7results about How to "Lower emission levels" patented technology

The invention discloses a fire grate, a combustor and a water heater, and relates to the technical field of water heaters, and the fire grate comprises a fire grate body and a spoiler. The fire grate body is provided with an air inlet runner and a mixing cavity, the mixing cavity is provided with an air outlet and communicates with the exhaust end of the air inlet runner, and an air inlet is formed in the air inlet end of the air inlet runner; the flow disturbing piece is arranged in the air inlet flow channel and comprises a flow disturbing body and a flow guiding part, the flow disturbing body is provided with a flow disturbing groove formed in a penetrating mode, the flow guiding part corresponds to the flow disturbing groove, the flow disturbing groove is used for dispersing and disturbing air flow flowing through the flow disturbing body, and the flow guiding part is used for guiding the air flow to flow from the flow disturbing groove to the mixing cavity. According to the technical scheme, the mixing uniformity of fuel gas and air in the fire grate can be enhanced, the stoichiometric ratio of the fuel gas at the fire grate outlet is reduced, the local high temperature phenomenon is avoided, and then flue gas emission of pollutants such as CO and NOx is reduced.

This disclosure provides a method, apparatus, device, storage medium, and program product for selecting a last-mile delivery station, which can be applied in the field of computer technology. The method includes: acquiring first travel characteristic data of the pickup party from a first starting point to multiple candidate stations; acquiring second travel characteristic data of the sender from a second starting point to multiple candidate stations; determining the influence coefficients corresponding to multiple sets of travel distance data and multiple sets of delivery distance data; and determining a target last-mile delivery station from multiple candidate stations based on the influence coefficients, the multiple sets of travel distance data, and the multiple sets of delivery distance data, wherein the influence coefficients are determined by a pre-trained prediction model.

This invention discloses a demand response scheduling method and system that considers the fairness of nodal carbon potential location, belonging to the field of low-carbon optimization scheduling technology. The demand response scheduling method considering the fairness of nodal carbon potential location includes: establishing a comprehensive energy system equipment model and a distribution network carbon emission flow model; establishing a hybrid index model considering the fairness of nodal carbon potential location; establishing a load-side dynamic demand response model based on the hybrid index model to generate carbon price signals; constructing a two-stage low-carbon optimization scheduling model considering the uncertainty of renewable energy output, with system operating costs, nodal carbon potential location fairness index, and carbon emission costs as optimization objectives. The optimal scheduling scheme is obtained through iterative solutions by determining unit output plans in the pre-scheduling stage and adjusting load distribution in the subsequent scheduling stage. This invention reduces system operating costs, achieves refined allocation and fair control of carbon emission responsibility in the comprehensive energy system, and improves the overall low-carbon operation level and the degree of reasonable carbon responsibility sharing of the system.

This disclosure provides a method, apparatus, device, storage medium, and program product for selecting a last-mile delivery station, which can be applied in the field of computer technology. The method includes: acquiring first travel characteristic data of the pickup party from a first starting point to multiple candidate stations; acquiring second travel characteristic data of the sender from a second starting point to multiple candidate stations; determining the influence coefficients corresponding to multiple sets of travel distance data and multiple sets of delivery distance data; and determining a target last-mile delivery station from multiple candidate stations based on the influence coefficients, the multiple sets of travel distance data, and the multiple sets of delivery distance data, wherein the influence coefficients are determined by a pre-trained prediction model.

The invention discloses a heavy duty gas turbine low-nitrogen combustion system and a control method, and relates to the technical field of gas turbine combustion, the system comprises a gradient aperture fuel nozzle, a self-adaptive premixing section, a dual-mode on-duty combustor and a steam dilution system; inlets of the gradient aperture fuel nozzles and the dual-mode on-duty burner are connected with the fuel distributor, the gradient aperture fuel nozzles are distributed in the circumferential direction of the head of the combustion chamber, outlets of the gradient aperture fuel nozzles are connected with the self-adaptive premixing section, the self-adaptive premixing section is connected with the combustion chamber, and the dual-mode on-duty burner is arranged in the center of the combustion chamber. A plurality of spray holes are formed in the wall surface of the gradient aperture fuel nozzle blade, and the apertures are gradually reduced along the fuel flowing direction; the self-adaptive premixing section comprises a front fuel section and a rear mixing section, the front fuel section is provided with guide vanes, and turbulent flow ribs are arranged on the inner wall of the rear mixing section. According to the control method, efficient and clean operation is achieved by obtaining operation data, digital twin model analysis and reinforcement learning optimization parameters.

This invention discloses a combined cooling, heating, and power (CCHP) system, relating to the field of solar energy utilization technology. It includes an energy storage medium, a heat collection subsystem, a supercritical organic Rankine cycle (SOCCR) subsystem, and a refrigeration subsystem. The SOCCR subsystem can output electricity and heat load; the refrigeration subsystem can output cooling load. The energy storage medium can be heated in the heat collection subsystem. Energy storage medium leaving the heat collection subsystem can enter the SOCCR subsystem, providing a first driving heat energy for the SOCCR subsystem's output of electricity and heat load. Energy storage medium leaving the SOCCR subsystem can enter the refrigeration subsystem, providing a second driving heat energy for the refrigeration subsystem's output of cooling load. This invention can improve energy conversion efficiency.

The application discloses a park heat energy grade layering matching and full privacy protection multi-agent energy-carbon collaborative scheduling method, relates to the technical field of comprehensive energy system operation and control, and comprises the following steps: S1: according to the temperature range, heat energy is divided into multiple grade levels, and actual heat of different grades is converted into equivalent work for representing the quality value; S2: a multi-agent energy-carbon collaborative optimization scheduling model is constructed, which comprises a local optimization objective function of each agent, a local constraint condition and a multi-agent global coupling constraint; S3: an alternating direction multiplier method is adopted, and only coupling variables are exchanged between each agent and a park collaborative layer for iterative solving, so that the multi-agent energy-carbon collaborative optimization scheduling model is solved under the premise of protecting the internal data privacy of each agent, and an optimal collaborative scheduling strategy is obtained. Therefore, the overall heat energy utilization efficiency of the park is significantly improved, and the collaborative optimization of energy efficiency and carbon emission reduction is realized.