Hydrothermal quality coupling driven industrial wastewater heat load prediction management and control method and system

Abstract

This invention relates to the field of wastewater treatment technology, and particularly to a method and system for predicting and controlling the heat load of industrial wastewater driven by hydrothermal-mass coupling. The method includes: S1. Multi-source data acquisition and feature engineering preprocessing; S2. Over-temperature early warning based on a machine learning model; S3. Multi-source heat sink identification and multi-path decoupling; S4. Quantitative analysis of system thermodynamic characteristics based on narrow temperature range linearization; S5. Targeted load control analysis based on transient thermal balance; and S6. Multi-objective optimization execution decision-making. This invention addresses the fundamental defects and shortcomings of existing technologies in areas such as heat source identification, control lag, single scheduling decision-making, and the collaborative framework of the thermal management system. It fundamentally achieves proactive prediction and scheduling of temperature risks in biochemical treatment systems, ensuring long-term stable, efficient, and low-carbon operation of industrial wastewater treatment systems under complex dynamic conditions.

Description

Technical Field

[0001] This invention relates to the field of wastewater treatment technology, and in particular to a method and system for predicting and controlling the heat load of industrial wastewater driven by hydrothermal-mass coupling. Background Technology

[0002] In industrial production, the efficient and stable operation of wastewater treatment systems is a crucial foundation for ensuring the stable profitability of upstream core production lines. However, in actual operation, biochemical treatment processes are highly susceptible to drastic influences from the environment and influent conditions, with temperature being a primary cause of unstable process performance. The aforementioned industrial wastewater typically contains high concentrations of organic matter and significant amounts of waste heat from production. Combined with the high-intensity metabolic exothermics of microorganisms, aeration compression heat, and high summer temperatures, the system is prone to continuous heat accumulation. Since activated sludge microorganisms are extremely sensitive to temperature (a deviation of ±3℃ from the optimal temperature can trigger the collapse of the microbial community structure), system overheating will directly lead to a sharp drop in treatment efficiency or even exceed effluent standards. Therefore, precise temperature control of the biochemical system is an absolute prerequisite for maintaining stable process performance. However, in existing technologies, the design and operation management of heat control and heat exchange systems for industrial water treatment exhibit significant limitations when dealing with high-concentration, highly fluctuating industrial wastewater.

[0003] (1) Compromise-based regulatory measures based on cost and compliance constraints.

[0004] In order to strike a balance between achieving effluent quality standards and operating costs, and to control the situation from worsening after overheating occurs, wastewater treatment plants generally adopt the following empirical compromise intervention methods:

[0005] Physical terminal coarse heat exchange: relying solely on cooling towers or heat exchangers for passive cooling.

[0006] High energy / high chemical consumption compensation: When temperature fluctuations cause the decline in bacterial activity, it is forced to compensate for the decline in biochemical treatment capacity by increasing the aeration volume of blowers, adding large amounts of chemical agents and external carbon sources.

[0007] While these measures can temporarily alleviate system fluctuations to some extent, they are essentially reactive responses that lack forward-looking control and dynamic feedback.

[0008] (2) Unplanned production stoppages and “hidden costs” caused by regulatory failure.

[0009] Industrial wastewater exhibits extreme fluctuations in quality and quantity. Large volumes of water, due to their significant heat storage capacity, are extremely difficult to cool rapidly, exhibiting high thermal inertia. This results in a severe lag between the actual effect of heating or cooling and external control commands. In practical engineering, the lack of current technology for predicting enthalpy fluctuations means that human experience is often insufficient to effectively and promptly suppress temperature increases. When control commands are severely delayed and the system faces the risk of irreversible failure, enterprises are often forced to resort to unplanned shutdowns or drastic production cuts.

[0010] The only solution is to cut off the high-temperature, high-concentration wastewater at the source, forcing the system into a rest period, where it relies on the slow, adaptive regulation of microorganisms to restore its treatment capacity. The cost of this compromise is catastrophic: in addition to the explicit costs of equipment maintenance and microbial activity recovery, the forced shutdown of high-value-added production lines at the front end results in enormous hidden economic losses due to capacity losses, personnel idleness, and system downtime. Essentially, it's paying the price for the uncontrolled operation of end-of-pipe water treatment at the expense of the company's core output value.

[0011] (3) High flexible carbon emission enterprises and the underlying defects of the existing control system.

[0012] A deeper problem lies in the fact that this vicious cycle of "fluctuation-remedy-shutdown-recovery" is a major driver of the current increase in carbon emissions across the entire industrial wastewater treatment industry. The overloaded chiller units operating in response to the shocks, along with the excessive consumption of electricity (aeration) and chemical agents, have generated an extremely large amount of previously controllable, elastic carbon emissions.

[0013] The reason existing technologies are in this predicament lies in their experience-based and extensive management systems. Traditional control logic treats wastewater treatment units as isolated, passive receivers, lacking mechanism-based heat calculations and failing to detect changes in heat input caused by dynamic production changes in upstream processes. Faced with highly nonlinear thermodynamic systems, the effectiveness and timeliness of conventional control methods are limited. In response to these industry challenges, and against the backdrop of the booming development of AI technology, deeply integrating artificial intelligence algorithms with the underlying mechanisms of water treatment provides a realistic path to achieving autonomous and intelligent temperature control of industrial wastewater. Driven by data models, the system is expected to accurately capture cutting-edge thermal shock trends, forming customized and refined management strategies tailored to each plant, completely changing the traditional delayed response model.

[0014] In the actual operation of industrial wastewater characterized by strong fluctuations and high concentrations, existing water treatment thermal control systems mainly rely on single heat exchange processes (such as cooling high-temperature wastewater through plate heat exchangers or cooling towers), post-process compensation (such as introducing cold water for physical dilution, or overdosing on growth promoters, adding external carbon sources, and increasing aeration when the bacterial community is damaged), or being forced to take measures such as front-end shutdown and production restriction when in extreme out-of-control situations. These technologies expose the following three fundamental defects when facing complex dynamic operating conditions:

[0015] (1) The lack of a feedforward mechanism leads to a serious lag in regulation, making it difficult to cope with the impact of "hot inertia".

[0016] Structures such as biological treatment tanks have large water storage capacities, resulting in significant time lag effects (i.e., large thermal inertia) during temperature rise and fall. Existing control logic primarily relies on passively triggering cooling equipment based on limit alarms from a single temperature sensor. Due to the lack of an effective feedforward control mechanism, the system is unable to predict heat accumulation trends, thus failing to identify and seize the appropriate intervention point (i.e., the optimal intervention window). This reactive feedback mechanism, which only activates when temperatures exceed limits and is too late to take effect, causes the system to completely lose control when facing sudden thermal shocks. This severe control lag inevitably exposes sensitive microbial populations to acute thermal damage and cold stress, making them highly susceptible to microbial community collapse. When the system faces irreversible collapse risks, companies are often forced to resort to unplanned shutdowns or significant production cuts in the upstream workshops as a fallback measure, sacrificing core business capacity to pay the price for the uncontrolled downstream situation.

[0017] (2) The identification of system heat sources has long been in the "black box" blind zone, and the lack of mechanism quantification has led to blind remedial control.

[0018] Current technologies generally fail to use "thermal balance" as a core monitoring and scheduling indicator, treating highly nonlinear biochemical reaction systems as isolated, passive receivers. Because the total system heat load cannot be scientifically traced, operators are unable to determine the true underlying cause when faced with reduced treatment efficiency due to overheating. This systemic blind spot in understanding the mechanisms leads to haphazard, superficial interventions based on experience, such as forced dilution with cold water, excessive carbon source addition, or significantly increased aeration. These "stopgap" measures not only disrupt the original process balance but also cause control actions to lag perpetually behind fluctuations in the pollution load.

[0019] (3) Cooling control measures are limited and lack a multi-dimensional optimization framework, and rigid interventions have significantly increased the industry’s flexible carbon emissions.

[0020] Faced with dynamic and intense enthalpy shocks, existing heat exchange systems mostly maintain static, fixed configurations and rely excessively on introducing external high-energy-consuming cooling sources (such as high-power chillers) for single-point "hard-line" resistance. When issuing control commands, traditional systems completely lack multi-objective economic considerations and plant-wide coordinated scheduling capabilities. For example, they ignore the combined value of "low marginal cost" multi-source heat sink paths such as fine-tuning influent flow, reducing aeration for heat reduction, and enhancing forced ventilation for heat dissipation. This cost-insensitive, high-energy-consuming, single-path compromise cooling mode not only seriously wastes the internal heat integration and self-heating potential of the wastewater treatment system, but also significantly increases the company's direct operating costs (OPEX), becoming a key factor in generating huge "flexible carbon emissions." Summary of the Invention

[0021] This invention provides a method and system for predicting and controlling the heat load of industrial wastewater driven by hydrothermal-mass coupling. It aims to solve the fundamental defects and deficiencies of existing technologies in terms of heat source identification, control lag, single scheduling decision and collaborative framework of thermal management system. It fundamentally realizes the proactive prediction and scheduling of temperature risks in biochemical treatment system, and ensures that industrial wastewater treatment system operates stably, efficiently and with low carbon emissions in the long term under complex dynamic conditions.

[0022] This invention provides a method for predicting and controlling the heat load of industrial wastewater driven by hydrothermal-mass coupling, comprising the following steps:

[0023] S1. Multi-source data acquisition and feature engineering preprocessing: Real-time sensing and acquisition of multi-source heterogeneous data, construction of standardized datasets, and completion of feature engineering preprocessing on standardized datasets;

[0024] S2. Over-temperature warning based on machine learning model: Receives standardized data features collected by S1, constructs a multi-step incremental prediction model using gradient boosting decision tree, performs the prediction task from real-time monitoring data to future thermal risk indicators, and outputs the future temperature trajectory and over-temperature risk points.

[0025] S3. Multi-source heat sink identification and multi-path decoupling: After receiving the S2 over-temperature warning, the total heat load of the system is traced to multiple paths within the time window, and the instantaneous heat contribution and controllable boundary of each path are identified.

[0026] S4. Quantitative analysis of system thermodynamic characteristics based on narrow temperature zone linearization: Building on the multi-path decoupling results of S3, the comprehensive net heating function of multi-path heat transfer under the current operating conditions is extracted, and narrow temperature zone first-order linearization is performed in the over-temperature risk domain of S2 to separate the linearization coefficients that characterize the "nodal base heat load" and the "nodal thermal conductivity".

[0027] S5. Targeted control load analysis based on transient thermal balance: Relying on the linearization coefficient output by S4, combined with the over-temperature risk time window and target control method predicted by S2, the instantaneous intervention power target value required to offset excess heat is deduced by using transient thermal balance logic, and the total intervention load within the control time window is calculated by time-domain integration.

[0028] S6. Multi-objective optimization execution decision: Based on the intervention heat load demand output by S5 and the system thermodynamic characteristics analyzed by S4, a constrained multi-objective optimization mathematical model is constructed with the stripped heat power allocation of each control path as the core decision variable.

[0029] As a further improvement of the present invention, the standardized dataset in S1 includes environmental meteorological conditions, water inflow characteristics, and operating parameters; the feature engineering preprocessing includes time alignment, sliding window sampling, and missing value imputation.

[0030] As a further improvement of the present invention, in S2, the multi-step incremental prediction model models the temperature increment for each future step and accumulates them to obtain the complete temperature trajectory and the final temperature:

[0031]

[0032] Indicates the prediction of the future. Absolute temperature of each step size; This indicates the actual observed temperature at the current moment; It represents the cumulative sum of temperature increments within all consecutive time steps from the current moment to the i-th future step, and outputs the future temperature trajectory and over-temperature risk points in parallel.

[0033] As a further improvement of the present invention, in S3, the first law of conservation of energy is applied to any wastewater treatment unit k to establish a lumped parameter transient thermal balance equation:

[0034]

[0035] in For the density of wastewater, For the specific heat capacity of water, For the unit's effective water volume, t represents the average temperature of the unit phase, and t represents time. Water-to-water convection heat, It is a heat of water metabolism. For water-air exchange heat, For the instantaneous intervention power target value, , , This constitutes a three-path decomposition architecture.

[0036] As a further improvement of the present invention, in S3, for water-water convection heat... The path is modeled as follows:

[0037]

[0038] In the formula, Indicates at time The collection of all water streams entering or leaving the reaction; The flow direction symbol is +1 for inflow and -1 for outflow; For flow Instantaneous mass flow rate, The temperature of the stream, The specific heat capacity of water at constant pressure. for The water temperature in the pool at all times.

[0039] As a further improvement of the present invention, in S3, for water-quality metabolic heat... The pathway decomposes biochemical metabolic heat into components including carbon metabolism pathway, nitrogen metabolism pathway, and endogenous respiration, and models them accordingly.

[0040]

[0041] The reaction rate term is corrected for temperature. In the formula, It represents the collection of all microbial metabolic processes in the pool that have significant heat production or endothermic characteristics, including but not limited to the aerobic degradation of carbon matrix by heterotrophic bacteria, nitrification of nitrogen-containing components by autotrophic bacteria, phosphorus metabolism by polyphosphate-accumulating bacteria, denitrification by denitrifying bacteria, and the endogenous respiration and decay processes of microorganisms. For the process The enthalpy change of the reaction, For the process The volumetric reaction rate, Reference temperature The baseline reaction rate is below. Let j be the temperature sensitivity coefficient of process j. For the substrate concentration vector Bacterial cell density vector Monod-type or other kinetic functions of dissolved oxygen and pH factor; This represents the effective volume of the reactor.

[0042] As a further improvement of the present invention, in S3, for water-air heat exchange... The path is modeled based on the different chemical conditions at each node, and the water-gas exchange heat sub-path within each node is modeled in detail:

[0043]

[0044] In the formula, This refers to the collection of physical mechanisms at the water-air interface that possess significant heat exchange characteristics, including but not limited to latent heat of vaporization, shortwave radiation absorption, net heat transfer from longwave radiation, and sensible heat from aeration. The effective heat exchange area of ​​the water surface / pool surface. For mechanism The heat flux per unit area function, where the driving force vector Covering, but not limited to, ambient temperature relative humidity Wind speed Solar irradiance Effective radiation temperature of the sky Environmental parameters; An engineering correction factor that reflects the influence of reactor configuration and environmental conditions.

[0045] As a further improvement of the present invention, S4 specifically includes:

[0046] Following the multipath decoupling results of S3, this module extracts the comprehensive net heating function of multipath heat transfer under the current operating conditions. And in the S2 over-temperature risk range [ Perform first-order linearization over a narrow temperature range: Through linearization feature analysis, the system separates coefficients a and b. Coefficient a represents the "node base heat load", which is formed by the aggregation of constant heat flux of each heat transfer path under the current operating conditions, reflecting the baseline thermal disturbance that the system is currently experiencing. Coefficient b represents the "node thermal conductivity", which reflects the net heat dissipation change of the system when the pool temperature increases by 1°C.

[0047] As a further improvement of the present invention, S6 specifically includes:

[0048] Using the total intervention heat load demand of S5 output as a constraint, the adjustable boundary of each control path under the condition of safe process operation is used as a safety constraint; the core objective function is to minimize the operating cost of the time window control cycle, while carbon emissions and process safety limits are incorporated as synergistic optimization objectives; an optimization algorithm is used to automatically find the optimal solution in this multi-dimensional constraint space, and output the optimal or Pareto optimal allocation scheme that can simultaneously meet the heat balance requirements, process stability and economic low carbon performance. The optimal allocation scheme is output in the form of structured instructions and sent to the underlying execution unit through the standard industrial control interface to complete the active steady-state control of temperature.

[0049] This invention also provides a hydrothermal-mass coupling driven industrial wastewater heat load prediction and control system, comprising:

[0050] Multi-source data perception and acquisition module: Real-time perception and acquisition of multi-source heterogeneous data, construction of standardized datasets, and feature engineering preprocessing of standardized datasets;

[0051] Over-temperature early warning and prediction module: Receives standardized data features collected by the multi-source data sensing and acquisition module, constructs a multi-step incremental prediction model using a gradient boosting decision tree, performs the prediction task from real-time monitoring data to future thermal risk indicators, and outputs the future temperature trajectory and over-temperature risk points;

[0052] Multi-path heat sink identification and decoupling module: After receiving the over-temperature warning from the over-temperature warning and prediction module, the module performs multi-path decomposition of the total heat load of the system within the time window, and identifies the instantaneous heat contribution and controllable boundary of each path.

[0053] Thermodynamic feature extraction module: It takes over the multi-path decoupling results of the multi-path heat sink identification and decoupling module, extracts the comprehensive net heating function of multi-path heat transfer under the current operating conditions, and performs narrow temperature zone first-order linearization processing in the over-temperature risk temperature domain of the over-temperature early warning prediction module to separate the linearization coefficients that characterize the "nodal base heat load" and "nodal thermal conductivity".

[0054] Targeted intervention load analysis module: Based on the linearization coefficient output by the thermodynamic feature extraction module, combined with the over-temperature risk time window and target control method predicted by the over-temperature early warning prediction module, the instantaneous intervention power target value required to offset excess heat is deduced by using transient thermal balance logic, and the total intervention load within the control time window is calculated by time-domain integration.

[0055] Multi-objective optimization decision-making module: Based on the intervention heat load demand output by the targeted intervention load analysis module and the system thermodynamic characteristics analyzed by the thermodynamic characteristic extraction module, a constrained multi-objective optimization mathematical model is constructed with the stripped heat power allocation of each control path as the core decision variable.

[0056] The beneficial effects of this invention are:

[0057] (1) A fundamental shift from “post-event feedback” to “feedforward early warning” has been achieved: By integrating a multi-step incremental prediction model based on gradient boosting decision tree, this invention can identify risk points and predict temperature evolution trajectory several hours before an overheating event occurs. This overcomes the fundamental defects of traditional feedback control, such as severe response lag caused by system thermal inertia and unavoidable microbial impact, and enables temperature regulation to shift from passive regulation to active prevention.

[0058] (2) Overcoming the technical bottleneck of heat source identification blind spot: The total heat load of the system is innovatively decoupled into three independent path components along the physical source: water-water convection heat, water-mass metabolism heat, and water-gas exchange heat. This enables precise positioning and quantitative diagnosis of heat disturbance in the pool, solves the blind spot problem of traditional experience-based operation and maintenance knowing the temperature rise but not the main cause, and provides a clear physical decision basis for targeted regulation.

[0059] (3) Provides a systematic optimization solution for multi-path collaboration: This invention unifies three often conflicting objectives—operating cost, carbon emissions, and process safety margin—within an optimization framework. It automatically finds the best balance point in the multi-dimensional constraint space according to the principle of "low marginal cost path priority," thereby achieving precise control and maximizing overall benefits.

[0060] (4) Effectively reduce “flexible carbon emissions” and hidden economic costs: Through precise feedforward temperature control, this invention prevents excessive dosing and over-aeration caused by thermal collapse. It not only curbs “flexible carbon emissions” at the end of the line, but also avoids the hidden losses caused by the shutdown of upstream production lines due to the paralysis of the water treatment system, achieving a win-win situation of “cost reduction in treatment” and “efficiency assurance in production”. Attached Figure Description

[0061] Figure 1 This is a flowchart of the industrial wastewater heat load prediction and control method driven by hydrothermal-mass coupling of the present invention.

[0062] Figure 2 This is a schematic diagram of the industrial wastewater heat load prediction and control method and system driven by water-thermal-mass coupling of the present invention;

[0063] Figure 3 This is a comparative analysis chart of the monitored temperature and the model-predicted temperature under continuous dynamic operating conditions according to the present invention.

[0064] Figure 4 This is a regression verification graph showing the full-range prediction accuracy of the prediction model of this invention against the actual value. Detailed Implementation

[0065] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0066] This invention proposes a method and system for predicting and controlling the heat load of industrial wastewater driven by water-heat-mass coupling, aiming to fill the gap in the industry's "water-heat-mass coordinated intelligent management and control" field. By achieving early warning and autonomous temperature control, it can not only effectively reduce the industry's huge and highly potential "flexible carbon emissions," but also fundamentally solve this common end-of-pipe pain point, ultimately achieving the dual macro-benefits of "stable improvement in upstream production efficiency and significant reduction in the environmental and economic costs of downstream wastewater treatment."

[0067] Combination Figure 1 and Figure 2 As shown, the water-thermal-mass coupling driven method for predicting and controlling industrial wastewater heat load of the present invention includes the following steps:

[0068] S1. Multi-source data acquisition and feature engineering preprocessing:

[0069] By deploying sensor networks, meteorological interfaces, and industrial control systems on-site, real-time sensing and acquisition of multi-source heterogeneous data are implemented. A standardized dataset covering environmental meteorological conditions (radiation, wind speed, temperature, etc.), water inflow characteristics (flow rate, COD concentration, etc.) and operating parameters is constructed, and feature engineering preprocessing such as time alignment, sliding window sampling, and missing value imputation is completed.

[0070] S2. Over-temperature warning based on machine learning model:

[0071] On the data-driven side, standardized data features collected by S1 are received, and a multi-step incremental prediction (Step-Delta) model is constructed using a gradient boosting decision tree. This model abandons the direct prediction of absolute temperature, and instead models and accumulates the temperature increment for each future step to obtain the complete temperature trajectory and the final temperature.

[0072]

[0073] : indicates a prediction of the future 1st Absolute temperature of a step size. : Represents the actual observed temperature at the current moment. This represents the cumulative sum of temperature increments across all consecutive time steps from the current moment to the i-th future step. It outputs the future temperature trajectory and over-temperature risk points in parallel, providing a temporal triggering basis for subsequent mechanism decomposition and targeted regulation. To objectively quantify the model's prediction accuracy, a coefficient of determination R is introduced. 2 The root mean square error (RMSE) and mean absolute error (MAE) are used to evaluate the model's accuracy. Furthermore, the system incorporates interpretable analysis algorithms (such as SHAP) to attribute the temperature rise increment to specific factors, directly providing diagnostic evidence for on-site analysis and validation for downstream mechanism decoupling.

[0074] S3. Multi-source heat sink identification and multi-path decoupling:

[0075] Upon receiving the S2 over-temperature warning, the system decomposes the total system heat load into multiple paths within the specified time window, identifying the instantaneous heat contribution and controllable boundary of each path, providing a physical basis for subsequent targeted regulation. Breaking away from the traditional reliance solely on influent temperature, the system abstracts the complex wastewater treatment system's various process units into an interconnected network of "heat sink nodes," applying the first law of energy conservation to any wastewater treatment unit k to establish a lumped-parameter transient heat balance equation:

[0076]

[0077] in Wastewater density (kg / m³) 3 ), is the specific heat capacity of water (kJ / (kg·℃)). Unit effective water volume (m³) 3 ), Let t be the average temperature of the unit phase (°C), t be the time (s), and T be the water temperature in the pool. This represents the instantaneous intervention power target value. Water-to-water convection heat (kW) Water-mass metabolic heat (kW) The water-air heat exchange (kW) constitutes a three-path decomposition architecture, which is refined by its unique process characteristics (such as the biochemical section can be divided into: anoxic anaerobic, open aerobic, semi-closed aerobic, etc.).

[0078] The first pathway is water-to-water convection heat. :

[0079]

[0080] In the formula, Indicates at time The collection of all water streams entering or leaving the reaction, including but not limited to influent streams, effluent streams, external recirculation, internal recirculation, sludge recirculation, and other engineering-identifiable hydraulic paths; The flow direction symbol is +1 for inflow and -1 for outflow. For flow The instantaneous mass flow rate (kg / s). The temperature of the stream (°C) is given. The specific heat capacity of water at constant pressure (approximately 4186 J / (kg·K)). for The water temperature in the pool at all times.

[0081] This path is essentially sensible heat transfer driven by the temperature difference between the inflow stream and the node temperature. This can be addressed by adjusting the inflow water temperature. or inflow rate The front-end interception is implemented, and the corresponding execution units are heat exchangers and variable frequency water pumps. In terms of heat exchange execution, the existing low-temperature / low-concentration water flow in the plant area (such as water collection wells, integrated regulating tanks, reclaimed water reuse tanks, etc.) is used as the indirect heat exchange cold source. When the cooling capacity of the above-mentioned low-temperature water flow is insufficient, the cooling tower circulating water system is then activated.

[0082] The second pathway is water-mass metabolism heat. The biochemical metabolic heat is broken down into multiple components, including the carbon metabolism pathway, nitrogen metabolism pathway, and endogenous respiration, and then modeled accordingly.

[0083]

[0084] The reaction rate term is corrected using an Arrhenius / Boltzmann type temperature correction. In the formula, It represents the collection of all microbial metabolic processes in the pool that have significant heat-generating (or heat-endothermic) characteristics, including but not limited to the aerobic degradation of carbon matrix by heterotrophic bacteria, the nitrification of nitrogen-containing components by autotrophic bacteria, the phosphorus metabolism of polyphosphate-accumulating bacteria, the denitrification of denitrifying bacteria, and the endogenous respiration and decay of microorganisms. For the process The enthalpy change of the reaction (J / mol or J / kg matrix). For the process Volumetric reaction rate (mol / (m) 3 ·s) or kg / (m 3 ·s)), Reference temperature (Typically, the baseline reaction rate is taken at 20°C) Let j be the temperature sensitivity coefficient of process j. For the substrate concentration vector Bacterial cell density vector Monod-type or other forms of kinetic functions for factors such as dissolved oxygen and pH; The effective volume of the reactor (m³) 3 ).

[0085] By adjusting the aeration rate, dissolved oxygen supply and microbial activity are affected, thereby regulating the biochemical reaction rate and metabolic heat production. The corresponding actuators are variable frequency blowers, etc.

[0086] The third pathway is heat exchange between water and air. Modeling is performed based on the different chemical conditions of each node (open aerobic, semi-closed aerobic, anaerobic / hydrolysis tank, etc.), and the water-air heat exchange pathways within each node are modeled in detail (e.g., natural evaporation, long-wave radiation, sensible heat entrained by aeration, aeration-enhanced evaporation, solar radiation, tank wall conduction, etc.).

[0087]

[0088] In the formula, It represents the collection of all physical mechanisms with significant heat exchange characteristics at the water-air interface, including but not limited to latent heat of vaporization, absorption of short-wave (solar) radiation, net heat transfer of long-wave radiation, and sensible heat of aeration. Effective heat exchange area of ​​water surface / pool surface (m²) 2 ), For mechanism Heat flux per unit area (W / m²) 2 ), where the driving force vector Covering, but not limited to, ambient temperature relative humidity Wind speed Solar irradiance Effective radiation temperature of the sky Environmental parameters; This is an engineering correction factor that reflects the influence of reactor configuration and environmental conditions. For different reactor configurations, the set... Specific composition and correction coefficients The value can be adjusted accordingly.

[0089] The adjustable parameters in this module include water-air contact area, ventilation area, shading degree, and aeration outlet temperature, and the corresponding actuators are sunshade canopy, variable frequency ventilation fan, blower aftercooler heat exchanger, etc.

[0090] S4. Quantitative Analysis of System Thermodynamic Characteristics Based on Narrow Temperature Range Linearization:

[0091] Following the multipath decoupling results of S3, this module extracts the comprehensive net heating function of multipath heat transfer under the current operating conditions. And in the S2 over-temperature risk range [ Perform first-order linearization over a narrow temperature range: .

[0092] Based on the current measured water temperature The future time-domain temperature prediction sequence output by module S2 With process safety threshold The rolling narrow temperature range defined by all three factors Perform first-order linearization within the bound, with a lower bound T. L The absolute minimum value between the current measured water temperature and the predicted water temperature within the future time domain is used to determine the lowest possible coldest limit the system may face; the upper limit T U The highest absolute value among the current water temperature, the predicted water temperature, and the upper limit of process safety is then taken, forcibly anchoring the evaluation boundary to the safety red line. This range is continuously updated with the latest data acquired in each control cycle, ensuring that the control algorithm's view always accurately matches the actual temperature fluctuation trajectory and constantly guards against the risk of overheating.

[0093] Through linearized feature analysis, the system isolates coefficient 'a' (unit: kW) to characterize the "nodal base heat load," which is the aggregation of constant heat fluxes of each heat transfer path under current operating conditions, reflecting the "baseline thermal disturbance" currently experienced by the system. Coefficient 'b' (unit: kW / ℃) characterizes the "nodal thermal conductivity," reflecting the system's net heat dissipation (or heat generation) change capacity for every 1℃ increase in pool temperature. This step transforms the traditional complex iterative simulation of differential equations into instantaneous system thermodynamic feature diagnosis analysis, providing a core method for subsequent rapid calculation of intervention quantification. Coefficient 'a' is then stripped away (its baseline disturbance is already reflected in the predicted change curve of S2). Within a strictly controlled, extremely narrow temperature range, the nonlinear change in the system's natural heat dissipation is minimal; therefore, the extracted value of 'b' can be considered as a time-invariant thermodynamic system constant within this control period, providing an accurate physical mapping benchmark for subsequent large-span time-domain integration.

[0094] S5. Targeted load control analysis based on transient thermal balance:

[0095] Based on the linearization coefficients (a,b) output by S4, combined with the overheat risk time window Δt predicted by S2 and the target control method (such as isothermal maintenance, controlled slow descent, and other typical operating conditions), the instantaneous intervention power target value required to offset excess heat is analyzed using transient thermal balance logic. Within this time window Δt, the total heat load to be stripped during the control period is obtained by integrating the instantaneous power along the time domain. .

[0096] S6. Multi-objective optimization execution decision:

[0097] Based on the intervention heat load demand output by S5 and the system thermodynamic characteristics analyzed by S4, a constrained multi-objective optimization mathematical model is constructed, with the stripped heat power allocation of each control path as the core decision variable. The total intervention heat load demand output by S5 is used as a constraint (i.e., the sum of the allocations of each path satisfies the overall control objective). The adjustable boundaries of each control path under process safety operating conditions (such as the lower limit of aeration volume, flow adjustment range, and upper limit of equipment capacity) are used as safety constraints. The core objective function is to minimize the operating cost of the time window control cycle, while simultaneously incorporating carbon emissions and process safety limits (such as meeting dissolved oxygen safety limits) as collaborative optimization objectives. An optimization algorithm is used to automatically find the optimal solution within this multi-dimensional constraint space, outputting an optimal or Pareto optimal allocation scheme that simultaneously satisfies heat balance requirements, process stability, and economic low-carbon performance. This optimal allocation scheme is output in the form of structured instructions and distributed to the underlying execution unit through a standard industrial control interface to complete the active steady-state control of temperature.

[0098] This invention also provides a hydrothermal-mass coupling driven industrial wastewater heat load prediction and control system, comprising:

[0099] Multi-source data perception and acquisition module: Real-time perception and acquisition of multi-source heterogeneous data, construction of standardized datasets, and feature engineering preprocessing of standardized datasets.

[0100] This module is responsible for the real-time sensing, acquisition, and standardized preprocessing of multi-source heterogeneous data, serving as the data foundation for the entire system. In terms of hardware, the system includes industrial-grade high-precision temperature sensors (Pt1000), electromagnetic flowmeters, online water quality analyzers (for monitoring parameters such as COD and NH4), blower power acquisition modules, and meteorological data interfaces (including solar radiation, wind speed, air temperature, and humidity) deployed at various heat sink nodes in the wastewater treatment site. In terms of data processing logic, the module incorporates outlier removal (based on the 3σ principle), missing value imputation (spline imputation), and time-series alignment functions. It also standardizes physical quantities of different dimensions to the [0,1] interval using the Min-Max method, constructing high-dimensional feature vectors suitable for downstream model training and inference.

[0101] Over-temperature early warning and prediction module: Receives standardized data features collected by the multi-source data sensing and acquisition module, constructs a multi-step incremental prediction model using a gradient boosting decision tree, performs the prediction task from real-time monitoring data to future thermal risk indicators, and outputs the future temperature trajectory and over-temperature risk points.

[0102] This module is responsible for predicting future thermal risk indicators from real-time monitoring data, acting as a "sentinel" to trigger subsequent targeted regulation. Algorithmically, it employs Gradient Boosting Decision Tree (GBDT) to construct a multi-step incremental prediction (Step-Delta) model. The training mechanism strictly follows a walk-forward validation strategy, and a purge gap is used to avoid data leakage risks. During the hyperparameter optimization phase, key parameters such as tree depth, number of leaves, and learning rate are fine-tuned while strictly controlling overfitting. The module outputs the future temperature trajectory, the first temperature exceedance time, and the probability of exceeding the temperature limit in parallel. When the prediction result reaches the process safety threshold, it activates the downstream modeling and decision-making process.

[0103] Multi-path heat sink identification and decoupling module: After receiving the over-temperature warning from the over-temperature warning and prediction module, it decomposes the total heat load of the system into multiple paths within the time window, and identifies the instantaneous heat contribution and controllable boundary of each path.

[0104] This module activates upon warning activation and physically decomposes the total heat load under the current operating conditions. Based on the first law of energy conservation, the module decouples the transient heat balance equation of the wastewater treatment unit along physical sources into three independent path components: water-water convection heat, water-mass metabolism heat, and water-air exchange heat. It then performs refined modeling of the sub-mechanisms of each path (such as carbon metabolism and nitrogen metabolism in the metabolic pathway; and natural evaporation / aeration entrainment / solar radiation in the water-air pathway). This module transforms the total thermal disturbance of the complex system into a path-level adjustable component structure, providing a physically feasible domain foundation for subsequent thermal characteristic analysis and multi-objective optimization.

[0105] Thermodynamic Feature Extraction Module: It inherits the multi-path decoupling results from the multi-path heat sink identification and decoupling module, extracts the comprehensive net heating function of multi-path heat transfer under the current operating conditions, and performs narrow temperature zone first-order linearization processing in the over-temperature risk temperature domain of the over-temperature early warning prediction module to separate the linearization coefficients that characterize the "nodal base heat load" and "nodal thermal conductivity".

[0106] This module, acting as the system's "mechanism engine," is responsible for reducing the complex nonlinear thermodynamic evolution process into intuitive physical diagnostic indicators. Upon receiving the upstream path components, the module performs first-order linearization on the comprehensive net heating function within the narrow temperature range of the actual operation of the biochemical tank, separating two core physical constants characterizing "nodal baseline heat load" and "nodal thermal conductivity." This mechanism reduction process achieves instantaneous locking of the system's thermodynamic state in milliseconds, effectively replacing traditional time-consuming iterative mechanism simulations and providing a high-fidelity physical benchmark for subsequent intervention load analysis.

[0107] Targeted intervention load analysis module: Based on the linearization coefficient output by the thermodynamic feature extraction module, combined with the over-temperature risk time window and target control method predicted by the over-temperature early warning prediction module, the instantaneous intervention power target value required to offset excess heat is deduced by using transient thermal balance logic, and the total intervention load within the control time window is calculated by time-domain integration.

[0108] This module is responsible for transforming abstract target temperature control commands into quantifiable energy allocation budgets. The system combines preset target temperature constant temperature maintenance and controlled slow-descent control modes, uses transient thermal balance logic to back-calculate the instantaneous intervention power target value required to offset excess heat, and calculates the total intervention load within the control time window through time-domain integration.

[0109] Multi-objective optimization decision-making module: Based on the intervention heat load demand output by the targeted intervention load analysis module and the system thermodynamic characteristics analyzed by the thermodynamic characteristic extraction module, a constrained multi-objective optimization mathematical model is constructed with the stripped heat power allocation of each control path as the core decision variable.

[0110] This module serves as the system's application decision layer, addressing the multi-path collaborative scheduling problem under complex operating conditions. The optimization model uses the stripping heat power allocation for each control path as the decision variable; minimizes the operating cost within the control cycle as the core objective function, while incorporating carbon emissions and process safety margins as collaborative optimization objectives; and sets multiple system physical boundaries as hard constraints (such as target stripping requirements, adjustable boundaries for each path under process safety operating conditions, and equipment capacity limits). Under the premise of satisfying these constraints, the module automatically seeks optimization in a multi-dimensional space according to the principle of "low marginal cost path priority," outputting the optimal or Pareto optimal multi-path allocation scheme. This scheme is then distributed to the underlying execution unit via a standard industrial control interface in the form of structured instructions, combining with the system's inherent thermal inertia to achieve proactive temperature control.

[0111] Based on the above-mentioned method and system for predicting and controlling the heat load of industrial wastewater driven by hydrothermal-mass coupling, this invention also provides an industrial water treatment heat control system, which is the hardware and control logic carrier medium for realizing the above-mentioned closed-loop control method, and is mainly composed of four interconnected functional units.

[0112] First, the multi-source data sensing and acquisition unit, which consists of an industrial-grade sensor network, a meteorological data interface, and a manufacturing execution system interface, is responsible for comprehensively collecting environmental, water quality, and process status data and completing standardized preprocessing.

[0113] Secondly, the over-temperature early warning and prediction unit deployed on the computing platform has a built-in multi-step incremental prediction model based on gradient boosting decision tree, which outputs the future temperature trajectory and over-temperature risk points, triggering the downstream targeted regulation decision link.

[0114] Subsequently, the multi-path heat sink identification and thermal characteristic analysis unit decouples and models the total heat load based on the first law of energy conservation, using three independent paths: water-water convection heat, water-mass metabolism heat, and water-gas exchange heat. It then performs narrow-temperature first-order linearization within the operating temperature range to extract two lumped thermal characteristic quantities: the node basic heat load and the node thermal conductivity coefficient.

[0115] Finally, the targeted intervention analysis and multi-objective optimization unit analyzes the required intervention load based on the above thermal characteristics and the control time window. It takes the stripping heat power allocation of each control path as the decision variable, the minimization of operating cost as the core objective, and carbon emissions and process safety margin as the synergistic optimization objectives. It automatically optimizes within the multi-dimensional constraint space and outputs the optimal or Pareto optimal allocation scheme. This scheme is issued to the underlying execution unit in the form of structured instructions through the standard industrial control interface to ensure the stable operation of the control system.

[0116] The invention will be further illustrated below with reference to application examples:

[0117] Comparative Example 1:

[0118] This embodiment uses a covered aerobic tank in a pharmaceutical factory's wastewater treatment system as the verification scenario. This node mainly undertakes the aerobic biochemical degradation of high-concentration raw material intermediate wastewater. Long-term operating data shows that, due to the huge "thermal inertia" of the large-volume biochemical tank, the factory's traditional experience-based operation and maintenance exhibits serious lag when facing complex enthalpy fluctuations.

[0119] Manual monitoring cannot predict temperature rise trends in advance, nor can it accurately pinpoint the optimal control window. When faced with high-temperature shocks, the system often only responds passively after the temperature exceeds the limit, forcing it to adopt crude methods such as increasing aeration and overdosing chemicals to forcibly maintain effluent quality. However, such remedial measures not only significantly increase the energy and chemical consumption of subsequent units, but also have minimal control effect. Every summer, the plant still experiences several extreme situations where upstream production is limited or even shut down due to system thermal collapse, resulting in huge hidden economic losses and high flexible carbon emissions.

[0120] Traditional experience-driven model (historical occurrences):

[0121] Operating condition evolution: At noon in summer, due to the high COD concentration of upstream wastewater, high influent temperature, and rising summer ambient temperature, the temperature monitored in the biological treatment tank rapidly increased from 39.92℃ to 40.70℃ within 6 hours.

[0122] Failure Process: Due to the lack of feedforward prediction, operators did not initiate passive intervention (fully operating the chiller and increasing aeration) until the temperature stabilized at 40℃. Because the large volume of water has extremely strong thermal inertia, and the sensible heat carried by aeration in summer exacerbates the heat accumulation in the semi-enclosed dome-shaped aerobic tank, and the cooling command is severely lagging behind the temperature rise trend, the average tank temperature remained above 40℃ for two consecutive days.

[0123] Process breakdown: High temperature caused large-scale disintegration and inactivation of aerobic bacteria, resulting in loss of system processing efficiency and forcing a 5-day shutdown and repair period.

[0124] Economic losses: The shutdown caused the upstream core production line to stop, resulting in a loss of millions of units of production capacity. In addition, the excessive addition of growth promoters and external carbon sources during the recovery period resulted in high additional electricity and chemical consumption.

[0125] Example 1:

[0126] Regarding the situation in the comparative example, combined with Figure 3 and Figure 4 As shown, the present invention adopts the following operating mode.

[0127] Data Sensing and Initialization: The system automatically calls the multi-source sensing interface deployed at the wastewater treatment site to collect and standardize real-time measured parameters (current tank temperature 38.88℃, safety threshold 39.0℃, safe floating temperature range 0.5℃, etc.) and influent parameters (total flow rate of two tanks 42.5 m³). 3 The system includes parameters such as influent COD (2573 mg / L), aeration parameters, meteorological parameters, and local economic parameters (e.g., electricity cost, electricity carbon emission factor).

[0128] Prediction unit startup: The system calls a pre-trained high-precision multi-step incremental prediction (Step-Delta) model based on the LightGBM gradient boosting decision tree framework (to predict temperature for the next six hours). 2 (>0.95, MAE=0.18℃), predicting the pool temperature evolution trajectory for the next 6 hours based on historical time-series characteristics. If the pool temperature is detected to exceed the safety threshold after 2 hours, an over-temperature risk warning is triggered, automatically activating the downstream targeted control decision-making link.

[0129] Identification Unit Intervention: The multi-path heat sink identification and decoupling unit, based on the first law of energy conservation, decomposes the current total heat load into three paths and analyzes the water-water convection heat one by one. =-9.38kW (inlet water temperature slightly lower than pool temperature, slight cooling effect), water-mass metabolism heat =+308.44kW (carbon metabolism pathway, nitrogen metabolism pathway, etc.), water-air heat exchange =-65.72kW; the decomposition results show that the current total net heating load of the system is approximately =+233.34kW.

[0130] Analysis of the unit identification: Within the process safety temperature range [38.0, 39.0], the system performs first-order linearization on the comprehensive net heating function, extracting the two core physical constants: nodal base heat load and nodal thermal conductivity. This confirms that the system currently has significant thermal inertia, requiring active control and an advance start-up of 1 hour to compensate for the response delay. The required instantaneous intervention power is then obtained. =441.86kW, with a cumulative load stripping of approximately 2651.15kWh during the 6-hour regulation period.

[0131] The optimization decision-making unit uses the heat load demand to be stripped as a constraint input to the multi-objective optimization unit. Using the stripping heat power allocation for each control path as the decision variable, and under hard constraints such as meeting the stripping demand, ensuring aeration is not lower than the safety lower limit, and meeting the equipment capacity upper limit, the system performs optimization calculations with minimizing operating costs as the core objective and carbon emissions and process safety margin as collaborative optimization objectives. The system completes all calculations within seconds and recommends the optimal allocation scheme based on the heat exchange design within the pharmaceutical plant area: the first path's inlet pre-cooling allocates approximately 259.2 kW; the second path's reduced aeration allocates 91 kW; the third path's forced ventilation allocates approximately 74.6 kW; and the third path's blower post-cooling allocates approximately 16.9 kW, totaling approximately 419.8 kW of stripping power. Using this scheme, the pool temperature steadily and gradually decreases from 38.88℃ to 38.54℃ during the 6-hour control period. Compared to the predicted peak of 39.20℃ without control, this creates a safety buffer of approximately 0.66℃ and meets the system's temperature change limit, avoiding cold stress on microorganisms.

[0132] Table 1: Predicted and Adjusted Temperatures of a System During Summer Nighttime

[0133] 03：00 38.89 38.82 04：00 38.89 38.75 05：00 38.91 38.69 06：00 38.98 38.62 07：00 39.09 38.58 08：00 39.20 38.54

[0134] Safety Assessment: The report clearly states that the process safety margin of this scheme is "comfortable". The aeration volume is reduced by 25% (but is still higher than the 40% safety limit of the design air volume), and the DO concentration can be stably maintained above 4.2 mg / L. The influent precooling and blower aftercooling are both set at around 28°C, the lower limit of the plate heat exchanger process. The system prioritizes the use of low-temperature, low-concentration wastewater from the plant's collection well / integrated regulating tank as the first-stage precooling source. When its cooling capacity is insufficient, the cooling tower circulating water system is used as a supplement, without the need to start high-energy-consuming chiller units as a backup.

[0135] Economic and Carbon Emission Analysis: The system automatically calculates that the total cost of this scheme during the 6-hour control period is approximately 57.13 yuan, the total power consumption is approximately 78 kWh, and the corresponding carbon emission is approximately 28.01 kg CO2. Compared with the traditional extensive mode of "fully operating the chiller unit as soon as the temperature exceeds the limit" (typically, a single 6-hour control consumes approximately 600 kWh of power, has an operating cost of approximately 390 yuan, and emits approximately 240 kg CO2), this optimized scheme saves approximately 85.3% in operating costs and reduces emissions by approximately 88%, while completely avoiding sludge deactivation, effluent water quality deterioration, and production line production restrictions caused by bacterial contamination.

[0136] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such modifications and substitutions should be considered within the scope of protection of the present invention.

Claims

1. A method for predicting and controlling the heat load of industrial wastewater driven by hydrothermal-mass coupling, characterized in that, Includes the following steps: S1. Multi-source data acquisition and feature engineering preprocessing: Real-time sensing and acquisition of multi-source heterogeneous data, construction of standardized datasets, and completion of feature engineering preprocessing on standardized datasets; S2. Over-temperature warning based on machine learning model: Receives standardized data features collected by S1, constructs a multi-step incremental prediction model using gradient boosting decision tree, performs the prediction task from real-time monitoring data to future thermal risk indicators, and outputs the future temperature trajectory and over-temperature risk points. S3. Multi-source heat sink identification and multi-path decoupling: After receiving the S2 over-temperature warning, the total heat load of the system is traced to multiple paths within the time window, and the instantaneous heat contribution and controllable boundary of each path are identified. S4. Quantitative analysis of system thermodynamic characteristics based on narrow temperature range linearization: Building on the multi-path decoupling results of S3, the comprehensive net heating function of multi-path heat transfer under the current operating conditions is extracted, and narrow temperature range first-order linearization is performed in the over-temperature risk domain of S2 to separate the linearization coefficients characterizing "nodal base heat load" and "nodal thermal conductivity". S5. Targeted control load analysis based on transient thermal balance: Relying on the linearization coefficient output by S4, combined with the over-temperature risk time window and target control method predicted by S2, the instantaneous intervention power target value required to offset excess heat is deduced by using transient thermal balance logic, and the total intervention load within the control time window is calculated by time-domain integration. S6. Multi-objective optimization execution decision: Based on the intervention heat load demand output by S5 and the system thermodynamic characteristics analyzed by S4, a constrained multi-objective optimization mathematical model is constructed with the stripped heat power allocation of each control path as the core decision variable.

2. The method for predicting and controlling industrial wastewater heat load driven by hydrothermal-mass coupling according to claim 1, characterized in that, The standardized dataset in S1 includes environmental meteorological conditions, water inflow characteristics, and operating parameters; feature engineering preprocessing includes temporal alignment, sliding window sampling, and missing value imputation.

3. The method for predicting and controlling industrial wastewater heat load driven by hydrothermal-mass coupling according to claim 1, characterized in that, In step S2, the multi-step incremental prediction model models the temperature increment for each future step and accumulates them to obtain the complete temperature trajectory and the final temperature. ， Indicates the prediction of the future. Absolute temperature of each step size; This indicates the actual observed temperature at the current moment; It represents the cumulative sum of temperature increments within all consecutive time steps from the current moment to the i-th future step, and outputs the future temperature trajectory and over-temperature risk points in parallel.

4. The method for predicting and controlling industrial wastewater heat load driven by hydrothermal-mass coupling according to claim 1, characterized in that, In S3, the first law of energy conservation is applied to any wastewater treatment unit k to establish a lumped parameter transient heat balance equation: ， in For the density of wastewater, For the specific heat capacity of water, For the unit's effective water volume, t represents the average temperature of the unit phase, and t represents time. Water-to-water convection heat, It is a heat of water metabolism. For water-air exchange heat, For the instantaneous intervention power target value, , , This constitutes a three-path decomposition architecture.

5. The method for predicting and controlling industrial wastewater heat load driven by hydrothermal-mass coupling according to claim 4, characterized in that, In S3, for water-to-water convection heat... The path is modeled as follows: ， In the formula, Indicates at time The collection of all water streams entering or leaving the reaction, including but not limited to influent streams, effluent streams, external recirculation, internal recirculation, sludge recirculation, and other engineering-identifiable hydraulic paths; The flow direction symbol is +1 for inflow and -1 for outflow; For flow Instantaneous mass flow rate, The temperature of the stream, The specific heat capacity of water at constant pressure. for The water temperature in the pool at all times.

6. The method for predicting and controlling industrial wastewater heat load driven by hydrothermal-mass coupling according to claim 4, characterized in that, In S3, for water-mass metabolic heat The pathway decomposes biochemical metabolic heat into components including carbon metabolism pathway, nitrogen metabolism pathway, and endogenous respiration, and models them accordingly. ， The reaction rate term is corrected for temperature. In the formula, It represents the collection of all microbial metabolic processes in the pool that have significant heat production or endothermic characteristics, including but not limited to the aerobic degradation of carbon matrix by heterotrophic bacteria, nitrification of nitrogen-containing components by autotrophic bacteria, phosphorus metabolism by polyphosphate-accumulating bacteria, denitrification by denitrifying bacteria, and the endogenous respiration and decay processes of microorganisms. For the process The enthalpy change of the reaction, For the process The volumetric reaction rate, Reference temperature The baseline reaction rate is below. Let j be the temperature sensitivity coefficient of process j. For the substrate concentration vector Bacterial cell density vector Monod-type or other kinetic functions of dissolved oxygen and pH factor; This represents the effective volume of the reactor.

7. The method for predicting and controlling industrial wastewater heat load driven by hydrothermal-mass coupling according to claim 4, characterized in that, In S3, for water-air heat exchange... The path is modeled based on the different chemical conditions at each node, and the water-gas exchange heat sub-path within each node is modeled in detail: ， In the formula, This refers to the collection of physical mechanisms at the water-air interface that possess significant heat exchange characteristics, including but not limited to latent heat of vaporization, shortwave radiation absorption, net heat transfer from longwave radiation, and sensible heat from aeration. The effective heat exchange area of ​​the water surface / pool surface. For mechanism The heat flux per unit area function, where the driving force vector Covering, but not limited to, ambient temperature relative humidity Wind speed Solar irradiance Effective radiation temperature of the sky Environmental parameters; An engineering correction factor that reflects the influence of reactor configuration and environmental conditions.

8. The method for predicting and controlling industrial wastewater heat load driven by hydrothermal-mass coupling according to claim 1, characterized in that, S4 specifically includes: Following the multipath decoupling results of S3, this module extracts the comprehensive net heating function of multipath heat transfer under the current operating conditions. And in the S2 over-temperature risk range [ Perform first-order linearization over a narrow temperature range: Through linearization feature analysis, the system separates coefficients a and b, where coefficient a represents the "node base heat load", which is formed by the aggregation of constant heat flux of each heat transfer path under the current operating conditions, reflecting the baseline thermal disturbance that the system is currently experiencing; coefficient b represents the "node thermal conductivity", reflecting the net heat dissipation change capacity of the system when the pool temperature increases by 1°C.

9. The method for predicting and controlling industrial wastewater heat load driven by hydrothermal-mass coupling according to claim 1, characterized in that, S6 specifically includes: Using the total intervention heat load demand of S5 output as a constraint, the adjustable boundary of each control path under the condition of safe process operation is used as a safety constraint; the core objective function is to minimize the operating cost of the time window control cycle, while carbon emissions and process safety limits are incorporated as synergistic optimization objectives; an optimization algorithm is used to automatically find the optimal solution in this multi-dimensional constraint space, and output the optimal or Pareto optimal allocation scheme that can simultaneously meet the heat balance requirements, process stability and economic low carbon performance. The optimal allocation scheme is output in the form of structured instructions and sent to the underlying execution unit through the standard industrial control interface to complete the active steady-state control of temperature.

10. A hydrothermal-mass coupling driven industrial wastewater heat load prediction and control system, characterized in that, include: Multi-source data perception and acquisition module: Real-time perception and acquisition of multi-source heterogeneous data, construction of standardized datasets, and feature engineering preprocessing of standardized datasets; Over-temperature early warning and prediction module: Receives standardized data features collected by the multi-source data sensing and acquisition module, constructs a multi-step incremental prediction model using a gradient boosting decision tree, performs the prediction task from real-time monitoring data to future thermal risk indicators, and outputs the future temperature trajectory and over-temperature risk points; Multi-path heat sink identification and decoupling module: After receiving the over-temperature warning from the over-temperature warning and prediction module, the module performs multi-path decomposition of the total heat load of the system within the time window, and identifies the instantaneous heat contribution and controllable boundary of each path. Thermodynamic feature extraction module: It takes over the multi-path decoupling results of the multi-path heat sink identification and decoupling module, extracts the comprehensive net heating function of multi-path heat transfer under the current operating conditions, and performs narrow temperature zone first-order linearization processing in the over-temperature risk temperature domain of the over-temperature early warning prediction module to separate the linearization coefficients that characterize "nodal base heat load" and "nodal thermal conductivity". Targeted intervention load analysis module: Based on the linearization coefficient output by the thermodynamic feature extraction module, combined with the over-temperature risk time window and target control method predicted by the over-temperature early warning prediction module, the instantaneous intervention power target value required to offset excess heat is deduced by using transient thermal balance logic, and the total intervention load within the control time window is calculated by time-domain integration. Multi-objective optimization decision-making module: Based on the intervention heat load demand output by the targeted intervention load analysis module and the system thermodynamic characteristics analyzed by the thermodynamic characteristic extraction module, a constrained multi-objective optimization mathematical model is constructed with the stripped heat power allocation of each control path as the core decision variable.

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