1000 results about "Gas emissions" patented technology

The invention discloses an intelligent regulation and control method and system for dust removal and air control of an underground high-gas heading face, and relates to the field of intelligent control. Firstly, an environment parameter set and an equipment parameter set are obtained in real time; constructing a digital twinborn model of the tunneling face and the ventilation system based on the environmental parameter set and the equipment parameter set, and simulating gas concentration and dust concentration under different working conditions; the prediction model predicts the gas emission amount trend and the dust concentration trend; and inputting the environment parameter set, the equipment parameter set and the risk level into an optimization model, and generating a target decision instruction by the optimization model. The operation parameters in the ventilation system can be timely regulated and controlled in real time based on the monitored environment parameter set of the tunneling face and the equipment parameter set in the ventilation system, so that the ventilation and dust removal efficiency of the ventilation system is improved. Moreover, the decision instruction is generated by referring to the trend of the gas emission amount and the dust concentration in the future, so that the accuracy of the decision instruction of the ventilation system is further improved.

The invention relates to the technical field of sewage plant-oriented carbon neutralization regulation and control scheme design, in particular to a sewage plant-oriented carbon neutralization regulation and control method and a sewage plant-oriented carbon neutralization regulation and control system. The method comprises the steps of collecting water quality, energy consumption and gas emission data in real time through a multi-source sensor, inputting the data into a space-time diagram convolutional network to predict a carbon emission trend after edge computing node cleaning, and dynamically optimizing an aeration rate and a sludge reflux ratio based on a fuzzy PID controller. A whole-process carbon footprint is tracked by adopting a life cycle evaluation method, a multi-target compensation strategy is generated in combination with an improved NSGA-III algorithm, and photovoltaic, biogas and green power resources are scheduled preferentially. A digital twin platform and a block chain evidence storage module are innovatively integrated, the former calibrates model parameters in real time, and the latter generates a non-tampering carbon neutralization voucher. According to the method, the problems of one-sided data, regulation and control lag and insufficient credibility of a traditional method are solved, and minute-level sensing, prediction and offset closed-loop control of carbon emission in the sewage treatment process is realized.

The invention discloses an operation balance optimization method for a waste incineration thermal power plant. Efficient and environment-friendly waste treatment and green energy recovery are achieved through regulation and control of core parameters. Comprising an intelligent control system, a carbon cyclic utilization technology and a flue gas purification technology, an optimization model taking comprehensive net income maximization and carbon emission intensity minimization as targets is constructed, a garbage heat value, incinerator temperature, steam parameters, power generation efficiency, station service power consumption rate and the like are selected as core parameters, an operation balance optimization target function is constructed, and the comprehensive net income maximization and the carbon emission intensity minimization are realized. And through an intelligent control system, key parameters such as the incinerator temperature, the excess air coefficient and the flue gas treatment temperature are monitored and adjusted in real time, it is ensured that garbage is fully combusted in the incinerator, harmful gas emission is reduced, and the power generation efficiency is improved. Compared with the prior art, efficient operation of the waste incineration thermal power plant and maximum utilization of green energy are achieved, and remarkable economic and environmental benefits are achieved.

The invention provides a Unipol polyethylene process tail gas recovery system, which belongs to the technical field of tail gas recovery, and comprises a fan, a gas inlet channel, a stripping unit, a movable unit, a turning unit, a gas inlet amount control unit, a movable impurity removal unit, a discharge channel A, an outer barrel, a liquid adding channel and a turning box, the upper end of the stripping unit is provided with a gas inlet channel, the movable unit and the turning unit are connected and located in the turning box, the liquid adding channel is arranged at the upper end of the outer barrel and downwards communicated with the turning box, an absorbent is added into the turning box through the liquid adding channel, and the movable impurity removing unit is arranged at the upper end of the gas inlet amount control unit. The discharge channel A is arranged at the upper end of the movable impurity removal unit, and the outer barrel is arranged on the outer side of the equipment. The problems that an existing Unipol polyethylene process tail gas recovery system cannot convey tail gas into the Unipol polyethylene process tail gas recovery system in a balanced mode to be treated in the operation period, a large amount of inrush tail gas easily causes insufficient tail gas treatment, then tail gas emission does not reach the standard, and the surrounding environment is polluted are solved.

The invention relates to the technical field of tail gas exhaust control, and particularly discloses an automatic control system and method for tail gas exhaust treatment, the system is provided with a data transmission detection module, a tail gas detection module and a tail gas exhaust detection module, and a data transmission quality index is obtained by performing transmission evaluation on a tail gas detection data transmission process; the stability and reliability of data transmission can be ensured, the tail gas pollution index is obtained by analyzing tail gas property data, the pollution degree of tail gas emission can be reflected, the tail gas treatment efficiency and effect can be improved, it is ensured that tail gas emission meets the environmental protection requirement, and the exhaust performance data in the tail gas exhaust treatment process is analyzed; according to the method, the operation state and performance of tail gas exhaust treatment can be comprehensively and accurately known, the exhaust performance index is obtained, the method is an important basis for evaluating the performance of a tail gas exhaust treatment system, the fault occurrence probability and the influence degree can be reduced, and stable operation of tail gas exhaust treatment is guaranteed.

The invention provides a single-glass crystalline silicon photovoltaic module recovery method, and aims to solve the problems that fluorine film residues easily exist on a packaging adhesive film and fluorine-containing waste gas is discharged during subsequent pyrolysis of the packaging adhesive film after a back plate is removed by an existing physical disassembly, pyrolysis, separation and recovery process, and in addition, most welding strip coatings on the market contain lead, so that when the adhesive film is removed by pyrolysis, the lead-containing waste gas cannot be discharged during the subsequent pyrolysis of the packaging adhesive film. The lead element diffuses to pollute the battery piece, and the subsequent recovery difficulty is increased. Before the packaging adhesive film and the battery piece are shoveled off by a heat knife, a packaging adhesive film thinning process and a welding strip removing process are added. The residual backboard and the residual fluorine film can be removed together through the adhesive film thinning process, and the problem of fluorine-containing tail gas emission is solved; in addition, the lead-containing solder strip is removed before pyrolysis, and the solder strip is not subjected to a high-temperature process, so that pollution of lead elements to a battery piece is reduced; by optimizing the disassembling process, the breakage rate of the removed back glass is reduced, and the production yield of a production line is increased. The method is not only suitable for recycling of complete single-glass assemblies, but also suitable for recycling of broken single-glass assemblies, and the process applicability is high.

The invention belongs to the technical field of gas emission concentration monitoring, and particularly discloses an Informer-based method for monitoring the emission concentration of harmful gas in a large-scale farm in real time. According to the method, aiming at the characteristics of high nonlinearity, variable redundancy, complex coupling and the like of the emission data of the farm, a fusion model based on an Informer model and an RLS model is constructed, and the perception and response capability to the harmful gas emission concentration change is improved. According to the fusion model, global trend prediction is achieved through an Informer model, then dynamic error correction is conducted through an RLS model, the real-time performance, self-adaptability and robustness of the fusion model are effectively enhanced in combination with a processing mechanism of prediction first and parameter lagging update, and the fusion model is obviously superior to a traditional static or single model prediction mode. The method provided by the invention effectively solves the problems of response lag and inaccurate prediction in the current harmful gas emission concentration detection technology.

The present disclosure relates a technique for determining gas emission parameters over a geospatial area. The system includes a non-transitory computer-readable medium for storing the spectral signals obtained from overhead sensors and one or more trained deep-learning classification models. The system further includes one or more processors configured to determine one or more gas emission parameters over the geospatial area based on the one or more spectral signals using the one or more trained deep-learning classification models. Each of the one or more trained deep-learning classification models is generated by generating training data based on training samples representative of spectral signals from the one or more geospatial areas at two or more different time-periods, forming a set of training data batches, and training a deep-learning classification model based on the set of training data batches by applying an iterative optimization procedure to adjust hyperparameters of the deep learning classification model.

The invention discloses an intelligent dynamic prediction method for a coal and gas outburst dangerous area of a working face, and the method comprises the steps: building an integrated early warning information analysis platform, dynamically collecting key parameters in a mine, and constructing a dynamic data set; and calculating a coal seam firmness factor and a gas emission factor by utilizing a data fusion technology and combining historical data and regression analysis. Furthermore, in combination with a static data set, such as geological and ventilation system distribution data, a multi-parameter coupling prediction model is constructed. The model is trained and verified through a machine learning method so as to predict the coal and gas outburst risk of the working face in real time. The system automatically triggers early warning according to a model prediction result and a preset threshold value, a scientific basis is provided for coal mine safety management, and coal and gas outburst accidents are effectively prevented.

The invention relates to the technical field of battery raw materials, in particular to a preparation method of lithium sulfide powder, the lithium sulfide powder and application, and the preparation method comprises the following steps: uniformly mixing lithium sulfate and a carbon source according to a preset proportion to obtain a first mixture; performing first heat treatment on the first mixture in inert gas to obtain a second mixture; and performing second heat treatment on the second mixture in reducing gas to obtain the lithium sulfide powder. The product obtained by the preparation method disclosed by the invention is the lithium sulfide powder, and the obtained lithium sulfide powder has the advantages of less oxygen and carbon impurities, higher purity and high reaction activity, and meets the synthesis requirements of the electrolyte; according to the preparation method, in the process of preparing lithium sulfide, toxic gas and organic solvent are not used, the reaction temperature is low, the energy consumption is low, the requirement on production equipment is low, the process is simple, the cost is low, harmful gas emission is avoided, safety and environment friendliness are achieved, and industrialization is easy to achieve.

The invention relates to the technical field of liquid hydrogen storage and transportation, and discloses a liquid hydrogen container heat insulation performance rapid detection method, which comprises: S1, measuring the specification of a liquid hydrogen container and the initial state parameter in the liquid hydrogen container; s2, calculating initial parameters of liquid hydrogen and hydrogen in the liquid hydrogen container; s3, the change rate of the state parameters in the liquid hydrogen container is measured, and the final state parameters in the liquid hydrogen container are monitored; s4, calculating final parameters of liquid hydrogen and hydrogen in the liquid hydrogen container; s5, calculating the evaporation mass flow rate of the gas-liquid interface of the liquid hydrogen container according to the change rate of the state parameters in the liquid hydrogen container; s6, the thermodynamic energy change rate of hydrogen and liquid hydrogen in the liquid hydrogen container is calculated; s7, calculating the total internal energy change of the liquid hydrogen container, and calculating the total heat transfer amount of the gas phase and the liquid phase of the liquid hydrogen container; and S8, comparing the total heat transfer amount of the gas phase and the liquid phase of the liquid hydrogen container with standard static evaporation rate data to obtain the heat insulation performance of the liquid hydrogen container. The detection method has the characteristics that hydrogen does not need to be discharged, and the detection precision and safety are improved.

The invention discloses a recovery device and a recovery method for a photovoltaic multi-layer stacked assembly. The recovery device comprises a dismounting unit, a pyrolysis unit and a waste gas treatment unit which are arranged in sequence, the pyrolysis unit comprises a tunnel kiln, a bearing assembly and a conveying assembly. The recycling method comprises the steps that S1, the disassembling unit is used for disassembling, and the frame is stripped; s2, the photovoltaic multi-layer laminated assembly is sent to a pyrolysis unit for pyrolysis, and a glue film layer in the photovoltaic multi-layer laminated assembly is removed; and S3, the waste gas is discharged after being treated. By means of the innovative disassembling system, the multi-plate stacking tunnel kiln pyrolysis scheme and the multi-stage waste gas treatment system, the industrial production capacity of treating 1-3 tons of photovoltaic panels per hour can be achieved, the pyrolysis effect is good, the silicon material recovery rate can reach 98.5% or above, energy consumption is low, and the tail gas emission index is superior to the national standard by 50% or above.

The invention relates to a partition loading drilling gas extraction dynamic simulation experiment device and method, and belongs to the field of coal mine safety. The device comprises an experiment cylinder body, a loading device and an air supply device. A hole sealing section confining pressure pressurizing device, an extraction section confining pressure pressurizing device and an axial pressurizing device are arranged in the experiment cylinder body, and the influence of coal body elasticoplastic deformation on permeability is truly reflected by accurately distinguishing stress loading of a hole sealing section and stress loading of an extraction section; the method comprises the following steps: analyzing a stress state, formulating an experimental scheme, preparing an experimental sample, simulating a loading experiment and analyzing experimental data. By combining drilling construction simulation and negative pressure extraction testing, gas emission rules and leakage characteristics under different hole sealing materials, processes and extraction parameters are systematically analyzed. The technology can accurately test the gas emission rule and the air leakage characteristic of drill holes with different hole sealing processes and hole sealing parameters under negative pressure extraction, and provides a scientific basis for optimizing the gas extraction process and improving the extraction efficiency.

The invention discloses an open pit mine stripping semi-continuous laying method based on a backfill type deeply-buried channel, and belongs to the technical field of transportation of open pit coal mine stripping systems. The method specifically comprises the steps that an optimal stripping semi-continuous belt conveyor system layout route starting from a stripping crushing station to an outer dumping site is planned; a space gully between the inner dumping site and the outer dumping site of the optimal route is filled with stripping materials to form a dumping bridge, a stripping semi-continuous belt conveyor is laid on the dumping bridge to the outer dumping site, and a backfill type deeply-buried channel is laid below the dumping bridge in advance. According to the transportation mode, the stripping and coal mining operation route is optimized, the truck transportation distance is shortened, and the comprehensive cost is reduced by 50%. Meanwhile, the dumping space is enlarged, and the tension of the dumping space is relieved. According to the arrangement mode, stripping and coal mining transportation routes are optimized, existing coal transportation and power transmission lines are reserved, and rerouting is avoided; tail gas emission is reduced, and environmental protection and energy conservation are achieved; hidden safety accidents in truck transportation are reduced, and manpower is released; and the strip mine is promoted to develop towards a safe, green, intelligent and efficient direction.

The invention relates to the technical field of industrial environmental protection, in particular to a double-hearth lime kiln low-emission collaborative dust removal system and a control method. The control method comprises the steps that a flue gas sensing module collects real-time flue gas parameters and kiln body working condition data; the working condition linkage regulation and control unit dynamically adjusts operation parameters of the dust removal equipment through a fuzzy PID working condition matching algorithm; the regional collaborative dust removal assembly performs differential dust removal according to flue gas characteristics and treatment requirements of different dust raising points, and flue gas flow guide is performed through a double-hearth time sequence valve group during kiln body switching; the noise elimination and vibration isolation cooperation unit adjusts the noise elimination amount and the vibration isolation damping according to the rotating speed of the draught fan and controls noise and vibration. The tail gas deep purification unit evaluates the dust removal state through a flue gas and dust coupling model, carries out deep treatment on the dedusted tail gas and controls the tail gas emission concentration; the anti-blocking optimization unit carries out cooperative anti-blocking through dynamic rapping and airflow purging based on historical operation data and real-time working conditions. Therefore, the problems of poor dust removal stability, high operation energy consumption and the like in the prior art are solved.

The invention discloses a gas extraction dynamic regulation and control system and method based on three-dimensional modeling, and relates to the technical field of three-dimensional modeling, and the method comprises the steps: constructing an underground multi-source sensor network, and collecting underground gas extraction multi-modal data flow; the method comprises the following steps: acquiring an underground gas historical extraction data set, performing dynamic early warning analysis in combination with an underground gas extraction multi-modal data flow, and establishing a gas emission abnormity early warning model and an extraction system failure early warning model; self-adaptive fusion modeling is carried out, and a three-dimensional model of the extraction drilling system is generated; and the gas emission abnormity early warning model and the extraction system failure early warning model are mapped to the extraction drilling system three-dimensional model for extraction regulation and control analysis to determine target gas extraction control parameters, and extraction closed-loop regulation and control are carried out. The technical problem of monitoring and regulation response lag caused by gas extraction data islanding in the prior art is solved, and the technical effects of real-time sensing, intelligent early warning and accurate regulation in the gas extraction process are achieved.

The invention provides equipment and a method for testing the emission amount of harmful gas in a tunnel, and relates to the technical field of tunnel construction. Comprising a sleeve, a gas parameter testing unit installed on the sleeve, a water tank communicated with the interior of the sleeve and a gassing module installed on the water tank, and the gassing module is connected with the gas parameter testing unit; one end of the sleeve can be dynamically sealed with the drill rod, and the other end of the sleeve is of an opening structure; the gassing module is used for separating out gas in water and conveying the gas to the gas parameter testing unit; and the gas parameter testing unit is used for detecting the concentration and flow velocity of harmful gas in the sleeve and the concentration of harmful gas separated out from water. A gas parameter testing unit is arranged to monitor the data change of gas gushing out of the drill hole in real time; the gassing module can separate out gas in water in real time and convey the gas to the gas parameter testing unit to measure the concentration of dissolvable gas, so that the calculation of the emission amount is more comprehensive and accurate.

The invention relates to the technical field of chemical environmental protection monitoring, in particular to a chemical waste gas emission alarm detection method, which comprises the following steps: carrying out multi-source data acquisition including pollutant concentration data, meteorological data and production process data; carrying out data preprocessing on the collected data and carrying out data fusion by adopting weighted fusion; a chemical waste gas emission alarm detection model is constructed, and model parameters are set and trained by combining a long short-term memory network with a support vector machine; setting an alarm threshold value and an alarm mechanism, monitoring and updating multi-source data in real time, inputting the multi-source data into the trained chemical waste gas emission alarm detection model for analysis, and dynamically adjusting the model and the alarm threshold value regularly according to actual monitoring data and alarm feedback. According to the invention, multi-source data acquisition is combined with an artificial intelligence technology to detect waste gas emission, so that the detection efficiency and timeliness of chemical waste gas emission are improved, and the harm of chemical waste gas to the environment is reduced.

The invention discloses an intelligent mine gas monitoring system and a gas safety risk identification and evaluation method. The system adopts a three-layer framework; the sensing layer is integrated with fixed monitoring equipment, mobile inspection equipment and methane emission detection equipment, and global data acquisition is realized through a multi-parameter sensor, an accurate positioning module and a gas emission calculation model; the transmission layer realizes underground data transmission through a wired-wireless hybrid network; and the application layer performs data fusion based on a digital twin mine model, and provides intelligent analysis, visual display and report generation functions. The risk assessment method comprises the following steps: collecting multi-source data in real time and carrying out space-time alignment and Kriging interpolation fusion; dynamically extracting four types of risk factors; identifying an abnormal aggregation area, and associating an equipment state traceability risk; and calculating a risk value through a weighting model, dividing four levels of risks, and triggering a grading response. According to the invention, gas risk dynamic identification, accurate traceability and automatic closed-loop control are realized, and the mine gas safety prevention and control capability is improved.

The invention relates to the technical field of pollution treatment, in particular to a full-automatic tail gas treatment remote control method applied to an intelligent cremation machine. The method comprises the following steps: acquiring tail gas emission data; according to the tail gas emission data, the emission standard exceeding trend is recognized, and tail gas emission standard exceeding data is obtained; determining an emission fluctuation rule based on the tail gas emission standard exceeding data; multi-stage tail gas purification parameter adjustment is conducted according to the emission fluctuation rule, and multi-stage tail gas purification adjustment parameters are obtained; performing full-automatic tail gas treatment simulation according to the multi-stage tail gas purification adjustment parameters to obtain tail gas treatment data; carrying out atomization failure detection according to the tail gas treatment data to obtain atomization failure data; performing nozzle blockage analysis based on the atomization failure data to obtain nozzle blockage data; and performing inner wall scaling detection based on the nozzle blockage data to obtain inner wall scaling data. The intelligent response rate and the purification standard-reaching rate of tail gas treatment of the intelligent cremation machine are improved based on the pollution treatment technology.

The invention relates to a mine gas emission quantity prediction method based on deep learning. The deep learning-based mine gas emission quantity prediction method comprises the following steps of (1) preparing a data set, (2) performing correlation analysis on the data set to determine main control elements of mine gas emission, and (3) constructing a VMD-WTC-PatchTST combined model according to time sequence data of the main control elements of mine gas emission. According to the VMD-WTC-PatchTST model prediction performance evaluation method, data and the model are improved through error analysis, and the final VMD-WTC-PatchTST model prediction performance evaluation result is that R2 reaches 0.894, RMSE is 1.80, and MAE is 1.38. Compared with an original PatchTST model, the fitting degree of the PatchTST model is improved by 2.6%, and due to the fact that the PatchTST is also suitable for long-term prediction, the performance of the model is expected to be further improved theoretically along with continuous supplementation of data.

The invention discloses a competitive evolution multi-task optimization method, system and equipment, and aims to solve the problem that an existing method is difficult to carry out collaborative optimization on fuel cost and gas emission under the condition that power balance and unit capacity constraint conditions are met. The method comprises the following steps: constructing main and auxiliary double-population parallel search; state indexes representing population convergence, diversity and feasibility are calculated in real time; based on the current state, a cooperation mode and evolution operator combined action is adaptively selected through a deep reinforcement learning agent; rewards are calculated according to improvement of the population on cost, emission and constraint satisfaction after action execution; and training the intelligent agent by using the state, the action and the reward, and iteratively optimizing the decision strategy until the Pareto optimal scheduling scheme meeting the constraint is output. According to the method, adaptive intelligent guidance of the evolutionary process is realized, and the optimization quality, the convergence speed and the algorithm robustness of the economic emission scheduling scheme of the power system are remarkably improved.

The invention belongs to the technical field of metallurgy, and relates to a carbon reduction method for a direct reduction device, which comprises the following steps: step 1, feeding a carbon-containing raw material and an oxidant into a conversion unit for oxidation reaction to obtain crude synthesis gas; 2, feeding the crude synthesis gas into a direct reduction unit for ironmaking, and respectively feeding furnace top gas discharged by the direct reduction unit into a conversion unit and a tail gas discharge unit after dust removal by a dust removal unit; 3, a part of the furnace top gas and a carbon-containing raw material are subjected to a reforming reaction in a conversion unit to obtain synthesis gas, and the synthesis gas is fed into a direct reduction unit for ironmaking; and the other part of furnace top gas is discharged after being treated by the tail gas discharge unit. According to the method, a carbon dioxide closed-loop conversion system is constructed, efficient carbon reduction is achieved through a coupling mechanism of non-catalytic partial oxidation and autothermal reforming, and compared with a traditional hydrogen-based shaft furnace ironmaking method, the carbon reduction method has the advantage that the carbon dioxide emission is reduced by at least 25%.

The invention provides a short-distance coal seam group Y-shaped ventilation gas extraction method, which belongs to the technical field of coal mining, and comprises the following steps: firstly, arranging a main drill hole vertical to a coal seam and branch drill holes extending to upstream and downstream at an angle of 45 degrees to form a Y-shaped three-dimensional extraction network; installing a gas concentration monitoring sensor network, and setting monitoring points to collect real-time data; constructing a gas concentration adjusting buffer system and an intelligent mixed gas distribution device, and maintaining the concentration of extracted gas to be 30-45%; establishing a predictive control model based on a long short-term memory neural network, predicting a gas emission rule in advance and dynamically adjusting extraction parameters; a gas concentration grading utilization system is implemented, and gas with different concentrations is guided into the corresponding utilization system; a gas extraction intelligent monitoring platform is deployed to realize remote monitoring and fault early warning; the technical problems that in the coal seam group gas extraction process, the extraction concentration is unstable, and prediction control is difficult are solved.

The utility model belongs to the technical field of on-line analysis of tail gas emission, and particularly relates to a stable-pressure sampling device of an FTA analyzer. Comprising a sampling probe, a high-temperature electric heat tracing integrated pipe cable, a stop valve, a bypass filter, a sample injection pressure reducing valve, a sample injection needle valve, a sample injection pneumatic valve, a sample injection pressure transmitter, a bypass flowmeter, an instrument air pressure reducing valve, an I / P controller, a PID control unit, an FTA analyzer, a box body heater, a pretreatment heat preservation box and the like. According to the pretreatment pressure stabilizing system disclosed by the utility model, through the design of the connection relation among the structures of the pretreatment pressure stabilizing system, pressure stabilizing sampling can be realized under the condition of pipeline pressure fluctuation, and the measurement accuracy of real-time monitoring of chemical tail gas is improved.

The invention belongs to the technical field of a VO2 low-dimensional structure growth process, and particularly relates to a chemical vapor deposition preparation method for realizing a natural bending VO2 nanowire with a controllable curvature radius. According to the method, a chemical vapor deposition system formed by a sliding rail type tube furnace is selected, a Si / SiO2 substrate is selected as a substrate, V2O5 powder is taken as a precursor, and controllable preparation of the bent VO2 nanowires with different curvature radiuses is successfully realized by comprehensively regulating and controlling growth parameters of chemical vapor deposition (CVD); the bottleneck that only conventional linear VO2 nanowires can be prepared through an existing chemical vapor deposition method is broken through. The bent VO2 nanowires with different curvature radiuses, which are prepared by the method disclosed by the invention, are relatively high in quality, good in crystallinity and uniformity, excellent in MIT characteristic and special in mechanical property. The method has the advantages of simplicity in operation, high repeatability, no toxic waste gas emission and the like, and is beneficial to large-scale popularization and application.

The invention relates to the technical field of sewage treatment and gas monitoring control, in particular to a distributed monitoring system for gas emission tracking. The system comprises a working condition data acquisition unit, an emission reference modeling unit, a model deviation analysis unit, a vulnerability deduction unit and an anti-vulnerability control unit. The system obtains a predicted emission value by constructing a physical information neural network model, compares the predicted emission value with a measured value to calculate a deviation, and generates an attack state flag; the method is characterized in that a microbial flora vulnerability index is calculated in combination with an attack state flag and a carbon source fluctuation rate; when the vulnerability index exceeds a preset biological safety threshold value, the system adaptively switches to a biological priority mode; according to the method, real-time quantification of the credibility of the model is realized, and invisible biochemical attacks with unknown toxicity sources can be effectively identified.

The invention discloses an oil gas treatment method and system based on pressure triggering and temperature feedback control, a medium and a product, and relates to the field of oil gas treatment. The method comprises the following steps: when a pressure value and an actual temperature of an oil tank meet trigger conditions, guiding oil gas in the oil tank into a condensation unit for condensation, liquefying preset high-boiling-point components in the oil gas into a liquid oil product, and adsorbing the condensed residual oil gas through a double-layer adsorption bed arranged in an adsorption tank in sequence; according to the temperature of the adsorption bed and the tail gas emission concentration, the saturation index is calculated through a composite saturation judgment algorithm, when adsorption is saturated, the vacuum pump is started to conduct vacuum desorption on the adsorption tank, oil gas adsorbed on the double-layer adsorption bed is desorbed, and the desorbed oil gas is circularly sent back to an inlet of the condensation unit to be subjected to secondary condensation liquefaction recovery. By implementing the technical scheme, oil gas can be effectively treated, and the oil gas recovery efficiency is improved.

The invention relates to the technical field of coal mine gas concentration prediction, in particular to a coal mine gas concentration time sequence prediction method and system based on LSTM. The method comprises the following steps: collecting multi-source data such as a mining machine pose time sequence, a micro-seismic monitoring event sequence and roadway environment monitoring, constructing a damage-permeability evolution equation by combining geological information, inverting a coal-rock mass dynamic stress damage field, calculating a gas dynamic permeability field and a gushing source intensity field, and finally predicting the gushing source intensity field by combining a space-time prediction model with historical data. And predicting future gas concentration distribution. And real-time space positioning of the coal mine gas emission source is realized.

The invention relates to the technical field of online monitoring and supervision, in particular to an environment online monitoring and supervision method and system based on multi-source data fusion, and the method comprises the following steps: collecting environment data from a sensor, including gas emission, water quality indexes and temperature readings, and generating an initial monitoring data set through time synchronization and data formatting; and performing unified standardization processing based on the initial monitoring data set to generate a standardized monitoring data set. According to the method, the accuracy of data classification is optimized through the application of the fuzzy clustering algorithm, and the similarity between the environmental data can be more effectively identified, so that the efficiency of data management is improved. The similarity evaluation further refines the comparison among the data points, enhances the judgment of the data typicality, allows the isolation of abnormal data, reduces the interference of error data on the analysis result, and improves the reliability of the monitoring data.