Automatic control system of oil field heating furnace

Abstract

The invention relates to the field of oil field production equipment, and discloses an automatic control system for an oil field heating furnace, and a control method of the system comprises the following steps: S1, describing gas flow, a temperature field and a concentration field in a combustion chamber by using computational fluid dynamics simulation; s2, simulating a combustion process by solving a Nav ier-Stokes equation, an energy equation and a substance conservation equation; s3, describing a reaction path and a reaction rate in a combustion process by using a chemical reaction mechanism; s4, solving a corresponding multiphase flow equation, and considering a local imbalance effect; and S5, implementing a model-based control strategy to adjust the air-fuel ratio in real time. According to the control method, the air-fuel ratio is accurately controlled, it is ensured that the combustion process is conducted at the optimal efficiency, fuel consumption is reduced, the energy conversion efficiency is improved, meanwhile, harmful emission is reduced, impact and vibration on the system are reduced by limiting the change of control input, and therefore the stability and reliability of the system are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of oilfield production equipment, specifically an automatic control system for oilfield heating furnaces. BACKGROUND

[0002] In the process of oilfield production, heating furnaces are widely used in oilfield exploration and production processes for heating oilfield production fluid, steam and other media. Traditional heating furnace control methods usually rely on experience or simple control strategies, which have problems such as low combustion efficiency, high emissions, poor system stability, short equipment life, etc. Therefore, it is of great practical significance to develop an efficient, accurate and automatic oilfield heating furnace control system. SUMMARY

[0003] In view of the deficiencies of the prior art, the present application provides an automatic control system for oilfield heating furnaces, which optimizes the air-fuel ratio control of the oilfield heating furnace to improve the combustion efficiency of fuel and reduce energy waste.

[0004] To achieve the above purpose, the present application is realized by the following technical scheme: an automatic control method for oilfield heating furnaces, comprising the following steps:

[0005] Using computational fluid dynamics simulation to describe the gas flow, temperature field and concentration field in the combustion chamber;

[0006] Simulating the combustion process by solving the Navier-Stokes equation, energy equation and mass conservation equation;

[0007] Using chemical reaction mechanisms to describe the reaction path and rate in the combustion process;

[0008] Solving the corresponding multiphase flow equation and considering the local imbalance effect;

[0009] Implementing a model-based control strategy for real-time adjustment of the air-fuel ratio.

[0010] Preferably, the Navier-Stokes equation is defined as:

[0011]

[0012] Where ρ represents the density, The velocity field is represented by p, and τ represents the stress tensor, The gravitational acceleration is represented by g.

[0013] Preferably, the energy equation is defined as:

[0014]

[0015] Where c pwhere cp represents the specific heat capacity, T represents the temperature, and k represents the thermal conductivity, represents the heat source term per unit volume.

[0016] Preferably, the mass conservation equation is defined as:

[0017]

[0018] where Y i represents the mass fraction of the i-th chemical species, D i represents the diffusion coefficient, represents the production / consumption rate of the i-th chemical species.

[0019] Preferably, the chemical reaction rate expression is defined as:

[0020]

[0021] where A j represents the pre-exponential factor, n j represents the temperature exponent, E a,j represents the activation energy, R represents the gas constant, T represents the temperature, [C i ] represents the concentration of the reactant i, v ij represents the stoichiometric number of the reactant i in the reaction j.

[0022] Preferably, the handling of multiphase flow and local imbalance includes:

[0023] Droplet evaporation model:

[0024] Particle combustion model: dm p / dt = -A p p p r p ;

[0025] where d l represents the droplet diameter, d l0 represents the initial diameter, K represents the evaporation constant, t represents time, dm p / dt represents the rate of change of particle mass, A p represents the particle surface area, p p represents the particle density, r p represents the particle combustion rate.

[0026] Preferably, the objective function of the model-based control strategy is defined as:

[0027]

[0028] where J represents the performance index, l(k) represents the air-fuel ratio at the prediction step k, lref represents a desired air-fuel ratio, Δu(k) represents a change in control input, and N represents a prediction horizon length.

[0029] Preferably, the constraint condition of the model-based control strategy comprises:

[0030] λ min ≤ λ(k) ≤ λ max

[0031] Δu min ≤ Δu(k) ≤ Δu max

[0032] wherein λ min and λ max respectively represent a minimum value and a maximum value of the air-fuel ratio, Δu min and Δu max respectively represent a minimum value and a maximum value of the change in control input.

[0033] The present application also provides an oilfield heater automation control system, characterized in that it comprises:

[0034] a sensor for measuring temperature, pressure and flow rate in the combustion chamber;

[0035] a data acquisition unit for collecting sensor data;

[0036] a control unit configured with a processor and a memory for implementing the oilfield heater automation control method;

[0037] an actuator for adjusting heat input and air-fuel ratio according to the instruction of the control unit.

[0038] Preferably, the control unit further comprises:

[0039] one or more computing modules for implementing CFD simulation, solving Navier-Stokes equation, energy equation and mass conservation equation;

[0040] an optimization module for implementing the model-based control strategy and calculating the adjustment instruction of the air-fuel ratio;

[0041] a communication module for data exchange with the sensor and the actuator.

[0042] The present application provides an oilfield heater automation control system. It has the following advantages:

[0043] The control method of the present application ensures that the combustion process is carried out with optimal efficiency by precisely controlling the air-fuel ratio, thereby saving fuel consumption and improving energy conversion efficiency, while helping to reduce harmful emissions, and by limiting the change of the control input, reducing the impact and vibration on the system, thereby improving the stability and reliability of the system. At the same time, the model-based control strategy can adapt to different working conditions and external disturbances according to real-time data and prediction models, thereby improving the adaptability and flexibility of the control system. BRIEF DESCRIPTION OF DRAWINGS

[0044] Figure 1 The method flowchart of the present application is shown in the figure;

[0045] Figure 2 The device structure diagram of the present application is shown in the figure. DETAILED DESCRIPTION

[0046] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the specification of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0047] Please refer to the drawings in the specification of the present application Figure 1 The oilfield heating furnace automatic control method provided by the embodiments of the present application includes steps S1-S5.

[0048] In step S1, computational fluid dynamics simulation is used to describe the gas flow, temperature field and concentration field in the combustion chamber.

[0049] In this step, computational fluid dynamics (CFD) simulation is used to describe the gas flow, temperature field and concentration field in the combustion chamber. CFD is a numerical simulation method that uses mathematical equations to describe fluid motion and heat and mass transfer processes. In oilfield heating furnaces, CFD can help understand and optimize the flow field distribution, temperature distribution and chemical concentration distribution in the combustion chamber.

[0050] CFD simulation can help understand the gas flow in the combustion chamber, including velocity distribution, turbulence characteristics, etc., as well as temperature and concentration distribution. These information is very important for optimizing the combustion process, reducing energy consumption and reducing pollutant emissions.

[0051] Through CFD simulation, it can be found that there are non-uniformity and local flow anomalies in the combustion chamber, which helps to adjust the combustion chamber structure and burner arrangement to improve the combustion efficiency; it can optimize the temperature distribution in the combustion chamber to reduce thermal stress and prolong the service life of the equipment; it helps to reduce pollutant emissions and improve environmental impact.

[0052] In this embodiment, the implementation steps of step S1 specifically include:

[0053] S11, select a suitable CFD software platform, such as ANSYS Fluent, COMSOL Multiphysics, etc.;

[0054] S12, construct a three-dimensional geometric model of the combustion chamber and set boundary conditions and initial conditions;

[0055] S13, use Navier-Stokes equation to describe fluid motion, energy equation to describe energy transfer, and mass conservation equation to describe mass transfer;

[0056] S14, solve the distribution of flow field, temperature field and concentration field by numerical method (such as finite volume method, finite element method).

[0057] Among them, step S2, by solving Navier-Stokes equation, energy equation and mass conservation equation to simulate the combustion process;

[0058] In this step, by solving Navier-Stokes equation, energy equation and mass conservation equation to simulate the combustion process. These equations describe the fluid motion, energy transfer and mass transfer, which are essential for understanding the combustion process in the combustion chamber.

[0059] In this embodiment, the implementation steps of step S2 specifically include:

[0060] S21, Navier-Stokes equation describes the motion of fluid, energy equation describes energy transfer, and mass conservation equation describes mass transfer;

[0061] S22, consider the non-equilibrium effects in the combustion chamber, such as local turbulence, chemical reaction, inhomogeneity, etc.;

[0062] S23, select a suitable numerical method to discretize and solve these equations.

[0063] As an embodiment of the present application, the Navier-Stokes equation is defined as:

[0064]

[0065] In this embodiment, the Navier-Stokes equation is used to describe the motion of fluid (usually gas and combustion produced flue gas) in the oilfield heating furnace combustion chamber. This equation expresses the relationship between fluid mass conservation and momentum conservation.

[0066] This part represents the change of fluid momentum, including local acceleration and convective acceleration Local acceleration describes the change of fluid velocity over time, while convective acceleration describes the change of fluid velocity as it moves through space.

[0067] This represents the force exerted by the pressure gradient on the fluid; spatial changes in pressure drive fluid motion.

[0068] The divergence of the stress tensor τ describes the viscous effect within a fluid. In Newtonian fluids, the stress tensor is proportional to the strain rate tensor, where the proportionality constant is the fluid's viscosity.

[0069] This term represents the effect of gravity on the fluid unit, which affects the vertical distribution and flow of the fluid.

[0070] In CFD simulations, this equation is discretized and solved over the computational domain within the combustion chamber. Numerical methods such as the finite volume method or the finite element method are typically used to discretize these equations, and appropriate solvers are used to obtain the velocity and pressure fields.

[0071] By solving the Navier-Stokes equations, the velocity and pressure distributions of the fluid inside the combustion chamber at any given time can be obtained. This is crucial for understanding gas flow, combustion efficiency, and the generation and emission of pollutants within the combustion chamber.

[0072] Accurate velocity and pressure field predictions can help optimize combustion chamber design, such as improving burner layout and combustion air supply methods; the temperature and pressure inside the combustion chamber can be controlled by adjusting the air-fuel ratio during combustion, thereby improving combustion efficiency and reducing the generation of pollutants such as NOx; dynamic prediction of fluids inside the combustion chamber helps to identify and prevent possible anomalies in advance, such as unstable combustion or flame extinction, enhancing the safety and reliability of the system.

[0073] In one embodiment of the present invention, the energy equation is defined as:

[0074]

[0075] In this embodiment, the energy equation is used to describe the changes in the temperature field and energy transfer within the combustion chamber of the oilfield heater. This equation expresses the principle of energy conservation.

[0076] This section represents the rate of change of energy per unit volume of fluid, including the change in temperature over time. and temperature convection due to fluid movement

[0077] This represents thermal conduction, the diffusion of heat from a high-temperature region to a low-temperature region. Here, k is thermal conductivity, a material property that indicates a material's ability to conduct heat.

[0078] This is the heat source term per unit volume, representing the heat generated per unit volume by chemical reactions or other heat sources (such as electric heating).

[0079] In CFD simulations, the energy equations are discretized and solved over the computational domain within the combustion chamber. Solving the energy equations using numerical methods (such as the finite volume method or the finite element method) yields the temperature field distribution.

[0080] The energy equation describes the conservation of energy per unit volume, including the storage, transfer, and generation of heat. In oilfield heating furnaces, the temperature field directly affects combustion efficiency and emission performance; therefore, solving the energy equation is crucial for the design and operation of the combustion chamber.

[0081] Accurate temperature field prediction helps optimize combustion chamber design, ensuring complete fuel combustion and efficient heat utilization; it can predict and control the heat load distribution within the combustion chamber, avoiding localized overheating or cold corners, thereby extending equipment life; precise control of combustion chamber temperature helps reduce the generation of pollutants such as NOx, achieving environmentally friendly combustion.

[0082] In one embodiment of the present invention, the matter conservation equation is defined as:

[0083]

[0084] In this embodiment, the mass conservation equation (also known as the mass species transport equation) is used to describe the change in the concentration of a certain chemical substance in the combustion chamber. This equation is based on the law of conservation of mass and takes into account the effects of fluid flow, molecular diffusion, and chemical reactions on the distribution of substances.

[0085] This term represents the rate of change of the mass fraction of the i-th chemical substance over time, describing the local accumulation or reduction of the substance.

[0086] This represents the convection term, that is, the transport of matter caused by fluid motion. Fluid velocity. With the mass fraction of the substance Y i The product of these two factors, and then the divergence, represents the net amount of material transported with the fluid flow.

[0087] This indicates that molecular diffusion of matter is driven by a concentration gradient. Diffusion system D i It describes the diffusion ability of the i-th substance in the fluid.

[0088] This is the production / consumption term for the i-th chemical substance. It can be positive or negative, depending on whether the substance is being produced or consumed. This term is typically related to the chemical reaction rate and may depend on temperature, pressure, and the concentration of other substances.

[0089] In CFD simulations, the mass conservation equations are discretized and solved over the computational domain within the combustion chamber. This typically involves solving numerical problems, such as mesh generation, boundary condition setting, and initial condition configuration, and then using appropriate numerical methods and algorithms to solve the equations.

[0090] By solving the mass conservation equation, the mass fraction distribution of various chemical substances during combustion can be predicted and calculated, which is very important for understanding combustion kinetics, optimizing the combustion process, and controlling pollutant emissions.

[0091] Precise prediction of chemical composition helps optimize burner design and combustion parameter selection to achieve complete fuel combustion and improve thermal efficiency; it allows for better control and optimization of chemical reaction pathways, thereby reducing the generation of harmful pollutants such as NOx and SOx; and precise control of chemical composition helps improve combustion chamber safety, such as preventing the formation of localized oxygen-rich or fuel-rich zones, thus avoiding the risk of explosion or flameout.

[0092] Among them, step S3 uses chemical reaction mechanisms to describe the reaction path and rate during combustion;

[0093] In this step, multiple chemical substances participate in the combustion process, generating heat and products. The pathways and rates of these reactions can be described using chemical reaction mechanisms, thereby enabling the simulation and control of the combustion process.

[0094] Chemical reaction mechanisms describe the key chemical reaction pathways and rates in the combustion process and are crucial for understanding the kinetic characteristics of combustion.

[0095] It helps predict heat release and product formation during combustion, providing a basis for optimizing temperature and concentration distribution in the combustion chamber; it can help design suitable burner structures and fuel ratios to improve combustion efficiency; and it helps reduce pollutant generation and improve environmental impact.

[0096] In this embodiment, the implementation steps of step S3 specifically include:

[0097] S31. Choose a suitable chemical reaction mechanism, such as GRI-Mech, JetSurf, etc.

[0098] S32. Introduce the chemical reaction rate equation into the energy equation and the mass conservation equation to describe the heat release and material transformation during combustion.

[0099] S33. Consider the chemical reaction characteristics of different fuels and oxidants, including fuel combustion and oxidant consumption.

[0100] In one embodiment of the present invention, the chemical reaction rate expression is defined as:

[0101]

[0102] In this embodiment, this expression can describe the rate of a specific reaction under given temperature and concentration conditions.

[0103] This represents the rate of the j-th chemical reaction, that is, the rate at which reactants are converted into products per unit time.

[0104] A j The pre-factor or frequency factor is related to the frequency of the reaction; it is related to the collision frequency and collision effectiveness of the reactants. A j The units are usually kept consistent with the units of the reaction rate to make the units on both sides of the equation match.

[0105] The exponential term of temperature, where n j It is a temperature index, representing the degree to which temperature affects the reaction rate. When n j When the value is positive, the reaction rate increases faster as the temperature rises.

[0106] The exponential term includes the activation energy E. a,j (Usually expressed in Joules or Calvius), it represents the energy barrier that must be overcome for the reaction to proceed. R is the gas constant, with a value of approximately 8.314 J / (mol·K). This term represents the effect of temperature on the reaction rate; reactions with higher activation energies are more sensitive to temperature.

[0107] This is the concentration term, where [C] i ] represents the concentration of the i-th reactant, v ij This is the stoichiometric coefficient of the i-th reactant in the j-th reaction. This term represents the relationship between the reaction rate and the reactant concentration, that is, the product of the reaction rate and the power of the reactant concentration.

[0108] In CFD simulations, chemical reaction rate expressions are used to calculate the production or consumption rate of each chemical substance. These rates are then substituted into the mass conservation equations to solve for the distribution and changes of different chemical substances in the combustion chamber.

[0109] Calculating chemical reaction rates is crucial for predicting chemical reactions within the combustion chamber, optimizing combustion efficiency, and reducing the formation of harmful pollutants. Accurate chemical reaction kinetic models allow for detailed analysis and control of the chemical reaction processes within the combustion chamber.

[0110] It provides detailed chemical reaction mechanisms, making the simulation of the combustion process closer to reality. This enables the optimization of combustion chamber design and operating conditions; by adjusting temperature, pressure, and reactant concentration, the reaction rate can be effectively controlled, thereby optimizing combustion efficiency and reducing pollutant emissions. It contributes to understanding complex chemical reaction networks and the interactions and influences between different reactants, which is of great significance for designing novel low-emission burners.

[0111] Among them, step S4 involves solving the corresponding multiphase flow equations and considering the local imbalance effect;

[0112] In this step, combustion typically involves multiphase flow, such as the combustion of solid particles or the evaporation of droplets. Furthermore, local imbalances also affect the combustion process. Therefore, these complex multiphase flow conditions and local imbalances must be considered when simulating combustion.

[0113] Multiphase flow equations describe the interactions and transformation processes between different material phases in the combustion chamber, while local imbalance effects describe the non-uniformity and abnormal conditions in the combustion process.

[0114] It helps to understand and optimize the combustion process of solid particles or droplets in the combustion chamber, thereby improving combustion efficiency; it can also predict and control the impact of local imbalance effects on the combustion process, ensuring the stability and reliability of the combustion process.

[0115] In this embodiment, the implementation steps of step S4 specifically include:

[0116] S41. Model the combustion process of solid particles or the evaporation process of liquid droplets, taking into account their mass change and heat release;

[0117] S42. Consider the effects of local turbulence, heterogeneity, and other factors on the flow field and temperature field;

[0118] S43. Select an appropriate multiphase flow model, such as the Euler-Lagrange method or the Euler-Euler method, to describe the multiphase flow.

[0119] As one embodiment of the present invention, the treatment of multiphase flow and local imbalance includes:

[0120] Droplet evaporation model:

[0121] Particle combustion model: dmp / dt=-A p ρ p r p ;

[0122] In this embodiment, the handling of multiphase flow and local imbalance involves two sub-models: droplet evaporation and particle combustion. These two phenomena are crucial in many industrial processes, such as combustion, spraying, and coating. The following is a further explanation of these two models:

[0123] 1. Droplet evaporation model:

[0124]

[0125] This model describes the change in droplet diameter during droplet evaporation, where d l It is the diameter of the droplet, d l0 The initial diameter of the droplet is denoted by , K is the evaporation constant, which is related to the droplet's physical properties (such as volatility), environmental conditions (such as temperature and humidity), and fluid flow conditions, and t is time. This model assumes that the evaporation rate of the droplet is proportional to the square of the droplet's diameter. It is used to describe the evaporation of fuel droplets during combustion and is an important component of combustion modeling.

[0126] 2. Particle combustion model:

[0127] dm p / dt=-A p ρ p r p

[0128] This model describes the rate of change of particle mass during the combustion of solid particles, where dm p / dt is the rate of change of particle mass, A p It is the surface area of ​​the particle, ρ p It is the density of the particles, r p This refers to the particle combustion rate. This model assumes that the particle combustion rate is proportional to the particle surface area and is used to describe the combustion process of pulverized coal or other solid particles.

[0129] In CFD simulations, these models can be integrated into multiphase flow simulations to predict and calculate the effects of droplet evaporation and particulate combustion on flow and reactions within the combustion chamber. These models help to simulate actual combustion processes more accurately, especially when dealing with liquid fuel spray combustion and solid particulate fuel combustion.

[0130] Modeling droplet evaporation and particulate combustion allows for a deeper understanding and prediction of phase transition and chemical reaction processes in multiphase flows, which is crucial for designing efficient and low-emission combustion systems.

[0131] It helps optimize combustion chamber design; accurate prediction of droplet and particle behavior allows for improved fuel injection and distribution strategies. It improves combustion efficiency; better control of the combustion process ensures complete fuel combustion, thereby increasing thermal efficiency. It reduces pollutant emissions; optimizing the combustion process reduces the formation of unburned carbon particles and other pollutants.

[0132] Step S5 involves implementing a model-based control strategy to adjust the air-fuel ratio in real time.

[0133] In this step, the Model Predictive Control (MPC) strategy can utilize the aforementioned simulations and models to achieve real-time adjustment of the air-fuel ratio during combustion. The MPC strategy can adjust control parameters in real time based on the current state and future predictions to maintain the combustion process in an optimal state.

[0134] The MPC strategy is based on models and predictions of the combustion process, and achieves accurate adjustment of the air-fuel ratio by optimizing control parameters in real time.

[0135] It can achieve precise control of the combustion process, keeping combustion efficiency and emission performance at their best; it helps to cope with uncertainties and changes in the combustion process, improving the stability and robustness of the combustion system; it can reduce energy consumption and pollutant emissions, lower operating costs, and improve environmental impact.

[0136] In this embodiment, the implementation steps of step S5 specifically include:

[0137] S51. Establish a mathematical model of the combustion process, including a flow field model, a temperature field model, and a chemical reaction model;

[0138] S52. Design an MPC controller, including state estimation, prediction model, optimization objective, and constraints.

[0139] S53. Real-time acquisition of combustion process status information, prediction and optimization calculation, and generation of control commands to adjust the air-fuel ratio.

[0140] As one embodiment of the present invention, the objective function of the model-based control strategy is defined as:

[0141]

[0142] In this embodiment, the model-based control strategy employs an objective function J, the purpose of which is to minimize the system performance index. This control strategy is commonly used in control systems, such as Model Predictive Control (MPC).

[0143] The specific meanings of each item are as follows:

[0144] J: Performance metric, used to measure the performance of the control strategy. In this case, it is a cost function, and the goal of the control strategy is to minimize this cost.

[0145] λ(k): The air-fuel ratio in the k-step prediction, i.e., the ratio of air to fuel during combustion. The air-fuel ratio is an important parameter for combustion efficiency and emission control.

[0146] λ ref The desired air-fuel ratio is the target air-fuel ratio that the control system is trying to achieve.

[0147] Δu(k): The change in control input at step k. Control input typically refers to the signal controlling the actuator, such as fuel flow regulation in a fuel supply system.

[0148] N: Forecast horizon length, which is the number of future steps the control strategy considers. This number determines the future time range that the controller considers when calculating the optimal control action.

[0149] The controller attempts to bring the air-fuel ratio λ(k) closer to the desired value λ by adjusting the control input u(k). ref At the same time, the change in control input Δu(k) should be kept as small as possible. This ensures a smooth system response and avoids system instability that may result from excessive input changes.

[0150] Precise control of the combustion process can be achieved by minimizing performance metrics, optimizing combustion efficiency and reducing emissions. Considering variations in control inputs can reduce system wear and fatigue, extending equipment life. Predictive control strategies can ensure performance while allowing for better adaptability to future uncertainties and external disturbances.

[0151] As one embodiment of the present invention, the constraints of the model-based control strategy include:

[0152] λ min ≤λ(k)≤λ max

[0153] Δu min ≤Δu(k)≤Δu max

[0154] In this embodiment, the model-based control strategy not only has an objective function but also a series of constraints. These constraints are used to ensure that the results of the control strategy are feasible in actual operation and meet safety and performance standards. Specifically, the constraints include limitations on the air-fuel ratio and limitations on changes in the control input.

[0155] λ min and λ maxThese two parameters define the minimum and maximum air-fuel ratio, ensuring that it remains within a specific range. This is important because excessively low or high air-fuel ratios can lead to undesirable combustion, such as reduced efficiency, increased emissions, or even engine damage.

[0156] Δu min and Δu max These two parameters limit the minimum and maximum values ​​of the control input variation, respectively, to prevent the control behavior from being too drastic, which could lead to unstable system response or excessive wear.

[0157] The control algorithm searches for the optimal control input u(k) in each prediction step, minimizing the objective function J while satisfying the aforementioned constraints. This means that the controller must not only consider performance indicators but also ensure that its control behavior remains within the set operating range.

[0158] By introducing constraints on air-fuel ratio and control input variations, the combustion process can be ensured to operate within a safe and efficient range. These constraints help prevent the control system from taking extreme measures that could damage the engine or lead to excessive emissions. Simultaneously, they provide the control system with a clear operating boundary, allowing the physical and engineering limitations of the system to be explicitly considered when designing the controller.

[0159] In summary, the control method of this invention ensures optimal combustion efficiency by precisely controlling the air-fuel ratio, thereby saving fuel consumption and improving energy conversion efficiency. It also helps reduce harmful emissions. By limiting changes in control input, it reduces the impact and vibration on the system, thus improving system stability and reliability. Furthermore, the model-based control strategy can adapt to different operating conditions and external disturbances based on real-time data and predictive models, thereby improving the adaptability and flexibility of the control system.

[0160] Please see the appendix Figure 2 The present invention also provides an automated control system for oilfield heating furnaces, comprising:

[0161] Sensors are used to measure key parameters such as temperature, pressure, and flow rate within the combustion chamber, providing real-time operational status data;

[0162] The data acquisition unit is responsible for collecting data acquired by the sensors and transmitting it to the control unit for processing and analysis.

[0163] The control unit, equipped with a processor and memory, is responsible for implementing automated control methods for oilfield heating furnaces, including monitoring and adjusting various parameters of the combustion process;

[0164] The actuator, according to the instructions of the control unit, is responsible for adjusting heat input and air-fuel ratio, etc., in order to achieve precise control of the combustion process.

[0165] Furthermore, the control unit includes:

[0166] One or more computing modules are used to perform computational fluid dynamics (CFD) simulations to solve the Navier-Stokes equations, energy equations, and mass conservation equations, thereby simulating and analyzing the flow field, temperature field, and mass distribution within the combustion chamber;

[0167] An optimization module implements a model-based control strategy, which calculates the air-fuel ratio adjustment command by analyzing CFD simulations and real-time data, and optimizes control parameters to achieve the best combustion process.

[0168] A communication module is used to exchange data with sensors and actuators, enabling the acquisition of sensor data and the issuance of actuator commands, thus ensuring the real-time performance and reliability of the control system.

[0169] Specifically, the automated control system for the oilfield heating furnace combines functions such as sensor measurement, data processing, model calculation, and control command issuance to achieve precise monitoring and automated control of the combustion process, thereby improving operational efficiency, safety, and reliability.

[0170] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method of automated control of an oilfield heater, characterized by, The method comprises the following steps: using computational fluid dynamics simulation to describe the gas flow, temperature field and concentration field in the combustion chamber; simulating the combustion process by solving Navier-Stokes equations, energy equations and mass conservation equations; using chemical reaction mechanism to describe the reaction path and rate in the combustion process; solving the corresponding multiphase flow equations and considering the local imbalance effect; implementing a model-based control strategy for real-time adjustment of air-fuel ratio.

2. The oilfield furnace automation control method of claim 1, wherein, The Navier-Stokes equations are defined as: where p denotes the density, denotes the velocity field, p denotes the pressure, and τ denotes the stress tensor, denotes the gravitational acceleration.

3. The oilfield furnace automation control method of claim 1, wherein, The energy equation is defined as: where c p represents the specific heat capacity, T represents the temperature, k represents the thermal conductivity, represents the heat source term per unit volume.

4. The oilfield furnace automation control method of claim 1, wherein, The mass conservation equation is defined as: where Y i represents the mass fraction of the i-th chemical species, D i represents the diffusion coefficient, represents the production / consumption rate of the i-th chemical species.

5. The oilfield furnace automation control method of claim 1, wherein, The chemical reaction rate expression is defined as: where A j represents a pre-exponential factor, n j represents a temperature exponent, E a,j represents an activation energy, R represents a gas constant, T represents a temperature, [C i ] represents a concentration of a reactant i, v ij represents a stoichiometric number of a reactant i in a reaction j.

6. The oilfield furnace automation control method of claim 1, wherein, The multiphase flow and local imbalance processing includes: Droplet evaporation model: Particle burning model: dm p / dt = -A p p p r p ; where d l represents the droplet diameter, d l0 represents the initial diameter, K represents the evaporation constant, t represents time, dm p / dt represents the particle mass change rate, A p represents the particle surface area, p p represents the particle density, r p represents the particle burning rate.

7. The oilfield furnace automation control method of claim 1, wherein, The objective function of the model-based control strategy is defined as: where J denotes a performance index, λ(k) denotes an air-fuel ratio at a prediction step k, λ ref denotes a desired air-fuel ratio, Δu(k) denotes a change in a control input, and N denotes a prediction horizon length.

8. The oilfield furnace automation control method of claim 7, wherein, The constraint conditions of the model-based control strategy include: λ min ≤ λ(k) ≤ λ max Δu min ≤ Δu(k) ≤ Δu max where λ min and λ max respectively represent the minimum and maximum values of the air-fuel ratio, Δu min and Δu max respectively represent the minimum and maximum values of the control input variation.

9. An automated control system for oilfield heating furnaces, characterized by, It comprises: sensors for measuring temperature, pressure and flow rate in the combustion chamber; a data acquisition unit for collecting sensor data; a control unit configured with a processor and a memory for implementing the oilfield furnace automatic control method of any one of claims 1-8; an actuator for adjusting heat input and air-fuel ratio according to the instructions of the control unit.

10. The oilfield furnace automation control system of claim 9, wherein, The control unit further comprises: one or more computing modules for implementing CFD simulation, solving Navier-Stokes equations, energy equations and mass conservation equations; an optimization module for implementing a model-based control strategy and calculating adjustment instructions for air-fuel ratio; a communication module for data exchange with sensors and actuators.

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