Agricultural fresh water-thermal energy cascade utilization system and intelligent control method thereof

Abstract

The application discloses an agricultural fresh water-thermal energy gradient utilization system, comprising a three-stage thermal energy utilization unit and a cloud-edge collaborative intelligent regulation and control unit; the three-stage thermal energy utilization unit comprises an MED fresh water production unit, a greenhouse heating unit and a preheating auxiliary unit; a steam inlet of the MED fresh water production unit is connected to a by-product steam source of a green ammonia production system through a desuperheating valve, steam temperature is accurately controlled at 280 DEG C, and gradient temperature reduction is realized through multi-effect heat exchange to drive MED water production; the greenhouse heating unit is connected to a last-effect waste heat outlet of the MED device through a plate heat exchanger, and waste heat after heating is used for preheating of water for ammonia production by the preheating auxiliary unit; the cloud-edge collaborative intelligent regulation and control unit integrates meteorological API, a crop water requirement model and real-time sensor data, dynamically optimizes a steam distribution ratio and outputs a control instruction. The application realizes full utilization of a 250-90 DEG C temperature gradient, improves energy utilization rate to 50%, reduces fresh water cost by 40%, reduces heating energy consumption by 65%, and forms an energy-agriculture-green ammonia closed-loop economic mode.

Description

Technical Field

[0001] This invention relates to an agricultural freshwater-thermal energy cascade utilization system and its intelligent control method, belonging to the technical fields of energy efficiency utilization and agricultural water resource management. Background Technology

[0002] The production of green ammonia typically generates a large amount of byproduct steam at around 400℃. Currently, the methods for utilizing this high-quality waste heat are relatively simple and inefficient. Common mainstream solutions include: Steam is used for power generation, but its efficiency is only about 35%. The low-temperature waste heat (<100℃) after power generation is often not utilized and is directly discharged, resulting in an overall thermal energy utilization rate of less than 40%.

[0003] Some waste heat is used directly for greenhouse heating, but this method fails to integrate freshwater production and the heating efficiency is unstable. During droughts, the agricultural water shortage rate often exceeds 30%, and the greenhouse temperature can fluctuate by ±5℃.

[0004] Existing technologies generally employ fixed processes and lack the ability to utilize heat energy in a tiered manner based on its grade. For example, they fail to utilize high-temperature steam (250-400℃) to drive low-temperature multi-effect distillation (MED) for freshwater production, while simultaneously using the waste heat from the medium-temperature range (70-90℃) after cooling for greenhouse heating, and then using the low-grade waste heat from heating for preheating feed water. This results in a waste of heat energy resources across a wide temperature gradient from 250℃ to 70℃.

[0005] In addition, the existing system lacks a dynamic control mechanism and cannot optimize the steam distribution ratio in real time according to weather forecasts (such as the need for increased water during droughts and increased heating during cold waves), resulting in insufficient freshwater supply or delayed heating response during critical periods, which cannot meet the needs of agricultural production. Summary of the Invention

[0006] The purpose of this invention is to solve the aforementioned problems in the existing technology and to provide an agricultural freshwater-thermal energy cascade utilization system and intelligent control method to achieve efficient cascade utilization of energy and precise management of agricultural water resources and thermal energy.

[0007] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: an agricultural freshwater-thermal energy cascade utilization system, comprising three-stage thermal energy utilization units, wherein the three-stage thermal energy utilization units include: The MED desalination unit includes a containerized MED device. Its steam inlet is connected to the by-product steam source of the green ammonia production system through a desuperheating valve. The desuperheating valve precisely controls the steam inlet temperature at 280°C. The MED device achieves gradient cooling through multi-effect heat exchange, with the first-stage effect chamber outlet temperature at 220°C, the second-stage effect chamber outlet temperature at 150°C, and the third-stage effect chamber outlet temperature at 90°C, so as to achieve 250-400°C steam driving MED water production and gradient utilization of thermal energy. The greenhouse heating unit is connected to the waste heat outlet of the MED device via a plate heat exchanger to receive waste heat of 70-90°C for greenhouse heating. A preheating auxiliary unit is connected to the waste heat outlet of the greenhouse heating unit via a plate heat exchanger, and uses the waste heat after greenhouse heating to preheat the incoming water and assist in ammonia production. It also includes a cloud-edge collaborative intelligent control unit, which integrates a meteorological API interface, a crop water requirement model and real-time sensor data. The cloud-edge collaborative intelligent control unit can dynamically optimize the steam distribution ratio, including water production priority and heating priority, and output control commands to the desuperheating valve and heat exchangers at all levels to realize closed-loop control of dynamic steam distribution and cascade utilization of thermal energy.

[0008] The aforementioned agricultural freshwater-thermal energy cascade utilization system, wherein the MED unit is a modular container structure with a single unit capacity of 200 tons / day.

[0009] The aforementioned agricultural freshwater-thermal energy cascade utilization system uses a plate heat exchanger, model HE-150, which is used to transfer the waste heat from the final effect of the MED (Medium-Dry Heater) to the greenhouse heating unit and to transfer the greenhouse waste heat to the preheating auxiliary unit.

[0010] The aforementioned agricultural freshwater-thermal energy cascade utilization system, wherein the cloud-edge collaborative intelligent control unit includes: The data sensing and acquisition layer includes a real-time sensor group deployed in each level of thermal energy utilization unit and an edge computing gateway. The edge computing gateway is connected to the real-time sensor group and is used to collect, process and upload real-time operating data. The cloud-based decision-making and modeling layer includes a meteorological API interface, a crop water requirement model library, and a dynamic optimization algorithm engine. The cloud-based decision-making and modeling layer is communicatively connected to the edge computing gateway and is used to receive real-time data and combine meteorological forecasts and crop water requirement models to generate a steam distribution strategy through a dynamic optimization algorithm. The instruction execution and control layer includes a programmable logic controller (PLC) and an actuator. The PLC is communicatively connected to the cloud-based decision and model layer and is used to receive the steam distribution strategy and convert it into control instructions. The actuator includes a desuperheating valve and an electric regulating valve connected to the steam pipeline. The PLC is electrically connected to the desuperheating valve and the electric regulating valve to precisely control the steam flow and temperature according to the instructions.

[0011] The aforementioned agricultural freshwater-thermal energy cascade utilization system includes at least a temperature sensor, a flow sensor, and a pressure sensor, which are respectively installed at the outlet of the desuperheating valve, the outlets of each effective chamber of the MED device, the inlet of the greenhouse heating unit, and the inlet of the preheating auxiliary unit.

[0012] In the aforementioned agricultural freshwater-thermal energy cascade utilization system, the dynamic optimization algorithm engine calculates water production priority and heating priority based on meteorological early warning, crop water requirement model and real-time sensor data, and generates opening commands for the desuperheating valve and each electric regulating valve to realize dynamic distribution of steam.

[0013] In the aforementioned agricultural freshwater-thermal energy cascade utilization system, the actuator further includes an electric regulating valve for adjusting the flow rate of the heat medium in the greenhouse heating unit and the preheating auxiliary unit. The programmable logic controller is electrically connected to the electric regulating valve to control the direction of heat energy flow.

[0014] In the aforementioned agricultural freshwater-thermal energy cascade utilization system, the cloud-edge collaborative intelligent control unit dynamically adjusts the steam distribution ratio based on weather forecasts, crop water requirement models, and real-time sensor data. The basic value for water production priority is 80%, and the basic value for heating priority is 20%. When the cloud-edge collaborative intelligent control unit receives a drought warning, it automatically raises the priority of water production to 90% and lowers the priority of heating accordingly. When the cloud-edge collaborative intelligent control unit receives a cold wave warning, it automatically increases the heating priority to 30% 48 hours in advance, and correspondingly reduces the water production priority.

[0015] A smart control method for the cascade utilization of agricultural freshwater and thermal energy, applied to the system described above, includes the following steps: S1: The 400℃ by-product steam generated during the green ammonia production process is precisely cooled to 280℃ via a desuperheating valve and then sent to the MED unit; S2: Steam is cooled by a multi-effect heat exchange gradient in the MED unit, at 220℃, 150℃ and 90℃ respectively, to drive the MED unit to produce fresh water, with a water production rate of 2.5 tons / ton of steam. S3: The waste heat of 70-90℃ from the final outlet of the MED unit is sent to the greenhouse heating unit through a plate heat exchanger to provide heat energy for the greenhouse. S4: The waste heat from the greenhouse heating is sent to the preheating auxiliary unit through a plate heat exchanger to preheat the inlet water and assist in ammonia production. S5: The cloud-edge collaborative intelligent control unit acquires meteorological API data, crop water requirement models and sensor data in real time, dynamically optimizes the steam distribution ratio, and controls the desuperheating valve and heat exchanger to achieve dynamic steam distribution and cascade utilization of thermal energy.

[0016] In the aforementioned intelligent control method, in step S5, the basic value of the steam distribution ratio is 80% for water production and 20% for heating; during drought warnings, it is automatically adjusted to 90% for water production and 10% for heating; during cold wave warnings, it is automatically adjusted to 70% for water production and 30% for heating 48 hours in advance.

[0017] Compared with the prior art, the present invention has at least the following beneficial effects: This invention has high energy efficiency. Through a three-stage thermal energy cascade utilization system, it achieves full utilization of the 250-90℃ temperature gradient, with waste heat utilization efficiency reaching 85% and overall energy utilization rate increased to 50%, which is significantly improved compared to the traditional solution (40-45%), and completely solves the problem of waste of temperature gradient resources.

[0018] The present invention has superior resource output. The water production rate of the MED device reaches 2.5 tons / ton of steam, which is higher than the industry standard (2.3 tons / ton), and the freshwater production cost is reduced by 40%. The energy consumption of greenhouse heating is reduced by 65% ​​compared with coal heating, the temperature fluctuation is controlled within ±0.5℃, and the crop survival rate can reach 100%.

[0019] This invention features precise intelligent control. Its cloud-edge collaborative intelligent control mechanism integrates meteorological and crop models and real-time data to dynamically optimize steam distribution, with a decision response time of ≤30 seconds. It automatically increases the water production ratio during drought periods and preheats and increases the heating ratio 48 hours in advance during cold waves, effectively solving the problems of delayed response and supply-demand imbalance in traditional systems.

[0020] The present invention has significant economic benefits. The system and green ammonia production form a closed-loop economic model of "energy-agriculture-green ammonia". The annual freshwater production is 153,000 tons, saving 306,000 yuan in water fees and 153,000 yuan in heating costs. The overall economic efficiency is improved by 35%, and the system failure rate is as low as 0.4%. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the structure and process of the agricultural freshwater-thermal energy cascade utilization system in an embodiment of the present invention.

[0022] The present invention will be further described below with reference to the accompanying drawings and specific embodiments. Detailed Implementation

[0023] This invention provides an agricultural freshwater-thermal energy cascade utilization system, comprising a three-stage thermal energy utilization unit and a cloud-edge collaborative intelligent control unit. The three-stage thermal energy utilization unit consists of a MED freshwater production unit, a greenhouse heating unit, and a preheating auxiliary unit. The cloud-edge collaborative intelligent control unit is responsible for the dynamic optimization decision-making of the entire system.

[0024] The three-stage thermal energy utilization unit includes: The MED freshwater production unit is a modular containerized structure with a single unit capacity of 200 tons / day. The MED freshwater production unit includes a containerized MED unit, whose steam inlet is connected to the by-product steam source of the green ammonia production system through a desuperheating valve. The desuperheating valve precisely controls the steam inlet temperature at 280℃. The MED unit achieves gradient cooling through multi-effect heat exchange, with the first-stage effect chamber outlet temperature at 220℃, the second-stage effect chamber outlet temperature at 150℃, and the third-stage effect chamber outlet temperature at 90℃, so as to achieve 250-400℃ steam driving MED water production and gradient utilization of thermal energy. The greenhouse heating unit is connected to the waste heat outlet of the MED device via a plate heat exchanger to receive waste heat of 70-90°C for greenhouse heating. The preheating auxiliary unit is connected to the waste heat outlet of the greenhouse heating unit through a plate heat exchanger. It uses the waste heat after the greenhouse heating to preheat the water intake and assist in ammonia production. The plate heat exchangers mentioned above are all model HE-150, used to transfer the waste heat from the final effect of the MED to the greenhouse heating unit, and to transfer the waste heat from the greenhouse to the preheating auxiliary unit.

[0025] The cloud-edge collaborative intelligent control unit can dynamically optimize the steam distribution ratio, including water production priority and heating priority, and output control commands to the desuperheating valve and heat exchangers at each stage to realize closed-loop control of dynamic steam distribution and cascade utilization of thermal energy. Specifically, it includes: The data sensing and acquisition layer includes a real-time sensor group deployed in each level of thermal energy utilization unit and an edge computing gateway. The edge computing gateway is connected to the real-time sensor group and is used to collect, process and upload real-time operating data. The real-time sensor group includes at least a temperature sensor, a flow sensor and a pressure sensor, which are respectively set at the outlet of the desuperheating valve, the outlet of each effect chamber of the MED device, the inlet of the greenhouse heating unit and the inlet of the preheating auxiliary unit. The cloud-based decision-making and modeling layer includes a meteorological API interface, a crop water requirement model library, and a dynamic optimization algorithm engine. The cloud-based decision-making and modeling layer is connected to the edge computing gateway to receive real-time data and combine it with meteorological forecasts and crop water requirement models. It generates a steam distribution strategy through a dynamic optimization algorithm. The dynamic optimization algorithm engine calculates water production priority and heating priority based on meteorological warnings, crop water requirement models, and real-time sensor data, and generates opening commands for the desuperheating valve and each electric regulating valve to achieve dynamic steam distribution. The instruction execution and control layer includes a programmable logic controller (PLC) and an actuator. The PLC is communicatively connected to the cloud-based decision and model layer to receive steam distribution strategies and convert them into control instructions. The actuator includes a desuperheating valve and an electric regulating valve connected to the steam pipeline. The PLC is electrically connected to the desuperheating valve and the electric regulating valve to precisely control the steam flow and temperature according to the instructions.

[0026] Specifically, the actuator also includes an electric regulating valve for adjusting the flow rate of the heat medium in the greenhouse heating unit and the preheating auxiliary unit. The programmable logic controller is electrically connected to the electric regulating valve to control the direction of heat flow.

[0027] The cloud-edge collaborative intelligent control unit dynamically adjusts the steam distribution ratio based on weather forecasts, crop water requirement models, and real-time sensor data. The base value for water production priority is 80%, and the base value for heating priority is 20%. When the cloud-edge collaborative intelligent control unit receives a drought warning, it automatically increases the water production priority to 90% and decreases the heating priority accordingly. When the cloud-edge collaborative intelligent control unit receives a cold wave warning, it automatically increases the heating priority to 30% 48 hours in advance and decreases the water production priority accordingly.

[0028] This invention also provides an intelligent control method for the cascade utilization of agricultural freshwater and thermal energy, applied to the above-mentioned system, comprising the following steps: S1: The 400℃ by-product steam generated during the green ammonia production process is precisely cooled to 280℃ via a desuperheating valve and then sent to the MED unit; S2: Steam is cooled by a multi-effect heat exchange gradient in the MED unit, at 220℃, 150℃ and 90℃ respectively, to drive the MED unit to produce fresh water, with a water production rate of 2.5 tons / ton of steam. S3: The waste heat of 70-90℃ from the final outlet of the MED unit is sent to the greenhouse heating unit through a plate heat exchanger to provide heat energy for the greenhouse. S4: The waste heat from the greenhouse heating is sent to the preheating auxiliary unit through a plate heat exchanger to preheat the inlet water and assist in ammonia production. S5: The cloud-edge collaborative intelligent control unit acquires meteorological API data, crop water requirement models and sensor data in real time, dynamically optimizes the steam distribution ratio, and controls the desuperheating valve and heat exchanger to achieve dynamic steam distribution and cascade utilization of thermal energy.

[0029] Specifically, in step S5, the basic value of the steam distribution ratio is 80% for water production and 20% for heating; during drought warnings, it is automatically adjusted to 90% for water production and 10% for heating; during cold wave warnings, it is automatically adjusted to 70% for water production and 30% for heating 48 hours in advance.

[0030] This invention achieves cascaded energy utilization and dynamically optimized closed-loop operation through the collaborative design of a three-stage thermal energy utilization unit and a cloud-edge collaborative intelligent control unit. The technical solution of this invention will be further described in detail below with reference to specific embodiments. These embodiments are for illustrative purposes only and are not intended to limit the scope of the invention.

[0031] In the following specific embodiments of the present invention, the system is deployed at the Huadian Green Ammonia production base in Aksu region, Xinjiang, and verified based on pilot-scale operation data. The core equipment of the system adopts commercially available mature products, including a 200-ton / day containerized MED unit, a HE-150 plate heat exchanger, and a cloud-edge collaborative intelligent control platform connected to the meteorological bureau's API interface. The raw materials are 400℃ steam, a byproduct of green ammonia production, and local groundwater at 15℃ with TDS ≤ 500 mg / L.

[0032] Example 1 of the present invention: This example uses a commercially available 200-ton / day containerized MED unit and a cloud-edge collaborative intelligent control system. The raw material is steam produced as a byproduct of green ammonia production, and the freshwater intake is taken from local groundwater (temperature 15℃, TDS≤500mg / L).

[0033] Steps: Steam first enters the first-stage effect chamber (280℃→220℃) to drive the MED unit to produce water, the second-stage effect chamber (220℃→150℃) continues to produce water, and the waste heat from the third-stage effect chamber (150℃→90℃) is sent to the greenhouse for heating via a plate heat exchanger (model HE-150); the intelligent system distributes steam according to the basic ratio (80% for water production / 20% for heating), with a decision response time of 25 seconds (based on meteorological API and real-time sensor data).

[0034] Parameters: MED water production rate 2.5 tons / ton steam, greenhouse heating temperature 12℃ (fluctuation ±0.5℃ to meet crop growth needs), preheating water temperature 45℃ (auxiliary ammonia production saves 12% energy).

[0035] Results: The daily freshwater production is 350 tons (annual output is 127,500 tons), the freshwater production cost is 6.2 yuan / ton (40% lower than the traditional distillation cost of 8.2 yuan / ton), the greenhouse heating energy consumption is 0.8 kWh / m² (60% lower than the coal-fired heating cost of 2.0 kWh / m²), the annual water cost savings are 300,000 yuan, the heating cost savings are 150,000 yuan, and the system operation stability is 99.2%.

[0036] Embodiment 2 of the present invention: The system deployment in this embodiment is completely consistent with that in Embodiment 1, except that the parameters are dynamically adjusted through an intelligent control system. The raw materials are steam from the by-product of homologous green ammonia (280℃, 15 tons / hour) and local groundwater (15℃, TDS≤500mg / L), with no new equipment added.

[0037] Steps: The intelligent control system receives drought early warning API data from the meteorological bureau in real time and automatically adjusts the steam distribution ratio from the basic value (80% water production / 20% heating) to 90% water production / 10% heating, with a decision response time of 28 seconds; the steam drives the MED to produce water through a three-stage gradient cooling process (280℃→220℃→150℃→90℃), and the waste heat is sent to the greenhouse for heating.

[0038] Parameters: MED water production rate 2.45 tons / ton of steam (temperature gradient slightly optimized due to increased water production ratio), greenhouse heating temperature 11℃ (fluctuation ±0.8℃), preheating inlet water temperature 43℃.

[0039] Results: The average daily freshwater production was 375 tons (7.1% higher than in Example 1), meeting the irrigation needs of 1,500 mu of farmland and avoiding the industry pain point of water shortage exceeding 30% during drought periods; the freshwater production cost was 6.4 yuan / ton (22% lower than the traditional 8.2 yuan / ton), the greenhouse heating energy consumption was 0.75 kWh / ㎡ (62.5% lower than coal-fired), and the annual additional water cost savings were 32,000 yuan (based on an average daily increase of 25 tons × 31 days × 4 yuan / ton during drought periods).

[0040] Example 3 of the present invention: The system deployment in this example is completely consistent with that in Example 1, with no new equipment. The raw materials are steam from the same source of green ammonia byproduct (measured at 280°C, flow rate of 15 tons / hour) and local groundwater (temperature 15°C, TDS ≤ 500 mg / L).

[0041] Steps: The intelligent control system receives the cold wave warning API 48 hours in advance and automatically adjusts the steam distribution ratio from the base value (80% water production / 20% heating) to 70% water production / 30% heating, with a decision response time of 30 seconds; the steam drives the MED to produce water through a three-stage gradient cooling process (280℃→220℃→150℃→90℃), and the waste heat is given priority for greenhouse heating.

[0042] Parameters: Greenhouse heating temperature 15℃ (fluctuation ±0.5℃, 3℃ higher than Example 1, to meet crop frost protection requirements), preheating water temperature 55℃ (auxiliary ammonia production saves 12% energy), MED water production rate 2.4 tons / ton steam.

[0043] Results: Greenhouse crop survival rate was 100% (15% higher than the traditional system's 85%), heating energy consumption was 0.6 kWh / m² (65% lower than coal-fired heating's 2.0 kWh / m²), annual heating cost savings were 180,000 yuan (based on a 1500-mu greenhouse × 15 days × 1000 m² × 0.8 yuan / m²), and frost damage losses were avoided by 200,000 yuan (compared to an average annual loss of 200,000 yuan for the traditional system). Freshwater production cost was 6.5 yuan / ton (20.7% lower than the traditional 8.2 yuan / ton), and annual freshwater production was 123,000 tons.

[0044] Comparative Example 1: Comparative Example 1 uses a traditional steam power generation + simple waste heat heating mode for verification.

[0045] Steps: 400℃ by-product steam is used to generate electricity via a steam turbine (efficiency 35%). The low-temperature waste heat (<100℃) after power generation is directly used for greenhouse heating, without any freshwater production process.

[0046] Parameters: The steam distribution ratio is fixed at 0% for water production and 100% for heating, with no meteorological API or dynamic control mechanism.

[0047] Results: The energy utilization rate was only 42% (compared to 50% in this patent). During droughts, due to the lack of freshwater production, freshwater had to be purchased externally (costing 10 yuan / ton), resulting in a 100% water shortage rate. The greenhouse heating temperature fluctuated by ±5℃ (unoptimized), and the annual operating cost was 220,000 yuan higher than that of this patent (including 150,000 yuan for purchased water and 70,000 yuan for heating). This solution failed to achieve the tiered utilization of 250-400℃ steam-driven freshwater production and 70-90℃ waste heat for heating, and the lack of intelligent control led to energy waste.

[0048] Comparative Example 2: Comparative Example 2 only uses 400℃ by-product steam to drive a low-temperature multi-effect distillation (MED) unit to produce fresh water, and the waste heat is directly discharged, without realizing the cascade utilization and intelligent control of thermal energy.

[0049] Steps: 400℃ steam enters the MED unit (water production rate 2.3 tons / ton of steam). After water production, the waste heat is directly discharged into the atmosphere.

[0050] Parameters: Water production rate of 2.3 tons / ton of steam (industry standard value), no greenhouse heating or preheating function.

[0051] Results: The energy utilization rate was only 45% (compared to 50% in this patent), requiring additional coal-fired heating for the greenhouse (energy consumption 2.0 kWh / m²), increasing annual heating costs by 180,000 yuan; freshwater production cost was 7.5 yuan / ton (21% higher than 6.2 yuan / ton in this patent), resulting in annual operating costs 150,000 yuan higher than this patent. This scheme failed to utilize the 250-400℃ steam gradient to drive freshwater production and the 70-90℃ waste heat for heating in a three-stage system, and the lack of dynamic control led to energy waste.

[0052] Example 4 of the present invention: This example uses a commercially available 200-ton / day containerized MED device and a cloud-edge collaborative intelligent control system, and is equipped with a 2,000-mu greenhouse farm (1,500 mu of vegetables and 500 mu of fruit trees).

[0053] Steps: The system receives meteorological data and crop water requirement models in real time, dynamically adjusts the steam distribution ratio (average water production ratio of 85%, heating ratio of 15%), stabilizes the daily steam flow at 15 tons (280℃), and maintains the MED water production rate at 2.45 tons / ton of steam.

[0054] Parameters: Annual average water production rate of 85% (90% during drought and 70% during cold wave), freshwater production cost of 6.2 yuan / ton (industry average of 8.2 yuan / ton), greenhouse heating energy consumption of 0.7 kWh / ㎡ (65% lower than coal-fired 2.0 kWh / ㎡).

[0055] Results: Annual freshwater production reached 153,000 tons (cost 6.2 yuan / ton), saving 306,000 yuan in water costs; greenhouse heating costs were reduced by 153,000 yuan (average annual savings of 1,500 mu × 1,000 square meters × 0.8 yuan / square meter × 12 months); an energy closed loop was formed with green ammonia production, improving overall economic efficiency by 35% (35% higher than operating alone); and the system failure rate was 0.4%. This embodiment innovatively embodies the "energy-agriculture-green ammonia closed-loop economic model".

[0056] Table 1: Comparison of Energy Utilization Efficiency and Cascade Utilization Effect

[0057] Table 2: Comparison of Economic Efficiency and Operational Effectiveness

[0058] Table 3: Efficiency of Temperature Gradient Utilization and Waste Heat Utilization

[0059] Table 4: Annual Economic Performance and Closed-Loop Operation Effect of Example 4

[0060] The working principle of one embodiment of the present invention: The present invention achieves energy cascade utilization and dynamic optimization closed-loop operation through the collaborative design of a three-level thermal energy utilization unit and a cloud-edge collaborative intelligent control unit.

[0061] The three-stage thermal energy utilization unit consists of a MED freshwater production unit, a greenhouse heating unit, and a preheating auxiliary unit. The MED unit adopts a containerized module. The steam inlet temperature is precisely controlled to 280℃ by a desuperheating valve. The MED produces water through a multi-effect heat exchange gradient cooling (280℃→220℃→150℃→90℃). The waste heat from the three-stage heat exchange chamber is sent to the greenhouse for heating via a plate heat exchanger. The waste heat from the greenhouse is used to preheat the inlet water via a plate heat exchanger to assist in ammonia production, achieving full utilization of the 250-90℃ temperature gradient.

[0062] The cloud-edge collaborative intelligent control unit integrates meteorological APIs, crop water requirement models, and real-time sensor data to dynamically optimize the steam distribution ratio. The system prioritizes water production at 80% and heating at 20%, with a decision response time of ≤30 seconds. Steam distribution is driven by real-time data, automatically increasing water production priority to 90% during droughts and heating priority to 30% during cold waves. The system boasts 99.2% operational stability, with freshwater production costs of 6.2 yuan / ton (40% lower than traditional methods) and greenhouse heating energy consumption of 0.7 kWh / m² (65% lower than coal-fired power). Annual savings are 306,000 yuan in water costs and 153,000 yuan in heating costs. The technical solution utilizes commercially available, mature equipment (MED unit, plate heat exchanger, meteorological API interface), with a clear deployment process: steam desuperheating → MED cascade water production → waste heat heating → waste heat preheating, requiring no additional equipment. This completely resolves the shortcomings of existing technologies, such as wasted energy in the 250-70℃ temperature gradient (energy utilization rate of only 40-45%), water shortage rates exceeding 30% during droughts, and heating fluctuations of ±5℃.

Claims

1. An agricultural freshwater-thermal energy cascade utilization system, characterized in that, It includes a three-stage thermal energy utilization unit, wherein the three-stage thermal energy utilization unit includes: The MED desalination unit includes a containerized MED device. Its steam inlet is connected to the by-product steam source of the green ammonia production system through a desuperheating valve. The desuperheating valve precisely controls the steam inlet temperature at 280°C. The MED device achieves gradient cooling through multi-effect heat exchange, with the first-stage effect chamber outlet temperature at 220°C, the second-stage effect chamber outlet temperature at 150°C, and the third-stage effect chamber outlet temperature at 90°C, so as to achieve 250-400°C steam driving MED water production and gradient utilization of thermal energy. The greenhouse heating unit is connected to the waste heat outlet of the MED device via a plate heat exchanger to receive waste heat of 70-90°C for greenhouse heating. A preheating auxiliary unit is connected to the waste heat outlet of the greenhouse heating unit via a plate heat exchanger, and uses the waste heat after greenhouse heating to preheat the incoming water and assist in ammonia production. It also includes a cloud-edge collaborative intelligent control unit, which integrates a meteorological API interface, a crop water requirement model and real-time sensor data. The cloud-edge collaborative intelligent control unit can dynamically optimize the steam distribution ratio, including water production priority and heating priority, and output control commands to the desuperheating valve and heat exchangers at all levels to realize closed-loop control of dynamic steam distribution and cascade utilization of thermal energy.

2. The agricultural freshwater-thermal energy cascade utilization system according to claim 1, characterized in that, The MED device has a modular container structure.

3. The agricultural freshwater-thermal energy cascade utilization system according to claim 1, characterized in that, The plate heat exchanger is used to transfer the waste heat from the final effect of the MED to the greenhouse heating unit, and to transfer the waste heat from the greenhouse to the preheating auxiliary unit.

4. The agricultural freshwater-thermal energy cascade utilization system according to claim 1, characterized in that, The cloud-edge collaborative intelligent control unit includes: The data sensing and acquisition layer includes a real-time sensor group deployed in each level of thermal energy utilization unit and an edge computing gateway. The edge computing gateway is connected to the real-time sensor group and is used to collect, process and upload real-time operating data. The cloud-based decision-making and modeling layer includes a meteorological API interface, a crop water requirement model library, and a dynamic optimization algorithm engine. The cloud-based decision-making and modeling layer is communicatively connected to the edge computing gateway and is used to receive real-time data and combine meteorological forecasts and crop water requirement models to generate a steam distribution strategy through a dynamic optimization algorithm. The instruction execution and control layer includes a programmable logic controller (PLC) and an actuator. The PLC is communicatively connected to the cloud-based decision and model layer and is used to receive the steam distribution strategy and convert it into control instructions. The actuator includes a desuperheating valve and an electric regulating valve connected to the steam pipeline. The PLC is electrically connected to the desuperheating valve and the electric regulating valve to precisely control the steam flow and temperature according to the instructions.

5. The agricultural freshwater-thermal energy cascade utilization system according to claim 4, characterized in that, The real-time sensor group includes at least a temperature sensor, a flow sensor, and a pressure sensor, which are respectively installed at the outlet of the desuperheating valve, the outlets of each effective chamber of the MED device, the inlet of the greenhouse heating unit, and the inlet of the preheating auxiliary unit.

6. The agricultural freshwater-thermal energy cascade utilization system according to claim 4, characterized in that, The dynamic optimization algorithm engine calculates water production priority and heating priority based on meteorological early warning, crop water requirement model and real-time sensor data, and generates opening instructions for desuperheating valve and each electric regulating valve to realize dynamic distribution of steam.

7. The agricultural freshwater-thermal energy cascade utilization system according to claim 4, characterized in that, The actuator also includes an electric regulating valve for adjusting the flow rate of the heat medium in the greenhouse heating unit and the preheating auxiliary unit. The programmable logic controller is electrically connected to the electric regulating valve to control the direction of heat flow.

8. The agricultural freshwater-thermal energy cascade utilization system according to claim 1, characterized in that, The cloud-edge collaborative intelligent control unit dynamically adjusts the steam distribution ratio based on weather forecasts, crop water requirement models, and real-time sensor data. The basic value for water production priority is 80%, and the basic value for heating priority is 20%. When the cloud-edge collaborative intelligent control unit receives a drought warning, it automatically raises the priority of water production to 90% and lowers the priority of heating accordingly. When the cloud-edge collaborative intelligent control unit receives a cold wave warning, it automatically increases the heating priority to 30% 48 hours in advance, and correspondingly reduces the water production priority.

9. A smart control method for the cascade utilization of agricultural freshwater and thermal energy, applied to the system described in any one of claims 1 to 8, characterized in that, Includes the following steps: S1: The 400℃ by-product steam generated during the green ammonia production process is precisely cooled to 280℃ via a desuperheating valve and then sent to the MED unit; S2: Steam is cooled by a multi-effect heat exchange gradient in the MED unit, at 220℃, 150℃ and 90℃ respectively, driving the MED unit to produce fresh water. S3: The waste heat of 70-90℃ from the final outlet of the MED unit is sent to the greenhouse heating unit through a plate heat exchanger to provide heat energy for the greenhouse. S4: The waste heat from the greenhouse heating is sent to the preheating auxiliary unit through a plate heat exchanger to preheat the inlet water and assist in ammonia production. S5: The cloud-edge collaborative intelligent control unit acquires meteorological API data, crop water requirement models and sensor data in real time, dynamically optimizes the steam distribution ratio, and controls the desuperheating valve and heat exchanger to achieve dynamic steam distribution and cascade utilization of thermal energy.

10. The intelligent control method according to claim 7, characterized in that, In step S5, the basic value of the steam distribution ratio is 80% for water production and 20% for heating; during drought warnings, it is automatically adjusted to 90% for water production and 10% for heating; during cold wave warnings, it is automatically adjusted to 70% for water production and 30% for heating 48 hours in advance.

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