101 results about "Urban solid waste" patented technology

The invention relates to the technical field of waste-free city carbon emission, and discloses a method for evaluating waste-free city construction carbon emission reduction benefits, which comprises the following steps: collecting related data of waste-free city solid wastes in a reference year and a target year; according to an emission factor method and a WARM model, determining a carbon footprint, and calculating the total carbon emission amount of the solid waste; setting a plurality of carbon emission reduction scenes of a waste-free city construction target year according to relevant national and urban plans; based on a WARM model and carbon footprint setting, carbon emission under each carbon emission reduction scene is calculated; based on a carbon emission calculation result, evaluating a carbon emission reduction benefit under a waste-free city policy; according to the evaluation result, a policy optimization scheme is provided, a carbon emission reduction monitoring system is arranged, and the purpose of medium-and-long-term carbon neutralization is achieved. The method effectively solves the problems that current carbon emission reduction evaluation is not accurate and systematic tools are lacked, helps local governments to realize accurate emission reduction and dynamic adjustment of waste-free targets, and has important practical significance and popularization value.

A system and process for sorting and recovery of recyclable materials, and in particular, sorting and recovery of recyclable materials from mixed waste comprising municipal solid waste in a materials recovery facility or “MRF” are provided. Advanced technology and machinery are integrated into a multi-step system and process that are designed to treat contaminated MSW streams to meet or exceed minimum material quality specifications while also reducing and minimizing headcount of sorter personnel. Because the mix of materials in MSW can be so varied and unpredictable, the system and process are designed to effectively sort and recover desired materials from MSW streams having a variety of material compositions.

The invention relates to the technical field of lithium ion battery recovery and wastewater treatment, and discloses a method for recovering valuable metals in waste lithium batteries and synchronously degrading antibiotics in water. The method comprises the following steps: adding a waste lithium battery positive electrode material and an oxidizing agent into antibiotic wastewater, and reacting to obtain a positive electrode metal leaching solution; adjusting the pH value of the anode metal leachate to 9-14, and carrying out solid-liquid separation to obtain a lithium-rich filtrate and a transition metal precipitate; and adding a precipitator into the lithium-rich filtrate, carrying out precipitation reaction, and collecting a lithium-rich precipitate. According to the method provided by the invention, based on an advanced oxidation process, the transition metal in the positive electrode material of the waste lithium battery can be oxidized and dissolved by an oxidizing agent to recover metal resources, and can also be used as a catalyst to activate the oxidizing agent to generate free radicals and degrade antibiotics, so that metal recovery and pollutant degradation are synchronously completed in a single system; the method is easy to operate, co-treatment of urban solid waste and wastewater is promoted, and good ecological environmental benefits are achieved.

A system and method for assessing surface methane concentrations through surface emissions monitoring utilizing unmanned aerial vehicles at municipal solid waste landfills. The method uses a combination of UAV-based and ground-based SEM techniques to measure methane concentrations at the surface of a landfill, to calculate zone-average methane concentrations on the surface and to identify methane leaks at landfill cover penetrations.

The invention discloses an image-based urban solid waste (MSW) calorific value prediction method. According to the method, on the basis of a monitoring video of a garbage storage pool, an in-furnace garbage image is captured and screened manually, and an efficient multi-module fusion learning framework is developed by using a deep learning technology. According to the framework, through extracting time sequence features and image visual features of calorific value labels and introducing a variational auto-encoder to process high-dimensional combined features, dynamic prediction of the incineration calorific value of the garbage entering the furnace can be realized. According to the actual working condition, two different time sequence information input strategies are explored, one strategy is that the real heat value at the previous moment is used as input, the other strategy is that the predicted heat value at the previous moment is used as input, and effective information reference and decision support can be provided for incineration intelligent control of the waste incineration power plant.

The invention discloses an intelligent classification and identification method and system for urban solid waste, and relates to the technical field of waste classification and identification. Comprising the following steps: acquiring collected historical junk pictures, and performing unified processing; performing multi-dimensional evaluation on the historical junk pictures after the unified processing, performing quantitative scoring, and screening out the pictures meeting the requirements; performing data enhancement processing on the screened pictures to obtain a historical data set; constructing a junk recognition model based on the historical data set, and training, verifying and testing the constructed junk recognition model to obtain a final junk recognition model; the technical key points are as follows: aiming at a core application scene of garbage mixing and stacking, a corresponding garbage recognition model for realizing high-precision recognition through multi-scale feature fusion and an attention mechanism is constructed, the problem that garbage recognition in a scene with a large amount of garbage and stacking cannot be realized in the prior art is effectively solved, the garbage recognition speed is high, and the recognition efficiency is high. And the requirement for real-time sorting is met, the using effect is good, and the good using prospect is achieved.

The invention relates to the technical field of urban solid waste energy treatment, in particular to a waste incineration combustion condition prediction method and device based on a combined rationalization mechanism and data driving. The method comprises the steps that on the basis of a physical and chemical mechanism involved in the waste incineration process, characteristic values are collected, and a chain type model of a multi-level chain type characteristic system is constructed; constructing a new characteristic value set and a new target value set which reflect dynamic changes; and for each mechanism reaction unit in the multi-level chain type characteristic system, independently training a hybrid model fused with physical constraints, training the hybrid model under physical mechanism constraints based on the pre-processed new characteristic value set and new target value set, and constructing a chain type prediction model for predicting the garbage incineration combustion condition. According to the method, the physical and chemical laws of the incineration process are embedded into the data driving model, accurate prediction of the key parameters of the waste incineration working condition is achieved, and the method is suitable for optimization control of the waste incineration power generation process.

The invention discloses an urban scale solid waste landfill site identification method based on an artificial intelligence target identification algorithm, and belongs to the technical field of urban solid waste management. Comprising the following steps: constructing a multi-source remote sensing image database of an urban scale solid waste landfill site through multi-source data; improving a YOLOv8 model to adapt to a remote sensing image recognition task, and training a solid waste landfill site recognition model for an urban scale by using the data subjected to data enhancement; acquiring a visible light remote sensing image of a target area, performing radiation correction, atmospheric correction and geometric correction, inputting the trained model for reasoning, and identifying a solid waste filling site; and sampling a model identification result, verifying the accuracy through field survey, and compiling an actual material diagram. According to the method, rapid and accurate identification of the urban scale solid waste landfill site is realized by using an artificial intelligence technology, the identification efficiency and accuracy are remarkably improved, and powerful support is provided for urban solid waste management and environmental governance.

Aspects of the invention are associated with the discovery of approaches for the conversion of carbonaceous feeds, such as biomass and biomass-containing solids via thermal treatment. Particular examples of biomass-containing solids are municipal solid waste (MSW), as well as waste plastics and waste tires. In some cases, this conversion, such as by pyrolysis, will allow for straightforward integration with gasification (e.g., entrained-flow gasification) or partial oxidation. Advantageously, processes and associated apparatuses / equipment described herein are tailored to the physical and chemical properties of the feeds. In this regard, important advantages reside in auger reactors that include electric heating elements within one or more auger shafts. Such centralized heating may be used in combination with external heating, for example also utilizing electric heaters. With centralized heating, the surface area available for heat transfer into the feedstock may be increased dramatically (e.g., by a factor of 3 to 5).

The application relates to a furnace temperature prediction method, system, device and medium for a solid waste incineration process. The prediction method comprises the following steps: acquiring a plurality of groups of characteristic variables affecting furnace temperature and corresponding current furnace temperature values and corresponding historical data groups of next-time furnace temperature values to form case descriptions, and constructing a difference database D in the order of the case descriptions; performing data preprocessing based on the difference database D to obtain a training set; constructing a case difference prediction model based on a deep Q network algorithm according to the training set; obtaining case difference data of the furnace temperature according to the characteristic variables of the current furnace temperature, and taking the case difference data as input data of the case difference prediction model; and obtaining a predicted value of the next-time furnace temperature according to output data of the case difference prediction model, so that the furnace temperature change trend of the urban solid waste incineration process can be accurately predicted, the optimization control of the incineration process is ensured, and the work efficiency is improved.

The present invention provides a method for washing chlorine bypass dust with water, which can reduce the selenium concentration in the wastewater discharged when chlorine bypass dust is washed with water, while keeping costs for equipment and chemicals down. [Solution] A method for washing chlorine bypass dust, comprising a slurrying step of adding washing water to chlorine bypass dust to form a slurry, and a solid-liquid separation step of separating the liquid phase from the slurry, wherein the slurry further contains municipal solid waste incineration fly ash in addition to the chlorine bypass dust, and the municipal solid waste incineration fly ash accounts for 15% by mass or more and 80% by mass or less of the total amount of the chlorine bypass dust and the municipal solid waste incineration fly ash.

The utility model relates to the field of municipal solid waste treatment, in particular to a small device for decomposing municipal solid waste brick slag. The utility model provides an urban solid waste brick slag breaking device which can automatically pour brick slag which is not completely broken into a slag breaking cylinder to be broken and broken again, and improves the solid waste brick slag breaking efficiency. A small urban solid waste brick slag dissolving device comprises a mounting frame, a first conveying belt, a slag dissolving barrel and the like, a left first rotating rod and a right first rotating rod are rotationally arranged on the lower portion of the mounting frame, the first conveying belt is connected between the first rotating rods in a wound mode through a belt wheel, and the slag dissolving barrel is connected to the upper side of the mounting frame. According to the brick slag crushing device, brick slag which is not completely crushed is screened and falls into the collecting hopper on the second conveying belt through the vibrating screen, then the second motor is started to enable the collecting hopper to move upwards, and the brick slag is poured into the slag crushing cylinder to be crushed again through the matching effect of the driving lever, so that the brick slag which is not completely crushed can be automatically poured into the slag crushing cylinder to be crushed again; and the solid waste brick slag degradation efficiency is improved.

The application discloses a kind of urban solid waste incineration process furnace temperature robust prediction method, comprising the following steps: S1, according to the initial furnace temperature model of preset training set is established;S2, assume that the prior distribution of noise in training data follows skew t distribution, the output weight and the set of hyperparameters of model are iteratively optimized by maximum likelihood estimation and expected conditional maximum algorithm, establish furnace temperature prediction model;S3, furnace temperature in incineration process is predicted by furnace temperature prediction model.The process data of urban solid waste incineration process is obtained in real time, and the furnace temperature prediction model based on robust random configuration network is established, the prior distribution of asymmetric abnormal value in incineration process operation data is simulated by skew t distribution with heavy tail characteristics, and the output weight of furnace temperature prediction model is solved by maximum likelihood estimation method, the robustness of furnace temperature prediction model to abnormal data is improved, and the real-time accurate prediction of urban solid waste incineration process furnace temperature is realized.

The invention relates to a construction material (S), wherein the construction material (S) comprises at least cement clinker (ZK) and at least one water hardening additive (X), wherein at least one water hardening additive (X) is mixed with the cement clinker (ZK) and is not a component of the cement clinker (ZK) itself. [0002] According to the invention, the at least one water hardening additive (X) contains conventionally ground and processed municipal solid waste incineration ash (gHMVA), wherein the conventionally ground and processed municipal solid waste incineration ash (gHMVA) has a particle size of < 200 μm, and contains thermally incinerated municipal solid waste (H), and is not subject to any material alteration and / or thermal treatment except during the incineration of the municipal solid waste (H) and / or after the conventional incineration and processing of the municipal solid waste (H).

The invention provides an urban solid waste incineration process nitrogen element product mechanism model construction and use method based on an improved mixed forest, and the method comprises the steps: carrying out the solid-phase and gas-phase combustion simulation of a combustion process, respectively obtaining a solid-phase combustion model comprising a moisture evaporation sub-model, a volatile component release sub-model, a volatile component combustion sub-model and a coke combustion sub-model and a gas-phase combustion model comprising a solid-phase combustion area, a gas-phase combustion area, a high-temperature combustion area and an SNCR purification area; performing an orthogonal experiment on the two combustion models to obtain simulation mechanism data; and constructing an NxOy model based on a width hybrid forest algorithm, and performing nitrogen element prediction on simulation mechanism data. According to the method, multi-working-condition virtual mechanism data is generated through coupling numerical simulation, an interpretable mapping model between manipulated variables and nitrogen element product concentration is constructed by using an improved width hybrid forest algorithm, and theoretical support is provided for strong uncertainty of industrial process modeling data.

The granulation apparatus (100) according to the present application allows for the production of solid recovered fuel pellets from municipal solid waste without the need to separate the constituents of the municipal solid waste prior to starting the granulation process. Thus, a larger amount of municipal solid waste can actually be used for the production of solid recovered fuel pellets. The granulation apparatus (100) and method according to the present application can be used, inter alia, for the production of solid recovered fuel pellets which can be used for the production of a synthesis gas rich in hydrogen and / or carbon dioxide by treating the torrefied solid recovered fuel pellets with a subsequent gas.

The present disclosure relates to embodiments of a process for producing asphalt binder containing a mixture of soft plastics from urban solid waste rich in polypropylene and polyethylene. The asphalt binder produced may be applied in the production of diluent suitable for the production of PAC with pre-salt RASF.

The embodiment of the invention provides a high-temperature heat storage structure based on solid waste-based variable stiffness support and dynamic stress compensation and a preparation method of the high-temperature heat storage structure. The heat storage structure comprises a core assembly filled with a solid-waste-based composite phase change material, an intelligent regulation and control assembly arranged on the periphery of the core assembly in a sleeving mode, a variable-rigidity flexible constraint assembly arranged on the periphery of the intelligent regulation and control assembly in a sleeving mode, and a heat blocking assembly arranged at the upper end of the core assembly in a sealed mode. Wherein the intelligent regulation and control assembly performs self-regulation according to the detected strain information of the core assembly and generates deformation for compensating thermal stress. The solid-waste-based high-energy-storage phase-change material is used as a core material of the core assembly to store heat, so that the urban solid waste emission can be effectively reduced, the environmental pollution is reduced, and the environmental protection is facilitated.

The invention discloses a method for preparing humus by using urban solid waste. According to the method, a humus solution is obtained through separation and purification by means of citric acid solution soaking, microbial culture, chelating agent chelation and the like, then the humus solution and an amino acid solution are mixed for a hydrothermal reaction, then freeze drying is conducted, and the high-activity humus product is obtained. The humus product prepared by the method is high in purity and high in biological activity, urban solid waste is effectively utilized, an innovative solution is provided for resource utilization of dredged sediment and perishable solid waste, and the humus product has remarkable environmental benefits and economic benefits.

Method for regulating and / or controlling lightweight packaging and / or municipal solid waste processing plants by changing a material flow, wherein the processing plant is first fed with material to be processed using a mobile technology, wherein the material is subsequently broken up and / or shredded by means of a shredder to form a uniform material flow on a conveying technology, wherein the material is then fed to at least one sorting stage using a conveying technology, wherein the following is used as a measurement variable of the method: - a fill level of a hopper of the shredder and / or - the volume flow rate of the material flow, wherein the measured quantity is measured contactlessly using sensor technology, wherein the following is used as the control and / or regulated variable of the process: - the shredding behavior of the shredder via its load capacity, and / or - the feeding of the processing plant with the mobile technology, and / or - Changes to plant operating parameters, where a feeding signal for the supply of the system is communicated to a loading personnel by means of a warning message.

The application provides a kind of urban solid waste combustion process pollutant emission model construction method, belongs to urban solid waste combustion field, including: constructing simulation model, carrying out multiple working condition orthogonal experiment, data preprocessing, data expansion, obtaining IT2FBLS network output, input-output matching, data screening, pollutant emission main model training, obtaining main model output and simulation error, calculating mean square error, determining segmentation variable, updating LRDT algorithm weight matrix, calculating leaf node output and carrying out weighted summation to main model output and leaf node output.The application reduces the time cost of simulation process by constructing simulation model, and improves the accuracy of emission model by using LRDT algorithm to establish compensation model.

The utility model discloses an urban solid waste mechanical biological treatment device which comprises a solid waste mechanical treatment frame assembly, the solid waste mechanical treatment frame assembly comprises a solid waste treatment frame, a top plate arranged at the top end of the solid waste treatment frame, a feeding pipe communicating with the solid waste treatment frame, a concave frame fixed to the bottom face of the solid waste treatment frame and an electric rotating shaft arranged in the concave frame. The cover plate is connected with the electric rotating shaft; the feeding hole is formed in the top surface of the feeding pipe; and a staggered linkage processing assembly. Through mutual cooperation of the solid waste mechanical treatment frame assembly and the staggered linkage treatment assembly, in the using process, differential rotation of a crushing cutter can be achieved through a driving structure, staggered crushing is achieved, garbage always moves inwards through surrounding type crushing, the contact probability of blades and the garbage is increased, and the crushing efficiency is improved. The urban solid waste treatment efficiency is greatly improved.

The application relates to a solid waste incineration process furnace temperature and flue gas oxygen content multi-target prediction method and system, and belongs to the technical field of solid waste incineration. The method comprises the following steps: collecting characteristic data of an incinerator, and collecting the furnace temperature and the flue gas oxygen content at the current moment, and constructing a training set and a test set; training a random configuration network prediction model based on the training set of the incinerator; inputting the test set into the trained prediction model, and recording the output value of the prediction model, so as to verify the prediction accuracy of the prediction model. The method can improve the prediction accuracy of the change trend of the furnace temperature and the flue gas oxygen content in the urban solid waste incineration process, so that the on-site operator can timely master the incineration working condition of the solid waste in the furnace, thereby guaranteeing the optimization control of the incineration process.

The invention belongs to the technical field of urban solid waste incineration, and provides an intelligent control method for the position of a combustion line in the urban solid waste incineration process, and the method comprises the steps of control quantity increment calculation, node increment learning, structure simplification, parameter compensation, and inter-layer parameter and intra-layer parameter updating. According to the method, incremental learning and a self-adaptive SPU strategy are embedded in the IT2FBNN, so that the calculation burden is remarkably reduced, and the control precision is improved and the controller structure is compact on the premise of ensuring the stability of the system.

The invention belongs to the technical field of urban solid waste incineration process dynamic optimization, and particularly relates to an urban solid waste incineration process dynamic optimization method and system based on multi-target particle swarm autonomous evolution. The method comprises the following steps: firstly, based on a random configuration integrated network, establishing an operation index model of nitrogen oxide emission concentration and waste heat power generation power; secondly, on the basis of a multi-target particle swarm optimization algorithm, a dynamic optimization strategy with the hearth temperature and the flue gas oxygen content as decision variables is developed; meanwhile, a particle distant parent hybridization strategy is introduced, and crossover variation is performed on population genes through particle swarm grid division, so that the diversity of the population is kept. And finally, a utility function is defined, the optimal set value of the hearth temperature and the optimal set value of the flue gas oxygen content are solved, and multi-target dynamic optimization of urban solid waste incineration is achieved.

Processes for removing oil and other organics especially lipids from process steams comprising lipids, brown grease, and water is disclosed and a process to remove metals and organics from leachate from landfills and other waste sites that generate contaminated water streams. The processes may involve adjusting pH and using electrical fields generated by a device comprising electrodes to induce gas bubbles. The gas bubbles facilitate movement toward the surface of the solution where they may be skimmed off and recovered. Processes are also described that upgrade brown grease to usable precursors for make beneficial products such as jet fuel. Other processes described herein include removal of carbon char from pyrolysis products like tires, plastics, rubbers, municipal solid waste, and oil or sewage sludge.

The application provides an intelligent optimization operation method for urban solid waste incineration process, and relates to the technical field of urban solid waste treatment and intelligent optimization. The method comprises the following steps: obtaining environmental data in the incinerator and concentration values of various incineration exhaust gases before a preset time length; iteratively optimizing the solution of the decision variable, so that the value of the target function obtained according to the solution of the decision variable is optimized in the direction of reduction; determining the solution of the target decision variable to obtain the set value of the oxygen content in flue gas and the hearth temperature in the current urban solid waste incineration process; the calculation step of the value of the target function comprises the following steps: inputting the solution of the decision variable, the environmental data in the current incinerator and the concentration values of various incineration exhaust gases before a preset time length into a pre-trained operation index model, outputting the predicted concentration values of various incineration exhaust gases, calculating the combustion efficiency and the concentration of flue gas pollutants, and obtaining the value of the target function. The combustion efficiency is improved while the emission of flue gas pollutants is reduced.

The application discloses a kind of urban solid waste incineration process intelligent control method, equipment, medium and product, it is related to urban solid waste incineration technical field, the method includes obtaining the current time of urban solid waste incineration process's pusher speed, grate speed, primary air flow, secondary air flow, combustion time, main steam flow and furnace oxygen content;According to the current time of pusher speed, grate speed, combustion time and main steam flow, utilize main steam flow prediction model, determine the main steam flow of next time;According to the current time of primary air flow, secondary air flow and furnace oxygen content, utilize furnace oxygen content prediction model, determine the furnace oxygen content of next time;Combination is based on the switching rule of expert knowledge, utilize gradient descent algorithm, to main steam flow controller and furnace oxygen content controller are solved, determine optimal control rate, to control urban solid waste incineration process.The application improves solid waste treatment efficiency, realizes the fast stable control of main steam flow and furnace oxygen content.

The method for sensing the temperature of urban solid waste incinerators based on a self-attention hybrid ensemble network belongs to the field of industrial process modeling and intelligent sensing. This method addresses the highly nonlinear, multi-condition variations, and complex physical-chemical reaction mechanisms in the MSWI process. Combining an ensemble learning framework and improved neural network structure, it introduces moving block bootstrap (MBB) sampling based on maximum likelihood estimation (MLE), a self-attention mechanism, and a hybrid ensemble strategy fusing AdaBoost and Bagging to achieve accurate modeling and dynamic prediction of the Fourier transform (FT). This results in good modeling accuracy, generalization ability, and engineering adaptability. Experimental verification under multiple benchmark problems and actual MSWI conditions demonstrates that the proposed FT sensing method exhibits superior performance in both prediction accuracy and robustness, possessing significant engineering application potential and widespread value.