1655 results about "Combustion process" patented technology

The invention provides a multi-target combustion optimization method based on economic predictive control, and aims to solve the problems that the power generation load and the in-furnace coal quality are frequently changed, conventional DCS control has defects in the aspects of air quantity and oxygen quantity optimization, boiler efficiency balancing, NOx emission, steam temperature control and the like, part of boiler parameters are difficult to accurately measure, and the existing combustion optimization technology is limited in applicability. The method comprises the following steps: collecting and preprocessing historical operation data, classifying and screening according to steady-state working conditions, and constructing a correlation model to perform preliminary combustion optimization; a load and coal quality change prediction model and a combustion process dynamic relation model are established according to unit characteristics and historical data; then, in the online secondary optimization stage, an initial parameter combination is selected according to the current state and the prediction state, and the combustion parameter adjustment amount meeting the multiple constraints is calculated through an economic prediction control algorithm and applied and corrected. Experiments show that the method can improve the boiler efficiency, reduce NOx emission, optimize steam temperature control, relieve the labor intensity of personnel and effectively improve the combustion performance.

The invention discloses a boiler combustion optimization method and device based on CFD numerical simulation and a medium, and belongs to the technical field of boiler combustion simulation. The method comprises the steps of obtaining operation parameters and historical operation data of a boiler combustion system; processing the data based on a three-dimensional modeling algorithm to generate a three-dimensional combustion model of the combustion area of the boiler; the three-dimensional combustion model is processed based on a CFD numerical simulation algorithm, and multi-physical field coupling data of the combustion process is generated; processing the multi-physical field coupling data and the historical operation data based on a multi-objective optimization algorithm to establish a combustion parameter optimization model; adjusting the ratio of the air speed at the inlet of the combustor to the fuel flow to update the model and optimize combustion parameters; verifying whether the optimized combustion parameters meet constraint conditions or not; and when the constraint conditions are met, finally optimized combustion parameters are output. By means of the method, multi-physical-field dynamic coupling optimization in the boiler combustion process is achieved, and the purposes of synchronously improving the combustion efficiency and the pollutant control effect are achieved.

The invention provides a method for constructing a differential diffusion small flame model for buoyancy-driven plume, and the method is applied and expanded to a pool fire combustion process in the field of fire safety. According to the method, the buoyancy driven plume is solved by using the small flame model, the solving number of component equations of the buoyancy plume is greatly reduced through coordinate conversion dimensionality reduction processing, and the main characteristics of the buoyancy plume can be reproduced with relatively high precision and a faster calculation rate. According to the method, high-precision numerical solution of buoyancy plume is realized, and pool fire combustion of gas fuel and liquid fuel and numerical solution of all plume driven by buoyancy can be processed.

The invention discloses a numerical calculation method for simulating combustion of iron powder group particles, belongs to the field of numerical simulation calculation of iron powder combustion, can perform numerical simulation on single-particle and iron powder group combustion under a microgravity condition, quantitatively analyzes the influence of differential diffusion on single-particle iron powder and iron powder group combustion flame temperature, and improves the calculation accuracy of the single-particle and iron powder group combustion flame temperature. And meanwhile, the influence of iron powder combustion characteristics and flame propagation speeds under different iron powder and oxygen concentration conditions is considered. The method has a certain guiding significance in research on low-propagation-speed lean-burn iron powder flames and subsequent application of iron powder fuel. The microgravity environment can eliminate the influence of convection and buoyancy, so that the combustion process is more stable and predictable, and the basic mechanisms of iron powder combustion, including combustion rate, oxidation process, phase change and the like, can be deeply researched.

The invention provides a method for constructing a thickened flame surface model of hydrogen turbulent combustion, which belongs to the technical field of hydrogen fuel engines, and specifically comprises the following steps: modeling a diffusion enhancement effect and a flame surface wrinkle effect caused by multi-scale turbulent flow by constructing a hydrogen fuel turbulent flame speed scale rate under a wide working condition; and the combustion process of the hydrogen fuel can be described more accurately. In addition, on the basis of a thin reaction zone combustion mode, key physical quantities are extracted, a combustion efficiency function is optimized, multi-scale turbulence and a difference diffusion effect are considered, and the thickened flame surface model is further perfected. The development of the method provides powerful technical support for the forward design of the hydrogen fuel engine based on numerical simulation, promotes the engineering application of the hydrogen fuel engine, and assists the hydrogen fuel engine to play a greater role in efficient, clean and safe energy transformation.

The invention relates to the technical field of boiler combustion control, and discloses a boiler closed-loop combustion dynamic optimization control system based on big data, and the system comprises an intelligent sensing module which is used for obtaining and outputting time-synchronized monitoring data in real time; the edge calculation module is used for processing multi-source sensor data in real time, realizing data credible processing and combustion state feature coding, and executing local caching of working condition perception; the digital twin modeling module is used for constructing a combustion process dynamic model and realizing real-time prediction of a combustion state; the optimization control module is used for dynamically adjusting combustion parameters and achieving collaborative optimization of the air-coal ratio, the opening degree of a secondary air door and the over-fire air ratio; and the closed-loop execution module is used for executing and feeding back the control instruction and realizing transmission and closed-loop verification of the control instruction. The combustion efficiency can be improved, pollutant emission can be reduced, and the stability and safety of boiler operation can be enhanced.

The invention belongs to the technical field of equipment intelligent control, and discloses a multi-objective optimization intelligent control method for a combustion carbon dioxide production system. The method comprises the following steps: constructing a combustion mechanism model; training a combustion data model; fusing the combustion mechanism model and the combustion data model to obtain a dual-drive model, and determining an optimization target; a dual-drive model is adopted to simulate the garbage combustion process, the control strategy is optimized according to the optimization target, and combustion equipment is adjusted; the advantages of the two models are effectively fused, advantage complementation between the models is achieved, and therefore the physical interpretability and adaptability of the models are improved; in the control strategy optimization process, not only is the combustion efficiency considered, but also carbon emission and pollutant emission are considered, the combustion efficiency is improved, and the carbon emission and the pollutant emission are reduced, so that multi-target optimization of the combustion carbon dioxide production system is achieved, and the control strategy optimization efficiency is improved. And the environment-friendly performance and the economic performance of the garbage combustion carbon dioxide production system are improved.

The invention relates to the technical field of boiler combustion optimization, and discloses an Agent-driven pulverized coal boiler combustion optimization closed-loop control method and system, and the method comprises the steps: obtaining combustion data in the combustion process of a pulverized coal boiler; according to the combustion data and a preset combustion task, generating an initial combustion control strategy through a multi-agent layered cooperation mode of the power system; according to the combustion data and an initial combustion control strategy, optimizing the initial combustion control strategy by predicting the combustion state of the boiler in a preset future time period to obtain an optimized combustion strategy, and guiding the boiler to carry out combustion adjustment so as to carry out closed-loop control on the combustion optimization process of the gas turbine; through closed-loop control over the combustion optimization process of the pulverized coal boiler, different combustion tasks can be responded in time, the control strategy is automatically adjusted according to the combustion tasks and the combustion state of the boiler, the control performance is continuously improved, the combustion state meets the task requirement all the time, and stable operation of the gas turbine is guaranteed.

The invention discloses a machine learning multi-objective optimization control method based on coal quality characteristics and combustion state feedback, and belongs to the technical field of industrial process automatic control. According to the method, collaborative optimization control over the combustion efficiency, NOx emission and combustion stability is achieved under the multi-working-condition operation condition of the combustor. According to the method, coal quality characteristic parameters and combustion state data are collected, working condition clustering is carried out after data fusion, and a historical combustion operation data set coupling coal quality characteristics and combustion states is constructed; a combustion state evaluation model and a combustion control model are established by adopting machine learning, on the basis of identifying working conditions in real time and evaluating combustion performance, a control parameter adjustment amount is generated in combination with trend extrapolation and a multi-objective cost function, and a combustion state feedback rolling correction model and a control strategy are utilized to realize combustion control. The self-adaptive closed-loop collaborative optimization control of the combustion process under multiple coal types and multiple working conditions is realized, the boiler efficiency is improved, the NOx emission is reduced, and the operation robustness is enhanced.

The invention discloses a gasification slag mixed biomass fuel blending combustion control method and system, and relates to the field of energy and environmental engineering. The method comprises the steps of collecting sensor data for data fusion, and preprocessing original data; according to the preprocessed data, an environment combustion synergy index is constructed, and the raw materials are classified; screening an optimal mixing ratio by utilizing dynamic game scores; temperature and flame stability parameters are collected in real time to construct a closed-loop feedback system, and intelligent adjustment of the combustion process is achieved; and a dynamic emission risk index is constructed, and closed-loop optimization control of pollutant emission is realized in combination with variable regression and a dynamic adjustment algorithm. Advanced technologies such as sensor data fusion, environment combustion synergy index, dynamic game scoring and feedback control are combined, on the premise that the combustion efficiency and stability are ensured, the blending combustion strategy of the gasified slag mixed biomass fuel can be optimized, and pollutant emission is effectively reduced.

The invention discloses a fluoride-free environment-friendly flame-retardant PC (polycarbonate) material and a preparation method thereof. The fluoride-free environment-friendly flame-retardant PC material is prepared from the following raw materials in parts by weight: 68-99.98% of polycarbonate resin; 0.01 to 25% of a flame retardant; 0.01 to 5% of a synergistic flame retardant; and 0-2% of a processing aid. The flame retardant does not contain any flame retardant, does not generate toxic gas or smoke in the combustion process and is harmless to the environment and human bodies, the components of the flame-retardant PC material meet the PFAS environment-friendly requirement, the flame-retardant PC material belongs to an environment-friendly material, and the prepared fluoride-free environment-friendly flame-retardant PC material is applied to the fields of the automobile industry, the electric appliance and instrument industry, the mechanical industry and the like.

The pulverized coal rotational flow stable combustion burner comprises a shell, a primary air pipe, a first swirler, a backflow cap and a laser generator assembly, a secondary air duct is defined between the primary air pipe and an air guide section, the first swirler is arranged in the secondary air duct, the backflow cap is arranged in the secondary air duct, and the laser generator assembly is arranged in the secondary air duct. The backflow cap is arranged in the combustion chamber and located at the outlet end of the primary air pipe. The laser generator assembly is used for emitting sheet-shaped laser beams into the combustion chamber so as to ignite pulverized coal in the combustion chamber. According to the invention, the size and the shape of the laser beam spot are adjusted, and the ignition efficiency and the stability in the pulverized coal combustion process are obviously improved by the aid of a cooling and combustion control strategy in which tertiary air and secondary air are matched; meanwhile, the laser power and mode can be flexibly switched, different coal quality and load working conditions can be adapted, the adaptability and combustion efficiency of the combustor are greatly improved, the service life of a hearth and related parts is prolonged, and the operation cost is reduced.

The invention discloses a thermal power plant boiler combustion optimization method and system, and the method comprises the steps: obtaining historical combustion data and historical control data of a thermal power plant boiler, and carrying out the analysis to determine control parameters affecting the combustion; extracting features of the control parameters, and constructing a combustion data prediction model based on the features and a preset neural network model for prediction to obtain combustion prediction data; analyzing the combustion prediction data, determining combustion data characteristics, and determining a combustion state coefficient of the thermal power plant boiler based on the combustion data characteristics; an expected combustion state coefficient preset in advance by the thermal power plant boiler is obtained, and the difference value between the combustion state coefficient and the expected combustion state coefficient is determined; and determining a parameter control optimization coefficient according to the difference value to optimize the current control parameter. According to the invention, refined and intelligent monitoring and optimal control of the combustion process can be realized, so that the combustion efficiency of the thermal power plant boiler is improved, the cost is reduced, the emission is reduced, and the stability and the safety of boiler operation are improved.

The invention relates to the technical field of boiler combustion control, in particular to a boiler combustion automatic detection control system based on a big data model. The system comprises a data acquisition module used for acquiring various parameter data in a boiler combustion process; the data processing module is used for preprocessing and fusing the collected data; the big data model module constructs a model based on the processed data, learns a combustion process rule and predicts the combustion process rule; the intelligent control module is used for generating a control instruction according to a model prediction result and performing optimization control on boiler combustion equipment; and the monitoring and alarming module is used for monitoring the combustion state and the equipment operation condition in real time, and giving an alarm and taking emergency measures when abnormity occurs. The system overcomes the limitation of traditional boiler combustion control means, realizes comprehensive and accurate monitoring, intelligent and efficient control and advanced fault prediction and treatment of the combustion process, effectively improves the combustion efficiency, reduces the energy consumption and pollutant emission, enhances the operation safety and reliability of the boiler, and is suitable for various industrial boiler application scenes.

The invention discloses a boiler combustion optimization method and system based on multi-parameter adjustment, and the method comprises the steps: obtaining and extracting combustion data related to the combustion efficiency of a boiler in the historical operation data of the boiler, and extracting the characteristics of the combustion data, constructing a boiler combustion prediction model based on the features and a preset neural network model to perform prediction to obtain combustion prediction data; analyzing the combustion prediction data to determine key combustion parameters influencing the combustion state of the boiler, and determining data characteristics of the key combustion parameters; evaluating the boiler combustion state based on the data characteristics to obtain a boiler state evaluation value, and determining the weight of each control parameter based on the boiler state evaluation value; and determining an adjustment coefficient corresponding to each control parameter based on the weight so as to optimize and adjust each control parameter. The advanced data analysis technology is combined with real-time monitoring data and model prediction, multiple control parameters in the boiler combustion process are intelligently adjusted and optimized, the boiler combustion efficiency is improved, energy consumption is reduced, emission is reduced, and the stability of the boiler is improved.

The invention relates to the technical field of model design, in particular to an integrated design method and system for a three-dimensional simulation model of a natural gas burning gun. The method comprises the following steps: collecting geometric parameters and operation parameters of a natural gas burning gun to obtain initial equipment parameter information; performing laser micro-texture treatment on the inner surface of a combustion chamber of the natural gas combustion gun to generate periodic micro-groove array data; constructing a three-dimensional initial model of the natural gas burning gun through three-dimensional modeling software based on the initial equipment parameter information and the periodic micro-groove array data; and acquiring natural gas fuel characteristic data, inputting the fuel characteristic data into the three-dimensional initial model for combustion process simulation, and performing multi-dimensional evaluation on the simulated combustion process to generate a combustion process evaluation result. Through three-dimensional modeling, combustion simulation and dynamic optimization adjustment, the problems of low efficiency, large optimization difficulty, lack of real-time feedback and the like in traditional design are solved, and the design precision and combustion performance of the natural gas combustion gun are remarkably improved.

The invention relates to a high-flame-retardant, high-thermal-conductivity and high-toughening epoxy resin packaging adhesive and a preparation method thereof. A self-developed flame retardant containing nitrogen, phosphorus and sulfur is added into an epoxy resin system, rich carbon residues hinder mass and heat exchange in the combustion process and also hinder combustible gas from being released to a combustion area, free radicals containing phosphorus and sulfur can capture active free radicals (H * and OH *), and the chain reaction of combustion is ended. The surface modifier is used for modifying the layered graphene composite boron nitride micro-powder heat-conducting filler, so that the interface interaction between the filler and the resin matrix is synergistically enhanced, and the compatibility and the heat-conducting effect of the filler and the resin matrix are improved. And the modified toughening agent is added to ensure that the epoxy resin packaging adhesive has good toughness. In addition, through further exploration and innovation, the specific proportion range of the nitrogen-phosphorus-sulfur flame retardant, the modified layered graphene composite carbon nitride micro powder and the flexibilizer is determined. The epoxy resin packaging adhesive prepared by the invention has excellent flame retardance, heat conductivity and toughness.

The invention relates to the technical field of intelligent monitoring of the waste incineration process, in particular to an intelligent monitoring method and system for the waste incineration process and electronic equipment. The method comprises the following steps: (1) collecting multi-dimensional historical data, and cleaning and normalizing the multi-dimensional historical data; (2) constructing a combustion process model by using a convolutional neural network (CNN) and a recurrent neural network (RNN), and weighting time sequence features through an attention mechanism; (3) introducing a transfer learning technology, and reducing cross-scene distribution difference based on a domain adaptive algorithm; and (4) fusing reinforcement learning to dynamically adjust equipment parameters, and giving consideration to thermal efficiency and pollution suppression targets by a reward function. According to the system, an edge-cloud collaborative architecture is adopted, the edge nodes complete real-time calculation, and the cloud executes model iteration. The incineration stability is remarkably improved, pollutant emission is reduced, and the method is suitable for complex working conditions.

The invention discloses a self-adaptive optimization control system and method for the combustion process of a biomass generator set. The self-adaptive optimization control system comprises a multi-dimensional sensing array module, an edge calculation module, a self-adaptive decision server, an execution mechanism cluster and a closed-loop verification terminal. The multi-dimensional sensing array module comprises a temperature field monitoring unit, a flue gas component analysis unit and a fuel characteristic detection unit; a data fusion processor is arranged in the edge calculation module; the self-adaptive decision-making server carries a dual-module decision-making engine; the executing mechanism cluster comprises a frequency conversion fan, a precise feeding machine and an adjustable fire grate. Through multi-source sensing data fusion and a self-adaptive decision-making mechanism, real-time response to biomass component fluctuation and load change is achieved, and the combustion efficiency is improved; a deep learning prediction model is combined, the air-coal ratio and the hearth temperature set value are adjusted in advance, and CO emission is reduced; the manual intervention frequency is reduced through an optimization mechanism, and the equipment maintenance period is prolonged.

The invention discloses a boiler combustion energy efficiency improving method and system based on digital twinning and a medium, and relates to the technical field of boilers, and the method comprises the steps that design information and historical data of a target boiler combustion system are obtained, digital twinning modeling is carried out, and a digital twinning model is built; key parts are identified based on design information, sensors are deployed, a combustion system sensing assembly is constructed, and real-time combustion state data are collected; constructing a boiler combustion energy efficiency evaluation system, performing multi-dimensional evaluation on real-time data, and determining a current energy efficiency coefficient; and performing energy efficiency improvement control on the target boiler combustion system based on the energy efficiency coefficient through a digital twin model. The technical problems of low combustion efficiency and high energy consumption caused by difficulty in real-time monitoring and optimization of energy efficiency in the boiler combustion process are solved, and accurate evaluation and intelligent optimization control of the boiler combustion state are realized through digital twin modeling and real-time sensing. The combustion energy efficiency of the boiler is improved, the energy consumption is reduced, and the operation stability is ensured.

The invention belongs to the field of combustion process monitoring, and particularly discloses a hearth flue gas CO and O2 measuring method which comprises the following steps: S1, acquiring original signals of CO concentration data and O2 concentration data; s2, performing data preprocessing on the obtained original signals of the CO concentration data and the O2 concentration data to generate a standardized electric signal sequence; s3, calculating the concentration of CO and the concentration of O2 through the standardized electric signal sequence; s4, acquiring environment temperature, humidity and concentration data of interference gas in flue gas to obtain a correction data set, and dynamically correcting the calculation results of the CO concentration and the O2 concentration according to the correction data set; according to the method, advanced data preprocessing and an accurate concentration calculation model are combined, the accuracy of CO and O2 concentration measurement is effectively improved, and the stability of a measurement system is enhanced by combining temperature drift compensation of the sensor and dynamic correction of the pre-trained neural network model on the detection concentration.

The invention relates to the technical field of garbage feeding control, in particular to a garbage feeding device for incineration power generation and an adjusting method. Garbage water content characteristics are analyzed through leachate flow time sequence data, density characteristics are quantified in combination with load coefficients, combustion characteristics are represented through carbon monoxide concentration changes, and a leachate characteristic coefficient, density characteristic coefficient and combustion characteristic coefficient three-dimensional index system is constructed. A clustering algorithm is adopted to carry out mode recognition on garbage grabbing operation, and clustering clusters with similar combustion characteristics are formed. In a cluster to which current garbage grabbing operation belongs, parameters of a feeding speed and a shake-off speed are dynamically optimized based on carbon monoxide concentration distribution characteristics, and a traditional fixed control mode is replaced. According to the method, intelligent matching of garbage component changes and process parameters is achieved, the combustion process is optimized through leachate management, pile compaction degree and the like, and garbage combustion sufficiency is remarkably improved.

The invention discloses a low-emission gas-fired boiler combustion device and method, the low-emission gas-fired boiler combustion device comprises an air premixing assembly, a combustion assembly and a burnout assembly, the air premixing assembly communicates with the combustion assembly, and the combustion assembly communicates with the burnout assembly; the air premixing assembly comprises a mixing tank, a premixing chamber, an air inlet pipe, a fuel gas inlet pipe, a first air distribution plate, two limiting rings, a rotating shaft, mixing blades and a driving impeller; the air inlet pipe and the fuel gas inlet pipe are both installed on the lower surface of the premixing chamber. Fuel gas and air are premixed in the premixing chamber, then gas preliminarily mixed in the premixing chamber flows into the mixing tank to be secondarily mixed, the fuel gas can be evenly distributed in the main combustion chamber after being evenly mixed, and therefore the situation that the concentration of the fuel gas in a local area is too high or too low is avoided; the uniform fuel gas distribution enables the combustion process to be more uniform, and the condition that the temperature of part of flames is relatively high does not exist, so that the generation rate and the generation amount of nitrogen oxides are reduced.

The invention discloses a method for predicting and controlling emissions from a thermal power plant, which relates to the field of electric power technology, and comprises the following steps: obtaining and preprocessing the combustion process data of a thermal power plant boiler; inputting the preprocessed combustion process data of the thermal power plant boiler into a pre-trained NO x The emission concentration prediction model predicts NO x Emission concentration data; the predicted NO x The emission concentration data and the pre-processed thermal power plant boiler combustion process data are input into the pre-trained control strategy optimization model based on reinforcement learning to output the optimal control parameters; the optimal control parameters are sent to the boiler's combustion system and SCR denitrification system through the PLC system to implement automatic adjustment control. This invention integrates advanced data processing and machine learning technologies to optimize NO in thermal power plants. x The prediction and control of emissions provides an efficient and accurate solution that helps achieve the dual goals of environmental protection and economic benefits of thermal power plants.

The invention relates to a combustion oscillation fault rapid detection method and device, and the method comprises the steps: obtaining a key signal of a combustion chamber in a combustion process, and determining a plurality of combustion states; the key signal comprises time sequence data; performing symbol segmentation on the time sequence data based on a maximum entropy segmentation method to obtain a symbol time sequence; obtaining a reference state according to the symbol time sequence and the plurality of combustion states; determining a current combustion state in the plurality of combustion states according to the reference state; when the current combustion state is a combustion oscillation mode, an early warning mechanism is started; according to the method, the time sequence data and the combustion state of the combustion chamber in the combustion process through the maximum entropy segmentation method can be processed, and the current combustion state is determined through the combustion state and the reference state, so that the combustion oscillation is monitored in real time through the current combustion state, complex sensor arrangement and algorithm processing are not needed, and the reliability is high. The cost is reduced, the calculation amount is reduced, and the early warning efficiency is improved.

The invention belongs to the technical field of hazardous waste treatment, and particularly relates to a treatment method of organic hazardous waste. An inner core is prepared from the organic hazardous waste and an additive, outer shell powder is obtained from an oxidizing agent and a reducing agent, then the core-shell structure energetic material is prepared in a coating mode, and finally the prepared core-shell structure energetic material is ignited for organic hazardous waste treatment. Compared with an existing incineration treatment technology, organic hazardous waste treatment can be conducted without traditional incineration equipment; as the oxidizing agent is in uniform and tight contact with the organic matter, large heat is released in the combustion process, the local temperature is high, oxygenolysis is rapid and thorough, and a good treatment effect is achieved; the method is short in technological process, low in energy consumption and cost and remarkable in economic and environmental benefits. According to the treatment method provided by the invention, inorganic gas and inorganic compounds are presented after combustion, so that group components forming the dioxin are fully dispersed, and generation of the dioxin is favorably inhibited.

The invention relates to a water heater control method and device, a water heater, a computer readable storage medium and a computer program product. The method comprises the steps that the target output power of the combustion system is obtained; determining a target driving signal of the fan assembly according to the target output power; in the process of controlling the fan assembly to operate based on the target driving signal, the current rotating speed of the fan assembly is obtained; and the opening degree of the fuel gas inlet assembly is adjusted according to the current rotating speed, so that the proportion of air entering the mixing cavity through the fan assembly to fuel gas entering the mixing cavity through the fuel gas inlet assembly is within a preset proportion range. By means of the method, the amount of fuel gas entering the mixing cavity can be matched with the amount of air, and then the proportion of the air and the fuel gas in the mixing cavity is kept within the preset proportion range. When the mixed gas in the mixing cavity is supplied to the combustion system, the combustion efficiency of the gas system can be improved, the situations of flame separation, backfire and the like possibly occurring in the combustion process are improved, and the operation safety of the water heater is improved.

The invention discloses a halogen-free flame-retardant nylon material which comprises PA (polyamide), a toughening agent, a P-series flame retardant, an N-series flame retardant, modified polyhedral oligomeric silsesquioxane and a derivative flame-retardant synergist of the modified polyhedral oligomeric silsesquioxane. The modified polyhedral oligomeric silsesquioxane and the derivative flame-retardant synergist thereof contain boron, an annular structure R2 with the carbon hydrogen ratio larger than 1 and two inorganic silicon cage cores, and the structures form a carbon layer, a ceramic barrier layer, a protective film or a heat-insulation coke layer and other protective layers in the combustion process, so that the anti-dripping effect is greatly improved, and meanwhile, the anti-dripping effect is greatly improved. Heat and oxygen transfer is isolated, combustible gas release is reduced, and the flame retardant property of the material is remarkably improved under the combined action of multiple mechanisms. Furthermore, the flame retardant synergist of the modified polyhedral oligomeric silsesquioxane and the derivative of the modified polyhedral oligomeric silsesquioxane is matched with the P-series flame retardant and the N-series flame retardant to realize synergistic flame retardance, so that the flame retardant property of the flame-retardant nylon material is improved while the dosage of the flame retardant is reduced. The halogen-free flame-retardant nylon material provided by the invention is excellent in flame retardance.

The invention provides a visualization method and device for the combustion process of a circulating fluidized bed boiler, and the method comprises the steps: collecting the operation data of the circulating fluidized bed boiler in real time through a distributed control system at an upper computer, and storing the collected data in a CSV format; in the lower computer, a three-dimensional modeling tool is used for establishing a three-dimensional model of the solid boiler, and grid division is carried out; controlling the upper computer to generate boundary condition data of numerical simulation according to the acquired CSV data, transmitting the boundary condition data to the lower computer, and reading the latest boundary condition data by using the lower computer to perform real-time simulation calculation; performing post-processing on a simulation calculation result by utilizing the lower computer to obtain a picture and curve data, and transmitting the picture and curve data to the upper computer through a network; and the upper computer is controlled to display the visual results of the temperature field, the velocity field, the pressure field, the gas component concentration field and the ash combustible content in real time through a platform interface in the forms of static diagrams, dynamic diagrams and trend curves. According to the method, the combustion process of the circulating fluidized bed boiler can be rapidly visualized, the operation state of the boiler can be predicted, and guidance is provided for operation optimization and parameter adjustment.

Disclosed in the present invention are a 90°C thermoplastic halogen-free low-smoke flame-retardant polyolefin sheath material and a preparation method therefor. The sheath material is prepared from raw materials comprising the following components in parts by mass: 20-40 parts of an ethylene-vinyl acetate copolymer, 20-40 parts of a linear low-density polyethylene, 2-10 parts of a high-density polyethylene, 20-40 parts of a metallocene ethylene-octylene copolymer, 10-15 parts of a maleic-anhydride-grafted metallocene linear low-density polyethylene copolymer, 58-73 parts of aluminum hydroxide, 20-25 parts of modified magnesium hydroxide, 64-84 parts of a synergistic flame retardant, 2-3 parts of a coupling agent, 1-4 parts of a lubricant, 1-3 parts of an antioxidant, and 2-7 parts of a coloring agent, wherein the synergistic flame retardant comprises lithium oxide. A matrix resin has low-temperature performance, mechanical properties and cracking resistance. With regard to flame retardance, a large amount of the synergistic flame retardant, i.e., lithium oxide, is added to a traditional magnesium-aluminum compound flame-retardant system, and therefore an off-white hard shell can be formed during a combustion process while low heat release is ensured, thereby ensuring no dripping during the combustion process.