Multi-mode biomass gasifier intelligent purification system and method thereof

Abstract

The invention relates to the field of biomass energy conversion, in particular to a multi-mode intelligent purification system and method for a biomass gasifier, and the system integrates five modules of self-adaptive multi-field coupling tar cracking, multi-dimensional real-time intelligent monitoring, in-situ nano catalyst regeneration, energy cascade recovery and digital twin driving. According to the system, through the synergistic effect of an electric field, ultrasonic waves, catalysis and a temperature gradient, the tar removal efficiency is remarkably improved to 99.5% or above, the content of tar at an outlet is lower than 5 mg / Nm < 3 >, an intelligent monitoring module collects technological parameters in real time, and precise control is ensured; the catalyst regeneration module performs in-situ regeneration on a catalyst based on monitoring data to maintain efficient operation of the system; the energy recovery module utilizes heat energy in a gradient mode and optimizes energy configuration, the digital twin module constructs a system digital model, optimization and predictive maintenance of real-time data driving are achieved, and the system achieves innovation breakthrough of intelligence and energy conservation while improving the biomass gasification purification efficiency.

Description

Technical Field

[0001] The present invention relates to the field of biomass energy conversion, and particularly to a multi-modal biomass gasifier intelligent purification system and method thereof, which are used for efficiently removing tar pollutants generated during the biomass gasification process and realizing the cascaded utilization of energy. Background Art

[0002] As a clean energy utilization method, biomass gasification technology can convert biomass such as agricultural and forestry waste into combustible gas, and plays an important role in the utilization of renewable energy. However, pollutants such as tar are inevitably generated during the biomass gasification process. These substances not only reduce the gas utilization efficiency, but also may cause equipment blockage and environmental pollution.

[0003] In the prior art, for example, Chinese Patent CN 106861328 B discloses "a purification system for a biomass gasification combustion device". The system includes a gasifier, a biomass boiler, a biomass burner, and a tail gas collection and purification device. This system mainly removes tar through physical methods such as a spray tower, a cyclone separator, and a spray system, and at the same time uses devices such as a circulating water tank and an atomizing nozzle to improve the purification efficiency.

[0004] However, this traditional purification system has the following deficiencies: First, the purification mechanism is single, mainly relying on physical methods to remove tar, and the treatment efficiency for ultrafine particles and specific tar compounds is limited; second, the tar cracking process lacks active control, the temperature changes greatly, and the cracking efficiency is unstable; third, there is no intelligent monitoring system, and it is impossible to real-time monitor the concentrations of various pollutants and the system operation parameters, making it difficult to carry out intelligent regulation; fourth, the energy utilization rate is low, and the heat energy in the system is not effectively recovered and utilized; fifth, the service life of the catalyst is short, there is no in-situ regeneration mechanism, and the long-term operation cost is high; sixth, the system adaptability is not strong, and it is difficult to cope with the changes in the characteristics of different biomass raw materials.

[0005] Therefore, there is an urgent need to develop an efficient, intelligent, and energy-saving biomass gasification tar purification system to solve the problems existing in the prior art. Summary of the Invention

[0006] The purpose of the present invention is to provide a multi-modal biomass gasifier intelligent purification system and method thereof. Through technologies such as multi-field coupling tar cracking, multi-dimensional real-time monitoring, in-situ catalyst regeneration, energy cascaded recovery, and digital twin drive, the problems existing in the existing biomass gasification purification system, such as low tar removal efficiency, low energy utilization rate, and insufficient intelligence, are solved.

[0007] The present invention proposes a multi-modal biomass gasifier intelligent purification system, including:

[0008] An adaptive multi-field coupling tar cracking module is used to process the tar-containing gas generated by biomass gasification. The adaptive multi-field coupling tar cracking module includes an electric field excitation unit, an ultrasonic cavitation field unit, a gradient catalytic field unit, and a temperature gradient control unit;

[0009] A multi-dimensional real-time intelligent monitoring module is electrically connected to the adaptive multi-field coupling tar cracking module and is used to collect process parameters during the tar cracking process. The multi-dimensional real-time intelligent monitoring module includes a multi-spectral online monitoring unit, a micro-mass spectrometry monitoring array, and an intelligent neural network controller;

[0010] An in-situ nano-catalyst regeneration module is communicatively connected to the multi-dimensional real-time intelligent monitoring module and the adaptive multi-field coupling tar cracking module, and is used to perform in-situ regeneration of the catalyst in the gradient catalytic field unit based on the catalyst activity data collected by the multi-dimensional real-time intelligent monitoring module;

[0011] An energy cascade recovery module is thermally connected to the adaptive multi-field coupling tar cracking module and is used to recover the multi-stage thermal energy generated by the adaptive multi-field coupling tar cracking module and distribute the recovered energy to each module of the system;

[0012] A digital twin driving module is communicatively connected to the multi-dimensional real-time intelligent monitoring module, the adaptive multi-field coupling tar cracking module, the in-situ nano-catalyst regeneration module, and the energy cascade recovery module, and is used to build a system digital model and perform system optimization and predictive maintenance based on real-time data.

[0013] Preferably, in the adaptive multi-field coupling tar cracking module:

[0014] The electric field excitation unit includes a needle-plate electrode array for generating a non-uniform dielectric barrier discharge of 15-30 kV / cm;

[0015] The ultrasonic cavitation field unit includes a 18-40 kHz frequency-tunable ultrasonic generator for generating cavitation bubbles;

[0016] The gradient catalytic field unit includes an inlet zone catalytic bed, a middle zone catalytic bed, and an outlet zone catalytic bed. The inlet zone catalytic bed uses a Ce-Ni / γ-Al2O3 catalyst, the middle zone catalytic bed uses an Fe-Mo / ZSM-5 molecular sieve catalyst, and the outlet zone catalytic bed uses a Ru-Pd / CeO2-ZrO2 composite catalyst;

[0017] The temperature gradient control unit includes independently controlled electric heating elements and a phase change material thermal management system for establishing a precisely controllable temperature gradient field of 350-850 °C.

[0018] Preferably, in the multi-dimensional real-time intelligent monitoring module:

[0019] The multispectral on-line monitoring unit includes an ultraviolet-visible-near-infrared spectral analyzer for monitoring the concentration of tar components in the gas phase;

[0020] The micro mass spectrometry monitoring array includes a high-temperature compatible micro quadrupole mass spectrometer for analyzing the molecular weight distribution of tar components;

[0021] The intelligent neural network controller includes a deep learning control system based on an LSTM-GNN hybrid architecture, and the control system includes a data preprocessing layer, a state estimation layer, a decision optimization layer, and an execution control layer.

[0022] Preferably, the in-situ nano-catalyst regeneration module includes:

[0023] A catalyst activity dynamic monitoring unit for real-time monitoring of catalyst activity changes by electrochemical impedance spectroscopy technology;

[0024] A multi-element cooperative regeneration unit, including a gas pulse device, a microwave-assisted device, and a plasma activation device, for cooperative regeneration of deactivated catalysts;

[0025] A nano-structure in-situ reconstruction unit, including a liquid-phase atomic layer deposition system, for selective nano-repair in the catalyst deactivation area;

[0026] An adaptive regeneration strategy optimization unit for judging the regeneration timing and selecting the regeneration method according to the catalyst activity decay curve and the system operation status.

[0027] Preferably, the energy cascade recovery module includes:

[0028] A high-temperature zone heat energy recovery unit, including a modified Ca-Co-based thermoelectric material power generation module for directly converting high-temperature heat energy of 600 - 850 °C into electrical energy;

[0029] A medium-temperature zone heat energy recovery unit, including a Li2CO3-K2CO3-Na2CO3 eutectic salt phase change material heat storage system for storing medium-temperature heat energy of 300 - 600 °C;

[0030] A low-temperature zone heat energy recovery unit, including an organic Rankine cycle power generation system for converting low-temperature heat energy of 80 - 300 °C into electrical energy;

[0031] An energy intelligent scheduling unit for dynamically adjusting the heat energy distribution ratio based on the system energy demand status.

[0032] Preferably, the digital twin driving module includes:

[0033] A multi-physical field coupling simulation unit for constructing a coupled digital model of an electric field - acoustic field - catalytic field - thermal field;

[0034] A chemical reaction network simulation unit for constructing a kinetic model of tar conversion containing more than 120 elementary reactions;

[0035] An equipment health status evaluation unit for constructing a model of equipment wear and performance degradation;

[0036] A predictive maintenance unit for analyzing characteristics such as equipment vibration, temperature, and pressure to predict potential faults;

[0037] A process parameter optimization unit for performing high-throughput experiments and optimization of process parameters in a virtual environment.

[0038] Preferably, the adaptive multi-field coupling tar cracking module, the multi-dimensional real-time intelligent monitoring module, the in-situ nano-catalyst regeneration module, and the energy cascade recovery module perform data transmission through a hierarchical communication architecture, and the hierarchical communication architecture includes:

[0039] The field layer, adopting industrial Ethernet and time-sensitive network technologies, realizes deterministic communication with a latency of less than 1 ms;

[0040] The control layer, adopting the OPC UA and MQTT protocols, ensures highly reliable transmission of control instructions;

[0041] The management layer, adopting the HTTP / HTTPS protocol, supports remote monitoring and maintenance.

[0042] Preferably, the energy cascade recovery module further includes:

[0043] A tar cracking product recovery unit for sending small molecule gases such as H2, CO, and CH4 generated by cracking back to the burner through selective membrane separation;

[0044] A light oil component extraction unit for fractionally extracting the condensed and recovered light tar components through molecular distillation technology;

[0045] A system self-power supply unit, including a DC microgrid and an energy priority manager, for achieving self-sufficiency of the system energy and two-way energy exchange with the external power grid according to the system operation status.

[0046] Preferably, the system further includes a biomass characteristic identification module, and the biomass characteristic identification module includes:

[0047] A near-infrared spectroscopy analysis unit for real-time identifying the characteristics of biomass components and predicting the tar production amount and type;

[0048] A microwave-assisted pretreatment unit for pretreating high-tar biomass to reduce the content of tar precursors;

[0049] The raw material mixing and optimization unit is used to calculate the optimal mixing ratio based on different biomass characteristics, and balance the system operation stability and purification efficiency.

[0050] The intelligent purification method for a multi-modal biomass gasifier includes the following steps:

[0051] Use an adaptive multi-field coupled tar cracking module to process the tar-containing gas generated by biomass gasification. The adaptive multi-field coupled tar cracking module realizes the directional cracking of tar through the synergistic action of four fields: electric field excitation, ultrasonic cavitation, gradient catalysis, and temperature gradient control;

[0052] Use a multi-dimensional real-time intelligent monitoring module to collect the process parameters during the tar cracking process, and generate adjustment instructions for the four-field parameters through an intelligent neural network controller according to the collected parameters;

[0053] Based on the catalyst activity data collected by the multi-dimensional real-time intelligent monitoring module, use an in-situ nano-catalyst regeneration module to regenerate the catalyst in the gradient catalysis field unit in-situ;

[0054] Use an energy cascade recovery module to recover the multi-stage thermal energy generated by the adaptive multi-field coupled tar cracking module, and distribute the recovered energy to each module of the system;

[0055] Use a digital twin-driven module to build a system digital model, and perform system optimization and predictive maintenance based on real-time data.

[0056] The beneficial effects of the present invention are:

[0057] 1. The tar removal efficiency is significantly improved: Through the synergistic action of multiple fields, the system tar removal rate is increased from the traditional 95% to more than 99.5%, and the outlet tar content is lower than 5mg / Nm 3 .

[0058] 2. The system energy efficiency is greatly improved: Through energy cascade utilization, the total system energy efficiency is increased from the traditional 40-50% to 70-85%, and the energy self-sufficiency rate reaches 50-70%.

[0059] 3. The service life of the catalyst is doubled: Through in-situ regeneration and activity control technology, the average service life of the catalyst is extended from 600-800 hours to 2500-3500 hours.

[0060] 4. The system intelligence level is improved: Based on multi-dimensional monitoring and deep learning control, the adaptability of the system to changes in biomass characteristics is increased by 300%, and the stable operation time is extended by 150%.

[0061] 5. The operation and maintenance cost is reduced: Through predictive maintenance driven by digital twin, the unplanned downtime is reduced by more than 50%, and the service life of the equipment is extended by 30%. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 It is a schematic diagram of the overall architecture of the intelligent purification system for the multi-modal biomass gasification furnace of the present invention;

[0063] Figure 2 It is a schematic diagram of the structure of the self-adaptive multi-field coupling tar cracking module of the present invention;

[0064] Figure 3 It is a schematic diagram of the structure of the multi-dimensional real-time intelligent monitoring module of the present invention;

[0065] Figure 4 It is a schematic diagram of the structure of the in-situ nano-catalyst regeneration module of the present invention;

[0066] Figure 5 It is a schematic diagram of the structure of the energy cascade recovery module of the present invention;

[0067] Figure 6 It is a schematic diagram of the structure of the digital twin drive module of the present invention;

[0068] Figure 7 It is a schematic diagram of the system hierarchical communication architecture of the present invention;

[0069] Figure 8 It is a schematic diagram of the structure of the tar cracking product recovery unit of the present invention;

[0070] Figure 9 It is a schematic diagram of the structure of the biomass characteristic identification module of the present invention;

[0071] Figure 10 It is a flowchart of the intelligent purification method for the multi-modal biomass gasification furnace of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0072] Please refer to the attached Figures 1-10 , and the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Those skilled in the art should understand that these embodiments are only used to illustrate the present invention and not to limit the scope of the present invention.

[0073] Embodiment 1: Overall Architecture of the Intelligent Purification System for the Multi-modal Biomass Gasification Furnace

[0074] As Figure 1 shown, the intelligent purification system for the multi-modal biomass gasification furnace provided by the present invention includes a self-adaptive multi-field coupling tar cracking module 1, a multi-dimensional real-time intelligent monitoring module 2, an in-situ nano-catalyst regeneration module 3, an energy cascade recovery module 4, and a digital twin drive module 5.

[0075] The adaptive multi-field coupling tar cracking module 1 is used to process the tar-containing gas generated by biomass gasification. Through the synergistic effect of four fields, namely electric field excitation, ultrasonic cavitation, gradient catalysis, and temperature gradient control, the module realizes the directional cracking of tar.

[0076] The multi-dimensional real-time intelligent monitoring module 2 is electrically connected to the adaptive multi-field coupling tar cracking module 1 and is used to collect process parameters during the tar cracking process, including tar component concentration, molecular weight distribution, temperature distribution, etc., providing a decision-making basis for the system.

[0077] The in-situ nano-catalyst regeneration module 3 is communicatively connected to the multi-dimensional real-time intelligent monitoring module 2 and the adaptive multi-field coupling tar cracking module 1. Based on the catalyst activity data collected by the monitoring module, it is used to regenerate the catalyst in the gradient catalysis field unit in-situ, extending the service life of the catalyst.

[0078] The energy cascade recovery module 4 is thermally connected to the adaptive multi-field coupling tar cracking module 1 and is used to recover the multi-stage heat energy generated during the tar cracking process and distribute the recovered energy to each module of the system, improving the energy utilization efficiency of the system.

[0079] The digital twin driving module 5 is communicatively connected to the multi-dimensional real-time intelligent monitoring module 2, the adaptive multi-field coupling tar cracking module 1, the in-situ nano-catalyst regeneration module 3, and the energy cascade recovery module 4. It is used to build a digital model of the system and perform system optimization and predictive maintenance based on real-time data.

[0080] Data exchange and control instruction transmission are realized among the modules of the system through a hierarchical communication architecture, forming a complete perception-decision-execution closed loop. The tar-containing gas generated by biomass gasification first enters the adaptive multi-field coupling tar cracking module 1 for processing. The multi-dimensional real-time intelligent monitoring module 2 monitors the process parameters in real-time and transmits the data to the digital twin driving module 5. The digital twin driving module 5 generates control strategies and feeds them back to the adaptive multi-field coupling tar cracking module 1 to achieve parameter optimization. At the same time, the in-situ nano-catalyst regeneration module 3 regenerates the catalyst according to the catalyst activity monitoring data, and the energy cascade recovery module 4 recovers the system heat energy and distributes it reasonably to achieve efficient energy utilization.

[0081] Example 2: Adaptive multi-field coupling tar cracking module

[0082] As Figure 2 shown, the adaptive multi-field coupling tar cracking module 1 includes an electric field excitation unit 11, an ultrasonic cavitation field unit 12, a gradient catalysis field unit 13, and a temperature gradient control unit 14.

[0083] The electric field excitation unit 11 includes a needle-plate electrode array for generating a non-uniform dielectric barrier discharge of 15 - 30 kV / cm. This unit excites molecules in the gas phase through a high-voltage electric field to generate electron impact and free radicals, breaking the chemical bonds of tar molecules. Preferably, the needle electrode is made of titanium alloy material with a nanoscale titanium oxide coating on its surface to improve the discharge uniformity and the electrode life; the plate electrode adopts a stainless steel mesh structure to improve the gas permeability. The electric field intensity can be adjusted in real time according to the tar concentration. When the tar concentration is higher than 500 mg / Nm 3 , the electric field intensity is adjusted to the range of 25 - 30 kV / cm; when the tar concentration is in the range of 200 - 500 mg / Nm 3 , the electric field intensity is adjusted to the range of 20 - 25 kV / cm; when the tar concentration is lower than 200 mg / Nm 3 , the electric field intensity is adjusted to the range of 15 - 20 kV / cm.

[0084] The ultrasonic cavitation field unit 12 includes a 18 - 40 kHz frequency-adjustable ultrasonic generator for generating cavitation bubbles. Preferably, the ultrasonic generator adopts a piezoelectric ceramic transducer with an energy conversion efficiency greater than 85%, and the frequency can be accurately adjusted within the range of ±0.5 kHz. Ultrasonic waves generate a cavitation effect at the gas-liquid interface. When the cavitation bubbles burst, local high temperatures (>5000 K) and high pressures (>100 MPa) micro-regions are generated, promoting the thermal cracking of tar. At the same time, the acoustic mechanical effect enhances the mass transfer process. When treating low molecular weight tar, the ultrasonic frequency is set in the range of 35 - 40 kHz; when treating medium molecular weight tar, the frequency is set in the range of 25 - 35 kHz; when treating high molecular weight tar, the frequency is set in the range of 18 - 25 kHz.

[0085] The gradient catalytic field unit 13 includes an inlet zone catalytic bed 131, a middle zone catalytic bed 132, and an outlet zone catalytic bed 133. The inlet zone catalytic bed 131 uses a Ce-Ni / γ-Al2O3 catalyst, where the Ce molar ratio is 8 - 12% and the Ni molar ratio is 20 - 25%, mainly targeting large molecular tar; the middle zone catalytic bed 132 uses an Fe-Mo / ZSM-5 molecular sieve catalyst with an Si / Al ratio of 30 - 50, mainly targeting medium molecular weight components; the outlet zone catalytic bed 133 uses a Ru-Pd / CeO2-ZrO2 composite catalyst with Ru:Pd = 3:1 and a loading of 1.5 - 2.5 wt%, mainly targeting residual small molecules. The three-stage catalytic bed forms a progressive catalytic conversion path to achieve the cascade cracking of large molecular tar → medium molecules → small molecules.

[0086] The temperature gradient control unit 14 includes independently controlled electric heating elements and a phase change material thermal management system for establishing an accurately controllable temperature gradient field of 350 - 850 °C. Preferably, the temperature in the inlet zone is controlled within the range of 350 - 450 °C, the temperature in the middle zone is controlled within the range of 500 - 650 °C, and the temperature in the outlet zone is controlled within the range of 700 - 850 °C. The temperature control accuracy is ±5 °C. The basic heat is provided by the electric heating elements, and the phase change material energy storage system provides temperature buffering and balancing. The phase change material uses a multi-component inorganic salt mixture, whose phase change temperature matches the operating temperature of each zone, and the heat storage density is greater than 300 kJ / kg.

[0087] During the actual operation process, the four physical fields form a data-driven field coupling effect through precise spatio-temporal synergy: the active groups excited by the electric field reduce the activation energy of tar cracking; the ultrasonic cavitation effect provides a local high-energy environment; the catalyst provides selective reaction sites; the temperature gradient provides the optimal reaction conditions. The intensity parameters of each field are adaptively adjusted in real time based on the downstream sensor data to achieve the maximum purification efficiency under the condition of minimizing energy consumption.

[0088] Example 3: Multidimensional Real-time Intelligent Monitoring Module

[0089] As Figure 3 shown, the multidimensional real-time intelligent monitoring module 2 includes a multi-spectral on-line monitoring unit 21, a micro mass spectrometry monitoring array 22, and an intelligent neural network controller 23.

[0090] The multi-spectral on-line monitoring unit 21 includes an ultraviolet-visible-near-infrared spectrometer with a working wavelength range of 200 - 2500 nm for monitoring the concentration of tar components in the gas phase. Preferably, an optical fiber probe is directly inserted into the gas flow pipeline, and the concentration of more than 20 kinds of tar components in the gas phase is monitored in real time through a multi-variable curve resolution algorithm, with a sampling frequency of up to 10 Hz and an analysis accuracy of ±2%. For the main components such as benzene, toluene, and naphthalene, the detection limit can reach 0.1 mg / Nm 3 . This unit adopts a variable-temperature cell technology to ensure stable operation within the temperature range of 50 - 250 °C and avoid the influence of tar condensation on the measurement accuracy.

[0091] The micro mass spectrometry monitoring array 22 includes a high-temperature compatible micro quadrupole mass spectrometer with an m / z range of 50 - 500 amu for analyzing the molecular weight distribution of tar components. Preferably, the mass spectrometer adopts a membrane separation injection technology, and the gas enters the mass spectrometry analysis area through a high-temperature selective membrane to reduce the interference of large particles and moisture on the analysis. The mass spectrometer is equipped with a micro turbo molecular pump with a pumping speed of up to 2 L / s to ensure a high vacuum degree (10 -6 Torr) in the analysis area. The mass spectrometry data can obtain the accurate molecular composition information of tar components and provide molecular-level feedback for the cracking process.

[0092] The intelligent neural network controller 23 includes a deep learning control system based on the LSTM-GNN (Long Short-Term Memory - Graph Neural Network) hybrid architecture. This control system includes a data preprocessing layer 231, a state estimation layer 232, a decision optimization layer 233, and an execution control layer 234.

[0093] The data preprocessing layer 231 normalizes, denoises, and extracts features from the collected sensor data. Preferably, wavelet transform is used for signal denoising, the sliding window technique is used for outlier detection, and the principal component analysis method is used for feature dimensionality reduction.

[0094] The state estimation layer 232 predicts the future state variables of the system based on historical data. This layer uses the LSTM network structure, which can capture the temporal characteristics of the system. Its core calculation process is:

[0095] h t = σ h (W x x t + W h h t-1 + b h ),

[0096] where: h t is the hidden state at time t, x t is the input feature at time t, W x is the input weight matrix, W h is the hidden state weight matrix, b h is the bias vector, and σ h is the activation function.

[0097] The decision optimization layer 233 calculates the optimal combination of four-field parameters (electric field intensity, ultrasonic frequency, catalytic bed temperature, etc.). This layer uses the graph neural network structure, models the mutual influence between various physical field parameters as a graph structure, and learns the optimal parameter combination through the information transfer mechanism.

[0098] The execution control layer 234 converts the decision into an execution instruction and sends it to each execution unit. This layer realizes the mapping from the high-level control strategy to the low-level execution command, ensuring the precise execution of the control instruction.

[0099] The intelligent neural network controller 23 uses a low-latency FPGA processing array, and the control response time is less than 10 ms, ensuring the real-time response ability under complex working conditions. The entire control system performs an online model update every 30 minutes, continuously optimizing the control strategy to adapt to the changes in the system characteristics.

[0100] Example 4: In-situ nanocatalyst regeneration module

[0101] As Figure 4As shown in the figure, the in-situ nano-catalyst regeneration module 3 includes a catalyst activity dynamic monitoring unit 31, a multi-element collaborative regeneration unit 32, a nano-structure in-situ reconstruction unit 33, and an adaptive regeneration strategy optimization unit 34.

[0102] The catalyst activity dynamic monitoring unit 31 is based on the electrochemical impedance spectroscopy (EIS) technology and real-time monitors the change of catalyst activity through an embedded microelectrode array. Preferably, the microelectrode array is made of platinum-iridium alloy with a diameter of 10 - 50 μm and is evenly distributed at different positions of the catalytic bed. By applying a small-signal AC excitation (5 - 10 mV), the impedance response of the measurement system at different frequencies (0.1 Hz - 100 kHz) is measured to construct an evaluation model of the catalyst "health index". The monitoring data is analyzed in real-time by a small embedded processor, and the activity data is updated every 5 minutes with an accuracy of ±3%.

[0103] The multi-element collaborative regeneration unit 32 includes a gas pulse device 321, a microwave-assisted device 322, and a plasma activation device 323, which are used for the collaborative regeneration of the deactivated catalyst. The gas pulse device 321 adopts a high-precision solenoid valve control system to generate alternating pulses of O2 / CO2 / H2O (pulse width 0.5 - 2 s), with the temperature controlled at 400 - 550 °C, selectively oxidizing carbon deposition and protecting the active metal sites. The microwave-assisted device 322 includes a 2.45 GHz variable-frequency microwave source with a power density of 1 - 3 W / cm 3 and precisely controls the microwave energy to act only on the carbon deposition on the catalyst surface. The plasma activation device 323 adopts argon dielectric barrier discharge with a power density of 0.5 - 1.5 W / cm 2 to reconstruct the active sites on the catalyst surface.

[0104] The nano-structure in-situ reconstruction unit 33 is based on the liquid-phase atomic layer deposition (ALD) technology and performs in-situ nano-repair when the catalyst is deactivated. This unit includes a precursor pulse introduction system 331, a selective surface deposition module 332, and a programmed control system 333. The precursor pulse introduction system 331 introduces metal-organic precursors through a precision micro-injection device (flow accuracy ±0.5 μL / min); the selective surface deposition module 332 controls the reaction temperature (150 - 250 °C) and pressure (10 - 100 Pa) to achieve selective reconstruction of active sites in the deactivated area of the catalyst; the programmed control system 333 automatically executes a three-step repair procedure of "cleaning - deposition - activation" based on the catalyst activity data.

[0105] The adaptive regeneration strategy optimization unit 34 intelligently determines the regeneration timing and method based on the catalyst activity decay curve and the system operating status. Preferably, an adaptive Bayesian optimization algorithm is used, combined with historical regeneration effect data, to determine the optimal combination of regeneration parameters. When the catalyst activity drops below 75% of the original activity, the basic regeneration program is started; when the activity drops below 60%, the enhanced regeneration program is started; when the activity drops below 40%, the reconstruction regeneration program is started. The system automatically balances the regeneration frequency and intensity to find the best balance point between maintaining high catalytic activity and extending the catalyst service life.

[0106] Through the closed-loop process of catalyst activity monitoring → deactivation judgment → regeneration method selection → in-situ regeneration execution → activity verification, this module extends the catalyst service life by 3 - 5 times, while avoiding the system downtime and waste treatment problems caused by traditional catalyst replacement.

[0107] Example 5: Energy cascade recovery module

[0108] As Figure 5 shown, the energy cascade recovery module 4 includes a high-temperature zone heat energy recovery unit 41, a medium-temperature zone heat energy recovery unit 42, a low-temperature zone heat energy recovery unit 43, and an energy intelligent scheduling unit 44.

[0109] The high-temperature zone heat energy recovery unit 41 includes a modified Ca-Co-based thermoelectric material power generation module for directly converting high-temperature heat energy of 600 - 850 °C into electrical energy. Preferably, the thermoelectric material adopts a nano-composite structure design with a thermoelectric figure of merit ZT > 1.2 and a conversion efficiency of 15 - 20%. The thermoelectric module adopts a cascade structure, arranging multiple stages of thermoelectric elements along the temperature gradient, with the temperature difference of each stage controlled within the range of 100 - 150 °C to maximize the energy conversion efficiency. The hot end of the thermoelectric module is in direct contact with the high-temperature zone heat source, and the cold end is thermally connected to the medium-temperature zone heat energy recovery unit to form a heat energy cascade utilization path.

[0110] The medium-temperature zone heat energy recovery unit 42 includes a Li2CO3-K2CO3-Na2CO3 eutectic salt phase change material heat storage system for storing medium-temperature heat energy of 300 - 600 °C. Preferably, the phase change temperature is 405 ± 10 °C, and the heat storage density > 450 kJ / kg. The heat storage system adopts a double-layer shell-and-tube structure, with the inner layer being the phase change material storage area and the outer layer being the heat-conducting fluid circulation area, and the heat transfer coefficient is greater than 500 W / (m 2 ·K). The heat storage system has a storage capacity of 10 - 20 kWh and can provide a heat energy buffer for 2 - 4 hours to effectively balance the heat energy supply and demand fluctuations of the system.

[0111] The low-temperature thermal energy recovery unit 43 includes an organic Rankine cycle (ORC) power generation system for converting low-temperature thermal energy of 80 - 300 °C into electrical energy. Preferably, cyclopentane is used as the working fluid, with an evaporation temperature of 180 - 220 °C, a condensation temperature of 30 - 50 °C, and a thermoelectric conversion efficiency of 8 - 12%. The ORC system uses a high-speed turbogenerator with a rotational speed of up to 30,000 rpm and a power range of 5 - 20 kW. The system is equipped with a microchannel heat exchanger to improve the heat transfer efficiency and reduce the system volume.

[0112] The energy intelligent scheduling unit 44 is used to dynamically adjust the thermal energy distribution ratio based on the system energy demand status. This unit includes a predictive energy management algorithm 441, a multi-objective optimization scheduler 442, and an energy flow visualization interface 443. The predictive energy management algorithm 441 predicts the system energy demand within the next 30 - 120 minutes based on Kalman filtering and time series analysis; the multi-objective optimization scheduler 442 dynamically adjusts the thermal energy distribution ratio (direct utilization, storage, or power generation) according to the energy supply and demand status; the energy flow visualization interface 443 real-time displays the energy flow path and conversion efficiency within the system.

[0113] Through the multi-stage recovery and intelligent distribution of energy, this module achieves 50 - 70% energy self-sufficiency while ensuring process requirements, significantly reducing the operating cost and carbon emissions.

[0114] Example 6: Digital twin-driven module

[0115] As Figure 6 shown, the digital twin-driven module 5 includes a multi-physical field coupling simulation unit 51, a chemical reaction network simulation unit 52, a device health status assessment unit 53, a predictive maintenance unit 54, and a process parameter optimization unit 55.

[0116] The multi-physical field coupling simulation unit 51 is used to construct a coupled digital model of the electric field - acoustic field - catalytic field - thermal field. Preferably, the finite element method is used to construct the multi-physical field coupling model, with the number of mesh elements being 1 million - 3 million and the calculation accuracy being better than 5%. The model takes into account the mutual influence between the electric field distribution, acoustic field propagation, catalytic reaction kinetics, and temperature field distribution, and can achieve accurate simulation of the four-field synergistic effect. This unit uses GPU accelerated computing to increase the simulation speed by 10 - 20 times and achieve near-real-time field distribution calculation.

[0117] The chemical reaction network simulation unit 52 is used to construct a kinetic model for tar conversion that includes more than 120 elementary reactions. Preferably, based on quantum chemical calculations and experimental data, a reaction network model including main tar components such as aromatic hydrocarbons, polycyclic aromatic hydrocarbons, and heterocyclic compounds is established. The model uses an adaptive simplification algorithm to automatically adjust the network complexity according to different operating conditions, improving the calculation efficiency while ensuring the calculation accuracy. This unit can predict the conversion paths and product distributions of tar components under different operating conditions, providing a theoretical basis for process optimization.

[0118] The equipment health status assessment unit 53 is used to construct a model for equipment wear and performance degradation. Preferably, based on historical operation data and fault records, a health status model for key equipment components (such as electrodes, catalytic beds, heating elements, etc.) is established. The model uses a multi-scale method to correlate microscopic failure mechanisms with macroscopic performance indicators, improving the prediction accuracy. This unit updates the equipment health status score (0 - 100 points) in real time, intuitively reflecting the equipment condition.

[0119] The predictive maintenance unit 54 is used to analyze characteristics such as equipment vibration, temperature, and pressure to predict potential faults. Preferably, a deep learning algorithm is adopted, combined with time-frequency domain analysis technology, to extract fault features from multi-source sensing signals. The system can predict potential faults 24 - 72 hours in advance, and the accuracy rate of fault type identification is greater than 90%. The prediction results are used to generate the optimal maintenance time window and maintenance operation suggestions, balancing the maintenance cost and equipment reliability.

[0120] The process parameter optimization unit 55 is used to conduct high-throughput experiments and optimization of process parameters in a virtual environment. Preferably, a multi-objective genetic algorithm is adopted to search for the optimal operating parameters in the three-dimensional space of efficiency - energy consumption - emissions. The algorithm design includes an elite retention strategy, an adaptive mutation rate, and a tabu search mechanism to avoid falling into local optimal solutions. This unit can automatically load the optimal control strategy according to different biomass characteristics and operating environments, improving the adaptability and stability of the system.

[0121] The entire digital twin drive module synchronizes the actual system data to the digital twin at a frequency of 100 Hz, with an error less than 3%, realizing the deep integration of the physical world and the digital world. Through the digital twin, the system performance is continuously optimized, equipment faults are predicted, the unplanned downtime is reduced by more than 50%, and the service life of the equipment is extended by more than 30%.

[0122] Example 7: System hierarchical communication architecture

[0123] As Figure 7 shown, data is transmitted among the system modules through a hierarchical communication architecture, which includes a field layer 71, a control layer 72, and a management layer 73.

[0124] The field layer 71 adopts industrial Ethernet and Time-Sensitive Networking (TSN) technologies to achieve deterministic communication with a latency of less than 1 ms. Preferably, the fieldbus adopts the EtherCAT protocol, supports various topological structures such as linear, star, and tree, with a transmission delay of less than 100 μs and a jitter of less than 1 μs. The field layer is responsible for connecting each execution unit and sensor, such as the electric field excitation unit, ultrasonic generator, temperature sensor, etc., to ensure the reliable transmission of real-time control signals.

[0125] The control layer 72 adopts the OPC UA and MQTT protocols to ensure the highly reliable transmission of control instructions. Preferably, the control layer network uses Gigabit Ethernet with a bandwidth greater than 100 Mbps, supporting simultaneous communication of more than 100 nodes. The OPC UA server provides a standardized data access interface, and the MQTT broker implements lightweight publish / subscribe messaging. The control layer is responsible for connecting the control units of each functional module, such as the intelligent neural network controller, energy intelligent scheduling unit, etc.

[0126] The management layer 73 adopts the HTTP / HTTPS protocol to support remote monitoring and maintenance. Preferably, the management layer uses a cloud platform based on a microservices architecture, supporting horizontal scaling, with a single-node processing capacity of up to 1000 requests per second. The platform provides a REST API interface, supports access from mobile devices, and enables system monitoring and management anytime and anywhere. The management layer is responsible for system data storage, analysis, and display, providing decision support functions.

[0127] A security isolation mechanism is adopted between each layer to ensure system security through encrypted communication and access control. The entire communication architecture has self-healing capabilities. When a certain node fails, the system can automatically reconstruct the communication path to ensure the transmission of critical data. The communication system supports online upgrades, and communication protocols and security policies can be updated without downtime.

[0128] Example 8: Recovery of Tar Pyrolysis Products by the Energy Cascade Recovery Module

[0129] As Figure 8 shown, the energy cascade recovery module 4 further includes a tar pyrolysis product recovery unit 45, a light oil component extraction unit 46, and a system self-power supply unit 47.

[0130] The tar pyrolysis product recovery unit 45 is used to send small molecule gases such as H2, CO, and CH4 generated by pyrolysis back to the burner through selective membrane separation. Preferably, a multi-layer composite membrane separation technology is adopted, the membrane material is a polyethersulfone / polyimide composite structure with a thickness of 1 - 5 μm, and the H2 permeability is greater than 100 GPU (1 GPU = 10 -6 cm 3 (STP) / (cm 2·s·cmHg)), the H2 / CO selectivity is greater than 50. The membrane separation system adopts a modular design, and the unit area is 0.5 - 2 m 2 , and can be flexibly combined according to the processing volume requirements. The separated hydrogen-rich gas can be directly used for system heating or enter the fuel cell for power generation.

[0131] The light oil component extraction unit 46 is used to fractionally extract the condensed and recovered light tar components through molecular distillation technology. Preferably, the working temperature of the molecular distillation system is 100 - 250 °C, the pressure is 1 - 10 Pa, and the evaporation area is 0.1 - 0.5 m 2 , and the separation efficiency is greater than 95%. The system can separate the tar mixture into light components (boiling point < 180 °C), medium components (boiling point 180 - 280 °C), and heavy components (boiling point > 280 °C). The light components mainly contain monocyclic aromatic hydrocarbons such as benzene and toluene, which can be recycled as chemical raw materials; the medium components mainly contain polycyclic aromatic hydrocarbons such as naphthalene and anthracene, which can be reused as system fuel; the heavy components are used as solid fuel or road asphalt additives after stabilization treatment.

[0132] The system self-powered unit 47 includes a DC microgrid 471 and an energy priority manager 472, which are used to achieve the self-sufficiency of the system energy and the two-way energy exchange with the external power grid according to the system operation status. The DC microgrid 471 includes a battery storage unit, a power regulator, and a power distribution system. The battery storage unit uses lithium iron phosphate batteries with a capacity of 5 - 20 kWh, a cycle life of more than 3000 times, and a charge and discharge efficiency of more than 95%; the power regulator uses a bidirectional DC / DC converter with an efficiency of more than 98%; the power distribution system uses a 380 V DC bus to provide a stable power supply to each load. The energy priority manager 472 formulates a priority power supply strategy according to the energy requirements of the monitoring and control unit, the catalyst regeneration unit, the thermal management unit, etc., to ensure the energy supply of the key system. When the system generates excess energy, it can send electricity back to the external power grid to achieve the efficient utilization of energy.

[0133] Through the recovery of tar cracking products and the cascade utilization of energy, the system realizes the closed-loop utilization of materials and energy, greatly improving the resource utilization efficiency and economy.

[0134] Example 9: Biomass characteristic identification module

[0135] As Figure 9 shown, the system further includes a biomass characteristic identification module 6, which includes a near-infrared spectroscopy analysis unit 61, a microwave-assisted pretreatment unit 62, and a raw material mixing and optimization unit 63.

[0136] The near-infrared spectroscopy analysis unit 61 is used to identify the characteristics of biomass components in real time and predict the tar production amount and type. Preferably, a near-infrared spectrometer with a wavelength range of 900 - 2500 nm is adopted, the spectral resolution is 2 nm, and the signal-to-noise ratio is greater than 10000:1. The spectrometer is equipped with a diffuse reflection probe, which can directly measure solid biomass samples, and the acquisition time is less than 1 second. Through the chemometric model, the rapid determination of the contents of cellulose, hemicellulose, and lignin in biomass is realized, and the prediction accuracy is better than ±2%. Based on the component data, the system can calculate the theoretical tar production amount and the main component distribution of biomass, providing a reference for subsequent processing.

[0137] The microwave-assisted pretreatment unit 62 is used to pretreat high-tar biomass to reduce the content of tar precursors. Preferably, an industrial microwave source of 2.45 GHz is adopted, the power density is 2 - 5 kW / L, the treatment temperature is 200 - 300 °C, and the treatment time is 5 - 20 minutes. Microwave heating generates a selective pyrolysis effect inside the biomass, causing components such as hemicellulose that are prone to generate tar to decompose in advance, while retaining the high calorific value components of cellulose and lignin. The tar production amount of the pretreated biomass can be reduced by 20 - 40%, and the calorific value loss is controlled within the range of 5 - 10%.

[0138] The raw material mixing and optimization unit 63 is used to calculate the optimal mixing ratio based on the characteristics of different biomasses, and balance the system operation stability and purification efficiency. Preferably, the response surface method is used to design the mixing scheme. Taking the tar purification efficiency, energy output, and system stability as the objective functions, and the mixing ratios of different biomasses as the independent variables, a multi-objective optimization model is established. The system can adjust the mixing strategy in real time according to the current biomass characteristics and system operation status, such as the mixing ratio of high-tar biomass and low-tar biomass, the mixing ratio of high-ash biomass and low-ash biomass, etc. The optimized mixing scheme can improve the system processing stability by 20 - 30% and reduce the tar treatment load by 15 - 25%.

[0139] This module reduces the system burden from the source by accurately identifying the biomass characteristics and intelligent pretreatment, and improves the overall operation efficiency and stability.

[0140] Example 10: Intelligent purification method for multi-modal biomass gasifier

[0141] As Figure 10 shown, the present invention also provides an intelligent purification method for a multi-modal biomass gasifier, including the following steps:

[0142] Step S1: Use an adaptive multi-field coupling tar cracking module to process the tar-containing gas generated by biomass gasification. Specifically, an inhomogeneous dielectric barrier discharge of 15 - 30 kV / cm is generated by an electric field excitation unit, an ultrasonic cavitation field unit generates ultrasonic waves of 18 - 40 kHz to form a cavitation effect, a gradient catalytic field unit provides a three-stage gradient catalytic conversion path, and a temperature gradient control unit establishes a temperature gradient field of 350 - 850 °C. The synergistic effect of the four fields realizes the directional cracking of tar, and the tar removal rate reaches over 99.5%.

[0143] Step S2: Use a multi-dimensional real-time intelligent monitoring module to collect the process parameters during the tar cracking process. Specifically, a multi-spectral on-line monitoring unit monitors the concentration of tar components in the gas phase in real time, a micro-mass spectrometry monitoring array analyzes the molecular weight distribution of tar components, and the collected data is transmitted to an intelligent neural network controller.

[0144] Step S3: The intelligent neural network controller generates adjustment instructions for the four-field parameters according to the collected parameters. Specifically, the data preprocessing layer standardizes and extracts features from the sensor data, the state estimation layer predicts the future state variables of the system, the decision optimization layer calculates the optimal combination of the four-field parameters, and the execution control layer converts the decision into an execution instruction and sends it to each execution unit.

[0145] Step S4: Based on the catalyst activity data collected by the multi-dimensional real-time intelligent monitoring module, use an in-situ nano-catalyst regeneration module to regenerate the catalyst in the gradient catalytic field unit in-situ. Specifically, a catalyst activity dynamic monitoring unit monitors the change of catalyst activity in real time, an adaptive regeneration strategy optimization unit judges the regeneration timing and method according to the activity decay curve, a multi-element synergistic regeneration unit performs triple synergistic regeneration of gas pulse - microwave assisted - plasma, and when necessary, a nano-structure in-situ reconstruction unit repairs the active sites through liquid-phase atomic layer deposition technology.

[0146] Step S5: Use an energy cascade recovery module to recover the multi-stage thermal energy generated by the adaptive multi-field coupling tar cracking module. Specifically, the high-temperature zone thermal energy recovery unit directly converts the high-temperature thermal energy of 600 - 850 °C into electric energy, the medium-temperature zone thermal energy recovery unit stores the medium-temperature thermal energy of 300 - 600 °C, and the low-temperature zone thermal energy recovery unit converts the low-temperature thermal energy of 80 - 300 °C into electric energy. At the same time, a tar cracking product recovery unit recovers the small molecule gas generated by cracking, and a light oil component extraction unit recovers the condensed light tar components.

[0147] Step S6: The energy intelligent scheduling unit distributes the recovered energy to each module of the system. Specifically, a predictive energy management algorithm predicts the system energy demand within the next 30 - 120 minutes, a multi-objective optimization scheduler dynamically adjusts the thermal energy distribution ratio, and the system self-power supply unit realizes the energy exchange with the external power grid according to the energy supply and demand situation.

[0148] Step S7: Use the digital twin drive module to build a system digital model, and perform system optimization and predictive maintenance based on real-time data. Specifically, the multi-physical field coupling simulation unit constructs an accurate model of the four-field synergy, the chemical reaction network simulation unit predicts the tar conversion path, the equipment health status evaluation unit updates the equipment status score in real time, the predictive maintenance unit predicts potential failures 24-72 hours in advance, and the process parameter optimization unit finds the optimal operating parameters in the virtual environment.

[0149] Through multi-field synergy, intelligent monitoring and control, energy cascade utilization, and predictive maintenance, this method realizes the efficient purification of biomass gasification tar and the intelligent operation of the system. The tar removal rate is increased to over 99.5%, the system energy efficiency is increased to 70-85%, and the operating cost is reduced by 40-50%.

[0150] The multi-modal biomass gasification furnace intelligent purification system of the present invention has been applied and verified in a biomass energy demonstration project. The processing scale of this project is 20 tons per day, and the raw materials are agricultural and forestry wastes (a mixture of rice husks, corn straws, and forestry wastes).

[0151] Before application, the project used a traditional spray tower and cyclone separator purification system, with a tar removal rate of about 93% and an outlet tar content of about 150 mg / Nm 3 , the system energy efficiency was about 45%, the average catalyst life was about 600 hours, and the annual unplanned downtime was about 480 hours.

[0152] After applying the system of the present invention, the tar removal rate is increased to 99.6%, the outlet tar content is reduced to 3.5 mg / Nm 3 , the system energy efficiency is increased to 78%, the average catalyst life is extended to 3200 hours, and the annual unplanned downtime is reduced to 180 hours. At the same time, the system energy self-sufficiency rate reaches 65%, and the annual operating cost is reduced by about 45%.

[0153] It is particularly worth mentioning that the adaptability of the system to different raw material combinations has been significantly improved. Under the extreme condition where the proportion of rice husks increases from 20% to 80%, the tar removal rate of the traditional system will drop to about 75%, while the system of the present invention can still maintain a tar removal rate of over 98%, showing excellent anti-interference ability.

[0154] This case verifies the feasibility and superiority of the present invention in actual industrial applications, and provides strong support for the popularization and application of biomass gasification technology.

[0155] The intelligent purification system and method for multi-modal biomass gasification furnace provided by the present invention combine modern electric field technology, ultrasonic technology, catalytic technology, thermal management technology, artificial intelligence and digital twin technology to achieve efficient purification of biomass gasification tar and cascaded utilization of energy. The system adopts a modular design and can be flexibly combined according to the treatment scale, meeting the biomass treatment capacity of 1-50 tons per day and being applicable to various biomass gasification projects. Each functional module uses industrial-grade materials and components, has high reliability and long service life, and meets the requirements of continuous operation for 7×24 hours. The system realizes predictive maintenance through digital twin technology, greatly reducing the unplanned downtime and improving the equipment utilization rate.

[0156] From the economic perspective, although the initial investment cost of the system increases by 25-35% compared with the traditional system, the operating cost is reduced by 40-50%, the investment payback period is only 3-4 years, and the life-cycle cost is reduced by more than 30%, showing good economic feasibility. From the perspective of environmental friendliness, the tar removal rate of the system is greater than 99.5%, and the energy self-sufficiency rate reaches 50-70%, significantly reducing environmental pollution and carbon emissions and meeting the requirements of the national energy conservation and emission reduction policies.

[0157] Therefore, the present invention has good industrial practicability and popularization value and can provide technical support for the clean and efficient utilization of biomass.

[0158] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. The intelligent purification system of the multi-modal biomass gasifier is characterized in that, Including: An adaptive multi-field coupled tar cracking module for treating tar-containing gas generated by biomass gasification. The adaptive multi-field coupled tar cracking module includes an electric field excitation unit, an ultrasonic cavitation field unit, a gradient catalytic field unit, and a temperature gradient control unit; A multi-dimensional real-time intelligent monitoring module electrically connected to the adaptive multi-field coupled tar cracking module for collecting process parameters during tar cracking. The multi-dimensional real-time intelligent monitoring module includes a multi-spectral online monitoring unit, a micro-mass spectrometry monitoring array, and an intelligent neural network controller; An in-situ nano-catalyst regeneration module communicatively connected to the multi-dimensional real-time intelligent monitoring module and the adaptive multi-field coupled tar cracking module for in-situ regenerating the catalyst in the gradient catalytic field unit based on the catalyst activity data collected by the multi-dimensional real-time intelligent monitoring module; An energy cascade recovery module thermally connected to the adaptive multi-field coupled tar cracking module for recovering multi-stage thermal energy generated by the adaptive multi-field coupled tar cracking module and distributing the recovered energy to each module of the system; A digital twin driving module communicatively connected to the multi-dimensional real-time intelligent monitoring module, the adaptive multi-field coupled tar cracking module, the in-situ nano-catalyst regeneration module, and the energy cascade recovery module for constructing a system digital model and performing system optimization and predictive maintenance based on real-time data.

2. The system according to claim 1, characterized in that In the adaptive multi-field coupled tar cracking module: The electric field excitation unit includes a needle-plate electrode array for generating a non-uniform dielectric barrier discharge of 15 - 30 kV / cm; The ultrasonic cavitation field unit includes an 18 - 40 kHz frequency-tunable ultrasonic generator for generating cavitation bubbles; The gradient catalytic field unit includes an inlet zone catalytic bed, a middle zone catalytic bed, and an outlet zone catalytic bed. The inlet zone catalytic bed uses a Ce-Ni / γ-Al2O3 catalyst, the middle zone catalytic bed uses an Fe-Mo / ZSM-5 molecular sieve catalyst, and the outlet zone catalytic bed uses a Ru-Pd / CeO2-ZrO2 composite catalyst; The temperature gradient control unit includes independently controlled electric heating elements and a phase change material thermal management system for establishing a precisely controllable temperature gradient field of 350 - 850 °C.

3. The system according to claim 1, wherein In the multi-dimensional real-time intelligent monitoring module: The multi-spectral online monitoring unit includes an ultraviolet-visible-near-infrared spectral analyzer for monitoring the concentration of tar components in the gas phase; The micro-mass spectrometry monitoring array includes a high-temperature compatible micro quadrupole mass spectrometer for analyzing the molecular weight distribution of tar components; The intelligent neural network controller includes a deep learning control system based on an LSTM-GNN hybrid architecture. The control system includes a data preprocessing layer, a state estimation layer, a decision optimization layer, and an execution control layer.

4. The system according to claim 1, characterized in that The in-situ nano-catalyst regeneration module includes: A catalyst activity dynamic monitoring unit for real-time monitoring of catalyst activity changes through electrochemical impedance spectroscopy technology; A multi-element collaborative regeneration unit including a gas pulse device, a microwave-assisted device, and a plasma activation device for collaboratively regenerating the deactivated catalyst. The in-situ nanostructure reconstruction unit includes a liquid-phase atomic layer deposition system for selective nano-repair in the catalyst deactivation area; The adaptive regeneration strategy optimization unit is used to judge the regeneration timing and select the regeneration method according to the catalyst activity decay curve and the system operation status.

5. The system according to claim 1, wherein The energy cascade recovery module includes: The high-temperature zone heat energy recovery unit includes a modified Ca-Co-based thermoelectric material power generation module for directly converting high-temperature heat energy of 600 - 850 °C into electric energy; The medium-temperature zone heat energy recovery unit includes a Li2CO3-K2CO3-Na2CO3 eutectic salt phase change material heat storage system for storing medium-temperature heat energy of 300 - 600 °C; The low-temperature zone heat energy recovery unit includes an organic Rankine cycle power generation system for converting low-temperature heat energy of 80 - 300 °C into electric energy; The energy intelligent scheduling unit is used to dynamically adjust the heat energy distribution ratio based on the system energy demand status.

6. The system according to claim 1, characterized in that, The digital twin-driven module includes: The multi-physical field coupling simulation unit is used to construct a coupled digital model of the electric field - sound field - catalytic field - thermal field; The chemical reaction network simulation unit is used to construct a tar conversion kinetic model containing more than 120 elementary reactions; The equipment health status assessment unit is used to construct an equipment wear and performance decay model; The predictive maintenance unit is used to analyze characteristics such as equipment vibration, temperature, and pressure to predict potential faults; The process parameter optimization unit is used to conduct high-throughput experiments and optimization of process parameters in a virtual environment.

7. The system according to claim 1, characterized in that The adaptive multi-field coupling tar cracking module, the multi-dimensional real-time intelligent monitoring module, the in-situ nano-catalyst regeneration module, and the energy cascade recovery module perform data transmission through a hierarchical communication architecture, and the hierarchical communication architecture includes: The field layer uses industrial Ethernet and time-sensitive network technology to achieve deterministic communication with a latency of less than 1 ms; The control layer uses the OPC UA and MQTT protocols to ensure the high-reliability transmission of control instructions; The management layer uses the HTTP / HTTPS protocol to support remote monitoring and maintenance.

8. The system according to claim 1, wherein The energy cascade recovery module also includes: The tar cracking product recovery unit is used to send small molecule gases such as H2, CO, and CH4 generated by cracking back to the burner through selective membrane separation; The light oil component extraction unit is used to fractionally extract the condensed and recovered light tar components through molecular distillation technology; The system self-powered unit includes a DC microgrid and an energy priority manager for achieving the self-sufficiency of the system energy and two-way energy exchange with the external power grid according to the system operation status.

9. The system according to claim 1, wherein The system also includes a biomass characteristic identification module, and the biomass characteristic identification module includes: The near-infrared spectroscopy analysis unit is used to identify the biomass composition characteristics in real time and predict the tar production amount and type; The microwave-assisted pretreatment unit is used to pre-treat high-tar biomass to reduce the content of tar precursors; The raw material mixing optimization unit is used to calculate the optimal mixing ratio based on different biomass characteristics to balance the system operation stability and purification efficiency.

10. The intelligent purification method of a multi-modal biomass gasifier adopts the system described in any one of claims 1-9, characterized in that, It includes the following steps: An adaptive multi-field coupling tar cracking module is used to process the tar-containing gas generated by biomass gasification. The adaptive multi-field coupling tar cracking module realizes the directional cracking of tar through the synergistic action of four fields: electric field excitation, ultrasonic cavitation, gradient catalysis, and temperature gradient control; A multi-dimensional real-time intelligent monitoring module is used to collect the process parameters during the tar cracking process, and adjustment instructions for the four-field parameters are generated through an intelligent neural network controller according to the collected parameters; Based on the catalyst activity data collected by the multi-dimensional real-time intelligent monitoring module, an in-situ nano-catalyst regeneration module is used to regenerate the catalyst in the gradient catalysis field unit in-situ; An energy cascade recovery module is used to recover the multi-stage thermal energy generated by the adaptive multi-field coupling tar cracking module and distribute the recovered energy to each module of the system; A digital twin-driven module is used to build a system digital model and perform system optimization and predictive maintenance based on real-time data.

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