207 results about "Thermodynamic model" patented technology

The invention belongs to the field of equipment coupling monitoring, and particularly relates to a dynamic coupling compensation method for thermal expansion and axial displacement, which comprises the following steps of: calculating a first characteristic value for representing reference dynamic offset of a sensor based on a three-dimensional thermal-structure coupling model by acquiring a thermal expansion parameter and axial displacement parameter sequence of a casing; calculating a second characteristic value representing the real position change of the rotor in combination with a rotor-casing axial thermodynamic model, establishing a piecewise coupling function considering a nonlinear effect, working condition self-adaption and cross interference, and eliminating the mechanical thermal inertia and measurement system lag influence through a dynamic delay compensation mechanism; an accurate axial displacement measurement total error compensation index is generated, and grading compensation actions are intelligently triggered according to error grades; the problem of measurement distortion caused by dynamic coupling of thermal expansion and axial displacement is effectively solved, and the accuracy and reliability of state monitoring of the rotating machine are remarkably improved.

The invention discloses a method and a system for monitoring the temperature of an energy storage battery in a high-altitude area. Through environment adaptive feature reconstruction, thermodynamic model correction and multi-frequency EIS fusion feature extraction are utilized to generate a feature vector adaptive to the plateau environment. The thermodynamic model correction adjusts the convective heat transfer coefficient by introducing an air pressure correction coefficient; the multi-frequency EIS fusion feature selects a specific frequency point impedance value to construct a vector, and the weight is dynamically adjusted through a random forest algorithm. The lightweight hybrid machine learning model integrates the advantages of LightGBM, 1D-CNN and a physical constraint particle filter model, and realizes accurate prediction for different working conditions. The plateau exclusive training mechanism covers data enhancement, transfer learning and online calibration, and the generalization ability and adaptability of the model are improved. According to the method, the problems of low temperature monitoring precision and poor model adaptability of the energy storage battery in the high-altitude area are solved, the monitoring precision and the system reliability are remarkably improved, and a guarantee is provided for safe and stable operation of a high-altitude energy storage system.

The invention discloses a fuel cell vehicle thermal management system and a construction method thereof, and the fuel cell vehicle thermal management system comprises a thermodynamic modeling module, a double-layer fuzzy sliding mode controller, an actuator cooperative control module, a working condition adaptive module and a fault protection module. According to the thermal management system of the fuel cell vehicle and the construction method of the thermal management system, the dynamic thermodynamic model is established, and the double-layer fuzzy sliding mode controller and the actuator cooperative control module are designed, so that millisecond-level high-precision tracking of the temperature of the electric pile and online real-time compensation of unknown disturbance are realized; meanwhile, stable operation of the system in the whole working condition domain range can be ensured through the working condition self-adaption module and the fault protection module, and the system has the advantages of being high in control precision, high in anti-interference capacity and good in system reliability.

The invention relates to the technical field of air conditioner temperature adjustment, in particular to an energy-saving control method, device and equipment for machine room air conditioner group control, a storage medium and a computer program product. The method comprises the following steps: acquiring temperature and humidity data in a machine room, power consumption data of load equipment and operation parameters of air conditioning equipment; synchronizing the data according to timestamps to generate a corresponding multi-source feature data matrix; constructing spatial thermodynamic model parameters based on the multi-source feature data matrix; on the basis of the space thermodynamic model parameters, the temperature evolution trend in the target time domain is predicted, and a control instruction sequence of the air conditioning equipment is generated on the basis of a prediction result; and if the control instruction sequence meets the preset temperature range constraint, the preset temperature change rate constraint and the preset control time interval constraint, the corresponding control instruction is sent to the air conditioner equipment, and the air conditioner group control energy-saving efficiency is improved.

The invention provides a cold heading steel wire rod rolling temperature closed-loop control method and system based on PID, and relates to the technical field of cold heading steel wire rod rolling process control, and the cold heading steel wire rod rolling temperature closed-loop control method comprises the following steps: determining temperature control parameters of a rolling area of a rolling mill in different rolling stages of a cold heading steel wire rod according to process requirements of the cold heading steel wire rod; constructing dynamic thermodynamic models of different rolling stages; the real-time state data and the real-time temperature data are preprocessed; the extracted temperature control parameters are compared with the preprocessed real-time temperature data; based on the dynamic thermodynamic model and the real-time temperature deviation, the control quantity is calculated through a PID algorithm, and a control instruction is output to an execution mechanism of the rolling mill; and an execution mechanism of the rolling mill receives the instruction and carries out temperature adjustment action. The method can effectively guarantee that the temperature in each rolling stage is stable, the quality consistency of cold heading steel wire rod products is improved, and the dynamic change requirement in the rolling process is met.

The invention provides a ship propeller design optimization method and system based on machine learning, and relates to the technical field of ship propeller design, and the method comprises the steps: building a parameterized curved surface based on a geometric control point coordinate set, converting the parameterized curved surface into a signed distance field (SDF), extracting an SDF zero contour surface to generate an initial grid, and carrying out the gradient optimization of control points to meet the demolding angle constraint, calculating the local thickness of the blade based on the SDF, and predicting shrinkage cavity defect probability distribution by adopting a differentiable thermodynamic model and a spatial-temporal characteristic network; fusing the defect probability and the hydrodynamic performance to construct a target function, reversely propagating the gradient to a geometric control point through a thermal field, a grid and an SDF through differentiable simulation, and driving the gradient to synchronously update; and iteratively executing the process, terminating after the defect probability threshold, the performance tolerance and the convergence condition are met, and outputting the propeller geometric model with excellent hydrodynamic performance and high manufacturability.

The invention discloses a mass concrete temperature control construction method and system based on phase change energy storage, and the method comprises the steps: S100, building a BIM model for a concrete member pouring region according to a design size, setting a temperature sensor in a concrete member to monitor data in real time, and predicting a temperature field in combination with the BIM model; s200, the space between the conventional cooling water pipe and the concrete protection layer is filled with phase change microcapsules in a gradient density mode, a phase change energy storage buffer layer is formed, and transfer of heat to the interior of the concrete member is delayed; s300, according to the predicted temperature field, the water inlet flow and the water temperature are dynamically adjusted, the temperature peak is controlled, and the cooling rate is controlled; s400, fiber grating sensors are arranged at the four corners and the midspan of the concrete member, and the strain change rate is detected; and S500, establishing a digital twin temperature control platform, integrating temperature, humidity and strain data, constructing a three-dimensional thermodynamic model, predicting the temperature rise trend of the concrete member, and performing adaptive regulation and control on the temperature. Temperature difference inside and outside the concrete is controlled through phase change temperature control.

The invention relates to the technical field of air source heat pump unit control, and discloses a control method for an air source heat pump unit. The method comprises the following steps: acquiring environment temperature and humidity data in real time, and performing noise filtering and time synchronization; generating a temperature prediction sequence based on the sliding window and the LSTM model; fluctuation characteristics are extracted through an optimization algorithm, and compressor control signals are generated; estimating the thickness of a frost layer in combination with a thermodynamic model and dynamically generating a defrosting instruction; closed-loop control parameters are generated through normalization processing and weight distribution; and finally, a system execution command is generated through multi-objective optimization, and compressor regulation and control and system health monitoring are achieved. The unit operation efficiency can be improved, the energy consumption can be reduced, and the defrosting control accuracy and the system stability can be enhanced.

The invention provides an intelligent blower temperature and wind speed adaptive control method and system, and relates to the field of intelligent control, and the method comprises the steps: collecting temperature, humidity and wind speed, and constructing a local feature map based on feature vectors; a dynamic evaporation thermodynamic model is established according to the local feature map and used for calculating the evaporation rate of each area, and then the energy requirement for achieving the needed drying effect of each area is calculated according to the evaporation rate; a competitive sensing type wind energy mapping model is established to map energy requirements into control signals used for controlling air outlet parameters, namely wind cavity control signals, and regional self-adaptive adjustment of wind temperature and wind speed in space is achieved; actual air flow output collected in real time is compared with an air cavity control signal, and a feedback difference mechanism is constructed to conduct iterative correction on the dynamic evaporation thermodynamic model and the energy requirement; the physical state change in the hair drying process can be perceived in real time, and dynamic balance can be realized by integrating the use habit and comfort perception of the user.

The invention belongs to the technical field of automobile electronics, and particularly relates to a tire pressure monitoring dynamic calibration method and system fused with vehicle speed data, and the method comprises the steps: obtaining real-time tire pressure, tire temperature, vehicle speed and environment temperature; based on the vehicle speed, the environment temperature and the calibration parameters, the tire theoretical temperature is estimated through a dynamic thermodynamic model; the real-time tire temperature and the theoretical temperature are fused through Kalman filtering, and the corrected internal temperature is obtained; and according to the real-time tire pressure, the corrected internal temperature and the cold tire reference temperature, a cold tire pressure equivalent value is calculated to judge the tire pressure state. By the adoption of the technical scheme, the interpretation ambiguity of traditional tire pressure monitoring can be overcome, the cold tire pressure equivalent value with more guiding significance is provided, and the accuracy, robustness and self-adaptive learning capacity of the system are enhanced.

The invention provides a PI control method and device for laser soldering based on observation compensation, a controller and a program product, and the method comprises the steps: building a laser soldering thermodynamic model containing a thermal disturbance term based on a lumped parameter method, and constructing a state-space equation; designing a state observer based on the state-space equation, and estimating a state variable of the state-space equation and external disturbance acting on the state variable in real time; calculating a feedforward control quantity according to a target temperature curve and the thermodynamic model, calculating a compensation control quantity according to a disturbance value estimated by an observer, and calculating a PI control quantity according to the deviation between the output temperature and the target temperature to obtain a total control quantity; and in each sampling period, the total control quantity is dynamically adjusted according to the deviation between the current welding spot temperature and the target temperature, so that the temperature error tends to zero. According to the method, the disturbance observation technology and the feedforward-PI control are combined, and high-precision and high-robustness control over the welding temperature is achieved.

According to the intelligent plunger pump temperature control method and system based on model predictive control, a thermodynamic model predictive control algorithm is introduced, key parameters such as the oil temperature of a plunger pump body, the water temperature of a water tank and the environment temperature are brought into a unified calculation model, and cooperative closed-loop control over a heating plate, a semiconductor chilling plate and a circulating pump is achieved. By constructing the heat balance equation of the lumped heat capacity system and solving the rolling optimization problem in the limited time domain, the heat load change of the plunger pump can be accurately predicted and adjusted in advance, the oil temperature is always stabilized in the preset target interval, and the temperature control precision is remarkably improved; meanwhile, mutual exclusion constraints of heating and refrigerating power are set, and weights are dynamically adjusted and optimized according to working conditions, so that meaningless consumption of energy is effectively avoided, and the operation cost of equipment is reduced; in addition, according to the control method, preheating, cooling and energy-saving modes can be automatically recognized, so that the plunger pump is rapidly started in the severe cold environment and stably operates in the high-temperature environment.

The invention discloses a heating system feedforward control method integrating building thermal inertia and meteorological prediction, which comprises the following steps of: firstly, dynamically identifying building thermal parameters of each room so as to quantify the thermal inertia; then, inputting the parameters and future meteorological prediction data into a feedforward controller; and the controller predicts a room temperature change trend caused by future meteorological disturbance based on a thermodynamic model, calculates a required feedforward compensation control quantity, and adjusts a heating system in advance so as to counteract the disturbance. Through combination of dynamic identification and predictive control, heating control is changed into active intervention from passive response, the problem of control lag caused by thermal inertia is fundamentally solved, the stability and comfort of indoor temperature are remarkably improved, and meanwhile energy waste is avoided through accurate compensation.

The invention relates to the technical field of metal smelting, in particular to a metal smelting temperature control method and system. The system comprises a temperature monitoring module, a gas analysis module, a thermodynamic simulation module and a dynamic regulation and control module, the thermodynamic simulation module sets a fitness function according to the Gibbs free energy value, and obtains the optimal temperature and oxygen potential through a particle swarm optimization algorithm; the dynamic regulation and control module is used for converting the theoretical optimal temperature of the thermodynamic model into precise operation, and efficient and stable control over the smelting temperature is achieved. According to the method, the reaction process in the furnace and the current optimal temperature and oxygen potential are accurately controlled on the basis of the Gibbs free energy value and the particle swarm optimization algorithm, the theoretical optimal temperature of a thermodynamic model is converted into precise operation through multi-PID cooperative control, and efficient and stable control over the antimony smelting temperature is achieved; and therefore, the problem that the oxygen-enriched smelting temperature is not accurately controlled is fundamentally solved, the extraction rate of metal is increased, and energy consumption is reduced.

The invention relates to the technical field of natural gas hydrates, in particular to a DSC (differential scanning calorimetry) correction-based multiphase flow system hydrate inhibitor loss prediction method, which comprises the following steps of: firstly, acquiring water activity and vaporization enthalpy of a sample under target salinity and ratio through two DSC tests, and regression a thermodynamic model according to the water activity and vaporization enthalpy to obtain a liquid-phase activity coefficient; the saturated vapor pressure of the inhibitor is calculated through the vaporization enthalpy obtained through DSC inversion and an anchor point method, a component distribution coefficient is obtained through combination of Poynting pressure correction and a gas phase fugacity coefficient, and a Rachford-Rice flash evaporation equation is solved to obtain an equilibrium phasor and composition; and then a first-order total mass transfer model is introduced to correct the actual vaporization loss, and droplet entrainment correlation is superposed to obtain the total flow loss of the inhibitor under the multiphase flow system. According to the method, the total flow loss of the hydrate inhibitor can be accurately calculated under the working conditions of high pressure, low temperature and salt-containing multiphase flow, so that a theoretical support is provided for adjusting an injection strategy of the inhibitor in real time.

The invention discloses a double-layer collaborative energy management and control method and system for a cold plate type liquid cooling data center, and belongs to the technical field of data center energy management. The method comprises the steps that upper-layer day-ahead economic dispatching is based on time-of-use electricity price and outdoor environment temperature prediction, and a server load scheme and a cooling system reference flow track are generated with the minimum total operation cost as the target; in lower-layer intraday dynamic regulation and control, model prediction control is adopted to track the trajectory, the load rate of a server and the rotating speed of a secondary pump are adjusted in real time, and it is ensured that the temperature of a chip is within the safety range of 45-70 DEG C; and the lower layer feeds back the actual chip temperature and energy consumption to the upper layer for online correction of dynamic thermodynamic model parameters to form closed-loop optimization. The system comprises a data acquisition module, a dynamic thermodynamic modeling module, an upper and lower layer optimization control module and an information interaction and feedback module. According to the invention, the overall operation cost and the PUE value of the data center are reduced on the premise of ensuring the temperature safety of the chip.

The invention relates to the technical field of insurance recommendation based on big data, and discloses an insurance recommendation method based on big data. Collecting user geographical behaviors and time information, mapping to a unified plane coordinate system, and dividing grids according to an equal area principle; counting the multi-period access times of each grid, calculating access density and logarithmic smooth heat, and constructing a dynamic thermodynamic model; extracting a median and a median absolute deviation in a historical period to generate a heat baseline, and judging an abnormal hotspot according to a deviation degree; the discrete hotspots are combined into a continuous region by adopting four-connection clustering, a composite score is generated by combining the average anomaly degree and the area of the region, candidate insurance products are mapped through a preset risk interval, weighted sorting is performed according to the score, and finally a personalized recommendation list is output. According to the scheme, short-time burst and large-range slight risks are taken into consideration, the fine mapping capacity of unification of multi-source data, high-robustness anomaly detection and scale perception is achieved, and the insurance recommendation accuracy and the user satisfaction degree are improved.

According to the ship power device cooling water temperature intelligent control system, the water temperature is monitored in real time through the temperature sensor arranged at the cooling water inlet, and the electric three-way flow regulating valve with the stepping motor is used for changing the mixing proportion of low temperature and bypass fresh water for control; a cooperative control framework is adopted; an upper-layer model prediction control module performs rolling time domain optimization based on a time-delay thermodynamic model which is identified and updated on line, and outputs a feedforward signal; and the lower-layer fuzzy self-adaptive PID controller dynamically adjusts parameters according to the temperature deviation and the change rate of the temperature deviation to generate compensation control quantity. And the two outputs are linearly superposed and then converted into a valve accurate action signal through the driving module. The cooperation method effectively fuses the feedforward optimization capability of the MPC and the strong robustness of the fuzzy PID to model mismatch and disturbance, significantly improves the control precision and stability of the water temperature of the main engine under the inertia and time-varying working conditions, reduces the equipment damage caused by temperature fluctuation, prolongs the service life of the main engine, and improves the navigation safety and the energy efficiency.

The invention discloses a refrigeration equipment energy-saving control method and system based on a computer, and relates to the technical field of refrigeration control, and the method comprises the steps: firstly, carrying out the precise evaluation of the current operation state of a refrigeration system through the real-time fusion of multi-source sensor information and external event information; based on this, a mechanism for prospective modeling and quantification of immediately occurring and future predictable disturbance events is introduced, and the discrete and pulsed disturbances are converted into continuous and accurate prediction of future thermal loads. And then, jointly inputting the evaluation result of the current state of the system and the prediction result of the future disturbance influence into a dynamic system thermodynamic model for deduction. And finally, based on the structured optimization framework, comprehensively weighing the energy consumption cost, the temperature control precision and the equipment operation constraint, and carrying out global optimization solution so as to generate and execute an optimal control instruction. In this way, strict temperature control requirements can be met, and meanwhile system energy consumption and equipment loss can be reduced to the maximum extent.

The invention discloses an organic Rankine cycle optimal configuration search method and device based on PINN and multi-objective optimization, and the method comprises the steps: firstly constructing a thermodynamic model of an organic Rankine cycle system, constructing a training data set and physical constraints based on the thermodynamic model, and then training a PINN pair network through the training data set, thereby obtaining the optimal configuration of the organic Rankine cycle system. Embedding a physical constraint in the loss function; the method comprises the following steps: training a PINN (Plural Index Neural Network), predicting a to-be-predicted design parameter by using the trained PINN to obtain a predicted cycle state parameter, finally calculating a plurality of organic Rankine cycle evaluation indexes according to the predicted cycle state parameter, constructing a target function, and carrying out multi-target optimization search by minimizing the target function to obtain final optimal configuration. According to the method, the calculation efficiency of system performance evaluation is remarkably improved, the optimal performance configuration of the ORC system under various working medium and operation conditions can be efficiently obtained by searching in a wide design space, and unification of optimization efficiency and prediction accuracy is achieved.

The invention discloses a turbine generator set power oscillation emergency valve switching control method, system, equipment and medium based on a thermodynamic model, and belongs to the technical field of electric power system automation control and thermal energy power engineering, and the method comprises the steps: monitoring the operation state of a set in real time, and collecting the current data of the set; calculating a theoretical balance valve position required for maintaining the current target power in real time by using the corrected inverse thermodynamic model; and the power fluctuation condition is monitored in real time, when it is detected that power oscillation happens to a unit and valve cutting logic is triggered, the system intercepts a conventional feedback instruction for tracking the actual valve position, the theoretical balance valve position is directly used as a target instruction, and a control valve is forcibly driven to act to the valve position through a rate limiter. According to the method, it can be ensured that after closed-loop control of the unit is cut off, the valve rapidly returns to the physical balance opening degree matched with the current load, an oscillation source is effectively eliminated, severe fluctuation of main steam pressure and temperature is stabilized, and the safety and stability of the unit under the abnormal working condition are remarkably improved.

This invention discloses a method and apparatus for adaptive coordinated control of building HVAC systems based on deep reinforcement learning. The method involves: constructing an indoor temperature change model; describing indoor temperature changes using the indoor temperature change model; constructing a building thermodynamic model based on the indoor temperature change model; constructing a real-time dynamic control model for the building HVAC system based on the building thermodynamic model and combining the EnergyPlus and Python co-simulation framework; transforming the real-time dynamic control model of the building HVAC system into a Markov decision process model; constructing a deep reinforcement learning algorithm EB-TD3 based on set expert rules; and iteratively optimizing the Markov decision process model using the deep reinforcement learning algorithm EB-TD3 according to a set optimization objective, thereby achieving adaptive real-time dynamic control of the building HVAC system. This invention improves the control efficiency and stability of HVAC systems in complex environments, reduces energy consumption, and enhances thermal comfort.

The invention discloses an oil and gas gathering and transportation pipeline sand deposition dynamic prediction method and system, and relates to the technical field of oil and gas gathering system simulation prediction, and the method comprises the steps: building a pipeline structure model, and carrying out the discretization of the pipeline structure model, so as to divide calculation units; a transient flow model is coupled with a thermodynamic model to carry out space iteration to calculate hydraulic and thermal parameters of the whole pipeline; calculating a sand deposition critical flow velocity along the pipeline by adopting a sand deposition critical flow velocity model, and judging a sand migration state; the height of a sedimentary sand bed along the pipeline is calculated according to the sand migration state, and pipeline structure parameters and sand attribute parameters are updated; repeating the steps to output the hydraulic and thermal parameters, the sand deposition critical flow velocity and the deposited sand bed height under each time and space as prediction results; the device can be suitable for gathering and transportation pipelines of different types of transportation media, and can reflect the dynamic influence of the transportation flow, the sand carrying property, the medium physical property and the like on the sand deposition characteristic in real time.

This application provides a method for coating positioning detection and control of sponge substrate, relating to the field of flexible composite material manufacturing, including the following steps: feeding and conveying the sponge substrate, and controlling the tension and lateral position of the sponge substrate; unwinding the coating material, applying adhesive, and preheating; acquiring the full-field deformation field of the sponge substrate surface through a binocular high-speed vision unit, calculating the strain energy density distribution based on nonlinear strain analysis, and generating a deformation risk digital map with energy release rate characteristics; based on the energy release rate characteristics in the deformation risk digital map, calculating control parameters through a dynamic thermodynamic model, and performing local thermal adaptation control of the coating material to match the energy field; aligning and pressing the coating material, which has undergone local thermal adaptation control to match the energy field, with the sponge substrate; and curing and quality verification of the pressed composite material.

A system and method for chemical process simulation integrates thermodynamic models into machine learning frameworks to enhance predictive accuracy and optimize chemical separation processes. The system employs a variety of methods, such as using a physics- informed neural network (PINN)-like approach and methods that embed thermodynamic equations directly into the architecture, ensuring adherence to physical laws such as conservation of energy and mass. Additionally, experimental data, virtual sampling points derived from thermodynamic models, and auxiliary predictions capturing interdependencies between physical and chemical variables are combined into an augmented dataset for training. A hybrid loss function balances contributions from experimental data and thermodynamic insights, dynamically adjusted via a scaling hyperparameter. The system predicts target properties, such as adsorption efficiency, under diverse operating conditions while maintaining compliance with thermodynamic constraints.

The present invention relates to the fields of material genetic engineering and integrated computational material engineering, and specifically discloses a multi-scale integrated temperature component phase diagram modeling method, which calculates the thermodynamic data of each material phase at a finite temperature based on the first principles, uses the calculated thermodynamic data as input data for the CALPHAD thermodynamic formula, and constructs a CALPHAD thermodynamic model. The present invention also discloses a multi-scale integrated temperature component phase diagram modeling device. The present invention uses the first principles thermodynamic data as the data source for phase diagram modeling, solving the problem of lack of experimental thermodynamic data. Bayesian sampling and Markov chain Monte Carlo methods are used to achieve automatic optimization of multiple parameters, and a core algorithm for developing independent and controllable phase diagram modeling software is proposed to accelerate the realization of domestic substitution of phase diagram modeling software. It can realize the large-scale, process-based, and automated construction of a highly reliable thermodynamic database under the premise of low cost and short R&D cycle, and assist in the design of new material components, optimization of process parameters, and the influence of added elements on phase stability.

The invention provides a Nusselt number experimental calculation method for a special flow channel structure, which comprises the following steps: determining structural parameters and basic input parameters of the special flow channel structure PCHE, and constructing a thermodynamic model of the special flow channel structure PCHE; based on a thermodynamic model, obtaining a total heat transfer coefficient and total heat transfer resistance through a thermal resistance method, and then determining an undetermined coefficient; a heat exchange experiment between the first working media is carried out, nonlinear regression fitting is carried out on the undetermined coefficient through an intelligent algorithm, and a first Nusselt number is obtained; and carrying out a heat exchange experiment between the first working medium and the second working medium, and carrying out nonlinear regression fitting on the undetermined coefficient by using the same intelligent algorithm in combination with the first Nusselt number to obtain a second Nusselt number. According to the method, step-by-step nonlinear regression is carried out on the undetermined coefficient in the thermal resistance model through two groups of heat exchange experiments in combination with an intelligent algorithm, so that the accuracy of measuring the wall temperature is improved, and the fitting precision of the Nusselt number is improved.

The application discloses a heating furnace control method, device, equipment and storage medium for delayed coking, and the method comprises the following steps: generating a three-dimensional thermodynamic model of the heating furnace and performing gridding according to heating furnace modeling; obtaining simulation results of the three-dimensional thermodynamic model according to preset input parameters; judging whether the outlet cross-section position of the heating furnace includes a grid exceeding a preset target temperature range, if yes, updating the heat source power and returning to the simulation result calculation step; if no and the current judgment is the first judgment, updating the feeding rate and returning to the simulation result calculation step; if no and the current judgment is not the first judgment, going to the next step; judging whether the heating furnace includes a grid exceeding the preset upper limit of free state coke content, if yes, updating the heat source power and the feeding rate and returning to the simulation result calculation step; if no, generating a control instruction according to the current heat source power and the feeding rate. The application can fully exert the production efficiency of the heating furnace.

The present application relates to natural gas hydrate technical field, especially relates to a kind of hydrate inhibitor loss prediction method of multiphase flow system based on DSC correction, first by two kinds of DSC test obtains the water activity and vaporization enthalpy of sample under target salinity and proportion, and accordingly regression thermodynamic model obtains liquid phase activity coefficient;Again, the saturated vapor pressure of inhibitor is calculated with DSC inversion vaporization enthalpy and anchor point method, combined with Poynting pressure correction and gas fugacity coefficient obtains component distribution coefficient, solves Rachford-Rice flash equation and obtains equilibrium phase and composition;Subsequently, the first-order overall mass transfer model is introduced to correct actual vaporization loss, and the total loss of inhibitor flow under multiphase flow system is obtained by superimposing droplet entrainment correlation.The present application can accurately calculate the total flow loss of hydrate inhibitor under high pressure, low temperature and salt-containing multiphase flow conditions, thereby providing theoretical support for real-time adjustment of inhibitor injection strategy.

The invention provides a ship energy consumption prediction method, device and equipment based on digital twinning and a medium, and belongs to the field of ship intelligent energy consumption management.The method comprises the steps that on the basis of a preprocessed real ship data set, a corresponding multi-physics field coupling digital twinning body is driven to operate, and real-time physics field distribution data is obtained; determining a real-time total energy consumption initial value through a thermodynamic model; calculating a deviation field between the real-time physical field and the reference physical field to generate energy consumption deviation data, positioning abnormal key parts of the ship power system, and analyzing energy consumption contribution degree factors of the abnormal key parts; and combining the real-time total energy consumption initial value with the component energy consumption influence quantity to generate an energy consumption prediction result. The method can accurately predict the ship energy consumption and trace the source of energy consumption abnormity, improves the energy consumption management precision, and effectively supports the predictive maintenance of the ship.