Multi-process waste heat staged recovery and cross-region cascade utilization system

Abstract

The application discloses a multi-process waste heat graded recovery and cross-region cascade utilization system, and belongs to the technical field of heat exchange and waste heat utilization. A heat source access module is used to detect the temperature, pressure and available energy index of a multi-process waste heat medium in real time. A graded processing module divides the waste heat into a high-grade channel, a medium-grade channel and a low-grade channel based on the sliding average value and the dynamic threshold value of the available energy index, and then outputs three intermediate waste heat flows. A heat exchange output module distributes the intermediate waste heat flows to users with different temperature requirements according to a preset temperature gradient, dynamically matches the heat supply load through supply-demand state monitoring, feedforward compensation and multi-stage compensation strategies. A capacity optimization module dynamically adjusts the grading threshold value, the bypass shunt ratio and the energy level improvement range based on real-time grade parameters and heat supply load data, and combines a prediction mechanism to optimize in advance. The multi-process waste heat is graded recovered and cross-region cascade utilized, and the comprehensive utilization rate of the waste heat is improved.

Description

Technical Field

[0001] This invention relates to the field of heat exchange and waste heat utilization technology, specifically to a multi-process waste heat graded recovery and cross-regional cascade utilization system. Background Technology

[0002] With the increasing demands for energy conservation and carbon reduction in industry, multi-process waste heat recovery and cross-regional cascade utilization systems are widely used in high-energy-consuming industries such as steel, chemical, and cement. Their core architecture involves collecting waste heat generated from different processes through a heat source access device, recovering it in stages according to temperature via multi-stage heat exchangers, and then transporting it to distant users through a cross-regional heating network. However, the existing technology still has the following prominent problems: High-grade waste heat is severely downgraded and lacks the channelization capability to automatically select the path according to grade; the existing system adopts a fixed series structure, and high-temperature waste heat is cooled and used for medium and low temperature heating regardless of whether the user needs it or not, and cannot be automatically switched to high-value conversion paths such as power generation or cooling, resulting in energy quality waste. Low-grade waste heat is directly emitted due to the lack of an economical way to improve it; the existing system does not have a dedicated way to improve low-grade waste heat, nor is it coordinated with the heat pump for control, resulting in a large amount of low-temperature waste heat that cannot be effectively utilized, and the overall recovery rate is limited. The system lacks proactive scheduling logic based on real-time quality and dynamic demand; the existing controller can only passively adjust the valves and cannot dynamically determine the direction and magnitude of waste heat flow according to changes in heat source quality and fluctuations in user demand. When the heat supply is insufficient, there is a lack of priority compensation strategy, resulting in a delayed response. Summary of the Invention

[0003] The purpose of this invention is to provide a multi-process waste heat graded recovery and cross-regional cascade utilization system. It achieves automatic route selection based on grade by using a diversion decision based on the available energy index moving average and hysteresis time delay, sets up low-grade channels and coordinates heat pump control with energy level enhancement devices, and adopts feedforward compensation and three-level graded compensation strategies combined with capacity optimization module dynamic self-tuning. This solves the problems in the prior art of high-grade waste heat downgrading and utilization, direct emission of low-grade waste heat, and the lack of active scheduling and priority compensation strategies based on real-time grade and dynamic demand.

[0004] The objective of this invention can be achieved through the following technical solution: This application provides a multi-process waste heat staged recovery and cross-regional cascade utilization system, comprising: The heat source access module is used to receive waste heat media from multiple processes, and to detect and record the waste heat media grade parameters in real time. The grade parameters include temperature value, pressure value, and available energy index, which characterizes the waste heat's ability to perform work, calculated based on the temperature value, pressure value, and a preset environmental reference temperature. The graded processing module is used to compare the available energy index with a preset threshold, and based on the comparison result, divert waste heat to high-grade channel, medium-grade channel and low-grade channel; and after energy conversion of the waste heat in the high-grade channel, output the first intermediate waste heat flow, after energy level enhancement of the waste heat in the low-grade channel, output the second intermediate waste heat flow, and the waste heat in the medium-grade channel is passed directly without processing and output the third intermediate waste heat flow. The heat exchange output module is used to receive the first intermediate waste heat flow, the second intermediate waste heat flow and the third intermediate waste heat flow, and distribute them to users with corresponding temperature requirements based on a preset temperature gradient. It also monitors and records the heating supply and demand status and heating load data in real time. When the heating supply and demand status is insufficient heating, it reduces the energy conversion power of the energy conversion device in the high-grade channel main road through a preset compensation strategy to increase the heating. The capacity optimization module is used to dynamically adjust the preset threshold in the graded processing module based on the real-time acquired waste heat medium grade parameters and heating load data, and to adjust the heat diversion ratio between the bypass valve in the high-grade channel and the main circuit of the energy conversion device, as well as the temperature rise of the energy level enhancement device in the low-grade channel.

[0005] The beneficial effects of this invention are as follows: This invention solves the problems of downgrading and using high-grade waste heat and directly discharging low-grade waste heat by using a graded processing module to automatically select the path according to grade based on the available energy index moving average and hysteresis delay. It also solves the problems of downgrading and using high-grade waste heat and directly discharging low-grade waste heat by using a graded processing module to automatically select the path according to grade and matching the energy level. By using the feedforward compensation and three-level graded compensation strategy of the heat exchange output module, the problem of lack of priority compensation when there is lag in response and insufficient heat supply is solved by reducing energy conversion power, bypassing high-grade waste heat, and increasing the temperature of low-grade waste heat, thus ensuring the stability of heating supply. By dynamically self-tuning the threshold and allocation ratio through the capacity optimization module and integrating the prediction unit for early optimization, the problem of mismatch between passive adjustment and dynamic demand is solved, realizing proactive and intelligent coordinated scheduling of waste heat recovery and heating, and significantly improving the system's energy efficiency and economy. Attached Figure Description

[0006] To better understand and implement this application, the technical solution is described in detail below with reference to the accompanying drawings.

[0007] Figure 1 A schematic diagram of a multi-process waste heat graded recovery and cross-regional cascade utilization system provided for this application; Figure 2 This application provides a schematic diagram of the available energy index moving average diversion and high-grade threshold, medium-grade threshold self-tuning coordination of a multi-process waste heat graded recovery and cross-regional cascade utilization system. Detailed Implementation

[0008] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, exemplary embodiments will be described in detail below, examples of which are illustrated in the accompanying drawings. In the following description, when referring to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application.

[0009] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0010] The following detailed description of the specific implementation methods, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided in detail.

[0011] Example 1 Please see Figures 1 to 2 This embodiment provides a multi-process waste heat graded recovery and cross-regional cascade utilization system, which is deployed in an industrial park containing three process production lines: steelmaking electric furnace, cement kiln, and chemical reactor. The system includes a heat source access module, a graded processing module, a heat exchange output module, and a capacity optimization module that are connected and coordinately execute the waste heat graded recovery and cross-regional cascade utilization process. Furthermore, the heat source access module is used to receive waste heat media from multiple processes, and to detect and record the waste heat media grade parameters in real time. The grade parameters include temperature, pressure, and available energy index. The heat source access module includes a data acquisition unit and a detection unit. The acquisition unit is connected to the waste heat discharge port of each process and acquires the temperature, pressure and flow rate of the waste heat medium in real time at a preset sampling period. In this embodiment, the sampling period can be set to 1 second; the acquisition unit continuously reads data by installing temperature sensors, pressure transmitters and vortex flow meters on the flue gas duct of steelmaking electric furnace, the exhaust gas outlet of cement kiln and the cooling water return pipe of chemical reactor. The detection unit calculates the available energy index based on the temperature, pressure and preset ambient reference temperature through a preset working fluid calculation model, and outputs the temperature value, pressure value and available energy index as quality parameters to the graded processing module and the heat exchange output module. Specifically, the preset working fluid calculation model refers to a model based on the thermodynamic properties of the working fluid, derived from measured temperature. and measured pressure Calculate the specific enthalpy under the current heating supply and demand conditions. Sum of entropy It can be obtained through the following formula:

[0012] in The preset ambient reference temperature, , The medium is at a preset ambient temperature. and environmental pressure The specific enthalpy and specific entropy are obtained by referring to the thermodynamic property table for pure working fluids or single-component gases with fixed composition. For multi-component industrial flue gases with varying compositions, such as steelmaking electric furnace flue gas and cement kiln exhaust gas, the detection unit further performs the following steps: The main components and their volume fractions of flue gas are obtained in real time by an online flue gas analyzer or by matching with a preset typical operating condition component database. The main components include, but are not limited to, N2, CO2, H2O, O2, and CO; when there is a delay in online detection, the most recent valid component data is used and an empirical correction coefficient is superimposed, or a confidence interval is output based on historical fluctuation amplitude; Based on measured temperature and pressure Calculate the specific enthalpy of each component in the current state. Sum of entropy and the environmental conditions of each component. enthalpy below Sum of entropy For operating conditions where the flue gas pressure is not higher than 0.5 MPa, the gas is treated as an ideal gas: the specific enthalpy is only a function of temperature, while the specific entropy is related to both temperature and pressure. The total specific enthalpy of the mixed flue gas was calculated according to the ideal gas mixing rules. Total entropy Specifically:

[0013] Where R is the universal gas constant (8.314 kJ / (kmol·K)). Components volume fraction, and Components Enthalpy and specific entropy in the current state; Similarly, calculate the total specific enthalpy under environmental conditions. Total entropy ; in, and Components In environmental conditions Enthalpy and specific entropy at the following values; The total specific enthalpy and total specific entropy are converted into a mass-based available energy index, and the average molar mass of the mixed flue gas is calculated. ( (where the molar mass of each component is given), then the mass-based available energy index is calculated using the following formula. :

[0014] When component data is unavailable, approximate calculations can be performed based on the default components of typical operating conditions (such as volume fractions N2=0.70, CO2=0.15, H2O=0.10, O2=0.05), and the calculation results can be multiplied by a correction factor of 0.9~1.1 as the confidence interval output. The detection unit calculates the available energy index of each process for each sampling cycle and outputs the temperature value, pressure value and available energy index to the graded processing module and the heat exchange output module. For example, during a certain sampling period, the temperature of the flue gas from a steelmaking electric furnace was measured to be 550℃ and the pressure to be 0.2MPa, with a calculated usable energy index of 210kJ / kg; the temperature of the exhaust gas from a cement kiln was measured to be 280℃ and the pressure to be 0.15MPa, with a usable energy index of 110kJ / kg; and the temperature of the chemical cooling water was measured to be 45℃ and the pressure to be 0.3MPa, with a usable energy index of 45kJ / kg. Specifically, the heat source access module acquires multi-process waste heat data in real time through the acquisition unit and calculates a unified available energy index through the detection unit, which solves the technical problem that the waste heat grades of different processes cannot be quantitatively compared, and provides an accurate grade basis for graded treatment. Furthermore, the graded processing module is used to compare the available energy index with a preset threshold, and based on the comparison result, divert the waste heat to the high-grade channel, the medium-grade channel and the low-grade channel; and after energy conversion, the waste heat in the high-grade channel is output as a first intermediate waste heat flow, the waste heat in the low-grade channel is output as a second intermediate waste heat flow after energy level enhancement, and the waste heat in the medium-grade channel is passed through directly without processing and output as a third intermediate waste heat flow. The high-grade, medium-grade, and low-grade channels are three parallel waste heat flow paths within the graded treatment module. The high-grade channel has a main path and a bypass path. An energy conversion device is installed in the main path, and a bypass valve is installed in the bypass path. The main path and the bypass path are connected in parallel. The medium-grade channel is a straight-through pipeline. An energy level enhancement device is installed in the low-grade channel. The graded treatment module includes a diversion unit. The splitting unit receives the available energy index, calculates the sliding average of the available energy index within a continuous sampling period using a sliding window of a preset fixed length, and compares the sliding average with a preset high-grade threshold and a medium-grade threshold, wherein the high-grade threshold is greater than the medium-grade threshold. Specifically, the formula for calculating the moving average is: Where k represents the sequence number of the current sampling period, Indicates the first Available energy index calculated from each sampling period N is the preset sliding window length (i.e., the number of sampling periods participating in the averaging). This is the sliding average value at the current moment; in this embodiment, the sliding window length N is preset to 10, meaning that the available energy index is taken for each of the current and previous 10 sampling periods. Calculate the arithmetic mean; Furthermore, when the sliding average value is greater than or equal to the high-grade threshold, the waste heat is diverted to the high-grade channel; when the medium-grade threshold is less than or equal to the sliding average value but less than the high-grade threshold, the waste heat is diverted to the medium-grade channel; when the sliding average value is less than the medium-grade threshold, the waste heat is diverted to the low-grade channel; and when the relationship between the sliding average value and the high-grade and medium-grade thresholds changes, the corresponding diversion action is executed after a preset fixed hysteresis time delay. In this embodiment, the hysteresis time can be set to 2 seconds. As an example, the current high-grade threshold is set at 150 kJ / kg, and the medium-grade threshold is set at 80 kJ / kg. If the sliding average of the available energy index of the steelmaking electric arc furnace flue gas is 210 kJ / kg, the diversion unit controls the three-way diversion valve to switch the flue gas to the high-grade channel. The sliding average of the cement kiln exhaust gas is 110 kJ / kg, and it is diverted to the medium-grade channel. The sliding average of the chemical cooling water is 45 kJ / kg, and it is diverted to the low-grade channel. When the sliding average of a certain process drops from 210 to 145, the diversion unit waits for 2 seconds to confirm that the new state is stable before executing the action of switching from the high-grade channel to the medium-grade channel. In the high-grade channel, the energy conversion device in the main path adopts a back-pressure steam turbine; the incoming high-temperature flue gas first flows through the steam turbine to drive the steam turbine to generate electricity, and the exhaust steam temperature is reduced and then output as the first intermediate waste heat flow; the bypass valve in the bypass is an electric regulating valve, which can adjust the opening degree according to the instructions issued by the capacity optimization module, so that some flue gas bypasses the steam turbine and is directly bypassed and output, mixed with the exhaust steam and supplied to the user; As an example, after the 550°C flue gas is used to generate electricity via a steam turbine, the exhaust steam temperature drops to 180°C before being output. The medium-grade channel adopts a straight-through pipeline, and the waste gas passes through directly without any treatment and outputs the third intermediate waste heat flow. As an example, 110℃ exhaust gas is directly output; The energy level enhancement device in the low-grade channel adopts an absorption heat pump, which uses steam from the plant area as the driving heat source to raise the low-temperature cooling water to the target temperature set by the capacity optimization module and then outputs the second intermediate waste heat flow. As demonstrated, the 45°C cooling water was raised to 70°C for output; Specifically, the graded processing module achieves graded processing by using three parallel channels and dynamically distributing the heat according to the sliding average value of the available energy index. This enables priority power generation from high-grade waste heat, direct supply of medium-grade waste heat, and upgraded utilization of low-grade waste heat, thus solving the technical problem of high-quality but low-use waste heat utilization. Furthermore, the heat exchange output module is used to receive the first intermediate waste heat flow, the second intermediate waste heat flow, and the third intermediate waste heat flow, and distribute them to users with corresponding temperature requirements based on a preset temperature gradient. It also monitors and records the heating supply and demand status and heating load data in real time. When the heating supply and demand status is insufficient, it reduces the energy conversion power of the energy conversion device in the high-grade channel main road through a preset compensation strategy to increase the heating capacity. The heat exchange output module includes a temperature gradient distribution unit, a supply and demand status monitoring unit, a feedforward compensation unit, and a compensation execution unit. The temperature gradient distribution unit receives the first intermediate waste heat flow, the second intermediate waste heat flow, and the third intermediate waste heat flow, and distributes them to high-temperature users, medium-temperature users, and low-temperature users respectively based on a preset temperature gradient. Specifically, the preset temperature gradient includes three temperature ranges, corresponding to high-temperature users, medium-temperature users, and low-temperature users, respectively; each temperature range is defined by a preset upper limit temperature and a lower limit temperature, and the lower limit temperature of the high-temperature range is greater than the upper limit temperature of the medium-temperature range, and the lower limit temperature of the medium-temperature range is greater than the upper limit temperature of the low-temperature range. In this embodiment, the high-temperature range can be set to 150°C to 250°C, the medium-temperature range to 80°C to 150°C, and the low-temperature range to 40°C to 80°C; the first intermediate waste heat flow (approximately 180°C) is distributed to the high-temperature user's drying kiln; the third intermediate waste heat flow (approximately 110°C) is distributed to the medium-temperature user's material preheater; and the second intermediate waste heat flow (approximately 70°C) is distributed to the low-temperature user's plant heating heat exchange station. The supply and demand status monitoring unit collects the return water temperature, supply water temperature, and flow rate of each user in real time. Based on the difference between the target supply water temperature and the actual supply water temperature and the flow rate of each user, it calculates the instantaneous heat load gap and compares the instantaneous heat load gap with a preset supply and demand deviation threshold: when the instantaneous heat load gap is greater than the supply and demand deviation threshold, it is determined that the user is in a state of insufficient heat supply; otherwise, it is determined that the user is in a state of non-insufficient heat supply. The determination result and the instantaneous heat load gap are used as the heating supply and demand status and heating load data, respectively. Specifically, the instantaneous heat load gap is calculated independently for each user, and the calculation formula is as follows:

[0015] in Let H, M, and L represent high-temperature users, medium-temperature users, and low-temperature users, respectively. This represents the instantaneous heat load shortfall for the current user, in kW. This refers to the specific heat capacity of the heating medium at the average temperature, expressed in kJ / (kg·K). This refers to the water supply flow rate on the user side, expressed in kg / s. The target water supply temperature for the user, in °C; The actual water supply temperature for the user, in °C; the supply-demand deviation threshold can be preset to 50kW; when hour, A positive value indicates insufficient heating; when hour, A non-positive value indicates sufficient or excessive heating; the supply-demand deviation threshold is a preset positive value, which is preset to 50kW in this embodiment; the supply-demand status monitoring unit will monitor each user's... Compared to 50kW, if a user If the power consumption is >50kW, the user is determined to be in a state of insufficient heating; otherwise, the user is determined to be in a state of non-insufficient heating. For the example, the target water supply temperature for high-temperature users is 200℃, the actual water supply temperature is 185℃, the flow rate is 10kg / s, and the specific heat capacity of the medium is taken as 4.2kJ / (kg·K). The instantaneous heat load gap is calculated to be 630kW, which is greater than 50kW, and is judged as a state of insufficient heat supply. The gaps for medium-temperature users and low-temperature users are 20kW and 10kW respectively, both of which are less than the threshold and are judged as a non-insufficient state. Furthermore, the feedforward compensation unit receives the temperature value, caches the temperature values ​​of multiple consecutive sampling periods to form a time series, calculates the time change rate of the temperature value as the temperature change rate based on the time series, and calculates the pre-opening degree of the bypass valve and the pre-increase power of the energy level enhancement device according to the temperature change rate and the heating load data, and sends the pre-opening degree and pre-increase power to the bypass valve and the energy level enhancement device. In this embodiment, temperature values ​​from 5 consecutive sampling periods are cached. The current sampling period is denoted as k, the previous sampling period as k-1, and the sampling interval is... =1 second; The rate of temperature change The unit is ℃ / s; when This indicates a drop in temperature and a decrease in waste heat quality, which may lead to insufficient heating. In this case, it is necessary to increase the bypass valve opening and heat pump power in advance; pre-opening increment. Pre-increase power ,in This is the pre-opening proportional coefficient of the bypass valve. This is the pre-power increase ratio coefficient for the heat pump. This refers to the rated power of the heat pump. As an example, in this embodiment, take (That is, for every 1℃ / s decrease in temperature, the pre-opening degree of the bypass valve increases by 5%). (That is, for every 1℃ / s decrease in temperature, the heat pump pre-boost power increases by 3% of the rated power); when the detected temperature change rate is... When = -3℃ / s, calculate , The feedforward compensation unit sends the command to the bypass valve actuator and the heat pump controller in advance to increase the bypass valve opening by 15% and the heat pump power by 9% of the rated value; when hour, , Maintain the current setting value; the proportional coefficient , The calibration can be based on the actual response characteristics of the system, and is not limited to the values ​​mentioned above; The compensation execution unit acquires the heating supply and demand status and heating load data. When the heating supply and demand status is insufficient, it executes multi-level compensation actions through a preset compensation strategy. The compensation execution unit has built-in decision logic to select the priority execution level according to the type of insufficient users: if high-temperature users are insufficient, the first level is executed first; if medium-temperature users are insufficient, the second level is executed first; if low-temperature users are insufficient, the third level is executed first. For tuning parameters such as step size and delay in the compensation strategy, this embodiment provides the following adaptive method: The thermal inertia time constant τ of the system was determined by a system identification experiment: under steady operating conditions, the opening of the bypass valve was changed stepwise (e.g., from 50% to 60%), and the time required for the user-side water supply temperature to reach the final change of 63.2% was recorded. The average value of multiple experiments was taken as τ. In this embodiment, τ = 30 seconds was measured. Step size of the first-level compensation (reducing energy conversion power) It is proportional to the current instantaneous heat load gap ΔQ: ,in The proportionality coefficient (% / kW) ranges from 0.01 to 0.1. In this embodiment, it is taken as... = 0.05; Decision delay Δt = 0.5·τ = 15 seconds, to ensure that the action effect is transmitted to the user before making a decision; The step size of the second-stage compensation (increasing the bypass valve opening) Calculate based on the ratio of the required additional heat to the maximum additional heat that can be added through the bypass flow path: ,in To address the current heat load deficit, This is the maximum amount of heat that can be added when the bypass valve is fully open; The step size of the third-level compensation (increasing the temperature rise of the energy level enhancement device) The temperature is set as an integer multiple of the system's minimum perceptible temperature control accuracy. In this embodiment, the minimum temperature control accuracy is 2℃, therefore... = 2℃, and after each increase, wait at least τ=30 seconds before proceeding to the next round of judgment; Specifically, the preset compensation strategy includes: Level 1: Reduce the energy conversion power of the energy conversion device in the main path of the high-grade channel; the compensation execution unit sends a command to the turbine controller to reduce the opening of the steam inlet valve, with each step size according to the... Calculate and re-detect the supply and demand status after a delay of Δt seconds; if the shortage is eliminated, stop; otherwise, continue stepping until the minimum allowable power is reached. Level Two: When the heating supply and demand status remains insufficient after Level One compensation, the required additional heat is calculated based on the current heating load data and the flow rate of the waste heat medium. The opening of the bypass valve is increased to directly bypass the corresponding amount of high-grade waste heat to the heat exchange output module until the heating supply and demand status changes to a non-insufficient state. The required additional heat is taken from the current user's heat load gap. The increment of the bypass valve opening is determined based on the required additional heat, the enthalpy of the waste heat medium, and the flow rate. Each step is calculated according to the... calculate; Third stage: When the bypass valve has reached its maximum opening after the second stage compensation and the heating supply and demand status is still insufficient, the required temperature increase is calculated based on the heat load gap and the target temperature of the low-temperature user. The temperature increase of the energy level enhancement device in the low-grade channel is then increased until the heating supply and demand status changes to a non-insufficient state. The required increase is determined based on the heat load gap, the specific heat capacity of the low-temperature medium, and the flow rate, according to the... Calculation; if the insufficient user is a high-temperature or medium-temperature user, the enhanced low-temperature waste heat will be transferred to the high-temperature or medium-temperature user through a heat exchanger. In the demonstration scenario, the instantaneous heat load deficit for a high-temperature user is 120kW. The first stage reduces the turbine's power output by 6% from 500kW to 470kW. After 10 seconds, the deficit is detected to have decreased to 30kW, which is below the 50kW threshold, and the compensation ends. If the deficit remains at 80kW after the first stage, the second stage is executed: The required additional heat is calculated to be 80kW. Given a high-grade flue gas flow rate of 20kg / s and an enthalpy drop of 100kJ / kg, an additional flow rate of 0.8kg / s is needed, and the bypass valve is opened. If the temperature is increased by 1% from the current 40% to 41%, the gap will be eliminated. If the bypass valve has reached 100% and the gap still exists, then the third stage will be executed: Calculate that 80kW needs to be added from the low temperature side, the low temperature water flow rate is 15kg / s, the specific heat capacity is 4.2kJ / (kg·K), and the temperature needs to be increased by 1.27℃. Each step of 2℃ is sufficient. Increase the heat pump temperature increase from 25℃ to 30℃, and introduce some low temperature hot water into the high temperature user heat exchanger through valve switching until the gap is eliminated. Specifically, the heat exchange output module achieves cross-regional tiered utilization through temperature gradient distribution, calculates the gap for each user in real time through supply and demand status monitoring, suppresses disturbances in advance through feedforward compensation, and makes up for deficiencies layer by layer through multi-level compensation matched according to user type, thus solving the problems of dynamic matching of waste heat supply and demand and heating reliability. Furthermore, the capacity optimization module is used to dynamically adjust the preset threshold in the graded processing module based on the real-time acquired waste heat medium grade parameters and heating load data, and to adjust the heat diversion ratio between the bypass valve in the high-grade channel and the main circuit of the energy conversion device, as well as the temperature rise of the energy level enhancement device in the low-grade channel; the capacity optimization module includes a data receiving unit, a threshold self-tuning unit, a distribution ratio adjustment unit, and a prediction unit; The data receiving unit receives the real-time grade parameters output by the heat source access module and the heating load data output by the heat exchange output module; the grade parameters include the temperature, pressure, and available energy index of each process, and the heating load data includes the instantaneous heat load gap of each user. The threshold self-tuning unit is used to calculate the sliding average of the available energy index within a continuous sampling period, and dynamically adjust the high-grade threshold and medium-grade threshold in the graded processing module based on the sliding average, so that the heat ratio of each channel corresponds to the current heat load demand on the user side; the sliding window length is the same as that of the aforementioned diversion unit, and is 10 sampling periods. In the threshold self-tuning unit, the threshold adjustment step size and judgment period are adaptively determined based on the current supply-demand deviation. The specific tuning method is as follows: Set the base step size The initial value of the high-grade threshold is 1%-5%, and in this embodiment, it is taken as 5 kJ / kg by default; the relative deviation between the waste heat of the high-grade channel and the heat load demand of high-temperature users is defined. ,in The actual heat output of the high-grade channel. To meet the heat load requirements of high-temperature users; when At that time, a large step size was adopted. ;when At that time, a small step size is adopted. Linear interpolation is used in the middle region: Similarly, the adjustment step size of the medium-grade threshold is based on Calculated according to the same rules; The number of consecutive sampling periods is set according to the fluctuation period of the available energy index moving average, with a typical value of 3-10, and the default value in this embodiment is 5; after each threshold adjustment, a preset hysteresis time (e.g., 10 minutes) is delayed before the next adjustment to avoid frequent oscillations; Specifically, the process of dynamically adjusting the high-grade threshold and the medium-grade threshold in the grading module is as follows: The sliding average of the available energy index is normalized based on the historical maximum and minimum values. Combined with the real-time heat load ratios of high-temperature, medium-temperature, and low-temperature users, the initial target values ​​for the high-grade and medium-grade thresholds are determined respectively. Execute closed-loop correction: When the waste heat in the high-grade channel exceeds the heat load demand of high-temperature users for multiple consecutive sampling periods, increase the high-grade threshold by a preset fixed step size to narrow the high-grade range; when the waste heat in the high-grade channel is less than the heat load demand of high-temperature users for multiple consecutive sampling periods, decrease the high-grade threshold by a preset step size to expand the high-grade range; when the waste heat in the medium-grade channel exceeds the heat load demand of medium-temperature users for multiple consecutive sampling periods, increase the medium-grade threshold by a preset fixed step size; when the waste heat in the medium-grade channel is less than the heat load demand of medium-temperature users for multiple consecutive sampling periods, decrease the medium-grade threshold by a preset step size. The preset fixed step size can be set to 5 kJ / kg. During the threshold adjustment process, if the calculated value of the medium-grade threshold is not less than the current high-grade threshold, the medium-grade threshold is limited to the high-grade threshold minus the preset minimum threshold interval, so that the high-grade threshold is always greater than the medium-grade threshold. The minimum threshold interval can be set to 10 kJ / kg. After each threshold adjustment, a preset hysteresis time (e.g., 10 minutes) is delayed before the next adjustment. For the example, the historical maximum value of the available energy index over the past 24 hours is 250 kJ / kg, and the minimum value is 30 kJ / kg; the current moving average of the available energy index is 120 kJ / kg, with a normalized value of 0.409; the heat load share of high-temperature users is 0.5, that of medium-temperature users is 0.3, and that of low-temperature users is 0.2; the initial target value for the high-grade threshold is calculated to be 165 kJ / kg, and the initial target value for the medium-grade threshold is 57 kJ / kg; then, the residual heat in the high-grade channel is detected: if the actual residual heat is greater than the demand of high-temperature users for three consecutive cycles, the high-grade threshold is increased by 5 kJ / kg each cycle; if it is less than the demand, it is decreased by 5 kJ / kg; after adjustment, if the medium-grade threshold becomes 65 and the high-grade threshold becomes 175, then the medium-grade threshold is limited to 175-10=165 kJ / kg; after adjustment, the next round is delayed by 10 minutes. Furthermore, the allocation ratio adjustment unit, based on the heating load data and the adjusted high-grade threshold and medium-grade threshold, combined with the real-time temperature and flow rate of the high-grade channel waste heat, the temperature of the low-grade channel waste heat, and the target heating temperature of low-temperature users, calculates the heat diversion ratio between the bypass valve and the energy conversion device with the goal of prioritizing the direct supply of high-grade channel waste heat to match the heat load gap of high-temperature users, and calculates the temperature increase of the energy level enhancement device with the goal of meeting the temperature and heat requirements of low-temperature users after the low-grade channel waste heat is enhanced, and sends the calculated diversion ratio and temperature increase to the graded processing module. Specifically, the principle for calculating the diversion ratio is as follows: Let the total heat input to the high-grade channel be... The efficiency of the energy conversion device is High-temperature user demand is The current gap is (Positive values ​​indicate insufficient supply, negative values ​​indicate surplus); An equation is established with the goal of meeting the needs of high-temperature users through direct supply from high-grade channels: Output bypass heat Diversion ratio And limit α to the [0,1] range, temperature increase ;in For low-temperature users, the target water supply temperature The inlet temperature of the low-grade channel, and Limit to interval, This represents the maximum permissible increase in energy level by the energy level enhancement device. For example, if the total input heat of the high-grade channel is 1000kW, the turbine efficiency is 0.25, the demand of high-temperature users is 800kW, and the current shortfall is 100kW, then the bypass heat is calculated to be 600kW, and the diversion ratio is 0.6, that is, 60% of the flue gas is bypassed; if the target temperature of low-temperature users is 70℃, and the inlet temperature of the low-grade channel is 45℃, then the temperature increase is 25℃; the distribution ratio adjustment unit sends the diversion ratio of 60% and the increase of 25℃ to the bypass valve and heat pump controller of the staged processing module; Furthermore, the prediction unit connects to an external data source to acquire weather forecast data for future time periods, production plan data for each process flow, and historical heating load data. It uses time series regression analysis to predict the changing trends of future heating load data and the changing trends of waste heat medium grade parameters. Based on the prediction results, the capacity optimization module adjusts the high-grade and medium-grade thresholds in the graded processing module, the heat diversion ratio in the high-grade channel, and the temperature rise of the energy level enhancement device in the low-grade channel before insufficient heating occurs. Specifically, the process of predicting the changing trends of future heating load data and waste heat medium grade parameters through time series regression analysis is as follows: The prediction unit extracts heating load data and available energy index data of waste heat from the historical database for a past period, such as 30 days, and constructs a time series at equal time intervals, such as 15 minutes. For exogenous variables, outdoor temperature from weather forecasts is directly input as a continuous variable. Production plans for each process (such as planned shutdowns and planned reductions) need to be quantified into numerical virtual variables: for example, full-load operation is coded as 1, complete shutdown is coded as 0, and 50% load operation is coded as 0.5, forming a step change at the time of plan change. For intermittent processes, such as the slag discharge cycle of steelmaking electric furnaces, it is coded as a periodic virtual variable according to a preset cycle, such as -1 for the 30 minutes before slag discharge, 1 for the slag discharge period, and 0 for the rest. The ARIMA model is used to fit the sequence, and the autoregressive order p, differencing order d, moving average order q, and seasonality orders P, D, Q, and s are automatically determined using the Bayesian Information Criterion (BIC). In this example, s=96, corresponding to a point every 15 minutes for 24 hours. The model parameters are trained using maximum likelihood estimation. A rolling time window method is used to update the model: for each new 15-minute measured data point, the latest data is added to the training set, and the oldest data point is removed, keeping the time window length fixed at 30 days, and then the model parameters are retrained. Time series cross-validation is used to evaluate the model performance: historical data is divided into training and validation sets, and the mean absolute percentage error (MAPE) of the predicted values ​​is calculated. If the MAPE exceeds 15%, the model hyperparameters are re-searched. After training, the prediction unit updates the model based on the latest measured data, outputting the predicted heating load and available energy index every 15 minutes for the next 2 hours, and outputting a 95% confidence interval. When the confidence interval width exceeds 30% of the predicted mean, the capacity optimization module reduces its dependence on the prediction result and switches to a conservative threshold adjustment strategy. As an example, based on historical data from the past 30 days, the weather forecast for the next hour (outdoor temperature dropping from 5°C to 0°C), and the production plan (steelmaking electric furnace shutting down in one hour), it is predicted that the heat load of high-temperature users will increase by 150kW in one hour, while the high-grade waste heat usable energy index will decrease from the current 210kJ / kg to 120kJ / kg; accordingly, the capacity optimization module will start gradually reducing the high-grade threshold and increasing the pre-opening degree of the bypass valve 20 minutes in advance to ensure a smooth transition; Specifically, the capacity optimization module achieves dynamic optimization and proactive adjustment of threshold, diversion ratio, and boost magnitude through data reception, threshold self-tuning, allocation ratio adjustment, and the coordination of the prediction unit, thus solving the problem of insufficient system adaptability to process fluctuations and load changes. This embodiment solves the problem of inconsistent quantification of waste heat grades from different processes by using the heat source access module to collect real-time temperature, pressure, and flow rate data of waste heat from multiple processes and calculating the available energy index. The tiered processing module distributes waste heat to high, medium, and low-grade channels based on the moving average of the available energy index, performing energy conversion, direct flow, and energy level enhancement respectively, thus addressing the issues of high-quality waste heat being underutilized and grade mismatch. The heat exchange output module distributes waste heat to different users based on temperature gradients, and combined with independent gap monitoring for each user, feedforward compensation, and a multi-level compensation strategy matching by user type, solves the problems of dynamic supply and demand matching and heating reliability in cross-regional cascade utilization. The capacity optimization module dynamically adjusts thresholds, diversion ratios, and enhancement magnitudes, and introduces a prediction mechanism, addressing the system's insufficient adaptability to process fluctuations and load changes. These features collectively achieve tiered recovery and cross-regional cascade utilization of waste heat from multiple processes, significantly improving the overall utilization rate of waste heat.

[0016] Example 2 This embodiment provides a multi-process waste heat graded recovery and cross-regional cascade utilization system. Compared with Embodiment 1, the core difference of this embodiment is that, in response to the strong coupling interference caused by the shared waste heat recovery pipeline network of steelmaking electric furnaces and cement kilns in large-scale steel-cement co-production parks, and the technical difficulties caused by the user-side response delay due to the long cross-regional heating pipelines, the system adds a pipeline thermal inertia compensation unit to the heat exchange output module, and adds waste heat source coupling degree assessment and thermal inertia correction functions to the prediction unit and allocation ratio adjustment unit of the capacity optimization module, respectively. This solves the problems of graded decision oscillation and cross-regional heating response lag in traditional solutions under multi-source coupling scenarios.

[0017] The system is deployed in a steel-cement co-production park that includes three process production lines: steelmaking electric furnace, cement kiln, and chemical reactor. The waste heat recovery pipelines of the steelmaking electric furnace and the cement kiln are interconnected. The system includes a heat source access module, a graded processing module, a heat exchange output module, and a capacity optimization module that are connected and coordinate to perform waste heat graded recovery and cross-regional cascade utilization processes.

[0018] Furthermore, the heat source access module is used to receive waste heat media from multiple processes, and to detect and record temperature, pressure, and availability index in real time. The heat source access module includes a data acquisition unit and a detection unit. The data acquisition unit is connected to the waste heat discharge port of each process and acquires the temperature, pressure, and flow rate of the waste heat media in real time at a preset sampling period. The detection unit calculates the availability index based on the temperature, pressure, and preset environmental reference temperature using a preset working fluid calculation model, and outputs the temperature, pressure, and availability index to the staged processing module and the heat exchange output module. As an example, the detection unit calculates the available energy index of steelmaking electric furnace flue gas and cement kiln exhaust gas in real time, providing a basis for subsequent classification. Furthermore, the graded processing module compares the available energy index with a preset threshold, diverts waste heat to high-grade, medium-grade, and low-grade channels, and performs energy conversion, direct flow, and energy level enhancement respectively, outputting three intermediate waste heat flows. The graded processing module includes a diversion unit and three parallel channels; the diversion unit calculates the sliding average of the available energy index using a preset fixed-length sliding window and compares it with preset high-grade and medium-grade thresholds. Based on the comparison result, waste heat is diverted to the corresponding channel, and the diversion action is delayed by a preset fixed hysteresis time when the magnitude relationship changes. As an example, when the energy availability index of the flue gas from the steelmaking electric furnace is high, the diversion unit sends it to the high-grade channel for power generation; when the energy availability index of the waste gas from the cement kiln is medium, it is sent to the medium-grade channel for direct heating.

[0019] Furthermore, the heat exchange output module is used to distribute intermediate waste heat flow to high-temperature users such as steel rolling heating furnaces, medium-temperature users such as raw material drying kilns and low-temperature users such as plant heating based on temperature gradients, monitor the heating supply and demand status in real time, and reduce the energy conversion power of high-grade channels to increase heating supply when the heating supply is insufficient through compensation strategies. The heat exchange output module includes a temperature gradient distribution unit, a supply and demand status monitoring unit, a feedforward compensation unit, a compensation execution unit, and a pipeline thermal inertia compensation unit.

[0020] Furthermore, the temperature gradient distribution unit distributes the intermediate waste heat flow to each user based on a preset temperature gradient, wherein the lower limit of the high temperature range is greater than the upper limit of the medium temperature range, and the lower limit of the medium temperature range is greater than the upper limit of the low temperature range. Furthermore, the supply and demand status monitoring unit collects the return water temperature, supply water temperature and flow rate of each user side in real time, calculates the instantaneous heat load gap and compares it with the supply and demand deviation threshold to determine the insufficient heat supply status. Furthermore, the feedforward compensation unit caches the temperature sequence to calculate the temperature change rate, and accordingly calculates the pre-opening degree of the bypass valve and the pre-increase power of the energy level enhancement device and sends them down. The pipeline thermal inertia compensation unit is connected to the supply and demand status monitoring unit and the compensation execution unit. The pipeline thermal inertia compensation unit acquires the length, diameter, and thermal resistance parameters of the insulation material of each user-side heating pipeline. Based on the ratio of pipeline length to medium flow velocity and the added delay increment caused by heat loss along the pipeline, it calculates the transmission delay of the heating medium from the heat exchange output module outlet to the user inlet. Based on this transmission delay, the pipeline thermal inertia compensation unit performs time-domain inversion compensation on the user-side return water temperature collected by the supply and demand status monitoring unit to generate an equivalent virtual return water temperature at the heat exchange output module outlet, which replaces the measured return water temperature for instantaneous heat load gap calculation. At the same time, the pipeline thermal inertia compensation unit sends the transmission delay and heat attenuation coefficient of each user side to the capacity optimization module in real time. As an example, when the heating pipeline from the steelmaking electric furnace to the high-temperature user is several kilometers long, the transmission delay can reach tens of minutes. The change in the actual measured return water temperature on the user side cannot reflect the temperature fluctuation at the outlet of the heat exchange output module in real time. The pipeline thermal inertia compensation unit can deduce the actual temperature at the pipeline inlet and determine whether the heat supply gap is caused by the fluctuation of the upstream residual heat, thus avoiding false triggering of compensation action. The compensation execution unit is linked with the pipeline thermal inertia compensation unit: when the compensation execution unit determines that the heat supply is insufficient, it first queries the transmission delay corresponding to the user; if the delay is greater than the preset delay threshold, it prioritizes feedforward compensation (increasing the pre-opening degree of the bypass valve or increasing the power of the heat pump), and maintains this action for at least the delay time before performing the first or second level compensation in the preset compensation strategy, and performs the third level compensation if necessary; to avoid repeated or excessive adjustment caused by thermal inertia; The capacity optimization module is used to dynamically adjust the threshold, diversion ratio, and temperature increase based on real-time grade parameters and heating load data; the capacity optimization module includes a data receiving unit, a threshold self-tuning unit, a diversion ratio adjustment unit, and a prediction unit. The data receiving unit receives the real-time grade parameters output by the heat source access module and the heating load data output by the heat exchange output module.

[0021] The threshold self-tuning unit calculates the moving average of the available energy index within a continuous sampling period, and dynamically adjusts the high-grade threshold and the medium-grade threshold based on the moving average, so that the heat ratio of each channel corresponds to the current heat load demand on the user side. The prediction unit adds a waste heat source coupling degree assessment function; the prediction unit obtains the available energy index change rate of each process waste heat medium in real time from the heat source access module, and calculates the ratio of covariance to standard deviation between any two processes as the Pearson correlation coefficient. The calculation formula is as follows: ,in For covariance, , These are the standard deviations of the change rates of the available energy index of the corresponding processes. When the absolute value of this coefficient is greater than the preset coupling threshold, it is determined that there is a strong coupling relationship between the two waste heat sources. The Pearson correlation coefficient is then used as an additional feature to input into the time series regression model to predict the joint change trend of the available energy index of each process at future moments and output a coupling fluctuation warning signal. As an example, in the steel-cement co-production park, there is a strong coupling between the waste heat sources of the steelmaking electric furnace and the cement kiln. When the slag discharge of the electric furnace causes a sudden drop in its flue gas temperature, the waste gas of the cement kiln will also fluctuate synchronously because they share the waste heat boiler system. The prediction unit predicts in advance that the available energy index of the cement kiln waste gas will drop significantly based on the slag discharge plan of the electric furnace and issues a coupling fluctuation warning. The allocation ratio adjustment unit receives the transmission delay and thermal attenuation coefficient of each user side from the pipeline thermal inertia compensation unit, as well as the coupling fluctuation warning signal output by the prediction unit. When calculating the diversion ratio of the high-grade channel bypass valve, the allocation ratio adjustment unit first calculates the basic diversion ratio, and then adds a thermal inertia correction term based on the deviation between the actual gap change rate and the predicted gap on the current high-temperature user side. When a coupling fluctuation warning is received, the allocation ratio adjustment unit temporarily increases the diversion ratio adjustment step size and shortens the adjustment interval to quickly respond to coupling interference. For example, when the prediction unit issues a coupling fluctuation warning caused by electric furnace slag discharge, the allocation ratio adjustment unit immediately increases the diversion ratio adjustment step size and shortens the adjustment interval, allowing more high-temperature flue gas to bypass to high-temperature users in advance to offset the upcoming heat load gap. This embodiment addresses the issue of delayed response in long-distance, cross-regional heating by employing a pipeline thermal inertia compensation unit. It identifies multi-source interference in advance through coupling degree assessment in the prediction unit, suppresses coupling fluctuations through thermal inertia correction and rapid response mechanisms in the allocation ratio adjustment unit, and avoids erroneous adjustments through time delay linkage in the compensation execution unit. Together, these measures solve the problems of coupling interference from multiple waste heat sources in large-scale steel-cement co-production parks and graded decision oscillations and heating response delays caused by cross-regional thermal inertia, further improving the stability and reliability of the system under complex operating conditions.

[0022] Example 3 This embodiment provides a multi-process waste heat graded recovery and cross-regional cascade utilization system. Compared with Embodiment 1, the core difference of this embodiment is that, in response to the technical problem that the online component analyzer of multi-component flue gas has a detection delay that causes the calculation of available energy index to lag behind the actual operating conditions, the system adds a delay compensation unit in the heat source access module, introduces a confidence-weighted diversion mechanism in the graded processing module, and adds robust constraints to the threshold self-tuning of the capacity optimization module. This embodiment is deployed in an industrial park that includes three process production lines: steelmaking electric furnace, cement kiln, and chemical reactor. The system includes a heat source access module, a graded processing module, a heat exchange output module, and a capacity optimization module that are connected and coordinate to perform waste heat graded recovery and cross-regional cascade utilization processes. Furthermore, the heat source access module is used to receive waste heat media from multiple processes, and to detect and record the waste heat media grade parameters in real time. The grade parameters include temperature value, pressure value, and available energy index calculated based on the temperature value, pressure value, and preset environmental reference temperature. The heat source access module includes a data acquisition unit, a detection unit, and a delay compensation unit. The acquisition unit is connected to the waste heat discharge port of each process and acquires the temperature, pressure and flow rate of the waste heat medium in real time at a preset sampling period (1 second in this embodiment); The detection unit calculates the available energy index based on the temperature, pressure, and preset environmental reference temperature using a preset working fluid calculation model. For multi-component industrial flue gas (such as steelmaking electric furnace flue gas and cement kiln exhaust gas), the detection unit obtains the main components and their volume fractions of the flue gas in real time through an online flue gas analyzer or by matching them with a preset typical operating condition component database, and calculates the available energy index according to the method described in Example 1. The detection unit outputs the calculated available energy index and its corresponding timestamp to the delay compensation unit. The delay compensation unit is used to address the detection delay of the component analyzer. The resulting computational lag problem; the delay compensation unit performs the following steps: Temperature T(t) and pressure p(t) are collected in real time, and the most recent data is cached with a period of 1 second. +10 seconds of temperature and pressure data; When the output of the detection unit is delayed Available energy index At the same time, the delay compensation unit extracts the historical temperature and pressure data at the same moment. and The available energy index after delay compensation is calculated. Then output it after marking it with a timestamp; To meet the needs of real-time control, the delay compensation unit also provides advanced prediction values. Based on recent Temperature change rate per sampling period and pressure change rate Based on the component data before the delay, the available energy index at the current moment is extrapolated and predicted using the following formula:

[0023] in and The partial derivative of the available energy index with respect to temperature and pressure is calculated based on the current composition through thermodynamic relationships or approximated using preset fitting coefficients. When the deviation between the predicted value and the most recent measured value exceeds a preset threshold (e.g., 10%), the delay compensation unit automatically switches to conservative mode and prioritizes output. And increase the diversion lag time; The heat source access module will measure the available energy index. Predicted available energy index The confidence weight w, calculated based on the rate of temperature change, is output as a grade parameter to the grading processing module and the heat exchange output module. Furthermore, the graded processing module is used to compare the available energy index with a preset threshold, and based on the comparison result, divert waste heat to high-grade channels, medium-grade channels, and low-grade channels; the graded processing module includes a diversion unit; The diversion unit receives the available energy index related parameters output by the heat source access module, calculates the moving average of the available energy index within a continuous sampling period using a preset fixed-length sliding window, and compares the moving average with preset high-grade thresholds and medium-grade thresholds. In this embodiment, the diversion unit does not directly use a single available energy index value, but instead uses a confidence-weighted fusion value.

[0024] Among them, confidence weight Based on the absolute value of the rate of temperature change Dynamic adjustment: when When w=0.8, the predicted value is preferred; when When w=0.2, the measured value after delay compensation is used first; the intermediate state is calculated by linear interpolation; when component data is missing, w=0, only the measured value is used and an alarm is triggered. The splitter unit will The waste heat is compared with preset high-grade and medium-grade thresholds, and the same diversion action as in Example 1 is performed: when the sliding average value is ≥ high-grade threshold, the waste heat is diverted to the high-grade channel; when the medium-grade threshold is ≤ sliding average value < high-grade threshold, the waste heat is diverted to the medium-grade channel; when the sliding average value is < medium-grade threshold, the waste heat is diverted to the low-grade channel; and when the relationship between the sliding average value and the high-grade and medium-grade thresholds changes, the corresponding diversion action is performed with a preset fixed hysteresis time delay; in this embodiment, the hysteresis time is dynamically adjusted according to the current confidence weight w: when w ≥ 0.6, the hysteresis time is set to 2 seconds; when w < 0.6, the hysteresis time is extended to 5 seconds to avoid erroneous actions caused by unreliable data; Furthermore, the structure of the heat exchange output module is the same as that in Embodiment 1, including a temperature gradient distribution unit, a supply and demand status monitoring unit, a feedforward compensation unit, and a compensation execution unit; its specific functions and connection relationships are consistent with those in Embodiment 1, and will not be repeated here; in this embodiment, when the feedforward compensation unit calculates the temperature change rate, it uses the temperature sequence output by the delay compensation unit to eliminate the phase error introduced by the component delay. Furthermore, the capacity optimization module is used to dynamically adjust the preset threshold in the graded processing module based on the real-time acquired waste heat medium grade parameters and heating load data, and to adjust the heat diversion ratio between the bypass valve in the high-grade channel and the main circuit of the energy conversion device, as well as the temperature rise of the energy level enhancement device in the low-grade channel; the capacity optimization module includes a data receiving unit, a threshold self-tuning unit, a distribution ratio adjustment unit, and a prediction unit; The data receiving unit receives the real-time grade parameters output by the heat source access module and the heating load data output by the heat exchange output module. The threshold self-tuning unit is used to calculate the moving average of the available energy index within a continuous sampling period, and dynamically adjust the high-grade threshold and medium-grade threshold in the graded processing module based on the moving average, so that the heat ratio of each channel corresponds to the current heat load demand on the user side. In this embodiment, the threshold self-tuning unit adds a robust constraint on the confidence level of the available energy index based on the adaptive step size in embodiment 1: when the confidence weight w < 0.3, the threshold adjustment step size is halved, and the number of judgment periods for multiple consecutive sampling periods is increased from the default 5 to 10, avoiding frequent changes in the threshold when the data is unreliable. When the component analyzer is detected to output the same component data for 3 consecutive samplings (possibly due to a jamming fault), the threshold self-tuning unit pauses automatic adjustment, switches to manual hold mode, and issues a maintenance alarm. Furthermore, based on the heating load data and the adjusted high-grade and medium-grade thresholds, combined with the real-time temperature and flow rate of the high-grade channel waste heat, the temperature of the low-grade channel waste heat, and the target heating temperature for low-temperature users, the heat diversion ratio between the bypass valve and the energy conversion device is calculated with the goal of prioritizing the direct supply of high-grade channel waste heat to match the heat load gap of high-temperature users. The temperature boosting range of the energy level enhancement device is calculated with the goal of meeting the temperature and heat requirements of low-temperature users after the low-grade channel waste heat is boosted. The calculated diversion ratio and temperature boosting range are then sent to the graded processing module. The prediction unit connects to an external data source to acquire weather forecast data for future time periods, production plan data for each process flow, and historical heating load data. It uses time series regression analysis to predict the changing trends of future heating load data and the changing trends of waste heat medium grade parameters. Based on the prediction results, the capacity optimization module adjusts the high-grade and medium-grade thresholds in the graded processing module, the heat diversion ratio in the high-grade channel, and the temperature rise of the energy level enhancement device in the low-grade channel before insufficient heating occurs. This embodiment, by setting up a delay compensation unit, utilizes the high-frequency response characteristics of temperature and pressure sensors to compensate for the detection delay of the component analyzer, significantly reducing the effective lag time of the available energy index and improving the real-time performance of diversion decisions. Through confidence-weighted fusion and dynamic lag time, it solves the system oscillation caused by measurement noise and component delay uncertainty, improving the operational stability of the multi-source waste heat graded recovery system. Through the robustness constraints of the capacity optimization module, it ensures that the system can still operate safely and stably when sensors fail or data is abnormal, enhancing the system's engineering practicality.

[0025] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A multi-process waste heat recovery and cross-regional cascade utilization system, characterized in that, include: The heat source access module is used to receive waste heat media from multiple processes, and to detect and record the waste heat media grade parameters in real time. The grade parameters include temperature value, pressure value, and available energy index, which characterizes the waste heat's ability to perform work, calculated based on the temperature value, pressure value, and a preset environmental reference temperature. The graded processing module is used to compare the available energy index with a preset threshold, and based on the comparison result, divert waste heat to high-grade channel, medium-grade channel and low-grade channel; The waste heat from the high-grade channel is converted into energy and output as the first intermediate waste heat flow; the waste heat from the low-grade channel is upgraded in energy level and output as the second intermediate waste heat flow; and the waste heat from the medium-grade channel is passed through directly without treatment and output as the third intermediate waste heat flow. The graded processing module includes a diversion unit: the diversion unit receives the available energy index, calculates the sliding average of the available energy index within a continuous sampling period using a preset fixed-length sliding window, and compares the sliding average with preset high-grade thresholds and medium-grade thresholds, wherein the high-grade threshold is greater than the medium-grade threshold; when the sliding average is ≥ the high-grade threshold, the waste heat is diverted to the high-grade channel; when the medium-grade threshold is ≤ the sliding average < the high-grade threshold, the waste heat is diverted to the medium-grade channel; when the sliding average is < the medium-grade threshold, the waste heat is diverted to the low-grade channel; and when the relationship between the sliding average and the high-grade and medium-grade thresholds changes, the corresponding diversion action is executed with a preset fixed hysteresis time delay. The heat exchange output module is used to receive the first intermediate waste heat flow, the second intermediate waste heat flow and the third intermediate waste heat flow, and distribute them to users with corresponding temperature requirements based on a preset temperature gradient. It also monitors and records the heating supply and demand status and heating load data in real time. When the heating supply and demand status is insufficient heating, it reduces the energy conversion power of the energy conversion device in the high-grade channel main road through a preset compensation strategy to increase the heating. The preset compensation strategy includes at least three compensation levels with decreasing priorities: reducing the energy conversion power in the high-grade channel, increasing the opening of the bypass valve to directly bypass the high-grade waste heat, and increasing the energy level enhancement in the low-grade channel. The capacity optimization module is used to dynamically adjust the preset threshold in the graded processing module based on the real-time acquired waste heat medium grade parameters and heating load data, and to adjust the heat diversion ratio between the bypass valve and the main circuit of the energy conversion device in the high-grade channel, as well as the temperature rise of the energy level enhancement device in the low-grade channel. The heat source access module includes: The data acquisition unit is connected to the waste heat discharge ports of each process and is used to acquire the temperature, pressure and flow rate of the waste heat medium in real time. The detection unit calculates the available energy index based on the temperature, pressure, and preset ambient reference temperature using a preset working fluid calculation model, and outputs the temperature, pressure, and available energy index as quality parameters to the graded processing module and the heat exchange output module. The heat exchange output module includes: The temperature gradient distribution unit receives the first intermediate waste heat flow, the second intermediate waste heat flow, and the third intermediate waste heat flow, and distributes them to high-temperature users, medium-temperature users, and low-temperature users respectively based on a preset temperature gradient. The supply and demand status monitoring unit collects the return water temperature, supply water temperature, and flow rate of each user in real time. Based on the difference between the target supply water temperature and the actual supply water temperature and the flow rate of each user, it calculates the instantaneous heat load gap and compares the instantaneous heat load gap with a preset supply and demand deviation threshold: when the instantaneous heat load gap is greater than the supply and demand deviation threshold, it is determined to be a state of insufficient heat supply; otherwise, it is determined to be a state of non-insufficiency. The determination result and the instantaneous heat load gap are respectively used as the heating supply and demand status and heating load data. The feedforward compensation unit receives the temperature value, constructs a time series by caching the temperature values ​​of multiple consecutive sampling periods, calculates the time change rate of the temperature value as the temperature change rate based on the time series, and calculates the pre-opening degree of the bypass valve and the pre-increase power of the energy level enhancement device according to the temperature change rate and the heating load data, and sends the pre-opening degree and pre-increase power to the bypass valve and the energy level enhancement device. The capacity optimization module includes: The data receiving unit receives the real-time grade parameters output by the heat source access module and the heating load data output by the heat exchange output module. The threshold self-tuning unit is used to calculate the moving average of the available energy index within a continuous sampling period, and dynamically adjust the high-grade threshold and medium-grade threshold in the graded processing module based on the moving average, so that the heat ratio of each channel corresponds to the current heat load demand on the user side. The distribution ratio adjustment unit, based on the heating load data and the adjusted high-grade and medium-grade thresholds, combined with the real-time temperature and flow rate of the high-grade channel waste heat, the temperature of the low-grade channel waste heat, and the target heating temperature for low-temperature users, calculates the heat diversion ratio between the bypass valve and the energy conversion device with the goal of prioritizing the direct supply of high-grade channel waste heat to match the heat load gap of high-temperature users. It also calculates the temperature boosting range of the energy-level enhancement device with the goal of meeting the temperature and heat demands of low-temperature users after the low-grade channel waste heat is boosted. The calculated diversion ratio and temperature boosting range are then sent to the graded processing module. The high-grade threshold and medium-grade threshold in the dynamic adjustment grading module based on the moving average include: The moving average of the available energy index is normalized according to the sampling period, and the initial target values ​​of the high-grade threshold and the medium-grade threshold are determined by combining the real-time heat load ratio of high-temperature users, medium-temperature users and low-temperature users. When the waste heat in the high-grade channel exceeds the heat load demand of high-temperature users for multiple consecutive sampling cycles, the high-grade threshold is increased by a preset fixed step size; when the waste heat in the high-grade channel is less than the heat load demand of high-temperature users for multiple consecutive sampling cycles, the high-grade threshold is decreased by a preset step size. When the residual heat in the medium-grade channel exceeds the heat load demand of medium-temperature users for multiple consecutive sampling cycles, the medium-grade threshold is increased by a preset fixed step size; when the residual heat in the medium-grade channel is less than the heat load demand of medium-temperature users for multiple consecutive sampling cycles, the medium-grade threshold is decreased by a preset step size. Specifically, during the threshold adjustment process, if the calculated value of the medium-grade threshold is not less than the current high-grade threshold, the medium-grade threshold is limited to the high-grade threshold minus the preset minimum threshold interval, so that the high-grade threshold is greater than the medium-grade threshold; and after each threshold adjustment, a preset hysteresis time is delayed before the next adjustment.

2. The multi-process waste heat staged recovery and cross-regional cascade utilization system according to claim 1, characterized in that, The preset temperature gradient includes three temperature ranges, corresponding to high-temperature users, medium-temperature users, and low-temperature users, respectively. Each temperature range is defined by a preset upper limit temperature and a lower limit temperature, and the lower limit temperature of the high-temperature range is greater than the upper limit temperature of the medium-temperature range, and the lower limit temperature of the medium-temperature range is greater than the upper limit temperature of the low-temperature range.

3. The multi-process waste heat staged recovery and cross-regional cascade utilization system according to claim 1, characterized in that, The capacity optimization module also includes a prediction unit, which is connected to an external data source to acquire weather forecast data for future time periods, production plan data for each process flow, and historical heating load data. It uses time series regression analysis to predict the changing trends of future heating load data and the changing trends of waste heat medium grade parameters. Based on the prediction results, the capacity optimization module adjusts the high-grade and medium-grade thresholds in the graded processing module, the heat diversion ratio in the high-grade channel, and the temperature rise of the energy level enhancement device in the low-grade channel before insufficient heating occurs.

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