Biomass fuel conversion full-process optimization management system based on digital twinning

Abstract

The invention discloses a biomass fuel conversion full-process optimization management system based on digital twinning, and relates to the technical field of biomass fuel conversion. Comprising a raw material detection module, a raw material quality analysis module, a conversion efficiency analysis module, a process optimization module, a processing scheme output module, a digital twin system module, a production processing module, a whole-process monitoring module, a real-time data display module, a data integration module, a fault prediction module and a maintenance output module. According to the biomass fuel conversion full-process optimization management system based on digital twinning, the treatment process can be optimized in a targeted mode according to the raw material quality of biomass fuel, the system monitors the water content, the heat value and other parameters of raw materials in real time, the pretreatment process is dynamically adjusted in combination with a digital twinning model, and the pretreatment efficiency is improved. The optimal matching of the raw materials and the subsequent gasification / liquefaction process is ensured, and the production efficiency and the environmental protection performance of the biomass fuel can be remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of biomass fuel conversion technology, specifically to a biomass fuel conversion end-to-end optimization management system based on digital twins. Background Technology

[0002] The conversion of biomass fuels into fuel oil is a crucial technological direction for addressing fossil fuel dependence and environmental pollution. Its core lies in converting biomass into liquid fuel through thermochemical or biochemical methods. Fossil fuels still account for a staggering 81% of the global energy mix, and their carbon emissions have prompted countries to seek renewable alternatives. Biomass fuels, due to their renewability and carbon neutrality, have become an ideal choice. The characteristics of the biomass feedstock directly affect conversion efficiency, requiring targeted process adjustments to achieve efficient and low-consumption fuel oil production. However, the complex characteristics of the feedstock make targeted process adjustments difficult. Therefore, a digital twin-based optimization management system for the entire biomass fuel conversion process is proposed. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a digital twin-based optimization management system for the entire biomass fuel conversion process, which solves the problems mentioned in the background section.

[0004] To achieve the above objectives, the present invention is implemented through the following technical solution: a biomass fuel conversion whole-process optimization management system based on digital twin, including a raw material detection module, a raw material quality analysis module, a conversion efficiency analysis module, a process optimization module, a processing solution output module, a digital twin system module, a production processing module, a whole-process monitoring module, a real-time data display module, a data integration module, a fault prediction module, and a maintenance output module; The raw material testing module is used to test the combustion and conversion performance, physical properties and chemical composition of biomass raw materials. Physical properties include particle size distribution, density and mechanical strength. Chemical composition includes elemental analysis, moisture content, ash content, volatile matter and fixed carbon, as well as sulfur and chlorine content. Combustion and conversion performance testing includes calorific value, combustion characteristics and biodegradability. The raw material quality analysis module is used to perform integrated analysis on the obtained data of combustion and conversion performance, physical properties and chemical composition of biomass raw materials, and to obtain the raw material quality of biomass raw materials through integrated analysis. The conversion efficiency analysis module is used to obtain the conversion efficiency of biomass raw materials into crude oil based on the raw material quality of biomass raw materials. The process optimization module is used to perform targeted process optimization based on the physical properties, chemical composition, and combustion and conversion performance parameters of biomass raw materials. The processing solution output module is used to send the process flow optimized by the process optimization module for this batch of biomass raw materials to the production processing module. The production processing module is used to control the processing production line to convert this batch of biomass raw materials into raw oil according to the process flow; The digital twin system module is used to establish a high-fidelity digital twin system for production line equipment such as boilers and gasifiers that convert biomass raw materials into feedstock oil. The full-process monitoring module is used to monitor the entire production line and acquire various data from the production line by using various sensors installed on the production line. The real-time data display module, relying on a 3D visualization interface and real-time data mapping technology, enables transparent monitoring of the entire production process, displaying equipment status, material flow, and personnel operations in real time. The data integration module is used to integrate various data from the production line obtained through the full-process monitoring module, and to combine various data from the production line in each time period. The fault prediction module is used to predict the faults of various equipment in the production line based on various data of the production line. The maintenance output module is used to adjust and output the maintenance plan for each piece of equipment on the production line based on the fault prediction results of each piece of equipment.

[0005] Preferably, the maintenance output module includes a processing solution input module, a processing solution library, a fault twin simulation module, a processing solution evaluation module, and a processing solution optimization module. The processing solution library is used to build a solution library based on fault repair and troubleshooting solutions from historical production line fault cases. The fault twin simulation module is used to determine equipment interaction relationships through value stream mapping based on production line fault types and KPIs, and to construct a twin simulation model considering dynamic characteristics and spatial dimensions. The processing solution input module is used to select corresponding fault repair and troubleshooting solutions based on fault prediction results and input them into the twin simulation model to deduce the fault repair and troubleshooting solutions. The processing solution evaluation module is used to evaluate the processing solutions based on the deduced fault repair and troubleshooting solutions. The processing solution optimization module is used to verify the technical feasibility, economy, and safety of the processing solutions from multiple dimensions, and to optimize the process and adjust the parameters of the processing solutions.

[0006] Preferably, the fault prediction module includes a work log module, an equipment data module, a working environment data module, and a historical data training module. The work log module is used to provide daily work data of the production line; the equipment data module is used to provide daily equipment status data; the working environment data module is used to provide working environment data of the production line; and the historical data training module trains the fault prediction model based on historical fault cases of the production line.

[0007] Preferably, the process optimization module includes a raw material parameter adjustment module, a process simulation module, a process library, a process selection module, a process scheme module, and a process parameter adjustment module. The raw material parameter adjustment module is used to adjust various parameters of biomass fuel; the process selection module is used to select various processes stored in the process library; the process simulation module is used to simulate the biomass fuel using the selected process; the process parameter adjustment module is used to adjust various process parameters of the selected process; and the process scheme module generates a process scheme based on the optimized processing technology and process parameters.

[0008] Preferably, the process includes a physical property optimization process, a chemical composition control process, and a combustion and conversion performance optimization process.

[0009] Preferably, the physical property optimization process includes particle size distribution control process and density and mechanical strength improvement process; the chemical composition control process includes moisture control, ash and volatile matter optimization and sulfur / chlorine removal; the combustion and conversion performance optimization process includes calorific value improvement process, combustion characteristic control process and biodegradability enhancement process.

[0010] Preferably, the high-fidelity digital twin system uses the Industrial Internet of Things (IIoT) to achieve multi-source heterogeneous data fusion, ensuring the real-time performance of the twin model.

[0011] This invention provides a digital twin-based optimization management system for the entire biomass fuel conversion process, which has the following advantages: 1. This digital twin-based biomass fuel conversion full-process optimization management system can optimize the processing technology according to the quality of biomass fuel raw materials. The system monitors parameters such as moisture content and calorific value of raw materials in real time, and dynamically adjusts the pretreatment process in combination with the digital twin model to ensure the best match between raw materials and subsequent gasification / liquefaction processes, which can significantly improve the production efficiency and environmental performance of biomass fuel.

[0012] 2. This digital twin-based biomass fuel conversion full-process optimization management system can significantly improve equipment reliability and ensure the technical feasibility, economy and safety of fault handling solutions by predicting faults in the biomass fuel conversion production line and optimizing handling solutions through real-time data fusion and intelligent simulation. Attached Figure Description

[0013] Figure 1 This is a schematic diagram of the system structure of the present invention; Figure 2 This is a schematic diagram of the maintenance output module structure of the present invention; Figure 3This is a schematic diagram of the fault prediction module structure of the present invention; Figure 4 This is a schematic diagram of the process optimization module structure of the present invention.

[0014] The diagram shows: 1. Raw material testing module; 2. Raw material quality analysis module; 3. Conversion efficiency analysis module; 4. Process optimization module; 5. Processing solution output module; 6. Digital twin system module; 7. Production processing module; 8. Full-process monitoring module; 9. Real-time data display module; 10. Data integration module; 11. Fault prediction module; 12. Maintenance output module; 13. Processing solution input module; 14. Processing solution library; 15. Fault twin simulation module; 16. Processing solution evaluation module; 17. Processing solution optimization module; 18. Work log module; 19. Equipment data module; 20. Working environment data module; 21. Historical data training module; 22. Raw material parameter adjustment module; 23. Process simulation module; 24. Process library; 25. Process selection module; 26. Process solution module; 27. Process parameter adjustment module. Detailed Implementation

[0015] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0016] Please see Figures 1 to 4 The present invention provides a technical solution: a biomass fuel conversion whole process optimization management system based on digital twin, including a raw material detection module 1, a raw material quality analysis module 2, a conversion efficiency analysis module 3, a process optimization module 4, a processing solution output module 5, a digital twin system module 6, a production processing module 7, a whole process monitoring module 8, a real-time data display module 9, a data integration module 10, a fault prediction module 11, and a maintenance output module 12; Raw material testing module 1 is used to test the combustion and conversion performance, physical properties and chemical composition of biomass raw materials. Physical properties include particle size distribution, density and mechanical strength. Chemical composition includes elemental analysis, moisture content, ash content, volatile matter and fixed carbon, as well as sulfur and chlorine content. Combustion and conversion performance testing includes calorific value, combustion characteristics and biodegradability. The raw material quality analysis module 2 is used to perform integrated analysis on the obtained data on the combustion and conversion performance, physical properties and chemical composition of biomass raw materials, and to obtain the raw material quality of biomass raw materials through integrated analysis; The conversion efficiency analysis module 3 is used to obtain the conversion efficiency of biomass raw materials into feedstock oil based on the raw material quality of biomass raw materials. Process optimization module 4 is used to perform targeted process optimization based on the physical properties, chemical composition, and combustion and conversion performance parameters of biomass raw materials. The process optimization module 4 includes a raw material parameter adjustment module 22, a process simulation module 23, a process library 24, a process selection module 25, a process scheme module 26, and a process parameter adjustment module 27. The raw material parameter adjustment module 22 is used to adjust various parameters of biomass fuel; the process selection module 25 is used to select various processes stored in the process library 24; the process simulation module 23 is used to simulate the biomass fuel using the selected process; the process parameter adjustment module 27 is used to adjust various process parameters of the selected process; and the process scheme module 26 generates a process scheme based on the optimized processing technology and process parameters. The processing solution output module 5 is used to send the process flow optimized by the process optimization module 4 for this batch of biomass raw materials to the production processing module 7. The process includes particle size distribution control technology: Target range: D50 should be controlled within 0.5-2mm (wood) or 1-3mm (straw). Too fine a D50 can lead to pyrolysis and coking, while too coarse a D50 can reduce heat transfer efficiency. Adjustment method: Use two-stage crushing (coarse crushing + fine crushing) combined with vibrating sieve (80-100 mesh screen), and add low-temperature crushing (-30℃ to -50℃) if necessary to reduce the loss of heat-sensitive components; Density and mechanical strength enhancement processes: Molding density: Compressed to 1.0–1.4 g / cm³ 3 (Wood particles) or 0.8–1.2 g / cm³ 3 (Straw pellets) Too high a concentration will increase energy consumption, while too low a concentration will affect pyrolysis stability; Strength enhancement: Add 5% to 10% lignin binder (such as alkali lignin) or enhance fiber bonding by steam explosion pretreatment (pressure 0.8-1.2MPa, time 5-10min); Moisture control process: Optimal range: Dry to ≤10% before pyrolysis; excessively high moisture content will lower the pyrolysis temperature and increase the tar moisture content. Drying process: fluidized bed drying (80-100℃) or vacuum drying (60-80℃), combined with online near-infrared spectroscopy (NIRS) for dynamic adjustment; Optimization of ash and volatile matter processes: Ash removal: Magnetic separation (magnetic field strength ≥1T) or acid washing (1%~3% dilute hydrochloric acid) reduces ash content to ≤5%, reducing the risk of catalyst poisoning; Volatile matter release: When the pyrolysis temperature is 500-600℃, the volatile matter release rate can reach 70% to 80%. It is necessary to avoid excessively high temperatures that may lead to secondary cracking. Sulfur / chlorine removal process: Water washing process: solid-liquid ratio 1:5, or alkali treatment (1%~2% NaOH solution) to reduce sulfur / chlorine content to ≤0.1%; Calorific value enhancement process: Catalytic cracking: Using HZSM-5 catalyst (Si / Al=25-35), reaction temperature 450-550℃, liquid yield increased to 60%-70%; Pyrolysis gasification: By controlling the gasifying agent (water vapor / oxygen ratio of 0.2-0.5 kg / kg), the calorific value of the syngas reaches 12-15 MJ / m³. 3 ; Combustion characteristic regulation technology: Combustion rate: Adjust particle size (3-8 mm) and porosity (20%-30%) to ensure a combustion rate of 0.5-2 g / s; Ash melting point enhancement: Adding CaO / MgO (1%–3%) increases the ash melting point to ≥1200℃ and reduces slagging; Biodegradability Enhancement Process: Enzymatic pretreatment: cellulase (30 FPU / g substrate) + xylanase (15 IU / g), saccharification rate >80%; Microwave-assisted treatment: 20W / g power density, 10 min treatment disrupts lignin structure and improves subsequent conversion efficiency; Production processing module 7 is used to control the processing production line to convert this batch of biomass raw materials into raw oil according to the process flow; Digital twin system module 6 is used to establish a high-fidelity digital twin system for production line equipment such as boilers and gasifiers that convert biomass raw materials into feedstock oil. The full-process monitoring module 8 is used to monitor the entire production line and acquire various data from the production line by using various sensors installed on the production line. The real-time data display module 9, relying on a 3D visualization interface and real-time data mapping technology, enables transparent monitoring of the entire production process, displaying equipment status, material flow, and personnel operations in real time. Data integration module 10 is used to integrate various data of the production line obtained through the full process monitoring module 8, and to combine various data of the production line in each time period. The fault prediction module 11 is used to predict the faults of each piece of equipment on the production line based on the vibration, temperature, current and other data detected by the sensors installed on the equipment on the production line. The fault prediction module 11 includes a work log module 18, an equipment data module 19, a working environment data module 20, and a historical data training module 21. The work log module 18 is used to provide daily work data of the production line; the equipment data module 19 is used to provide daily equipment status data; the working environment data module 20 is used to provide working environment data of the production line; and the historical data training module 21 is used to train the fault prediction model based on historical fault cases of the production line. The maintenance output module 12 is used to adjust and output the maintenance plan for each piece of equipment on the production line based on the fault prediction results of each piece of equipment on the production line. The maintenance output module 12 includes a solution input module 13, a solution library 14, a fault twin simulation module 15, a solution evaluation module 16, and a solution optimization module 17. The solution library 14 is used to build a solution library based on fault repair and troubleshooting solutions from historical production line fault cases. The fault twin simulation module 15 is used to determine equipment interaction relationships through value stream mapping based on production line fault types and KPIs, and to construct a twin simulation model considering dynamic characteristics and spatial dimensions. The solution input module 13 is used to select corresponding fault repair and troubleshooting solutions based on fault prediction results and input them into the twin simulation model to simulate the fault repair and troubleshooting solutions. The solution evaluation module 16 is used to evaluate the solution based on the simulation results of the fault repair and troubleshooting solutions. The solution optimization module 17 is used to verify the technical feasibility, economy, and safety of the solutions from multiple dimensions, and to optimize the process and adjust parameters of the solutions. All standard parts used in this application can be purchased from the market, and can be customized according to the description and drawings. The specific connection methods of each part adopt conventional methods such as bolts, rivets, and welding that are mature in the prior art. The machinery, parts and equipment all adopt conventional models in the prior art. The installation methods between equipment are also the same as conventional installation methods in the prior art. For example, the two ends of shaft-shaped parts are connected by bearings, the connection position of valve components is provided with anti-leakage rubber strips, the outside of threaded rods or lead rods is provided with dust covers, and the equipment can be driven by either built-in batteries or external power supply. The control method is automatic control by a controller. The control circuit of the controller can be implemented by simple programming by those skilled in the art and is common knowledge in the field. Since this invention is mainly used to protect mechanical devices, this invention will not explain the control method and circuit connection in detail. The external controller mentioned in the specification can play a control role for the electrical components mentioned herein, and the external controller is a conventional known device.

[0017] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A biomass fuel conversion end-to-end optimization management system based on digital twins, characterized in that: It includes a raw material testing module (1), a raw material quality analysis module (2), a conversion efficiency analysis module (3), a process optimization module (4), a processing solution output module (5), a digital twin system module (6), a production processing module (7), a full-process monitoring module (8), a real-time data display module (9), a data integration module (10), a fault prediction module (11), and a maintenance output module (12). The raw material testing module (1) is used to test the combustion and conversion performance, physical properties and chemical composition of biomass raw materials. Physical properties include particle size distribution, density and mechanical strength. Chemical composition includes elemental analysis, moisture content, ash content and volatile matter and fixed carbon as well as sulfur and chlorine content. Combustion and conversion performance testing includes calorific value, combustion characteristics and biodegradability. The raw material quality analysis module (2) is used to perform integrated analysis based on the obtained data on the combustion and conversion performance, physical properties and chemical composition of biomass raw materials, and to obtain the raw material quality of biomass raw materials through integrated analysis; The conversion efficiency analysis module (3) is used to obtain the conversion efficiency of biomass raw materials into raw oil based on the raw material quality of biomass raw materials. The process optimization module (4) is used to perform targeted process optimization based on the physical properties, chemical composition, and combustion and conversion performance parameters of biomass raw materials. The processing scheme output module (5) is used to send the process flow optimized by the process optimization module (4) for this batch of biomass raw materials to the production processing module (7). The production processing module (7) is used to control the processing production line to convert this batch of biomass raw materials into raw oil according to the process flow; The digital twin system module (6) is used to establish a high-fidelity digital twin system for production line equipment such as boilers and gasifiers that convert biomass raw materials into raw oil. The full-process monitoring module (8) is used to monitor the entire production line and obtain various data of the production line by using various sensors installed on the production line. The real-time data display module (9) is used to achieve transparent monitoring of the entire production process by relying on a three-dimensional visualization interface and real-time data mapping technology, and to display equipment status, material flow and personnel operation in real time; The data integration module (10) is used to integrate the various data of the production line obtained through the full-process monitoring module (8) and combine the various data of the production line in each time period. The fault prediction module (11) is used to predict the faults of each piece of equipment in the production line based on various data of the production line. The maintenance output module (12) is used to adjust and output the maintenance plan for each piece of equipment in the production line based on the fault prediction results of each piece of equipment in the production line.

2. The biomass fuel conversion whole-process optimization management system based on digital twin as described in claim 1, characterized in that: The maintenance output module (12) includes a processing solution input module (13), a processing solution library (14), a fault twin simulation module (15), a processing solution evaluation module (16), and a processing solution optimization module (17). The processing solution library (14) is used to build a solution library based on fault repair and troubleshooting solutions in historical production line fault cases. The fault twin simulation module (15) is used to determine the equipment interaction relationship through value stream mapping based on the production line fault type and KPI, and to construct a twin simulation model considering dynamic characteristics and spatial dimensions; the processing scheme input module (13) is used to select the corresponding fault repair and elimination scheme based on the fault prediction result, and input it into the twin simulation model to deduce the fault repair and elimination scheme; The processing scheme evaluation module (16) is used to evaluate the processing scheme based on the deduction results of the fault repair and troubleshooting scheme; the processing scheme optimization module (17) is used to verify the technical feasibility, economy and safety in multiple dimensions, and to optimize the process and adjust the parameters of the processing scheme. ‌ 3. The biomass fuel conversion whole-process optimization management system based on digital twin as described in claim 1, characterized in that: The fault prediction module (11) includes a work log module (18), an equipment data module (19), a work environment data module (20), and a historical data training module (21). The work log module (18) is used to provide daily work data of the production line; the equipment data module (19) is used to provide daily status data of the equipment; the work environment data module (20) is used to provide work environment data of the production line; and the historical data training module (21) trains the fault prediction model based on historical fault cases of the production line.

4. The biomass fuel conversion whole-process optimization management system based on digital twin as described in claim 1, characterized in that: The process optimization module (4) includes a raw material parameter adjustment module (22), a process simulation module (23), a process library (24), a process selection module (25), a process scheme module (26), and a process parameter adjustment module (27). The raw material parameter adjustment module (22) is used to adjust various parameters of biomass fuel. The process selection module (25) is used to select various processes stored in the process library (24). The process simulation module (23) is used to simulate the biomass fuel using the selected process. The process parameter adjustment module (27) is used to adjust various process parameters of the selected process; The process scheme module (26) generates a process scheme based on the preferred processing technology and process parameters.

5. The biomass fuel conversion whole-process optimization management system based on digital twin as described in claim 1, characterized in that: The process includes physical property optimization, chemical composition control, and combustion and conversion performance optimization.

6. The biomass fuel conversion whole-process optimization management system based on digital twin as described in claim 1, characterized in that: The physical property optimization process includes particle size distribution control process and density and mechanical strength improvement process; the chemical composition control process includes moisture control, ash and volatile matter optimization and sulfur / chlorine removal; the combustion and conversion performance optimization process includes calorific value improvement process, combustion characteristic control process and biodegradability enhancement process.

7. The biomass fuel conversion whole-process optimization management system based on digital twin as described in claim 1, characterized in that: The high-fidelity digital twin system uses the Industrial Internet of Things (IIoT) to achieve multi-source heterogeneous data fusion, ensuring the real-time performance of the twin model.

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