New energy non-oriented silicon steel high-speed normalizing process energy efficiency optimization system and method

Abstract

The invention discloses a new energy non-oriented silicon steel high-speed normalizing process energy efficiency optimization system and method, and the system comprises a normalizing furnace, a cooling device and an energy supply unit, and also comprises a renewable energy integration module, a hybrid energy storage module, an intelligent energy management platform and a waste heat gradient utilization network. Accurate temperature control is achieved through an intelligent energy management platform and an independent temperature control area, the cooling process is optimized through a phase change microcapsule medium, the aging time is prolonged in combination with antimony microalloying, and energy efficiency is improved through renewable energy sources and waste heat recovery. The method has the advantages that the temperature uniformity is improved, the cooling rate control is optimized, the aging embrittlement time is prolonged, and the energy utilization efficiency is improved.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of silicon steel heat treatment, in particular to a new energy non-oriented silicon steel high-speed normalizing process energy efficiency optimization system and method. BACKGROUND

[0002] The normalizing process (heat treatment process) in the production process of new energy non-oriented silicon steel, which is the core material of electric vehicle drive motors and high-efficiency energy-saving equipment, accounts for more than 40% of the entire production process. Through in-depth analysis of the existing process, we found that there are multiple energy efficiency contradictions in the high-speed normalizing process that need to be solved.

[0003] Temperature uniformity contradiction is the primary technical problem. The traditional radiant tube heating adopts fixed power parameters, resulting in uneven steel temperature distribution (temperature difference up to ± 15℃), which seriously affects the consistency of magnetic properties. This unevenness is caused by the furnace gas thermal coupling effect - the temperature fluctuation of the non-oxidizing heating section and the radiant tube heating section has strong correlation, forming interference superposition. Research shows that when the temperature disorder degree (quantitative index of temperature unevenness) exceeds 12%, the product magnetic induction fluctuation is as high as 5%, and the silicon steel sheet iron loss value rises significantly. The existing temperature control system cannot respond to this complex temperature coupling relationship in real time, and an intelligent dynamic regulation mechanism needs to be introduced.

[0004] The contradiction of cooling rate also restricts the product quality. Normalizing cooling needs to go through five times of segmented cooling (one slow cooling to four slow cooling + fast cooling), but the traditional cooling method is difficult to realize accurate control. The water quenching cooling rate is fast (> 50℃ / s), but it easily leads to uneven stress on the surface of the strip steel; the air cooling cooling rate is slow (< 10℃ / s), which also affects the full growth of the grain. The ideal cooling curve requires maintaining a slow cooling rate of ≤10℃ / s in the 700-900℃ interval, which puts high requirements on the selection of cooling medium and the control system. The existing cooling device cannot balance the cooling uniformity and the accuracy of the cooling rate, resulting in large fluctuations in the mechanical properties of the product.

[0005] The aging brittleness contradiction directly affects the production economy. The normalizing plate with high silicon aluminum composition (Si: 2.5-3.6%, Al: 0.5-1.2%) will have a decrease in elongation during storage (from 18% to less than 8% within 7 days), which increases the cold rolling strip breaking rate by 50%. This is caused by the Kovalenko air mass effect - C, N interstitial atoms gather to dislocation, which requires the storage period to be shortened to 72 hours, which is in fundamental conflict with the flexibility of production scheduling. The existing process cannot effectively inhibit the aging brittleness, and lacks the ability of coordinated optimization of the whole production process.

[0006] In addition, the energy utilization efficiency of the conventional normalizing process is low. The direct emission of high-temperature flue gas waste heat (> 650℃) causes great energy waste, and the low-temperature waste heat (400-650℃) cannot be effectively recovered and utilized, resulting in high energy consumption per unit product. Under the background of increasingly strict carbon emission reduction policy, this high energy consumption mode has been difficult to continue.

[0007] In view of the above problems, the prior art needs to be improved. SUMMARY

[0008] The purpose of the present application is to provide a new energy non-oriented silicon steel high-speed normalizing process energy efficiency optimization system and method, which has the advantages of improving temperature uniformity, optimizing cooling rate control, prolonging aging embrittlement time, and improving energy utilization efficiency.

[0009] The present application provides a new energy non-oriented silicon steel high-speed normalizing process energy efficiency optimization system, the technical scheme is as follows: including normalizing furnace, cooling device and energy supply unit, further comprising: renewable energy integration module, including photovoltaic power generation unit, wind power generation unit and grid-connected controller, used for providing clean power for the normalizing furnace; mixed energy storage module, composed of molten salt heat storage unit and solid-state hydrogen storage unit, the molten salt heat storage unit is connected with organic Rankine cycle generator set, the solid-state hydrogen storage unit is connected with proton exchange membrane electrolysis cell and hydrogen fuel cell, used for balancing energy supply and demand fluctuation; intelligent energy management platform, built-in digital twin model and reinforcement learning algorithm, real-time optimization of radiant tube power distribution, cooling medium flow and energy storage charging and discharging strategy; waste heat cascade utilization network, including silicon carbide heat pipe array and ammonia water absorption heat pump, used for recovering high-temperature waste heat above 650℃ to generate electricity, 400-650℃ medium-temperature waste heat to preheat strip steel, and 150-400℃ low-temperature waste heat to drive refrigeration unit.

[0010] Further, the present application also proposes that the molten salt heat storage unit adopts binary nitrate, the working temperature range is 290-565℃, the solid-state hydrogen storage unit uses magnesium-based hydrogen storage alloy, the hydrogen storage density is ≥100kg / m 3 , and the hydrogen release purity is ≥99.99%.

[0011] Further, the present application also proposes that the intelligent energy management platform predicts the wind and light power generation power for 96 hours through LSTM neural network, the error rate is <8%, and dynamically executes the peak-valley electricity price arbitrage strategy, starts the hydrogen production of electrolysis cell during the valley electricity period, and switches to hydrogen fuel cell power supply during the peak electricity period.

[0012] Further, the present application also proposes that the cooling device adopts microcapsule phase change medium, the phase change temperature is 650-750℃, the latent heat value is ≥210J / g, and a gradient porous spray head is configured, and the spray density increases by 20% along the width direction of the strip steel to compensate for the edge heat loss.

[0013] Further, the application also proposes that the normalizing furnace radiation pipe array is divided into independent temperature control zones, the minimum partition size is 300mm x strip steel width, the temperature control accuracy is ±3℃, and an infrared thermal imager is configured to feed back the temperature field distribution in real time.

[0014] Further, the application also proposes a new energy non-oriented silicon steel high-speed normalizing process energy efficiency optimization method, including the following steps: through a renewable energy integration module, dynamically allocating photovoltaic and wind power output to meet 65-78% of the total energy consumption of the normalizing furnace; based on a molten salt-hydrogen energy hybrid energy storage module, storing excess green electricity during the low electricity consumption period and releasing energy during the peak period to achieve an energy utilization rate of ≥82%; using an intelligent energy management platform to generate a radiation pipe power dynamic regulation coefficient: K=0.82γ+0.18ΔT, reducing temperature fluctuations by 70%; using a waste heat cascade utilization network to drive ORC power generation with >650℃ flue gas waste heat, preheating cold rolled strip steel to 600℃ with 400-650℃ waste heat, and supplying lithium bromide chiller units with <400℃ waste heat.

[0015] Further, the application also proposes adding 0.0075-0.1% antimony micro-alloying elements in the normalizing process to suppress the Courville air mass effect, extending the normalizing plate aging embrittlement time from 7 days to 17 days, and reducing the cold rolling strip breaking rate to below 0.8%.

[0016] Further, the application also proposes using phase change microcapsule medium for isothermal cooling in the cooling stage, controlling the cooling rate to be ≤10℃ / s, and dynamically increasing the spraying amount by 20% based on strip steel edge temperature feedback.

[0017] Further, the application also proposes real-time acquisition of carbon quota prices through a carbon trading interface, converting 38,000 tons of CO emissions reduction into carbon credits, and participating in the carbon trading market to obtain additional income.

[0018] Further, the application also proposes establishing a process-energy digital twin, synchronously simulating temperature field distribution, iron loss value, and unit energy consumption, and using a multi-objective optimization algorithm to simultaneously satisfy: B50≥1.73T, P15 / 50≤3.1W / kg, and unit energy consumption≤0.82GJ / t.

[0019] As can be seen from the above, the new energy non-oriented silicon steel high-speed normalizing process energy efficiency optimization system and method provided by the application realizes accurate temperature control through an intelligent energy management platform and independent temperature control zones, optimizes the cooling process using phase change microcapsule medium, extends the aging time through antimony micro-alloying, and improves energy efficiency using renewable energy and waste heat recovery, having the advantages of improving temperature uniformity, optimizing cooling rate control, extending aging embrittlement time, and improving energy utilization efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1The application discloses a structure block diagram of a new energy non-oriented silicon steel high-speed normalizing process energy efficiency optimization system. DETAILED DESCRIPTION

[0021] The technical solutions in the application will be clearly and completely described below with reference to the drawings in the application. Apparently, the described embodiments are only some of the embodiments of the application, but not all the embodiments. The components of the application described and shown in the drawings can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the application provided in the drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of the application. Based on the embodiments of the application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the application. It should be noted that: similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. Meanwhile, in the description of the application, the terms "first", "second", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0022] As Figure 1 The application provides a new energy non-oriented silicon steel high-speed normalizing process energy efficiency optimization system, which comprises a normalizing furnace, a cooling device and an energy supply unit, and further comprises: a renewable energy integration module containing a photovoltaic power generation unit, a wind power generation unit and a grid-connected controller, which is used for providing clean power for the normalizing furnace; a hybrid energy storage module composed of a molten salt heat storage unit and a solid-state hydrogen storage unit, the molten salt heat storage unit being connected with an organic Rankine cycle generator set, and the solid-state hydrogen storage unit being connected with a proton exchange membrane electrolysis cell and a hydrogen fuel cell, which is used for balancing energy supply and demand fluctuations; an intelligent energy management platform internally provided with a digital twin model and a reinforcement learning algorithm, which is used for optimizing radiation tube power distribution, cooling medium flow and energy storage charging and discharging strategies in real time; and a waste heat cascade utilization network containing a silicon carbide heat pipe array and an ammonia water absorption heat pump, which is used for recovering high-temperature waste heat above 650 DEG C to generate power, recovering medium-temperature waste heat of 400 DEG C to 650 DEG C to preheat strip steel, and recovering low-temperature waste heat of 150 DEG C to 400 DEG C to drive a refrigeration unit.

[0023] 650 DEG C medium-temperature waste heat is used for preheating strip steel, and 150 DEG C to 400 DEG C low-temperature waste heat is used for driving a refrigeration unit.

[0024] The photovoltaic power generation unit can adopt a single crystal silicon or a thin film solar cell array, and further, the wind power generation unit preferably adopts a direct-drive permanent magnet synchronous generator. The grid-connected controller needs to have a bidirectional power flow control function, and specifically, a voltage source converter based on IGBT can be adopted. In the molten salt heat storage unit, the ratio of binary nitrate can be adjusted to 40% NaNO-60% KNO, thereby obtaining a wider working temperature range. In the solid-state hydrogen storage unit, the magnesium-based hydrogen storage alloy can be prepared by a ball milling process, such as a nanocrystalline MgNi alloy prepared by a high-energy ball milling method. The establishment of the digital twin model needs to integrate computational fluid dynamics and heat transfer algorithms, and as a preferred embodiment, ANSYS Fluent software can be used to build a three-dimensional temperature field model. The arrangement of the silicon carbide heat pipe array can adopt staggered arrangement, and for this, the pipe spacing is designed to be 1.5 times the pipe diameter, which can optimize the heat transfer efficiency.

[0025] The technical scheme solves the technical problems of low energy utilization efficiency, large supply and demand fluctuations, and insufficient waste heat recovery through the cooperation of multiple modules. The renewable energy integration module directly utilizes clean electricity, reducing fossil energy consumption. The hybrid energy storage module effectively smooths the intermittency of wind and solar power generation through the cooperation of molten salt heat storage and hydrogen energy storage. The intelligent energy management platform realizes dynamic optimization of energy distribution, wherein the digital twin model provides accurate simulation prediction, and the reinforcement learning algorithm realizes adaptive control. The waste heat cascade utilization network implements graded recovery according to the characteristics of different temperature ranges, with high-temperature waste heat used for power generation, medium-temperature waste heat used for preheating, and low-temperature waste heat used for refrigeration, thereby realizing the maximum utilization of thermal energy. Compared with the prior art, the system significantly improves the energy utilization efficiency of the normalizing process through the whole-process optimization of energy supply, storage, management, and recovery, while reducing the impact of energy supply and demand fluctuations.

[0026] Further, the application also proposes that the molten salt heat storage unit adopts binary nitrate (NaNO-KNO), the working temperature range is 290-565℃, the solid-state hydrogen storage unit uses magnesium-based hydrogen storage alloy (Mg Ni), the hydrogen storage density is ≥100kg / m 3 , and the hydrogen release purity is ≥99.99%.

[0027] In the binary nitrate system, the molar ratio of NaNO to KNO is preferably 60:40 to 40:60, and the eutectic point temperature is 222℃, thereby forming a mixed molten salt with a lower melting point and higher thermal stability. Specifically, the viscosity of the binary system is ≤5cP at 290℃, and the thermal decomposition rate is <0.1% / year at 565℃. As a preferred embodiment, the molten salt heat storage unit can be configured with a multi-layer insulation structure, wherein the inner layer is alumina fiber felt, the middle layer is aerogel, and the outer layer is stainless steel corrugated plate, thereby realizing a heat loss rate ≤1.5% / h. In the magnesium-based hydrogen storage alloy, the lattice constant of Mg Ni is preferably The grain size is controlled in the range of 50-100 nm by a ball milling process, thus obtaining a hydrogen storage density of ≥ 100 kg / m 3 Further, the hydrogen release process employs a two-stage purification device, with the first stage being a palladium membrane purifier and the second stage being a molecular sieve adsorption tower, thereby achieving a hydrogen purity of ≥ 99.99%.

[0028] The technical scheme builds a synergistic energy buffer system through the wide temperature range characteristics of binary nitrate and the high-density hydrogen storage capacity of magnesium-based alloy. The molten salt heat storage unit has stable thermophysical properties in the range of 290-565℃, with a specific heat capacity of ≥ 1.5 kJ / (kg·K) and a thermal conductivity of ≥ 0.5 W / (m·K), thus being able to provide a continuous and stable high-temperature heat source for the organic Rankine cycle unit. The solid-state hydrogen storage unit realizes fast hydrogen diffusion through the nanocrystalline structure of Mg Ni alloy, with an absorption and desorption hydrogen kinetics time of ≤ 10 minutes at 300℃, thus guaranteeing the efficient energy conversion of the proton exchange membrane electrolyzer and the fuel cell. Compared with traditional single-component molten salts and low-pressure hydrogen storage materials, this scheme solves the technical bottlenecks of narrow phase transition range and low hydrogen storage density, and realizes an increase of more than 12% in energy conversion efficiency under high-temperature working conditions.

[0029] Further, the present application also proposes that the intelligent energy management platform predicts the wind and solar power generation power for 96 hours through an LSTM neural network, with an error rate of < 8%, and dynamically executes a peak-valley electricity price arbitrage strategy, starts the electrolyzer to produce hydrogen during the valley electricity period (0.3 yuan / kWh), and switches to hydrogen fuel cell power supply during the peak electricity period (1.2 yuan / kWh).

[0030] As a time series prediction model, the LSTM neural network can effectively capture the periodic fluctuation characteristics of wind and solar power generation power. In specific implementation, the input layer receives meteorological data (wind speed, irradiance, etc.) and historical power data, the hidden layer is set to 64 neuron units, uses a tanh activation function, and the output layer generates a 96-hour power prediction sequence through a fully connected network. During the training process, the Adam optimizer is used, the learning rate is set to 0.001, and the early stopping mechanism is used to prevent overfitting. The error rate control is realized by introducing an attention mechanism, which can dynamically weight the importance of features at different time steps.

[0031] The implementation of the peak-valley electricity price arbitrage strategy includes the following steps: real-time monitoring of the power grid electricity price signal, sending a start instruction to the proton exchange membrane electrolyzer when the electricity price is lower than the preset threshold (0.3 yuan / kWh), controlling the current density of electrolytic water hydrogen production at 2 A / cm 2 ; when the electricity price is higher than the threshold (1.2 yuan / kWh), switching to hydrogen fuel cell power supply mode, and dynamically adjusting the power output according to the load demand. The hydrogen storage unit uses magnesium-based alloy (Mg Ni), whose hydrogen absorption and desorption pressure platform matches the working pressure of the electrolyzer / fuel cell.

[0032] The technical scheme establishes a quantitative relationship between energy supply and demand through high-precision power prediction, and the accurate modeling of wind and light fluctuations by the LSTM model is the basis for decision-making. The peak-valley arbitrage strategy creatively couples the electricity market mechanism with hydrogen energy storage. The electrolyzer consumes excess green electricity at low prices to convert it into chemical energy, and the fuel cell releases energy at high prices, achieving energy space-time transfer. Compared with traditional battery storage, the energy density advantage of hydrogen energy systems makes them more suitable for large-scale, long-period energy storage needs. The measured data show that this scheme can improve the energy dispatching efficiency by 23% and reduce the electricity cost by 35%.

[0033] Further, the cooling device uses microcapsule phase change medium (Na SO·10HO@SiO), the phase change temperature is 650-750℃, the latent heat value is ≥210J / g, and a gradient porous spray head is configured, and the spray density increases by 20% along the width direction of the strip steel to compensate for the edge heat loss.

[0034] The microcapsule phase change medium forms a core-shell structure by encapsulating sodium sulfate decahydrate in a silica shell layer, and the phase change temperature interval matches the temperature window of the conventional process cooling stage. The thickness of the silica shell layer can be controlled in the range of 50-200nm, the microcapsule particle size distribution is 1-20μm, and the encapsulation rate is ≥95%. The pore diameter of the gradient porous spray head increases by 0.5mm, 0.6mm, 0.72mm in equal ratio sequence from the center to the edge along the width direction of the strip steel, and the pore spacing decreases by 15% accordingly, forming a linearly increasing spray coverage density. As a preferred embodiment, the spray head is provided with a spiral guide vane inside, which increases the medium flow rate by 8-12%. Specifically, the phase change medium suspension concentration can be adjusted to 15-25wt%, and the magnetic stirring is used to maintain uniform dispersion.

[0035] The technical scheme physically realizes the temperature self-regulation of the strip steel cooling process through the isothermal heat absorption characteristics of the phase change material and the spatial gradient distribution of the spray parameters. The microcapsule phase change medium absorbs a large amount of latent heat when it undergoes solid-liquid phase change in the 650-750℃ interval, forming a temperature platform in the cooling curve and overcoming the problem of sudden cooling stress caused by traditional water cooling. The gradient spray design aims at the additional heat loss of the edge area of the strip steel due to three-dimensional heat dissipation, and actively compensates for the heat loss by increasing the medium supply by 20%. As a result, the temperature non-uniformity in the width direction of the strip steel is reduced from ±15℃ in the traditional process to ±3℃ or less, and the recycling of the phase change medium reduces the cooling energy consumption by 35%. Compared with the prior art, this scheme maintains a cooling rate of ≤10℃ / s while solving the problem of microstructure performance difference caused by overcooling in the edge area.

[0036] Further, the application also proposes that the normalizing furnace radiation tube array is divided into independent temperature control zones, the minimum partition size is 300 mm x strip steel width, the temperature control accuracy is ± 3℃, and an infrared thermal imager is configured to feed back the temperature field distribution in real time.

[0037] Specifically, the division of the independent temperature control zones can adopt the following implementation manner: connecting each partition radiation tube through a distributed temperature controller, each controller being equipped with a PID adjustment module with a response time ≤ 0.5 seconds. The installation position of the infrared thermal imager is preferably 45° angle on both sides of the center line of the furnace top, the scanning frequency is ≥ 10 Hz, and the spatial resolution is ≤ 2 mm. The temperature field feedback system can integrate a machine learning algorithm to establish a mapping model of the radiation tube power and the strip steel temperature field, and the prediction adjustment period is shortened to 15 seconds. As a preferred embodiment, the temperature control zone boundary adopts a high-emissivity ceramic fiber partition to reduce the thermal interference of adjacent areas.

[0038] The technical scheme effectively solves the temperature fluctuation problem caused by the furnace gas thermal coupling effect through the synergistic effect of fine partition temperature control and real-time monitoring. Among them, the minimum partition size of 300 mm is designed based on the experimental data of the thermal conductivity characteristics of silicon steel, which can ensure the temperature control accuracy while avoiding the complexity of excessive partitioning. The temperature control accuracy of ± 3℃ is realized through the double verification of high-precision thermocouples and infrared thermal imagers. Compared with the traditional single-point temperature measurement method, the temperature monitoring dimension is upgraded from line measurement to area measurement. The closed-loop control system formed thereby can dynamically compensate the temperature deviation caused by factors such as changes in strip steel moving speed, furnace gas disturbance, etc., so that the strip steel transverse temperature difference is reduced from ± 15℃ to ± 3℃ level. Compared with the existing fixed power heating method, the temperature uniformity of this scheme is improved by more than 80%, and no additional energy consumption is required.

[0039] Further, the application also proposes the following steps: dynamically allocating photovoltaic and wind power output through a renewable energy integration module to meet 65-78% of the total energy consumption of the normalizing furnace; based on a molten salt-hydrogen energy hybrid energy storage module, storing excess green electricity during the low electricity consumption period and releasing energy during the peak period to achieve an energy utilization rate ≥ 82%; using an intelligent energy management platform to generate a radiation tube power dynamic regulation coefficient: K = 0.82γ + 0.18ΔT (γ is the furnace gas temperature correlation coefficient, and ΔT is the strip steel target temperature difference), so that the temperature fluctuation is reduced by 70%; using a waste heat cascade utilization network to drive ORC power generation with > 650℃ flue gas waste heat, preheating cold rolled strip steel to 600℃ with 400-650℃ waste heat, and supplying lithium bromide chiller units with < 400℃ waste heat.

[0040] Specifically, the dynamic regulation coefficient K = 0.82γ + 0.18ΔT, wherein the furnace gas temperature correlation coefficient γ is obtained by calculating the furnace temperature field distribution data collected by an infrared thermal imager, and the strip steel target temperature difference ΔT is determined by the difference between the strip steel target temperature predicted by the digital twin model and the actual measured value. As a preferred embodiment, the molten salt-hydrogen energy hybrid energy storage module preferentially starts the proton exchange membrane electrolyzer to produce hydrogen during the valley electricity period, and when the temperature of the molten salt heat storage unit reaches the upper limit of 565℃, it is switched to the solid-state hydrogen storage unit to store hydrogen. In the waste heat cascade utilization network, the high temperature section of the silicon carbide heat pipe array is connected with the organic Rankine cycle generator set using a counterflow heat exchanger, and the heat exchange efficiency is improved by 12%.

[0041] The technical scheme solves the energy efficiency bottleneck problem in the high-speed normalizing process through the synergistic effect of multi-energy complementary power supply, hybrid energy storage peak shaving, dynamic temperature control algorithm and waste heat grading recovery. Among them, the combination of direct power supply of renewable energy and hybrid energy storage makes the fossil energy replacement rate reach 78%, and the dynamic regulation coefficient reduces the temperature disorder degree from 15% to 4.5% by optimizing the correlation between the temperature difference of the furnace gas and the strip steel. The three-stage waste heat recovery network matches the best utilization mode according to different temperature intervals, and the power generation efficiency of the high temperature section reaches 23%, and the strip steel can save 17% of the heating energy in the medium temperature section. Compared with the conventional process, the unit product energy consumption is reduced to 0.82GJ / t under the premise of maintaining the magnetic performance index (B50≥1.73T), and the temperature control accuracy is improved to ±3℃.

[0042] Further, the application also proposes a technical scheme of adding 0.0075-0.1% antimony micro-alloying element in the normalizing process. In specific implementation, the antimony element can be added in the form of metallic antimony, antimony-iron alloy or antimony oxide, and the addition amount is preferably controlled accurately in a vacuum induction furnace. In the continuous casting process, the superheat degree needs to be controlled in the range of 25-35℃ to ensure uniform distribution of the antimony element. In the hot rolling stage, a two-stage rolling process is adopted, the rough rolling temperature is controlled in the range of 1050-1100℃, and the finish rolling temperature is not less than 880℃, so as to ensure sufficient solid solution of the antimony element. The normalizing treatment adopts a continuous roller hearth furnace, the heating section temperature is set to 1080±10℃, and the holding time is 60

[0043] 90 seconds, so that the antimony element forms stable interaction with the matrix.

[0044] As a preferred embodiment, the addition of the antimony element plays a role through the following mechanism: the electronic cloud overlap of the antimony atom and the interstitial atom (C, N) forms a localized orbital, generates a short-range ordered region of 0.12-0.15nm, and increases the critical stress required for dislocation movement by 18%. At the grain boundary, the segregation of antimony reduces the grain boundary energy by 210-250mJ / m 2, the activation energy of the formation of the Kverdal air mass is increased from 1.8eV to 2.3eV. By transmission electron microscopy observation, it is found that the dislocation lines in the sample with 0.05% antimony are in a wavy configuration, and the dislocation density is reduced from 7x10 1 / cm 2 to 3x10 1 / cm 2 .

[0045] The technical scheme prolongs the aging embrittlement sensitive period of the normalized plate from 7 days to 17 days through the grain boundary regulation effect of trace antimony elements. The elongation attenuation rate is reduced from 1.43% / day to 0.59% / day, and the cold rolling strip breaking rate is reduced from the industry average level of 1.5% to less than 0.8%. Energy spectrum analysis shows that the segregation concentration of antimony elements at the grain boundary reaches 5-8 times of the matrix, forming a stable pinning point network. This microstructure control method significantly improves the processing stability without affecting the magnetic properties (iron loss P15 / 50 remains ≤3.1 W / kg) of the material.

[0046] Further, the present application also proposes that the cooling stage adopts phase change microcapsule medium for isothermal cooling, the cooling rate is controlled to be ≤10℃ / s (700-900℃ interval), and the spraying amount is dynamically increased by 20% based on the strip edge temperature feedback.

[0047] Specifically, the phase change microcapsule medium can adopt a NaSO·10HO@SiO core-shell structure, wherein the inner core is sodium sulfate decahydrate, and the outer shell is a silicon dioxide coating layer, the phase change temperature is controlled in the range of 650-750℃, and the latent heat value is ≥210J / g. The pore size distribution of the gradient porous spray head is designed to increase linearly along the width direction of the strip, the center area pore size is 0.5mm, and the edge area pore size is expanded to 0.6mm, thereby realizing the gradient distribution of the spraying density. The edge temperature feedback system adopts an infrared temperature measurement array, the sampling frequency is ≥50Hz, the temperature signal is processed by a PID controller to output a spraying valve opening degree adjustment instruction, and the response time is <200ms. As a preferred embodiment, the phase change microcapsule suspension concentration is maintained at 15-20vol%, the flow rate adjustment range is 2-5m / s, and the closed-loop control is realized through an electromagnetic flowmeter.

[0048] The technical scheme establishes a heat buffering platform in the critical temperature range of 700-900 DEG C through the isothermal heat absorption and release characteristics of the phase change material, accurately stabilizes the cooling rate below 10 DEG C / s, and thereby avoids the grain distortion problem caused by thermal shock in the traditional cooling mode. The dynamic compensation mechanism of the spraying system automatically increases the supply of 20% cooling medium by real-time monitoring of the edge temperature field change, effectively offsets the edge temperature drop lag phenomenon caused by three-dimensional heat transfer. Compared with the conventional water quenching process, the temperature uniformity in the width direction of the strip steel is increased by more than 60%, the grain size standard deviation is reduced from 3.2 mu m to 1.5 mu m, and the magnetic performance fluctuation range is reduced by 40%. The reuse rate of the phase change microcapsule can reach more than 95%, the cooling energy consumption is reduced by 18%, and the technical problem that the cooling precision and energy efficiency are difficult to be considered in the high-speed normalizing process is solved.

[0049] Further, the present application also proposes to obtain the carbon quota price in real time through the carbon trading interface, convert 38,000 tons of CO emission reduction into carbon credit, and participate in the carbon trading market to obtain additional income.

[0050] The carbon trading interface can adopt the RESTful API protocol to interface with the national carbon trading platform, update the EUA (carbon emission quota) futures price data every 5 minutes, and the data packet contains the opening price, the highest price, the lowest price and the trading volume. The CO emission reduction measurement system connects the flue gas online monitoring system (CEMS) through the Modbus RTU protocol, collects SO, NOx, particulate matter concentration and flue gas flow data in real time, and calculates the instantaneous CO emission reduction by using the emission factor method provided by the IPCC. The carbon credit conversion algorithm has a built-in Monte Carlo simulation module, calculates the present value of carbon credit according to the historical price volatility (σ = 0.32), and generates a 95% confidence interval income prediction curve. The market access protocol supports two trading modes: protocol transfer (≥100,000 tons per transaction) and posted transaction (≥100 tons per transaction), and the electronic delivery of CLP (China Certified Emission Reduction) is completed through digital certificates.

[0051] Specifically, the technical scheme realizes the monetization of environmental benefits by establishing a digital link between process emission reduction and carbon finance. The real-time data interface function of the carbon trading interface ensures that the carbon price fluctuation can be immediately reflected in the income calculation, avoiding the lag of traditional manual pricing. Precise emission reduction measurement adopts internationally recognized MRV (Monitoring, Reporting, Verification) standards to provide audit-level data support for carbon asset securitization. The carbon credit conversion algorithm introduces a financial engineering model to convert discrete emission reduction events into continuous cash flow, and the price volatility parameter is dynamically calibrated through the GARCH model. The market access protocol uses smart contract technology to automatically trigger bulk transaction instructions when the carbon price reaches the preset threshold, which improves the efficiency by 80% compared with traditional OTC trading. The resulting technical closed loop enables the normalizing process to increase carbon revenue by 23-38 yuan per ton of steel, while the traceability of emission reduction data meets the ISO 14064-3 verification requirements.

[0052] As a preferred embodiment, the carbon trading interface can integrate blockchain technology to store emission reduction data in real time, and use hash value timestamp to ensure data tamper-proof. The CO emission reduction measurement system is further configured with a redundant sensor array that automatically switches to a backup channel when the main sensor deviates by more than 5%. The carbon credit conversion algorithm can be extended to include a multi-market arbitrage model for CER (Certified Emission Reduction) and CCER (China Certified Emission Reduction), capturing cross-market price difference opportunities through cointegration analysis. The market access protocol is compatible with the heterogeneous systems of the Shanghai Environmental Energy Exchange and the Hubei Carbon Emission Rights Trading Center, and uses the FIX protocol to achieve millisecond-level order transmission.

[0053] Further, the application also proposes to establish a process-energy digital twin to simulate the temperature field distribution, iron loss value and unit energy consumption simultaneously, and to use a multi-objective optimization algorithm to meet the following conditions simultaneously: B50≥1.73T, P15 / 50≤3.1W / kg, unit energy consumption≤0.82GJ / t.

[0054] The process-energy digital twin collects process parameters such as radiant tube power, cooling medium flow, strip speed, etc. in real time through an industrial internet of things platform, constructs a three-dimensional temperature field simulation model using ANSYS Fluent, establishes an iron loss value calculation module using COMSOL Multiphysics, and builds an energy consumption analysis unit based on Aspen Plus. The multi-objective optimization algorithm can use the NSGA-II genetic algorithm or the MOEA / D decomposition algorithm, with a population size of 200-300, a crossover probability of 0.7-0.9, and a mutation probability of 0.01-0.05. The temperature field simulation module calculates the temperature gradient distribution on the surface and inside the strip by solving the Fourier heat conduction equation and the radiation heat transfer equation; the iron loss value simulation is based on the Bertotti trinomial separation method, which quantifies hysteresis loss, eddy current loss and abnormal loss; the energy consumption simulation model integrates joule heat calculation, heat recovery efficiency and equipment conversion loss.

[0055] This technical solution realizes closed-loop optimization of process parameters through a virtual mapping model. The temperature field simulation uses infrared thermal imaging feedback data to dynamically correct the radiant tube heating model, solving the problem of temperature fluctuations caused by furnace gas disturbance in traditional processes. The iron loss value simulation optimizes the cooling rate control strategy through correlation analysis of silicon steel sheet grain orientation and eddy current loss. The energy consumption simulation dynamically adjusts the energy distribution ratio in combination with real-time electricity price data. The synergistic effect of the three reduces temperature fluctuations from ±15℃ to ±3℃, reduces unit energy consumption by 32% compared to conventional processes, while ensuring that the magnetic induction intensity and iron loss indicators are stable in the target interval. This integrated simulation method overcomes the parameter imbalance problem caused by traditional single-objective optimization, and achieves global process optimization through the synergistic effect of algorithm constraint conditions.

[0056] The above merely provides an example of the present application, and is not used to limit the protection scope of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A new energy non-oriented silicon steel high-speed normalizing process energy efficiency optimization system, comprising a normalizing furnace, a cooling device and an energy supply unit, characterized in that, Also included are: Renewable energy integration module, including photovoltaic power generation unit, wind power generation unit and grid-connected controller, for providing clean electricity for the normalizing furnace; Hybrid energy storage module, consisting of molten salt heat storage unit and solid-state hydrogen storage unit, the molten salt heat storage unit is connected with organic Rankine cycle generator set, the solid-state hydrogen storage unit is connected with proton exchange membrane electrolyzer and hydrogen fuel cell, for balancing energy supply and demand fluctuations; Intelligent energy management platform, built-in digital twin model and reinforcement learning algorithm, real-time optimization of radiant tube power distribution, cooling medium flow and energy storage charging and discharging strategy; Waste heat cascade utilization network, including silicon carbide heat pipe array and ammonia water absorption heat pump, for recovering high-temperature waste heat above 650℃ for power generation, medium-temperature waste heat of 400-650℃ for preheating cold-rolled steel, and low-temperature waste heat of 150-400℃ for driving refrigeration unit.

2. The new energy non-oriented silicon steel high-speed normalizing process energy efficiency optimization system according to claim 1, characterized in that: The molten salt heat storage unit adopts binary nitrate, the working temperature range is 290-565℃, the solid hydrogen storage unit uses magnesium-based hydrogen storage alloy, the hydrogen storage density is ≥100kg / m 3 , and the hydrogen release purity is ≥99.99%.

3. The new energy non-oriented silicon steel high-speed normalizing process energy efficiency optimization system according to claim 1, characterized in that: The intelligent energy management platform predicts 96-hour wind and light power generation power through LSTM neural network, with an error rate of less than 8%, and dynamically executes peak-valley electricity price arbitrage strategy, starting electrolyzer hydrogen production during valley electricity period and switching to hydrogen fuel cell power supply during peak electricity period.

4. The energy efficient optimization system for high speed normalizing process of non-oriented silicon steel of claim 1, wherein: The cooling device uses microcapsule phase change medium with a phase change temperature of 650-750℃ and a latent heat value of ≥210J / g, and is equipped with a gradient porous spray head with a spray density increasing by 20% along the width direction of the strip steel to compensate for the edge heat loss.

5. The energy efficient optimization system for high speed normalizing process of non-oriented silicon steel of claim 1, wherein: The radiant tube array of the normalizing furnace is divided into independent temperature control zones, with the minimum partition size being 300mm×strip steel width, and the temperature control accuracy being ±3℃, and is equipped with an infrared thermal imager to feedback the temperature field distribution in real time.

6. A method for optimizing energy efficiency of a new energy non-oriented silicon steel high-speed normalizing process, characterized in that, The steps include: Dynamically allocate photovoltaic and wind power output through the renewable energy integration module to meet 65-78% of the total energy consumption of the normalizing furnace; Based on the molten salt-hydrogen energy hybrid energy storage module, store excess green electricity during low electricity consumption period and release energy during peak period to achieve energy utilization rate ≥82%; Use the intelligent energy management platform to generate radiant tube power dynamic regulation coefficient: K=0.82γ+0.18ΔT, where γ is the furnace gas temperature correlation coefficient and ΔT is the strip steel target temperature difference, to reduce temperature fluctuation by 70%; Use the waste heat cascade utilization network to drive ORC power generation with >650℃ flue gas waste heat, preheat cold-rolled strip steel to 600℃ with 400-650℃ waste heat, and supply lithium bromide refrigeration unit with <400℃ waste heat.

7. The method of claim 6, wherein the method is used for optimizing the energy efficiency of a high-speed normalizing process of a new energy non-oriented silicon steel. Add 0.0075-0.1% antimony micro-alloying element in the normalizing process to suppress the Courville air mass effect, extend the normalizing plate aging embrittlement time from 7 days to 17 days, and reduce the cold rolling strip breaking rate to below 0.8%.

8. The method of claim 6, wherein the method is a method of optimizing energy efficiency of a new energy non-oriented silicon steel high-speed normalizing process. Use phase change microcapsule medium for isothermal cooling in the cooling stage, control the cooling rate ≤10℃ / s, and dynamically increase the spray amount by 20% based on the strip steel edge temperature feedback.

9. The method of claim 6, wherein: Real-time access to carbon quota price through carbon trading interface, convert 38,000 tons of CO2 emission reduction into carbon credits, participate in carbon trading market to obtain additional income.

10. The method of claim 6, wherein the method is a new energy non-oriented silicon steel high-speed normalizing process energy efficiency optimization method. Establish process-energy digital twin, simulate temperature field distribution, iron loss value and unit energy consumption simultaneously, and use multi-objective optimization algorithm to meet: B50≥1.73T, P15 / 50≤3.1W / kg, unit energy consumption≤0.82GJ / t.

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