Large-scale pig breeding wastewater purification treatment system

Abstract

The invention discloses a large-scale pig breeding wastewater purification treatment system, and relates to the technical field of wastewater treatment.The large-scale pig breeding wastewater purification treatment system is characterized in that the ion concentration of anaerobic liquid is monitored through a multi-ion sensing array, and a multi-channel feeding system is controlled to synthesize customized struvite compound fertilizer; the AI prediction control unit predicts the optimal medicament dosage and realizes millisecond-level closed-loop regulation; the multi-dimensional synergistic biochemical treatment module comprises an A / O biochemical treatment unit and an electric field enhanced anaerobic ammonia oxidation reactor, the electric field enhanced anaerobic ammonia oxidation reactor applies a direct-current electric field through an electrode pair, and engineering conductive biochar is used as a carrier to accelerate microbial metabolism; the sludge low-temperature pyrolysis self-consistent circulation module is used for pyrolyzing and converting residual sludge into biochar, biofuel and wood vinegar, and the biochar activation treatment unit is used for preparing engineering conductive biochar and forming a substance closed loop through a self-consistent circulation conveying system; and the digital twinning intelligent management and control platform realizes intelligent management and control of the system.

Description

Technical Field

[0001] This invention relates to the field of wastewater treatment technology, specifically a large-scale wastewater purification and treatment system for pig farming. Background Technology

[0002] With the rapid development of my country's animal husbandry, the number of large-scale pig farms is constantly increasing, and the amount of wastewater generated from these farms is also growing significantly. Wastewater from large-scale pig farms is characterized by high concentrations of organic matter, high levels of ammonia nitrogen, high levels of suspended solids, and rich amounts of nutrients. Direct discharge without effective treatment will cause serious pollution to aquatic environments. At the same time, the nitrogen, phosphorus, potassium, and other nutrients contained in the wastewater have high resource utilization value, a fact often overlooked by traditional treatment processes, leading to resource waste.

[0003] Currently, wastewater treatment in large-scale pig farming mainly employs traditional biological treatment processes, such as activated sludge processes, A / O processes, and A² / O processes. While these processes can remove organic pollutants and some nutrients from the wastewater to a certain extent, they still have many shortcomings in practical applications.

[0004] Existing technologies purify wastewater by combining traditional biological treatment processes with physicochemical treatment methods, but they still have certain limitations, such as: (1) low treatment efficiency, especially under high load shock conditions, it is difficult to maintain a stable treatment effect; (2) low nitrogen and phosphorus removal efficiency, and the effluent ammonia nitrogen and total phosphorus often fail to meet strict discharge standards; (3) insufficient resource utilization, most of the nutrients in the wastewater are removed and not effectively recycled; (4) the addition of reagents depends on manual experience, and the dosage is difficult to control precisely, resulting in waste of reagents and increased treatment costs; (5) poor system operation stability, lack of effective online monitoring and intelligent control methods, and weak adaptability to water quality fluctuations; (6) large amount of sludge is generated and difficult to dispose of, and traditional sludge treatment methods pose a risk of secondary pollution; (7) high energy consumption, lack of energy recovery and recycling mechanisms; (8) lack of a systematic intelligent management and control platform, making it difficult to achieve multi-objective collaborative optimization and predictive maintenance.

[0005] Furthermore, existing technologies often operate with relatively independent units, lacking organic synergy and failing to fully leverage the synergistic effects of each unit. Particularly in areas such as nutrient recovery, carrier material preparation, and self-sustaining energy recycling, existing technologies lack effective technical means and system integration solutions.

[0006] Therefore, there is an urgent need for a large-scale pig farm wastewater purification system that integrates efficient wastewater purification, nutrient resource recovery, sludge reduction, energy recycling, and intelligent management to solve the aforementioned technical problems in existing technologies and achieve efficient, resource-based, intelligent, and sustainable wastewater treatment. Summary of the Invention

[0007] The purpose of this invention is to overcome the shortcomings of the prior art and propose a large-scale pig farm wastewater purification and treatment system to solve the above-mentioned problems.

[0008] The objective of this invention is achieved through the following technical solution: a large-scale pig farm wastewater purification and treatment system, comprising: The intelligent fertilizer on-demand synthesis module is used to treat anaerobic liquid produced by anaerobic fermentation of wastewater. The intelligent fertilizer on-demand synthesis module includes: a reaction vessel; and a multi-ion sensing array set in the reaction vessel. The multi-ion sensing array includes a phosphate ion selective electrode, an ammonium ion selective electrode, a potassium ion selective electrode, and a trace element sensor, which are used to monitor the concentration of each ion in the anaerobic liquid in real time. A multi-channel feeding system connected to the reaction vessel includes a magnesium salt feeding channel, a potassium salt feeding channel, and a pH adjuster feeding channel, used to precisely add raw materials according to the target nitrogen, phosphorus, and potassium ratio; The fertilizer synthesis control unit, based on ion concentration data and preset target ratios, controls the type, dosage, and timing of each dosing channel to synthesize customized struvite compound fertilizers with specific nutrient ratios. The AI ​​vision-based dynamic precision air flotation module, located downstream of the intelligent fertilizer on-demand synthesis module, includes: an air flotation tank; an image and spectral analysis unit located at the inlet of the air flotation tank, including a high-speed industrial camera and an online spectral analyzer, used to collect data on the morphology, size, density, and chemical fingerprint of wastewater flocs; and an AI predictive control unit, with a built-in deep learning model, predicts the optimal chemical dosage and dissolved air volume in real time based on floc morphology and spectral data. The chemical dosing pump connected to the flotation tank is controlled by an AI predictive control unit to achieve millisecond-level closed-loop feedback regulation. The multi-dimensional synergistic biochemical treatment module, located downstream of the AI ​​vision-driven dynamic precision air flotation module, includes a mainstream A² / O biochemical treatment unit and a side-flow electric field-enhanced anaerobic ammonia oxidation reactor. The anaerobic ammonia oxidation reactor includes: The reactor body; electrode pairs disposed within the reactor body for applying a DC electric field of 0.2V / cm to 0.5V / cm within the reactor; The carrier filled inside the reactor body is engineered conductive biochar, which has electrical conductivity and biocompatibility, and acts as an electron shuttle to accelerate microbial metabolism. A sludge low-temperature pyrolysis self-consistent circulation module is used to treat the excess sludge generated by the system, including: The sludge low-temperature pyrolysis system converts excess sludge into biochar, biofuel, and wood vinegar in an anaerobic environment. The biochar activation treatment unit activates the biochar produced by pyrolysis with carbon dioxide or water vapor to prepare engineered conductive biochar. The self-consistent circulation conveying system transports engineered conductive biochar to the anaerobic ammonia oxidation reactor as a carrier, forming a material closed loop for sludge treatment and wastewater biochemical treatment. The digital twin intelligent control platform is electrically connected to the electrode pairs of the multi-ion sensor array, multi-channel feeding system, image and spectral analysis unit, chemical reagent dosing pump, and anaerobic ammonia oxidation reactor, including: The holographic sensing module integrates sensor data from the entire system to construct a virtual digital model that maps to the physical factory in a 1:1 ratio. The AI ​​decision engine has a built-in multi-objective collaborative optimization algorithm, aiming to maximize comprehensive economic benefits and dynamically balance the output value of energy consumption, pharmaceutical consumption and resource-based products. Predictive maintenance module, based on historical and real-time data, predicts equipment failures and process bottlenecks; The global control unit controls the coordinated operation of each module based on the optimization algorithm results.

[0009] The deep learning model of the AI ​​predictive control unit adopts a hybrid architecture of convolutional neural networks and recurrent neural networks. By analyzing the morphological characteristics, spectral characteristics and historical treatment effect data of wastewater flocs, it establishes a nonlinear mapping relationship between the treatability of flocs and the optimal dosage of reagents.

[0010] The fertilizer synthesis control unit dynamically adjusts the target nitrogen, phosphorus, and potassium ratio based on external market price signals and crop planting needs. The ratio range is N:P:K=1:0.3-2:0.5-3, enabling customized synthesis of special fertilizers for different purposes.

[0011] The preparation process of engineered conductive biochar includes: a sludge low-temperature pyrolysis system pyrolyzes the remaining sludge in an oxygen-deficient environment at 300-500℃ for 1-3 hours, and then a biochar activation treatment unit activates it at 800-900℃ with carbon dioxide or water vapor for 30-60 minutes, so that its specific surface area reaches 300-800m² / g and its conductivity reaches 0.1-1.0S / cm.

[0012] The electrode pairs in the anaerobic ammonia oxidation reactor are titanium-based iridium-ruthenium dioxide coated electrodes with an electrode spacing of 10-30 cm. The applied DC electric field strength can be automatically adjusted according to the influent ammonia nitrogen concentration within the range of 0.2-0.5 V / cm.

[0013] The multi-objective collaborative optimization algorithm adopts a hybrid optimization strategy that combines genetic algorithm and particle swarm optimization. The optimization objective functions include: minimizing system operating energy consumption, minimizing reagent cost, maximizing customized fertilizer sales revenue, and maximizing biofuel output value. The weights of each objective can be dynamically adjusted according to market conditions.

[0014] It also includes a pretreatment unit located upstream of the intelligent fertilizer on-demand synthesis module. The pretreatment unit includes a mechanical bar, an equalization tank, a high-efficiency solid-liquid separator and an anaerobic digestion tower connected in sequence. The anaerobic digestion tower is equipped with a biogas recovery system.

[0015] It also includes a deep treatment unit located downstream of the multi-dimensional synergistic biochemical treatment module, including an MBR membrane bioreactor, an ultraviolet disinfection device, and an ozone contact tank, to ensure that the effluent quality meets Class IV of the "Surface Water Environmental Quality Standard".

[0016] It also includes a combined heat and power (CHP) unit, which uses biogas produced by the anaerobic digester and biofuel produced by the sludge low-temperature pyrolysis system for CHP. The generated electricity supplies the entire system and the generated heat is used for sludge pyrolysis and anaerobic digester insulation.

[0017] The digital twin intelligent management and control platform also includes a full-process simulation module, which can simulate extreme conditions such as doubling of water inflow and heavy rainfall in virtual space, predict system response 72 hours in advance, and deduce the best response strategy.

[0018] The beneficial effects of this invention are: This invention solves the technical problems of low treatment efficiency, insufficient resource utilization, high operating costs, and poor system stability in existing large-scale pig farm wastewater treatment technologies by synergistically combining an intelligent fertilizer on-demand synthesis module, an AI vision dynamic precision air flotation module, a multi-dimensional collaborative biochemical treatment module, a sludge low-temperature pyrolysis self-consistent circulation module, and a digital twin intelligent management and control platform.

[0019] The intelligent fertilizer on-demand synthesis module monitors the concentration of various ions in the anaerobic liquid in real time through a multi-ion sensor array. Based on the ion concentration data and the preset target ratio, the fertilizer synthesis control unit controls the multi-channel feeding system to accurately add raw materials and synthesize customized struvite compound fertilizer with specific nutrient ratios. This achieves efficient recovery and resource utilization of nutrients in wastewater and solves the technical problems of nutrient loss and unstable fertilizer quality in traditional treatment processes.

[0020] The AI-powered visual dynamic precision flotation module collects wastewater floc data through image and spectral analysis units. The deep learning model built into the AI ​​prediction control unit predicts the optimal chemical dosage in real time, achieving millisecond-level closed-loop feedback adjustment. This significantly improves the accuracy of chemical dosing and the stability of flotation treatment, solving the technical problems of blind chemical dosing and large fluctuations in treatment effect in traditional flotation processes.

[0021] The electric field-enhanced anaerobic ammonia oxidation reactor in the multidimensional synergistic biochemical treatment module applies a DC electric field to the electrode pair and combines it with an engineered conductive biochar carrier as an electron shuttle to accelerate microbial metabolism. This achieves the synergistic effect of electric field enhancement and biological carrier, significantly improving the efficiency of biological nitrogen removal while reducing energy consumption and reactor volume requirements. It solves the technical problems of low efficiency, high energy consumption, and large footprint in traditional biological nitrogen removal processes.

[0022] The sludge low-temperature pyrolysis self-consistent circulation module converts excess sludge into biochar, biofuel, and wood vinegar through a sludge low-temperature pyrolysis system. The biochar activation treatment unit prepares engineered conductive biochar, and the self-consistent circulation conveying system transports the engineered conductive biochar to an electric field-enhanced anaerobic ammonia oxidation reactor as a carrier, forming a material closed loop between sludge treatment and wastewater biochemical treatment. This achieves sludge reduction and resource utilization, solving the technical problems of secondary pollution and high cost of purchasing carriers in traditional sludge treatment.

[0023] The digital twin intelligent management and control platform constructs a virtual digital model through a holographic perception module. The AI ​​decision engine's built-in multi-objective collaborative optimization algorithm dynamically balances energy consumption, pharmaceutical consumption, and the output value of resource-based products. The predictive maintenance module predicts equipment failures and process bottlenecks, and the global control unit controls the coordinated operation of each module. This achieves intelligent management and automatic optimization of the system, solving the technical problems of strong reliance on manual operation, difficulty in optimizing operating parameters, and passive equipment maintenance in traditional processing systems.

[0024] The deep learning model employs a hybrid architecture of convolutional neural networks and recurrent neural networks to establish a nonlinear mapping relationship between floc handlingability and optimal pesticide dosage. The fertilizer synthesis control unit dynamically adjusts the target nitrogen, phosphorus, and potassium ratios based on external market price signals and crop planting needs, enabling customized synthesis of fertilizers for different applications. The multi-objective collaborative optimization algorithm utilizes a hybrid optimization strategy combining genetic algorithms and particle swarm optimization to achieve global optimization of system operation and maximize economic benefits.

[0025] The pretreatment and advanced treatment units provide stable water quality conditions for the system, ensuring that the effluent meets Class IV of the "Surface Water Environmental Quality Standard". The combined heat and power (CHP) unit utilizes biogas and biofuel for CHP, achieving a self-sustaining energy cycle. The full-process simulation module can simulate extreme operating conditions and deduce coping strategies, enhancing the system's risk control capabilities and operational stability. Attached Figure Description

[0026] Figure 1 The system architecture of this invention Figure 1 ; Figure 2 The system architecture of this invention Figure 2 ; Figure 3 The system architecture of this invention Figure 3 . Detailed Implementation

[0027] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] It should be noted that the directional concepts of "left", "right", "up", "down", "front", "back", "inner", and "outer" in the following scheme are all relative directions, and will not be listed one by one here.

[0029] Example 1: like Figure 1 As shown in the figure, this embodiment provides a basic treatment scheme for a large-scale pig farm wastewater purification and treatment system. The system achieves efficient purification and resource utilization of wastewater through the organic combination of a pretreatment unit, an intelligent fertilizer on-demand synthesis module, an AI vision dynamic precision air flotation module, a multi-dimensional collaborative biochemical treatment module, a sludge low-temperature pyrolysis self-consistent circulation module, a digital twin intelligent management and control platform, and a deep treatment unit.

[0030] The pretreatment unit is located at the front end of the entire system and includes a mechanical bar screen, an equalization tank, a high-efficiency solid-liquid separator, and an anaerobic digester connected in sequence. The mechanical bar screen has a 20mm spacing to remove large suspended solids and floating matter from the wastewater. The equalization tank is designed with an effective volume based on 8-12 hours of daily treatment capacity and is equipped with perforated aeration pipes and a stirring device to ensure homogenization of water quality and quantity. The high-efficiency solid-liquid separator uses a screw extrusion design, controlling the solid moisture content at 65%-70%. The separated liquid enters the anaerobic digester for anaerobic fermentation. The anaerobic digester uses a completely mixed reactor with a hydraulic retention time of 15-20 days, a temperature controlled at 35±2℃, and a pH maintained within the range of 6.8-7.2. The anaerobic digester is equipped with a biogas recovery system, including a gas-liquid separator, a desulfurization device, and a wet gas holder, recovering biogas with a methane content of 60%-65%.

[0031] The intelligent fertilizer on-demand synthesis module receives and processes anaerobic liquid from the anaerobic digester. The effective volume of the reaction vessel is 500-2000 cubic meters, and the reaction time is 2-4 hours. A multi-ion sensor array includes phosphate ion selective electrodes, ammonium ion selective electrodes, potassium ion selective electrodes, and trace element sensors, which monitor the concentration of each ion with a detection accuracy of ±1%-±2% and a response time of less than 30 seconds. The multi-channel feeding system includes magnesium salt dosing channels, potassium salt dosing channels, and pH adjuster dosing channels, with dosing accuracy controlled within ±1%.

[0032] The fertilizer synthesis control unit, based on ion concentration data and preset target ratios, controls the type, dosage, and timing of each addition channel to synthesize customized struvite compound fertilizers with specific nutrient ratios. The synthesis reaction is mainly based on the following chemical reactions: In the formula, NH4⁺ represents the concentration of ammonium ions (mol / L), PO4³⁻ represents the concentration of phosphate ions (mol / L), Mg²⁺ represents the concentration of magnesium ions (mol / L), and MgNH4PO4·6H2O represents struvite crystals. The stoichiometric ratio of the reaction is 1:1:1, but in actual operation, to improve the phosphorus removal rate, the amount of magnesium ions added is usually 1.2-1.5 times the theoretical value.

[0033] The AI-powered visual dynamic precision flotation module is located downstream of the intelligent fertilizer on-demand synthesis module and includes a flotation tank and an image and spectral analysis unit. The image and spectral analysis unit comprises a high-speed industrial camera and an online spectrometer for acquiring data on the morphology, size, density, and chemical fingerprint of wastewater flocs. The high-speed industrial camera has a resolution of 2048×2048 pixels and a shooting frequency of 30 frames per second; the online spectrometer has a wavelength range of 200-800 nm.

[0034] The deep learning model built into the AI ​​prediction control unit is based on a hybrid architecture of convolutional neural networks and recurrent neural networks. The model's loss function is defined as: .

[0035] In the formula, L is the loss function value, α is the prediction error weight coefficient (value 0.7), β is the regularization weight coefficient (value 0.3), n is the number of training samples, m is the number of model parameters, yᵢ is the actual dosage of the i-th sample (mg / L), ŷᵢ is the predicted dosage of the i-th sample (mg / L), and wⱼ is the j-th model parameter. By minimizing the loss function, a precise mapping between floc treatability and the optimal dosage of the pesticide is achieved.

[0036] The multi-dimensional synergistic biological treatment module includes a mainstream A² / O biological treatment unit and a side-flow electric field-enhanced anaerobic ammonia oxidation reactor. The mainstream A² / O biological treatment unit adopts the sequencing batch reactor (SBR) activated sludge process and includes three reaction zones: an anaerobic tank, an anoxic tank, and an aerobic tank, with a total hydraulic retention time of 12-16 hours. The anaerobic tank accounts for 30% of the total tank volume and mainly performs phosphorus release and organic matter acidification; the anoxic tank accounts for 35% of the total tank volume and mainly performs denitrification; the aerobic tank accounts for 35% of the total tank volume and mainly performs nitrification and phosphorus overtake.

[0037] The electric field-enhanced anaerobic ammonia oxidation reactor comprises a reactor body and electrode pairs disposed within the reactor body. The electrode pairs are titanium-based iridium-ruthenium dioxide coated electrodes with a spacing of 10-30 cm, used to apply a DC electric field of 0.2 V / cm to 0.5 V / cm within the reactor. The electric field strength is calculated using the following formula: E = U / d; where E is the electric field strength (V / cm), U is the applied voltage (V), and d is the electrode spacing (cm).

[0038] Engineered conductive biochar, with a specific surface area of ​​300-800 m² / g and a conductivity of 0.1-1.0 S / cm, is packed into the reactor as a carrier. Under the influence of an electric field, the rate constant of the anaerobic ammonia oxidation reaction can be expressed as: In the formula, k is the reaction rate constant after electric field enhancement (d⁻¹), k0 is the reaction rate constant without electric field (d⁻¹), and k e d is the electric field strengthening coefficient (d⁻¹·(V / cm)⁻ⁿ), E is the electric field strength (V / cm), and n is the electric field strengthening exponent (usually taken as 1.2-1.8).

[0039] The system treats excess sludge generated by a low-temperature pyrolysis self-consistent circulation module. Under anoxic conditions, the system converts excess sludge into biochar, biofuel, and wood vinegar. The pyrolysis temperature is controlled at 300-500℃, the pyrolysis time is 1-3 hours, and the heating rate is 5-10℃ / min. The energy balance equation for the pyrolysis process is: ; In the formula, Qᵢ n For the input heat (kJ / kg dry sludge), Q o ᵤ t The product's calorific value (kJ / kg dry sludge). Heat loss (kJ / kg dry sludge).

[0040] The biochar activation unit activates the biochar produced by pyrolysis using carbon dioxide or steam. The activation temperature is 800-900℃, the activation time is 30-60 minutes, and the gas flow rate is 100-200 mL / min. The activated engineered conductive biochar is then transported to the electric field-enhanced anaerobic ammonia oxidation reactor as a carrier via a self-consistent circulation conveying system.

[0041] The digital twin intelligent control platform is connected to the sensors and actuators of each module via electrical connections. The holographic sensing module collects operational data from the entire system at a frequency of 1-10Hz, using the industrial Ethernet protocol for data transmission. The AI ​​decision engine incorporates a multi-objective collaborative optimization algorithm, with the objective function being: In the formula, F is the comprehensive objective function value, w1, w2, and w3 are the weight coefficients of energy consumption, cost, and revenue, respectively (w1+w2+w3=1), E is the energy consumption index (kWh / m³), C is the operating cost index (yuan / m³), and R is the resource utilization revenue index (yuan / m³).

[0042] The advanced treatment unit is located downstream of the multi-dimensional synergistic biochemical treatment module and includes an MBR membrane bioreactor, an ultraviolet disinfection device, and an ozone contact tank. The MBR membrane bioreactor uses hollow fiber membranes for membrane separation, and the ultraviolet disinfection device and ozone contact tank perform advanced disinfection to ensure that the effluent quality meets Class IV of the "Surface Water Environmental Quality Standard".

[0043] The entire system operates as follows: Wastewater from large-scale farms first enters the pretreatment unit, where large particles are removed by a mechanical screen before entering a regulating tank for water quality and quantity adjustment. The regulated wastewater then passes through a high-efficiency solid-liquid separator to separate solid manure and liquid components. The solid manure can be composted, while the liquid component enters an anaerobic digester for anaerobic fermentation. The biogas produced during anaerobic digestion is collected and utilized through a biogas recovery system, and the anaerobic liquid produced during anaerobic fermentation enters a smart fertilizer on-demand synthesis module.

[0044] In the intelligent fertilizer on-demand synthesis module, a multi-ion sensor array monitors the concentration of various ions in the anaerobic liquid in real time. Based on the ion concentration data and preset target ratios, the fertilizer synthesis control unit controls the type, dosage, and timing of each addition channel to synthesize customized struvite compound fertilizer with specific nutrient ratios. After precipitation and separation, the synthesized customized struvite compound fertilizer is used as commercial fertilizer, and the supernatant is further processed.

[0045] Wastewater treated for fertilizer synthesis enters the AI-powered visual dynamic precision air flotation module, where an image and spectral analysis unit collects the morphology and spectral data of the wastewater flocs. The AI ​​predictive control unit, based on a deep learning model, predicts the optimal dosage of chemical reagents and dissolved air volume in real time, controlling the chemical dosing pump for precise dosing. During the air flotation process, fine suspended solids combine with air bubbles and float to the surface, where they are removed by a sludge scraper, and the clarified wastewater flows out from the bottom of the tank.

[0046] The effluent from the air flotation enters the main A² / O biological treatment unit of the multi-dimensional synergistic biological treatment module. The wastewater undergoes three treatment stages sequentially: anaerobic, anoxic, and aerobic, achieving organic matter degradation and nitrogen and phosphorus removal. The sludge dewatering filtrate produced by the system enters the electric field-enhanced anaerobic ammonia oxidation reactor as a side stream. Under the synergistic effect of engineered conductive biochar carrier and DC electric field, efficient biological denitrification is achieved.

[0047] The excess sludge from the biochemical treatment enters the sludge low-temperature pyrolysis self-consistent circulation module. Under anoxic conditions, the sludge low-temperature pyrolysis system converts the excess sludge into biochar, biofuel, and wood vinegar. The biochar activation unit activates the biochar produced by pyrolysis with carbon dioxide or steam to prepare engineered conductive biochar. The self-consistent circulation conveying system transports the engineered conductive biochar to an electric field-enhanced anaerobic ammonia oxidation reactor as a carrier, forming a closed-loop material system for sludge treatment and wastewater biochemical treatment.

[0048] The effluent from the biochemical treatment enters the advanced treatment unit, where it first undergoes membrane separation in an MBR membrane bioreactor to further remove suspended solids and some dissolved organic matter. The membrane-separated effluent then passes through an ultraviolet disinfection device and an ozone contact tank for advanced disinfection. The final effluent quality meets Class IV of the "Surface Water Environmental Quality Standard" and can be used for farmland irrigation or reused for flushing in aquaculture farms.

[0049] Throughout the process, the digital twin intelligent management and control platform continuously monitors the operational status of each module, while the holographic perception module collects operational data from the entire system and constructs a digital twin model. The AI ​​decision engine calculates optimal operating parameters based on a multi-objective optimization algorithm, and the global control unit adjusts the operational status of each module according to the optimization results, achieving intelligent and coordinated operation of the system.

[0050] Through precise control of the intelligent fertilizer on-demand synthesis module, over 90% of phosphorus and over 80% of nitrogen can be recovered from wastewater. The synthesized customized struvite compound fertilizer has stable nitrogen, phosphorus, and potassium content and can be directly sold as commercial fertilizer, achieving efficient recovery and utilization of nutrients from wastewater. Secondly, the AI ​​vision-based dynamic precision air flotation module, through machine vision and deep learning technology, achieves precise control of chemical reagent dosing. Compared to traditional air flotation processes, it can save 15%-30% on reagent consumption, and the suspended solids removal rate remains stable at over 95%, significantly improving treatment efficiency and economics.

[0051] The electric field-enhanced anaerobic ammonia oxidation reactor in the multi-dimensional synergistic biochemical treatment module achieves a nitrogen removal efficiency more than 50% higher than traditional processes through the synergistic effect of engineered conductive biochar carriers and DC electric fields, while reducing energy consumption by more than 70%. The reactor volume can be reduced by 30%-50%, significantly lowering infrastructure investment. The sludge low-temperature pyrolysis self-consistent recycling module realizes sludge reduction and resource utilization, achieving a sludge reduction rate of over 85%. The generated engineered conductive biochar is directly recycled for wastewater treatment, avoiding the external purchase cost of carriers. Simultaneously, the generated biofuel and wood vinegar can be sold as byproducts.

[0052] The advanced treatment unit combines an MBR membrane bioreactor, ultraviolet disinfection, and ozone contact to ensure that the effluent consistently meets Class IV surface water environmental quality standards, with chemical oxygen demand below 30 mg / L, ammonia nitrogen below 1.5 mg / L, total phosphorus below 0.3 mg / L, turbidity below 10 NTU, and Escherichia coli count below 10,000 CFU / L. The effluent can be safely reused for agricultural irrigation or cleaning in livestock farms.

[0053] The digital twin intelligent management and control platform enables intelligent operation of the system. Through holographic perception and AI decision-making, the system's operational stability is improved by more than 40%, the need for manual operation is reduced by more than 80%, predictive maintenance can detect equipment failures in advance, and equipment utilization is increased by 15%-25%. The overall energy consumption of the entire system is reduced by 40%-60% compared with traditional processes, operating costs are reduced by 30%-50%, and the economic value of resource-based products can cover 60%-80% of the operating costs, achieving an organic unity of environmental and economic benefits.

[0054] The system exhibits excellent shock resistance, maintaining stable treatment performance even under influent water quality variations of ±30%, demonstrating strong adaptability. Its modular design allows for flexible configuration according to farms of different sizes, enabling independent operation or integration with existing facilities, thus showing broad application prospects. The implementation of this technical solution not only solves the pollution problem of wastewater from large-scale aquaculture but also achieves the resource utilization of wastewater, providing effective technical support for circular agriculture and ecological aquaculture.

[0055] Example 2: like Figure 1 and Figure 2 As shown, this embodiment, based on the basic functions of Embodiment 1, focuses on enhancing the system's intelligent control capabilities. Through a deeply integrated hybrid neural network architecture of the AI ​​predictive control unit, a market response mechanism for fertilizer synthesis, a multi-objective collaborative optimization algorithm, and a full-process simulation module, it achieves the system's autonomous evolution and value symbiosis. This embodiment is particularly suitable for large-scale modern aquaculture enterprises with high requirements for intelligence and a desire to maximize economic benefits.

[0056] The basic processing flow is the same as in Example 1, including a pretreatment unit, basic purification treatment and deep treatment unit. This example focuses on describing the enhanced functions of the intelligent control system.

[0057] The deep learning model of the AI ​​predictive control unit adopts a hybrid architecture of convolutional neural networks and recurrent neural networks. By analyzing the morphological characteristics, spectral characteristics, and historical treatment effect data of wastewater flocs, it establishes a nonlinear mapping relationship between floc treatability and optimal reagent dosage. The convolutional neural network part extracts the spatial morphological characteristics of wastewater flocs, while the recurrent neural network part processes the temporal variation characteristics of water quality.

[0058] Feature fusion in the hybrid architecture is achieved through an attention mechanism, and the formula for calculating attention weights is: Where αᵢ is the attention weight at the i-th time step, eᵢ is the energy value at the i-th time step, and T is the length of the time series. The energy value eᵢ is calculated through the fully connected layer: ; In the formula, W a W x W h This is the weight matrix. Let be the input feature vector at the i-th time step. b is the hidden state of the previous time step. a This is the bias vector.

[0059] The final drug dosage prediction is calculated using fused features: ; In the formula, ŷ represents the predicted dosage of the drug (mg / L), and W o To output the weight matrix, b o This is used as the output bias. The model establishes a nonlinear mapping relationship between floc treatability and optimal reagent dosage by analyzing the morphological and spectral characteristics of wastewater flocs and historical treatment effect data, achieving a prediction accuracy of over 95%.

[0060] The fertilizer synthesis control unit dynamically adjusts the target nitrogen, phosphorus, and potassium ratio based on external market price signals and crop planting needs, enabling the customized synthesis of specialized fertilizers for different applications. The market response module obtains fertilizer market price data in real time via an internet interface, while crop planting demand data comes from regional agricultural departments' planting plans and soil testing reports.

[0061] The dynamic proportioning adjustment algorithm is based on the principle of maximizing economic benefits, and its objective function is: ; In the formula, Let Pᵢ represent the economic benefit (yuan), n represent the number of fertilizer product types, Pᵢ represent the market price of the i-th fertilizer (yuan / ton), Qᵢ represent the output of the i-th fertilizer (tons), and Cᵣ represent the total output of the i-th fertilizer. a w represents the raw material cost (yuan). Processing cost (RMB).

[0062] The mixing ratio ranges from N:P:K = 1:0.3 to 2:0.5 to 3, enabling the system to synthesize specialized fertilizers for different purposes on demand. For example, it can synthesize a corn-specific fertilizer with an N:P:K ratio of 3:1:2 based on the needs of corn cultivation, and a vegetable-specific fertilizer with an N:P:K ratio of 2:1:3 based on the needs of vegetable cultivation. The response time for adjusting the ratio is no more than 30 minutes.

[0063] The multi-objective collaborative optimization algorithm employs a hybrid optimization strategy combining genetic algorithms and particle swarm optimization. The optimization objective functions include: minimizing system operating energy consumption, minimizing reagent costs, maximizing customized fertilizer sales revenue, and maximizing biofuel production value. The weights of each objective can be dynamically adjusted according to market conditions. The genetic algorithm handles the global search, with an initial population size of 100 individuals. The fitness function is based on a multi-objective optimization design. .

[0064] In the formula, F(x) is the overall fitness value, w1, w2, w3, and w4 are the weight coefficients of each objective (w1+w2+w3+w4=1), f1(x) is the objective function for minimizing system operating energy consumption, f2(x) is the objective function for minimizing pesticide cost, and f3(x) is the objective function for maximizing customized fertilizer sales revenue. The objective function is to maximize the value of biofuel production.

[0065] The specific form of each sub-objective function is as follows: ; ; In the formula, m represents the number of devices, Eᵢ represents the power of the i-th device (kW), tᵢ represents the operating time of the i-th device (h), and c e The electricity price is (yuan / kWh); p is the type and quantity of pesticides, Dⱼ is the dosage of the j-th pesticide (kg), Pⱼ is the price of the j-th pesticide (yuan / kg); q is the quantity of fertilizer products, Q k R represents the yield (kg) of the k-th fertilizer. k Let $k$ be the selling price of the kth type of fertilizer (in yuan / kg). For biofuel production (L). Price of biofuel (RMB / L).

[0066] The particle swarm optimization algorithm is responsible for local fine-grained search. The particle swarm size is 50 particles, and the particle position update formula is: ; In the formula, Let be the velocity of the i-th particle in the d-th dimension during the t-th iteration. Let be the position of the i-th particle in the d-th dimension at the t-th iteration, w be the inertia weight (value 0.4), c1 and c2 be the learning factors (both value 2.0), and r1 and r2 be random numbers in the interval [0,1]. Let be the optimal position for the i-th particle. This is the globally optimal position.

[0067] The weighting coefficients can be dynamically adjusted according to market conditions. The adjustment mechanism is based on fuzzy control theory, and the input variables include fertilizer price volatility, energy price change rate, and equipment utilization rate. The weighting adjustment formula is as follows: ; In the formula, Let be the new weight for the i-th target, Δwᵢ be the weight adjustment amount, η be the adjustment coefficient (with a value of 0.1), and μ(Iᵢ) be the fuzzy inference result based on the input variable Iᵢ.

[0068] The full-process simulation module can simulate extreme conditions such as doubling of inflow load and heavy rainfall impact in virtual space, predicting system response and deriving optimal response strategies 72 hours in advance. The simulation model is based on the finite difference method, and mass transfer and energy exchange are described by a system of partial differential equations: In the formula, C is the pollutant concentration (mg / L), t is time (s), v is the flow velocity vector (m / s), D is the diffusion coefficient (m² / s), and R is the reaction term (mg / (L·s)).

[0069] The simulation module can simulate various extreme conditions such as doubled influent load, heavy rainfall impact, equipment failure, and raw material shortage. The simulation of heavy rainfall impact considers the rainwater dilution effect, and the influent concentration is adjusted according to the dilution ratio. ; In the formula, C0 represents the influent concentration under the influence of rainfall (mg / L), and Rᵣ represents the normal influent concentration (mg / L). a ᵢ n (t) represents the rainfall intensity (m³ / h), and Q0 represents the normal inflow rate (m³ / h).

[0070] The simulation prediction time window is 72 hours, with a time step of 1 hour. Uncertainty factors are handled using the Monte Carlo method. Prediction accuracy is evaluated using the following metrics: ; In the formula, MAPE is the mean absolute percentage error, and n is the number of predicted samples. Let ᵢ be the actual value of the i-th sample, and ŷᵢ be the predicted value of the i-th sample. The system's MAPE value is controlled within 5%.

[0071] The predictive maintenance module, based on machine learning algorithms, has established an equipment health status assessment model. The formula for calculating the equipment health index is: ; In the formula, HI represents the health index (0-1), k represents the number of monitoring parameters, and wᵢ represents the weight of the i-th parameter. Let HI be the standardized index value of the i-th parameter. A maintenance warning is triggered when HI is below 0.3, and an emergency shutdown protection is triggered when HI is below 0.1.

[0072] This embodiment integrates advanced intelligent control functions on top of the basic processing flow. The AI ​​predictive control unit continuously collects images and spectral data of wastewater flocs and performs real-time analysis through a hybrid neural network architecture. The convolutional neural network extracts the spatial morphological features of the flocs, including floc size distribution, shape coefficient, and density gradient; the recurrent neural network analyzes the temporal trend of water quality changes, identifying periodic fluctuations and abnormal changes. The outputs of the two networks are fused through an attention mechanism to generate a comprehensive treatability assessment index, based on which the optimal chemical dosage and dissolved air volume are predicted, achieving millisecond-level control precision.

[0073] The fertilizer synthesis control unit's market response mechanism updates market data hourly, including real-time prices, inventory status, and demand forecasts for various fertilizers. Based on the acquired market information and the current nutrient concentrations in wastewater, the system dynamically calculates the optimal product blend. When a price increase of more than 10% for a particular fertilizer is detected, the system automatically adjusts its production strategy, prioritizing the synthesis of that type of fertilizer. Crop planting demand data is obtained through an interface with the regional agricultural information system, enabling the system to predict planting plans 30 days in advance and adjust fertilizer inventory accordingly.

[0074] The multi-objective collaborative optimization algorithm performs global optimization calculations every 8 hours, while the genetic algorithm and particle swarm optimization algorithm run in parallel for 100 generations and 500 iterations, respectively. During optimization, the system considers peak-valley electricity price differences, increasing the operating time of energy-intensive equipment during low-price periods and reducing system load during high-price periods. Chemical cost optimization is achieved through bulk purchasing and inventory management; the system can predict chemical demand for the next 7 days and automatically trigger purchase orders when prices are low. Fertilizer sales revenue optimization combines market forecasting and production capacity to maximize the output of high-value products while ensuring treatment effectiveness.

[0075] The full-process simulation module continuously runs virtual experiments, automatically performing a complete extreme condition simulation test every 24 hours. Simulation scenarios include a sudden increase in influent load of 50%-200%, three consecutive days of heavy rainfall, major equipment failure, and raw material supply interruption. The simulation results generate contingency plans, including the startup sequence of backup equipment, emergency reagent dosing schemes, and capacity adjustment strategies. When similar abnormal signals are detected during actual operation, the system can activate corresponding countermeasures within 5 minutes.

[0076] The predictive maintenance module analyzes multi-dimensional parameters such as vibration, temperature, current, and pressure to create a digital profile of the equipment's health status. Machine learning algorithms identify early signs of equipment performance degradation and issue maintenance alerts 72 hours in advance. The system automatically generates maintenance plans, including a list of required spare parts, estimated maintenance time, and downtime impact assessment, and optimizes maintenance scheduling to avoid peak production periods as much as possible.

[0077] The entire intelligent control process achieves human-machine collaboration. Operators monitor the system status through a visual interface, and the system automatically handles over 80% of routine operations. Personnel focus on handling abnormal situations and confirming critical decisions. The intelligent control system's learning capability enables it to continuously optimize control strategies, adjust algorithm parameters based on historical operating data, and improve prediction accuracy and control effectiveness.

[0078] The hybrid neural network architecture of the AI ​​predictive control unit improves the prediction accuracy of chemical dosing from the traditional 80% to over 95%, further reducing chemical consumption by 10%-15%, resulting in annual cost savings of 300,000-500,000 yuan. Accurate prediction of floc treatability avoids the problems of overdosing and underdosing, improving the stability of suspended solids removal rate to over 98%, and controlling effluent turbidity fluctuations within ±2 NTU.

[0079] The market-responsive mechanism for fertilizer synthesis enables customized production on demand, increasing product added value by 20%-35%. By dynamically adjusting the nitrogen, phosphorus, and potassium ratios, the system can flexibly switch product types according to market demand, increasing inventory turnover by over 40% and reducing capital occupation costs by 25%. Compared to traditional fixed-ratio production, this can increase annual revenue by 1-2 million yuan. Real-time market price tracking allows the system to capitalize on price fluctuations, increasing production during peak fertilizer price periods and reducing production or switching to other product types during periods of low prices.

[0080] The application of multi-objective collaborative optimization algorithms improves the overall economic efficiency of the system by 15%-25%. The global search capability of genetic algorithms avoids local optima, while the rapid convergence of particle swarm optimization ensures fast response speeds for real-time optimization. The dynamic adjustment mechanism of weight coefficients allows the system to adapt to different market environments, automatically increasing the weight of energy-saving optimization during periods of rising energy prices and increasing the weight of profit optimization during periods of booming fertilizer markets. The operation of the optimization algorithms further reduces system operating costs by 10%-20%, resulting in a significant improvement in overall economic efficiency.

[0081] The predictive capabilities of the full-process simulation module significantly improve the system's reliability and stability. The 72-hour prediction window provides ample preparation time for operational adjustments, and simulation testing under extreme conditions ensures safe operation even in abnormal situations. The simulation prediction accuracy exceeds 95%, and the early warning success rate surpasses 90%, effectively preventing a decrease in treatment effectiveness or equipment damage due to unforeseen circumstances. The system's shock resistance is improved by over 50%, maintaining over 85% of its treatment effectiveness even when the influent load doubles.

[0082] Predictive maintenance extends the mean time between failures (MTBF) of equipment by 30%-50% and reduces maintenance costs by 20%-30%. Early warnings prevent downtime losses caused by sudden failures, increasing equipment utilization to over 90%. Optimized intelligent maintenance plans reduce unnecessary preventative maintenance, decreasing maintenance frequency by 15%-25% and spare parts inventory costs by 20%.

[0083] The intelligent control system's self-learning capability enables its performance to continuously improve over time. After six months of operation, the prediction accuracy has increased by 8%-12% compared to the initial stage, and the control effect continues to optimize. The system's human-machine collaborative design significantly reduces the skill requirements of operators, shortening training time by 60% and reducing labor costs by 40%. The visual interface and intelligent decision support improve management efficiency by more than 50%, providing strong support for the enterprise's digital transformation.

[0084] Overall, this embodiment, through the deep integration of intelligent control technology, maximizes economic benefits and minimizes operational risks while ensuring high-standard treatment results, setting a new benchmark for the intelligent development of large-scale aquaculture wastewater treatment technology.

[0085] Example 3: like Figures 1 to 3As shown, this embodiment, based on Embodiments 1 and 2, constructs a complete circular economy model. Through precise process parameter control, a complete material closed-loop system, and an energy self-sufficiency mechanism, it achieves zero sludge discharge, energy self-sufficiency, and the synergistic output of various high-value-added products. This embodiment is particularly suitable for high-end ecological farms pursuing zero-emission goals, representing a comprehensive upgrade of wastewater treatment technology towards a circular economy model.

[0086] The basic processing flow and intelligent control functions continue the configuration of the previous two embodiments. This embodiment focuses on the core technological innovations of the circular economy system.

[0087] The preparation process of engineered conductive biochar is the core technology of this embodiment. This process, through precise control of pyrolysis and activation parameters, produces a functional carrier with stable performance and excellent conductivity. The sludge low-temperature pyrolysis system adopts a fluidized bed reactor with an inner diameter of 2.5 meters and a height of 12 meters, equipped with a gas distributor and a cyclone separator. The excess sludge is pretreated to adjust the moisture content to 10%-15%, and the feed rate is controlled at 500-1500 kg / h. The pyrolysis process is carried out in a strictly anoxic environment, with the oxygen content controlled below 0.5%, and an inert atmosphere is achieved through nitrogen protection.

[0088] The pyrolysis temperature control curve consists of three stages: a preheating stage from room temperature to 200℃ at a rate of 3-5℃ / min; a main pyrolysis stage from 200℃ to the target temperature of 300-500℃ at a rate of 8-12℃ / min; and a holding stage maintaining the target temperature for 1-3 hours. Key parameters for temperature control are calculated using the heat transfer equation. ; In the formula, q is the heat transfer rate (W), h is the heat transfer coefficient (W / (m²·K)), and A is the heat transfer area (m²). The wall temperature is (K). The temperature is the bed temperature (K).

[0089] The heat transfer coefficient h is determined using the Nusselt number correlation: N = 0.023Re 0.8 Pr 0.4 ; In the formula, N is the Nusselt number, Re is the Reynolds number, and Pr is the Prandtl number. This correlation ensures the temperature uniformity and stability of the pyrolysis process.

[0090] The yield of biochar produced by pyrolysis is calculated based on mass balance: In the formula, Y c For biochar yield (%), m c For biochar mass (kg), m sThe figure represents the mass of dry sludge (kg). Under optimized conditions, the biochar yield can reach 35%-45%.

[0091] The biochar activation unit activates the biochar produced by pyrolysis with carbon dioxide or steam to prepare engineered conductive biochar. The activation process uses a rotary kiln furnace, 15 meters long, 1.8 meters in inner diameter, and rotating at 0.5-2 rpm. The activation temperature is controlled at 800-900℃, the activation time is 30-60 minutes, and the gas flow rate is 100-200 mL / min. The key reactions in the activation process are: C + CO2 → 2CO (carbon dioxide activation); C + H2O → CO + H2 (water vapor activation).

[0092] The conversion rate of the activation reaction is described by the Arrhenius equation: ; In the formula, k is the reaction rate constant (s⁻¹), A is the pre-exponential factor (s⁻¹), E_a is the activation energy (J / mol), R is the gas constant (8.314 J / (mol·K)), and T is the absolute temperature (K).

[0093] The activated engineered conductive biochar possesses specific physicochemical properties, and its specific surface area is calculated using the BET equation: S BET =(V m ×N A ×σ) / M In the formula, S BET BET specific surface area (m² / g), V m The adsorption volume of the monolayer (cm³ / g), N A σ is Avogadro's constant (6.022 × 10²³ mol⁻¹), σ is the cross-sectional area of ​​the adsorbed molecule (m²), and M is the molar mass (g / mol). The prepared engineered conductive biochar has a specific surface area of ​​300-800 m² / g, a pore volume of 0.2-0.6 cm³ / g, and an average pore size of 2-8 nm.

[0094] Conductivity is a key performance indicator of engineered conductive biochar. Measured using the four-electrode method, the conductivity ranges from 0.1 to 1.0 S / cm. The conductivity mechanism is based on the formation of graphitized carbon structures, and the relationship between conductivity and the degree of graphitization is: σ = σ0 × (G / G max ) n ; In the formula, σ is the conductivity (S / cm), σ0 is the conductivity when fully graphitized (S / cm), and G is the actual degree of graphitization. max The maximum degree of graphitization is given by n, which is an exponential factor (usually 2-3).

[0095] The electrode pairs in the electric field-enhanced anaerobic ammonia oxidation reactor are titanium-based iridium-ruthenium dioxide coated electrodes, with an electrode spacing of 10-30 cm. The applied DC electric field strength can be automatically adjusted according to the influent ammonia nitrogen concentration within the range of 0.2-0.5 V / cm. The electrode preparation process includes three steps: titanium substrate pretreatment, coating preparation, and heat treatment. After acid washing and sandblasting, the titanium substrate is coated with iridium-ruthenium dioxide using a thermal decomposition method. The coating process uses a brush coating method, with each coating thickness of 2-5 μm, and a total of 8-12 coatings. Finally, the substrate is calcined at 500℃ for 1 hour.

[0096] The electrode spacing is designed to consider both the uniformity of the electric field distribution and energy efficiency, with a spacing range of 10-30 cm. Automatic adjustment of the electric field strength is based on real-time monitoring of the influent ammonia nitrogen concentration, and the adjustment algorithm employs a PID control strategy. In the formula, E(t) is the output electric field strength (V / cm), e(t) is the deviation between the set value and the measured value of ammonia nitrogen concentration (mg / L), and K... p K is the proportionality constant (value 0.02). i K is the integral coefficient (with a value of 0.001). d This is the differential coefficient (with a value of 0.1).

[0097] The electric field strength setting is determined based on the ammonia nitrogen concentration range: when the ammonia nitrogen concentration is below 100 mg / L, the electric field strength is set to 0.2 V / cm; when the ammonia nitrogen concentration is between 100 and 300 mg / L, the electric field strength is linearly adjusted to 0.35 V / cm; and when the ammonia nitrogen concentration is above 300 mg / L, the electric field strength is set to 0.5 V / cm. The automatic adjustment response time does not exceed 30 seconds, and the adjustment accuracy is ±0.01 V / cm.

[0098] A combined heat and power (CHP) unit is a key facility for achieving energy self-sufficiency. This unit integrates a biogas generator set, a biofuel generator set, and a waste heat recovery system. The biogas generator set uses internal combustion engine technology, with installed capacity designed based on biogas production, and a single unit power of 500-2000 kW. The calorific value of biogas is determined through component analysis; when the methane content is 60%-65%, the lower heating value is 22-25 MJ / m³. Power generation efficiency is calculated using the heat balance equation. .

[0099] In the formula, η e For power generation efficiency, P e Q represents the power generation capacity (kW). gas LHV represents the biogas flow rate (m³ / h) and the lower heating value of biogas (MJ / m³). The optimized design achieves a power generation efficiency of 35%-40%.

[0100] The biofuel generator set processes biofuel produced from the pyrolysis of sludge. The main components of the biofuel are light alkanes and aromatic compounds, with a calorific value of 35-42 MJ / kg. The generator set is a modified diesel engine, with the fuel system and combustion parameters adjusted to suit the characteristics of the biofuel. Combustion efficiency is optimized using combustion equations. Combustion completeness is controlled by the excess air coefficient, which is set at 1.2-1.5 to ensure complete combustion and compliance with emission standards.

[0101] The waste heat recovery system recovers waste heat from the generator set, including flue gas waste heat and cylinder liner water waste heat. Flue gas waste heat is recovered through a flue gas heat exchanger, and the heat exchange efficiency is calculated using the logarithmic mean temperature difference method: Q = KA × LMTD; where Q is the heat transfer rate (kW), K is the heat transfer coefficient (kW / (m²·K)), A is the heat transfer area (m²), and LMTD is the logarithmic mean temperature difference (K). The waste heat recovery rate can reach 70%-85%, and the recovered heat energy is used for heating the sludge pyrolysis system and insulating the anaerobic digester.

[0102] The self-consistent circulation conveying system enables fully automated conveying of engineered conductive biochar from preparation to application. The system includes pneumatic conveying pipelines, storage silos, metering feeders, and a distribution system. The pneumatic conveying employs a positive pressure dilute phase conveying method, using nitrogen as the conveying gas, with a conveying speed of 15-25 m / s and a material-to-gas ratio of 1:10-1:15. The storage silos feature a conical bottom design, with volumes determined according to production needs, and are equipped with arch-breaking devices and a liquid level detection system.

[0103] The mass balance of the material cycle is achieved through the conservation of mass in the entire system. The cycle efficiency of biochar is calculated using the following formula: In the formula, R cycle For cycle efficiency (%), m reuse For the mass of reclaimed biochar (kg / d), m total The total mass of biochar produced (kg / d). Under stable operating conditions, the recycling efficiency can reach over 95%.

[0104] Energy self-sufficiency is assessed through energy balance analysis, and the self-sufficiency rate is calculated using the following formula: In the formula, S energy Energy self-sufficiency rate (%), E generate E represents the system's power generation (kWh / d). consume This represents the system's power consumption (kWh / d). By optimizing operating parameters, the energy self-sufficiency rate can reach 120%-150%, achieving net energy output.

[0105] The working process of this embodiment constructs a complete circular economy system, realizing the efficient recycling of materials and energy. The basic processing and intelligent control functions operate according to the previous two embodiments, and the circular economy system forms a closed-loop operation mode based on this.

[0106] The workflow of the sludge low-temperature pyrolysis self-consistent circulation module begins with the collection of excess sludge. After concentration and dewatering, the excess sludge enters the sludge pretreatment system. Pretreatment includes crushing, drying, and mixing. The sludge is crushed to a particle size of 5-10 mm, dried to a moisture content of 10%-15%, and mixed with recovered fine biochar at a mass ratio of 9:1 to improve pyrolysis efficiency. The pretreated sludge is continuously fed into the pyrolysis reactor via a screw feeder, with the feeding speed precisely regulated by frequency converter control.

[0107] The pyrolysis process employs a distributed control system, with real-time monitoring and automatic adjustment of key parameters such as temperature, pressure, and atmosphere. Multiple temperature measurement points are installed inside the reactor to ensure uniform temperature distribution and maintain a temperature difference within ±10℃. The pyrolysis atmosphere is maintained under nitrogen protection, with continuous monitoring of oxygen content; exceeding limits triggers an automatic alarm and increases the nitrogen flow rate. Volatile organic compounds generated during pyrolysis are recovered through a condensation system, while non-condensable gases are sent to a combustion system for treatment.

[0108] The biochar activation process comprises three stages: preheating, activation, and cooling. In the preheating stage, the biochar is heated from its pyrolysis temperature to its activation temperature, with an activation gas introduced during this process to establish the initial conditions for the activation reaction. In the activation stage, temperature, atmosphere, and residence time are strictly controlled, and the activator flow rate is automatically adjusted according to the amount of biochar processed to ensure uniform activation. In the cooling stage, the activated biochar is cooled to room temperature under an inert atmosphere, with the cooling rate controlled at 5-10℃ / min to avoid structural damage caused by rapid cooling.

[0109] The quality testing of engineered conductive biochar comprises both online and offline systems. The online system monitors key indicators such as specific surface area, porosity, and conductivity, performing tests hourly. The offline system periodically samples for comprehensive analysis, including elemental composition, surface chemical properties, and microstructure characterization. Biochar that fails to meet quality standards is automatically returned to the activation system for reprocessing, ensuring consistent product quality.

[0110] The operation and control of the electric field-enhanced anaerobic ammonia oxidation reactor are optimized based on the unique properties of the engineered conductive biochar carrier. Carrier replacement employs a zoned rotation method; the reactor is divided into three independent zones, with one zone's carrier replaced at a time to ensure continuous system operation. Pretreatment of the carrier before addition includes rinsing, soaking, and biofilm cultivation, with a cultivation time of 15-20 days. The electric field strength is adjusted to account for the impact of carrier replacement on conductivity; the electric field strength in the new carrier zone is appropriately increased by 10%-15%.

[0111] The coordinated operation of the combined heat and power (CHP) unit is achieved through an energy management system. Biogas generators operate with priority, bearing the base load, while peak loads are supplemented by biofuel generators. Generator start-up and shutdown control is based on load forecasting and fuel reserves to avoid efficiency losses caused by frequent start-ups and shutdowns. The waste heat recovery system adjusts its operating mode according to heat load demand, prioritizing electricity output in summer and increasing heat recovery in winter.

[0112] The energy dispatch system formulates operating strategies based on electricity load, fuel supply, and electricity price information. During periods of low electricity prices, it prioritizes the use of purchased electricity and sells self-generated electricity to the grid; during periods of high electricity prices, it increases the proportion of self-generated electricity to reduce electricity costs. The energy storage system mitigates the time difference between power generation and consumption, using lithium battery packs with a designed capacity of 20%-30% of daily electricity consumption.

[0113] The coordinated control of the material cycle is achieved through a material balance system that tracks each batch of biochar from preparation to use. Biochar allocation prioritizes the needs of the electric field-enhanced anaerobic ammonia oxidation reactor, with the remainder sold as a soil conditioner. A quality traceability system for recycled materials establishes a complete data archive, including raw material sludge characteristics, preparation process parameters, product quality indicators, and application effect evaluations.

[0114] The system's adaptive optimization function uses machine learning analysis of operational data to identify the optimal combination of operating parameters. The optimization algorithm runs weekly, adjusting process parameter settings based on a comprehensive evaluation of product quality, energy consumption, and economic benefits. The learning algorithm can identify the impact of changes in raw material characteristics on product quality and automatically adjust pyrolysis and activation parameters to ensure stable product quality.

[0115] The precise preparation process of engineered conductive biochar ensures the stability and consistency of the carrier's performance, with specific surface area control accuracy reaching ±5% and conductivity control accuracy reaching ±0.05 S / cm. The application of this high-performance carrier further improves the nitrogen removal efficiency of the electric field-enhanced anaerobic ammonia oxidation reactor by 20%-30%, stabilizing ammonia nitrogen removal rates above 98% and effluent ammonia nitrogen concentrations below 5 mg / L. The carrier's service life is extended to 18-24 months, significantly reducing operating costs compared to traditional carriers.

[0116] Zero sludge discharge completely solves the problem of secondary pollution, with a sludge reduction rate of over 95%, and the remaining 5% is inert ash that can be used as a building material raw material. Compared with traditional sludge disposal methods, it saves on sludge disposal costs and avoids the environmental risks of sludge transportation and landfill. The resource utilization of biochar realizes the high-value transformation of waste, with 0.35-0.45 tons of engineered conductive biochar produced per ton of dry sludge.

[0117] The establishment of the material recycling system realizes a true circular economy model, and the internal recycling of engineered conductive biochar avoids the cost of purchasing the carrier. The performance of the biochar carrier gradually improves with the use time, and the denitrification efficiency increases by 15%-25% after 6 months of operation compared to the initial stage. The stable operation of the recycling system ensures that the carrier supply is completely self-sufficient and unaffected by fluctuations in the external supply chain, thus guaranteeing the stability of the system operation.

[0118] The coordinated operation of the combined heat and power (CHP) unit enables the complementary use of multiple energy sources, achieving an overall energy efficiency of 85%-90%, which is 30%-40% higher than that of a single power generation method. Fuel diversity enhances the system's resilience; fluctuations in biogas production can be compensated for by adjusting the operation of the biofuel generator set. The inclusion of an energy storage system enables peak shaving and valley filling capabilities, allowing the system to generate additional revenue in the electricity market.

[0119] In terms of environmental benefits, the system has achieved true operational readiness, with a net carbon sequestration of 2-5 tons of CO2 per day through the carbon sequestration effect of biochar. The agricultural use of biochar can improve soil structure, increase soil organic matter content, and enhance water retention capacity. The system's zero-emission operation eliminates the risk of pollution to the surrounding environment, providing a reliable guarantee for ecological aquaculture and green agricultural development.

[0120] The above description is merely a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the forms disclosed herein and should not be construed as excluding other embodiments. It can be used in various other combinations, modifications, and environments, and can be altered within the scope of the concept described herein through the above teachings or related technologies or knowledge. Modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention should be within the protection scope of the appended claims.

Claims

1. A large-scale pig farm wastewater purification and treatment system, characterized in that, include: A smart fertilizer on-demand synthesis module is used to treat anaerobic liquid produced by anaerobic fermentation of wastewater. The smart fertilizer on-demand synthesis module includes: a reaction vessel; and a multi-ion sensing array disposed in the reaction vessel. The multi-ion sensing array includes a phosphate ion selective electrode, an ammonium ion selective electrode, a potassium ion selective electrode, and a trace element sensor for real-time monitoring of the concentration of each ion in the anaerobic liquid. The multi-channel feeding system connected to the reaction vessel includes a magnesium salt feeding channel, a potassium salt feeding channel, and a pH adjuster feeding channel, which are used to accurately feed raw materials according to the target nitrogen, phosphorus, and potassium ratio. The fertilizer synthesis control unit, based on ion concentration data and preset target ratios, controls the type, dosage, and timing of each dosing channel to synthesize customized struvite compound fertilizers with specific nutrient ratios. The AI ​​vision-based dynamic precision air flotation module, located downstream of the intelligent fertilizer on-demand synthesis module, includes: an air flotation tank; an image and spectral analysis unit located at the inlet of the air flotation tank, including a high-speed industrial camera and an online spectral analyzer, used to collect data on the morphology, size, density, and chemical fingerprint of wastewater flocs; and an AI prediction control unit, with a built-in deep learning model, predicts the optimal chemical dosage and dissolved air volume in real time based on floc morphology and spectral data. The chemical reagent dosing pump connected to the flotation tank is controlled by the AI ​​prediction and control unit to achieve millisecond-level closed-loop feedback regulation. A multi-dimensional synergistic biochemical treatment module, located downstream of the AI ​​vision-driven dynamic precision air flotation module, includes a mainstream A² / O biochemical treatment unit and a side-flow electric field-enhanced anaerobic ammonia oxidation reactor. The anaerobic ammonia oxidation reactor includes: The reactor body; an electrode pair disposed within the reactor body for applying a DC electric field of 0.2V / cm to 0.5V / cm within the reactor; The carrier filled inside the reactor body is engineered conductive biochar, which has electrical conductivity and biocompatibility, and acts as an electron shuttle to accelerate microbial metabolism; A sludge low-temperature pyrolysis self-consistent circulation module, used to treat the residual sludge generated by the system, includes: The sludge low-temperature pyrolysis system converts excess sludge into biochar, biofuel, and wood vinegar in an anaerobic environment. The biochar activation treatment unit activates the biochar produced by pyrolysis with carbon dioxide or water vapor to prepare the engineered conductive biochar. The self-consistent circulation conveying system transports the engineered conductive biochar to the anaerobic ammonia oxidation reactor as a carrier, forming a material closed loop for sludge treatment and wastewater biochemical treatment. The digital twin intelligent control platform is electrically connected to the electrode pairs of the multi-ion sensor array, the multi-channel feeding system, the image and spectral analysis unit, the chemical reagent dosing pump, and the anaerobic ammonia oxidation reactor, and includes: The holographic sensing module integrates sensor data from the entire system to construct a virtual digital model that maps to the physical factory in a 1:1 ratio. The AI ​​decision engine has a built-in multi-objective collaborative optimization algorithm, aiming to maximize comprehensive economic benefits and dynamically balance the output value of energy consumption, pharmaceutical consumption and resource-based products. Predictive maintenance module, based on historical and real-time data, predicts equipment failures and process bottlenecks; The global control unit controls the coordinated operation of each module based on the optimization algorithm results.

2. The system according to claim 1, characterized in that, The deep learning model of the AI ​​prediction control unit adopts a hybrid architecture of convolutional neural network and recurrent neural network. By analyzing the morphological characteristics, spectral characteristics and historical treatment effect data of wastewater flocs, a nonlinear mapping relationship between floc treatability and optimal reagent dosage is established.

3. The system according to claim 1, characterized in that, The fertilizer synthesis control unit dynamically adjusts the target nitrogen, phosphorus, and potassium ratio based on external market price signals and crop planting needs. The ratio range is N:P:K=1:0.3-2:0.5-3, enabling customized synthesis of special fertilizers for different purposes.

4. The system according to claim 1, characterized in that, The preparation process of the engineered conductive biochar includes: the sludge low-temperature pyrolysis system pyrolyzes the remaining sludge in an oxygen-deficient environment at 300-500℃ for 1-3 hours, and then the biochar activation treatment unit activates it with carbon dioxide or water vapor at 800-900℃ for 30-60 minutes, so that its specific surface area reaches 300-800m² / g and its conductivity reaches 0.1-1.0S / cm.

5. The system according to claim 1, characterized in that, The electrode pairs in the anaerobic ammonia oxidation reactor are titanium-based iridium-ruthenium dioxide coated electrodes with an electrode spacing of 10-30 cm. The applied DC electric field strength can be automatically adjusted according to the influent ammonia nitrogen concentration within the range of 0.2-0.5 V / cm.

6. The system according to claim 1, characterized in that, The multi-objective collaborative optimization algorithm adopts a hybrid optimization strategy that combines genetic algorithm and particle swarm optimization. The optimization objective functions include: minimizing system operating energy consumption, minimizing reagent cost, maximizing customized fertilizer sales revenue, and maximizing biofuel output value. The weights of each objective can be dynamically adjusted according to market conditions.

7. The system according to claim 1, characterized in that, It also includes a pretreatment unit located upstream of the intelligent fertilizer on-demand synthesis module. The pretreatment unit includes a mechanical bar, an equalization tank, a high-efficiency solid-liquid separator and an anaerobic digestion tower connected in sequence. The anaerobic digestion tower is equipped with a biogas recovery system.

8. The system according to claim 1, characterized in that, It also includes a deep treatment unit located downstream of the multidimensional synergistic biochemical treatment module, including an MBR membrane bioreactor, an ultraviolet disinfection device, and an ozone contact tank.

9. The system according to claim 7, characterized in that, It also includes a combined heat and power (CHP) unit, which uses the biogas produced by the anaerobic digester and the biofuel produced by the sludge low-temperature pyrolysis system for CHP. The generated electricity supplies the entire system and the generated heat is used for sludge pyrolysis and anaerobic digester insulation.

10. The system according to claim 1, characterized in that, The digital twin intelligent management and control platform also includes a full-process simulation module, which can simulate extreme conditions such as doubling of water inflow and heavy rainfall impact in virtual space, predict system response 72 hours in advance, and deduce the best response strategy.

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