Method and system for controlling hotspots in furnaces in industrial plants

Abstract

The present disclosure relates to method and control system (106) for controlling hotspots in furnaces. The method comprises receiving thermal image (104) of plurality of furnace tubes (110). Further, the method comprises receiving values of inlet material flow rates of furnace (102) and fuel flow rates of plurality of burners associated with furnace (102). The method comprises obtaining predictions of plurality of thermal images by varying values of inlet material flow rates and fuel flow rates, using predictive model (108). The method comprises determining relationship between plurality of thermal images and varied values of inlet material flow rates and fuel flow rates, using predictive model (108). Thereafter, the method comprises determining optimal values of inlet material flow rates and fuel flow rates, based on relationship and control objective comprising predefined setpoints and constraints for outlet flow rate, outlet temperature and skin temperature of plurality of furnace tubes (110).

Description

TITLE: “METHOD AND SYSTEM FOR CONTROLLING HOTSPOTS IN FURNACES IN INDUSTRIAL PLANTS”TECHNICAL FIELD

[0001] The present disclosure generally relates to industrial furnaces. More particularly, the present disclosure relates to a method and a system for controlling hotspots in furnaces in industrial plants.BACKGROUND

[0002] Industrial furnaces are widely used in various sectors, including oil and gas, chemical, metal industries, etc. Industrial furnaces generate high temperatures, typically exceeding 400 degrees Celsius (752 degrees Fahrenheit). This facilitates heating of materials which serves different purposes such as providing necessary heat for chemical reactions, pre-heating for fractionation, inducing phase change etc. Industrial furnaces generate heat primarily through the combustion of fuels, such as natural gas, oil, or coal. Some furnaces may also use electricity as a heat source. Furnace control is a critical requirement in industrial plants. For example, in oil and gas industry, ethylene plants can have 12-15 industrial furnaces. The large number of furnaces in industrial settings necessitates robust control systems.

[0003] The information disclosed in this background of the disclosure section is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.SUMMARY

[0004] In an embodiment, the present disclosure discloses a method for controlling hotspots in furnaces in industrial plants. The method comprises receiving a thermal image of a plurality of furnace tubes associated with a furnace in an industrial plant, from one or more sources. Further, the method comprises receiving values of inlet material flow rates of the furnace and fuel flow rates of a plurality of burners associated with the furnace. Furthermore, the method comprises obtaining predictions of a plurality of thermal images by varying values of the inlet material flow rates and the fuel flow rate, using a predictive model associated with a control system. The method comprises determining a relationship between the plurality of thermalimages and varied values of the inlet material flow rates and the fuel flow rate, using the predictive model. Thereafter, the method comprises determining optimal values of the inlet material flow rates and the fuel flow rates, based on the relationship.

[0005] In an embodiment, the present disclosure discloses a control system for controlling hotspots in furnaces in industrial plants. The control system comprises a processor and a memory. The processor is configured to receive a thermal image of a plurality of furnace tubes associated with a furnace in an industrial plant, from one or more sources. Further, the processor is configured to receive values of inlet material flow rates of the furnace and fuel flow rates of a plurality of burners associated with the furnace. Furthermore, the processor is configured to obtain predictions of a plurality of thermal images by varying values of the inlet material flow rates and the fuel flow rate, using a predictive model associated with a control system. The processor is configured to determine a relationship between the plurality of thermal images and varied values of the inlet material flow rates and the fuel flow rates, using the predictive model. Thereafter, the processor is configured to determine optimal values of the inlet material flow rates and the fuel flow rates, based on the relationship.

[0006] The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features will become apparent by reference to the drawings and the following detailed description.BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWINGS

[0007] The novel features and characteristics of the disclosure are set forth in the appended claims. The disclosure itself, however, as well as a preferred mode of use, further objectives, and advantages thereof, will best be understood by reference to the following detailed description of an illustrative embodiment when read in conjunction with the accompanying figures. One or more embodiments are now described, by way of example only, with reference to the accompanying figures wherein like reference numerals represent like elements and in which:

[0008] Figure 1 illustrates an exemplary environment for controlling hotspots in furnaces in industrial plants, in accordance with some embodiments of the present disclosure;

[0009] Figure 2 illustrates a detailed block diagram of a control system, in accordance with some embodiments of the present disclosure;

[0010] Figures 3A and 3B illustrate exemplary training of a predictive model, in accordance with some embodiments of the present disclosure;

[0011] Figure 3C shows an exemplary illustration for controlling hotspots in furnaces in industrial plants, in accordance with some embodiments of the present disclosure;

[0012] Figure 4 shows an exemplary flow chart illustrating method steps for controlling hotspots in furnaces in industrial plants, in accordance with some embodiments of the present disclosure; and

[0013] Figure 5 shows a block diagram of a general-purpose computing system for controlling hotspots in furnaces in industrial plants, in accordance with embodiments of the present disclosure.

[0014] It should be appreciated by those skilled in the art that any block diagram herein represents conceptual views of illustrative systems embodying the principles of the present subject matter. Similarly, it will be appreciated that any flow charts, flow diagrams, state transition diagrams, pseudo code, and the like represent various processes which may be substantially represented in computer readable medium and executed by a computer or processor, whether or not such computer or processor is explicitly shown.DETAILED DESCRIPTION

[0015] In the present document, the word "exemplary" is used herein to mean "serving as an example, instance, or illustration." Any embodiment or implementation of the present subject matter described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.

[0016] While the disclosure is susceptible to various modifications and alternative forms, specific embodiment thereof has been shown by way of example in the drawings and will be described in detail below. It should be understood, however that it is not intended to limit the disclosure to the particular forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternatives falling within the scope of the disclosure.

[0017] The terms “comprises”, “comprising”, or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, device or method that comprises a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or device or method. In other words, one or more elements in a system or apparatus proceeded by “comprises... a” does not, without more constraints, preclude the existence of other elements or additional elements in the system or apparatus.

[0018] Furnaces are essential in industrial plants such as oil and gas, chemical, metal industries, etc. While the typical objective of the furnace is to obtain desired outlet flow rate and temperature of materials being processed, a crucial aspect to be continuously monitored is outer temperature or skin temperature of furnace tubes inside the furnace. Traditionally, the skin temperature of the furnace tubes is measured using a pyrometer by an operator intermittently. In a scenario of certain section of the furnace tubes exceeds maximum temperature limits, a fuel flow rate of a burner close to the said section is reduced or the inlet material flowrates of corresponding tube pass(es) / branch(es) are increased. Hence, the fuel flow rates of the burners or appropriate inlet material flowrates are controlled manually to maintain the skin temperature within the maximum temperature limits.

[0019] There are significant advances in thermal sensing with the advent of Infrared (IR) cameras, which can measure spatial temperatures effectively covering significant areas of the furnace tubes. While the IR cameras are already being used for monitoring of the industrial plants, the fuel flow rate of the burners or the inlet material flowrates in the furnaces are manually controlled only when the temperatures are exceeding or close to the maximum temperature limits.

[0020] While infrared imaging helps in monitoring temperature violations to enable operator actions, they are not capable of avoiding these violations. There is a need for autonomous predictive control of plants without much intervention of operators and enhance the control performance.

[0021] The present disclosure provides a method and a control system for controlling hotspots in furnace tubes in industrial plants. The present disclosure utilizes infrared thermal images of the furnace tubes in the furnace. A predictive model is trained to vary values of controlvariables such as inlet material flow rates of the furnace and fuel flow rates at different burner locations. The predictive model is used to generate predictions of thermal images for the variations. The predictive model is trained to relate the thermal images and the varied values of control variables. A control system obtains relationship between the thermal images and the varied values of control variables, from the predictive model. The control system determines optimal values of the inlet material flow rates of the furnace and fuel flow rates at different burner locations, based on the relationship. In this way, the control system can analyse and predict the effect of variations in the values of control variables on temperature profile of the furnace tubes. Accordingly, the control system can optimize the values of the control variables. This essentially predicts any violation in temperatures much ahead in time and incorporates corrective actions continuously through control of the inlet material flow rates and fuel flow rates of the burners. In this way, the hotspots in the furnace tubes is controlled. This ensures safe, autonomous, and optimal operation of the furnaces.

[0022] The present disclosure considers pre-defined setpoints and constraints on outlet flow rate, outlet temperature of the furnace, and skin temperature of the furnace tubes, while ensuring control of hotspots in the furnace tubes. The present disclosure utilizes infrared-based temperature sensing which provides better spatial information about the spatially distributed tubes. The material processing in the furnaces can be increased (increased productivity) as the operation of the furnace can be pushed to the upper limits of temperatures, while still maintaining the safety of the furnaces by incorporation of the predictive model.

[0023] Figure 1 illustrates an exemplary environment 100 for controlling hotspots in furnaces in industrial plants, in accordance with embodiments of the present disclosure. The environment 100 comprises an industrial furnace 102 (also referred to as furnace 102) and a control system 106. Industrial furnaces (or the furnace 102) are used in a variety of industries to provide heat for various processes. Industrial furnaces are designed to generate high levels of heat for industrial processes, typically exceeding 400° Celsius. They are used in a wide range of applications, including metal processing, chemical reactions, oil refining, glass and ceramic production, power generation, waste incineration, and the like. Industrial furnaces are utilized in industrial plants such as oil and gas plants, chemical plants, etc. Industrial furnaces generate heat primarily through the combustion of fuels, such as natural gas, oil, or coal. Some furnaces may also use electricity as a heat source.

[0024] Furnace tubes are essential components of the industrial furnaces. They are designed to withstand high temperatures and pressures while providing a conduit for heat transfer, combustion, or material processing. Furnace tubes facilitate the transfer of heat from the combustion process or heating elements to the materials being processed. This can involve heating liquids, gases, or solids. Figure 1 illustrates a plurality of furnace tubes 110 associated with the furnace 102.

[0025] In the present disclosure, the control system 106 is configured to control hotspots in the plurality of furnace tubes 110. Herein, the control system 106 receives a thermal image 104 of the plurality of furnace tubes 110. In an example, the control system 106 may receive a realtime thermal image 104 of the plurality of furnace tubes 110 from a capturing unit. The control system 106 also receives values of inlet material flow rates of the furnace 102 and fuel flow ratesof each of a plurality of burners associated with the furnace 102. In an embodiment, the control system 106 may receive a value or multiple values of the inlet material flow rate. For instance, the control system 106 may receive multiple values when different values of the inlet material flow rate are associated with different passes in the furnace. In the present disclosure, the control system 106 determines or predicts optimal values (current and future values) of the inlet material flow rates and the fuel flow rates, based on a current thermal image and past values of the inlet material flow rates and the fuel flow rates.

[0026] The control system 106 utilizes a predictive model 108 to determine the optimal values of the inlet material flow rates and the fuel flow rates. Herein, the predictive model 108 is trained to vary values of the inlet material flow rates and the fuel flow rates and generate predictions of thermal images for the variations. Further, the predictive model 108 is trained to determine a relationship between the thermal images and the varied values. The control system 106 determines optimal values of the inlet material flow rates and the fuel flow rates for the plurality of burners, based on the relationship. Further, the determined optimal values of the inlet material flow rates and the fuel flow rates are fed back to the furnace 102, for controlling hotspots in the plurality of furnace tubes 110 in the furnace 102. In this way, any violation in temperatures can be predicted much ahead in time and incorporates corrective actions continuously through control of the inlet material flow rates and the fuel flow rates.

[0027] In an embodiment, the control system 106 may be implemented in a variety of computing systems, such as a laptop computer, a desktop computer, a Personal Computer (PC), a notebook, a smartphone, a tablet, e-book readers, a server, a network server, a cloud-basedserver, and the like. In an embodiment, the control system 106 may be implemented in the industrial system, an edge device, and / or a cloud server. In an embodiment, the predictive model 108 may be an integral part of the control system 106. In another embodiment, the predictive model 108 may reside outside the control system 106 and may be communicatively coupled to the control system 106.

[0028] Figure 2 illustrates a detailed block diagram 200 of the control system 106 for controlling hotspots in furnaces in the industrial plants, in accordance with some embodiments of the present disclosure. The control system 106 may include Central Processing Units 206 (also referred as “CPUs” or “a processor 206”), Input / Output (I / O) interface 202, and a memory 204. In some embodiments, the memory 204 may be communicatively coupled to the processor 206. The memory 204 stores instructions executable by the processor 206. The processor 206 may comprise at least one data processor for executing program components for executing user or system-generated requests. The memory 204 may be communicatively coupled to the processor 206. The memory 204 stores instructions, executable by the processor 206, which, on execution, may cause the processor 206 to control hotspots in furnaces in the industrial plants. In an embodiment, the memory 204 may include one or more modules 210 and computation data 208. The one or more modules 210 may be configured to perform the steps of the present disclosure using the computation data 208, to control hotspots in furnaces in the industrial plants. In an embodiment, each of the one or more modules 210 may be a hardware unit which may be outside the memory 204 and coupled with the control system 106. As used herein, the term modules 210 refers to an Application Specific Integrated Circuit (ASIC), an electronic circuit, a Field-Programmable Gate Arrays (FPGA), Programmable System-on-Chip (PSoC), a combinational logic circuit, and / or other suitable components that provide described functionality. The one or more modules 210 when configured with the described functionality defined in the present disclosure will result in a novel hardware. Further, the I / O interface 202 is coupled with the processor 206 through which an input signal or / and an output signal is communicated. For example, the control system 106 may receive the thermal image 104 from the one or more sources, using the I / O interface 202.

[0029] In one implementation, the modules 210 may include, for example, a communication module 218, an optimal value determination module 220, and auxiliary modules 222. It will be appreciated that such aforementioned modules 210 may be represented as a single module or a combination of different modules. In one implementation, the computation data 208 mayinclude, for example, communication data 212, optimal value determination data 214, and auxiliary data 216.

[0030] In one implementation, the communication module 218 may comprise a transmitter and a receiver. The communication module 218 may include antennas, transceivers, network interface controllers (NICs), and the like to transmit and receive data. For instance, the transmitter of the communication module 218 may be configured to transmit the optimal values of the inlet material flow rate and the fuel flow rate to one or more operators in the industrial plant. The receiver of the communication module 218 may be configured to receive the thermal image from the one or more sources (for example, capturing units). Further, the communication module 218 may comprise protocol handling units such as Transmission Control Protocol / Internet Protocol (TCP / IP), User Datagram Protocol (UDP) etc. The protocol handling units may be configured for encoding or decoding data, routing of data, etc. For instance, the control system 106 may be implemented in a central plant server. In such a case, the protocol handling units may be used to manage thermal images received from multiple industrial plants. Further, the communication module 218 may comprise data buffers, for instance, to temporality store the thermal images received from the industrial plants. Also, the communication module 218 may include interfaces such as serial ports, ethernet ports, etc. The communication module 218 may include a signal pre-processing unit to eliminate noise from the data. The communication module 218 may transfer the data (the thermal images) to the memory 204 of the control system. A person skilled in the art will appreciate that the communication module 218 may comprise fewer or additional components, and the above-mentioned components should not be considered as limiting.

[0031] In one implementation, the optimal value determination module 220 may be coupled to the memory 204 of the control system. In an embodiment, the optimal value determination module 220 may be configured to retrieve the thermal images from the memory 204. In an embodiment, the optimal value determination module 220 may be communicatively coupled with various hardware units such as sensors, capturing units, databases, etc. with the industrial plant. The communication module 218 may include interfaces such as serial ports, ethernet ports, etc to receive data for determining the optimal values from the hardware units. A person skilled in the art will appreciate that the optimal value determination module 220 may comprise fewer or additional components, and the above-mentioned components should not beconsidered as limiting. The functionalities of the above-stated modules are explained in subsequent paragraphs of the detailed description.

[0032] In an embodiment, the communication module 218 may be configured to receive the thermal image 104 of the plurality of furnace tubes 110 associated with the furnace 102. In an embodiment, the communication module 218 may receive the thermal image 104 of the plurality of furnace tubes 110 from the one or more sources. In an embodiment, the one or more sources may include one or more capturing units. For instance, the one or more capturing units may include thermal camera(s) (for example, IR cameras) placed at specific positions in the furnace 102. The one or more capturing units may transmit real-time thermal images of the furnace 102.

[0033] In another embodiment, the one or more sources may include a database in the industrial plant. In yet another embodiment, the one or more sources may include a central plant monitoring system. For instance, the control system 106 may be implemented across a plurality of industrial plants. In such a case, the communication module 218 may receive the thermal image 104 of the furnace 102 associated with each of the plurality of industrial plants. Referring to Figure 3C, the thermal image 104 of the plurality of furnace tubes 110 associated with the furnace 102 may be received. Referring back to Figure 2, in an embodiment, the thermal image 104 may be projected onto a lower dimensional space for determining the optimal values of the inlet material flow rates and the fuel flow rates. The thermal image 104 of the plurality of furnace tubes 110 may be stored as the communication data 212 in the memory 204.

[0034] In an embodiment, the communication module 218 may be configured to receive values of the inlet material flow rates of the furnace 102 and the fuel flow rates of the plurality of burners. The inlet material flow rates of a furnace refer to the quantity of material that is fed into the furnace for processing within a given time period. The fuel flow rates of burners in a furnace refers to the amount of fuel consumed by the burners within a specific time period. These are crucial parameters in furnace operation and control, as they directly affect the furnace's performance, efficiency, and the quality of the output material. In the present disclosure, the values of the inlet material flow rates and the fuel flow rates of the plurality of burners are received, to control the hotspots in the plurality of furnace tubes 110. In an embodiment, the communication module 218 may receive measured values of the inlet material flow rates and the fuel flow rates of the plurality of burners associated with the furnace 102.The values of the inlet material flow rates and the fuel flow rates may be stored as the communication data 212 in the memory 204.

[0035] In an embodiment, the optimal value determination module 220 may be configured to obtain predictions of a plurality of thermal images using the predictive model 108. The functionalities of the predictive model 108 are explained in detail in subsequent paragraphs of the present description.

[0036] In an embodiment, the predictive model 108 may be used to assist the optimal value determination module 220 in determining or predicting the optimal values of the inlet material flow rates and the fuel flow rates. In an embodiment, the predictive model 108 may be deployed using a Model Predictive Control (MPC) framework. Herein, the predictive model 108 helps to analyze how system's outputs change over time in response to different inputs. This prediction is crucial for MPC to calculate optimal control actions. A person skilled in the art will appreciate that the predictive model 108 may be deployed using other frameworks.

[0037] In an embodiment, the predictive model 108 may be a dynamic Multi-Input Multi-Output (MIMO) model deployed by relating thermal images with the inlet material flow rates and the fuel flow rates. In an exemplary embodiment, the dynamic MIMO model may be deployed using dynamic mode decomposition or any appropriate model order reduction techniques may be deployed to reduce the number of states and obtain a predictive model suitable for modelbased control application (for instance, computationally efficient model). In an embodiment, the predictive model 108 may be trained to vary training values of the inlet material flow rates and the fuel flow rates over a pre-defined range corresponding to the inlet material flow rates and the fuel flow rates, respectively. These variations help to analyze the effect of change in input(s) on output(s) in the industrial plant. Figure 3A illustrates graphs representing variations of the control variables (also referred to as manipulated variables) over the pre-defined range or time.

[0038] The predictive model 108 is trained to generate predictions of thermal images of the plurality of furnace tubes 110, corresponding to these variations. Referring to Figure 3B, the predictions of plurality of thermal images 304 are generated corresponding to the variations in the control variables as shown in 302. In an embodiment, the predictions of thermal images may include information of all image pixels associated with a typical thermal image or only a portion of image pixels that are important for analysis of the hotspots that are chosen selectively.In this way, multiple predictions of thermal images with varied scenarios (manipulated variables) of the fuel flow rates and the inlet material flow rates are considered for training the predictive model 108. Further, the predictive model 108 is trained to relate the thermal images with the varied training values of the control variables with appropriate model order reduction. This determines a relationship between the thermal images and the varied training values of the inlet material flow rates and the fuel flow rates.

[0039] Referring back to Figure 2, in an embodiment, the predictive model 108 may be an integral part of the control system 106. In another embodiment, the predictive model 108 may reside outside the control system 106 and may be communicatively coupled to the control system 106. In an embodiment, when the predictive model 108 is an integral part of the control system 106, the control system 106 may optionally comprise further modules such as a generation module to generate the predictions of thermal images and a relationship determination module to determine the relationship between the thermal images and the varied training values of the inlet material flow rates and the fuel flow rates. Such modules may be integral to the predictive model 108 within the control system 106.

[0040] In an embodiment, the optimal value determination module 220 may obtain predictions of a plurality of thermal images from the predictive model 108. Also, the optimal value determination module 220 may obtain the relationship between the plurality of thermal images and varied values of the inlet material flow rates and the fuel flow rates, from the predictive model 108. The optimal value determination module 220 may determine optimal values of the inlet material flow rates and the fuel flow rates, based on the relationship. The optimal value determination module 220 may analyze effects of variations in the values of the inlet material flow rates and the fuel flow rates on a future temperature profile of the plurality of furnace tubes 110, from the predictions of thermal images. The optimal value determination module 220 may determine the optimal values of the inlet material flow rates and the fuel flow rates, based on such analysis. Figure 3C shows the optimal value determination module 220 determining the optimal values using the predictive model 108.

[0041] In an embodiment, the optimal value determination module 220 may determine the optimal values of the inlet material flow rates and the fuel flow rates for the plurality of burners based on one or more pre-defined setpoints and constraints. The one or more pre-defined setpoints and constraints comprises at least one of, but not limited to, an outlet flow rate and outlet temperature of the furnace 102, and a skin temperature associated with each of theplurality of furnace tubes 110. In an embodiment, the one or more pre-defined constraints further comprises a variable indicating a temperature difference between one or more regions of the plurality of furnace tubes 110.

[0042] In an embodiment, the optimal value determination module 220 may obtain the one or more pre-defined setpoints corresponding to the outlet flow rate and the outlet temperature of the furnace 102. Further, the optimal value determination module 220 may obtain upper and lower constraints on the skin temperature of the plurality of furnace tubes 110. The optimal value determination module 220 may predict values of the outlet flow rate, the outlet temperature, and the skin temperature associated with the plurality of furnace tubes 110 with respect to the one or more pre-defined setpoints and constraints. The optimal value determination module 220 may perform the prediction over a user specified prediction horizon. Further, the optimal value determination module 220 may determine the optimal values of the inlet material flow rates and the fuel flow rates for the plurality of burners, based on the predictions. Referring again to Figure 3C, the optimal value determination module 220 obtains the one or more pre-defined setpoints and the constraints related to the controlled variables (as shown in 306). The optimal value determination module 220 determines the optimal values of the control variables based on the one or more pre-defined setpoints and the constraints related to the controlled variables i.e., the outlet flow, the outlet temperature, and the skin temperature, along with the optimal values. The present disclosure ensures that the main controlled variables of the furnace 102 i.e., the outlet flow rate and the outlet temperature of the furnace 102 and the skin temperature are controlled to avoid overheating of tubes, while determining the optimal value of the inlet material flow rates and a fuel flow rates for the plurality of burners.

[0043] In an embodiment, the optimal value determination module 220 may be configured to receive an input or feedback from one or more operators on the optimal value of the inlet material flow rates and a fuel flow rates for the plurality of burners. The optimal value determination module 220 may update the optimal value, based on the input. Further, the predictions generated by the predictive model 108 and used by the control system 106 may provide guidance to the one or more operators to manipulate the inlet material flow rates and the fuel flow rates to predictively / proactively avoid / eliminate hotspots in the plurality of furnace tubes 110.

[0044] The auxiliary data 216 may store data, including temporary data and temporary files, generated by the one or more modules 210 for performing the various functions of the controlsystem 106. The one or more modules 210 may also include the auxiliary modules 222 to perform various miscellaneous functionalities of the control system 106. The auxiliary data 216 may be stored in the memory 204. It will be appreciated that the one or more modules 210 may be represented as a single module or a combination of different modules.

[0045] The applications of the present disclosure include controlling hotspots in furnace tubes of a furnace in oil and gas, chemical, metal industries, etc. The present disclosure is now explained considering its application in oil and gas industry. Consider a naphtha cracking furnace where liquid hydrocarbons along with steam in certain ratio are fed into tubular reactors. The outlet temperature of the furnace to be maintained is around 800 to 840 °C depending on desired ratio of ethylene to propylene at the output while maintaining the total feed volume with minimal variations. The heat is introduced by manipulating the fuel to air ratio in the burner or multiple burners. The tubular reactors in the furnace further comprise of certain number of furnace tubes, for example, eight set of furnace tubes, where the temperature in each branch / pass to be balanced by manipulating the flows entering each of the passes.

[0046] This is a multi-input multi-output problem control problem, where the inputs are the inlet material flow rates at each of the passes and fuel to air ratios / fuel flow rates at one or more burners while the outputs are the temperatures at the outlet of individual passes and the overall material outlet flowrate. In addition to the traditional objective of maintaining the outlet flow rate and the temperature of the material, it is also important to monitor the skin temperatures of the furnace tubes at different spatial positions to ensure that the skin temperatures are below design limits of the furnace tubes. This is traditionally addressed more reactively by an operator by intermittently scanning the different regions of the furnace using a pyrometer and subsequent reactive actions in the event of any issues.

[0047] The thermal image-based control of furnaces in the present disclosure simultaneously and autonomously achieves traditional control objectives along with maintaining skin temperatures of the furnace tubes based on thermal images of the furnace tubes. The thermal images are additional outputs of the furnace apart from the traditional outputs while receiving the same inputs. This image-based predictive model along with traditional variables is used in linear / nonlinear model predictive control framework with upper and lower constraints on the skin temperatures of the furnace tubes to simultaneously achieve desired outlet temperatures and outlet flow rate with minimal variability. Additionally, upper and lower constraints ondifference between skin temperatures of adjacent passes are considered, to maintain near to uniform heating.

[0048] In an embodiment, the furnace 102 may be an electrical furnace. In such a case, the control system 106 may receive the thermal image 104 of the plurality of furnace tubes 110. Further, the furnace 102 may receive values of the inlet material flow rates of the furnace 102 and heating rates of a plurality of coils associated with the furnace 102. In such a case where the furnace 102 is an electrical furnace, the predictive model 108 associated with the control system 106 may be trained on the inlet material flow rate and the heating rate of each of the plurality of coils. The predictive model 108 may be trained to vary the inlet material flow rate and the heating rate over a pre-defined range. Further, the predictive model 108 may be trained to generate thermal image predictions of the plurality of furnace tubes for such variations. The predictive model 108 may determine a relationship between the thermal image predictions and the varied values of the inlet material flow rate and the heating rate. This enables the control system 106 to analyze the variations in the controlled variables such as the heating rate of the plurality of coils on a temperature profile of the plurality of furnace tubes 110

[0049] Figure 4 shows an exemplary flow chart illustrating method steps for controlling hotspots in furnaces in industrial plants, in accordance with some embodiments of the present disclosure. As illustrated in Figure 4, the method 400 may comprise one or more steps. The method 400 may be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, and functions, which perform particular functions or implement particular abstract data types.

[0050] The order in which the method 400 is described is not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement the method. Additionally, individual blocks may be deleted from the methods without departing from the scope of the subject matter described herein. Furthermore, the method can be implemented in any suitable hardware, software, firmware, or combination thereof.

[0051] At step 402, the control system 106 receives the thermal image 104 of the plurality of furnace tubes 110 associated with the furnace 102. In an embodiment, the control system 106 may receive the thermal image 104 of the plurality of furnace tubes 110 from the one or moresources. In an embodiment, the one or more sources may include one or more capturing units. In another embodiment, the one or more sources may include a database in the industrial plant. In yet another embodiment, the one or more sources may include a central plant monitoring system.

[0052] At step 404, the control system 106 obtains values of the inlet material flow rates of the furnace 102 and the fuel flow rates of the plurality of burners. In an embodiment, the control system 106 may receive measured values of the inlet material flow rates and the fuel flow rates of the plurality of burners associated with the furnace 102.

[0053] At step 406, the control system 106 obtains predictions of a plurality of thermal images using the predictive model 108. Herein, the predictive model 108 is trained to vary training values of the inlet material flow rates and the fuel flow rates over a pre-defined range corresponding to the inlet material flow rates and the fuel flow rates, respectively. The predictive model 108 is trained to generate predictions of thermal images of the plurality of furnace tubes 110, corresponding to these variations.

[0054] At step 408, the control system 106 determines a relationship between the plurality of thermal image and varied values of the inlet material flow rates and the fuel flow rates, using the predictive model 108.

[0055] At step 410, the control system 106 determines optimal values of the inlet material flow rates and the fuel flow rates, based on the relationship. The control system 106 may analyze effects of variations in the values of the inlet material flow rates and the fuel flow rates on a future temperature profile of the plurality of furnace tubes 110, from the predictions of thermal images. The control system 106 may determine the optimal values of the inlet material flow rates and the fuel flow rates, based on such analysis.COMPUTER SYSTEM

[0056] Figure 5 illustrates a block diagram of an exemplary computer system 500 for implementing embodiments consistent with the present disclosure. In an embodiment, the computer system 500 may be the control system 106. Thus, the computer system 500 may be used to control hotspots in furnaces in industrial plants.

[0057] The computer system 500 may communicate with one or more Input / Output (I / O) devices, using an I / O interface 502. For example, an input device 520 may be an antenna,keyboard, mousejoystick, (infrared) remote control, camera, card reader, fax machine, dongle, biometric reader, microphone, touch screen, touchpad, trackball, stylus, scanner, storage device, transceiver, video device / source, etc. An output device 522 may be a printer, fax machine, video display (e.g., cathode ray tube (CRT), liquid crystal display (LCD), light-emitting diode (LED), plasma, Plasma display panel (PDP), Organic light-emitting diode display (OLED) or the like), audio speaker, etc.

[0058] The computer system 500 may comprise a Central Processing Unit 504 (also referred as “CPU” or “processor”). The processor 504 may comprise at least one data processor. The processor 504 may include specialized processing units such as integrated system (bus) controllers, memory management control units, floating point units, graphics processing units, digital signal processing units, etc.

[0059] The processor 504 may be disposed in communication with one or more input / output (I / O) devices (not shown) via the I / O interface 502. The I / O interface 502 may employ communication protocols / methods such as, without limitation, audio, analog, digital, monoaural, RCA, stereo, IEEE (Institute of Electrical and Electronics Engineers) -1394, serial bus, universal serial bus (USB), infrared, PS / 2, BNC, coaxial, component, composite, digital visual interface (DVI), high-definition multimedia interface (HDMI), Radio Frequency (RF) antennas, S-Video, VGA, IEEE 802. n / b / g / n / x, Bluetooth, cellular (e.g., code-division multiple access (CDMA), high-speed packet access (HSPA+), global system for mobile communications (GSM), long-term evolution (LTE), WiMax, or the like), etc.

[0060] The processor 504 may be disposed in communication with the communication network 518 via a network interface 506. The network interface 506 may communicate with the communication network 518. The network interface 506 may employ connection protocols including, without limitation, direct connect, Ethernet (e.g., twisted pair 10 / 100 / 1000 Base T), transmission control protocol / intemet protocol (TCP / IP), token ring, IEEE 802.11a / b / g / n / x, etc. The communication network 518 may include, without limitation, a direct interconnection, local area network (LAN), wide area network (WAN), wireless network (e.g., using Wireless Application Protocol), the Internet, etc. The network interface 506 may employ connection protocols include, but not limited to, direct connect, Ethernet (e.g., twisted pair 10 / 100 / 1000 Base T), transmission control protocol / internet protocol (TCP / IP), token ring, IEEE 802.11a / b / g / n / x, etc.

[0061] The communication network 518 includes, but is not limited to, a direct interconnection, an e-commerce network, a peer to peer (P2P) network, local area network (LAN), wide area network (WAN), wireless network (e.g., using Wireless Application Protocol), the Internet, WiFi, and such. The first network and the second network may either be a dedicated network or a shared network, which represents an association of the different types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol / Internet Protocol (TCP / IP), Wireless Application Protocol (WAP), etc., to communicate with each other. Further, the first network and the second network may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, etc.

[0062] In some embodiments, the processor 504 may be disposed in communication with a memory 510 (e.g., RAM, ROM, etc. not shown in Figure 5) via a storage interface 508. The storage interface 508 may connect to a memory 510 including, without limitation, memory drives, removable disc drives, etc., employing connection protocols such as serial advanced technology attachment (SATA), Integrated Drive Electronics (IDE), IEEE- 1394, Universal Serial Bus (USB), fiber channel, Small Computer Systems Interface (SCSI), etc. The memory drives may further include a drum, magnetic disc drive, magneto-optical drive, optical drive, Redundant Array of Independent Discs (RAID), solid-state memory devices, solid-state drives, etc.

[0063] The memory 510 may store a collection of program or database components, including, without limitation, user interface 512, an operating system 514, a web browser 516 etc. In some embodiments, the computer system 500 may store user / application data, such as, the data, variables, records, etc., as described in this disclosure. Such databases may be implemented as fault-tolerant, relational, scalable, secure databases such as Oracle® or Sybase®.

[0064] The operating system 514 may facilitate resource management and operation of the computer system 500. Examples of operating systems include, without limitation, APPLE MACINTOSH® OS X, UNIX®, UNIX-like system distributions (E.G., BERKELEY SOFTWARE DISTRIBUTION™ (BSD), FREEBSD™, NETBSD™, OPENBSD™, etc.), LINUX DISTRIBUTIONS™ (E.G., RED HAT™, UBUNTU™, KUBUNTU™, etc.), IBM™ OS / 2, MICROSOFT™ WINDOWS™ (XP™, VISTA™ / 7 / 8, 10 etc.), APPLE® IOS™, GOOGLE® ANDROID™, BLACKBERRY® OS, or the like.

[0065] In some embodiments, the computer system 500 may implement the web browser 516 stored program component. The web browser 516 may be a hypertext viewing application, for example MICROSOFT® INTERNET EXPLORER™, GOOGLE® CHROME™, MOZILLA® FIREFOX™, APPLE® SAFARI™, etc. Secure web browsing may be provided using Secure Hypertext Transport Protocol (HTTPS), Secure Sockets Layer (SSL), Transport Layer Security (TLS), etc. Web browsers 516 may utilize facilities such as AJAX™, DHTML™, ADOBE® FLASH™, JAVASCRIPT™, JAVA™, Application Programming Interfaces (APIs), etc. In some embodiments, the computer system 500 may implement a mail server (not shown in Figure) stored program component. The mail server may be an Internet mail server such as Microsoft Exchange, or the like. The mail server may utilize facilities such as ASP™, ACTIVEX™, ANSI™ C++ / C#, MICROSOFT®, .NET™, CGI SCRIPTS™, JAVA™, JAVASCRIPT™, PERL™, PHP™, PYTHON™, WEBOBJECTS™, etc. The mail server may utilize communication protocols such as Internet Message Access Protocol (IMAP), Messaging Application Programming Interface (MAPI), MICROSOFT® exchange, Post Office Protocol (POP), Simple Mail Transfer Protocol (SMTP), or the like. In some embodiments, the computer system 500 may implement a mail client stored program component. The mail client (not shown in Figure) may be a mail viewing application, such as APPLE® MAIL™, MICROSOFT® ENTOURAGE™, MICROSOFT® OUTLOOK™, MOZILLA® THUNDERBIRD™, etc.

[0066] Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present disclosure. A computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term “computer- readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e., be non-transitory. Examples include Random Access Memory (RAM), Read-Only Memory (ROM), volatile memory, non-volatile memory, hard drives, Compact Disc Read-Only Memory (CD ROMs), Digital Video Disc (DVDs), flash drives, disks, and any other known physical storage media.

[0067] The present disclosure provides a method and a control system for controlling hotspots in furnace tubes in industrial plants. The present disclosure utilizes infrared thermal images of the furnace tubes in the furnace. A predictive model is trained to vary values of control variablessuch as inlet material flow rates of the furnace and fuel flow rates at different burner zones or locations. The predictive model is trained to relate thermal images and the varied values of control variables. The control system determines optimal values of the inlet material flow rates of the furnace and fuel flow rates at different burner locations, based on the relationship. In this way, the control system can analyse and predict the effect of variations in the values of control variables on temperature profile of the furnace tubes. Accordingly, the control system can optimize the values of the control variables. This essentially predicts any violation in temperatures much ahead in time and incorporates corrective actions continuously through manipulation of the inlet material flow rates and the fuel flow rates of the burners. In this way, the hotspots in the furnace tubes are controlled. This ensures safe, autonomous, and optimal operation of the furnaces.

[0068] The present disclosure considers pre-defined setpoints and constraints such as outlet flow rate, outlet temperature of the furnace, and skin temperature of the furnace tubes, while ensuring control of hotspots in the furnace tubes. The present disclosure utilizes infrared-based temperature sensing which provides better spatial information about the spatially distributed tubes. The material processing in the furnaces can be increased (increased productivity) as the operation of the furnace can be pushed to the upper limits of temperatures, while still maintaining the safety of the furnaces by incorporation of the predictive model.

[0069] The terms "an embodiment", "embodiment", "embodiments", "the embodiment", "the embodiments", "one or more embodiments", "some embodiments", and "one embodiment" mean "one or more (but not all) embodiments of the invention(s)" unless expressly specified otherwise.

[0070] The terms "including", "comprising", “having” and variations thereof mean "including but not limited to", unless expressly specified otherwise.

[0071] The enumerated listing of items does not imply that any or all of the items are mutually exclusive, unless expressly specified otherwise. The terms "a", "an" and "the" mean "one or more", unless expressly specified otherwise.

[0072] A description of an embodiment with several components in communication with each other does not imply that all such components are required. On the contrary, a variety of optional components are described to illustrate the wide variety of possible embodiments of the invention.

[0073] When a single device or article is described herein, it will be readily apparent that more than one device / article (whether or not they cooperate) may be used in place of a single device / article. Similarly, where more than one device or article is described herein (whether or not they cooperate), it will be readily apparent that a single device / article may be used in place of the more than one device or article, or a different number of devices / articles may be used instead of the shown number of devices or programs. The functionality and / or the features of a device may be alternatively embodied by one or more other devices which are not explicitly described as having such functionality / features. Thus, other embodiments of the invention need not include the device itself.

[0074] The illustrated operations of Figure 4 show certain events occurring in a certain order. In alternative embodiments, certain operations may be performed in a different order, modified, or removed. Moreover, steps may be added to the above-described logic and still conform to the described embodiments. Further, operations described herein may occur sequentially or certain operations may be processed in parallel. Yet further, operations may be performed by a single processing unit or by distributed processing units.

[0075] Finally, the language used in the specification has been principally selected for readability and instructional purposes, and it may not have been selected to delineate or circumscribe the inventive subject matter. It is therefore intended that the scope of the invention be limited not by this detailed description, but rather by any claims that issue on an application based here on. Accordingly, the disclosure of the embodiments of the invention is intended to be illustrative, but not limiting, of the scope of the invention, which is set forth in the following claims.

[0076] While various aspects and embodiments have been disclosed herein, other aspects and embodiments will be apparent to those skilled in the art. The various aspects and embodiments disclosed herein are for purposes of illustration and are not intended to be limiting, with the true scope being indicated by the following claims.Referral Numerals:

Claims

We claim:

1. A method of controlling hotspots in furnaces in industrial plants, the method comprising:receiving, by a processor (206), a thermal image (104) of a plurality of furnace tubes (110) associated with a furnace (102) in an industrial plant, from one or more sources;receiving, by the processor (206), values of inlet material flow rates of the furnace (102) and fuel flow rates of a plurality of burners associated with the furnace (102);obtaining, by the processor (206), predictions of a plurality of thermal images by varying values of the inlet material flow rates and the fuel flow rates, using a predictive model (108) associated with the processor (206);determining, by the processor (206), a relationship between the plurality of thermal images and varied values of the inlet material flow rates and the fuel flow rates, using the predictive model (108); anddetermining, by the processor (206), optimal values of the inlet material flow rates and the fuel flow rates, based on the relationship.2 The method as claimed in claim 1, wherein determining the optimal values of the inlet material flow rates and the fuel flow rates is further based on one or more pre-defined setpoints and constraints comprising at least one of an outlet flow rate and outlet temperature of the furnace (102), and a skin temperature associated with each of the plurality of furnace tubes (110).3 The method as claimed in claim 2, wherein determining the optimal values of the inlet material flow rates and the fuel flow rates based on the one or more pre-defined setpoints and the constraints comprising:obtaining the one or more pre-defined setpoints corresponding to the outlet flow rate and the outlet temperature, and upper and lower constraints on the skin temperature of the plurality of furnace tubes (110);predicting values of the outlet flow rate, the outlet temperature, and the skin temperature with respect to the one or more pre-defined setpoints and the constraints; and determining the optimal values of the inlet material flow rates and the fuel flow rates for the plurality of burners, based on the predictions.4 The method as claimed in claim 2, wherein the constraints further comprise a variable indicating a temperature difference between one or more regions of the plurality of furnace tubes (110).The method as claimed in claim 1, wherein training the predictive model (108) comprising: receiving training values of inlet material flow rates of the furnace (102) and fuel flow rates of the plurality of burners;varying the training values of the inlet material flow rates and the fuel flow rates over a pre-defined range corresponding to the inlet material flow rates and the fuel flow rates, respectively;generating predictions of thermal images s of the plurality of furnace tubes (110); and determining a relationship between the thermal images and the varied training values of the inlet material flow rates and the fuel flow rates.The method as claimed in claim 1, comprising:receiving an input from one or more operators on the optimal values of the inlet material flow rates and the fuel flow rates; andupdating the optimal values, based on the input.A control system (106) for controlling furnaces in industrial plants, the control system (106) comprises:a processor (206); anda memory (204), wherein the memory (204) stores processor-executable instructions, which, on execution, causes the processor (206) to:receive a thermal image (104) of a plurality of furnace tubes (110) associated with a furnace (102) in an industrial plant, from one or more sources;receive values of inlet material flow rates of the furnace (102) and fuel flow rates of a plurality of burners associated with the furnace (102);obtain predictions of a plurality of thermal images by varying values of the inlet material flow rates and the fuel flow rates, using a predictive model (108) associated with the processor (206);determine a relationship between the plurality of thermal images and varied values of the inlet material flow rates and the fuel flow rates, using the predictive model (108); anddetermine optimal values of the inlet material flow rates and the fuel flow rates, based on the relationship.

8. The control system (106) as claimed in claim 7, wherein the processor (206) is configured to determine the optimal values of the inlet material flow rates and the fuel flow rates based on one or more pre-defined setpoints and constraints comprising at least one of an outlet flow rate and outlet temperature of the furnace (102), and a skin temperature associated with each of the plurality of furnace tubes (110).

9. The control system (106) as claimed in claim 8, wherein the processor (206) is configured to determine the optimal values of the inlet material flow rates and the fuel flow rates based on the one or more pre-defined setpoints and the constraints by:obtaining the one or more pre-defined setpoints corresponding to the outlet flow rate and the outlet temperature, and upper and lower constraints on the skin temperature of the plurality of furnace tubes (110);predicting values of the outlet flow rate, the outlet temperature, and the skin temperature with respect to the one or more pre-defined setpoints and the constraints; and determining the optimal values of the inlet material flow rates and the fuel flow rates for the plurality of burners, based on the predictions.

10. The control system (106) as claimed in claim 8, wherein the constraints further comprise a variable indicating a temperature difference between one or more regions of the plurality of furnace tubes (110).

11. The control system (106) as claimed in claim 7, wherein training the predictive model (108) comprises:receiving training values of inlet material flow rates of the furnace (102) and fuel flow rates of the plurality of burners;varying the training values of the inlet material flow rates and the fuel flow rates over a pre-defined range corresponding to the inlet material flow rates and the fuel flow rates, respectively;generating predictions of thermal images of the plurality of furnace tubes (110); and determining a relationship between the thermal images and the varied training values of the inlet material flow rates and the fuel flow rates.

12. The control system (106) as claimed in claim 7, wherein the processor (206) is configured to:receive an input from one or more operators on the optimal values of the inlet material flow rates and the fuel flow rates; andupdate the optimal values, based on the input.

Images