1. A method for optimizing energy consumption in an industrial automation process using artificial intelligence techniques, comprising: collecting data from sensors and devices within the industrial automation process; processing the collected data using an artificial intelligence algorithm to identify patterns and trends in energy consumption; identifying opportunities for energy savings based on the analysis of the data; and implementing energy-saving measures within the industrial automation process based on the identified opportunities.

2. The method of claim 1, wherein the data collected from sensors and devices within the industrial automation process comprises data related to energy consumption, process parameters, and environmental conditions.

3. The method of claim 1, wherein the artificial intelligence algorithm comprises machine learning techniques such as neural networks, decision trees, and support vector machines.

4. The method of claim 1, wherein identifying opportunities for energy savings comprises comparing the current energy consumption levels to a predetermined baseline or target energy consumption level.

5. The method of claim 1, wherein implementing energy-saving measures comprises adjusting process parameters, modifying the operation of equipment or devices, or scheduling operations to optimize energy consumption.

6. The method of claim 1, further comprising monitoring and evaluating the effectiveness of the implemented energy-saving measures over time and refining the energy optimization process based on the results.

7. The method of claim 1, wherein the energy-saving measures are implemented in a hierarchical manner based on their potential energy savings and impact on the industrial automation process.

8. The method of claim 1, wherein the energy-saving measures are implemented in real-time or near real-time to continuously optimize energy consumption within the industrial automation process.

9. The method of claim 1, wherein the energy-saving measures are prioritized based on their impact on energy consumption, operational efficiency, and environmental impact.

10. A system for optimizing energy consumption in an industrial automation process using artificial intelligence techniques, comprising: one or more sensors and devices configured to collect data related to energy consumption within the industrial automation process; a processor configured to execute an artificial intelligence algorithm to analyze the collected data and identify opportunities for energy savings; and an actuator or control system configured to implement energy-saving measures within the industrial automation process based on the identified opportunities. Optimization of energy consumption in industrial automation processes using artificial intelligence techniques Abstract The present disclosure may include a method for optimizing energy consumption in an industrial automation process using techniques from artificial intelligence, such as collecting data from sensors and devices contained within the industrial automation process. This method may be included in embodiments of the present disclosure. Processing the acquired data using an artificial intelligence system in order to find patterns and trends in energy use is another possible aspect of embodiments. In certain implementations, one of the goals is to find ways to reduce energy use by analyzing data in order to spot potential possibilities. Using energy-saving techniques inside the industrial automation process in order to capitalize on possibilities that have been recognized is another potential aspect of embodiments. , Claims:Claims :

1. A method for optimizing energy consumption in an industrial automation process using artificial intelligence techniques, comprising: collecting data from sensors and devices within the industrial automation process; processing the collected data using an artificial intelligence algorithm to identify patterns and trends in energy consumption; identifying opportunities for energy savings based on the analysis of the data; and implementing energy-saving measures within the industrial automation process based on the identified opportunities.

2. The method of claim 1, wherein the data collected from sensors and devices within the industrial automation process comprises data related to energy consumption, process parameters, and environmental conditions.

3. The method of claim 1, wherein the artificial intelligence algorithm comprises machine learning techniques such as neural networks, decision trees, and support vector machines.

4. The method of claim 1, wherein identifying opportunities for energy savings comprises comparing the current energy consumption levels to a predetermined baseline or target energy consumption level.

5. The method of claim 1, wherein implementing energy-saving measures comprises adjusting process parameters, modifying the operation of equipment or devices, or scheduling operations to optimize energy consumption.

6. The method of claim 1, further comprising monitoring and evaluating the effectiveness of the implemented energy-saving measures over time and refining the energy optimization process based on the results.

7. The method of claim 1, wherein the energy-saving measures are implemented in a hierarchical manner based on their potential energy savings and impact on the industrial automation process.

8. The method of claim 1, wherein the energy-saving measures are implemented in real-time or near real-time to continuously optimize energy consumption within the industrial automation process.

9. The method of claim 1, wherein the energy-saving measures are prioritized based on their impact on energy consumption, operational efficiency, and environmental impact.

10. A system for optimizing energy consumption in an industrial automation process using artificial intelligence techniques, comprising: one or more sensors and devices configured to collect data related to energy consumption within the industrial automation process; a processor configured to execute an artificial intelligence algorithm to analyze the collected data and identify opportunities for energy savings; and an actuator or control system configured to implement energy-saving measures within the industrial automation process based on the identified opportunities.

Description:OPTIMIZATION OF ENERGY CONSUMPTION IN INDUSTRIAL AUTOMATION PROCESSES USING ARTIFICIAL INTELLIGENCE TECHNIQUES
Field of the Invention
[0001] The invention pertains to a predictive maintenance system for industrial equipment that utilizes machine learning algorithms to analyze real-time data from the equipment and accurately predict maintenance requirements. The system is designed to improve the reliability, efficiency, and safety of industrial operations by enabling proactive maintenance instead of reactive maintenance.
Background
[0002] The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] The present invention relates to the optimization of energy consumption in industrial automation processes, and in particular, to a method and system that uses artificial intelligence techniques to optimize energy consumption within an industrial automation process.
[0004] Energy consumption is a significant cost in industrial processes, and optimizing energy consumption can lead to significant cost savings. Industrial automation processes, which rely on the use of sensors, devices, and control systems, have the potential to improve energy efficiency and reduce energy consumption. Few of the prior arts are listed below.
[0005] EP3696622A1 (By: ROCKWELL AUTOMATION TECHNOLOGIES) Industrial smart data tags conforming to structured data types serve as the basis for creating a digital twin of an industrial asset. The digital twin can comprise an automation model and a mechanical model or other type of non-automation model, both of which reference the smart tags in connection with digitally modeling the industrial asset. The structured data topology offered by the smart tags allows the digital twin to be readily interfaced with artificial intelligence (AI) systems. AI analysis can leverage the smart tags to discover new relationships between key performance indicators and other variables of the asset and encode these relationships in the smart tags themselves. These enhanced smart tags can also be leveraged to perform Al-based validation the digital twin. Additional contextualization provided by the enhanced smart tags can simplify AI analysis and assist in quickly converging on desired analytic results.
[0006] US20190179300A1 (By: STRONG FORCE IOT PORTFOLIO 2016) Systems and methods for data collection and predication of future states of components are disclosed. A system can include a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to input received from a detection package, the detection package comprising at least one of a plurality of input sensors, each of the plurality of input sensors operatively coupled to a component of an industrial system. The system can also include a data analysis circuit to predict a future state of the component based, at least in part, upon the plurality of detection values and data received from data collection band circuit.
[0007] US9729639B2 (By: ROCKWELL AUTOMATION) The invention provides control systems and methodologies for controlling a process having computer-controlled equipment, which provide for optimized process performance according to one or more performance criteria, such as efficiency, component life expectancy, safety, emissions, noise, vibration, operational cost, or the like. More particularly, the subject invention provides for employing machine diagnostic and/or prognostic information in connection with optimizing an overall business operation over a time horizon.
[0008] Traditional methods for optimizing energy consumption in industrial processes rely on manual analysis of data, which can be time-consuming and may not be able to identify all opportunities for energy savings. Furthermore, traditional methods may not be able to adapt to changes in the industrial process, which can limit their effectiveness.
[0009] To overcome these limitations, the use of artificial intelligence techniques has been proposed to optimize energy consumption in industrial automation processes. These techniques involve the use of machine learning algorithms to analyze data from sensors and devices within the industrial process to identify patterns and trends in energy consumption.
[00010] However, there is a need for an improved method and system for optimizing energy consumption in industrial automation processes using artificial intelligence techniques that can adapt to changes in the industrial process and provide real-time or near real-time optimization of energy consumption.
[00011] Therefore, the present invention provides a novel method and system that uses artificial intelligence techniques to optimize energy consumption in industrial automation processes. The method and system collect data from sensors and devices within the industrial process, analyze the data using machine learning algorithms to identify patterns and trends in energy consumption, and implement energy-saving measures based on the identified opportunities for energy savings.
[00012] All references, including publications, patent applications, and patents, cited herein are hereby incorporated by reference to the same extent as if each reference were individually and specifically indicated to be incorporated by reference and were set forth in its entirety herein.
[00013] It also shall be noted that as used herein and in the appended claims, the singular forms “a”, “an”, and “the” include plural referents unless the context clearly dictates otherwise. This invention can be achieved by means of hardware including several different elements or by means of a suitably programmed computer. In the unit claims that list several means, several ones among these means can be specifically embodied in the same hardware item. The use of such words as first, second, third does not represent any order, which can be simply explained as names.
Summary
[00014] The following presents a simplified summary of various aspects of this disclosure in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements nor delineate the scope of such aspects. Its purpose is to present some concepts of this disclosure in a simplified form as a prelude to the more detailed description that is presented later.
[00015] The following paragraphs provide additional support for the claims of the subject application.
[00016] The invention pertains to a predictive maintenance system for industrial equipment that utilizes machine learning algorithms to analyze real-time data from the equipment and accurately predict maintenance requirements. The system is designed to improve the reliability, efficiency, and safety of industrial operations by enabling proactive maintenance instead of reactive maintenance.
[00017] Embodiments of the present disclosure may include a method for optimizing energy consumption in an industrial automation process using artificial intelligence techniques, wherein the method includes collecting data from sensors and devices within the industrial automation process. Embodiments may also include processing the collected data using an artificial intelligence algorithm to identify patterns and trends in energy consumption. Embodiments may also include identifying opportunities for energy savings based on the analysis of the data. Embodiments may also include implementing energy-saving measures within the industrial automation process based on the identified opportunities.
[00018] In some embodiments, the data collected from sensors and devices within the industrial automation process may include data related to energy consumption, process parameters, and environmental conditions. In some embodiments, the artificial intelligence algorithm may include machine learning techniques such as neural networks, decision trees, and support vector machines.
[00019] Embodiments may also include identifying opportunities for energy savings may include comparing the current energy consumption levels to a predetermined baseline or target energy consumption level. Embodiments may also include implementing energy-saving measures, which may include adjusting process parameters, modifying the operation of equipment or devices, or scheduling operations to optimize energy consumption.
[00020] In some embodiments, the method may include monitoring and evaluating the effectiveness of the implemented energy-saving measures over time and refining the energy optimization process based on the results. In some embodiments, the energy-saving measures may be implemented in a hierarchical manner based on their potential energy savings and impact on the industrial automation process. In some embodiments, the energy-saving measures may be implemented in real-time or near real-time to continuously optimize energy consumption within the industrial automation process. In some embodiments, the energy-saving measures may be prioritized based on their impact on energy consumption, operational efficiency, and environmental impact.
[00021] Embodiments of the present disclosure may also include a system for optimizing energy consumption in an industrial automation process using artificial intelligence techniques, including one or more sensors and devices configured to collect data related to energy consumption within the industrial automation process. Embodiments may also include a processor configured to execute an artificial intelligence algorithm to analyze the collected data and identify opportunities for energy savings. Embodiments may also include an actuator or control system configured to implement energy-saving measures within the industrial automation process based on the identified opportunities.
Brief Description of the Drawings
[00022] The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
[00023] FIG. 1 is a flowchart illustrating a method for optimizing energy consumption, according to some embodiments of the present disclosure.
Detailed Description
[00024] In the following detailed description of the invention, reference is made to the accompanying drawings that form a part hereof, and in which is shown, by way of illustration, specific embodiments in which the invention may be practiced. In the drawings, like numerals describe substantially similar components throughout the several views. These embodiments are described in sufficient detail to claim those skilled in the art to practice the invention. Other embodiments may be utilized and structural, logical, and electrical changes may be made without departing from the scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined only by the appended claims and equivalents thereof.
[00025] The use of the terms “a” and “an” and “the” and “at least one” and similar referents in the context of describing the invention (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The use of the term “at least one” followed by a list of one or more items (for example, “at least one of A and B”) is to be construed to mean one item selected from the listed items (A or B) or any combination of two or more of the listed items (A and B), unless otherwise indicated herein or clearly contradicted by context. The terms “comprising,” “having,” “including,” and “containing” are to be construed as open-ended terms (i.e., meaning “including, but not limited to,”) unless otherwise noted. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., “such as”) provided herein, is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the invention..
[00026] The invention pertains to a predictive maintenance system for industrial equipment that utilizes machine learning algorithms to analyze real-time data from the equipment and accurately predict maintenance requirements. The system is designed to improve the reliability, efficiency, and safety of industrial operations by enabling proactive maintenance instead of reactive maintenance.
[00027] Figure 1 presents a flowchart that illustrates one potential implementation of a method for cutting down on total energy use. This approach is only one of several that are detailed in the disclosure that has been presented here. Step 110 of the approach may, in some implementations, contain the additional step of collecting data from sensors and other devices that are employed in the industrial automation process. However, including this step is entirely optional. In the 120th step, the method may entail conducting an analysis of the gathered data with the assistance of an artificial intelligence system in order to identify patterns and trends in the use of energy. The method may, as part of step 130, include, on the basis of the analysis of the data, the identification of possibilities for cost savings in terms of energy usage. In response to the opportunities that have been identified, step 140 of the method may include the implementation of energy-saving measures within the framework of the industrial automation process.
[00028] In some configurations, the information acquired from the sensors and devices that are active throughout the course of the industrial automation process. Details on the quantity of energy that was expended, the attributes of the procedure, and the environmental conditions that were present. In certain implementations of the artificial intelligence programmed, certain kinds of machine learning techniques, such as neural networks, decision trees, and support vector machines, might be utilized. These are just a few examples of the many types of machine learning methods that could be employed. One way for assessing where there may be opportunities to make savings on energy is to conduct a comparison of the current levels of energy consumption to a baseline or target level of energy consumption that has been set. In some implementations, this technique is used instead of another.
[00029] Changing the parameters of the process, modifying the operation of the equipment or devices, or rescheduling the operations in order to achieve optimum energy usage are all examples of how many types of energy-saving strategies might be applied. In certain applications, the process of optimizing energy consumption involves monitoring and evaluating the effectiveness of the energy-saving measures that have been implemented over the course of time, as well as making adjustments to the process based on the conclusions reached from the evaluations. In some implementations, it is conceivable that the energy-saving methods will be implemented in a hierarchical way. This would be assessed based on the potential energy savings of each measure in addition to their effect on the process of industrial automation. To consistently optimize the amount of energy that is consumed by the industrial automation process, various implementations of energy-saving measures may be put into effect in real time or very close to real time. This is done in order to reduce the amount of energy that is wasted. It is possible that the energy-saving measures might be placed in order of priority in specific implementations according to the effect that they have on the amount of energy that is used, the operational efficiency that they provide, and the impact that they have on the environment.
[00030] In some embodiments, there may be included one or more sensors and devices that are intended to acquire data related to the amount of energy that is used within the process of industrial automation. This data may be utilized to help optimize the usage of the energy that is used. A processor that has been configured to carry out an artificial intelligence algorithm in order to analyze the data that has been obtained and uncover prospective places where energy may be saved. This is done in order to find potential locations where energy may be saved. An actuator or control system that has been designed to carry out energy-saving measures within the context of an industrial automation process in order to capitalize on possibilities that have been identified as being available.
[00031] The present invention is directed to a method and system for optimizing energy consumption in an industrial automation process using artificial intelligence techniques. The method involves collecting data from sensors and devices within the industrial automation process, processing the collected data using an artificial intelligence algorithm to identify patterns and trends in energy consumption, identifying opportunities for energy savings based on the analysis of the data, and implementing energy-saving measures within the industrial automation process based on the identified opportunities.
[00032] In one embodiment, the data collected from sensors and devices within the industrial automation process includes data related to energy consumption, process parameters, and environmental conditions. The data is collected in real-time or near real-time to provide up-to-date information for analysis.
[00033] The artificial intelligence algorithm used in the method may include machine learning techniques such as neural networks, decision trees, and support vector machines. The algorithm processes the collected data to identify patterns and trends in energy consumption, which may include identifying energy consumption outliers, peak usage times, and other trends that may impact energy consumption.
[00034] Once opportunities for energy savings are identified based on the analysis of the data, energy-saving measures are implemented within the industrial automation process. These measures may include adjusting process parameters, modifying the operation of equipment or devices, or scheduling operations to optimize energy consumption. In one embodiment, the energy-saving measures are implemented in a hierarchical manner based on their potential energy savings and impact on the industrial automation process.
[00035] To continuously optimize energy consumption, the effectiveness of the implemented energy-saving measures is monitored and evaluated over time. The energy optimization process is refined based on the results of the monitoring and evaluation. In addition, the energy-saving measures may be prioritized based on their impact on energy consumption, operational efficiency, and environmental impact.
[00036] The system for optimizing energy consumption in an industrial automation process using artificial intelligence techniques includes one or more sensors and devices configured to collect data related to energy consumption within the industrial automation process, a processor configured to execute an artificial intelligence algorithm to analyze the collected data and identify opportunities for energy savings, and an actuator or control system configured to implement energy-saving measures within the industrial automation process based on the identified opportunities.
[00037] In one embodiment, the system is integrated with the industrial automation process and is configured to operate in real-time or near real-time to continuously optimize energy consumption. The system may also include a user interface for displaying information related to energy consumption and the effectiveness of implemented energy-saving measures.
[00038] The present disclosure may include the method for optimising energy consumption in an industrial automation process using techniques from artificial intelligence, such as collecting data from sensors and devices contained within the industrial automation process. Processing the acquired data using an artificial intelligence system in order to find patterns and trends in energy use is another possible aspect of embodiments. In certain implementations, one of the goals is to find ways to reduce energy use by analysing data in order to spot potential possibilities. Using energy-saving techniques inside the industrial automation process in order to capitalise on possibilities that have been recognised is another potential aspect of embodiments.
[00039] The information that is gathered from the sensors and devices that are a part of the industrial automation process might, in certain implementations, contain details about the energy usage, the parameters of the process, and the ambient conditions. The artificial intelligence algorithm may, in certain implementations, make use of machine learning methods including neural networks, decision trees, and support vector machines.
[00040] Comparison of the current energy consumption levels to a specified baseline or target energy consumption level may be included as an additional step in the process of finding possibilities for energy savings in accordance with certain embodiments. In other embodiments, energy-saving techniques may further comprise altering process parameters, modifying the functioning of equipment or devices, or scheduling processes in such a way as to maximise efficiency in terms of energy usage.
[00041] The approach may, in some implementations, call for continuously monitoring and assessing the efficiency of the energy-saving measures that have been put into place over the course of time, as well as continuously improving the energy optimisation process on the basis of the findings. It is possible that the energy-saving measures might be applied in a hierarchical fashion in some embodiments. This would be determined by the potential energy savings of each measure as well as their influence on the industrial automation process. In some implementations, the energy-saving measures may be put into action in real time or very close to real time in order to continually optimise the amount of energy that is used by the industrial automation process. In certain implementations, the energy-saving measures may be ranked in order of priority according to the influence that they have on energy consumption, operational efficiency, and the impact that they have on the environment.
[00042] A system for optimising energy consumption in an industrial automation process using artificial intelligence techniques may also be included in embodiments of the present disclosure. This system may comprise one or more sensors and devices that are designed to collect data related to energy consumption within the industrial automation process. A processor that is capable of analysing the acquired data and determining chances for energy savings may also be included in certain embodiments. This processor would be designed to carry out an artificial intelligence algorithm. In some embodiments, there is additionally an actuator or control system that is designed to execute energy-saving measures within the industrial automation process based on the possibilities that have been discovered.
[00043] Example embodiments herein have been described above with reference to block diagrams and flowchart illustrations of methods and apparatuses. It will be understood that each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations, respectively, can be implemented by various means including hardware, software, firmware, and a combination thereof. For example, in one embodiment, each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations can be implemented by computer program instructions. These computer program instructions may be loaded onto a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions which execute on the computer or other programmable data processing apparatus create means for implementing the functions specified in the flowchart block or blocks.
[00044] Throughout the present disclosure, the term ‘Artificial intelligence (AI)’ as used herein relates to any mechanism or computationally intelligent system that combines knowledge, techniques, and methodologies for controlling a bot or other element within a computing environment. Furthermore, the artificial intelligence (AI) is configured to apply knowledge and that can adapt it-self and learn to do better in changing environments. Additionally, employing any computationally intelligent technique, the artificial intelligence (AI) is operable to adapt to unknown or changing environment for better performance. The artificial intelligence (AI) includes fuzzy logic engines, decision-making engines, preset targeting accuracy levels, and/or programmatically intelligent software.
[00045] Throughout the present disclosure, the term ‘processing means’ or ‘microprocessor’ or ‘processor’ or ‘processors’ includes, but is not limited to, a general purpose processor (such as, for example, a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a microprocessor implementing other types of instruction sets, or a microprocessor implementing a combination of types of instruction sets) or a specialized processor (such as, for example, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), or a network processor).
[00046] The term “non-transitory storage device” or “storage” or “memory,” as used herein relates to a random access memory, read only memory and variants thereof, in which a computer can store data or software for any duration.
[00047] Operations in accordance with a variety of aspects of the disclosure is described above would not have to be performed in the precise order described. Rather, various steps can be handled in reverse order or simultaneously or not at all.
[00048] While several implementations have been described and illustrated herein, a variety of other means and/or structures for performing the function and/or obtaining the results and/or one or more of the advantages described herein may be utilized, and each of such variations and/or modifications is deemed to be within the scope of the implementations described herein. More generally, all parameters, dimensions, materials, and configurations described herein are meant to be exemplary and that the actual parameters, dimensions, materials, and/or configurations will depend upon the specific application or applications for which the teachings is/are used. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific implementations described herein. It is, therefore, to be understood that the foregoing implementations are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, implementations may be practiced otherwise than as specifically described and claimed. Implementations of the present disclosure are directed to each individual feature, system, article, material, kit, and/or method described herein. In addition, any combination of two or more such features, systems, articles, materials, kits, and/or methods, if such features, systems, articles, materials, kits, and/or methods are not mutually inconsistent, is included within the scope of the present disclosure.
 

Claims
I/We Claim:
1. A method for optimizing energy consumption in an industrial automation process using artificial intelligence techniques, comprising: collecting data from sensors and devices within the industrial automation process; processing the collected data using an artificial intelligence algorithm to identify patterns and trends in energy consumption; identifying opportunities for energy savings based on the analysis of the data; and implementing energy-saving measures within the industrial automation process based on the identified opportunities.
2. The method of claim 1, wherein the data collected from sensors and devices within the industrial automation process comprises data related to energy consumption, process parameters, and environmental conditions.
3. The method of claim 1, wherein the artificial intelligence algorithm comprises machine learning techniques such as neural networks, decision trees, and support vector machines.
4. The method of claim 1, wherein identifying opportunities for energy savings comprises comparing the current energy consumption levels to a predetermined baseline or target energy consumption level.
5. The method of claim 1, wherein implementing energy-saving measures comprises adjusting process parameters, modifying the operation of equipment or devices, or scheduling operations to optimize energy consumption.
6. The method of claim 1, further comprising monitoring and evaluating the effectiveness of the implemented energy-saving measures over time and refining the energy optimization process based on the results.
7. The method of claim 1, wherein the energy-saving measures are implemented in a hierarchical manner based on their potential energy savings and impact on the industrial automation process.
8. The method of claim 1, wherein the energy-saving measures are implemented in real-time or near real-time to continuously optimize energy consumption within the industrial automation process.
9. The method of claim 1, wherein the energy-saving measures are prioritized based on their impact on energy consumption, operational efficiency, and environmental impact.
10. A system for optimizing energy consumption in an industrial automation process using artificial intelligence techniques, comprising: one or more sensors and devices configured to collect data related to energy consumption within the industrial automation process; a processor configured to execute an artificial intelligence algorithm to analyze the collected data and identify opportunities for energy savings; and an actuator or control system configured to implement energy-saving measures within the industrial automation process based on the identified opportunities.

Optimization of energy consumption in industrial automation processes using artificial intelligence techniques
Abstract
The present disclosure may include a method for optimizing energy consumption in an industrial automation process using techniques from artificial intelligence, such as collecting data from sensors and devices contained within the industrial automation process. This method may be included in embodiments of the present disclosure. Processing the acquired data using an artificial intelligence system in order to find patterns and trends in energy use is another possible aspect of embodiments. In certain implementations, one of the goals is to find ways to reduce energy use by analyzing data in order to spot potential possibilities. Using energy-saving techniques inside the industrial automation process in order to capitalize on possibilities that have been recognized is another potential aspect of embodiments. , Claims:Claims
I/We Claim:
1. A method for optimizing energy consumption in an industrial automation process using artificial intelligence techniques, comprising: collecting data from sensors and devices within the industrial automation process; processing the collected data using an artificial intelligence algorithm to identify patterns and trends in energy consumption; identifying opportunities for energy savings based on the analysis of the data; and implementing energy-saving measures within the industrial automation process based on the identified opportunities.
2. The method of claim 1, wherein the data collected from sensors and devices within the industrial automation process comprises data related to energy consumption, process parameters, and environmental conditions.
3. The method of claim 1, wherein the artificial intelligence algorithm comprises machine learning techniques such as neural networks, decision trees, and support vector machines.
4. The method of claim 1, wherein identifying opportunities for energy savings comprises comparing the current energy consumption levels to a predetermined baseline or target energy consumption level.
5. The method of claim 1, wherein implementing energy-saving measures comprises adjusting process parameters, modifying the operation of equipment or devices, or scheduling operations to optimize energy consumption.
6. The method of claim 1, further comprising monitoring and evaluating the effectiveness of the implemented energy-saving measures over time and refining the energy optimization process based on the results.
7. The method of claim 1, wherein the energy-saving measures are implemented in a hierarchical manner based on their potential energy savings and impact on the industrial automation process.
8. The method of claim 1, wherein the energy-saving measures are implemented in real-time or near real-time to continuously optimize energy consumption within the industrial automation process.
9. The method of claim 1, wherein the energy-saving measures are prioritized based on their impact on energy consumption, operational efficiency, and environmental impact.
10. A system for optimizing energy consumption in an industrial automation process using artificial intelligence techniques, comprising: one or more sensors and devices configured to collect data related to energy consumption within the industrial automation process; a processor configured to execute an artificial intelligence algorithm to analyze the collected data and identify opportunities for energy savings; and an actuator or control system configured to implement energy-saving measures within the industrial automation process based on the identified opportunities.