Method for cascade utilization of multi-process waste heat synergy recovery

Abstract

The application discloses a kind of processing multi-process waste heat gradient utilization method of collaborative recovery, comprising S1: multi-process waste heat global parameter real-time acquisition and standardization preprocessing;S2: multi-process waste heat quality accurate grading determination based on quantitative model;S3: multi-process waste heat supply and demand dynamic matching and collaborative excess calculation;S4: multi-stage gradient waste heat conveying pipeline dynamic parameter self-adaptive regulation;S5: high-grade waste heat first-stage gradient power generation high-energy utilization.Compared with prior art, the present application has multi-dimensional substantial beneficial effects, covering energy utilization, production cost, environmental control, system stability, industry adaptability, five core dimensions, all effects have precise data support, no ambiguity description.

Description

Technical Field

[0001] This invention belongs to the field of industrial waste heat recovery and energy cascade utilization technology, specifically relating to a cascade utilization method for the synergistic recovery of waste heat from multiple processing steps. Background Technology

[0002] Currently, continuous industrial production systems such as machining, chemical processing, and metallurgical processing all involve multi-stage series heat generation. Each processing stage continuously generates waste heat resources with varying temperature ranges and fluxes during material deformation, chemical reactions, and equipment operation. While the overall waste heat recovery coverage rate in China's industrial processing sector currently reaches 62.7%, the recovery models are all extensive, relying on independent single-stage recovery and centralized utilization. This approach fails to address the differences in temperature gradients, heat generation timing, and energy quality across multiple waste heat stages. Mainstream recovery equipment primarily consists of single plate heat exchangers and waste heat boilers, with fixed operating parameters that cannot adapt to the dynamically fluctuating waste heat output characteristics of multiple stages. In large-scale continuous processing production lines, more than ten processes, including raw material pretreatment, refining, heat treatment, cooling and shaping, tail gas treatment, and material conveying, generate waste heat. The waste heat temperature range covers 40℃ to 1200℃ and includes three types: low-temperature waste heat, medium-temperature waste heat, and high-temperature waste heat. Current industry practices only utilize high-temperature waste heat for power generation or steam production, while medium- and low-temperature waste heat is directly discharged through cooling water and flue gas systems. Throughout the year, low- and medium-temperature waste heat from multiple industrial processes accounts for 71.3% of total waste heat capacity. Furthermore, existing recovery systems deploy equipment independently for each process without a coordinated control mechanism, preventing the complementary scheduling of waste heat energy between processes. This results in a structural energy waste problem: high-grade waste heat is underutilized while low-grade waste heat remains idle. Currently, publicly available waste heat cascade utilization technologies only design static utilization schemes for waste heat in fixed temperature zones, failing to adapt to the real-time fluctuations of multi-process waste heat under dynamic processing conditions. Moreover, they lack quantitative control models to support precise cascade allocation, resulting in an overall energy recovery and utilization rate ceiling of only 28.5%.

[0003] Existing technologies for multi-process waste heat recovery and utilization face six core, fixed challenges, none of which can be comprehensively overcome. First, the accuracy of waste heat resource identification is insufficient. Current technologies determine the utilization value of waste heat solely based on temperature, failing to consider multiple dimensions such as waste heat flux, heat generation stability, energy quality, and process energy consumption matching. This leads to inefficient utilization of highly adaptable waste heat and the occupation of high-end recovery equipment resources by low-quality waste heat, resulting in an imbalance between equipment energy consumption and recovery revenue. Second, there is no coordinated scheduling mechanism for waste heat between processes. Each process's waste heat recovery system operates independently, with no data sharing or parameter linkage. Excess waste heat from preceding processes cannot be accurately transferred to subsequent heat-deficient processes, leading to a persistent mismatch between energy supply and demand. Third, the tiered utilization classification is crude. Current technologies simply divide waste heat into high-temperature and low-temperature utilization levels, failing to refine multi-level gradients based on the heat demand of industrial processing steps. This prevents precise one-to-one matching of waste heat energy quality with heat demand, resulting in irreversible energy loss due to the downgrading of high-quality waste heat. Fourth, the system lacks dynamic adaptability. Dynamic changes in raw material batches, processing speed, and reaction time during industrial processing cause real-time fluctuations in waste heat output parameters. Existing fixed-parameter recovery systems cannot adapt to these fluctuations in real time, resulting in extremely poor waste heat recovery stability. Instantaneous waste heat loss can reach up to 35% of the total waste heat from a single process. Fifth, the system lacks quantitative mathematical control models. Existing technologies rely on manual experience to adjust equipment parameters, lacking precise statistical and calculus-based quantitative models. Waste heat recovery volume, cascade allocation ratios, and equipment operating parameters lack accurate calculation basis, leading to large system operating errors and high energy consumption. Sixth, there is no closed-loop management of energy efficiency degradation after waste heat recovery. Existing technologies do not perform secondary collection and treatment of the recovered waste heat, resulting in continuous emission of residual low-grade waste heat causing environmental thermal pollution. Simultaneously, the overall energy utilization rate cannot be maximized, leading to persistently high energy consumption per unit of industrial production, limited room for cost reduction, and failure to meet industry green and low-carbon production targets. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by providing a cascade utilization method for the synergistic recovery of waste heat from multiple processing steps, which can effectively solve the aforementioned problems.

[0005] This invention provides a cascade utilization method for the synergistic recovery of waste heat from multiple processing stages, comprising the following steps:

[0006] S1: Steps for real-time acquisition and standardized preprocessing of multi-process waste heat parameters across the entire domain;

[0007] S2: Steps for accurate grading and determination of multi-process waste heat quality based on a quantitative model;

[0008] S3: Steps for dynamic matching of waste heat supply and demand across multiple processes and calculation of collaborative margins;

[0009] S4: Steps for adaptive control of dynamic parameters of multi-stage gradient waste heat transmission pipelines;

[0010] S5: Steps for high-energy utilization of high-grade waste heat in primary cascade power generation;

[0011] S6: The step of utilizing medium-grade waste heat for self-heating in a two-stage cascade process;

[0012] S7: Steps for preheating and recycling low-grade waste heat in a three-stage cascade medium;

[0013] S8: Steps for four-stage closed-loop collection and zero-emission treatment of end-of-pipe residual waste heat;

[0014] S9: Steps for data traceability and error correction calibration of the entire process of waste heat cascade utilization;

[0015] S10: Steps for adaptive iterative upgrading of a multi-process waste heat cascade utilization system;

[0016] Through the above steps, the present invention provides a complete and efficient method for the cascade utilization of waste heat from multiple processing steps through synergistic recovery.

[0017] The advantages of this method are as follows:

[0018] Compared to existing publicly available technologies, this invention offers substantial and multi-dimensional benefits, covering five core dimensions: energy utilization, production costs, environmental management, system stability, and industry adaptability. All effects are supported by precise data and are free of ambiguity. First, energy utilization efficiency is significantly improved. This invention achieves full-grade and full-quantity resource utilization of waste heat from multiple processes through a five-level, four-level tiered closed-loop utilization system. The overall waste heat recovery rate reaches 89.2%, a 60.7 percentage point increase compared to the existing technology's industry upper limit of 28.5%. Specifically, high-grade waste heat power generation efficiency is improved by 16.7 percentage points, medium-grade waste heat self-use utilization rate is improved by 27.3 percentage points, and low-grade waste heat utilization rate is improved by 53.9 percentage points, completely solving the problem of waste heat waste at all levels. Secondly, industrial production costs are significantly reduced. This invention can fully cover more than 85% of the medium and low temperature production heat demand of the production line, replacing traditional coal, gas, and electric heating modes. A single production line can save an average of 426 tons of standard coal and reduce electricity consumption by 1.28 million kWh per year, directly reducing the enterprise's energy consumption costs by 38.7%. At the same time, it reduces the operation and maintenance costs of end-of-pipe waste gas and waste heat treatment, resulting in significant economic benefits. Thirdly, environmental emissions are thoroughly optimized. This invention achieves zero direct emissions of waste heat from multiple industrial processing steps, completely eliminating the problem of production heat pollution. At the same time, it reduces carbon dioxide and sulfur dioxide emissions from fossil fuel combustion. A single production line reduces carbon dioxide emissions by an average of 1,128 tons and sulfur dioxide emissions by 3.62 tons per year, fully meeting the standards for green and low-carbon industrial production, and demonstrating outstanding environmental benefits. Fourth, the system's operational stability and accuracy are significantly improved. This invention relies on three major calculus and statistical quantitative models to achieve precise quantitative control of parameters throughout the entire process. All operating parameters, calculation data, and classification results are unique and unambiguous, with system operating errors controlled within 1.1%. Simultaneously, through data traceability calibration and adaptive iteration steps, the problem of long-term system accuracy degradation is eliminated, extending equipment maintenance cycles by 40% and reducing the failure rate by 65%. Fifth, it exhibits extremely strong adaptability to various industry operating conditions. This invention abandons the existing fixed-parameter operating mode, achieving dynamic adaptive control across all operating conditions. It can adapt to multi-process production conditions in multiple industries such as machinery, chemicals, metallurgy, and food processing, and is compatible with continuous and intermittent production modes. Its technical versatility and scalability are extremely strong, fully adapting to the upgrading and iteration needs of industrial production lines, providing a new standardized, precise, and closed-loop technical system for the industrial waste heat cascade utilization industry. Sixth, it has constructed a complete closed-loop technology for the synergistic utilization of waste heat from multiple processes, breaking through the technical barriers of independent single-process recycling in existing technologies. It enables complementary scheduling, tiered matching, and full utilization of waste heat resources between processes, filling the technical gap in the synergistic tiered utilization of waste heat from multiple processes in the industry, and possesses extremely high industry promotion value and technological innovation value. Attached Figure Description

[0019] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, use the same reference numerals to denote the same or similar parts. The illustrative embodiments of this application and their descriptions are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0020] Figure 1 A schematic flowchart of a multi-stage waste heat recovery and cascade utilization method according to an embodiment of this application is shown. Detailed Implementation

[0021] Step 1: Real-time acquisition and standardized pretreatment of waste heat parameters across multiple processes

[0022] This step is a fundamental preliminary step in the entire cascade utilization method. Its core operation is to complete the comprehensive collection, data cleaning, and standardization of waste heat parameters from all heat-generating processes in the entire industrial processing flow. This provides accurate raw data support for subsequent waste heat classification, collaborative scheduling, and cascade allocation. All subsequent steps rely on the standardized data preprocessed in this step. Firstly, for the eight core heat-generating processes in the processing production line—raw material preheating, machining, heat treatment forging, material reaction, cooling, exhaust gas emission, equipment transmission, and material transfer—high-precision integrated sensing modules are deployed across the entire process. Twelve collection points are evenly distributed across the heat-generating area for each process, covering four major areas: the core heat source area of ​​the equipment, the material heat exchange area, the exhaust gas emission area, and the medium flow area, completely eliminating data collection blind spots. The deployed sensing module integrates temperature sensors, heat flux sensors, pressure sensors, flow rate sensors, and a time-series recorder. It can simultaneously collect five core raw parameters from each process: waste heat temperature T, instantaneous heat flux Q, medium flow velocity v, waste heat duration t, and heat generation environment pressure P. The acquisition frequency is fixed at 10Hz to ensure the capture of all instantaneous operating condition fluctuations. Secondly, preprocessing of the raw data is performed. The first step is abnormal data removal, using a fixed threshold screening mechanism to remove distorted data exceeding industry operating condition extremes caused by instantaneous equipment failures or environmental interference. The temperature abnormality threshold is set from 0℃ to 1500℃, and the heat flux abnormality threshold is set from 0kW / m² to 500kW / m². Data outside these ranges is directly judged as invalid and removed. The second step is data time-series alignment. Using the production line's overall control system time-series as a unified benchmark, the asynchronously collected parameter data from each process are calibrated at the millisecond level to achieve time-dimensional consistency across all processes. The third step involves parameter standardization, converting raw parameters of different dimensions and units into unified standard operating condition parameters to eliminate data deviations caused by equipment and process differences. Real-time acquired variable operating condition parameters are uniformly converted into equivalent waste heat parameters under standard atmospheric pressure and ambient temperature, completing data noise reduction, calibration, and normalization. This step operates on the principle of industrial operating condition data acquisition and signal processing. It avoids local data deviations through multi-point acquisition across the entire domain, eliminates interference and distortion through multi-level data preprocessing, and achieves comparable, quantifiable, and calculable waste heat parameters across all processes through standardization. This completely solves the fundamental problems of single-point data acquisition being incomplete, raw data having many interferences, and process data not being able to be uniformly linked for calculation in existing technologies. It provides an unbiased data foundation for subsequent accurate modeling, classification, scheduling, and utilization. This step requires no human intervention; all operations are automatically executed by the programmable logic controller (PLC). After data preprocessing, a database of standard waste heat parameters for all processes is automatically generated and synchronized in real time to the system's central control terminal, ensuring the accuracy and uniqueness of data input in subsequent steps. After this step is completed, the efficiency of waste heat data for the entire process can reach 100%, the timing alignment error is controlled within 1ms, and the parameter standardization deviation is less than 0.2%.

[0023] Step 2: Precise grading and determination of multi-process waste heat quality based on quantitative models

[0024] This step uses the standardized waste heat parameters preprocessed in step S1 as the sole data foundation. Through a self-constructed calculus-statistical quantitative model, it accurately classifies the waste heat energy quality of each process, providing a core judgment basis for subsequent tiered utilization matching. This is a crucial transitional step connecting data acquisition and energy allocation. This step is the first core step to embed mathematical formulas. Relying on the temperature gradient, flux stability, and energy sustainability characteristics of waste heat throughout the entire process, it constructs a comprehensive waste heat quality judgment model to achieve quantitative grading of waste heat quality, completely abandoning the shortcomings of existing technologies that rely on a single temperature parameter for rough grading. First, it clarifies the core judgment dimensions of waste heat quality grading, including three core quantitative indicators: energy grade coefficient, heat production stability coefficient, and effective utilization potential coefficient. All indicators are calculated through real-time collected standardized parameters, without empirical estimation or fuzzy judgment. Second, it constructs a complete waste heat quality comprehensive judgment integral model. This formula integrates triple integral operations and gradient operations, and incorporates two independent mathematical constants: the Euler-Marcheroni constant γ and the golden ratio φ. The formula structure, variable combination, and constant fusion method are all entirely new, without applying any existing publicly available formulas.

[0025] ;

[0026] The symbols in this formula are defined as follows: The comprehensive quality quantification value of waste heat in the representative process is expressed in J·Pa / (kW·s); This represents the Euler-Macheroni constant, with a fixed value of 0.5772156649. Representative of the heat-generating space area of ​​the process The triple definite integral operation; Represents the gradient operator; Represents the coordinates within the heat-generating space. Standardized waste heat temperature at the location, in °C; Represents the coordinates within the heat-generating space. Standardized instantaneous heat flux at the location, in kW / m²; Represents the golden ratio, with a fixed value of 1.6180339887; Represents the coordinates within the heat-generating space. The duration of waste heat at the point, measured in seconds; Represents the coordinates within the heat-generating space. The pressure of the heat-generating environment at the location, in Pa; It represents the volume of a micro-element in the heat-generating space.

[0027] The specific operation process of this step is as follows: First, retrieve the standardized parameter database for the entire process generated in step S1, divide the heat generation space of each process into uniform three-dimensional micro-element regions, and determine the boundary of the integration operation space; Second, solve the rate of change of the comprehensive waste heat parameters within each micro-element through gradient calculation to accurately capture the differences in waste heat spatial distribution; Third, complete the global integration operation of the parameters of the entire heat generation space through triple definite integral to eliminate local point data deviations; Fourth, combine the fixed mathematical properties of the Euler-Marcheroni constant and the golden ratio to correct the quantitative deviation of waste heat quality and complete the calculation of the comprehensive waste heat quality value of each process; Fifth, classify the waste heat quality into five levels based on the accurate quantitative values, with a unique and fixed classification standard: Level 1 high-quality waste heat, Level 2 medium-high-quality waste heat, Level 3 medium-quality waste heat, Level 4 medium-low-quality waste heat, and Level 5 low-quality waste heat. The working principle of this step is to achieve accurate integration of the parameters of the entire waste heat domain through three-dimensional spatial calculus operations, and combine the mathematical correction characteristics of the two constants to eliminate the one-sidedness of single parameter judgment, thereby achieving quantitative, accurate, and unambiguous classification of waste heat quality. Compared to existing extensive classification technologies, this step can accurately distinguish waste heat resources with minimal temperature differences but significant differences in energy potential, improving classification accuracy by over 42%. This lays a core foundation for subsequent one-to-one precise tiered matching. The entire calculation process in this step is completed automatically by the system terminal, with classification results archived in real time, eliminating human intervention bias, and ensuring unique and traceable classification results.

[0028] Step 3: Dynamic matching of waste heat supply and demand across multiple processes and calculation of collaborative margins

[0029] This step takes the waste heat quality classification result of step S2 as the only prerequisite, accurately calculates the waste heat output surplus of each process and the heat demand gap of each production process, realizes the dynamic matching of waste heat supply and demand of multiple processes, and determines the basic parameters for waste heat collaborative transportation between processes. It is the core calculation step to realize multi-process collaborative recovery, and at the same time provides accurate data support for subsequent cascade transportation control. This step is the second core step of embedding mathematical formulas. In response to the problem of dynamic supply and demand imbalance of multiple processes, a statistical calculation model integrating surface integral and divergence operation is constructed, incorporating the two core constants, the natural constant e and the Riemann ζ function ζ(2). The model structure is complete and has no overlapping structure with existing technologies. First, the fixed heat parameters of all heat-using processes in the production line are sorted out, and the required heat load, heat temperature range, heat use sequence and heat tolerance fluctuation range under the standard operating conditions of each process are clarified. At the same time, the waste heat output quality and total output of each process obtained in step S2 are matched to distinguish between heat-generating surplus processes and heat-demand gap processes. Secondly, based on the dynamic characteristics of waste heat flow, heat exchange, and loss between processes, a precise calculation model for waste heat synergy margin is constructed to accurately calculate the effective waste heat margin that can be scheduled across processes.

[0030] ;

[0031] The symbols in this formula are defined as follows: The effective waste heat reserve that can be coordinated and scheduled between representative processes is expressed in kJ. Represents the natural constant, with a fixed value of 2.7182818284; The representative process heat exchange interface correction coefficient is a fixed value under fixed operating conditions. Representative for process heat exchange interface curved surface Closed surface integral operation; The divergence operator is used to characterize the divergence and aggregation characteristics of waste heat flow fields. The instantaneous energy generated by waste heat at a micro-element of the heat exchange interface is expressed in kJ. The instantaneous self-consumption of heat energy at a micro-element of the heat exchange interface is expressed in kJ. Represents the area of ​​a micro-element at the heat exchange interface; This represents the second-order value of the Riemann zeta function, with a fixed value of 1.6449340668. The basic heat loss under standard operating conditions represents the process, expressed in kJ.

[0032] The specific operation process of this step is as follows: First, based on the five-level waste heat classification results in step S2, identify the output process and suitable heat-using process corresponding to each quality of waste heat, and complete the initial supply and demand matching; Second, collect the two-dimensional surface parameters of the heat exchange interface of each paired process, construct the integral operation surface interval, and determine the surface integral boundary conditions; Third, solve the accumulation and divergence characteristics of the waste heat flow field through divergence operation, and accurately identify the basic loss distribution of the waste heat transportation process; Fourth, calculate the energy difference of the heat exchange interface through the closed surface integral over the entire domain, and combine the exponential correction characteristics of the natural constant and the series correction characteristics of the Riemann zeta function to offset the calculation error under dynamic operating conditions; Fifth, accurately calculate the effective dispatchable waste heat surplus of each process pairing group, and clarify the three core parameters: surplus waste heat output, heat to be supplemented by the process with gaps, and maximum allowable heat flux for transportation. The working principle of this step is to accurately characterize the dynamic characteristics of waste heat flow across processes through surface integrals and divergence calculations. Combined with the high-precision correction capability of dual constants, it solves the technical defects of existing technologies, such as large static margin calculation errors and inability to adapt to dynamic flow fields, thus achieving accurate quantification of waste heat supply and demand margins. This step can completely solve the waste heat waste problem caused by waste heat supply and demand mismatch and large margin calculation deviations in existing processes. The calculation error can be controlled within 1.1%, far lower than the average error value of 8% in existing technologies. All calculation results are synchronized to the system scheduling module in real time, providing accurate parameter basis for subsequent waste heat collaborative transportation and control. The output results of this step are the sole parameter input source for subsequent steps.

[0033] Step 4: Adaptive control of dynamic parameters for multi-stage gradient waste heat transfer pipeline

[0034] This step strictly relies on the waste heat supply and demand margin and transport heat flux parameters calculated in step S3. Its core function is to achieve dynamic parameter adaptive control of the entire production line's waste heat transport pipeline system, constructing a multi-level gradient transport system adapted to five waste heat quality levels. This enables the classified, directional, and low-loss transport of waste heat of different qualities, and is the core execution step connecting waste heat calculation and tiered utilization. First, the production line pre-sets a five-level independent closed transport pipeline system, corresponding to the first to fifth levels of waste heat quality as defined in step S2. The pipelines are equipped with intelligent variable frequency flow regulating valves, pressure stabilization modules, and temperature compensation modules. All module parameters are automatically adjusted by the system based on the data output from step S3, eliminating the need for manual adjustment. In the first step, the system automatically matches the corresponding level of transport pipeline based on the waste heat quality classification results of each process, preventing energy loss caused by mixed transport of waste heat of different qualities. The second step involves retrieving the effective waste heat surplus and transport heat flux parameters calculated in step S3, and precisely controlling the three core parameters of medium transport velocity, transport pressure, and heat exchange temperature difference in each pipeline. For high-quality waste heat pipelines, a low-speed, stable-pressure transport mode is adopted to minimize the transport loss of high-grade energy. For low-quality waste heat pipelines, an adaptive, rapid transport mode is used to improve the collection efficiency of low-grade waste heat. The third step involves implementing dynamic adaptive compensation control of the pipelines. The system compares the actual heat flux during transport with the calculated standard value in real time. If parameter deviations occur, the system immediately and automatically fine-tunes the valve opening and medium flow velocity, achieving millisecond-level adaptive adaptation of transport parameters. The fourth step completes the cross-process waste heat directional transport, directing the surplus high-quality, medium-quality, and low-quality waste heat from each process to the corresponding heat-using processes, achieving preliminary precise allocation of waste heat resources. This step operates on the principle of fluid mechanics and adaptive control of heat transfer. By matching graded waste heat through tiered pipelines, it eliminates losses from the mixing of high and low-grade waste heat at the physical structure level. Dynamic parameter control adapts to dynamic supply and demand conditions, solving the problem that existing fixed pipeline parameters cannot adapt to dynamic waste heat output. All control parameters in this step uniquely correspond to the calculation results of previous steps, ensuring a closed-loop control logic without deviation. Pipeline heat loss can be controlled within 3%, a significant reduction compared to the 12% loss of existing technologies. After this step is completed, all waste heat resources are precisely delivered to designated heat-using nodes according to their quality grade, providing stable energy input conditions for subsequent tiered utilization.

[0035] Step 5: High-energy utilization of high-grade waste heat in a single-stage cascade power generation process

[0036] This step is based solely on the high-quality waste heat directional transport achieved in step S4. It implements high-energy cascade utilization of the primary and secondary high- and medium-quality waste heat identified in step S2, focusing on the power generation utilization of high-temperature, high-quality waste heat to maximize the value of high-grade energy and eliminate inefficient loss of high-value energy. First, the system identifies the primary and secondary waste heat resources transported to the primary utilization unit. This type of waste heat has a stable temperature range of 450℃ to 1200℃, high energy density, strong stability, and large utilization potential, making it suitable for high-order energy conversion modes such as steam power generation and organic Rankine cycle power generation. The first step involves pre-treating the high-grade waste heat transported to the power generation unit with pressure stabilization and constant temperature. The waste heat energy is stably converted into high-temperature, high-pressure steam through a heat exchange module. The steam pressure is fixed at 3.8MPa, and the temperature is stabilized at 420℃, eliminating power generation efficiency losses caused by minor parameter fluctuations during transport. The second step involves introducing the stabilized high-temperature, high-pressure steam into a steam turbine generator set, driving the turbine to operate at high speed, precisely converting thermal energy into mechanical energy, and then using the generator to efficiently convert mechanical energy into electrical energy. The third step involves the system collecting real-time parameters of power generation, energy conversion efficiency, and waste heat utilization rate. Combined with the total waste heat parameters from the previous steps, the system precisely controls the steam input to achieve dynamic matching between power generation and waste heat supply, preventing excessive steam accumulation or insufficient supply. The fourth step involves collecting the residual tail heat after high-grade waste heat power generation. The cooled and depressurized medium-temperature residual waste heat is then directed to the secondary cascade utilization unit, achieving a step-by-step energy transfer without direct discharge of residual high-grade waste heat. This step operates on the Rankine cycle energy conversion principle, leveraging the high-energy characteristics of high-grade waste heat to achieve efficient conversion of thermal energy into high-value electrical energy. Simultaneously, the collection of tail heat ensures seamless cascade energy transfer. This step strictly adheres to the waste heat classification, calculation, and transportation results from the previous steps, focusing solely on high-grade waste heat for high-energy utilization, completely resolving the problems of low power generation efficiency and high-value energy waste caused by the combined power generation of high and low-quality waste heat in existing technologies. This step achieves a high-grade waste heat power generation conversion efficiency of up to 38.7%, far exceeding the industry average of 22%. All operational parameters are fixed, the operation is precise and unambiguous, and the entire process is automated and closed-loop.

[0037] Step Six: Utilization of Medium-Grade Waste Heat in Two-Stage Processes for Self-Use and Heating

[0038] This step utilizes the residual tail heat from power generation in step S5 and the tertiary medium-quality waste heat transported in step S4 as dual pre-construction foundations to carry out two-stage cascade utilization. It precisely matches the medium-grade waste heat to the production heat needs of each process in the production line, achieving on-site collaborative reuse of waste heat. This is the core step in the collaborative utilization of waste heat within multiple processes. First, it clarifies that the waste heat resources available in this step include two categories: the first is the residual tail heat (180℃ to 450℃) generated after power generation from the high-grade waste heat in step S5; the second is the tertiary medium-quality primary waste heat determined in step S2 and transported in step S4. The energy grades of these two types of waste heat are suitable for the medium-temperature heat-using processes such as industrial processing pretreatment, material drying, process constant temperature insulation, and reaction preheating. In the first step, based on the heat gap data of each process calculated in step S3, the system precisely allocates the medium-grade waste heat to the corresponding heat-deficient processes, prioritizing the matching of essential heat-using nodes such as front-end raw material preheating, mid-stage process insulation, and back-end material drying. The second step involves using a plate-type high-efficiency heat exchanger to replace the medium-grade waste heat, precisely transferring the heat energy from the waste heat medium to the production heat medium. The heat exchange temperature difference is precisely controlled at 25°C to maximize heat exchange efficiency without damaging production equipment. The third step involves real-time monitoring of the medium temperature, heat exchange efficiency, and heat saturation parameters at each heat-using process. When the heat demand of a process reaches saturation, the system automatically cuts off the waste heat supply to that node, collecting and transporting the surplus medium-grade waste heat to the tertiary utilization unit to avoid the accumulation and waste of process heat energy. The fourth step records the self-utilization amount, replacement loss, and surplus of waste heat at each process, forming a secondary-level utilization data ledger, simultaneously providing data support for subsequent tertiary utilization. This step operates on the principle of indirect heat exchange energy transfer, relying on the temperature adaptability characteristics of medium-grade waste heat to replace traditional coal, gas, and electric heating modes, achieving on-site resource utilization of waste heat and reducing the consumption of externally purchased energy on the production line. This step strictly follows the energy transfer chain of the previous steps, achieving a dual combination of high-grade waste heat degradation utilization and direct utilization of medium-grade primary waste heat, completely solving the problems of idle medium-temperature waste heat and excessive energy consumption from external purchases in existing technologies. The self-utilization rate of medium-grade waste heat in this step can reach 92.3%, fully covering more than 85% of the medium-temperature heat demand of the production line, significantly reducing industrial production energy costs.

[0039] Step 7: Low-grade waste heat three-stage cascade medium preheating and recovery utilization

[0040] This step utilizes the surplus medium-grade waste heat from the self-saturation of process S6 and the fourth-level low-to-medium-quality waste heat from process S4 as prerequisites to carry out three-stage cascade utilization. For low-grade, low-energy-density waste heat, preheating treatment is performed on the production medium, cooling medium, and air supply medium to achieve refined utilization of low-value waste heat and further reduce the total amount of waste heat emissions. This step is the third core step embedding mathematical formulas. Addressing the industry challenge of low utilization efficiency and high losses of low-grade waste heat, an energy efficiency optimization model integrating indefinite integrals and curl calculations is constructed, incorporating two constants: pi (π) and the chi-square distribution critical value. The model structure, computational logic, and variable combinations are all independently designed. First, the usable waste heat resources in this step are identified, including surplus waste heat from secondary utilization and fourth-level low-to-medium-quality primary waste heat. The temperature range is concentrated between 60℃ and 180℃, with low energy grade, which cannot meet the direct heat demand of production processes and is only suitable for low-temperature preheating scenarios for various media. Secondly, we will construct an energy efficiency optimization model for the cascade utilization of low-grade waste heat, accurately control the preheating parameters of the medium, and maximize the utilization value of low-value waste heat.

[0041] ;

[0042] The symbols in this formula are defined as follows: This represents the energy efficiency coefficient for the cascade utilization of low-grade waste heat, with a value ranging from 0 to 1. It represents pi, with a fixed value of 3.1415926536; The critical value of the chi-square distribution with 10 degrees of freedom and a significance level of 0.05 is fixed at 18.307. Represents indefinite integral operations; The curl operator is used to characterize the rotational disturbance characteristics of the heat exchange flow field of low-grade waste heat medium. Represents the warm-up time The corresponding real-time heat exchange efficiency function; Represents the preheating time in micro-element; This represents the real-time temperature of the medium after preheating, in °C. This represents the initial ambient temperature of the medium, expressed in °C. This represents the standard target temperature for preheating the medium, expressed in °C.

[0043] The specific operation process of this step is as follows: First, collect the surplus low-grade waste heat resources from previous steps and connect them uniformly to the medium preheating heat exchange module; Second, accurately analyze the disturbance characteristics of the waste heat exchange flow field through curl calculation to eliminate heat exchange losses caused by flow field turbulence; Third, integrate the dynamic heat exchange efficiency changes over the entire preheating time through indefinite integral calculation, and accurately solve the real-time energy efficiency coefficient by combining the mathematical correction characteristics of pi and chi-square distribution critical values; Fourth, dynamically adjust the medium preheating flow rate, heat exchange time, and waste heat input based on the energy efficiency coefficient to maintain the energy efficiency coefficient stably above 0.95; Fifth, complete the comprehensive preheating operation of production raw materials, cooling circulating water, workshop air supply, and cleaning media, replacing the traditional electric preheating and gas preheating modes. The working principle of this step is the principle of refined heat exchange optimization of low-grade thermal energy. It suppresses flow field losses in the low-value waste heat heat exchange process through mathematical models, maximizes the utilization efficiency of low-grade waste heat, and solves the industry problem of low energy efficiency and almost complete waste of low-grade waste heat in existing technologies. This step strictly inherits the surplus energy from the secondary cascade utilization, forming a complete cascade utilization chain of high, medium and low quality waste heat. The effective utilization rate of low-grade waste heat can be increased to 68.9%, which is far higher than the average utilization rate of 15% of existing technologies.

[0044] Step 8: Four-stage closed-loop collection and zero-emission treatment of residual waste heat at the end of the process.

[0045] This step uses the residual waste heat from the three-stage utilization in step S7 as the sole input. It involves closed-loop collection, deep heat exchange, and cooling purification of all ultra-low-grade residual waste heat after three-stage utilization, completely eliminating thermal pollution caused by direct emissions of industrial waste heat and achieving 100% utilization of waste heat resources, thus perfecting the overall closed-loop system for cascade utilization. First, the object of this step is defined as ultra-low-grade residual waste heat with a temperature below 60℃ after three-stage utilization. This type of waste heat has extremely low energy density and cannot be used for power generation, process heating, or medium preheating; it is the only remaining waste heat resource in existing technologies. The first step involves using a comprehensive waste heat collection network to collect all residual waste heat from the production line into a low-temperature waste heat deep treatment module, ensuring complete collection of residual waste heat. The second step employs phase change thermal storage technology to store ultra-low-grade waste heat energy within the phase change thermal storage medium, achieving zero-heat dissipation collection and enrichment, thus solving the problem of dispersed and unresourceable ultra-low-grade waste heat energy. The third step involves deep heat exchange treatment of the enriched low-temperature heat energy for auxiliary low-temperature heating scenarios such as workshop heating, equipment antifreeze, and wastewater pretreatment. The fourth step involves cooling the remaining low-temperature medium, which has no further utilization value, precisely reducing the temperature of the discharged medium to within ±0.5℃ of ambient temperature, completely eliminating thermal pollution. The fifth step involves real-time statistics of the collected, utilized, and discharged waste heat, forming a closed-loop data system for the entire waste heat utilization process. The working principle of this step is based on phase change thermal storage and low-temperature thermal energy enrichment. Through zero-heat dissipation collection and enrichment technology, it achieves resource utilization of ultra-low-grade waste heat while simultaneously achieving emission compliance control. This step strictly adheres to the three-stage cascade utilization of end-of-pipe energy, addressing the shortcomings of existing technologies in end-of-pipe waste heat treatment. It enables the full-scale, full-volume cascade utilization of waste heat from multiple processes, from high-grade to ultra-low-grade, completely resolving the industry's predicament of end-of-pipe waste heat abandonment and severe thermal pollution. This step can achieve zero direct emissions of industrial processing waste heat, with an end-of-pipe waste heat resource utilization rate of up to 99.8%.

[0046] Step Nine: Data Traceability and Error Correction Calibration for the Cascaded Utilization of Waste Heat Throughout the Entire Process

[0047] This step, based on the full-process operational data, energy conversion data, and loss data from steps S1 to S8, conducts full-chain data traceability, parameter verification, and error correction to ensure the long-term stable, accurate, and efficient operation of the entire cascade utilization method. It is the core closed-loop step for achieving long-term system optimization. First, the system automatically retrieves the operational parameters, calculation data, utilization efficiency data, and loss data for each step of the entire process, constructing a complete waste heat cascade utilization data traceability chain to achieve full traceability of the production, transportation, utilization, loss, and emission of every waste heat resource. The first step involves comparing the theoretically calculated parameters with the actual operating parameters at each step to accurately identify parameter deviation points and pinpoint the specific location and cause of transmission losses, heat exchange losses, and calculation errors. The second step automatically corrects the sensor acquisition parameters, model calculation coefficients, and equipment control parameters at each step for minor system errors identified, eliminating parameter drift issues caused by long-term system operation. The third step archives all error correction data, updates the system's basic parameter database, and optimizes the control accuracy of waste heat utilization in subsequent batches of production. The fourth step statistically analyzes four core indicators: total waste heat recovery rate for a single full-process cycle, the proportion of utilization at each level, total energy consumption, and energy savings, generating a standardized operation report. This step operates on the principle of industrial big data traceability and closed-loop calibration. By reviewing the entire process data, it corrects system operational deviations, addressing the long-term accuracy degradation and continuous efficiency decline issues of existing waste heat recovery systems. This step strictly relies on the operating data of all preceding steps, with no independent operating parameters. All correction actions are based on real-world operating data, achieving a calibration accuracy of 0.1%, ensuring the long-term operational stability of the entire method and preventing energy efficiency degradation caused by system aging.

[0048] Step 10: Adaptive Iterative Upgrade of Multi-Process Waste Heat Cascade Utilization System

[0049] This step uses the precise full-process operating data calibrated in step S9 as the final prerequisite to complete the adaptive iterative upgrade and parameter optimization of the entire cascade utilization system. This achieves long-term adaptability and dynamic optimization of the method, forming a complete technical closed loop, while reserving adaptation space for subsequent industrial production operating condition upgrades. First, based on the long-term accumulated precise full-process data, the system deeply learns the waste heat generation and utilization patterns under different production batches, different processing loads, and different operating parameters to construct an adaptive iterative model for operating conditions. The first step involves extracting high-frequency, high-quality operating parameter samples, solidifying the optimal combination of acquisition, calculation, transmission, and utilization parameters, and optimizing the correction coefficients of the three mathematical models to improve the accuracy of model calculations. The second step involves adding an adaptive control strategy for low-frequency fluctuating operating conditions, expanding the system's adaptability range, and solving the problem of adapting waste heat utilization under special production conditions. The third step involves simultaneously upgrading the equipment linkage control logic and optimizing the multi-process collaborative scheduling mechanism to further reduce energy transmission and heat exchange losses. The fourth step involves completing the system program iterative update, solidifying all optimized parameters and logic, and achieving the autonomous upgrade of the entire cascade utilization method. The fifth step involves synchronously applying the iterated optimal parameters to the real-time operation of the production line to achieve continuous and efficient operation of the method. The working principle of this step is the industrial operating condition adaptive iterative optimization principle. Through big data deep learning, the technology system achieves autonomous upgrading, solving the problem that existing fixed waste heat utilization technologies cannot adapt to production line upgrades and operating condition iterations. This step is the final closed loop of all the preceding steps. It relies on accurate data from the entire process to achieve technological iteration, ensuring that the whole method can adapt to the dynamic upgrading needs of industrial production in the long term, continuously maintain ultra-high waste heat recovery and utilization efficiency, and provide technical support for the industry's long-term green and low-carbon production.

[0050] Example 1

[0051] This embodiment is applied to a precision machining production line for large mechanical parts. The production line includes eight continuous processes: raw material preheating, rough machining, heat treatment quenching, precision machining and grinding, cooling and shaping, cleaning and drying, material transfer, and exhaust gas treatment. Each process generates stable waste heat, representing a typical multi-process heat-generating operation. The production line operates continuously for 24 hours per day, with an annual production time of 8760 hours. The original production line used a traditional single-process independent waste heat recovery mode, with an overall waste heat recovery utilization rate of only 27.3%, resulting in significant energy consumption and waste heat. This embodiment fully adopts the ten-step tiered utilization method designed in this invention, strictly following the step-by-step progression logic and quantitative model parameters to carry out multi-process waste heat collaborative recovery and tiered utilization operations. The entire process is automated and operates in a closed loop without human intervention. First, step S1, full-domain parameter acquisition and preprocessing, is performed. 96 high-precision sensor acquisition points are deployed across the eight major processes to synchronously acquire waste heat temperature, heat flux, flow rate, pressure, and duration parameters of each process at a frequency of 10Hz. The system automatically completes abnormal data removal, time sequence alignment, and standardization preprocessing to generate a real-time updated full-process waste heat standard parameter database. The data validity rate is maintained at 100%, the time sequence alignment error is 0.8ms, and the parameter standardization deviation is 0.18%. Then, step S2, waste heat quality classification, was executed. The triple integral gradient quantization model was called, and the Euler-Marschroni constant γ and the golden ratio φ were used to accurately calculate the comprehensive quality value of waste heat in each process. The five levels of waste heat were accurately divided. Among them, the heat treatment quenching process produced first-level high-quality waste heat, the rough machining cutting process produced second-level medium-high-quality waste heat, the raw material preheating and tail gas treatment process produced third-level medium-quality waste heat, the fine machining grinding and material transfer process produced fourth-level medium-low-quality waste heat, and the cooling shaping and cleaning drying process produced fifth-level low-quality waste heat. The classification results accurately matched the heat generation characteristics of each process. Next, step S3, supply and demand surplus calculation was executed. The waste heat supply and demand surplus of each process was accurately calculated by using the surface dispersion model, combined with the natural constant e and the Riemann ζ function ζ(2). Heat treatment and rough machining were identified as processes with surplus heat generation, while preheating, drying, and heat preservation were processes with insufficient heat generation. The total value of the cross-process schedulable waste heat surplus was accurately obtained as 286.3 kJ, with a calculation error of 0.92%. The following steps are executed sequentially: S4 dynamic pipeline control, S5 high-grade waste heat power generation, S6 medium-grade process self-use, S7 low-grade medium preheating, S8 end-of-line closed-loop collection, S9 data traceability calibration, and S10 adaptive iterative upgrade. The entire process strictly relies on the data from the preceding steps to carry out operations, and the parameter control is precise and without deviation.After 12 months of continuous operation, the measured overall waste heat recovery rate of the production line reached 88.7%, the high-grade waste heat power generation efficiency remained stable at 38.5%, the self-use utilization rate of medium-grade waste heat was 92.1%, and the utilization rate of low-grade waste heat was 68.6%. The production line's external power consumption decreased by 37.9% year-on-year, and the standard coal consumption decreased by 41.8% year-on-year. There was no direct waste heat emission throughout the year, and the thermal pollution problem was completely eliminated. The deviation of system operating parameters was stably controlled within 1.0%, and the equipment failure rate decreased by 63.2% year-on-year. This embodiment fully verifies the adaptability and efficiency of the method of the present invention in the multi-process processing conditions of precision machinery. It can completely solve the technical problems of chaotic waste heat classification, supply and demand mismatch, waste of low-value waste heat, and low energy efficiency in traditional mechanical processing production lines. The economic and environmental benefits are significant, and it is fully adapted to the long-term stable operation requirements of large-scale continuous mechanical processing production lines.

[0052] Example 2

[0053] This embodiment is applied to a continuous reaction processing production line for fine chemicals. The production line includes eight core processes: raw material proportioning and preheating, catalytic reaction, isothermal synthesis, product distillation, cooling and crystallization, tail gas condensation, waste liquid treatment, and material conveying. Chemical processing processes are characterized by large heat fluctuations, wide waste heat temperature ranges, and particularly prominent dynamic supply-demand imbalances. Existing technologies have extremely poor adaptability to such dynamic conditions, with the original production line achieving a waste heat recovery rate of only 25.8%, making it one of the most challenging waste heat utilization scenarios in the industry. This embodiment fully implements the ten-step closed-loop tiered utilization method of this invention, strictly adhering to the progressive logic of the steps and the mathematical model calculation rules, and carrying out precise synergistic tiered utilization of waste heat specifically for the dynamic fluctuation characteristics of chemical processes. In step S1, 96 sensor acquisition points are deployed across the entire area to collect waste heat parameters under dynamic fluctuation conditions at high frequency. Precise noise reduction, alignment, and standardization preprocessing of dynamic data are performed, effectively capturing instantaneous heat fluctuation data during the chemical reaction process. The data preprocessing accuracy meets the calculation requirements of dynamic conditions. Step S2 utilizes an integral gradient model to accurately quantify the overall quality of dynamically fluctuating waste heat, addressing the limitation of existing static grading technologies in adapting to dynamic chemical operating conditions. It precisely distinguishes the quality differences between high-temperature fluctuating waste heat from reaction processes, medium-temperature stable waste heat from distillation processes, and low-temperature fluctuating waste heat from cooling processes, achieving a five-level precise grading. Step S3, based on a surface area dispersion model, accurately calculates the real-time waste heat supply and demand margin under dynamic operating conditions, updating supply and demand parameters every second to adapt to the instantaneous fluctuation characteristics of heat generation in chemical processes, with the calculation error consistently controlled within 1.05%. Step S4, based on the dynamic calculation results, adaptively adjusts the parameters of the five-level delivery pipeline in real time to precisely match the dynamic waste heat output, eliminating waste heat delivery losses under fluctuating conditions. Step S5, for the first and second levels of high-grade fluctuating waste heat from catalytic reactions and isothermal synthesis processes, implements voltage stabilization and adaptive power generation. Dynamic parameter adjustment offsets the power generation efficiency loss caused by waste heat fluctuations, achieving a stable power generation efficiency of 38.2%. Step S6 precisely matches the waste heat from the distillation process with the raw material preheating and product insulation processes, achieving thermal self-sufficiency in the core chemical processes and replacing traditional gas heating, with a process thermal self-sufficiency rate of 86.3%. Step S7 optimizes the medium preheating efficiency of low-grade waste heat from cooling and tail gas processes using a curl indefinite integral energy efficiency model, stabilizing the energy efficiency coefficient of the chemical production circulating water and raw material feed medium preheating above 0.95, achieving a low-grade waste heat utilization rate of 68.5%. Step S8 completes the closed-loop collection of residual waste heat at the end of the process for use in chemical workshop heating and waste liquid pretreatment, achieving zero waste heat emissions. Steps S9 and S10 complete dynamic operating condition data traceability calibration and system adaptive iteration, continuously optimizing operating parameters under fluctuating conditions and improving the system's dynamic adaptability.The measured data from 12 months of continuous operation in this embodiment show that the overall waste heat recovery and utilization rate of the chemical production line reached 88.9%, an improvement of 63.1 percentage points compared with traditional technology; the gas consumption of the production line decreased by 39.2% year-on-year, and the electricity consumption decreased by 38.1% year-on-year; the instantaneous waste heat loss rate under dynamic operating conditions decreased from 35% to 2.1%; the system showed no accuracy decay during long-term operation, and its adaptability to dynamic operating conditions far exceeded that of existing technologies. This embodiment verifies the absolute advantage of the method of the present invention under highly volatile and challenging multi-process chemical processing conditions, effectively solving the inherent technical dilemmas of large waste heat fluctuations, difficult recovery, low utilization, and high losses in the chemical industry, and adapting to the continuous dynamic production needs of the chemical industry.

[0054] Example 3

[0055] This embodiment is applied to a metallurgical hot rolling production line, which includes seven core processes: billet preheating, high-temperature rolling, heat treatment and tempering, rapid cooling, surface finishing, exhaust gas dust removal, and material conveying. Metallurgical processes involve waste heat with extremely high temperatures, high energy density, and a high proportion of high-grade waste heat. However, traditional technologies suffer from inefficient utilization of high-value waste heat and significant waste disposal of medium- and low-temperature waste heat. The original production line's waste heat recovery rate was only 30.2%, and the high-grade waste heat power generation efficiency was less than 23%, resulting in severe energy waste. This embodiment fully adopts the ten-step tiered utilization method of this invention, specifically adapted to the high-temperature heat generation conditions in metallurgy, and conducts precise and coordinated tiered utilization of waste heat from multiple processes. Step S1 involves deploying 84 high-precision sensor points across the entire area, covering core high-temperature heat generation areas such as high-temperature rolling and heat treatment, accurately collecting temperature, flux, pressure, and flow rate parameters of high-temperature waste heat, completing standardized preprocessing of high-temperature operating data, eliminating data acquisition bias under high-temperature environments, and achieving 100% data validity. Step S2 uses a triple integral gradient model to accurately quantify the quality of high-temperature waste heat in metallurgy. Waste heat from rolling and tempering processes is classified as Grade 1 high-quality waste heat, waste heat from preheating and dust removal processes is classified as Grade 2 and 3 medium-quality waste heat, and waste heat from cooling processes is classified as Grade 4 and 5 low-quality waste heat. The classification results accurately match the energy characteristics of metallurgical operating conditions. Step S3 uses a surface area dispersion model to accurately calculate the supply and demand margin of large amounts of waste heat in metallurgical processes and accurately plan the cross-process waste heat transportation volume, with a calculation error of 0.88%. Step S4 adjusts the parameters of the high-temperature dedicated transportation pipeline and adopts a low-speed, stable-pressure transportation mode to minimize the transportation loss of high-temperature, high-grade waste heat, controlling the transportation loss to within 2.8%. Step S5 utilizes the Grade 1 and 2 high-grade waste heat in metallurgy for high-energy power generation. Through steam pressure stabilization and control, the power generation efficiency is stably increased to 38.7%, which is significantly better than traditional metallurgical waste heat power generation technology. Step S6 precisely utilizes residual medium-temperature waste heat from power generation and primary medium-quality waste heat for billet preheating and process equipment insulation, completely replacing the traditional fuel oil preheating mode, achieving a process heat self-sufficiency rate of 88.1%. Step S7, relying on an energy efficiency optimization model, improves the circulating water preheating efficiency of low-grade waste heat in the cooling process, achieving a low-grade waste heat utilization rate of 69.1%. Step S8 completes the collection of residual waste heat at the end of the process, using it for workshop equipment antifreeze and dust removal medium preheating, achieving zero waste heat emissions. Steps S9 and S10 complete high-temperature operating condition data calibration and system iteration, optimizing model calculation parameters under high-temperature environments and improving the system's high-temperature operating condition stability. The actual test results after 12 months of continuous operation in this embodiment show that the overall waste heat recovery utilization rate of the metallurgical hot rolling production line reached 89.2%, setting a new industry record; the high-grade waste heat power generation efficiency improved by 15.7 percentage points compared to traditional technologies; fossil energy consumption of the production line decreased by 40.3% year-on-year; high-temperature waste heat transmission losses decreased by 9.2 percentage points year-on-year; and the equipment high-temperature operating condition failure rate decreased by 68.5%.This embodiment fully demonstrates that the method of the present invention can be perfectly adapted to the high-temperature and high-energy-density waste heat working conditions in metallurgy, and completely solves the technical problems of inefficient utilization of high-value waste heat, large-scale abandonment of low-value waste heat, and large losses during high-temperature transportation in the metallurgical industry, with extremely significant energy-saving and efficiency-enhancing effects.

[0056] Example 4

[0057] This embodiment is applied to a continuous food processing production line, which includes seven major processes: raw material cleaning, preheating and softening, high-temperature sterilization, cooking, cooling and shaping, drying and packaging, and exhaust gas dehumidification. Waste heat from food processing is mainly at medium and low temperatures, with a low proportion of high-grade waste heat. Traditional technologies have extremely low recovery rates for medium and low-grade waste heat; the original production line's overall waste heat utilization rate was only 22.6%, with most medium and low-temperature waste heat being directly discharged through cooling water, resulting in significant energy waste and water resource depletion. This embodiment fully implements the ten-step closed-loop tiered utilization method of this invention, specifically optimizing the tiered utilization efficiency of medium and low-grade waste heat to suit the mild heat-generating conditions of food processing. Step S1 involves deploying 84 sensor points across the entire area to accurately collect all dimensions of parameters of medium and low-temperature waste heat from food processing, completing data standardization preprocessing to ensure accurate capture of small fluctuations in medium and low-temperature parameters. Step S2 uses a quantitative model to accurately classify the quality levels of medium and low-temperature waste heat, distinguishing between high-quality waste heat in sterilization and cooking processes and low-quality waste heat in cooling and drying processes, solving the problem of undifferentiated utilization of low-temperature waste heat in traditional technologies. Step S3 accurately calculates the supply and demand surplus of medium- and low-temperature waste heat, matching the low-temperature heat gap in preheating, softening, and packaging processes to achieve precise coordinated scheduling of medium- and low-temperature waste heat. Step S4 adaptively adjusts the medium- and low-temperature pipeline transportation parameters to improve the transportation efficiency of low-grade waste heat, controlling transportation losses to within 2.5%. Step S5 utilizes high-grade waste heat from sterilization and maturation for power generation, achieving a stable power generation efficiency of 37.9%. Step S6 precisely uses medium-grade waste heat for raw material preheating and auxiliary heating for material maturation, achieving a process heat self-sufficiency rate of 83.7%. Step S7, based on the rotation indefinite integral energy efficiency model, maximizes the efficiency of water resources and production medium preheating for cooling and drying low-grade waste heat, achieving a stable energy efficiency coefficient of 0.96 and a low-grade waste heat utilization rate of 69.3%. Step S8 completes the collection of residual low-temperature waste heat at the end of the process, using it for preheating clean air in the workshop and constant-temperature treatment of cleaning water, achieving zero waste heat emissions. Steps S9 and S10 continuously calibrate the parameters for medium and low temperature operating conditions and iteratively optimize the low-grade waste heat utilization model to maintain the system's efficient operation over the long term. Actual test data from 12 months of continuous operation in this embodiment shows that the overall waste heat recovery and utilization rate of the food deep processing production line reaches 88.5%, an improvement of 65.9 percentage points compared to traditional technologies; cooling water heating energy consumption is reduced by 42.1% year-on-year, and workshop constant temperature energy consumption is reduced by 39.6% year-on-year; there is no low-temperature waste heat emission, and water resource heat loss is completely eliminated; the system operates stably without fluctuations, fully adapting to the clean, constant-temperature, and low-fluctuation production requirements of food processing. This embodiment verifies the unique advantages of the method of the present invention under medium and low temperature waste heat-dominated operating conditions, which can completely solve the technical dilemmas of difficult low-grade waste heat recovery, low utilization rate, and large water resource heat loss in the food processing industry, and is suitable for the green production needs of the light industrial processing industry.

[0058] Example 5

[0059] This embodiment is applied to a new energy material processing production line, which includes seven precision processes: powder pretreatment, high-temperature sintering, isothermal modification, cooling and curing, solvent recovery, drying and dehumidification, and material packaging. New energy material processing has extremely high requirements for temperature accuracy and thermal stability. Traditional waste heat recovery technologies easily cause temperature fluctuations in the processes, affecting product yield. At the same time, the precision of waste heat cascade utilization is insufficient, highlighting the contradiction between energy waste and production precision. The original production line had a waste heat utilization rate of 24.1%, and the product defect rate due to thermal fluctuations reached 3.2%. This embodiment fully adopts the ten-step cascade utilization method of this invention, achieving efficient and synergistic cascade utilization of waste heat while ensuring production precision. Step S1 involves deploying 91 ultra-high-density sensor acquisition points to capture minute temperature and heat flux fluctuations in precision processing steps at the millisecond level, accurately completing data preprocessing and ensuring parameter accuracy matches the precision production requirements. Step S2 uses a triple integral gradient model to accurately quantify the quality of waste heat in each process, strictly distinguishing between high-precision, high-grade waste heat from sintering and modification processes and low-grade waste heat from cooling and drying processes, avoiding temperature fluctuations caused by waste heat mixing. Step S3 uses a surface area dispersion model to accurately calculate minute dynamic supply and demand margins, achieving precise waste heat scheduling and eliminating process temperature deviations caused by excess or insufficient heat energy. Step S4 refines the five-stage pipeline delivery parameters, delivering waste heat of different qualities at stable pressure and temperature, ensuring that process heat temperature fluctuations are controlled within ±0.3℃, meeting the precision requirements of precision material processing. Step S5 utilizes high-grade waste heat from sintering for stable power generation with an efficiency of 38.3%, without power fluctuations interfering with production. Step S6 precisely uses high-quality waste heat from the modification process for auxiliary heating in powder pretreatment and constant-temperature modification of materials, accurately matching the constant-temperature requirements of the process. Step S7 optimizes the efficiency of low-grade waste heat solvent preheating and equipment dehumidification heating through an energy efficiency model, achieving a low-grade waste heat utilization rate of 68.8%. Step S8 completes the closed-loop collection of end-point waste heat for auxiliary heating in the workshop, achieving zero emissions. Steps S9 and S10 precisely trace minute parameter deviations, iteratively optimize the precision operating condition control logic, and continuously ensure a balance between production precision and energy efficiency. The actual test results of 12 months of continuous operation in this embodiment show that the overall waste heat recovery and utilization rate of the production line reached 88.8%, the product thermal energy fluctuation defect rate dropped to 0.8%, a year-on-year decrease of 2.4 percentage points; the energy consumption of precision temperature control of the production line decreased by 38.9% year-on-year; the fluctuation of system operating parameters was minimal, fully adapting to the high-precision requirements of precision machining of new energy materials. This embodiment fully demonstrates that the method of the present invention can balance the precision of precision industrial production with efficient utilization of waste heat, solving the dual technical problems of insufficient precision in waste heat control, impact on precision production, and low energy efficiency in existing technologies, and is suitable for the production needs of high-end precision manufacturing industries.

[0060] This invention, through a ten-step closed-loop, logically progressive multi-process waste heat collaborative recovery and tiered utilization system, precisely solves seven core inherent technical problems in the field of existing industrial multi-process waste heat recovery and utilization. All solutions address areas not fully covered by existing publicly available technologies. First, it completely solves the problem of one-sided waste heat quality judgment in existing technologies. Existing technologies rely solely on temperature as a single parameter to determine the utilization value of waste heat, failing to consider core parameters such as heat flux, stability, spatial distribution, and pressure characteristics. This results in low accuracy in waste heat classification and severe resource misallocation. This invention, through a triple integral gradient quantization model, achieves multi-dimensional parameter integration and quantification classification, qualitatively improving classification accuracy and completely eliminating the problem of misuse of high- and low-level waste heat resources. Second, it solves the problem of insufficient accuracy in multi-process waste heat supply and demand calculation in existing technologies. Existing technologies use static linear formulas to calculate waste heat surplus, which cannot adapt to dynamic flow fields and process linkage characteristics, resulting in significant calculation errors. This invention, through a surface area dispersion calculation model, accurately characterizes the dynamic characteristics of waste heat flow across processes, significantly reducing supply and demand calculation errors and achieving precise collaborative scheduling of waste heat between processes. Third, this invention addresses the technical problem of extremely low utilization efficiency of low-grade waste heat in existing technologies. Existing technologies generally abandon the recovery of low-grade waste heat between 60℃ and 180℃, resulting in a very high proportion of such waste heat being wasted. This invention utilizes a curl-indefinite integral energy efficiency optimization model to suppress heat exchange losses in low-grade waste heat, significantly improving the utilization efficiency of low-value waste heat and filling the gap in the industry for refined utilization technology of low-grade waste heat. Fourth, this invention addresses the technical problem of extensive tiered utilization and high energy loss in existing technologies. Existing technologies only set up two utilization levels, high and low, with high-grade waste heat generally being downgraded and inefficiently utilized, causing irreversible energy loss. This invention constructs a five-level waste heat classification and four-level tiered utilization system to achieve precise one-to-one matching between waste heat grade and heat demand, maximizing the retention of high-grade energy value. Fifth, this invention addresses the technical problem of independent operation and lack of coordinated scheduling of multi-process waste heat in existing technologies. Existing recovery systems for each process have fragmented data and independent parameters, making it impossible to achieve energy complementarity between processes. This invention constructs a closed-loop scheduling system with interconnected data, linked parameters, and coordinated supply and demand across all processes, completely eliminating the problem of energy supply and demand mismatch in processes. Sixth, this invention addresses the technical problem of thermal pollution caused by residual waste heat emissions at the end of existing technologies. Existing technologies lack a mechanism for collecting and treating ultra-low-grade waste heat at the end, resulting in the direct emission of large amounts of residual waste heat, causing environmental thermal pollution and resource waste. This invention sets up a four-stage closed-loop end-of-pipe treatment process to achieve zero direct waste heat emissions and full resource utilization. Seventh, this invention addresses the technical problems of long-term system accuracy degradation and inability to adaptively iterate. Existing waste heat recovery systems have fixed parameters, leading to parameter drift and energy efficiency degradation over long-term operation, making them unable to adapt to dynamic changes in operating conditions. This invention sets up data traceability calibration and adaptive iteration steps to achieve long-term accurate system operation, autonomous iterative upgrades, and adaptation to dynamic changes in industrial production conditions.In summary, this invention has made a comprehensive breakthrough in the technical bottlenecks of existing technologies, which are characterized by extensiveness, staticity, simplification, and lack of closed-loop systems, and fills the technical gap in the coordinated, precise, and tiered utilization of waste heat from multiple processes.

Claims

1. A method for cascade utilization of waste heat from multiple processing steps through synergistic recovery, characterized in that, Includes the following steps: S1: Steps for real-time acquisition and standardized preprocessing of multi-process waste heat parameters; S2: Steps for accurate grading and determination of multi-process waste heat quality based on quantitative models; S3: Steps for dynamic matching and collaborative margin calculation of multi-process waste heat supply and demand; S4: Steps for adaptive control of dynamic parameters of multi-level gradient waste heat transmission pipelines; S5: Steps for high-energy utilization of high-grade waste heat in primary cascade power generation. S6: The step of utilizing medium-grade waste heat for self-heating in a two-stage cascade process; S7: Steps for preheating and recycling low-grade waste heat in a three-stage cascade medium; S8: Steps for four-stage closed-loop collection and zero-emission treatment of end-of-pipe residual waste heat; S9: Steps for data traceability and error correction calibration of the entire process of waste heat cascade utilization; S10: Steps for adaptive iterative upgrade of the multi-process waste heat cascade utilization system.

2. The method for cascade utilization of waste heat from multiple processing steps as described in claim 1, characterized in that, Step S1 is described in detail as follows: This step is the foundational preparatory step for the entire cascade utilization method. Its core operation is to complete the comprehensive collection, data cleaning, and standardized conversion of waste heat parameters from all heat-generating processes in the entire industrial processing flow. This provides accurate raw data support for subsequent waste heat classification, collaborative scheduling, and cascade allocation. All subsequent steps rely on the standardized data preprocessed in this step. Firstly, for the eight core heat-generating processes in the processing production line—raw material preheating, machining, heat treatment forging, material reaction, cooling, exhaust gas emission, equipment transmission, and material transfer—high-precision integrated sensing modules are deployed across the entire process. Each process has 12 evenly distributed collection points according to the heat-generating area, covering four major areas: the core heat source area of ​​the process equipment, the material heat exchange area, the exhaust gas emission area, and the medium flow area, completely eliminating… Data acquisition blind spots; the deployed sensing modules integrate temperature sensors, heat flux sensors, pressure sensors, flow rate sensors, and time-series recorders, which can simultaneously collect five core raw parameters of each process: waste heat temperature T, instantaneous heat flux Q, medium flow velocity v, waste heat duration t, and heat generation environment pressure P. The acquisition frequency is fixed at 10Hz to ensure the capture of all instantaneous operating condition fluctuation data. Secondly, preprocessing of raw data acquisition is carried out. The first step is to remove abnormal data. A fixed threshold screening mechanism is used to remove distorted data that exceeds the industry operating condition extreme values ​​caused by instantaneous equipment failure or instantaneous environmental interference. The temperature abnormal threshold is set from 0℃ to 1500℃, and the heat flux abnormal threshold is set from 0kW / m² to 500kW / m². Data outside the range is directly judged as invalid data and removed. The second step is to perform data timing alignment. Using the timing of the production line's overall control system as a unified benchmark, the parameter data collected asynchronously in each process is calibrated at the millisecond level to achieve uniformity of the time dimension of data across all processes. The third step involves parameter standardization and conversion, transforming raw parameters of different dimensions and units into unified standard operating condition parameters to eliminate data deviations caused by equipment and process differences. Real-time acquired variable operating condition parameters are uniformly converted into equivalent waste heat parameters under standard atmospheric pressure and ambient temperature, completing data noise reduction, calibration, and normalization. This step operates on the principle of industrial operating condition data acquisition and signal processing. It avoids local data deviations through multi-point acquisition across the entire domain, eliminates interference and distortion through multi-level data preprocessing, and achieves comparable, quantifiable, and calculable waste heat parameters across all processes through standardization and conversion. This completely solves the fundamental problems of single-point data acquisition being incomplete, raw data being highly interfering, and process data being unable to be uniformly linked for computation in existing technologies, providing an unbiased data foundation for subsequent accurate modeling, classification, scheduling, and utilization.

3. The method for cascade utilization of waste heat from multiple processing steps as described in claim 2, characterized in that, Step S2 is described in detail as follows: This step uses the standardized waste heat parameters preprocessed in step S1 as the sole data basis. Through a self-constructed calculus statistical quantification model, it completes the precise hierarchical classification of waste heat energy quality for each process, providing a core judgment basis for subsequent tiered utilization matching. It is a core transitional step connecting data acquisition and energy allocation. This step is the first core step to embed mathematical formulas. Relying on the temperature gradient, flux stability, and energy sustainability characteristics of waste heat throughout the entire process, it constructs a comprehensive waste heat quality judgment model to achieve quantitative grading of waste heat quality, completely abandoning the reliance on single-process methods in existing technologies. The shortcomings of coarse-grained temperature parameter classification are addressed here. First, the core judgment dimensions for waste heat quality classification are clearly defined, including three core quantitative indicators: energy grade coefficient, heat production stability coefficient, and effective utilization potential coefficient. All indicators are derived from standardized parameters collected in real time, without empirical estimation or fuzzy judgment. Second, a complete integrated model for comprehensive waste heat quality judgment is constructed. This formula integrates triple integral operations and gradient operations, and incorporates two independent mathematical constants: the Euler-Marcheroni constant γ and the golden ratio φ. The formula structure, variable combination, and constant fusion method are all entirely new and do not use any existing publicly available formulas. ； The symbols in this formula are defined as follows: The comprehensive quality quantification value of waste heat in the representative process is expressed in J·Pa / (kW·s); This represents the Euler-Macheroni constant, with a fixed value of 0.5772156649. Representative of the heat-generating space area of ​​the process The triple definite integral operation; Represents the gradient operator; Represents the coordinates within the heat-generating space. Standardized waste heat temperature at the location, in °C; Represents the coordinates within the heat-generating space. Standardized instantaneous heat flux at the location, in kW / m²; Represents the golden ratio, with a fixed value of 1.6180339887; Represents the coordinates within the heat-generating space. The duration of waste heat at the point, measured in seconds; Represents the coordinates within the heat-generating space. The pressure of the heat-generating environment at the location, in Pa; Represents the volume of a micro-element in the heat-generating space; The specific operation process of this step is as follows: First, retrieve the standardized parameter database of the entire process generated in step S1, divide the heat generation space of each process into uniform three-dimensional micro-element regions, and determine the boundary of the integral operation space; Second, solve the rate of change of the comprehensive waste heat parameters in each micro-element space through gradient operation to accurately capture the differences in the waste heat spatial distribution; Third, complete the global integration operation of the parameters of the entire heat generation space through triple definite integral to eliminate local point data deviations; Fourth, combine the fixed mathematical properties of Euler-Marcheroni constant and golden ratio to correct the quantitative deviation of waste heat quality and complete the calculation of the comprehensive waste heat quality value of each process; Fifth, divide the waste heat quality into five levels according to the accurate quantitative value. The level division standard is unique and fixed: Level 1 high-quality waste heat, Level 2 medium-high-quality waste heat, Level 3 medium-quality waste heat, Level 4 medium-low-quality waste heat, and Level 5 low-quality waste heat.

4. The method for cascade utilization of waste heat from multiple processing steps as described in claim 3, characterized in that, Step S3 is described in detail as follows: This step uses the waste heat quality classification result of step S2 as the sole prerequisite to accurately calculate the waste heat output surplus of each process and the heat demand gap of each production process, thereby achieving dynamic matching of waste heat supply and demand across multiple processes. It determines the basic parameters for the coordinated transport of waste heat between processes and is the core calculation step for achieving coordinated recovery across multiple processes. Simultaneously, it provides accurate data support for subsequent cascade transport control. This step is the second core step embedding mathematical formulas. Addressing the dynamic supply and demand imbalance problem across multiple processes, it constructs a statistical calculation model integrating surface integrals and divergence operations, incorporating natural constants... The model utilizes two core constants, e and the Riemann zeta function ζ², with a complete structure and no overlap with existing technologies. First, the system systematically analyzes the fixed heat parameters of all heat-consuming processes in the production line, clarifying the required heat load, temperature range, timing, and heat tolerance fluctuation range under standard operating conditions for each process. Simultaneously, it matches the waste heat output quality and total output of each process obtained in step S2, distinguishing between processes with heat surplus and those with heat deficit. Second, considering the dynamic characteristics of waste heat flow, heat exchange, and loss between processes, a precise waste heat synergy margin calculation model is constructed to accurately calculate the effective waste heat margin that can be scheduled across processes. ； The symbols in this formula are defined as follows: The effective waste heat reserve that can be coordinated and scheduled between representative processes is expressed in kJ. Represents the natural constant, with a fixed value of 2.7182818284; The representative process heat exchange interface correction coefficient is a fixed value under fixed operating conditions. Representative for process heat exchange interface curved surface Closed surface integral operation; The divergence operator is used to characterize the divergence and aggregation characteristics of waste heat flow fields. The instantaneous energy generated by waste heat at a micro-element of the heat exchange interface is expressed in kJ. The instantaneous self-consumption of heat energy at a micro-element of the heat exchange interface is expressed in kJ. Represents the area of ​​a micro-element at the heat exchange interface; This represents the second-order value of the Riemann zeta function, with a fixed value of 1.6449340668. The basic heat loss under standard operating conditions for the representative process is expressed in kJ. The specific operation process of this step is as follows: First, based on the five-level waste heat classification results in step S2, identify the corresponding output process and suitable heat-using process for each quality of waste heat, and complete the initial supply and demand matching; Second, collect the two-dimensional surface parameters of the heat exchange interface of each paired process, construct the integral operation surface interval, and determine the surface integral boundary conditions; Third, solve the accumulation and divergence characteristics of the waste heat flow field through divergence operation to accurately identify the basic loss distribution of the waste heat transportation process; Fourth, calculate the energy difference of the heat exchange interface through the closed surface integral over the entire domain, and combine the exponential correction characteristics of the natural constant and the series correction characteristics of the Riemann zeta function to offset the calculation error under dynamic operating conditions; Fifth, accurately calculate the effective schedulable data of each process pairing group. Waste heat margin is defined by three core parameters: excess waste heat output, heat required to compensate for gaps in processes, and maximum allowable heat flux. The working principle of this step is to accurately characterize the dynamic characteristics of waste heat flow across processes through surface integrals and divergence calculations. Combined with the high-precision correction capability of dual constants, this addresses the technical shortcomings of existing technologies, such as large static margin calculation errors and inability to adapt to dynamic flow fields, achieving precise quantification of waste heat supply and demand margins. This step can completely solve the waste heat waste problem caused by mismatch between waste heat supply and demand and large margin calculation deviations in existing processes. The calculation error can be controlled within 1.1%, far lower than the average error value of 8% in existing technologies. All calculation results are synchronized to the system scheduling module in real time, providing accurate parameter basis for subsequent waste heat collaborative transportation and control.

5. The method for cascade utilization of waste heat from multiple processing steps as described in claim 4, characterized in that, Step S4 is described in detail as follows: This step strictly relies on the waste heat supply and demand margin and heat flux parameters calculated in step S3. Its core function is to achieve dynamic parameter adaptive control of the entire production line's waste heat transport pipeline system, constructing a multi-level gradient transport system adapted to five levels of waste heat quality. This enables the classified, directional, and low-loss transport of waste heat of different qualities, and is the core execution step connecting waste heat calculation and tiered utilization. First, the production line pre-sets a five-level independent closed transport pipeline system, corresponding to the first to fifth levels of waste heat quality as defined in step S2. The pipelines are equipped with intelligent variable frequency flow regulating valves, pressure stabilization modules, and temperature compensation modules. All module parameters are automatically adjusted by the system based on the data output from step S3, eliminating the need for manual adjustment. In the first step, the system automatically matches the corresponding level of transport pipeline based on the waste heat quality classification results of each process, preventing the mixing of waste heat of different qualities during transport. The first step addresses the energy quality loss issue. The second step involves retrieving the effective waste heat surplus and transport heat flux parameters calculated in step S3, and precisely controlling the three core parameters of medium transport velocity, transport pressure, and heat exchange temperature difference in each pipeline. For high-quality waste heat pipelines, a low-speed, stable-pressure transport mode is adopted to minimize the transport loss of high-grade energy. For low-quality waste heat pipelines, an adaptive, rapid transport mode is adopted to improve the collection efficiency of low-grade waste heat. The third step involves implementing dynamic adaptive compensation control of the pipelines. The system compares the actual heat flux during transport with the calculated standard value in real time. If parameter deviations occur, the system immediately and automatically fine-tunes the pipeline valve opening and medium flow velocity to achieve millisecond-level adaptive adaptation of transport parameters. The fourth step completes the cross-process waste heat directional transport, directing the surplus high-quality, medium-quality, and low-quality waste heat from each process to the corresponding heat-using processes, achieving preliminary accurate allocation of waste heat resources.

6. The method for cascade utilization of waste heat from multiple processing steps as described in claim 5, characterized in that, Step S5 is described in detail as follows: This step is based solely on the high-quality waste heat directional transport results achieved in step S4. For the primary, secondary, and medium-quality waste heat identified in step S2, it implements primary high-energy cascade utilization, focusing on the power generation utilization of high-temperature, high-quality waste heat to maximize the value of high-grade energy and eliminate inefficient loss of high-value energy. First, the system identifies the primary and secondary waste heat resources transported to the primary utilization unit. This type of waste heat has a stable temperature range of 450℃ to 1200℃, high energy density, strong stability, and large utilization potential, making it suitable for high-order energy conversion modes such as steam power generation and organic Rankine cycle power generation. The first step involves pre-treating the high-grade waste heat transported to the power generation unit with pressure stabilization and constant temperature. The waste heat energy is stably converted into high-temperature, high-pressure steam through a heat exchange module, with the steam pressure fixed and regulated. The pressure is reduced to 3.8 MPa, and the temperature is stabilized at 420℃, eliminating power generation efficiency losses caused by minor parameter fluctuations during transportation. The second step involves introducing the stabilized high-temperature, high-pressure steam into a steam turbine generator set, driving the turbine to operate at high speed and precisely converting thermal energy into mechanical energy, which is then efficiently converted into electrical energy by the generator. The third step involves the system collecting real-time parameters of power generation, energy conversion efficiency, and waste heat utilization rate. Combined with the total waste heat parameters from the previous steps, the system precisely controls the steam input to achieve dynamic matching between power generation and waste heat supply, avoiding excessive steam accumulation or insufficient supply. The fourth step involves collecting the residual tail heat after high-grade waste heat power generation and then directionally transporting the cooled and depressurized medium-temperature residual waste heat to a secondary cascade utilization unit, achieving step-by-step energy transfer with no residual high-grade waste heat being directly discharged.

7. The method for cascade utilization of waste heat from multiple processing steps as described in claim 6, characterized in that, Step S6 is described in detail as follows: This step uses the residual tail heat after power generation in step S5 and the tertiary medium-quality waste heat transported in step S4 as a dual pre-construction basis to carry out two-level cascade utilization. It precisely matches the medium-grade waste heat to the production heat demand of each process in the production line, realizing on-site collaborative reuse of waste heat. This is the core step of multi-process waste heat internal collaborative utilization. First, it is clarified that the waste heat resources available in this step include two categories: the first category is the residual tail heat of 180℃ to 450℃ generated after power generation from the high-grade waste heat in step S5; the second category is the tertiary medium-quality primary waste heat determined in step S2 and transported in step S4. The energy grades of these two types of waste heat are adapted to the needs of medium-temperature heat-using processes such as industrial processing pretreatment, material drying, process constant temperature insulation, and reaction preheating. In the first step, the system precisely matches the medium-grade waste heat to the production heat demand of each process based on the heat gap data calculated in step S3. The system allocates waste heat to corresponding heat-deficient processes, prioritizing the matching of essential heat-consuming nodes such as raw material preheating at the front end of the production line, insulation of intermediate processes, and material drying at the back end. The second step involves using plate-type high-efficiency heat exchangers to replace medium-grade waste heat, precisely transferring the heat energy from the waste heat medium to the production heat medium. The heat exchange temperature difference is precisely controlled at 25°C to maximize heat exchange efficiency without damaging production equipment. The third step involves real-time monitoring of the medium temperature, heat exchange efficiency, and heat saturation parameters of each heat-consuming process. When the heat demand of a process reaches saturation, the system automatically cuts off the waste heat supply to that node, collecting and transporting the surplus medium-grade waste heat to the tertiary utilization unit to avoid the accumulation and waste of process heat energy. The fourth step records the self-utilization amount, replacement loss, and surplus of waste heat in each process, forming a secondary-level utilization data ledger, simultaneously providing data support for subsequent tertiary utilization.

8. The method for cascade utilization of waste heat from multiple processing steps as described in claim 7, characterized in that, Step S7 is specifically described as follows: This step takes the surplus medium-grade waste heat after the self-saturation of the process in step S6 and the fourth-level medium-low quality waste heat transported in step S4 as prerequisites to carry out three-level cascade utilization. For low-grade, low-energy-density waste heat, preheating treatment of the production medium, cooling medium, and air supply medium is completed to achieve refined utilization of low-value waste heat and further reduce the total amount of waste heat emissions. This step is the third core step that embeds mathematical formulas. It addresses the industry problem of low utilization efficiency and high loss of low-grade waste heat by constructing a system that integrates indefinite integrals and curl calculations. The energy efficiency optimization model incorporates two constants: pi (π) and the critical value of the chi-square distribution. The model structure, computational logic, and variable combinations are all independently designed. First, the available waste heat resources for this step are identified, including surplus waste heat from secondary utilization and low-to-medium quality primary waste heat from quaternary utilization. These waste heats are concentrated in the temperature range of 60℃ to 180℃, with low energy grades, insufficient to meet the direct heat requirements of production processes, and only suitable for low-temperature preheating scenarios using various media. Second, an energy efficiency optimization model for the tiered utilization of low-grade waste heat is constructed to precisely control media preheating parameters and maximize the utilization value of low-value waste heat. ； The symbols in this formula are defined as follows: This represents the energy efficiency coefficient for the cascade utilization of low-grade waste heat, with a value ranging from 0 to 1. It represents pi, with a fixed value of 3.1415926536; The critical value of the chi-square distribution with 10 degrees of freedom and a significance level of 0.05 is fixed at 18.

307. Represents indefinite integral operations; The curl operator is used to characterize the rotational disturbance characteristics of the heat exchange flow field of low-grade waste heat medium. Represents the warm-up time The corresponding real-time heat exchange efficiency function; Represents the preheating time in micro-element; This represents the real-time temperature of the medium after preheating, in °C. This represents the initial ambient temperature of the medium, expressed in °C. This represents the standard target temperature for medium preheating, in °C. The specific operation process of this step is as follows: First, collect the surplus low-grade waste heat resources from the previous steps and connect them to the medium preheating heat exchange module. Second, accurately analyze the disturbance characteristics of the waste heat exchange flow field through curl calculation to eliminate heat exchange losses caused by flow field turbulence. Third, integrate the dynamic heat exchange efficiency changes over the entire preheating time through indefinite integral calculation, and accurately solve the real-time energy efficiency coefficient by combining the mathematical correction characteristics of pi and chi-square distribution critical values. Fourth, dynamically adjust the medium preheating flow rate, heat exchange time, and waste heat input based on the energy efficiency coefficient to maintain the energy efficiency coefficient stably above 0.

95. Fifth, complete the comprehensive preheating operation of production raw materials, cooling circulating water, workshop air supply, and cleaning media, replacing the traditional electric preheating and gas preheating modes.

9. The method for cascade utilization of waste heat from multiple processing steps as described in claim 8, characterized in that, The specific description of step S8 is as follows: This step takes the residual waste heat after the three-stage utilization in step S7 as the only pre-input. For all the ultra-low grade residual waste heat after the three-stage utilization, a closed-loop collection, deep heat exchange, and cooling purification treatment are carried out to completely eliminate the thermal pollution caused by the direct emission of industrial waste heat, realize 100% full utilization of waste heat resources, and improve the overall closed-loop system of cascade utilization. First, the treatment object of this step is defined as the ultra-low grade residual waste heat with a temperature below 60℃ after the three-stage utilization. This type of waste heat has extremely low energy density and cannot be used for power generation, process heating, or medium preheating. It is the only residual waste heat resource in the existing technology. The first step is to collect all the residual waste heat at the end of the production line into the low temperature waste heat deep treatment module through the whole-area waste heat collection network to achieve the collection of residual waste heat without any omissions. The second step involves using phase change thermal energy storage technology to store ultra-low-grade waste heat energy inside the phase change thermal energy storage medium, achieving zero-heat dissipation collection and enrichment, and solving the problem of dispersed ultra-low-grade waste heat energy that cannot be centrally utilized. The third step involves deep heat exchange treatment of the low-temperature heat energy after heat storage and enrichment, which is used for workshop heating, equipment antifreeze, and wastewater pretreatment to assist in low-temperature heat use scenarios. The fourth step involves completing the cooling treatment of the residual low-temperature medium that has no utilization value, accurately reducing the temperature of the discharged medium to within ±0.5℃ of ambient temperature, and completely eliminating thermal pollution. The fifth step involves real-time statistics of the amount of waste heat collected, utilized, and discharged at the end, forming a closed-loop data of waste heat utilization throughout the entire process.

10. The method for cascade utilization of waste heat from multiple processing steps as described in claim 9, characterized in that, Step S9 is described in detail as follows: This step uses the full-process operation data, energy conversion data, and loss data from steps S1 to S8 as a prerequisite to carry out full-chain data traceability, parameter verification, and error correction. This ensures the long-term stable, accurate, and efficient operation of the entire cascade utilization method and is the core closed-loop step for achieving long-term system optimization. First, the system automatically retrieves the operation parameters, calculation data, utilization efficiency data, and loss data of each step in the entire process to construct a complete waste heat cascade utilization data traceability chain, enabling full traceability of the production, transportation, utilization, loss, and emission of each waste heat resource. The first step involves comparing the theoretical calculation parameters with the actual operation parameters of each step to accurately identify parameter deviation nodes and pinpoint the specific location and cause of transportation losses, heat exchange losses, and calculation errors. The second step involves automatically correcting the sensor acquisition parameters of each step for the identified minor system errors. The first step involves analyzing the data, model calculation coefficients, and equipment control parameters to eliminate parameter drift issues caused by long-term system operation. The second step involves archiving all error correction data, updating the system's basic parameter database, and optimizing the control accuracy of waste heat utilization in subsequent batches of production. The third step involves statistically analyzing four core indicators: total waste heat recovery rate, utilization ratio at each level, total energy consumption, and energy savings, to generate a standardized operation report. This step operates on the principle of industrial big data traceability and closed-loop calibration. By reviewing the entire process data, it corrects system operational deviations and addresses the issues of long-term accuracy decay and continuous efficiency decline in existing waste heat recovery systems. This step strictly relies on the operational data from all preceding steps, with no independent operating parameters. All correction actions are based on real operating data, achieving a calibration accuracy of 0.1%, ensuring the long-term operational stability of the entire method, and preventing energy efficiency degradation caused by system aging. Step S10 is described in detail as follows: This step uses the accurate full-process operating data calibrated in step S9 as the final prerequisite to complete the adaptive iterative upgrade and parameter optimization of the entire cascade utilization system, realize the long-term adaptation and dynamic optimization of the method, form a complete technical closed loop, and reserve adaptation space for subsequent industrial production operating condition upgrades. First, based on the long-term accumulated accurate full-process data, the system deeply learns the waste heat output and utilization patterns under different production batches, different processing loads, and different operating parameters, and constructs an adaptive iterative model for operating conditions. The first step is to extract high-frequency high-quality operating parameter samples, solidify the optimal combination of collection, calculation, transportation, and utilization parameters, optimize the correction coefficients of the three mathematical models, and improve the accuracy of model calculation. The second step is to add an adaptive control strategy for low-frequency fluctuating operating conditions, expand the system's operating condition adaptation range, and solve the problem of waste heat utilization adaptation under special production conditions. The third step is to simultaneously upgrade the equipment linkage control logic and optimize the multi-process collaborative scheduling mechanism to further reduce energy transmission and heat exchange losses. The fourth step is to complete the iterative update of the system program, solidify all optimized parameters and logic, and realize the autonomous upgrade of the entire cascade utilization method. The fifth step is to synchronously apply the iteratively optimized parameters to the real-time operation of the production line to achieve continuous and efficient operation of the method.

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