchovers is already limited by the initial sequence, the described approach does not risk frequent switchovers. There is also the possibility to apply conditional constraints, such as not setting u1=1 unless u2=1 or u3=1 beforehand, since this can be directly applied to the sequence to filter forbidden combinations.

[0065] The method of the present application may initially appear disadvantageous due to the large number of possible combinations required for the initial sequence. However, surprisingly, the inventors have discovered that this concern is misplaced. It is noteworthy that the initial sequence need only include the optimal sequence, and the power set grows exponentially. Furthermore, the number of variables does not need to increase. For example, in multiple shooting methods, STO can reduce the number of variables compared to prior art relaxation problems by fixing u(t) and substituting a small number w.

[0066] Embodiments of the sequence optimization method can be configured to repeatedly select only one state as a candidate for removal, or to process multiple states in parallel to reduce the number of required iterations. Similarly, branch and bound schemes for constraint satisfaction and state removal selection can be provided to ensure sequence optimality. Finally, the sequence optimization can also be configured to insert required states rather than remove them. As is readily apparent from the above advantages, the disclosed method and system provide significantly greater flexibility than known in the prior art, in addition to reducing compressor system wear, energy requirements, and system operating times.

[0067] According to the present embodiment, the scheduling of compressor system components is handled in a special way, resulting in significant improvements over the prior art. The described iterative STO can advantageously handle a wider range of operating units or components than conventional approaches, such as compressors, such as positive displacement compressors, turbo compressors, boosters, and blowers (low pressure); air utilities, such as dryers, valves, aftercoolers, chillers, O2 generators, and N2 generators; refrigeration or oil cooling circuits; and energy recovery systems. The described iterative STO can further consider passive air utility elements, such as filters, vessels, and pipes, which is not achieved with known methods and systems.

[0068] The described embodiments further realize the additional benefit of iterative STO, which can generate several predictions, such as predicting profiles of humidity, temperature, impurities, etc., as well as handling long periods of time with high accuracy. For example, prediction periods according to the present disclosure can be up to 6 hours, up to 8 hours, up to 10 hours, and preferably up to 8 hours. Similarly, the accuracy of iterative STO of the described methods and systems can be between 0.5 seconds and 5 minutes, more particularly between 0.5 seconds and 3 minutes, or between 3 seconds and 3 minutes, between 10 seconds and 2.5 minutes, less than 5 minutes, less than 4 minutes, less than 3 minutes, less than 2 minutes, less than 1 minute, less than 45 seconds, less than 30 seconds, less than 10 seconds, or less than 5 seconds.

[0069] Embodiments within the scope of the present disclosure also include physical and other computer-readable media for carrying or storing computer-executable instructions and / or data structures. Such computer-readable media may be any available media that can be accessed by a general-purpose or special-purpose computer system. Computer-readable media that store computer-executable instructions and / or data structures are computer storage media. Computer-readable media that carry computer-executable instructions and / or data structures are transmission media. Thus, by way of example, embodiments of the present disclosure may include at least two different types of computer-readable media: computer storage media and transmission media.

[0070] Computer storage media are physical storage media that store computer-executable instructions and / or data structures. Physical storage media include computer hardware such as RAM, ROM, EEPROM, solid-state drive (SSD), flash memory, phase-change memory (PCM), optical disk storage, magnetic disk storage, or other magnetic storage devices, or any other hardware storage device(s) that can be used to store program code in the form of computer-executable instructions or data structures, which can be contained within or accessed and executed by the controller 106, a general-purpose computer system, or a special-purpose computer system to implement the disclosed functions of the present disclosure.

[0071] Transmission media can be used to carry program code in the form of computer-executable instructions or data structures and can include networks and / or data links that can be accessed by a general-purpose or special-purpose computer system. A "network" can be defined as one or more data links that enable the transmission of electronic data between computer systems and / or modules and / or other electronic devices. When information is transferred or provided to a computer system over a network or other communications connection (either hardwired, wireless, or a combination of hardwired and wireless), the computer system can view the connection as a transmission medium. Combinations of the above are also intended to be included within the scope of computer-readable media.

[0072] Furthermore, upon reaching the various computer system components, program code in the form of computer-executable instructions or data structures may be automatically transferred from transmission media to computer storage media (or vice versa). For example, computer-executable instructions or data structures received over a network or data link may be buffered in RAM within a network interface module (e.g., a "NIC") and eventually transferred to the computer system's RAM and / or less-volatile computer storage media. Thus, it should be understood that computer storage media may be included in computer system components that also (or primarily) utilize transmission media.

[0073] Computer-executable instructions may include, for example, instructions and data that, when executed by one or more processors, cause a general-purpose computer system, special-purpose computer system, or special-purpose processing device to perform a certain function or group of functions. Computer-executable instructions may be, for example, binary instructions, intermediate format instructions such as assembly language, or source code.

[0074] The present disclosure may be practiced in networked computing environments having many types of computer system configurations, including, but not limited to, personal computers, desktop computers, laptop computers, message processors, handheld devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, cellular phones, PDAs, tablets, pagers, routers, switches, etc. The present disclosure may also be practiced in distributed system environments where tasks are performed by both local and remote computer systems that are linked through a network (either by hardwired data links, wireless data links, or a combination of hardwired and wireless data links). Thus, in a distributed system environment, the computer system may include multiple constituent computer systems. In a distributed system environment, program modules may be located in both local and remote memory storage devices.

[0075] The present disclosure may also be implemented in a cloud computing environment. A cloud computing environment may be distributed, but this is not required. If distributed, the cloud computing environment may be distributed internationally within an organization and / or have components held across multiple organizations. For purposes of this specification and the claims that follow, "cloud computing" is defined as a model that enables on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services). The definition of "cloud computing" is not limited to any of the many other benefits that such a model can provide when properly deployed.

[0076] Cloud computing models can be configured with a variety of characteristics, such as on-demand self-service, extensive network access, resource pooling, fast elasticity, and measured service. Cloud computing models can also be offered in the form of various service models, such as Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). Cloud computing models can also be deployed using different deployment models, such as private cloud, community cloud, public cloud, and hybrid cloud.

[0077] Some embodiments, such as a cloud computing environment, may include a system including one or more hosts, each capable of running one or more virtual machines. During operation, the virtual machines emulate an operational computing system, supporting an operating system and, possibly, one or more other applications as well. In some embodiments, each host includes a hypervisor that emulates the virtual machine's virtual resources using abstracted physical resources that are invisible to the virtual machines. The hypervisor also enables proper isolation between the virtual machines. Thus, from the perspective of any virtual machine, the hypervisor provides the illusion that the virtual machine is interfacing with physical resources, even though the virtual machine only interfaces with the appearance (e.g., virtual resources) of physical resources. Examples of physical resources may include processing power, memory, disk space, network bandwidth, and media drives.

[0078] Throughout this specification and claims, certain terms are used to refer to particular methods, features, or components. As one skilled in the art will appreciate, different people may refer to the same method, feature, or component by different names. This disclosure does not intend to distinguish between methods, features, or components that differ in name but not function. The figures are not necessarily drawn to scale. Certain features and components herein may be shown on an exaggerated scale or in somewhat schematic form, and some details of conventional elements may not be shown or described for the sake of clarity and conciseness.

[0079] Although various exemplary embodiments have been described in detail herein, those skilled in the art will readily appreciate in light of this disclosure that many modifications are possible in the exemplary embodiments without substantially departing from the concepts of the present disclosure. Accordingly, such modifications are intended to be included within the scope of the present disclosure. Similarly, while the present disclosure contains many specific details, these specific details should not be construed as limiting either the scope of the present disclosure or the scope of the appended claims, but merely as providing information related to one or more specific embodiments that may be included within the scope of the present disclosure and the appended claims. Any described features from the various disclosed embodiments may be used in combination. In addition, other embodiments of the present disclosure may be conceived that are within the scope of the present disclosure and the appended claims. Each addition, deletion, and modification to the embodiments that falls within the spirit and scope of the claims is intended to be encompassed within the scope of the claims.

[0080] Certain embodiments and features may be described with a set of upper numerical limits and a set of lower numerical limits. It is understood that ranges including any combination of two values ​​are contemplated, for example, any lower limit with any upper limit, any two lower limits, and / or any two upper limits, unless otherwise indicated. Particular lower limits, upper limits, and ranges may be set forth in one or more claims below. Any numerical value is "about" or "approximately" the stated value and accounts for experimental error and variations that one of ordinary skill in the art would expect.

[0081] It should be noted that in the above embodiments, the set of components fluidly connected to a common compressed air distribution network is a finite set of components.

[0082] The present disclosure provides various examples, embodiments, and features, which are understood to be combinable with other examples, embodiments, or features described herein unless expressly stated otherwise or are mutually exclusive.

[0083] In addition to the above, further embodiments and examples include the following.

[0084] 1. A compressor system comprising a set of components fluidly connected to a common compressed air distribution network and a controller, wherein the controller is configured to: predict future demands of the compressor system; determine an initial switching sequence for operation of at least one component of the set of components that meets the predicted future demands; determine a set of switching times for the initial switching sequence; refine the initial switching sequence based on the set of switching times to form an improved switching sequence; iteratively determine a set of switching times for the improved switching sequence and refine the improved switching sequence based on the set of switching times until a final switching sequence and a set of final switching times are obtained; and control the operation of the set of components based on the final switching sequence and the set of final switching times.

[0085] 2. A compressor system according to any one or combination of 1 and 3-8, wherein at least one component of the set of components includes one or more compressors.

[0086] 3. A compressor system in which the initial switching sequence represents a unique operating sequence of at least one component, either 1-2 or 4-8, or a combination thereof.

[0087] 4. A compressor system according to any one or combination of 1-3 and 5-8, wherein at least one component of the set of components includes one or more dryers.

[0088] 5. A compressor system according to any one or combination of 1-4 and 6-8, wherein at least one component of the set of components includes one or more valves in the compressed air distribution network.

[0089] 6. A compressor system according to any one or combination of 1-5 and 7-8, further including a plurality of sensors arranged to monitor an operating parameter of at least one component.

[0090] 7. A compressor system according to any one or combination of 1-6 and 8, wherein the set of switching times for the initial switching sequence is determined by switching time optimization.

[0091] 8. A compressor system according to any one of 1-7 or a combination thereof, wherein each of the initial changeover sequence refinement and the refined changeover sequence refinement includes removing any portion of the respective sequence that has zero time assigned to it.

[0092] 9. A controller configured to operate a compressor system having a set of components fluidly connected to a common compressed air distribution network, the controller comprising a computer-readable storage medium and a processor, the processor being configured to: predict future demands of the compressor system; determine an initial switchover sequence for operation of at least one component of the set of components that meets the predicted future demands; determine a set of switch times for the initial switchover sequence; refine the initial switchover sequence based on the set of switchover times to form an improved switchover sequence; iteratively determine a set of switchover times for the improved switchover sequence and refine the improved switchover sequence based on the set of switchover times until a final switchover sequence and final switchover times are obtained; and control the operation of the set of components based on the final switchover sequence and the set of final switchover times.

[0093] 10. A controller according to any one or combination of 9 and 10-13, wherein the initial switching sequence represents a unique sequence of operation of at least one component.

[0094] 11. The set of switching times for the initial switching sequence is determined by switching time optimization, the controller according to any one or combination of 9-10 and 12-13.

[0095] 12. A controller according to any one or combination of 9-11 and 13, wherein the refinement of the initial switching sequence and the refinement of the refined switching sequence each include removing any portion of the respective sequence that is assigned zero time.

[0096] 13. Switching time optimization

number

[0097] 14. A computer-implemented method for controlling a compressor system having a set of components fluidly connected to a common compressed air distribution network, the method comprising: predicting future demand for the compressor system; determining an initial switchover sequence for operation of at least one component of the set of components that meets the predicted future demand; determining a set of switch times for the initial switchover sequence; refining the initial switchover sequence based on the set of switch times to form an improved switchover sequence; iteratively determining a set of switch times for the improved switchover sequence and refining the improved switchover sequence based on the set of switch times until a final switchover sequence and a set of final switchover times are obtained; and controlling operation of the set of components based on the final switchover sequence and the set of final switchover times.

[0098] 15. A method according to any one or combination of 14 and 16-20, wherein the initial switching sequence represents a unique sequence of operation of at least one component.

[0099] 16. The set of switching times for the initial switching sequence is determined by switching time optimization, according to any one or combination of methods 14-15 and 17-20.

[0100] 17. The method according to any one or combination of 14-16 and 18-20, wherein the refinement of the initial switching sequence and the refinement of the improved switching sequence each include removing any portion of the respective sequence that is assigned a zero time.

[0101] 18.Switching time optimization

number

[0102] 19. The initial switching sequence is determined from predicted future demand by one or more of the following methods: dynamic programming, analytical dynamic programming (ADP), artificial intelligence (AI), heuristic methods, branch and bound schemes, and linear program simplex solvers, according to any one or combination of methods 14-18 and 20.

[0103] 20. The predicted future demand represents the predicted future pressure and / or airflow demand for the compressor system by any of the methods in 14-19 or a combination thereof.

Claims

1. a set of components fluidly connected to a common compressed air distribution network; A controller; A compressor system comprising: The controller forecasting future demand for the compressor system; determining an initial switchover sequence for operation of at least one component of the set of components that meets the predicted future demand; determining a set of switching times for the initial switching sequence; modifying the initial switching sequence based on the set of switching times to form an improved switching sequence; iteratively determining a set of switching times for the improved switching sequence and improving the improved switching sequence based on the set of switching times until a final switching sequence and a final set of switching times are obtained; controlling an operation of the set of components based on the set of final switching sequences and the set of final switching times; The compressor system is configured as follows:

2. The compressor system of claim 1 , wherein the at least one component of the set of components includes one or more compressors.

3. The compressor system of claim 1 , wherein the initial switching sequence represents a unique operating sequence of the at least one component.

4. The compressor system of claim 1 , wherein the at least one component of the set of components includes one or more dryers.

5. The compressor system of claim 1 , wherein the at least one component of the set of components comprises one or more valves in the compressed air distribution network.

6. The compressor system of claim 1 , further comprising a plurality of sensors positioned to monitor operating parameters of the at least one component.

7. The compressor system of claim 1 , wherein the set of switch times for the initial switch sequence is determined by switch time optimization.

8. 2. The compressor system of claim 1, wherein the refinement of the initial switching sequence and the refinement of the refined switching sequence each include removing any portion of the respective sequence that is assigned a zero time.

9. 1. A controller configured to operate a compressor system having a set of components fluidly connected to a common compressed air distribution network, the controller comprising: a computer readable storage medium; a processor; Equipped with The processor: forecasting future demand for the compressor system; determining an initial switchover sequence for operation of at least one component of the set of components that meets the predicted future demand; determining a set of switching times for the initial switching sequence; modifying the initial switching sequence based on the set of switching times to form an improved switching sequence; iteratively determining a set of switching times for the improved switching sequence and improving the improved switching sequence based on the set of switching times until a final switching sequence and a final set of switching times are obtained; controlling the operation of the component set based on the final switching sequence and the final switching time; The controller is configured as follows:

10. The controller of claim 9 , wherein the initial switching sequence represents a unique operating sequence of the at least one component.

11. The controller of claim 9 , wherein the set of switching times for the initial switching sequence is determined by switching time optimization.

12. The controller of claim 9 , wherein the refinement of the initial switching sequence and the refinement of the refined switching sequence each include removing any portion of the respective sequence that is assigned a zero time.

13. The switching time optimization includes: [Equation 1] The controller of claim 11 , further comprising: calculating the set of switch times based on:

14. 1. A computer-implemented method for controlling a compressor system comprising a set of components fluidly connected to a common compressed air distribution network, comprising: forecasting future demand for the compressor system; determining an initial switchover sequence for operation of at least one component of the set of components that meets the predicted future demand; determining a set of switching times for the initial switching sequence; improving the initial switching sequence based on the set of switching times to form an improved switching sequence; iteratively determining a set of switching times for the improved switching sequence and refining the improved switching sequence based on the set of switching times until a final switching sequence and a final set of switching times are obtained; controlling operation of the set of components based on the set of final switching sequences and final switching times; A method comprising:

15. The method of claim 14 , wherein the initial switching sequence represents a unique operating sequence of the at least one component.

16. The method of claim 14 , wherein the set of switch times for the initial switch sequence is determined by switch time optimization.

17. 15. The method of claim 14, wherein the refinement of the initial switching sequence and the refinement of the refined switching sequence each include removing any portion of the respective sequence that is assigned a zero time.

18. The switching time optimization includes: [Equation 2] 17. The method of claim 16, comprising calculating the set of switch times based on:

19. 15. The method of claim 14, wherein the initial switching sequence is determined from the predicted future demand by one or more of dynamic programming, analytical dynamic programming (ADP), artificial intelligence (AI), heuristics, branch and bound schemes, and linear program simplex solvers.

20. The method of claim 14 , wherein the predicted future demand represents a predicted future pressure and / or airflow demand for the compressor system.

21. The switching time optimization includes: [Equation 3] The controller of claim 11 , further comprising: calculating the set of switch times based on:

22. The switching time optimization includes: [Equation 4] 17. The method of claim 16, comprising calculating the set of switch times based on:

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