Method for intelligently regulating and controlling carbon-nitrogen ratio of straw and livestock and poultry manure and efficiently decomposing straw and livestock and poultry manure

Abstract

The invention discloses a method for intelligently regulating and controlling the carbon nitrogen ratio of straw and livestock and poultry manure and efficiently decomposing the straw and the livestock and poultry manure, and belongs to the technical field of agricultural waste resourceful treatment technology and environmental engineering, and the method comprises the following steps: S1, carrying out online component detection on input straw and livestock and poultry manure, automatically mixing the materials to a preset initial carbon-nitrogen ratio based on a detection result, and inoculating a microbial agent; meanwhile, on the basis of the initial material component data, a personalized digital twinborn body of the batch of materials is constructed; the in-situ spectrum technology is used for monitoring the change of the carbon-nitrogen ratio in the pile body in real time, the unbalance trend of the pile body is predicted in combination with the digital twinborn model, the execution unit is driven to carry out local and trace material supplement adjustment, and the lag mode of traditional static matching and off-line sampling inspection is fundamentally changed. The dynamic closed-loop regulation and control can accurately control the fluctuation range of the carbon-nitrogen ratio in the whole decomposition period within + / -5, and a continuous and stable optimal nutrition environment is created for microorganisms.

Description

Technical Field

[0001] This invention relates to the fields of agricultural waste resource utilization technology and environmental engineering technology, specifically a method for intelligent control and efficient composting of the carbon-nitrogen ratio of straw and livestock manure. Background Technology

[0002] The co-processing of straw and livestock manure is an important way to realize the resource utilization of agricultural waste. This process is essentially a microbial-driven biochemical transformation, and the core control parameter for its efficiency and product quality is the carbon-nitrogen ratio of the materials. Maintaining the initial carbon-nitrogen ratio within the theoretically optimal range of 25:1-30:1 helps microorganisms obtain balanced nutrition, thereby promoting rapid start-up and efficient transformation.

[0003] However, existing technologies for the co-processing of straw and livestock manure have significant shortcomings in terms of precise control of the carbon-nitrogen ratio and efficiency of the composting process. These shortcomings are mainly manifested in three major bottlenecks: static nature, opaque process, and extensive operation, which seriously restrict processing efficiency, product quality, and environmental benefits.

[0004] Specifically, existing methods mainly rely on a one-time mixing ratio based on the estimated composition of materials before fermentation. This not only ignores the huge fluctuations in the composition of the raw materials themselves, but more importantly, it completely fails to respond to the real-time changes in the carbon-nitrogen ratio caused by the dynamic consumption of carbon and nitrogen by microorganisms during fermentation. The entire lengthy composting process is actually in a state of carbon-nitrogen imbalance for a long time. When there is excess carbon, the fermentation cycle is unnecessarily prolonged, and when there is excess nitrogen, a large amount of nitrogen is lost in the form of ammonia and produces a foul odor.

[0005] Furthermore, current processes generally rely on monitoring physical parameters such as temperature, oxygen concentration, and humidity to indirectly infer the fermentation state. These parameters cannot directly reveal the core biochemical nature of the process, such as the degree of degradation of complex organic matter, the activity of the microbial community, and metabolic pathways. For example, an increase in temperature may only stem from the decomposition of easily degradable sugars, and does not necessarily indicate the effective conversion of lignin. Due to the lack of in-situ, real-time sensing methods for material components and microbial activity, process control lacks precise basis and relies primarily on empirical judgment.

[0006] Corresponding to the black-box perception, key operations such as turning and aeration often adopt fixed time or cycle procedures. This one-size-fits-all approach ignores the significant spatial heterogeneity within the pile caused by uneven material distribution and limitations in heat and mass transfer, resulting in wasted energy, interference with efficient fermentation, and exacerbation of localized anaerobic digestion and odor generation.

[0007] The interior of the straw compost pile is subjected to an extreme environment of high temperature, high humidity, and highly corrosive gases for extended periods. Conventional industrial sensors are prone to failure, drift, or contamination under these conditions, posing a significant engineering challenge to achieving reliable in-situ real-time monitoring and further exacerbating the aforementioned deficiencies. Therefore, we propose an intelligent control and efficient composting method for the carbon-nitrogen ratio of straw and livestock manure to alleviate or solve these problems.

[0008] The information disclosed above in this background section is only for enhancing the understanding of the background section of this invention, and therefore may include prior art that is not known to those skilled in the art. Summary of the Invention

[0009] To address the aforementioned technical problems, this invention provides an intelligent method for regulating and efficiently composting the carbon-nitrogen ratio of straw and livestock manure, thereby solving the problems of lagging carbon-nitrogen ratio regulation, unclear fermentation process status, coarse execution control, and difficulty in achieving reliable online monitoring under harsh working conditions in existing technologies.

[0010] To achieve the above objectives, this invention provides a method for intelligent control and efficient composting of the carbon-nitrogen ratio of straw and livestock manure, comprising the following steps:

[0011] S1. Perform online component detection on the input straw and livestock manure, automatically mix the materials to a preset initial carbon-nitrogen ratio based on the detection results, and inoculate with microbial agents; at the same time, construct a personalized digital twin of the batch of materials based on the initial material composition data;

[0012] S2. During the fermentation process, a multimodal bio-information sensor network deployed inside the pile is used to acquire in-situ monitoring data in real time that reflects changes in the chemical composition of the material and the metabolic activity of microorganisms.

[0013] S3. The in-situ monitoring data is synchronized to the personalized digital twin in real time, and the fermentation state diagnosis and trend prediction are performed through its built-in hybrid model. Based on the multi-objective optimization algorithm, precise control instructions containing spatial positioning information are generated.

[0014] S4. Based on the precise control command, control the autonomous mobile execution unit to perform local addition or turning operations of materials in specific coordinate areas of the pile, and / or control the zoned intelligent aeration system to perform differentiated oxygen supply to different areas of the pile, so as to realize dynamic closed-loop adjustment of carbon-nitrogen ratio.

[0015] S5. Based on the predicted output of the personalized digital twin and the real-time data of the multimodal bio-information sensor network, the end point of composting is determined together, and a full-process digital traceability file of the batch of materials is generated.

[0016] Preferably, in step S1, the personalized digital twin integrates a mechanistic model based on microbial reaction kinetics and a data-driven model trained on historical data; the data-driven model is a long short-term memory neural network model.

[0017] Preferably, in step S2, the multimodal bio-information sensor network includes:

[0018] Microbial electronic nose array for continuous detection of characteristic spectra of volatile organic compounds inside a stack;

[0019] Miniature spectroscopic probes for in-situ, continuous monitoring of carbon-nitrogen ratio and lignocellulose degradation at different depths of a stack.

[0020] Temperature field sensing unit used to construct the three-dimensional spatial metabolic thermal field distribution of the stack.

[0021] Preferably, in step S3, the fermentation state diagnosis includes:

[0022] By comparing real-time sensor data with digital twin predictions, local imbalances in the carbon-nitrogen ratio, abnormal nitrogen volatilization areas, or areas with abnormal metabolic activity are identified; the trend prediction is a rolling prediction of the decomposition process and resource consumption within a set future time period.

[0023] Preferably, in step S3, the multi-objective optimization algorithm uses at least two of the following as simultaneous optimization objectives: shortening the composting cycle, reducing total nitrogen loss, and reducing total process energy consumption, to calculate the optimal control strategy.

[0024] Preferably, in step S4, the autonomous mobile execution unit is a robot that can move along a track above the fermentation facility, which can receive coordinate information from the precise control instructions and perform at least one of the following operations:

[0025] Through its onboard dosing device, straw powder or concentrated manure slurry conditioner is quantitatively added to the designated coordinate area of ​​the pile.

[0026] Using its onboard robotic arm end effector, it performs depth-controlled local micro-tumbling and throwing operations on designated coordinate areas of the stack.

[0027] Preferably, in step S4, the zoned intelligent aeration system includes:

[0028] The bottom of the fermentation facility is divided into multiple independently controlled air chambers, each covered with a smart breathable membrane made of electroactive material. By adjusting the voltage applied to each smart breathable membrane, its pore size can be changed independently and continuously, thereby achieving precise control of the oxygen supply rate of each zone.

[0029] Preferably, in step S5, the conditions for jointly determining the end point of decomposition are simultaneously satisfied:

[0030] The predicted maturity value output by the personalized digital twin reaches and stabilizes at a preset threshold.

[0031] The temperature field sensing unit detected that the difference between the overall temperature of the stack and the ambient temperature was consistently lower than a set threshold.

[0032] In the volatile organic compound spectrum detected by the microbial electronic nose array, the characteristic peak signal of malodorous substances disappeared and a stable humification characteristic peak appeared.

[0033] Preferably, in step S5, the fully digital traceability file includes time-series sensor data from the initial detection of materials to the end of the composting process, records of all executed control commands, digital twin simulation curves, and key parameter analysis reports; this file is uniquely bound to the batch of composted materials produced.

[0034] A system for intelligent regulation and efficient composting of the carbon-nitrogen ratio of straw and livestock manure, characterized by comprising:

[0035] A central control and computing platform is used to run the personalized digital twin and multi-objective optimization algorithms;

[0036] A multimodal bioinformatics sensor network is deployed within the fermentation facility to execute step S2;

[0037] An autonomous mobile actuator and a zoned intelligent aeration system are used to execute step S4;

[0038] The central control and computing platform is communicatively connected to the sensor network, execution unit and aeration system, forming a closed-loop control system.

[0039] Compared with the prior art, the beneficial effects of the present invention are:

[0040] This invention utilizes in-situ spectroscopy to monitor changes in the carbon-nitrogen ratio within the compost pile in real time. Combined with a digital twin model, it predicts imbalance trends and drives the execution unit to perform localized, minute-level material replenishment adjustments. This fundamentally changes the lagging mode of traditional static proportioning and offline sampling. This dynamic closed-loop control can precisely control the carbon-nitrogen ratio fluctuation range within ±5 throughout the entire composting cycle, creating a continuously stable and optimal nutrient environment for microorganisms.

[0041] This invention is the first to introduce multi-dimensional biochemical information, including volatile organic compound spectra reflecting microbial metabolic activity, in-situ spectral information reflecting material component transformation, and metabolic thermal field distribution, into a real-time monitoring system. This allows control decisions to be based directly on insights into the essence of the process, rather than indirect physical parameters.

[0042] This invention abandons the traditional, timed, and extensive operation mode, and uses an autonomous mobile robot to perform localized micro-turning and feeding where imbalances occur, avoiding the huge heat and moisture losses caused by traditional overall turning. At the same time, precise oxygen supply and timely local adjustment effectively inhibit the formation of anaerobic zones, significantly reducing the emission of odorous gases such as hydrogen sulfide and methane, as well as greenhouse gases, from the source, resulting in significant environmental benefits.

[0043] The above overview is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features of the invention will become readily apparent from the accompanying drawings and the following detailed description. Attached Figure Description

[0044] Figure 1 This is a flowchart of the intelligent control and efficient composting method for the carbon-nitrogen ratio of straw and livestock manure according to the present invention. Detailed Implementation

[0045] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. It should be noted that the drawings are schematic and not illustrated to scale. For clarity and convenience, the relative sizes and proportions of the parts shown in the drawings have been exaggerated or reduced in size. Any size is only illustrative and not limiting.

[0046] To ensure the long-term stable operation of the sensor network deployed inside the fermentation pile in extreme environments of high temperature, high humidity, corrosiveness, and mechanical disturbance, the sensors are specially designed with protective features. All sensor probes that come into contact with materials use engineering ceramic or special stainless steel housings, and optical windows are made of sapphire or quartz glass. Electronic components are thermally isolated from high-temperature components via vacuum insulation chambers or aerogel materials. The sensor signal processing module is equipped with miniature heat pipes or forced air cooling channels to maintain its operating temperature within the chip's allowable range. The sensors employ a modular, pluggable design, with all interfaces achieving an IP68 or higher protection rating for easy maintenance and replacement. Meanwhile, the core sensing unit is mounted on a liftable robotic arm. It automatically inserts into the pile to a specified depth only when measurement is needed and quickly retracts to a protected position after measurement, significantly reducing high-temperature exposure time. For parameters such as temperature, armored mineral-insulated thermocouples or distributed optical fibers are used, which can withstand long-term high temperatures. For gas analysis, high-temperature resistant PTFE pipes draw gases from inside the pile to an analysis module located outside the fermentation facility at ambient temperature for processing, preventing the precision instruments from being affected by harsh environments.

[0047] Example 1: Large-scale centralized processing center trough-type intelligent composting system

[0048] This embodiment is applied to a regional centralized treatment center that processes 100,000 tons of agricultural waste annually, using a closed long-tank fermentation system.

[0049] 1. System Configuration:

[0050] Fermentation facilities: 10 parallel, enclosed intelligent fermentation tanks, each measuring 50m × 4m × 2.5m (length × width × height). The tanks are made of insulated steel and have double tracks on the top for robots to move along.

[0051] In the deployment of the sensing network, spectral probes are used: a monitoring profile is set every 5 meters along the length of the slot, and a miniature Raman and near-infrared fusion spectral probe is implanted in the upper, middle and lower layers of each profile, for a total of 30 probes / slot.

[0052] Electronic nose array: VOCs sensor units are arranged near each spectral probe to form a distributed metabolic activity sensing network.

[0053] Thermal monitoring: An infrared thermal imager is installed on the top of the tank, and 30 distributed fiber optic temperature measurement points are buried in the side walls and inside the tank to jointly construct a three-dimensional thermal field.

[0054] Intelligent robots in the execution system: Each pair of troughs shares a heavy-duty autonomous mobile robot, which carries a 2-cubic-meter straw powder silo and a 1-cubic-meter concentrated manure slurry tank. The addition accuracy can reach ±0.5kg, and the micro-tilting throwing arm has a maximum working depth of 25cm.

[0055] The aeration system in the execution system: the bottom of the tank is divided into 25 independent air chambers along the length direction, each chamber is 2m×4m, and each air chamber is covered with a 2 square meter flexible electrodeformable polymer smart membrane.

[0056] The digital twin platform in the execution system is deployed on a central server cluster, with an independent twin instance running for each fermentation tank, and communicates with all terminal devices in real time via a 5G industrial network.

[0057] 2. Raw materials and parameters:

[0058] Raw materials: corn stalks (initial C / N ≈ 65, moisture content 15%) and dairy cow manure (initial C / N ≈ 18, moisture content 75%). Primarily for producing high-quality organic substrate for landscaping, requiring thorough decomposition, freedom from weed seeds, and stable properties.

[0059] Initial mixing: After online detection, the system automatically mixes materials according to a straw:manure dry weight ratio of 1.3:1, resulting in an initial C / N ratio of approximately 30 and a moisture content of 55%. Inoculation is then performed with a specialized compound microbial agent targeting lignocellulose, containing white-rot fungi and cellulose-decomposing bacteria.

[0060] The target C / N fluctuation range during the main fermentation period is set at 28±4; the oxygen concentration is controlled to be no less than 8% during the high-temperature period above 55℃; and forced cooling regulation is triggered when the local temperature exceeds 70℃ for 2 consecutive hours.

[0061] 3. On the fourth day of the first batch of fermentation, the system monitored and implemented the following intelligent closed-loop control:

[0062] The spectral probe at the 12-meter section of the No. 3 tank showed that the C / N ratio increased to 36.5; the corresponding electronic nose detected that the intensity of the ammonia characteristic peak was 40% lower than that of other areas, but the peaks of organic acids such as acetic acid were slightly higher; the thermal image showed that this area was a temperature island of 48°C.

[0063] Diagnostic analysis using digital twin data revealed a relative carbon surplus in the area, and slight acidification may have inhibited the activity of ammonifying bacteria, leading to asynchronous nitrogen mineralization and carbon degradation. Predictions indicated that without intervention, complete decomposition in this area would be delayed by 5 days, potentially becoming an uncooked point in the final product.

[0064] The multi-objective optimization algorithm outputs the following instructions: ① Precisely add 8 kg of calcium carbonate and 5 kg of soybean meal to the coordinate (trough 3, 12m, center) area to adjust the pH level; ② Instruct the intelligent breathable membrane below this coordinate area to expand its pore size by 50% for 3 hours to improve ventilation and dissipate some organic acids.

[0065] After receiving the instruction, the robot moved to the coordinate point and completed the dosing operation. The aeration system responded synchronously. A follow-up inspection 6 hours later showed that the C / N ratio in the area had dropped to 31.2, the temperature had risen to 52℃ and merged into the mainstream high-temperature zone, and the electronic nose signal had returned to normal.

[0066] The results showed that this batch of material reached the maturity endpoint on day 14, with only one overall turning and turning performed on day 7 throughout the entire cycle; the rest were localized micro-adjustments. The final product achieved a GI index of 95% and a stable C / N ratio of 17:1, with all indicators exceeding national standards. Compared to traditional trough composting of the same scale, the fermentation cycle was shortened by 35%, nitrogen loss was reduced by approximately 28%, and energy consumption per unit product was reduced by 22%.

[0067] Example 2: Intelligent Static Composting System with Film Covering for Medium-Sized Farms

[0068] This embodiment is applicable to medium-sized farms with 2,000 pigs, and uses a membrane-covered static aerobic composting process to treat pig manure and rice straw.

[0069] 1. System Configuration:

[0070] Fermentation facilities: Construct four concrete compost beds, each measuring 25m × 6m. A perforated aeration pipe network is laid at the bottom of each bed, dividing it into six aeration zones. After the compost pile is formed, it is covered with a special molecular membrane that is waterproof, breathable, and odor-blocking.

[0071] In the deployment of the sensing network, mobile sensing rods are used: two movable vertical sensing rods are deployed on the tracks on both sides of the pile bed. The rods are equipped with miniature spectral probes, VOCs intake ports and temperature probes. They can perform scanning measurements on the cross-section of the pile body according to a preset program to obtain profile data.

[0072] Fixed sensors: Several wireless temperature and humidity sensor nodes are buried at key points inside the stack.

[0073] The aeration system in the execution system: Each aeration zone is supplied with air by an independent variable frequency fan, and the air volume can be precisely controlled.

[0074] The auxiliary control unit in the execution system: A movable liquid spraying system and a dry material spreader are installed on one side of the stockpile, which are controlled by the central system.

[0075] The digital twin platform in the execution system: uses an edge computing gateway to run a lightweight digital twin model locally.

[0076] 2. Raw materials and parameters:

[0077] Raw materials: rice straw (C / N≈80, high silicon content) and fresh pig manure (C / N≈13, high moisture content). The C / N ratios of the raw materials differ greatly, and the pig manure is sticky and has poor air permeability.

[0078] Intelligent pretreatment: Based on the detection and instructions, the system crushes the straw to 3-5cm and mixes it at a high intensity with a straw:pig manure volume ratio of 2.5:1. It also adds 10% of well-rotted recycled material as a conditioner to make the initial pile porosity greater than 40%.

[0079] This embodiment focuses on nitrogen conservation and anaerobic digestion prevention. The model uses real-time monitored ammonia and oxygen concentrations inside the reactor as key inputs, prioritizing adjustments to avoid nitrogen loss and odor generation.

[0080] 3. During days 2-5 of the high-temperature fermentation period, the system should implement the following adjustments:

[0081] The scanning of the mobile sensor rod revealed that the oxygen concentration in the third zone of pile 2 dropped rapidly to below 3% within 30 minutes after the ventilation was stopped, while the ammonia concentration increased.

[0082] The digital twin analysis indicates that the area is experiencing excessive density due to the accumulation of pig manure, making it difficult to maintain an aerobic environment. It is currently in an unfavorable state of anaerobic conditions with rapid nitrogen mineralization and volatilization. The analysis predicts that the area will lose a significant amount of nitrogen and produce hydrogen sulfide within the next 12 hours.

[0083] To avoid damaging the membrane system by overall overturning, the optimized algorithm outputs the following instructions: ① Instruct the third zone fan to operate intermittently at maximum airflow, i.e., on for 2 minutes and off for 5 minutes, to provide impact oxygenation; ② Calculate the amount of carbon source to be added and instruct the dry material spreader to evenly spread a layer of crushed straw along the corresponding top position of the zone.

[0084] The aeration system immediately initiated impact aeration. The spreader operated automatically. A follow-up inspection 24 hours later showed that the oxygen level in the area remained at a minimum of 8%, the ammonia concentration decreased by 60%, and the thermal image showed that the temperature in the area rose uniformly from 59℃ to 65℃, merging into the mainstream high-temperature zone.

[0085] The results showed that under static conditions without any turning or turning, the compost pile successfully matured after 18 days of intelligent aeration and localized conditioning. The final product had a nitrogen content 15% higher than that of traditional static composting, with almost no odor leakage, fully meeting the standards for organic fertilizer used on-site. The automated operation of the system greatly saved manpower.

[0086] Example 3: Small-scale distributed processing - containerized intelligent fermentation system

[0087] This embodiment is designed for small-scale, multi-location scenarios such as family farms and eco-parks, and adopts a standardized and modular containerized intelligent fermentation system.

[0088] 1. System Configuration:

[0089] Fermentation facility: A smart fermentation chamber converted from a standard 20-foot shipping container. The chamber has an effective volume of approximately 25 cubic meters and integrates a complete system.

[0090] Sensing network: Fixed spectral sensors, gas sensors, and temperature sensors are integrated on the bulkhead and stirring shaft. Due to the relatively homogeneous space, the number of sensing points is reduced, but the frequency is higher.

[0091] The built-in intelligent mixer in the execution system not only mixes, but its blades can also be replaced with a fertilizer applicator to achieve localized mixing with the material during rotation. If necessary, liquid regulators can be precisely injected through the hollow shaft.

[0092] The system integrates aeration and exhaust gas treatment: uniform aeration is achieved at the bottom, and exhaust gas is treated by a biological filter before being discharged.

[0093] The digital twin platform adopts a cloud-edge collaborative model. Each container has a built-in edge controller that runs a basic model. Data is synchronized to the cloud for deep learning and model optimization, and then the optimized parameters are distributed.

[0094] 2. Raw materials and parameters:

[0095] Raw materials: A mixture of garden straw (wide C / N range) and rabbit manure, sheep manure, etc. (C / N≈20). This product is a high-quality, refined organic fertilizer suitable for potted plants and seedling cultivation, requiring a short production cycle.

[0096] A high-temperature aerobic rapid fermentation process is employed. During system initialization, if complex raw materials are detected, a broad-spectrum and highly effective compound microbial agent will be automatically recommended and inoculated. The digital twin model focuses on predicting and maintaining the optimal high-temperature window of 55-65℃ to rapidly kill pathogens and weed seeds.

[0097] 3. In a single processing run, the system aims to complete the primary fermentation within 7 days:

[0098] During the 36th hour of fermentation, the temperature rapidly rose to 68°C and continued to rise, while the oxygen concentration dropped to 5%.

[0099] The digital twin analysis indicates that the rapid temperature rise is due to the concentrated decomposition of easily degradable sugars, posing a risk of composting and potentially killing beneficial microorganisms. It predicts that simply increasing aeration may lead to excessive heat loss, affecting the depth of later composting.

[0100] The algorithm provides a combined instruction for temperature control and heat preservation: ① Immediately start the agitator to stir at low speed for 2 minutes to evenly distribute the heat; ② Increase the aeration rate by 15%, but use a high-frequency short pulse mode, with strong aeration for 1 minute every 10 minutes, rather than continuous high air volume, to achieve a balance between oxygen supply and heat preservation; ③ Inject a small amount of water mist (about 5L) into the center of the material through the hollow pipe in the agitator shaft to assist in cooling by evaporative heat absorption.

[0101] The system executed the commands sequentially. Two hours later, the temperature dropped to 62°C, the oxygen concentration recovered to 10%, and the microbial activity was maintained.

[0102] The results showed that the system completed the entire process from feeding to decomposition and output within 9 days. The product particles were uniform, odorless, and had a GI index of 98%, making it suitable for direct use in the high-end horticulture market. The system's modular design allows for flexible deployment, and cloud platform management enables users to monitor the operational status and product quality of multiple sites via a mobile app, achieving intelligent and high-quality small-scale processing.

[0103] Example 4: Intelligent Anaerobic-Aerobic Cogeneration Process Guided by Energy Recovery

[0104] This embodiment is applied to the dual recovery of energy and fertilizer, and is suitable for large-scale farms to process high-nitrogen chicken manure and corn stalks.

[0105] 1. System Configuration:

[0106] The first stage is intelligent high-solids anaerobic fermentation to produce biogas, using a vertical anaerobic fermenter with leachate recirculation.

[0107] During the anaerobic phase, the system monitors pH, volatile fatty acid concentration, daily gas production, and gas composition (CH4 / CO2) to assess system stability in real time. A digital twin can predict acidification risks. When VFA accumulation is predicted, the system automatically adjusts the proportion of straw in the feed or initiates leachate recirculation for dilution to maintain an optimal environment for methanogens.

[0108] In the intelligent aerobic composting stage of the biogas residue, after being discharged, the residue enters the intelligent aerobic composting system described in this embodiment. The C / N ratio of the biogas residue has already undergone the first round of adjustment, but it still has a high moisture content and poor air permeability. During system initialization, a priority decision will be made to add a large amount of loosening materials such as straw, and a higher aeration intensity target will be set. The model will focus on oxygen penetration depth and dehumidification efficiency.

[0109] The anaerobic stage's output characteristics serve as key initial parameters, which are input into the digital twin of the aerobic stage, enabling more proactive prediction and control. Waste heat generated in the aerobic stage can be partially recovered to maintain the digestion temperature in the anaerobic digester. The system comprehensively manages the energy consumption and output of both stages to achieve optimal overall energy efficiency.

[0110] The results showed that the biogas yield per ton of raw material increased by approximately 15%, and the system stability was enhanced. The final biogas residue organic fertilizer underwent more thorough decomposition, with better heavy metal passivation and higher commercial value. A tiered utilization of carbon, nitrogen, and energy was achieved, and odor and greenhouse gas emissions were precisely controlled throughout the process.

[0111] Comparative Example 1: Traditional static composting that relies on human experience

[0112] This comparative example uses the traditional static composting method commonly used in rural areas, which relies entirely on manual experience for management and lacks any online monitoring or dynamic control methods.

[0113] 1. Experimental Setup: The raw materials were the same as in Example 1 of this invention, namely corn stalks and dairy cow manure, with an initial C / N ratio of approximately 65. During pretreatment, a rough estimate was made manually, and the stalks and manure were mixed at a volume ratio of approximately 1:1, without precise testing or calculation. The mixture was simply rolled twice using a forklift. No commercial microbial agents were inoculated; fermentation relied on natural microbial fermentation in the environment. The pile was constructed in an open field, forming a trapezoidal long strip pile with dimensions of approximately 10m × 3m × 1.5m and a total volume of approximately 45m³. 3 The throughput is comparable to that of a single tank in Example 1. No sensors are used for monitoring or management.

[0114] Turning the pile is done entirely by hand, relying on the temperature of the pile by touch or the amount of surface steam. A small turner is used to turn the pile over approximately every 5-7 days. Moisture is sprayed onto the surface manually via water pipes when the pile feels dry. There is no aeration system; oxygen is supplied through natural diffusion and turning. The maturity of the compost is judged primarily by experience: when the pile no longer heats up, its volume shrinks by about one-third, the material turns dark brown, and there is no foul odor, it is considered mature, which typically takes more than 60 days.

[0115] 2. To facilitate quantitative comparison, manual sampling and offline testing were conducted periodically in this comparative study, and data from key nodes were recorded.

[0116] Table 1: Key Process Monitoring Data (Comparative Example 1)

[0117] Fermentation time (days) Sampling location Temperature (°C) Offline C / N detection Scent description Mainly manual operation 0 After mixing Ambient temperature 38.2 The feces smelled strongly heap building 7 30cm below the surface 68 Not detected Strong ammonia smell, pungent First overall flip 14 center 55 32.5 The ammonia smell has weakened, and a sour smell has emerged. Feeling dry, sprinkle water on the surface. 21 center 62 28.1 Slight odor Second overall flip 30 center 45 24.7 It smells like soil, and occasionally has an unpleasant odor. No operation 45 Center and edge 35 (center), 30 (edge) 22.1 (center), 18.5 (edge) The center is almost odorless, while the edges have a musty smell. Third overall flip 60 Mixed sample Ambient temperature 19.3 Overall earthy smell Determine if it is fully decomposed, then discharge.

[0118] In summary, it took up to 60 days to be judged as fully decomposed based on experience. There were significant differences in C / N ratio and decomposition maturity between the center and periphery materials, resulting in poor product uniformity. Periphery materials, due to prolonged exposure to low temperatures and oxygen deficiency, may contain incompletely decomposed portions.

[0119] The carbon-to-nitrogen ratio control and nitrogen loss were hampered by inaccurate initial mixing and the inability to ascertain the C / N ratio changes throughout the process. The strong ammonia odor observed on day 7 suggests that a significant amount of nitrogen was lost in the early stages as ammonia gas. The estimated nitrogen loss rate throughout the process, based on material balance calculations, was as high as 40-50%.

[0120] Turning and turning the material relies entirely on experience, and the timing may be inappropriate. Although there is no energy consumption for aeration, the total energy consumption for turning and turning is not low due to the long treatment cycle. It has a significant negative impact on the environment, with a pervasive odor in the early stages and leachate potentially forming at the edges in the later stages.

[0121] The final product was tested and found that the seed germination index (GI) of the mixed product was 78%, which did not fully meet the standard of high-quality organic fertilizer (GI>80%), and may contain weed seeds and pathogens.

[0122] Comparative Example 2: Conventional mechanized trough composting with timed control

[0123] This comparative example uses an improved technology adopted by most organic fertilizer plants, employing mechanical equipment such as fermentation tanks, turning machines, and aeration fans. However, the control method is a simple timed program, lacking real-time sensing and intelligent decision-making.

[0124] 1. Experimental setup: The raw materials and pretreatment were the same as in Example 1 and Comparative Example 1. The fermentation facility used a fermentation tank with the same structure as in Example 1, equipped with a trough-type turner and bottom-fixed aeration pipes.

[0125] Turning is set to be performed every two days, regardless of the actual condition of the compost pile. The aeration blowers are set to operate intermittently in a fixed cycle. There are no online component and gas sensors; only several temperature sensors are installed on the tank walls for monitoring. Completion is determined primarily by the temperature dropping to near ambient temperature under fixed fermentation time and temperature.

[0126] 2. Operate according to a fixed procedure and conduct sampling and testing at fixed points periodically.

[0127] Table 2: Key Process Monitoring Data and Fixed Operations in Comparative Example 2

[0128] Fermentation time (days) Average temperature inside the tank (°C) Preset operation execution status Offline detection of C / N (fixed sampling points) Notes (based on the observed phenomena) 0 Ambient temperature Feeding begins, aeration starts. 29.5 Initial mixing is good 2 58 First overall flip Not detected The temperature dropped sharply by 10°C after being turned over. 4 65 Second overall flip 31.2 The ammonia smell was obvious when turning the pot. 6 60 Third overall flip Not detected Temperature rises slowly 8 62 Fourth overall flip 28.8 10 52 Fifth overall flip Not detected After being turned over, the temperature dropped to 45℃, interrupting the high-temperature period. 12-20 45-50 Turn it over every 2 days. 25.1 (Day 15) Sustained medium temperature, high energy consumption but low efficiency 22 40 Eleventh overall flip 22.5 30 35 Stop aeration and turn over 20.1 Material output as planned

[0129] In summary, although the equipment is advanced, the fixed-cycle turning severely interferes with the sustained high-temperature activity of microorganisms. Each turning results in a significant loss of heat and moisture, making it difficult to maintain the pile temperature within the efficient 55-65℃ range for decomposition. The actual effective high-temperature period is less than 10 days, resulting in incomplete decomposition despite the 30-day harvest period.

[0130] Due to the inability to detect ammonia volatilization, frequent turning during high-temperature periods exacerbated nitrogen loss, with an estimated nitrogen loss rate of approximately 30-35%. The entire process passively controlled C / N ratio changes, making optimization impossible. Furthermore, the mechanical execution was extremely inefficient, turning when unnecessary, resulting in energy waste; and coinciding with periods of ventilation shutdown when increased ventilation was needed. Turning and aeration were energy-intensive but inefficient, with energy consumption per unit product exceeding 1.8 times that of Example 1 of this invention. When the final product was discharged on day 30, the product temperature was still high, with a slight ammonia odor. The GI index was measured at 82%, just reaching the acceptable level, but product stability was questionable, potentially requiring further post-fermentation.

[0131] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0132] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for intelligent control and efficient composting of the carbon-nitrogen ratio of straw and livestock manure, characterized in that, Includes the following steps: S1. Perform online component detection on the input straw and livestock manure, automatically mix the materials to a preset initial carbon-nitrogen ratio based on the detection results, and inoculate with microbial agents; at the same time, construct a personalized digital twin of the batch of materials based on the initial material composition data; S2. During the fermentation process, a multimodal bio-information sensor network deployed inside the pile is used to acquire in-situ monitoring data in real time that reflects changes in the chemical composition of the material and the metabolic activity of microorganisms. S3. The in-situ monitoring data is synchronized to the personalized digital twin in real time, and the fermentation state diagnosis and trend prediction are performed through its built-in hybrid model. Based on the multi-objective optimization algorithm, precise control instructions containing spatial positioning information are generated. S4. Based on the precise control command, control the autonomous mobile execution unit to perform local addition or turning operations of materials in specific coordinate areas of the pile, and / or control the zoned intelligent aeration system to perform differentiated oxygen supply to different areas of the pile, so as to realize dynamic closed-loop adjustment of carbon-nitrogen ratio. S5. Based on the predicted output of the personalized digital twin and the real-time data of the multimodal bio-information sensor network, the end point of composting is determined together, and a full-process digital traceability file of the batch of materials is generated.

2. The method for intelligent control and efficient composting of the carbon-nitrogen ratio of straw and livestock manure according to claim 1, characterized in that, In step S1, the personalized digital twin integrates a mechanistic model based on microbial reaction kinetics and a data-driven model trained on historical data; the data-driven model is a long short-term memory neural network model.

3. The method for intelligent control and efficient composting of the carbon-nitrogen ratio of straw and livestock manure according to claim 1, characterized in that, In step S2, the multimodal bio-information sensor network includes: Microbial electronic nose array for continuous detection of characteristic spectra of volatile organic compounds inside a stack; Miniature spectroscopic probes for in-situ, continuous monitoring of carbon-nitrogen ratio and lignocellulose degradation at different depths of a stack. Temperature field sensing unit used to construct the three-dimensional spatial metabolic thermal field distribution of the stack.

4. The method for intelligent control and efficient composting of the carbon-nitrogen ratio of straw and livestock manure according to claim 1, characterized in that, In step S3, the fermentation status diagnosis includes: By comparing real-time sensor data with digital twin predictions, local imbalances in the carbon-nitrogen ratio, abnormal nitrogen volatilization areas, or areas with abnormal metabolic activity are identified; the trend prediction is a rolling prediction of the decomposition process and resource consumption within a set future time period.

5. The method for intelligent control and efficient composting of the carbon-nitrogen ratio of straw and livestock manure according to claim 1, characterized in that, In step S3, the multi-objective optimization algorithm uses at least two of the following as simultaneous optimization objectives: shortening the composting cycle, reducing total nitrogen loss, and reducing total process energy consumption, to calculate the optimal control strategy.

6. The method for intelligent control and efficient composting of the carbon-nitrogen ratio of straw and livestock manure according to claim 1, characterized in that, In step S4, the autonomous mobile execution unit is a robot that can move along a track above the fermentation facility. It can receive coordinate information from the precise control instructions and perform at least one of the following operations: Through its onboard dosing device, straw powder or concentrated manure slurry conditioner is quantitatively added to the designated coordinate area of ​​the pile. Using its onboard robotic arm end effector, it performs depth-controlled local micro-tumbling and throwing operations on designated coordinate areas of the stack.

7. The method for intelligent control and efficient composting of the carbon-nitrogen ratio of straw and livestock manure according to claim 1, characterized in that, In step S4, the zoned intelligent aeration system includes: The bottom of the fermentation facility is divided into multiple independently controlled air chambers, each covered with a smart breathable membrane made of electroactive material. By adjusting the voltage applied to each smart breathable membrane, its pore size can be changed independently and continuously, thereby achieving precise control of the oxygen supply rate of each zone.

8. The method for intelligent control and efficient composting of the carbon-nitrogen ratio of straw and livestock manure according to claim 1, characterized in that, In step S5, the conditions for jointly determining the end point of decomposition are simultaneously satisfied: The predicted maturity value output by the personalized digital twin reaches and stabilizes at a preset threshold. The temperature field sensing unit detected that the difference between the overall temperature of the stack and the ambient temperature was consistently lower than a set threshold. In the volatile organic compound spectrum detected by the microbial electronic nose array, the characteristic peak signal of malodorous substances disappeared and a stable humification characteristic peak appeared. The carbon-nitrogen ratio of the material detected by the micro-spectral probe is 15:1-20:

1.

9. The method for intelligent control and efficient composting of the carbon-nitrogen ratio of straw and livestock manure according to claim 1, characterized in that, In step S5, the fully digital traceability file includes time-series sensor data from the initial detection of materials to the end of the composting process, records of all executed control commands, digital twin simulation curves, and key parameter analysis reports; this file is uniquely bound to the batch of composted materials produced.

10. A system for implementing the method according to any one of claims 1-9, characterized in that, include: A central control and computing platform is used to run the personalized digital twin and multi-objective optimization algorithms; A multimodal bioinformatics sensor network is deployed within the fermentation facility to execute step S2; An autonomous mobile actuator and a zoned intelligent aeration system are used to execute step S4; The central control and computing platform is communicatively connected to the sensor network, execution unit and aeration system, forming a closed-loop control system.

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