EP4643020A1

EP4643020A1 - Method, system and device for the control of a compressor system

Info

Publication number: EP4643020A1
Application number: EP23837420.1A
Authority: EP
Inventors: Wim Van Roy; Ramin Abbasi ESFEDEN
Original assignee: Katholieke Universiteit Leuven; Atlas Copco Airpower NV
Current assignee: Katholieke Universiteit Leuven; Atlas Copco Airpower NV
Priority date: 2022-12-29
Filing date: 2023-12-18
Publication date: 2025-11-05
Also published as: AU2023416393A1; CN120604039A; TW202436758A; MX2025007670A; BE1031134B1; JP2025542480A; BE1031134A1; KR20250121128A; WO2024141848A1

Abstract

Methods, systems, and apparatuses are provided for improving control and efficiency of a compressor system(100). The compressor system includes a controller (106). The controller is configured to predict a future demand for the compressor system, determine an initial sequence based on the predicted future demand, determine an initial set of switching times for the initial sequence, refine the initial switching sequence on the basis of the set of switching times to form a refined switching sequence, iteratively determine a set of switching times for the refined switching sequence and refine the refined switching sequence on the basis of the set of switching times until a final switching sequence and a final set of switching times are obtained, and control operation of the set of components on the basis of the final switching sequence and the final set of switching times.

Description

METHOD, SYSTEM AND DEVICE FOR THE CONTROL OF A COMPRESSOR SYSTEM

[01] FIELD OF THE DISCLOSURE

[02] The present disclosure relates to methods, systems, and apparatuses for monitoring and controlling a compressor system, and particularly for monitoring, controlling, and optimizing the efficiency of components of a compressor system for providing compressed air or gases to consumers.

[03] BACKGROUND

[04] It is known that compressors are used to compress air or gases in one or more compression stages. The compressed air or gas is then provided to one or more consumers. The distribution thereof may be provided through a compressed air or gas system.

[05] Since the number of consumers may be vast and spatially distributed over a significant area, for example in an industrial plant or in a hospital, usually a central hub is installed for providing therefrom the compressed air or gases.

[06] A central hub normally comprises one or more compressor rooms wherein in each room one or more compressors are installed. Further, auxiliary devices such as valves, filters, dryers, vessels, sensors, controlling components, and/or other devices for managing and/or controlling the compressor rooms are likewise installed. Next, from the one or more compressor rooms onward pipes or ducts depart for supplying the consumers. As a last part in the chain, the compressed air or gas is utilized by the consumers for a variety of applications.

[07] Furthermore, between the compressors and the consumers another set of devices may be present as well such as safety valves, distribution valves, control sensors, or other devices for controlling and safeguarding the distribution of the compressed air or gases.

[08] The described set-up will further be identified as a compressor system. Hence, a compressor system may comprise one compressor supplying one consumer but will generally be regarded as more extensive, thus comprising a multitude of components and constituting a complex system of several elements interacting with each other. [09] To make use of the compressor system, the different parts thereof need to be controlled. It is already known to separately control compressors by means of independent local controllers, whereby the different controllers are set at a predefined pressure value thereby switching the compressors sequentially on or off, depending on the consumption of compressed air.

[10] It is further known to apply a method for controlling a compressor system by a number of communicating controllers for controlling components that are part of the compressor system, whereby the components are controlled such that none of the controllers determines the operational condition of any component that is controlled by other controllers. In W02008/009073 such a method is disclosed.

[11] In W02008/009072 another method is disclosed for controlling a compressed air unit which consists of several compressed air or gas networks having at least one commonly controllable component, whereby, on the basis of measurement data of at least one of the compressed air or gas networks, at least the common component is controlled by at least one controller.

[12] However, a disadvantage of these controlling methods that has been identified by the inventors is that they solely operate based on the current state of the compressor system, meaning that they are incapable of considering a prediction of any kind. This leads to suboptimal control and higher energy cost.

[13] A compressor system may be provided as a switched dynamical system, a continuous-time nonlinear system defined by multiple subsystems and nonlinear switching rules. Switched dynamical systems show a large flexibility in modeling a wide range of real-world applications, however, due to the discrete nature of switched dynamics, it has proven difficult to realize advantageous control of such a system.

[14] Problems in controlling switched dynamical systems identified by the inventors may take the following form:

Where x(t) is the state of the system and u(t) and v(t) are the inputs. The switching nature of the systems is captured in the variable u(t), which can only take values from a finite discrete set of options. This is known as a Mixed-Integer Optimal Control Problem (MIOCP). [15] Known approaches for addressing MIOCPs include Mixed-Integer Nonlinear Programming (MINLP), the relaxed solution, the control parameterization technique (CPET), and combinatorial integral approximation (CIA). However, the inventors have identified that currently known methods all exhibit significant shortcomings. In particular, the inventors have identified that such methods and systems are overly complex, requiring significant computational capacity and time delay due to the required computations, and may be inaccurate.

[16] It has been found by the inventors of the present application that the known calculations and optimizations are indeed unnecessary and inefficient, require more time and processing capacity than is necessary, and the inventor of the present application has found a robust and efficient method and system to accurately control and optimize performance efficiency of a compressor system.

[17] SUMMARY

[18] A compressor system is provided comprising: a set of components which are fluidly connected to a common compressed air distribution network; and a controller configured to: predict a future demand for 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 demand; determine a set of switching times for the initial switching sequence; refine the initial switching sequence on the basis of the set of switching times to form a refined switching sequence; iteratively determine a set of switching times for the refined switching sequence and refine the refined switching sequence on the basis of the set of switching times until a final switching sequence and a final set of switching times are obtained; and control operation of the set of components on the basis of the final switching sequence and the final set of switching times.

[19] In another embodiment, a controller of a compressor system is also provided. The controller configured to operate a compressor system having a set of components which are fluidly connected to a common compressed air distribution network, the controller comprising: a computer readable storage medium; and a processor configured to: predict a future demand for 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 demand; determine a set of switching times for the initial switching sequence; refine the initial switching sequence on the basis of the set of switching times to form a refined switching sequence; iteratively determine a set of switching times for the refined switching sequence and refine the refined switching sequence on the basis of the set of switching times until a final switching sequence and a final set of switching times are obtained; and control operation of the set of components on the basis of the final switching sequence and the final set of switching times.

[20] In another embodiment, a computer-implemented method for controlling a compressor system comprising a set of components which are fluidly connected to a common compressed air distribution network is also provided for improving efficiency of the compressor system. The method comprising: predicting a future demand for 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 demand; determining a set of switching times for the initial switching sequence; refining the initial switching sequence on the basis of the set of switching times to form a refined switching sequence; iteratively determining a set of switching times for the refined switching sequence and refining the refined switching sequence on the basis of the set of switching times until a final switching sequence and a final set of switching times are obtained; and controlling operation of the set of components on the basis of the final switching sequence and the final set of switching times.

[21] A hardware storage device having stored thereon computer executable instructions which, when executed by one or more processors of a computing system, configure the computing system to perform the method for controlling a compressor system, including predicting a future demand for 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 demand; determining a set of switching times for the initial switching sequence; refining the initial switching sequence on the basis of the set of switching times to form a refined switching sequence; iteratively determining a set of switching times for the refined switching sequence and refining the refined switching sequence on the basis of the set of switching times until a final switching sequence and a final set of switching times are obtained; and controlling operation of the set of components on the basis of the final switching sequence and the final set of switching times.

BRIEF DESCRIPTION OF THE DRAWINGS [22] FIG. 1 shows an embodiment of a compressor system.

[23] FIG. 2 shows an embodiment of a controller from the embodiment of FIG. 1.

[24] FIG. 3 shows an embodiment and further details of a model predictive control from the embodiment of FIG. 2.

[25] FIG. 4 shows an embodiment and further details of a future prediction from the embodiment of FIG. 3.

[26] FIG. 5 shows an embodiment of a method for iterative switching time optimization.

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

[28] DETAILED DESCRIPTION OF VARIOUS EMBODIMENTS

[29] The inventive concepts of the present disclosure will be described below with reference to embodiments and with reference to the drawings. But the claimed invention is not limited thereto. The drawings described are only schematic and are non-limiting in scope. In the drawings, the size of some of the elements may be exaggerated and not drawn on scale; this is for ease of illustration. The dimensions and relative dimensions do not necessarily correspond to practical embodiments of the invention.

[30] Furthermore, the terms first, second, third and the like may be used for distinguishing between similar elements and not necessarily for describing a sequential or chronological order. The terms are interchangeable under appropriate circumstances and the embodiments of the invention can be practiced in sequences other than those described or illustrated herein.

[31] In addition, the various embodiments which may be described as “preferred embodiments” are to be construed as merely illustrative of ways and modes for carrying out the invention and not as limitations on the scope of the invention.

[32] The terms “comprising”, “including”, or “having” as used in the claims should not be interpreted as being limited to the means or steps mentioned thereafter. The terms are to be interpreted as specifying the presence of the stated features, elements, steps or components as referred to, but do not preclude 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 taken as being limited to an apparatus or device consisting only of components A and B. It is intended that for the purposes of this disclosure, only the parts A and B of the device are specifically mentioned, but the claims should be further construed to include equivalents of these parts.

[33] Generally, the compressor systems of the present disclosure comprise one or more compressors configured to provide compressed air or gas to a client network. As described herein, a compressor is provided as a compressed-gas source, however, the compressor system may be provided with other compressed-gas sources, such as a pre -compressed gas tank, reservoir, or a supply pipe or line. The compressor systems may further comprise a vessel or tank for storing compressed air or gas and a valve connected to the client network, where one or more consumers may be present. Further devices may also be included, such as dryers, filters, regulators, and/or lubricators.

[34] FIG. 1 illustrates a compressor system 100 comprising three compressors 101, 101’ and 101” configured to provide compressed air or gas to a client network 105. The compressor system 100 further comprises a vessel or tank 103 for storing compressed air or gas and a valve 104 connected to the client network 105. At the client network 105 one or more consumers are present. It should be further understood that the compressor system 100 may further comprise other devices such as dryers, filters, regulators, and/or lubricators, as indicated above, but in the continuation of this text, embodiments will be illustrated with reference to FIG. 1 as a set-up of the compressor system 100. In FIG. 1, full lines indicate fluid connections whereas broken lines indicate data connections.

[35] The compressors 101, 101’, 101” may each be locally controllable by a respective controller 102, 102’, 102”. Further, to efficiently control the compressor system 100, the controllers 102, 102’, 102” may be controlled in a coordinated manner. In other words, it may be avoided that the controllers 102, 102’, 102” each individually control their respective compressor 101, 101’, 101”. Yet, the controllers 102, 102’, 102” may be instructed by a controller 106 such that the overall performance and efficiency of the compressor system 100 is increased.

[36] Controllers 102, 102’, 102”, 106 may comprise a processor, for example, a microprocessor, a memory storage, an output interface, and an input interface. Controllers 102, 102’, 102”, 106 may be configured to receive input signals through input interface, which may be received through wired or wireless means, and process received sensor signals obtained from components and related sensors within the compressor system 100. And as described herein, controllers 102, 102’, 102”, 106 may output control signals to components of the compressor system 100 through output interface. As described in more detail below, based on an iterative STO determination of an optimal schedule for the compressor system 100, at controller 106 transmits control signals to adjust operational parameters of the compressor system. In certain embodiments, controller 100 is configured to transmit control signals to 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, depending on the optimal schedule determined by the controller 106.

[37] The controller 106 may be located near the controllers 102, 102’, 102” but may also be located at a remote position compared to other components of the compressor system 100. For example, the controller 106 is not necessarily formed integrally with or coupled to the compressor system 100. The controller 106 may be provided in proximity to the compressor system 100, for example, within a same room volume, or housing. Or, the controller 106 may be remote from the compressor system and components thereof, while still being able to receive signals from and transmit signals to the components of the compressor system 100. Further, the controller 106 may be communicatively connected to a remote computer system, e.g. for remote monitoring, control, adjustment and/or software updating, etc., and data obtained by the controller or control unit 106 and operation parameters transmitted by controller or control unit 106 as control signals may be transmitted to the remote computer system or a data storage device for further analysis and/or processing.

[38] Controller or control unit 106 may include or use a special -purpose or general- purpose computer system, or a computing system, particularly in control unit or controller 106 or alternatively in communication with controller 106, that includes computer hardware, such as, for example, a processor or more than one processor and system memory, as discussed in greater detail below. Controller 106 may be in relatively close proximity to the compressor system 100, and receive hardwire or wireless signals from other components of the compressor system 100 and send hardwire or wireless signals to other components of the compressor system 100. Alternatively, controller 106 may be arranged remotely from other components of the compressor system and may receive signals from other components of the compressor system, including from one more sensors providing data indicative of one or more operating characteristics in the compressor system, and transmit signals to other components of the compressor system over 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 act as the controller 106 for controlling all the compressors 100, 100’, 100”.

[39] Through the controller 106 the running, switching and idle costs of the compressor system 100 may be managed, thereby reducing wear of components of the different devices while at the same time reducing or otherwise improving the energy consumption of the compressor system 100. To this end, the controller 106 may be configured to schedule operation of components of the compressor system 100 in an optimal manner according to varying embodiments of the present disclosure.

[40] In one aspect of the disclosure, the controller 106 receives characterizing data 110 which describes the technical or functional properties of one or more parts of the compressor system 100. This characterizing data may be obtained from a database, a model, measurements effected on one or more parts of the compressor system 100 or any other suitable means. Further, the controller 106 also receives prediction data 120 which describes at least the future predicted airflow and/or pressure demand of the client network 105. Again, this prediction data may be obtained from a database, a model, measurements effected on one or more parts of the compressor system 100 or client network 105 or any other suitable means. Based on the characterizing data 110, the prediction data 120 and an action profde determined for the compressor system 100 according to the current disclosure, the controller 106 may send configuration data 130 to the controllers 102, 102’, 102” to coordinate the control of the compressors 101, 101’, 101”.

[41] An example of how the controller 106 may control the compressor system 100 is illustrated in the flow diagram of FIG. 2. In the depicted embodiment, the controller 106 controls and communicates with the compressor system 100 through an output 210 and optionally an input 211. Different modules or elements of the controller 106 may be arranged for providing data to a model predictive control (MPC) block 205 for determination of the output 210. [42] According to varying embodiments, the controller 106 may include a database 200, a set of compressor models and/or a model of the compressor system 201, one or more estimators 202, a flow prediction block 203, and a sampling block 204 for providing the initial sequence to the MPC block 205. Although these blocks 200, 201, 202, 203, 204, 205 are illustrated as being part of one controller 106, it should be noted that they may be physically or even virtually distributed with respect to each other. For example, the database 200 may be located on a remote server and accessible via a custom-made data connection. In like manner, it is noted that the controller 106 may include more or less than these blocks 200, 201, 202, 203, 204, 205, and may be configured with varying architecture for determination of the output 210.

[43] As shown in FIG. 2, the one or more estimators 202 may be configured to receive 220 measurements 211 of the compressor system 100. The one or more estimators 202 may receive further inputs 221 from the database 200 and may, for example, use an existing set of compressor models 201 as another input 222. In varying embodiments, the set of compressor models 201 may also be incorporated 223 into the database 200 itself. The set of compressor models 201 may be representative of the compressor system 100. For example, a model may be a digital twin of a compressor system, may comprise a set of differential equations representative of a compressor system, or may comprise a black box approach.

[44] The estimator block 202 may estimate a current state of the compressor system 100 based on received 220 measurements 211, and optionally based on the models 201. Additionally, former estimations 221 may be uploaded from the database 200 to increase the accuracy of the estimation. The output of the estimator block 202 may be used as an input 224, 227 for the flow prediction block 203 and/or the MPC block 205.

[45] 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 on data 226 stored in the database 200. The prediction block 203 may use current process variables and agent state data of the compressor system 100 to calculate a desired state of the compressor system 100 for an appropriate time horizon. For example, a vessel pressure and a flow demand may be expressed in a future process variable profile as a predicted future demand on the compressor system 100. [46] According to the current disclosure, the terms “prediction,” “predict,” “predicted” and the like are concerned with estimating outcomes for unseen data, while forecasting is a sub-discipline of prediction in which predictions are made about a future using time-series data. For example, a difference between prediction and forecasting is that in the latter a temporal dimension is considered. In this way, the term “prediction” may also be interpreted as forecasting, yet in the continuation of this description, the term “prediction” will be used.

[47] The prediction block 203 may be configured to consider past process variable data, through 226 the database 200, and current process variable data, through 224 the estimator block 202. Additionally, other input data like sensor data, past and future state agent data, production planning, calendar data, holiday data, and/or weather forecasting data may be considered.

[48] The output 225 of the prediction block 203 comprises a data profile of a predicted process variable given for a predefined time horizon which may be set by a user, or by an MPC program which will be further discussed. In the latter, the setting of the time horizon may be automated.

[49] The prediction block 203 may be a predictor function block based on an input-output model with inputs, outputs, model parameters and hyper parameters. As exemplary illustrations, four prediction paradigms are discussed which are suitable for the prediction block 203.

[50] First, a multiple output prediction strategy may be used that directly estimates or trains the predictor function for a given fixed time horizon H using any function approximator. This approach is further known as a multi-step approach, where the multivariate predictor function is directly trained given current and past observations. Second, a recursive multi-step prediction method may be used wherein a suitable (I)/O model is chosen. From trained parameters of the (I)/O model, the predictor function is constructed, and the output may be simulated or forecasted recursively for a given time horizon H. Third, a direct multi-step prediction strategy may be used which comprises a construction for each forecast time step a separate predictor. As a fourth prediction paradigm, a hybrid prediction strategy may be used combining two or more of the above-mentioned paradigms. Other prediction strategies may of course be used, as would be apparent to one skilled in the art from the instant disclosure.

[51] In a sampling block 204, the output 225 may be sampled at a sampling frequency suitable for the MPC block 205. If needed, the sampling frequency may be reset or may be varying in time.

[52] FIG. 3 illustrates a flow diagram of an MPC block 205 configured for controlling a compressor system 100 according to varying embodiments of the disclosure. As shown in FIG. 3, the MPC block 205 operates on the basis of one or more requirements 300, one or more constraints 301, and future predictions 302 of demands for the compressor system 100.

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

[54] The system constraints may be included for any location or component of the system. For example, system constraints may include maximum and minimum limits of temperature at an inlet of an air utility or booster, at motor components such as windings or a converter, at compressor elements, at water of a cooling system, at oil of the compressor, on the outlet of a compressor for an energy recovery system, or the like. Humidity limits, flow limits, and impurity limits may be included for an air utility, booster, or the like, such as at an inlet thereof.

[55] Further, important setpoints and/or any of the mentioned constraints may be tracked, improving quality of the output air and, as a result, improving the quality of the end product. In a related aspect, using weights, a combination of the mentioned constraints can be created in the same framework without significant adaptations.

[56] In view of the identified constraints, embodiments of a compressor system may include one or more sensors located at a plurality of predefined positions in the system. For example, the compressor system may include any or a combination of temperature sensors, humidity sensors, flow sensors, velocity sensors, acceleration sensors, imaging sensors, current sensors, vibration sensors, particle sensors, oxygen sensors, nitrogen sensors, positions sensors, pressure sensors, pressure dewpoint sensors, rotational speed sensors, and related components.

[57] As would be understood from the current description by one skilled in the art, the compressor system may be configured to include any known sensors relevant to compressor systems and/or compressed gas. For example, temperature sensors may include one or more thermocouples, liquid or gas thermometers, electrical thermometers including, for example, an electric resistance thermometer, silicone diode, bimetallic devices, bulb and capillary sensors, sealed bellows, and/or a radiation thermometry device, or any other type of temperature sensing device. Even further, the one or more sensors of the compressor system may be remote from a wall or side wall of a component of the system, such as a pressure vessel or line or pipes while still obtaining their respective sensor data based, for example, on radiation thermometry or other remote sensing means.

[58] Any of the above sensors may be provided with means for communicating with the controller 106. The communication connection may be wireless or wired; for the sake of clarity, the sensors and related communication sensors are not shown. Respective output signals or data from these sensors are transmitted, either through hard wiring or wireless communication, to controller 106, and may be further used by controller 106 to adjust or modify inputs for determining an optimal schedule for the compressor system and/or to track operational characteristics of the system. The described embodiments further or optionally include writing sensor and/or constraint data into a memory. The memory may be a component of or exterior to the controller 106.

[59] In embodiments of the current disclosure, the MPC block 205 may employ an iterative switching time optimization (STO) to define an optimal sequence and switching timing for the compressor system 100, in the form of an action profile 320 or schedule for the compressor system. The action profile 320 may comprise instructions for improved operation of the compressor system 100, such that energy requirements of the compressor system 100 and wear to the components thereof are reduced. The iterative STO may comprise an STO module 310 and a refinement module 311 for determining the action profile 320, as discussed in greater detail below.

[60] According to the current disclosure, the controller is configured to determine an action profile for the compressor system using an MPC framework taking into account future consumer demand. As shown in the plot of FIG. 4, setpoint 405, past data 410, models and a predicted demand may be used to form inputs for the MPC. The past data 410 may comprise past setpoints 402 and their actual values 403, as well as the actions 404 previously taken by the compressor system. In varying aspects, the models may include static or dynamic machine models, static or dynamic aimet models, and/or instantaneous flow demand vs. horizon flow demand. A prediction for the parameter under control 406 may be generated as well as a limited subset of states from the compressor system, such as the state of the compressors or other components, the generated flow and pressures for the prediction horizon 409 at each timestep k until k+n, a prediction of inlet air or atmospheric conditions based on weather information, or the like. These inputs may be provided as part of or with an initial sequence for the iterative STO of the current disclosure, and may be prepared, for example, with dynamic programming, analytical dynamic programming (ADP), artificial intelligence (Al), a heuristic, a branch and bound scheme, a linear program simplex solver, or similar methods.

[61] Using this initial sequence 407, STO may be applied to determine optimal switching times for an action profile of the compressor system 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 explained previously, the initial sequence may be provided using dynamic programming, ADP, Al, a heuristic, a branch and bound scheme, a linear program simplex solver, or similar methods. In a second step 502, the STO module establishes the switching times as variables to be optimized for the compressor system based on the assumption of the initial sequence. The STO module optimizes the cost including constraints and requirements of the compressor system to determine time values for each part of the initial sequence 407 in a third step 503. [62] The STO module may calculate the set of switching times based on the following equation, constraints and requirements:

[63] In this example, Ck represents the capacity of a unit, S_on,k represents the running state, Sio,_k represents the load state, Pk represents the power in function of the capacity for unit k and the states, p(t) represents the pressure of the system, p(t) represents the first derivative of the pressure, f represents the system dynamics, t(S_on,k, Si₀,k) represents the timing constraints, to represents the starting time (now), tf represents the final time or the end time of the horizon.

[64] According to varying embodiments, the STO module may calculate the set of switching times based on the following equation, constraints and requirements, using at least in part representations for similar notations of constants, variables, and parameters as above: [65] Further alternatives, variants and combinations are of course contemplated and are not excluded from the current disclosure.

[66] Based on the STO results, the refinement module 311 identifies any part of the initial sequence 407 where the STO indicates the part should be allocated zero time and removes the identified parts to form a new sequence in a refinement step 504. 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 result of the preceding iterations. The method may be iteratively performed until an optimal sequence and optimal switching times are determined, the optimal sequence and the optimal switching times forming an action profile or schedule for operation of the compressor system.

[67] Advantageously, the iterative STO of the described embodiments allows for more accurate timing of starts and stops in the compressor system compared to known methods, allows for including a larger set of constraints, and can handle a broader scope of air utilities and objectives.

[68] The iterative approach of the current disclosure recognizes two subproblems to optimization of schedule for a compressor system: (1) finding a switching sequence and (2) when a switching sequence is given, optimizing the switching instances. Advantageously, any remaining problems can be cast as continuous linear programming, for which efficient numerical algorithms exist.

[69] In embodiments of the disclosure, a sequence may be defined as an ordered set S = of states ordered by their happening in time. To specify the switching instances, the set may be defined based on wi defined as the time the system spends in the state Si. Accordingly, the STO determines W given S.

[70] Surprisingly, to find the optimum sequence according to the described embodiments, the method starts from an initial sequence S that is neither required to be optimal nor based on the relaxed solution. Instead, the method advantageously only needs to include the optimal sequence as a set. Extra states that are not part of the optimal sequence are iteratively removed from the initial sequence by the STO and refinement of the described method. In other words, given a sequence, the STO is used to find optimal switching times and, given the optimal switching times, the sequence may then be refined. This is to be done iteratively: following each removal, the STO is solved again for the new sequence, from which other candidates for removal are recognized. As the initial sequence is finite, the iteration comes to an end with the identification of the optimal sequence and optimal switching times.

[71] While not bound to any specific theory, the efficiency of the iterative STO method of the current disclosure is believed to be derived from the fact that it changes the mixed- integer problem into a continuous one, in such a way that many constraints can enter the problem in a natural and simple way, e.g. w > 0.5 for adding a minimum uptime as a constraint for a compressor of the compressor system. There is no risk in the described approach of frequent switching, as the maximum number of switches is already limited by the initial sequence. There is also the possibility of applying conditional constraints such as do not set u1 = 1 unless u2 = 1 or u3 = 1 beforehand, as they can be applied directly to the sequence to filter the prohibited combinations.

[72] The method of the current application may initially appear disadvantageous based on the large number of possible combinations that may be required for the initial sequence. Surprisingly, however, the inventors have discovered that this concern is misplaced. Notably, the initial sequence only needs to include the optimal sequence, and the power set grows exponentially. Further, there does not have to be an increase in the number of variables. For example, in a multiple shooting scheme, the STO fixes u(t) and substitutes them with a small number of w, which reduces the number of variables in comparison to the prior art relaxed problem.

[73] Embodiments of the method of sequence optimization can be arranged for iteratively selecting only one state as a candidate to be removed, or for performing parallel processing of multiple states to reduce a number of required iterations. In a similar way, there may be provided a branch and bound scheme regarding the choice between constraint satisfaction and state removal to ensure the optimality of the sequence. Finally, the sequence optimization may be configured to insert a necessary state rather than removing states. As is readily apparent from the above advantages, the method and system of the current disclosure provides much more flexibility than known in the prior art, in addition to a reduction in wear on the compressor system, energy requirements, and system run time. [74] A vast improvement can be made compared to the state of the art by handling the scheduling of components of the compressor system in a special manner according to the current embodiments. The described iterative STO can advantageously handle a broader scope of actuated units or components than conventional approaches, including compressors such as volumetric compressors, turbo compressors, boosters, blower (low pressure), and the like; air utilities such as dryers, valves, aftercoolers, chillers, O2 generators, N2 generators, and the like; cooling circuits or oil cooling circuits; and energy recovery systems. The described iterative STO can further take into account passive air utility elements, such as filters, vessels, pipes, etc., which is not achieved in known methods and systems.

[75] The described embodiments further realize the added advantages of the iterative STO being capable of creating several predictions, such as predicting profiles of humidity, temperature, impurities, etc., as well as the ability to handle long horizons accurately. For example, a prediction horizon according to the current disclosure can be up to 6 hours, up to 8 hours, up to 10 hours, preferably up to 8 hours. In like manner, the accuracy of the 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.

[76] 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 can 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 disclosure can comprise at least two distinctly different kinds of computer-readable media: computer storage media and transmission media.

[77] 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 drives (“SSDs”), flash memory, phase-change memory (“PCM”), optical disk storage, magnetic disk storage or other magnetic storage devices, or any other hardware storage device(s) which can be used to store program code in the form of computer-executable instructions or data structures, which can be included within or accessed and executed by controller 106, a general -purpose, or a special -purpose computer system to implement the disclosed functionality of the disclosure.

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

[79] Further, upon reaching various computer system components, program code in the form of computer-executable instructions or data structures can be transferred automatically 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 can be buffered in RAM within a network interface module (e.g., a “NIC”), and then eventually transferred to computer system RAM and/or to less volatile computer storage media at a computer system. Thus, it should be understood that computer storage media can be included in computer system components that also (or even primarily) utilize transmission media.

[80] Computer-executable instructions may comprise, for example, instructions and data which, 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, binaries, intermediate format instructions such as assembly language, or even source code. [81] The disclosure of the present application may be practiced in network computing environments with many types of computer system configurations, including, but not limited to, personal computers, desktop computers, laptop computers, message processors, hand-held devices, multi-processor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, mobile telephones, PDAs, tablets, pagers, routers, switches, and the like. The disclosure may also be practiced in distributed system environments where local and remote computer systems, which are linked (either by hardwired data links, wireless data links, or by a combination of hardwired and wireless data links) through a network, both perform tasks. As such, in a distributed system environment, a computer system may include a plurality of constituent computer systems. In a distributed system environment, program modules may be located in both local and remote memory storage devices.

[82] The disclosure of the present application may also be practiced in a cloud-computing environment. Cloud computing environments may be distributed, although this is not required. When distributed, cloud computing environments may be distributed internationally within an organization and/or have components possessed across multiple organizations. In this description and the following claims, “cloud computing” is defined as a model for enabling 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 other numerous advantages that can be obtained from such a model when properly deployed.

[83] A cloud-computing model can be composed of various characteristics, such as on- demand self-service, broad network access, resource pooling, rapid elasticity, measured service, and so forth. A cloud-computing model may also come in the form of various service models such as, for example, Software as a Service (“SaaS”), Platform as a Service (“PaaS”), and Infrastructure as a Service (“laaS”). The cloud-computing model may also be deployed using different deployment models such as private cloud, community cloud, public cloud, hybrid cloud, and so forth.

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

[85] Certain terms are used throughout the description and claims to refer to particular methods, features, or components. As those having ordinary skill in the art will appreciate, different persons may refer to the same methods, features, or components 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 in exaggerated scale or in somewhat schematic form and some details of conventional elements may not be shown or described in interest of clarity and conciseness.

[86] Although various example embodiments have been described in detail herein, those skilled in the art will readily appreciate in view of the present disclosure that many modifications are possible in the example embodiments without materially departing from the concepts of present disclosure. Accordingly, any such modifications are intended to be included in the scope of this disclosure. Likewise, while the disclosure herein contains many specifics, these specifics should not be construed as limiting the scope of the disclosure or of any of the appended claims, but merely as providing information pertinent to one or more specific embodiments that may fall within the scope of the disclosure and the appended claims. Any described features from the various embodiments disclosed may be employed in combination. In addition, other embodiments of the present disclosure may also be devised which lie within the scopes of the disclosure and the appended claims. Each addition, deletion, and modification to the embodiments that falls within the meaning and scope of the claims is to be embraced by the claims.

[87] Certain embodiments and features may have been described using a set of numerical upper limits and a set of numerical lower limits. It should be appreciated that ranges including the combination of any two values, e.g., the combination of any lower value with any upper value, the combination of any two lower values, and/or the combination of any two upper values are contemplated unless otherwise indicated. Certain lower limits, upper limits and ranges may appear in one or more claims below. Any numerical value is “about” or “approximately” the indicated value, and takes into account experimental error and variations that would be expected by a person having ordinary skill in the art.

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

[89] This disclosure provides various examples, embodiments, and features which, unless expressly stated or which would be mutually exclusive, should be understood to be combinable with other examples, embodiments, or features described herein.

[90] In addition to the above, further embodiments and examples include the following:

[91] 1. A compressor system comprising: a set of components which are fluidly connected to a common compressed air distribution network; and a controller configured to: predict a future demand for 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 demand; determine a set of switching times for the initial switching sequence; refine the initial switching sequence on the basis of the set of switching times to form a refined switching sequence; iteratively determine a set of switching times for the refined switching sequence and refine the refined switching sequence on the basis of the set of switching times until a final switching sequence and a final set of switching times are obtained; and control operation of the set of components on the basis of the final switching sequence and the final set of switching times.

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

[93] 3. The compressor system according to any or a combination of 1-2 and 4-8, wherein the initial switching sequence represents a unique sequence of operation of the at least one component.

[94] 4. The compressor system according to any or a combination of 1-3 and 5-8, wherein the at least one component of the set of components includes one or more dryers. [95] 5. The compressor system according to any or a combination of 1-4 and 6-8, wherein the at least one component of the set of components includes one or more valves in the compressed air distribution network.

[96] 6. The compressor system according to any or a combination of 1-5 and 7-8, further comprising a plurality of sensors positioned to monitor operational parameters of the at least one component.

[97] 7. The compressor system according to any or a combination of 1-6 and 8, wherein the set of switching times for the initial switching sequence are determined by switching time optimization.

[98] 8. The compressor system according to claim any or a combination of 1-7, wherein the refining the initial switching sequence and the refining the refined switching sequence each include removing any part of the respective sequence that is allocated zero time.

[99] 9. A controller configured to operate a compressor system having a set of components which are fluidly connected to a common compressed air distribution network, the controller comprising: a computer readable storage medium; and a processor configured to: predict a future demand for 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 demand; determine a set of switching times for the initial switching sequence; refine the initial switching sequence on the basis of the set of switching times to form a refined switching sequence; iteratively determine a set of switching times for the refined switching sequence and refine the refined switching sequence on the basis of the set of switching times until a final switching sequence and a final set of switching times are obtained; and control operation of the set of components on the basis of the final switching sequence and the final set of switching times.

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

[101] 11. The controller according to any or a combination of 9-10 and 12-13, wherein the set of switching times for the initial switching sequence are determined by switching time optimization. [102] 12. The controller according to any or a combination of 9-11 and 13, wherein the refining the initial switching sequence and the refining the refined switching sequence each include removing any part of the respective sequence that is allocated zero time.

[103] 13. The controller according to any or a combination of 9-12, wherein the switching time optimization comprises calculating the set of switching times based on the following:

[104] 14. A computer-implemented method for controlling a compressor system comprising a set of components which are fluidly connected to a common compressed air distribution network, the method comprising: predicting a future demand for 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 demand; determining a set of switching times for the initial switching sequence; refining the initial switching sequence on the basis of the set of switching times to form a refined switching sequence; iteratively determining a set of switching times for the refined switching sequence and refining the refined switching sequence on the basis of the set of switching times until a final switching sequence and a final set of switching times are obtained; and controlling operation of the set of components on the basis of the final switching sequence and the final set of switching times.

[105] 15. The method according to any or a combination of 14 and 16-20, wherein the initial switching sequence represents a unique sequence of operation of the at least one component. [106] 16. The method according to any or a combination of 14-15 and 17-20, wherein the set of switching times for the initial switching sequence are determined by switching time optimization.

[107] 17. The method according to any or a combination of 14-16 and 18-20, wherein the refining the initial switching sequence and the refining the refined switching sequence each include removing any part of the respective sequence that is allocated zero time.

[108] 18. The method according to any or a combination of 14-17 and 19-20, wherein the switching time optimization comprises calculating the set of switching times based on the following:

[109] 19. The method according to any or a combination of 14-18 and 20, 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 (Al), a heuristic, a branch and bound scheme, and a linear program simplex solver.

[110] 20. The method according to any or a combination of 14-19, wherein the predicted future demand represents predicted future pressure and/or airflow demand for the compressor system.

Claims

CLAIMS:

1. A compressor system comprising: a set of components which are fluidly connected to a common compressed air distribution network; and a controller configured to: predict a future demand for 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 demand; determine a set of switching times for the initial switching sequence; refine the initial switching sequence on the basis of the set of switching times to form a refined switching sequence; iteratively determine a set of switching times for the refined switching sequence and refine the refined switching sequence on the basis of the set of switching times until a final switching sequence and a final set of switching times are obtained; and control operation of the set of components on the basis of the final switching sequence and the final set of switching times.

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

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

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

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

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

7. The compressor system according to claim 1, wherein the set of switching times for the initial switching sequence are determined by switching time optimization.

8. The compressor system according to claim 1, wherein the refining the initial switching sequence and the refining the refined switching sequence each include removing any part of the respective sequence that is allocated zero time.

9. A controller configured to operate a compressor system having a set of components which are fluidly connected to a common compressed air distribution network, the controller comprising: a computer readable storage medium; and a processor configured to: predict a future demand for 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 demand; determine a set of switching times for the initial switching sequence; refine the initial switching sequence on the basis of the set of switching times to form a refined switching sequence; iteratively determine a set of switching times for the refined switching sequence and refine the refined switching sequence on the basis of the set of switching times until a final switching sequence and a final set of switching times are obtained; and control operation of the set of components on the basis of the final switching sequence and the final set of switching times.

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

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

12. The controller according to claim 9, wherein the refining the initial switching sequence and the refining the refined switching sequence each include removing any part of the respective sequence that is allocated zero time.

13. The controller according to claim 11, wherein the switching time optimization comprises calculating the set of switching times based on the following:

14. A computer-implemented method for controlling a compressor system comprising a set of components which are fluidly connected to a common compressed air distribution network, the method comprising: predicting a future demand for 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 demand; determining a set of switching times for the initial switching sequence; refining the initial switching sequence on the basis of the set of switching times to form a refined switching sequence; iteratively determining a set of switching times for the refined switching sequence and refining the refined switching sequence on the basis of the set of switching times until a final switching sequence and a final set of switching times are obtained; and controlling operation of the set of components on the basis of the final switching sequence and the final set of switching times.

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

16. The method according to claim 14, wherein the set of switching times for the initial switching sequence are determined by switching time optimization.

17. The method according to claim 14, wherein the refining the initial switching sequence and the refining the refined switching sequence each include removing any part of the respective sequence that is allocated zero time.

18. The method according to claim 16, wherein the switching time optimization comprises calculating the set of switching times based on the following:

19. The method according to 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 (Al), a heuristic, a branch and bound scheme, and a linear program simplex solver.

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

21. The controller according to claim 11, wherein the switching time optimization comprises calculating the set of switching times based on the following:

22. The method according to claim 16, wherein the switching time optimization comprises calculating the set of switching times based on the following: