Description:FORM 2
THE PATENTS ACT 1970
[39 OF 1970]
&
THE PATENTS RULES, 2003

COMPLETE SPECIFICATION
[See section 10; Rule 13]

TITLE: “METHOD AND SYSTEM FOR DYNAMICALLY CONTROLLING HEAT IN A COKE OVEN BATTERY”

Name and Address of the Applicant:
TATA STEEL LIMITED of JAMSHEDPUR – 831 001, JHARKHAND, INDIA.

Nationality: Indian

The following specification particularly describes the invention and the manner in which it is to be performed.

TECHNICAL FIELD

[001] The present disclosure generally relates to coke oven battery and more particularly, to a method and system for dynamically controlling heat in a coke oven battery.

BACKGROUND
[002] Steel forms a vital part of the global economy and is one of the most widely used engineering material in modern society owing to its superior properties, abundance and cost -efficiency. Generally, coke is used as a fuel and as a reducing agent for smelting iron ore in a blast furnace for the production of steel. Coke is also used by a number of other industries, namely iron foundries, nonferrous smelters, chemical plants, and the like.
[003] Conventionally, coke is prepared in a coke oven battery of ovens sandwiched between heating walls which are carbonized at high temperatures (for example, 1000°C to 1200°C) where coal undergoes destructive distillation to produce metallurgical coke of desired mechanical and thermo-chemical properties. In general, coking is a complex intermittent thermal process in which the coals are solid when charged, become fluid to varying degrees, then with further increase in temperature, become the solid, hard porous substance, known as coke. As such, coke oven battery is a key thermal equipment which consumes energy massively in the metallurgy industry. Moreover, this entire process of coke making is manually managed by operators, for example, coal preparation, managing heating control in battery, managing moisture control, managing battery temperature, operating and maintaining quenching system, coke handling, etc. In an example, heat input to the coke oven battery is controlled by controlling a pause time. More specifically, a target regenerator temperature in the coke oven may be determine based on number of pushing and is preset by the operator of the coke oven battery for controlling the heat input to the coke oven battery. Further, desired under firing gas flow for the coke oven battery may be maintained by manually setting a cellar pressure and combustion air is adjusted manually by changing the set point for draft. In another example, there are variations in incoming gas pressure and Calorific value and again manual interventions are required to maintain constant heat input in the heating system of the coke oven battery. As such, a lot of manual interference is required by the operator of the coke oven battery. Such manual interference may include the operator or support staff to manually identify a current condition of the coke oven battery and predict next course of action for managing the heat in the coke oven battery. However, differences may arise due to varying actions adapted by operators based on operators skill, evaluation and timeliness. Accordingly, variations caused due to manual interference result in additional gas consumption in the coke oven battery. Moreover, the coke making process is associated with various safety and health hazards such as, entanglement with mobile equipment, burns, fire, exposure to dust, noise, heat, toxic gases etc.
[004] In view of the above discussion, there is a need to efficiently manage heat energy in the coke oven battery for promoting production of quality coke whilst reducing the manual interference of operators.
SUMMARY
[005] In an embodiment, a method for dynamically controlling heat in a coke oven battery is disclosed. The method includes receiving, by a system, heat energy estimate for a coke oven, a calorific value of a gas mixture in the coke oven, and one or more feedback parameters from the coke oven battery. The method includes predicting, by the system, a condition of the coke oven battery based on the one or more feedback parameters. The method includes dynamically determining, by the system, a target regenerator temperature value based on the one or more feedback parameters and the predicted condition of the coke oven battery. The method includes determining, by the system, a temperature difference between the target regenerator temperature value and an actual temperature value of the coke oven battery. The actual temperature value is measured using one or more sensors. The method includes controlling, by the system, the heat in the coke oven battery by dynamically adapting one or more control parameters based on the temperature difference, the heat energy estimate, and the calorific value of the gas mixture.
[006] In another embodiment, a system for dynamically controlling heat in a coke oven battery is disclosed. The system includes a memory and a processor. The memory is configured to store instructions and the processor is configured to execute the instructions stored in the memory and thereby cause the system to perform the method. The system is caused to receive heat energy estimate for a coke oven, a calorific value of a gas mixture in the coke oven, and one or more feedback parameters from the coke oven battery. The system is caused to predict a condition of the coke oven battery based on the one or more feedback parameters. The system is caused to dynamically determine a target regenerator temperature value based on the one or more feedback parameters and the predicted condition of the coke oven battery. The system is caused to determine a temperature difference between the target regenerator temperature value and an actual temperature value of the coke oven battery. The actual temperature value is measured using one or more sensors. The system is caused to control the heat in the coke oven battery by dynamically adapting one or more control parameters based on the temperature difference, the heat energy estimate, and the calorific value of the gas mixture.
[007] In yet another embodiment, a non-transitory computer-readable medium is disclosed. The non-transitory computer-readable medium stores instructions for dynamically controlling heat in a coke oven battery. The instructions when executed by a processor cause a system to perform a method. The method includes receiving, by a system, heat energy estimate for a coke oven, a calorific value of a gas mixture in the coke oven, and one or more feedback parameters from the coke oven battery. The method includes predicting, by the system, a condition of the coke oven battery based on the one or more feedback parameters. The method includes dynamically determining, by the system, a target regenerator temperature value based on the one or more feedback parameters and the predicted condition of the coke oven battery. The method includes determining, by the system, a temperature difference between the target regenerator temperature value and an actual temperature value of the coke oven battery. The actual temperature value is measured using one or more sensors. The method includes controlling, by the system, the heat in the coke oven battery by dynamically adapting one or more control parameters based on the temperature difference, the heat energy estimate, and the calorific value of the gas mixture.
[008] 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 DRAWINGS

[009] The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles. The same numbers are used throughout the figures to reference like features and components. Some embodiments of device and/or methods in accordance with embodiments of the present subject matter are now described, by way of example only, and with reference to the accompanying figures, in which:

[010] FIG. 1 illustrates an example representation of an environment, in which at least some example embodiments of the present disclosure can be implemented;
[011] FIG. 2 illustrates a system for dynamically controlling heat in a coke oven battery, in accordance with an embodiment of the present disclosure;
[012] FIG. 3A illustrates a block diagram representation of dynamically controlling heat in a coke oven battery, in accordance with an embodiment of the present disclosure;
[013] FIG. 3B illustrates a schematic block diagram representing processing performed by the heating control module of FIG. 3A for dynamically controlling heat in the coke oven battery, in accordance with an embodiment of the present disclosure;
[014] FIG. 4 is a flowchart illustrating a method for dynamically controlling heat in a coke oven battery, in accordance with an embodiment of the present disclosure; and
[015] FIG. 5 shows a block diagram of a general-purpose computer for controlling heat in a coke oven battery, in accordance with an embodiment of the present disclosure.
[016] It should be appreciated by those skilled in the art that any block diagrams herein represent 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
[017] 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.
[018] 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 alternative falling within the spirit and the scope of the disclosure.
[019] 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 device or system or apparatus proceeded by “comprises… a” does not, without more constraints, preclude the existence of other elements or additional elements in the device or system or apparatus.
[020] In the following detailed description of the embodiments of the disclosure, reference is made to the accompanying drawings that form a part hereof, and in which are shown by way of illustration specific embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the present disclosure. The following description is, therefore, not to be taken in a limiting sense.
[021] FIG. 1 illustrates an example representation of an environment 100, in which at least some example embodiments of the present disclosure can be implemented. The environment 100 exemplarily depicts a coke oven battery 102. The coke oven battery 102 is a group of ovens connected by common walls for processing coal 104 to produce coke 106. In one example, a coke oven battery 102 includes 20 to 100 ovens arranged adjacent to each other with common side walls made of high-quality silica and other types of refractory bricks. It shall be noted that the coke oven battery 102 shown in FIG. 1 is for exemplary purposes and the coke oven battery 102 is in general a complex structure comprising multiple ovens, heating flues, coking chamber shaft, regenerators, brickwork of coking chambers and heating walls, vaults, and the like for processing coal 104.
[022] Coal 104 is usually received in railway wagons and prepared for processing. More specifically, preparation of coal 104 for the coking process include reception, preliminary crushing, storing, proportioning, coke making wastes addition to the coal blend, final crushing, mixing and transportation of the coal blend to a coal tower. The coal blend undergoes thermal distillation (i.e., coking process) in the coke oven battery 102 to produce coke 106. More specifically, the coal 104 is heated to 1200°C for upto 18 hours. During coking process, two opposite reactions take place, i.e., condensation and pyrolysis. When the coal 104 are heated to high temperatures (i.e., 1100°C to 1200°C) in the absence of air, the coal 104 melts to form plastic layers adjacent to the heating walls and with progress of time and heat, resolidify to produce a solid coherent mass of coke 106 while volatiles of the coal 104 are driven into the offgas as coke oven gas 108. The coke 106 when exposed to oxygen, will immediately ignite and begin to burn. When the coke 106 is pushed from the oven into a railcar, it is quickly quenched to cook the coke and stop the burning process. The cooled coke 106 is then dumped onto a coal wharf where it is taken to a facility to be screened and sized prior to being charged into a blast furnace 110. It shall be noted that the coking process may be performed by processes/techniques other than the process described herein and it must be apparent to a person skilled in the art that embodiments of the present disclosure may be practiced with such different processes and techniques.
[023] The coke 106 is a solid carbon fuel which is used in the blast furnace 108 as a reductant and as a source of thermal energy. More specifically, the coke 106 is used to melt iron ore to liquid metal 112 in the blast furnace 110, reduce and refine the liquid metal in convertor to form steel. In general, the properties of coke 106 and performance of the coke oven battery 102 are influenced by coal quality and battery operating variables such as, coal grade, characteristics of coal 104, particle size, moisture content, coking temperature and coking rate, etc. Traditionally, the coal quality and battery operating variables may be controlled manually by an operator of the coke oven battery 102 to control the heat within the coke oven battery 102.
[024] Various embodiments of the present disclosure disclose a system 150 for dynamically controlling heat in the coke oven battery 102. In general, heat input in the coke oven battery 102 is automatically controlled by the system 150 to reduce energy consumption and improve coke quality. Accordingly, the system 150 adapts one or more control parameters of the coke oven battery 102 based on a temperature difference for controlling the heat in the coke oven battery 102. More specifically, a multi-level approach to control heating process of the coke oven battery 102 is performed using feedback and closed loop control thereby precluding manual intervention of operators. Moreover, integration of one or more feedback parameters ensure that the system 150 analyzes and updates heating gas flow, air flow, calorific value (CV) and pause time to realize steady heating of the coke oven. The system 150 for dynamically controlling heat in the coke oven battery 102 is explained in detail next with reference to FIG. 2.
[025] FIG. 2 illustrates the system 150 for dynamically controlling heat in the coke oven battery 102, in accordance with an embodiment of the present disclosure. In an embodiment, the system 150 may be a remote server capable of receiving one or more feedback parameters related to operation of the coke oven battery 102 and generating optimized control parameters for operating the coke oven battery 102. More specifically, the system 150 is implemented at Level 2 system of a coke plant and removes dependency of manual action by plant operators. In general, the system 150 is a server configured to solve complex problems and send outputs to control circuits (i.e., Level 1 systems) which control instruments/devices of the coke oven battery 102 such as sensors, transmitters, temperature indicators, valves, motors, etc. In other words, control parameters of the coke oven battery 102 are computed by the system 150 (i.e., Level 2 system) and forwarded to a control circuit such as, SCADA systems that control and monitor DCS or PLC systems, for managing the coke oven battery 102.
[026] It is understood that the control circuits (not shown in figures) of the coke oven battery 102 may be in operative communication with a communication network 120 (see, FIG. 1). The control circuits may connect to the communication network 120 using a wired network, a wireless network, or a combination of wired and wireless networks. Some non-limiting examples of the wired networks may include the Ethernet, the Local Area Network (LAN), a fiber-optic network, and the like. Some non-limiting examples of the wireless networks may include the Wireless LAN (WLAN), cellular networks, Bluetooth or ZigBee networks, and the like.
[027] The system 150 is depicted to include a processor 202, a memory 204, an Input/Output module 206, and a communication interface 208. It shall be noted that, in some embodiments, the system 150 may include fewer or more components than those depicted herein. The various components of the system 150 may be implemented using hardware, software, firmware or any combinations thereof. Further, the various components of the system 150 may be operably coupled with each other. More specifically, various components of the system 150 may be capable of communicating with each other using communication channel media (such as buses, interconnects, etc.). It is also noted that one or more components of the system 150 may be implemented in a single server or a plurality of servers, which are remotely placed from each other.
[028] In one embodiment, the processor 202 may be embodied as a multi-core processor, a single core processor, or a combination of one or more multi-core processors and one or more single core processors. For example, the processor 202 may be embodied as one or more of various processing devices, such as a coprocessor, a microprocessor, a controller, a digital signal processor (DSP), a processing circuitry with or without an accompanying DSP, or various other processing devices including, a microcontroller unit (MCU), a hardware accelerator, a special-purpose computer chip, or the like. The processor 202 includes a heat energy estimation module 210, a gas flow balance module 212 and a heating control module 214 which are explained in detail later. It shall be noted that each of the modules 210, 212 and 214 may be implemented as separate processors in the system 150 and may be communicably coupled to the processor 202 for performing one or more of the operations described herein.
[029] In one embodiment, the memory 204 is capable of storing machine executable instructions, referred to herein as instructions 205. In an embodiment, the processor 202 is embodied as an executor of software instructions. As such, the processor 202 is capable of executing the instructions 205 stored in the memory 204 to perform one or more operations described herein. The memory 204 can be any type of storage accessible to the processor 202 to perform respective functionalities, as will be explained in detail with reference to FIGS. 2 to 5. For example, the memory 204 may include one or more volatile or non-volatile memories, or a combination thereof. For example, the memory 204 may be embodied as semiconductor memories, such as flash memory, mask ROM, PROM (programmable ROM), EPROM (erasable PROM), RAM (random access memory), etc. and the like.
[030] In an embodiment, the processor 202 is configured to execute the instructions 205 for: (1) estimating heat energy required for the coke oven, (2) estimating a calorific value of the gas mixture for the coke oven battery 102, (3) adapting a flow rate of coke oven gas 108 for controlling the calorific value of the gas mixture, (4) predicting a condition of the coke oven battery 102, (5) dynamically determining a target regenerator temperature value, (6) determining a temperature difference between the target regenerator temperature value and an actual temperature value, (7) adapting one or more control parameters based on the temperature difference for controlling heat in the coke oven battery 102, (8) determining a pause time period based on the temperature difference, (9) determining a scaling factor for adapting the pause time period based on coke oven characteristics. The processor 202 may also execute instructions 205 for using one or more AI models for determining pause periods based on temperature difference.
[031] In an embodiment, the I/O module 206 may include mechanisms configured to receive inputs from and provide outputs to peripheral devices such as, control circuits that control and monitor DCS or PLC systems operating the coke oven battery 102, and/or an operator of the system 150. The term ‘operator of the system 150’ as used herein may refer to one or more individuals, whether directly or indirectly, associated with managing the coke oven battery 102. To enable reception of inputs and provide outputs to the system 150, the I/O module 206 may include at least one input interface and/or at least one output interface. Examples of the input interface may include, but are not limited to, a keyboard, a mouse, a joystick, a keypad, a touch screen, soft keys, a microphone, and the like. Examples of the output interface may include, but are not limited to, a display such as a light emitting diode display, a thin-film transistor (TFT) display, a liquid crystal display, an active-matrix organic light-emitting diode (AMOLED) display, a microphone, a speaker, a ringer, and the like.
[032] In an embodiment, the communication interface 208 may include mechanisms configured to communicate with other entities in the environment 100. In other words, the communication interface 208 is configured to collate data for processing by the processor 202. Referring now to FIG. 3A in conjunction with FIG. 2, the communication interface 208 is configured to receive data related to a pushing plan 302 (see, FIG. 3A), one or more coal properties 304 (see, FIG. 3A), a target calorific value 306 (see, FIG. 3A), one or more coke oven characteristics, actual temperature value and measurements of the gas mixture 307 (see, FIG. 3A). The pushing plan 302 includes information related to number of batches, weight of each batch, specified timing at which each load needs to be provided to the coke oven battery 102, time interval between providing loads to the coke oven battery 102, pushing delay between loading two different coal loads, etc. In one example, the pushing plan 302 depicts 30 batches of coal with 20 tons of coal for each batch that is charged into single slot ovens grouped together in batteries to be loaded at time intervals of 5 minutes. The pushing delay may be specified based on work plan of the operator of the system 150, for example, 1 hour. It shall be noted that the number of batches may be specified over different periods of time, for example, number of batches for specific time in a day (i.e., morning, afternoon, evening), number of batches for a day (for example, weekday/weekend), or more than a day (for example, a pushing plan for a week or month) and the like.
[033] In an embodiment, the one or more coal properties 304 include information related to an amount of moisture, percentage of volatile matter, concentrations of ash, sulfur, alkalis, phosphorus, carbon content, coal grade, ash fusion temperature, and the like that define a type of coal used for producing coke 106. In one example, if bituminous coal or anthracite may be used for producing coke 106 and accordingly, the one or more coal properties 304 correspond to information related to the bituminous coal/anthracite.
The target calorific value 306 of a gas mixture indicates a calorific value of the fuel gas for each oven in the coke oven battery 102 and is preset by the operator of the system 150. In an embodiment, the target calorific value 306 is preset based on operational characteristics of the coke oven battery 102, for example, number of coke ovens, number of heating flues, maximum temperature that may be provided by the heating flues, etc. The gas mixture includes at least a blast furnace gas 308, a Linz-Donawitz gas 310 and a coke oven gas 312 (see, figure 3 for 308, 310 and 312) that may be used as fuel for the coke oven battery 102. It shall be noted that the coke oven gas 108 is a raw gas produced during the manufacture of metallurgical coke in the coke oven battery 102 and is processed to remove tar, ammonia, phenol, naphthalene, light oil, and sulphur. As such, the coke oven gas 312 is recovered from the raw form of the coke oven gas 108 and is used as fuel for heating the coke ovens in the coke oven battery 102. It shall be noted that the gases in the gas mixture mentioned herein are for exemplary purposes and the gas mixture may include fewer or more gases in any combination thereof. Further, it shall be noted that the target calorific value 306 may be preset for different periods of time such as, for specific time in a day, specific days (weekday/weekend), one day or over period of days (i.e., months/weeks). Alternatively, the target calorific value 306 may be preset for each batch or each pushing plan.
[034] The one or more coke oven characteristics include information related to a type of coke oven, number of coke ovens, a type of coke oven battery 102, a type of sensor in the coke oven, and an average number of pushings in the coke oven battery 102. In an embodiment, the actual temperature value of the coke oven battery 102 is measured using one or more sensors. The actual temperature value may be determined at predefined time intervals. For example, the temperature value within the coke oven battery 102 may be determined at predefined time intervals such as, every 5 second, every 1 minute, etc.
[035] In addition, the communication interface 208 is configured to receive one or more feedback parameters (see, feedback parameters 316 in FIG. 3A) from the coke oven battery 102. In an embodiment, the one or more feedback parameters 316 (hereinafter referred to interchangeably as ‘feedback parameters 316’) include information related to coke time, coke battery temperature, coke end temperature, pushing delay, one or more properties of coke in the coke oven battery 102 and an amount of heat loss at an exhaust of the coke oven battery 102. In an embodiment, the feedback parameters 316 are collated over defined time periods. More specifically, the feedback parameters 316 may be collated from the coke oven battery 102 every few seconds, every minute or specified time intervals of 2 minutes. As such, one or more sensors may be deployed for measuring the feedback parameters 316 in the coke oven battery 102. In one example, a temperature sensor may be used for measuring coke battery temperature every minute. In another example, a moisture sensor may be used for determining moisture content in the coke every 30 minutes. It shall be noted that different sensors may be installed within the coke oven battery 102 to sense various feedback parameters 316 which are collated at defined time periods. Further, it shall be noted that each feedback parameter may be measured and provided at a different time period. For example, coke end temperature is determined only after coal 104 is processed to form coke 106 such as, after 15-20 hours of coking time whereas properties of coke 106 in the coke oven battery 102 may be defined every 10 minutes.
[036] Further, the communication interface 208 is configured to provide values for one or more control parameters (see, control parameters 314). In an embodiment, the one or more control parameters 314 (also referred to herein as ‘control parameters 314’) are at least one of: a gas mixture flow, air flow and pause time period. Pause time period is the time between two gas reversals when both the gas mixture flow and air flow is stopped. The control parameters 314 provided by the communication interface 208 are dynamically adapted to control the heat in the coke oven battery 102. In an embodiment, the pushing plan 302, the one or more coal properties 304, the target calorific value 306, the one or more coke oven characteristics and the measurements of the gas mixture 307 are forwarded to the processor 202 which performs one or more operations described herein to dynamically control heat in the coke oven battery 102.
[037] The system 150 is depicted to be in operative communication with a database 220. In one embodiment, the database 220 is configured to store one or more prediction models in a prediction model pool 222 for predicting condition of the coke oven battery 102. In one example scenario, a prediction model M1 may be trained to predict condition of the coke oven battery 102 during the coking process and as such may be based on a first set of feedback parameters from the coke oven such as, coke time, coke battery temperature, one or more properties of coke 106 in the coke oven battery 102 and an amount of heat loss at an exhaust of the coke oven battery 102. In another example scenario, another prediction model M2 may be trained to predict condition of the coke oven battery 102 after processing one batch and receiving a new batch of coal 104. As such, the prediction model M2 may be configured to predict condition of the coke oven battery 102 based on a second set of feedback parameters from the coke oven such as, coke end temperature, coke time, coke battery temperature, one or more properties of coke 106 in the coke oven battery 102, pushing delay and an amount of heat loss at an exhaust of the coke oven battery 102. Accordingly, each prediction model may be trained using a variety of feedback parameters. It shall be noted that the first set of feedback parameters and the second set of feedback parameters are subsets of the feedback parameters 316 provided for exemplary purposes and the prediction models may be trained on any subset of feedback parameters 316. The prediction model pool 222 is also configured to store one or more AI models for determining pause time period for controlling heat in the coke oven battery 102. In one embodiment, the AI model is an expert system configured to identify pause time period based on feedback parameters 316 and temperature difference as will be explained in detail later. Some other examples of the AI models in the prediction model pool 222 include, but not limited to, machine learning models, statistical models, neural network models, and the like. The database 220 may also store historical values related to the coke oven battery 102 in a historical profile pool 224 such as, historical values of pushing plans, pushing delays, heat energy levels with time stamps, predicted conditions of the coke oven battery 102 and associated feedback parameters, and the like.
[038] The database 220 may include multiple storage units such as hard disks and/or solid-state disks in a redundant array of inexpensive disks (RAID) configuration. In some embodiments, the database 220 may include a storage area network (SAN) and/or a network attached storage (NAS) system. In one embodiment, the database 220 may correspond to a distributed storage system, wherein individual databases are configured to store custom information, such as scheduling policies, pushing plans, scaling factor, coke plant specific data, etc.
[039] In some embodiments, the database 220 is integrated within the system 150. For example, the system 150 may include one or more hard disk drives as the database 220. In other embodiments, the database 220 is external to the system 150 and may be accessed by the system 150 using a storage interface (not shown in FIG. 2). The storage interface is any component capable of providing the processor 202 with access to the database 220. The storage interface may include, for example, an Advanced Technology Attachment (ATA) adapter, a Serial ATA (SATA) adapter, a Small Computer System Interface (SCSI) adapter, a RAID controller, a SAN adapter, a network adapter, and/or any component providing the processor 202 with access to the database 220.
[040] As already explained, the communication interface 208 is configured to receive the data related to processing of coal 104 in the coke oven battery 102 such as, the pushing plan 302, the one or more coal properties 304, the target calorific value 306, the one or more coke oven characteristics and the measurements of the gas mixture 307. The communication interface 208 forwards the pushing plan 302, the one or more coal properties 304, the target calorific value 306, the one or more coke oven characteristics and the measurements of the gas mixture 307 to the processor 202. The modules of the processor 202 in conjunction with the instructions in the memory 204 are configured to process the data (i.e., the pushing plan 302, the one or more coal properties 304, the target calorific value 306, the one or more coke oven characteristics and the measurements of the gas mixture 307) for dynamically controlling heat in the coke oven battery 102. The processor 202 is configured to forward the pushing plan 302, the one or more coal properties 304, and feedback parameters 316 such as, amount of heat loss at the exhaust of the coke oven battery 102 and one or more coke properties to the heat energy estimation module 210.
[041] The heat energy estimation module 210 in conjunction with the instructions 205 in the memory 204 is configured to estimate the heat energy required for the coke oven based on the pushing plan 302, the one or more coal properties 304 and the feedback parameters 316 (i.e., amount of heat loss at the exhaust of the coke oven battery 102 and one or more coke properties). In an embodiment, empirical, experimental or mathematical models may be used for estimating the heat energy required for the coke oven battery 102. In another embodiment, an AI model such as, a machine learning model, neural network model or statistical model may be used for estimating the heat energy based on the pushing plan 302, the one or more coal properties 304, the amount of heat loss at the exhaust of the coke oven battery 102 and one or more coke properties. In one example scenario, if 15 tonnes of bituminous coal with low ash levels and low phosphorus are provided in 30 batches to the coke oven battery 102, the heat energy estimate for the coke oven may be 3.32 GJ ton-1 coke and 20 tonnes of bituminous coal with different grade (i.e., slightly high ash levels and phosphorus levels) when provided in 30 batches to the coke oven may require 3.77 GJ ton-1 coke. In another example scenario, a neural network model such as, linear regression model, may be trained to predict the heat energy estimate for a given pushing plan, coal properties and one or more feedback parameters 316 (i.e., the amount of heat loss at the exhaust of the coke oven battery 102 and one or more coke properties). It shall be noted that if the coke oven battery 102 is operated initially after installation or after maintenance, the feedback parameters 316 (i.e., amount of heat loss at the exhaust of the coke oven battery 102 and one or more coke properties) may not be available and as such, the heat energy estimate will depend only on the pushing plan 302, and the one or more coal properties 304. The heat energy required for heating the coke oven may be provided by the gas mixture (i.e., gases 308, 310, 312) which acts as fuel for the coke oven battery 102. As such, the thermal energy estimated by the heat energy estimation module 210 may be provided to the gas flow balance module 212 and the heating control module 214.
[042] The gas flow balance module 212 in conjunction with the instructions 205 stored in the memory 204 is configured to estimate a calorific value of the gas mixture for the coke oven battery 102 based at least on the target calorific value and the heat energy estimate received from the heat energy estimation module 210. More specifically, the gas flow balance module 212 is configured to adapt a flow rate of the coke oven gas for controlling the calorific value of the gas mixture provided to the coke oven battery 102. In general, there is a constant flow of blast furnace gas whereas the Linz-Donawitz (LD) gas flows intermittently and as such, whenever there is a flow of the LD gas, the calorific value of the gas mixture rises. This change in calorific value of the gas mixture from the target calorific value 306 of the gas mixture in the coke oven may be regulated by manipulating a flow rate of coke oven gas (i.e., a level of coke oven gas) in the coke oven. As such, the gas flow balance module 212 determines any deviation from the target calorific value 306 based on the heat energy estimate and determines a level of coke oven gas that needs to flow at any particular instant for controlling (i.e., increasing/decreasing) the calorific value of the gas mixture. It shall be noted that the gas mixture of the blast furnace gas, the LD gas and the coke oven gas for providing heat energy in the coke oven are for exemplary purposes and the gas mixture may include fewer or more gases and/or gases different from the above-mentioned gases to provide the required thermal energy. The calorific value estimated by the gas flow balance module 212 is provided to the heating control module 214. The operations of the heating control module 214 are explained next with reference to FIG. 3B.
[043] FIG. 3B illustrates processing of the heat energy estimate, the calorific value estimate of gas mixture and the feedback parameters 316 by the heating control module 214 of FIG. 3A to control the heat in the coke oven battery 102, in accordance with an embodiment of the present disclosure. More specifically, the heating control module 214 is configured to dynamically adapt one or more control parameters 314 of the coke oven battery 102 for controlling the heat in the coke oven battery 102. In an embodiment, the one or more control parameters 314 are at least one of: a gas mixture flow 366, air flow 368 and pause time period 364 in the coke oven. It shall be noted that at least one of the control parameters 314 or all the control parameters 314 may be adapted to control the heat in the coke oven battery 102. In one embodiment, the gas mixture flow 366 and the pause time period may be adapted to control the heat in the coke oven battery 102. In another embodiment, the pause time period 364 alone may be controlled to control the heat in the coke oven battery 102. In general, heating control of the coke oven battery 102 may be performed by dynamically controlling different control parameters 314 as will be explained in detail. The processing performed by the heating control module 214 is depicted by processing steps 352-364 and 370, 372.
[044] At 352, the heating control module 214 controls the gas flow 366 to the coke oven battery 102. As already explained the calorific value of the gas mixture is adapted to conform to the target calorific value 306 and the gas flow 366 may be regulated based on the heat energy estimate, the calorific value of the gas mixture and the feedback parameters 316. In one example, the feedback parameters 316 may indicate a decrease in heat energy level within the coke oven and as such, the amount of gas mixture flow 366 needs to be increased to provide the required thermal energy within the coke oven. In an example, if the pushing plan is adapted due to scheduled shutdown, for example, a change in pushing delay or change in amount of coal provided, the heat energy required may also decrease and as such, the gas mixture flow needs to be decreased to reduce the thermal energy provided to the coke oven battery 102. Accordingly, the heating control module 214 adapts an amount of the gas flow 366 to the coke oven battery 102.
[045] At 354, the heating control module 214 is configured to predict the condition of the coke oven battery 102 based on the feedback parameters 316 received from the coke oven battery 102. The condition of the coke oven battery 102 is predicted to be at least one of: stable operating condition or unstable operating condition. More specifically, the heating control module 214 may determine a likelihood of deterioration in performance of the coke oven battery 102. The deterioration in performance may be due to temperature fluctuations which depends on numerous factors such as, amount of air flow, amount of gas mixture flow and pause time which may vary with one or more coal properties 304 and desired quality of coke required. The deterioration in performance may be identified from the feedback parameters 316. To that effect, one or more sensors are configured to measure the feedback parameters 316 such as, coke time, coke battery temperature, coke end temperature, pushing delay, in the coke oven battery 102. In an embodiment, the feedback parameters 316 are collated over defined time periods for predicting the condition of the coke oven battery 102. For instance, the feedback parameters 316 are collated for 5 minutes and may be used for predicting the condition of the coke oven battery 102. For example, the coke time, coke battery temperature, coke end temperature are determined every 1 minute and averaged out for 5 minutes to predict the condition of the coke oven battery 102. In an embodiment, a prediction model from the prediction model pool 222 of the database 220 may be used for predicting the condition of the oven. As such, the predicted condition of the coke oven battery 102 may be used for adapting control parameters of the coke oven battery 102 as will be explained hereinafter.
[046] At 356, a target regenerator temperature value (TR) may be determined for controlling the control parameters 314 of the coke oven battery 102. The target regenerator temperature value (TR) indicates a desired temperature which needs to be maintained in the coke oven battery 102 to produce best quality coke. In an embodiment, the heating control module 214 is configured to dynamically determine the target regenerator temperature value (TR) based on the predicted condition of the coke oven battery 102, and the feedback parameters 316 such as, coke time, coke battery temperature, coke end temperature and pushing delay. In one example, the coke oven battery 102 may need a maintenance and as such there may be a pushing delay of 1 hour, while the battery temperature is 500°C, coke temperature is 300°C, coke time is 1 hours and the condition of the coke oven battery 102 is predicted as ‘unstable’ condition, then the target regenerator temperature value (TR) may be determined as 1200°C. In an embodiment, the target regenerator temperature value (TR) is predicted based on the feedback parameters 316. More specifically, AI models may be trained to predict the target regenerator temperature value (TR) based on the predicted condition of the coke oven battery 102 and detected feedback parameters 316 (i.e., coke time, coke battery temperature, coke end temperature and pushing delay.
[047] At 360, the heating control module 214 is configured to determine a temperature difference (T) between the target regenerator temperature value (TR) and an actual temperature value (TA) of the coke oven battery 102. Accordingly, the actual temperature value (TA) is measured using one or more sensors (see, 358). In an embodiment, the heating control module 214 is configured to control the heat in the coke oven battery 102 by dynamically adapting the more control parameters 314 based on the temperature difference, the heat energy estimate, and the calorific value of the gas mixture. For instance, control parameters 314 such as, air flow, gas mixture flow and the pause time period may be adapted to control the heat in the coke oven battery 102. It shall be noted that the actual temperature values (i.e., TA1, TA2) determined by the one or more sensors sensed at defined time intervals may be averaged (i.e., (TA1 + TA2)/2) to determine the actual temperature value (TA) or actual temperature values sensed at more than two defined time intervals (i.e., TAt1, TAt2) may be averaged to compare with the target regenerator temperature value (TR).
[048] At 362, the temperature difference (T) may be used by one or more AI models to determine a pause time period. In some embodiments, the one or more AI models may also determine the pause time period based on the feedback parameters 316 and the temperature difference (T). In one illustrative example, the AI model may be an expert system that determines the pause time period based on the temperature difference and the feedback parameters 316. More specifically, the one or more AI models are configured to mimic human decision making skills based on observed feedback parameters 316 and the temperature difference.
[049] In some example embodiments, the pause time period may be adapted based on one or more coke oven characteristics using a scaling factor (see, 364). The one or more coke oven characteristics includes least one of: a type of coke oven, a type of coke oven battery 102, a type of sensor in the coke oven, and an average number of pushings in the coke oven. More specifically, the scaling factor is plant specific i.e., specific to a coke plant and may vary based on equipments and number of pushings in the coke oven battery 102.
[050] In an embodiment, the gas mixture flow 372 and the air flow 372 to the coke oven battery 102 may be adapted based on the pause time period 370. At 368, the gas mixture flow 372 may be adapted based on the pause time period. In one example scenario, there may be a pushing delay in the coke oven battery 102 and as such, the heat in the coke oven battery 102 may be high. As such, the condition of the coke oven battery 102 may be predicted as unstable operating condition based on feedback parameters 316. If the gas mixture flow 372 to the coke oven battery 102 is 200 m3/sec, the heating control module 214 may determine a pause time period 370 of 10 minutes, and accordingly, the gas mixture flow 372 may also be adapted based on the pause time period 370, for example, 150 m3/sec. Similarly, the air flow 374 may also be controlled based on the pause time period 370, for example, the air flow 374 may be reduced from 100 CFM to 80 CFM. It shall be noted that the pause time period, gas mixture flow values and the air flow values provided above are for exemplary purposes and the heating control module 214 may dynamically determine values for control parameters 314 based on other input data and feedback parameters 316.
[051] In one example, coke oven battery 102 may have faced a temporary power outrage and as such the condition of the coke oven battery 102 may be predicted as an unstable condition. Moreover, the feedback parameters 316 indicate a decrease in heat energy within the coke oven battery 102 and as such, the feedback parameters 316 may be used for determining the control parameters 314. More specifically, in on embodiment, the gas mixture flow 372 alone may be increased to increase the heat energy within the coke oven battery 102. In another embodiment, the gas mixture flow 372 and the air flow 374 may be increased while the pause time period 370 may be decreased for achieving the desired heat in the coke oven battery 102. In other words, when the condition of the coke oven battery 102 indicates if the control parameters 314 need to be adapted (increased/decreased) to maintain ideal temperature (i.e., heat energy) in the coke oven battery 102. In another example, an unplanned delay (i.e., pushing delay) in pushing plan 302 may result in increase in thermal energy (i.e., increased temperature) within the coke oven battery 102 and as such, the heating control module 214 adapts the control parameters 314 by decreasing the gas mixture flow 372 and/or air flow 374 and increasing the pause time period 370 to decrease the heat in the coke oven battery 102. It shall be noted that the heating control unit 214 dynamically adapts one or all the control parameters 314 for maintaining desired heat energy in the coke oven battery 102.
[052] It shall be noted that although the system 150 is explained with reference to coke oven battery 102 for production of steel, various embodiments of the present disclosure may also be used in industrial infrastructures using coke for blast furnaces without departing from the scope of the present disclosure. A method for dynamically controlling heat in the coke oven battery 102 is explained next with reference to FIG. 4.
[053] FIG. 4 is a flowchart illustrating a method 400 for dynamically controlling heat in the coke oven battery 102, in accordance with an embodiment of the present disclosure. The method 400 depicted in the flow diagram may be executed by, for example, the system 150. Operations of the flow diagram, and combinations of operation in the flow diagram, may be implemented by, for example, hardware, firmware, a processor, circuitry and/or a different device associated with the execution of software that includes one or more computer program instructions. The operations of the method 400 are described herein with help of the system 150. It is noted that the some operations of the method 400 can be described and/or practiced by using one or more processors of a system/device other than the system 150, such as a control circuit deployed in SCADA systems that control and monitor DCS or PLC systems, for managing the coke oven battery 102. The method 400 starts at operation 402
[054] At operation 402 of the method 400, heat energy estimate for a coke oven, a calorific value of a gas mixture in the coke oven, and one or more feedback parameters 316 from the coke oven battery 102 are received by a system such as, the system 150 shown and explained with reference to FIG. 2. As already explained, the system 150 may be embodied within a remote server monitoring the coke oven battery 102 or maybe configured as a standalone processor configured to perform one or more of the operations herein.
[055] At operation 404 of the method 400, a condition of the coke oven battery 102 is predicted based on the one or more feedback parameters 316. The condition of the coke oven battery 102 is predicted to be at least one of: stable operating condition or unstable operating condition. More specifically, the heating control module 214 may determines a likelihood of deterioration in performance of the coke oven battery 102. The deterioration in performance may be due to temperature fluctuations which depends on numerous factors such as, amount of air flow, amount of gas mixture flow and pause time which may vary with one or more coal properties 304 and desired quality of coke required. The deterioration in performance may be identified from the feedback parameters 316. To that effect, one or more sensors are configured to measure the feedback parameters 316 such as, coke time, coke battery temperature, coke end temperature, pushing delay, in the coke oven battery 102. In an embodiment, the feedback parameters 316 are collated over defined time periods for predicting the condition of the coke oven battery 102.
[056] At operation 406 of the method 400, a target regenerator temperature value is dynamically determined based on the one or more feedback parameters 316 and the predicted condition of the coke oven battery 102. More specifically, the target regenerator temperature value (TR) is dynamically determined based on the predicted condition of the coke oven battery 102, and the one or more feedback parameters 316 such as, coke time, coke battery temperature, coke end temperature and pushing delay. In one embodiment, AI models may be trained to predict the target regenerator temperature value (TR) based on the predicted condition of the coke oven battery 102 and detected feedback parameters 316.
[057] At operation 408 of the method 400, a temperature difference between the target regenerator temperature value and an actual temperature value of the coke oven battery 102 is determined. The actual temperature value is measured using one or more sensors.
[058] At operation 410 of the method 400, the heat in the coke oven battery 102 is controlled by dynamically adapting one or more control parameters 314 based on the temperature difference, the heat energy estimate, and the calorific value of the gas mixture. More specifically, pause time period may be determined based on the temperature difference, and the gas mixture flow may be determined based on the heat energy estimate and the calorific value of the gas mixture. In an embodiment, the gas mixture flow and the air flow may be controlled by adapting the pause time period. Adapting one or more control parameters 314 is explained in detail with reference to FIG. 3B and is not explained herein for the sake of brevity.
[059] The sequence of operations of the method 400 need not be necessarily executed in the same order as they are presented. Further, one or more operations may be grouped together and performed in form of a single step, or one operation may have several sub-steps that may be performed in parallel or in sequential manner.
[060] The disclosed methods with reference to FIGS. 1 to 4, or one or more operations of the flow diagram 400 may be implemented using software including computer-executable instructions stored on one or more computer-readable media (e.g., non-transitory computer-readable media, such as one or more optical media discs, volatile memory components (e.g., DRAM or SRAM), or non-volatile memory or storage components (e.g., hard drives or solid-state non-volatile memory components, such as Flash memory components) and executed on a computer (e.g., any suitable computer, such as a laptop computer, net book, Web book, tablet computing device, smart phone, or other mobile computing device). Such software may be executed, for example, on a single local computer
[061] FIG. 5 shows a block diagram of a general-purpose computer for dynamically controlling heat in the coke oven battery 102, in accordance with an embodiment of the present disclosure. The computer system 500 may comprise a central processing unit (“CPU” or “processor”) 502. The processor 502 may comprise at least one data processor. The processor 502 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. The computer system 500 may be analogous to the system 150 (shown in FIG. 2).
[062] The processor 502 may be disposed in communication with one or more input/output (I/O) devices (not shown) via I/O interface 501. The I/O interface 501 may employ communication protocols/methods such as, without limitation, audio, analog, digital, monoaural, RCA, stereo, IEEE-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.
[063] Using the I/O interface 501, the computer system 500 may communicate with one or more I/O devices. For example, the input device 510 may be an antenna, keyboard, mouse, joystick, (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. The output device 511 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.
[064] In some embodiments, the computer system 500 is connected to the remote devices 512 through a communication network 509. The remote devices 512 may be peripheral devices tracking data related to the coke oven battery 102. The processor 502 may be disposed in communication with the communication network 509 via a network interface 503. The network interface 503 may communicate with the communication network 509. The network interface 503 may employ connection protocols including, without limitation, 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. The communication network 509 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. Using the network interface 503 and the communication network 509, the computer system 500 may communicate with the remote devices 512. The network interface 503 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.
[065] The communication network 509 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, Wi-Fi, 3GPP 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.
[066] In some embodiments, the processor 502 may be disposed in communication with a memory 505 (e.g., RAM, ROM, etc. not shown in FIG. 5) via a storage interface 504. The storage interface 504 may connect to memory 505 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.
[067] The memory 505 may store a collection of program or database components, including, without limitation, user interface 506, an operating system 507, web server 508, etc. In some embodiments, 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®.
[068] The operating system 507 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.
[069] In some embodiments, the computer system 500 may implement a web browser 508 stored program component. The web browser 508 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 508 may utilize facilities such as AJAX™, DHTML™, ADOBER FLASH™, JAVASCRIPT™, JAVA™, Application Programming Interfaces (APIs), etc. In some embodiments, the computer system 500 may implement a mail server 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 may be a mail viewing application, such as APPLE® MAIL™, MICROSOFT® ENTOURAGE™, MICROSOFT® OUTLOOK™, MOZILLA® THUNDERBIRD™, etc.
[070] 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, CD (Compact Disc) ROMs, DVDs, flash drives, disks, and any other known physical storage media.
[071] Various embodiments of the present disclosure provide numerous advantages. Embodiments of the present disclosure provide a data driven analysis for dynamically controlling heat in the coke oven battery. More specifically, the data driven analysis uses a combination of feed forward – feedback control to achieve the desired heat within the coke oven battery. The feedback parameters ensure accurately establishing the target regenerator value and enable the system 150 to dynamically adapt to changing conditions of the coke oven battery. In general, real-time collection of data from the coke oven battery 102 ensures efficient adaptation of control parameters to establish/maintain the requisite heat within the coke oven battery 102. Moreover, data analysis of feedback parameters ensures dynamic adaptation of one or more control parameters (i.e., air flow, gas mixture flow, pause time period) for intermittent and efficient heating control of the coke oven battery. The system 150 not only makes production of coke independent of manual control and intervention but also makes the coke production to be of higher quality. Moreover, the realization of steady heating in the coke oven battery enhances production of coke oven, quality of coke, reducing energy consumption prolonging coke oven service life, and decreasing environment pollution in the course of coking production.
[072] It will be understood by those within the art that, in general, terms used herein, and are generally intended as “open” terms (e.g., the term “including” should be interpreted as “including but not limited to,” the term “having” should be interpreted as “having at least,” the term “includes” should be interpreted as “includes but is not limited to,” etc.). For example, as an aid to understanding, the detail description may contain usage of the introductory phrases “at least one” and “one or more” to introduce recitations. However, the use of such phrases should not be construed to imply that the introduction of a recitation by the indefinite articles “a” or “an” limits any particular part of description containing such introduced recitation to inventions containing only one such recitation, even when the introductory phrases “one or more” or “at least one” and indefinite articles such as “a” or “an” (e.g., “a” and/or “an” should typically be interpreted to mean “at least one” or “one or more”) are included in the recitations; the same holds true for the use of definite articles used to introduce such recitations. In addition, even if a specific part of the introduced description recitation is explicitly recited, those skilled in the art will recognize that such recitation should typically be interpreted to mean at least the recited number (e.g., the bare recitation of “two recitations,” without other modifiers, typically means at least two recitations or two or more recitations).
[073] 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 and spirit being indicated by the following detailed description.
, Claims:WE CLAIM:
1. A method for dynamically controlling heat in a coke oven battery (102), the method comprising:
receiving, by a system (150), heat energy estimate for a coke oven, a calorific value of a gas mixture in the coke oven, and one or more feedback parameters (316) from the coke oven battery (102);
predicting, by the system (150), a condition of the coke oven battery (102) based on the one or more feedback parameters (316);
dynamically determining, by the system (150), a target regenerator temperature value (356) based on the one or more feedback parameters (316) and the predicted condition of the coke oven battery (102);
determining, by the system (150), a temperature difference between the target regenerator temperature value (356) and an actual temperature value (358) of the coke oven battery (102), wherein the actual temperature value (358) is measured using one or more sensors; and
controlling, by the system (150), the heat in the coke oven battery (102) by dynamically adapting one or more control parameters (314) based on the temperature difference, the heat energy estimate, and the calorific value of the gas mixture.

2. The method as claimed in claim 1, further comprises:
estimating, by the system (150), the heat energy required for the coke oven based on at least a pushing plan (302), one or more coal properties (304) and the one or more feedback parameters (316).

3. The method as claimed in claim 2, further comprises:
estimating, by the system (150), a calorific value of the gas mixture for the coke oven battery (102) based at least on a target calorific value (306) and the heat energy estimate, wherein the gas mixture comprises at least a blast furnace gas, a Linz-Donawitz gas and a coke oven gas (108); and
adapting, by the system (150), a flow rate of the coke oven gas (108) for controlling the calorific value of the gas mixture provided to the coke oven battery (102).

4. The method as claimed in claim 1, wherein the one or more feedback parameters (316) comprise at least one of: a coke time, a coke battery temperature, a coke end temperature, a pushing delay, one or more properties of coke in the coke oven battery (102) and an amount of heat loss at an exhaust of the coke oven battery (102).

5. The method as claimed in claim 1, wherein the one or more control parameters (314) comprise at least one of: a gas mixture flow, air flow and pause time period.

6. The method as claimed in claim 1, further comprises:
determining, by the system (150), a pause time period based on the temperature difference using one or more AI models; and
determining, by the system (150), a scaling factor for adapting the pause time period based on one or more coke oven characteristics, wherein the one or more coke oven characteristics comprises at least one of: a type of coke oven, a type of coke oven battery (102), a type of sensor in the coke oven, and an average number of pushings in the coke oven.

7. The method as claimed in claim 1, wherein the one or more feedback parameters (316) are collated over defined time periods for predicting the condition of the coke oven battery (102).

8. A system (150) for dynamically controlling heat in a coke oven battery (102), the system (150) comprising:
a memory (204) configured to store instructions (205); and
a processor (202) configured to execute the instructions (205) stored in the memory (204) and thereby cause the system (150) to:
receive heat energy estimate for a coke oven, a calorific value of a gas mixture in the coke oven, and one or more feedback parameters (316) from the coke oven battery (102);
predict a condition of the coke oven battery (102) based on the one or more feedback parameters (316);
dynamically determine a target regenerator temperature value (356) based on the one or more feedback parameters (316) and the predicted condition of the coke oven battery (102);
determine a temperature difference between the target regenerator temperature value (356) and an actual temperature value (358) of the coke oven battery (102), wherein the actual temperature value (358) is measured using one or more sensors; and
control the heat in the coke oven battery (102) by dynamically adapting one or more control parameters (314) based on the temperature difference, the heat energy estimate, and the calorific value of the gas mixture.

9. The system (150) as claimed in claim 8, wherein the system (150) is further caused to:
estimate the heat energy required for the coke oven based on at least a pushing plan (302), one or more coal properties (304) and the one or more feedback parameters (316).

10. The system (150) as claimed in claim 9, wherein the system (150) is further caused to:
estimate a calorific value of the gas mixture for the coke oven battery (102) based at least on a target calorific value (306) and the heat energy estimate, wherein the gas mixture comprises at least a blast furnace gas, a Linz-Donawitz gas and a coke oven gas (108); and
adapt a flow rate of the coke oven gas (108) for controlling the calorific value of the gas mixture provided to the coke oven battery (102).

11. The system (150) as claimed in claim 8, wherein the one or more feedback parameters (316) comprise at least one of: a coke time, a coke battery temperature, a coke end temperature, a pushing delay, one or more properties of coke in the coke oven battery (102) and an amount of heat loss at an exhaust of the coke oven battery (102).

12. The system (150) as claimed in claim 8, wherein the system (150) is further caused to:
determine a pause time period based on the temperature difference using one or more AI models; and
determine a scaling factor for adapting the pause time period based on one or more coke oven characteristics, wherein the one or more coke oven characteristics comprises at least one of: a type of coke oven, a type of coke oven battery (102), a type of sensor in the coke oven, and an average number of pushings in the coke oven.

13. The system (150) as claimed in claim 8, wherein the one or more control parameters (314) comprise at least one of: a gas mixture flow, air flow and pause time period.

14. The system (150) as claimed in claim 8, wherein the one or more feedback parameters (316) are collated over defined time periods for predicting the condition of the coke oven battery (102).

Dated this 30th day of August, 2022
Thanking you,

Madhusudan S. T
OF K&S PARTNERS
AGENT FOR THE APPLICANT
IN/PA-1297