Abstract: A system for adjusting and controlling temperature of an intelligent new energy farmhouse integrated with tunnel wind and solar energy. The system includes a house, a solar chimney system, a solar power supply system and a tunnel wind system. The house includes walls and a sunroom is mounted on outside of one of the walls; the house is provided with detection module being connected to master control module; the solar chimney system includes first Venturi tube provided on top of the house and connected to solar chimney which extends longitudinally along inside of the one of the walls; the solar power supply system is provided on top of the house; and the tunnel wind system comprises a water cellar with duct assembly provided inside the water cellar, one end of the duct assembly is connected to forced draft system and the other end is connected to interior of the house.

Abstract: A spray device with an adjustable spray range is provided, including a supporting wall frame, an upper top plate, a movable horizontal plate, a lifting and telescoping device, and an adjustable nozzle. The upper top plate is arranged at a first end of the supporting wall frame away from a ground, the movable horizontal plate is slidably connected to a side of the upper top plate away from the ground, the movable horizontal plate is slidably arranged along a length direction of the upper top plate, and the lifting and telescoping device is fixed at an end of the movable horizontal plate away from the upper top plate; and an end of the lifting and telescoping device away from the movable horizontal plate is connected to a support plate, the lifting and telescoping device is telescopic along a height direction of the support plate, and the adjustable nozzle is rotatably arranged on the support plate.

Abstract: Provided are an end-edge-cloud collaborative furnace temperature (FT) control method and system in a municipal solid waste incineration (MSWI) process, which relates to the field of FT control. Process data of the MSWI process are obtained in real time, and the acquire process data are processed. An FT prediction model is established according to received data, the prediction model is updated based on a self-correcting mechanism (SCM), and the established model is delivered to an edge side. An FT prediction model established and updated online on a cloud side is used to predict FT on the edge. An objective function is optimized by using a gradient descent method, an optimal control law (OCL) is solved, and an execution device is adjusted according to the calculated control law. This implements stable and accurate control of the FT in the MSWI process.

Abstract: Provided are an end-edge-cloud collaborative furnace temperature (FT) control method and system in a municipal solid waste incineration (MSWI) process, which relates to the field of FT control. Process data of the MSWI process are obtained in real time, and the acquire process data are processed. An FT prediction model is established according to received data, the prediction model is updated based on a self-correcting mechanism (SCM), and the established model is delivered to an edge side. An FT prediction model established and updated online on a cloud side is used to predict FT on the edge. An objective function is optimized by using a gradient descent method, an optimal control law (OCL) is solved, and an execution device is adjusted according to the calculated control law. This implements stable and accurate control of the FT in the MSWI process.

Abstract: Provided is an intelligent operational optimization method in the municipal solid waste incineration process, which belongs to both the field of municipal solid waste treatment and the field of intelligent optimization. The method includes: constructing a sample data set by collecting historical data in the municipal solid waste incineration process; with optimization objectives of nitrogen oxide emissions and combustion efficiency, establishing an SORBF neural network-based indicator model to characterize the mapping relationship between operational variables and optimization objectives in the municipal solid waste incineration process; and setting the established indicator model as evaluation functions of a multi-objective optimization algorithm, and obtaining optimal setting values of the operational variables by the multi-objective particle swarm optimization algorithm, so as to improve combustion efficiency while reducing concentrations of nitrogen oxide emissions.

Abstract: Provided is an intelligent operational optimization method in the municipal solid waste incineration process, which belongs to both the field of municipal solid waste treatment and the field of intelligent optimization. The method includes: constructing a sample data set by collecting historical data in the municipal solid waste incineration process; with optimization objectives of nitrogen oxide emissions and combustion efficiency, establishing an SORBF neural network-based indicator model to characterize the mapping relationship between operational variables and optimization objectives in the municipal solid waste incineration process; and setting the established indicator model as evaluation functions of a multi-objective optimization algorithm, and obtaining optimal setting values of the operational variables by the multi-objective particle swarm optimization algorithm, so as to improve combustion efficiency while reducing concentrations of nitrogen oxide emissions.

Abstract: A system for adjusting and controlling temperature of an intelligent new energy farmhouse integrated with tunnel wind and solar energy. The system includes a house, a solar chimney system, a solar power supply system and a tunnel wind system. The house includes walls and a sunroom is mounted on outside of one of the walls; the house is provided with detection module being connected to master control module; the solar chimney system includes first Venturi tube provided on top of the house and connected to solar chimney which extends longitudinally along inside of the one of the walls; the solar power supply system is provided on top of the house; and the tunnel wind system comprises a water cellar with duct assembly provided inside the water cellar, one end of the duct assembly is connected to forced draft system and the other end is connected to interior of the house.

Abstract: A dynamic modular neural network (DMNN) for NOx emission prediction in MSWI process is provided. First, the input variables are smoothed and normalized. Then, a feature extraction method based on principal component analysis (PCA) was designed to realize the dynamic division of complex conditions, and the prediction task to be processed was decomposed into sub-tasks under different conditions. In addition, aiming each sub-tasks, a long short-term memory (LSTM)-based sub-network is constructed to achieve accurate prediction of NOx emissions under various working conditions. Finally, a cooperative strategy is used to integrate the output of the sub-networks, further improving the accuracy of prediction model. Finally, merits of the proposed DMNN are confirmed on a benchmark and real industrial data of a municipal solid waste incineration (MSWI) process. The problem that the NOx emission of MSWI process is difficult to be accurately predicted due to the sensor limitation is effectively solved.

Abstract: The present application provides a spatial layered transmission method and a spatial layered transmission device. The spatial layered transmission method includes steps of: dividing to-be-transmitted data into at least two layers of spatial data; determining a symbol rate for each layer of spatial data, to enable an error between SERs for the layers of spatial data to be smaller than a predetermined error threshold; and encoding each layer of spatial data in accordance with the determined symbol rate, and transmitting the encoded layer of spatial data.

Abstract: The present application provides a spatial layered transmission method and a spatial layered transmission device. The spatial layered transmission method includes steps of: dividing to-be-transmitted data into at least two layers of spatial data; determining a symbol rate for each layer of spatial data, to enable an error between SERs for the layers of spatial data to be smaller than a predetermined error threshold; and encoding each layer of spatial data in accordance with the determined symbol rate, and transmitting the encoded layer of spatial data.