899 results about "Low-carbon emission" patented technology

The invention discloses a power grid dispatching method and system adapting to power system requirements, and relates to the field of industrial big data, and the method comprises the following operation steps: S1, multi-source heterogeneous data collection and edge preprocessing; s2, knowledge graph construction and data fusion; s3, load prediction and renewable energy output prediction based on deep learning; s4, generating a dynamic optimization scheduling strategy; s5, carrying out security and credible execution on the data endowed by the block chain; and S6, real-time monitoring and closed-loop feedback optimization are carried out. According to the power grid scheduling method and system adapting to the power system demand, the scheduling method integrates edge calculation, block chain, deep learning and reinforcement learning, can realize multi-source data real-time processing, dynamic optimization strategy generation and data security and credibility, improves the power grid operation efficiency, stability and renewable energy consumption capability, and improves the power grid scheduling efficiency. And the dynamically optimized scheduling strategy can reduce the operation cost of the power grid, and can reduce carbon emission at the same time.

The invention discloses a carbon emission monitoring method and system for a data center, and the method comprises the steps: collecting the power generation proportion data of different energy types in real time, predicting the power supply change trend of renewable energy in combination with meteorological information, calculating the power supply proportion of each energy type, and constructing a dynamic weight matrix; constructing a time sequence prediction model by using historical and real-time renewable energy data, and predicting the future renewable energy power supply capability; carbon emission factors and priorities of different data centers are calculated, a task migration feasibility matrix is constructed, calculation tasks are dynamically migrated to a low-carbon emission data center according to a scheduling revenue function, a task scheduling path is optimized, cross-regional data transmission time delay and energy consumption are reduced, and cross-regional optimization scheduling of computing power is achieved; a multi-dimensional time sequence prediction model is constructed, the carbon emission trend is predicted, and carbon emission abnormity is monitored in real time. According to the invention, the problem of matching of computing power load and renewable energy of a data center can be solved.

The invention relates to the technical field of boiler engineering and artificial intelligence modeling, in particular to a coal-fired boiler biomass blending combustion working condition intelligent optimization method, which comprises the following steps: constructing a three-dimensional mathematical model of coal-fired boiler biomass blending combustion based on experimental test data; performing multi-working-condition numerical simulation according to boiler combustion working condition fluctuation in the three-dimensional mathematical model; according to the numerical simulation data, constructing a time-space coupling model for co-combustion of biomass in the coal-fired boiler; the modeling accuracy is improved through a numerical simulation technology, the boiler operation condition is dynamically optimized in combination with space-time modeling, and theoretical support is provided for energy conservation and emission reduction and blending combustion proportion optimization; the method is suitable for environment-friendly transformation and operation optimization of the coal-fired boiler, and carbon emission and pollutant emission can be effectively reduced.

The invention discloses an energy efficiency management system for low-carbon and energy-saving operation of building electromechanical equipment, and belongs to the technical field of building energy saving and intelligent control. The system comprises a data acquisition module, an energy efficiency analysis module, an optimization decision module and an execution control module; the data acquisition module acquires information such as equipment operation parameters in real time through the heterogeneous sensor network and the LoRaWAN edge gateway; the energy efficiency analysis module performs multi-dimensional evaluation on the basis of a dynamic energy efficiency reference algorithm by fusing an equipment energy efficiency ratio, carbon emission per unit area and the like; the optimization decision module adopts a mixed integer nonlinear programming algorithm, simulates working conditions through a mixed digital twin model, and generates a control strategy for collaborative optimization of energy consumption, carbon emission and equipment life; the execution control module dynamically adjusts equipment parameters by using a fuzzy PID composite controller; the method breaks through the limitation of static evaluation and single-target optimization of a traditional system, the comprehensive energy efficiency can be improved by 15%-30%, carbon emission is reduced by 20% or above, the service life of equipment is prolonged, and the credibility requirement of carbon checking is met.

The invention relates to an electricity-carbon cooperative scheduling optimization method and device for a low-carbon park integrated energy system, and the method comprises the steps: carrying out the cooperative prediction of a multi-state parameter through employing a panoramic situation deduction model, and generating a panoramic dynamic situation scene set; establishing an electricity-carbon cooperative scheduling model considering a carbon transaction mechanism, and deeply embedding the real-time carbon cost into a target function to carry out Pareto optimization of economic cost and carbon emission cost; an electricity-carbon cooperative scheduling model is converted into a standard mixed integer linear programming model, a situation deduction-day-ahead optimization-rolling correction hierarchical calculation framework is adopted to decompose a cooperative scheduling optimization problem to different time scales for decision making, and a global optimization plan is made on the day-ahead layer based on a panoramic dynamic situation. Deviation is corrected on line through rolling optimization in the intraday layer; and the integrated energy system executes the corrected scheduling plan. Compared with the prior art, the method has the advantages that the consumption rate of renewable energy sources can be remarkably increased and carbon emission can be effectively reduced while the operation economy of the system is ensured.

The invention relates to the technical field of carbon emission data processing, in particular to a carbon emission data processing method, device and equipment and a readable storage medium. According to the technical scheme, the method comprises the steps of data acquisition and preprocessing, abnormal data identification and correction based on a hybrid machine learning model, energy optimization configuration assistance and real-time monitoring and feedback adjustment, and data acquisition and preprocessing: constructing a multivariate data acquisition network and preprocessing the acquired data; therefore, standard carbon emission data for subsequent analysis can be obtained. Through a multi-element acquisition network and fine preprocessing, the comprehensiveness, accuracy and timeliness of data are greatly improved, a foundation is built for accurate analysis, abnormity is accurately recognized and corrected through a hybrid machine learning model, the accuracy is improved, data reliability is guaranteed, association between carbon emission and energy is mined, enterprises are assisted in optimizing energy configuration, and the energy utilization efficiency is improved. The carbon emission is reduced, and the system stability is enhanced by real-time monitoring feedback.

The invention discloses a cementing material for a solid waste-based road of a coal-fired power plant and a preparation method of the cementing material. The cementing material mainly comprises a cementing component, an alkali-activated component and a reinforcing component, the gelling component is a mixture of coal-fired power plant fly ash and furnace slag; the alkali-activated component is a mixture of alkali residues, red mud, blast furnace slag and coal-fired power plant desulfurized gypsum; the reinforcing component is rice hull ash or peanut hull ash. The mass ratio of the gelling component to the alkali-activated component is (4 / 1)-(10 / 1); and the addition amount of the reinforcing component is 1-3% of that of the main body material. The coal-fired power plant solid waste fly ash, the furnace slag and the desulfurized gypsum are used as main bodies, a small amount of other industrial solid waste is added, and the cementing material belongs to a full-solid-waste low-carbon cementing material and can be applied to a road base, a subbase or a base layer. The invention has the characteristics of low carbon emission, high solid waste consumption, simple production process and strong pavement performance, and in addition, the invention has a good stabilization effect on the problem that part of solid waste heavy metals and sulfate ions in a coal-fired power plant exceed the standard.

The invention provides a port energy dynamic optimization method and system based on digital twinning, and relates to the technical field of port energy optimization. According to the method, the full-link closed-loop control of the port energy data is realized by constructing a physical-edge-virtual three-layer digital twin architecture, and the utilization rate of renewable energy sources is improved and the carbon emission is reduced through real-time data synchronization and multi-time scale optimization strategies. Through combination of edge layer preprocessing and a virtual layer physical constraint model, the problem that dynamic response is delayed and a physical rule is disjointed in a traditional method is solved.

The invention discloses a high-content steel slag-based functionalized nano-composite cementing material and a preparation method thereof, and belongs to the technical field of solid waste resource comprehensive utilization. The nano composite cementing material is prepared from the following raw materials in parts by mass: 50 to 65 parts of steel slag, 10 to 20 parts of fly ash, 10 to 25 parts of carbide slag, 8 to 15 parts of desulfurized gypsum, 0.3 to 1.0 part of nano silicon dioxide and 0.003 to 0.03 part of silane coupling agent. On the basis of a solid waste synergism theory and an internal alkali excitation effect, quaternary solid wastes such as steel slag and fly ash are optimized and compounded by adopting methods of mechanical-thermal activation, addition of modified nano silicon dioxide, synergistic gelling between solid wastes and the like; the paint is prepared through gradient grinding and a medium-temperature wet curing process. The preparation method is simple, high-temperature calcination is not needed, cement clinker and exogenous excitant do not need to be added, the obtained cementing material can replace part of cement to be applied to the fields of road bases, mine filling, low-carbon building material products and the like, resource utilization and high-value utilization of regional bulk industrial solid waste can be achieved, and carbon emission and production cost are effectively reduced.

According to the carbon emission factor dynamic matching method based on the large language model, efficient and accurate matching is achieved through multi-step optimization. The method comprises the following steps: firstly, collecting carbon emission factors and activity data from multiple sources, and cleaning and normalizing the data; then, performing deep semantic analysis on the data by utilizing a pre-trained large language model subjected to field fine adjustment to generate a high-quality semantic vector; the system unifies data formats and completes physical quantity conversion through a unit identification and conversion mechanism; in the matching process, the system performs intelligent matching by calculating cosine similarity of semantic vectors, and under the condition that the similarity is close, a weighted average mechanism is applied to optimize a result, so that the matching precision is further improved; meanwhile, the system analyzes the relevance of newly submitted data by utilizing a factor library dynamic updating mechanism, dynamically expands the factor library and continuously optimizes the matching performance; the whole method improves the matching precision and efficiency, reduces manual intervention and errors, and reduces the calculation cost of carbon emission.

The invention discloses a deep reinforcement learning-based multimodal transport path optimization method, which belongs to the field of logistics management, and comprises the following steps of S1, forming a plurality of feasible paths from a starting point to an end point as training samples, calculating transportation time, freight cost, carbon emission, accident rate and maximum load index of each sub-path as quality evaluation indexes of the corresponding feasible path; s2, constructing a deep reinforcement learning agent based on the state space, the action space and the reward function; s3, selecting a sample by the intelligent agent by adopting a Sum Tree priority sampling mechanism, and generating an optimal path; and S4, in the intermodal process, monitoring the quality evaluation index of the path, and dynamically generating an optimal path according to the real-time path state data. The method can adapt to dynamic factors such as real-time traffic changes, manual intervention is reduced, the emergency response efficiency is high, the occurrence probability of accidents can be reduced, and carbon emission and emission of other pollutants are reduced.

The invention discloses a method for low-carbon preparation of a high-reactivity blast furnace burden from magnetite concentrate and the blast furnace burden, and belongs to the technical field of ferrous metallurgy. According to the method, an iron-containing raw material, an organic-inorganic composite binder, a multifunctional additive and water are sequentially subjected to uniform mixing, high-pressure double-roller forming and consolidation, and the finished product cold pressing block is obtained. According to the method, the strength and reducibility of the cold pressing block can be remarkably improved by optimizing the particle size composition of the raw materials and adding the multifunctional additive, the technological process is simple, a high-temperature process is not needed, and carbon emission is low.

The invention discloses a power distribution network transformer carbon footprint analysis method based on life cycle evaluation, which is based on a life cycle evaluation method, takes a transformer with the capacity of 200kVA as an example, calculates the carbon emission and the total energy demand of the whole life cycle, and further analyzes the carbon emission in each stage. Research finds that the carbon emission with the highest proportion is the operation stage and accounts for 99.03% of the whole life cycle, the waste recovery stage effectively reduces the influence of the carbon emission on the environment, the technology in the production and manufacturing process is improved, more energy-saving and efficient devices are adopted, and the carbon emission can be effectively reduced. Sensitivity analysis shows that the silicon steel sheets have obvious influence on carbon emission in the life cycle of the power distribution network transformer, and the higher the silicon steel sheets are, the lower the carbon emission is.

Disclosed is a marine fuel cell-based integrated heat, electricity, and cooling supply system comprising a power supply system and a waste heat recovery system; the power supply system comprises wind turbine generator sets, solar generator sets, and a fuel cell power module; the waste heat recovery system encompasses a turbine power generation module and a lithium bromide refrigeration module; the fuel cell power module is connected to both the turbine power generation module and the lithium bromide refrigeration module; the turbine power generation module is used to generate electricity using waste heat. This approach fully exploits the waste heat from the exhaust gas generated by the fuel cell power module, resulting in a high overall energy utilization rate. The self-consumption electricity and pure hydrogen fuel for the integrated energy supply system can be obtained from solar and wind energy, ensuring low carbon emissions for the entire system.

The invention belongs to the technical field of novel power system photovoltaic and energy storage system optimization configuration, and discloses an optical storage system optimization method based on carbon emission and line loss rate collaborative modeling, which comprises the following steps: constructing a double-layer planning structure of an upper layer planning model and a lower layer operation model, a carbon emission calculation model and an improved line loss rate calculation model are introduced into the lower layer, joint optimization of configuration and operation is realized through parameter coupling, and the charge and discharge efficiency loss power consumption of the energy storage device is introduced as an independent parameter in line loss rate calculation, so that the line loss calculation precision is improved; and solving by adopting an improved particle swarm optimization algorithm, and objectively sorting candidate schemes in combination with an information entropy method and a TOPSIS comprehensive evaluation method. A simulation result based on an IEEE33 node power distribution system shows that the model can effectively reduce carbon emission and line loss rate, improves node voltage level, and has good convergence and engineering applicability.

The invention provides an industrial manufacturing industry-oriented carbon asset full life cycle management method and system, and the method comprises the steps: carrying out the cross-link feature fusion processing through obtaining an original carbon activity data set of an industrial manufacturing whole process, and generating carbon footprint feature data; and calling a pre-trained deep learning model to perform carbon asset state evolution analysis on the carbon footprint feature data, predicting a carbon asset evolution path, generating a carbon asset optimization strategy set based on a prediction result, including equipment energy efficiency regulation and control and a production plan recombination strategy, and pushing the carbon asset optimization strategy set to an industrial manufacturing execution system. And triggering an automatic control instruction reconstruction operation to realize automation and intelligence of carbon asset management, thereby comprehensively and accurately mastering a carbon asset state and an evolution rule, formulating a scientific and effective optimization strategy, reducing carbon emission, improving energy utilization efficiency, and improving green production capacity of an enterprise.

According to the method, a set of parameterized simulation optimization design method integrating passive low carbon emission and high comfort is constructed for the design stage of a building scheme, and the method is a building low-carbon optimization design method based on an energy consumption-thermal comfort correlation model and comprises the steps that based on a parameterized building performance optimization model, an energy consumption-thermal comfort correlation model is established; establishing a parameterized model, performing microenvironment simulation by using a plug-in, and collecting data; sensitivity analysis of the parameterized model: carrying out sensitivity analysis by adopting random forest regression, gradient enhanced regression and decision tree regression, and discussing a relationship between a design variable and an environment target; and performing multi-target optimization based on performance simulation to obtain a compromise optimal result under multiple targets. According to the method, corresponding geometric and enclosure structure design parameter strategies can be provided from the aspects of building greenhouse gas emission, indoor and outdoor thermal environment comfort and the like, efficient design iteration and decision support is provided for designers, and sustainable development of green communities is promoted.

The invention discloses a low-carbon path real-time planning method and system based on multi-source data fusion and reinforcement learning. According to the invention, through a dynamic weight self-adjustment mechanism, the real-time adaptation capability of low-carbon path planning is significantly improved. Meanwhile, scenes such as traffic flow abrupt change, carbon price abnormity and energy station high load are recognized in real time through high-frequency scanning of multi-source fusion data, weights are dynamically adjusted through a formula, and planning can quickly respond to emergencies. For example, energy weight is reduced during charging pile queuing, emission constraint is enhanced when carbon price rises, planning lag caused by weight fixing in a traditional method is avoided, path selection better fits current environment changes, the problems of congestion and energy supplement shortage can be solved more timely in actual use, emission, time and cost are more accurately balanced under low-carbon constraint, and the method is suitable for popularization and application. In practical application, carbon emission can be effectively reduced, and user time and energy expenditure are saved.

The invention relates to a data center energy system scheduling method, device and equipment, a medium and a product in the technical field of intelligent management systems. The method comprises the following steps: constructing an economical objective function for minimizing the operation cost and an environmental objective function for minimizing the carbon emission; constructing a computing power side model and an energy supply side model; according to the supply data information and the demand data information in the data center energy system and constraint conditions, solving a supply and demand matching optimal solution of the data center energy system; and configuring the data center energy system according to a supply and demand matching optimal configuration scheme corresponding to the supply and demand matching optimal solution so as to realize an optimal operation target. According to the method, under the condition that the minimum operation cost and the minimum carbon emission serve as target functions, the operation models of the computing power side and the energy supply side and the constraint conditions are combined, and the targets of optimal energy system operation, optimal computing power resource allocation, the minimum carbon emission and the minimum energy operation cost of the data center are achieved.

The invention relates to a power distribution network partition and transformer substation power-carbon collaborative planning method considering multi-terminal uncertainty. The method comprises the following steps: extracting a representative wind-light output curve and a load curve; constructing an electrical coupling degree index, a load carbon flux index and a load balance degree index, calculating a comprehensive division index, and performing dynamic partitioning; and planning the transformer substation capacity which meets the main transformer N-1 verification, considers the influence of the load coincidence rate and the load prediction coefficient, and can meet the maximum load demand in the network element. The node carbon flux clustering index is combined with the traditional electrical coupling degree and load balance in the partition index, the limitation that traditional partition only depends on the electrical topological relation is broken through, the real-time carbon emission intensity of the load nodes is accurately quantified, and balanced partition of the power distribution network elements is achieved; carbon loss can be obviously reduced, and joint optimization of low carbon emission and high safety in the planning process of the transformer substation is realized; a typical scene expected value curve is used as input, and the influence of uncertainty on power distribution network planning is remarkably reduced.

The invention relates to a digital intelligent regulation and control preparation method and system for a high-solid-waste low-carbon high-performance grouting material, and solves the problems that a traditional preparation technology lacks an autonomous and controllable digital intelligent regulation and control system and is weak in adaptability to fluctuation of raw materials, and the method comprises the following steps: obtaining XRF chemical components and XRD mineral composition data of industrial solid waste raw materials; constructing a material gene database; based on the database, screening a proportioning scheme by using a performance prediction model, and predicting workability, strength development and shrinkage performance; inputting a prediction result as a fitness function into a multi-objective optimization algorithm, and outputting an optimal material gene combination; a batching scheme is generated based on the optimal combination, and a stirring process is started after technological parameters are preset; collecting data through a real-time monitoring system, and comparing the data with the digital twin model; and based on a comparison result, automatically adjusting material proportioning parameters. The high-solid-waste, low-carbon and high-performance grouting material has the advantages that accurate design and regulation of the high-solid-waste, low-carbon and high-performance grouting material are achieved, material performance is improved, and carbon emission and cost are reduced.

The common rail oil injector with the static leakage structure comprises an oil injector body, a control valve seat, a control valve matching part, a needle valve assembly and a needle valve body, and is characterized in that a middle hole pressure storage cavity is formed in the oil injector body and used for containing the control valve matching part and the needle valve assembly; the control valve matching part serves as the only static leakage matching part and is composed of a control valve sleeve and a control valve rod in clearance fit with the control valve sleeve, a static oil return channel comprising a first inclined oil return hole, a first vertical oil return hole and an annular groove is constructed in the control valve sleeve, a second oil return through hole in the high-pressure sealing face of the control valve seat is matched, and a specific fuel leakage backflow path is formed. Compared with a traditional 2000 bar common rail oil injector, static leakage matching parts are reduced, the fuel oil leakage amount is greatly reduced, oil consumption is effectively reduced, carbon emission is reduced, meanwhile, the reliability of the oil injector is improved, and the service life of the oil injector is prolonged. The middle hole pressure storage cavity can reduce oil injection pressure fluctuation, and the average effective injection pressure and the multi-time oil injection precision are improved. The oil injector body and the needle valve body are simplified in structure, and machining difficulty and manufacturing cost are reduced.

The present application discloses a method, device, electronic device and storage medium for tracing the flow of equivalent carbon emissions from electricity consumption, wherein the method includes: obtaining real-time flow data of the power grid, establishing a power grid flow matrix, and calculating the flow process of equivalent carbon emissions of the power system in the power grid based on the node average distribution rule, and finally establishing a user-power plant carbon tracking matrix and a line-power plant carbon tracking matrix. These matrices can directly characterize which power plants in the power grid the indirect carbon emissions of the user's electricity consumption come from and the proportion of each power plant. By using this method to measure the carbon emissions of user electricity consumption, the relationship between the user's carbon emissions and the carbon emissions of a specific power plant can be traced in real time, thereby improving the carbon measurement efficiency of the power grid, providing decision-making reference and support for power grid operators, and providing a reference basis for users to reduce carbon emissions. In this way, problems such as accurate measurement of equivalent carbon emissions of the power system are solved.

The invention discloses a comprehensive control system and method for carbon emission of a methanol dual-fuel power ship. The method comprises the following steps: analyzing carbon emission characteristics at different moments by collecting and acquiring sensor data in real time; solving based on the constructed carbon emission optimization model in combination with a particle swarm algorithm to obtain the optimal ship navigational speed, adsorbent circulation rate and methanol fuel injection pressure corresponding to the minimum carbon emission of the methanol dual-fuel power ship; cO2 in tail gas is captured in real time through the ship carbon capture unit, the circulation rate of an adsorbent is adjusted in time according to optimization information of the ship navigation optimization control unit, the circulation efficiency of the adsorbent is calculated according to the carbon emission amount, the adsorbent is updated or replaced in time, and the comprehensive control process of the ship carbon emission is comprehensively evaluated. And comprehensive control of ship carbon emission is realized according to the comprehensive evaluation result. The problem of poor carbon emission control effect caused by insufficient cooperative control of a methanol dual-fuel power ship carbon capture device and a power system is solved.

The invention discloses a park zero carbon detection management planning method, which belongs to the technical field of carbon emission management and comprises the steps of park data implementation and acquisition, park total carbon emission analysis, carbon reduction measure formulation and implementation, carbon reduction measure adjustment and equipment maintenance, park carbon reduction measure evaluation, park carbon reduction efficiency scoring and the like. The real-time data acquisition unit covers key information of carbon emission, carbon reduction equipment use cost, renewable energy quantity and abnormal carbon emission condition processing duration, direct and indirect carbon emission source emission data are acquired, the carbon emission intensity of each source is calculated, and accurate total carbon emission analysis is realized; a constraint and scoring mechanism for the implementation effect of the carbon reduction measures is provided, whether the carbon reduction measures meet requirements can be accurately judged according to preset parameters, the optimal carbon reduction strategy is selected, and the efficiency and effect of carbon reduction measure selection are remarkably improved; by continuously optimizing and adjusting carbon reduction measures and regularly maintaining related equipment, the overall energy efficiency of the park is improved, and carbon emission is reduced.

The invention relates to the technical field of energy system optimization and low-carbon economy crossing, in particular to a low-carbon agricultural and pastoral park energy system operation evaluation method considering electricity-carbon transaction, which comprises the following steps: S1, constructing an agricultural and pastoral park energy system operation model based on the energy supply and demand characteristics and morphological structure of an agricultural and pastoral park; s2, calculating the carbon emission intensity of the main network based on a carbon emission flow theory, and establishing an actual carbon emission responsibility model of the agricultural and pastoral park; s3, based on the carbon emission responsibility model of the agricultural and pastoral park energy system and the operation model of the agricultural and pastoral park energy system, in combination with the influence of renewable energy power generation on the total carbon emission amount, establishing an agricultural and pastoral park energy system economic operation evaluation model considering the electricity-carbon transaction; and S4, solving the economic operation evaluation model of the agricultural and pastoral park energy system, obtaining an optimal operation strategy of the agricultural and pastoral park comprehensive energy system, and realizing operation of the agricultural and pastoral park comprehensive energy system. The total amount of carbon emission is effectively reduced, and the economic cost caused by carbon emission is reduced.

The invention relates to the technical field of micro-grid energy management, in particular to a micro-grid composite energy storage system monitoring and regulation method and system, and the method comprises the steps: carrying out the butt joint of energy supply side and user side energy flow change data predicted by an energy supply side energy prediction model and a user side energy prediction model; the environment data of the energy supply side are analyzed through a machine learning algorithm, an optimization target is set according to an analysis result, and the optimization target comprises the steps that energy provided by the energy supply side is preferentially called, and in the peak period of power grid demand, energy storage electric power is used for balancing supply and demand and reducing power consumption cost; the energy provided by the energy supply side and the carbon emission value of the energy storage electric power used in the peak electricity time period are monitored through the monitoring model, the process of balancing supply and demand by using the energy storage electric power is optimized, and the carbon emission is reduced while the electricity consumption cost is reduced.

The invention discloses a load response type coal-fired boiler multi-coal-type-ammonia collaborative blending optimization method and system, and belongs to the technical field of coal-fired boiler combustion optimization and low-carbon power generation. The method comprises the following steps: establishing a coal quality database and a compatibility matrix, and generating an initial blending scheme by adopting a multi-objective optimization algorithm; combining real-time load rate data of the unit, and dynamically adjusting the blending proportion through a three-section type load interval division model; and calculating the ammonia doping amount based on the calorific value compensation model to realize calorific value notch compensation, and finally generating an ammonia-coal collaborative blending combustion scheme and forming a cost evaluation report. The system is correspondingly provided with a data acquisition module, a scheme generation and optimization module, an ammonia blending module and a cost evaluation module. According to the method, the technical aims of improving the utilization rate of inferior coal and reducing carbon emission under the variable-load working condition of the coal-fired unit are achieved.

The invention relates to a building engineering green low-carbon construction evaluation and decision support system, in particular to the field of building engineering, and aims to construct a space-time atlas to comprehensively characterize a construction process by integrating an Internet of Things sensor, a building information model and meteorological data, and accurately estimate a future carbon emission trend by using a dynamic prediction mechanism. And key influence factors and contribution degrees thereof are identified through factor analysis, and finally real-time optimization suggestions are provided by virtue of a visual interface, so that scientific decision support for green low-carbon construction is realized, carbon emission is effectively reduced, the resource utilization efficiency is improved, and the environmental sustainability of constructional engineering is promoted.

The invention belongs to the technical field of masonry cement, and particularly discloses all-solid-waste low-carbon-emission masonry cement and a preparation method thereof. Comprising the following steps: 1, finely grinding iron tailings, carbide slag, fly ash and silica fume, and proportioning according to a design to obtain a cement raw material; 2, agglomerating or pelletizing the cement raw material, and calcining according to a designed temperature to obtain a cement clinker; and 3, mixing the cement clinker with a certain proportion of gypsum, and grinding to the target fineness to obtain the masonry cement. The masonry cement is prepared from all solid wastes, meanwhile, a calcium source is mainly provided by the carbide slag with low carbon emission, compared with cement fired by calcium carbonate calcium sources, the carbon emission is greatly reduced, the goal of high-valued and green utilization of various solid wastes including iron tailings is achieved, and the method is suitable for industrial production. The compressive strength, the sulfur trioxide content and the screen residue of the prepared masonry cement meet the national standard requirements of masonry cement. In addition, the masonry cement is prepared from all solid waste raw materials, so that the preparation cost of the masonry cement is reduced.