4423 results about "Heat supply" patented technology

The invention relates to the technical field of system control data processing, in particular to a waste heat recovery heat supply system based on an injection type heat pump, which comprises a multi-source data acquisition module, a genetic algorithm optimization module, a dynamic mode switching module, a thermal stress suppression module and a self-learning updating module. The multi-source data collection module collects industrial waste heat temperature, power grid load and heat supply network backwater flow data through a temperature sensor array and a flow metering device, and feature vectors are extracted through sliding window standardization and principal component analysis. And heat transfer end difference fluctuation is restrained through closed-loop feedback. And the self-learning updating module constructs a multi-parameter incidence matrix to dynamically adjust the weight of a fitness function, and realizes parameter iterative optimization in combination with a historical optimal solution caching mechanism. The problems that the industrial waste heat recovery efficiency is low, thermoelectric coupling adjustment is delayed, and thermal stress of equipment is accumulated are solved.

The invention discloses an intelligent heat supply pipe network optimization control method and system based on the Internet of Things, and relates to the technical field of heat supply pipe network optimization control, and the system comprises environmental parameter collection modules which are distributed in different areas covered by a heat supply pipe network and are used for collecting environmental data of each area in real time; and the Internet of Things control platform is used for receiving the environmental data from the environmental parameter acquisition module. According to the intelligent heat supply pipe network optimization control method and system based on the Internet of Things, flow and temperature data are monitored in real time, the valve opening degree is accurately controlled according to a PID control algorithm, a closed-loop control system is formed, heat supply demand changes are quickly responded, energy waste is reduced, heat supply quality is guaranteed, meanwhile, energy consumption is reduced, and the energy utilization efficiency is improved; environmental parameters and user heat using habit changes are monitored in real time, a heat supply strategy is adjusted in time, heat supply demand fluctuation is quickly responded, and it is ensured that heat supply always meets the actual demand of a user.

The invention relates to the technical field of heat supply system temperature control, and discloses a temperature control method of an air source heat pump heat supply system. The method comprises the following steps: establishing a temperature field model according to temperature-pressure-flow data of the head and tail ends of a building and each region, and obtaining distribution characteristics; analyzing and predicting a thermal load based on the temperature rise and drop rate and the heat storage coefficient; determining a water supply temperature correction coefficient and a valve opening degree through fuzzy control in combination with an outdoor temperature trend; generating a partition start-stop sequence according to the partition start-stop sequence; and finally calculating heat pump operation frequency and water pump rotation speed. According to the method, the partition control strategy based on temperature field modeling is established, the technical means such as unit pressure compensation and partition alternate starting are combined, accurate temperature control over all the heat supply areas is achieved, and the problem of uneven heat supply in the prior art is effectively solved.

The invention provides a heat supply load prediction and scheduling method based on energy storage peak regulation, and the method comprises the steps: obtaining multi-modal data of a heat supply system, carrying out the preprocessing of the multi-modal data, and constructing a space-time joint feature matrix based on the preprocessed multi-modal data; inputting the spatio-temporal joint feature matrix into a multi-modal depth load prediction model for heat supply load prediction to obtain a heat supply load prediction result; the multi-modal deep load prediction model is obtained by training a deep learning prediction model fused with physical constraints through system historical data; based on the heat supply load prediction result, a distributed energy storage scheduling strategy is generated in combination with a multi-agent reinforcement learning algorithm based on an MADDPG framework, and scheduling operation of the heat supply system is carried out according to the distributed energy storage scheduling strategy; and the energy utilization efficiency and the power grid stability are improved.

The invention discloses a cold and heat supply unit and process capable of deeply utilizing a heat source in the technical field of absorption refrigeration. The cold and heat supply unit comprises a refrigerating unit, a heating unit, a heat source flash tank and a high-pressure absorber. The refrigerating unit utilizes an external heat source to produce cold energy, the heating unit utilizes the heat source used by the refrigerating unit to produce cold energy so as to cool circulating water of the refrigerating unit, and the heat source flash tank utilizes the heat source discharged by the high-pressure absorber to heat; the absorption refrigerating unit is optimally designed, so that the absorption refrigerating unit can deeply utilize a heat source and supply cold and high-level heat energy at the same time, the problem that the heat source utilization rate of the unit is not high is solved, and the requirement for high-temperature heat energy in the production process is met; and low-grade waste heat is converted into cold energy and high-level heat energy, transition and upgrading of energy quality can be achieved, the unit has the cold and heat combined supply function, and the application scene is greatly expanded.

The invention discloses an intelligent heat supply two-network balance control method and system based on fuzzy reinforcement learning, and the method comprises the steps: obtaining real-time operation parameters which comprise the return water temperature, the heat load and the environment temperature; based on the real-time operation parameters, preliminarily generating a preliminary adjusting quantity of the opening degree of the valve; operation data are analyzed in real time, fuzzy rule parameters are adjusted according to feedback of the reinforcement learning module, and the fuzzy rule parameters comprise priority weights and membership functions of return water temperature deviation; and according to the preliminary adjustment effect, a deep learning algorithm is utilized to construct a state-action space equation, an energy consumption design reward function is considered, the priority weight is further optimized and fed back to a dynamic optimization module of a fuzzy rule, and secondary adjustment of the valve opening degree is output to an execution mechanism. The problems that a traditional fuzzy control method cannot adapt to complex working condition changes in the operation of the heat supply two-network, the adjusting strategy lags behind, the adjusting effect is not accurate enough, and the adjusting efficiency and energy consumption are affected are solved.

The invention discloses a heat supply system multi-working-condition scheduling control method based on a large model and multiple agents. The method comprises the steps that a heat supply system digital twinborn model is established; the method comprises the following steps: setting a scheduling control center management agent, predicting a thermal load demand by adopting a large language model and combining a thermal load prediction model, dividing a system into a plurality of operation conditions, and setting a plurality of condition sub-agents corresponding to the operation conditions; the dispatching control center management agent judges whether the working condition sub-agents under the corresponding operation working conditions are triggered to start working or not, and meanwhile heat load demand values, predicted through a large language model, of all time periods in the future and the heat production amount of the new energy heat supply unit are transmitted to the corresponding working condition sub-agents; and the working condition sub-agent adopts a large language model to establish a system optimization scheduling control knowledge graph based on the large language model, obtains a scheduling control strategy of each device on the source network load side of the heat supply system, and feeds back the scheduling control strategy to the digital twinborn model for guiding the actual operation management of the heat supply system.

The invention relates to the technical field of communication and network transmission, in particular to a terminal data acquisition and transmission method for a long-distance heat supply pipeline. The method comprises the following steps that long-distance heat supply pipeline laying area data are obtained, pipe network laying form analysis is carried out, and single-pipe branched heat supply pipe network data are obtained; constructing a pipeline laying model based on the data of the single-pipe branched heat supply network; performing intelligent heat energy flow simulation based on the pipeline laying model, and calculating heat loss in the heat energy flow simulation process to obtain heat loss data; performing thermal protection thermal insulation material integration on the pipeline laying model based on the heat loss data to obtain composite thermal insulation material data; and identifying a straight pipe section of the pipeline laying model, and calculating the minimum thick wall of the straight pipe section to obtain minimum thick wall data. The heat efficiency, the stability and the intelligent management capability of the pipeline system are improved based on the communication and network transmission technology.

The invention discloses a low-vacuum circulating water heating dynamic load matching method based on AI regulation and control, and the method comprises the following steps: step S100, multi-source data real-time collection: building thermal environment parameters are synchronously collected through a temperature sensor array, a vacuum pressure transmitter, a flowmeter and an environment monitoring unit which are deployed in a distributed manner; according to the method, the control period is compressed to the minute level, compared with traditional PID control, the response speed is increased by several times, the indoor temperature can be controlled more accurately, and meanwhile the failure rate of equipment is reduced. According to the sudden weather coping mechanism, heat supply strengthening can be started 30 minutes ahead of time under extreme weather such as snowstorm weather, and the stability of the system is ensured. The method is characterized in that the prediction advantage of deep learning and the optimization capability of model prediction control are deeply combined, and the control problem of multivariable strong coupling of the low vacuum system is solved through a verification system of multi-source data fusion and virtual-real combination.

The invention discloses a heat supply system capable of storing energy across seasons based on renewable energy sources, relates to the technical field of renewable energy source utilization, and solves the problems of response lag, energy efficiency reduction and poor system stability caused by reaction kinetics nonlinearity and long-term operation uncertainty in an existing heat supply system. The system comprises an environmental data acquisition module, a dynamic load prediction module, a heat storage reaction control module, a nonlinear compensation module, a material performance monitoring module and a long-term operation management module. Real-time response to external environment changes, accurate prediction of heat supply requirements, dynamic adjustment of reaction conditions, continuous monitoring of material performance and optimization adjustment of operation strategies are achieved; through cooperative work of multiple modules, the quick response capacity and energy conversion efficiency of the system on external load changes are remarkably improved, the side reaction risk is effectively restrained, the service life of the system is prolonged, and long-term stable and efficient operation of the energy storage and heat supply system in a complex environment is guaranteed.

The invention provides an optimization method of a heat supply system, which comprises the following steps: acquiring a real-time heat source operation state, a power grid load, a user demand and an environment constraint condition of the heat supply system, extracting characteristic information of sudden cooling and equipment fault information, and determining a heat source priority and a response speed initial value; acquiring parameters required by a cost function corresponding to the adjusted heat source priority sequence, determining cost weights of different heat sources after adjustment through a linear weighting method, and outputting a cost optimization sequence; identifying a power grid load according to the cost optimization sorting and the environment constraint condition, and if the power grid load is lower than a preset threshold value, calculating a heat source output distribution initial scheme through a dynamic programming algorithm to obtain a preliminary output matrix; and according to the optimized output distribution scheme, summarizing the total heat source emission amount, and if the total heat source emission amount exceeds a preset threshold value, reducing the high-emission heat source output proportion through an iterative adjustment algorithm, and outputting a target output distribution matrix.

The invention relates to the technical field of collaborative optimization, and discloses a gas power generation and heat supply collaborative optimization method and system based on a particle swarm algorithm. The method comprises the following steps: acquiring and preprocessing operation parameters of the gas power generation and heat supply system to obtain state characterization data, constructing gas resource limitation and equipment operation constraints based on the data, and generating an optimization objective function; importing the optimized objective function into a particle swarm algorithm for initialization to obtain an initial particle set; a particle swarm is evolved through a search operation based on a gas energy critical path, near-end strategy optimization is carried out, particle swarm parameters are dynamically adjusted, and a target configuration scheme is obtained. And finally, establishing a multi-level cooperative control structure, executing hierarchical scheduling, and generating a cooperative optimization control instruction. The gas power generation and heat supply system is subjected to collaborative optimization control through an intelligent optimization algorithm, the influence of dynamic factors such as gas concentration fluctuation and energy demand change is overcome, and efficient utilization of gas energy is achieved.

The invention belongs to the technical field of building heat supply and intelligent power grid demand response control, and particularly relates to an intelligent power grid-oriented heat pump dynamic preheating and demand response control method, which comprises the following steps of: identifying building thermal characteristic parameters, introducing real-time environment variables such as external meteorological parameters and the like and thermal resistance and thermal capacity characteristics of a building, excessive or insufficient preheating caused by only relying on a timer is effectively avoided, and the balance of accurate heat supply and energy conservation is achieved; by adjusting the preheating starting time and the peak period temperature control strategy, the heat pump can dynamically respond according to the electricity price or the power grid demand signal, and the peak load is translated or reduced, so that the dual purposes of economic benefits, energy conservation and emission reduction are achieved; and formulating a peak period control strategy and formulating a non-peak period control strategy. According to the control method, actual thermal characteristics and external meteorological conditions of a building are taken as cores, load reduction in the peak period of a power grid and indoor thermal comfort are taken as double targets, and preheating time self-adaption, demand response flexibility and system operation reliability are achieved at the same time.

The invention relates to the field of heat supply heat load operation adjustment, and provides a continuous optimization heat supply load prediction method and system based on big data analysis learning, and the method comprises the steps: S1, configuring a data interface on a real-time library service module to receive real-time and historical operation data, the operation data comprises meteorological information data, heat exchange station heat load and room temperature qualification rate; and S2, preprocessing the collected operation data through a data processing module integrated on the heat supply platform service module, and analyzing the preprocessed operation data through an analysis algorithm stored in a relation module to determine a predicted heat load value of a next control period. The heat supply platform is combined, the big data analysis technology is used for learning and analyzing historical heat supply network operation data, proper correction and adjustment are carried out, the heat load of the heat supply system is effectively predicted, the method is closely combined with actual operation, prediction accuracy is remarkably improved, the operation process is simplified, energy use is optimized, and the operation efficiency and the system response speed are improved.

The invention discloses a secondary network heat supply temperature control method and device based on a negative pressure water mixing technology, and relates to the technical field of secondary network heat supply control. The secondary network heat supply temperature control method and device based on the negative pressure water mixing technology comprises the steps that S1, operation data, structure steady state data and thermal environment data in a current heat supply task are collected, and a standardized heat supply working condition data set is constructed after preprocessing; s2, local hydraulic fluctuation characteristics are evaluated, and the water mixing proportion, the water supplementing flow and bypass path configuration are dynamically adjusted; s3, jointly analyzing the temperature difference of supply water and return water, the heat deviation and the valve state, and driving water mixing adjustment and path optimization; s4, evaluating adjustment intensity and heat response deviation, and realizing abnormal disturbance response and adjustment strategy correction; and S5, recording negative pressure, uneven heat and cold and response data for subsequent diagnosis and steady state identification. The problems that a traditional water mixing control means is difficult to effectively inhibit pressure fluctuation of the water return end, local negative pressure is easily formed, and local energy waste and uneven heat distribution are caused are solved.

The invention provides an energy storage coupling heat pump heat supply system and a heat supply method. The system comprises a heat pump device, an energy storage device and a control module. The heat pump device heats the heat carrier to a first temperature, and the energy storage device further heats the heat carrier to a second temperature for heat supply. The control module dynamically regulates and controls operation of the heat pump device and the energy storage device according to the heat supply demand quantity and the external heat source state, the heat pump device and the energy storage device are preferentially operated in the off-peak electricity period to complete heat storage, and first enthalpy increment provided by the heat pump device and second enthalpy increment released by the energy storage device are dynamically distributed in the heat supply period. And diversified thermal load requirements are met. The system supports real-time adjustment of power distribution, preheating optimization and waste heat utilization, and has the characteristics of high efficiency, flexibility and energy conservation.

The invention discloses a dynamic load adaptive charging pile liquid cooling control method, relates to the technical field of thermal management, and solves the problems of temperature prediction deviation, local hot spot leak detection and protective supercooling caused by a simplified thermal model based on fixed parameters and key temperature points under rapid load fluctuation. Parameterization time-varying heat supply network parallel multi-rate observation is constructed, a heat exchange coefficient and node heat capacity are identified and calibrated through recursion with physical constraints, transmission time delay is merged into an extended state in an explicit mode, short-window high-frequency sampling and local temperature field reconstruction are triggered based on residual errors, and the time-varying heat supply network parallel multi-rate observation is achieved. A control sequence is issued in parallel by adopting hierarchical rolling type control with a second-order moment uncertainty constraint; based on the scheme, the short-term temperature field prediction precision and the local hot spot identification capability are improved, and the cooling resource directional utilization efficiency and the charging system operation continuity are remarkably improved.

The multi-energy complementary heat supply system comprises an external basic heat source, a middle-grade synergistic heat source, a high-grade energy storage heat source, an intelligent heating power mixing center and a central controller, and the intelligent heating power mixing center is used for heating heat source equipment of all levels and operation instructions of associated assemblies according to the operation instructions of the heat source equipment of all the levels and the operation instructions of the associated assemblies. Heat energy of different grades is mixed and allocated, and heat loads meeting the grade requirements of heat consumers are output to the heat consumers; the central controller is used for establishing a multi-target optimization model taking the minimum total operation cost of the system as a core target based on the electricity price prediction data and the different-grade thermal load prediction data, solving the multi-target optimization model and generating a system operation strategy under the condition that preset basic guarantee logic, economic operation logic and peak response and power grid interaction logic are met; and the generated system operation strategy is converted into an operation instruction for the heat source equipment of each level and the associated assembly, so that the reliability and the adaptability of operation control of the heat supply system are effectively improved.

The invention provides a heat supply digital twinborn intelligent regulation and control method, and relates to the technical field of intelligent regulation and control, and the method comprises the steps: executing millisecond-level high-concurrency numerical simulation according to a dynamic digital twinborn body, so as to obtain a simulation result; based on the simulation result, coupling the electricity price fluctuation data and the weather prediction data, performing future heat supply system multi-working-condition deduction, and dynamically optimizing a heat source unit start-stop combination and pipe network valve opening strategy to obtain a deduction result and an optimization strategy; generating an equipment regulation and control instruction set according to the deduction result and the optimization strategy; and an equipment regulation and control instruction set is issued to an edge computing node, a variable-frequency circulating pump and an electric control valve executing mechanism are controlled, and dynamic closed-loop regulation of the flow and the pressure of the heat supply pipe network is achieved. According to the invention, the pipe network flow and pressure regulation response time can be shortened to a second level.

The invention discloses an intelligent building heating and ventilation automatic control method and system based on digital twinning, and relates to the technical field of intelligent heat supply. The method comprises the specific steps that entity data of a building and a heat supply system are obtained, a building heat supply system twinborn model is constructed, and a digital twinborn automatic control system is constructed based on the building heat supply system twinborn model; deploying a multi-modal sensing network, and constructing a distributed data monitoring system; real-time data collected by the distributed data monitoring system is input into the digital twin automatic control system, and a dynamic control strategy is generated in combination with historical data; and the heat supply system is controlled to execute actions based on the dynamic control strategy, and feedback data are sent to the digital twin automatic control system for control strategy optimization. According to the invention, the response speed of the system is obviously improved, and the adaptability to complex and changeable environments is enhanced.

The invention relates to the technical field of heat transmission, in particular to a heat transmission data storage and management system based on an industrial big data platform, and the system comprises a self-adaptive acoustic baseline modeling module which generates a self-adaptive baseline model library for storing the mapping relation between a working condition area and a model; the abnormal deviation degree calculation module is used for calculating and generating an abnormal deviation degree; the health state evaluation module is used for generating a comprehensive health index representing the long-term service performance of the pipe network; and the closed-loop correction and scheduling module is used for determining a comprehensive risk level according to the comprehensive health index and the change trend thereof, generating an operation and maintenance scheduling instruction for dynamically adjusting an abnormal deviation degree calculation process and pipe network operation parameters, and realizing closed-loop feedback control. According to the invention, accurate identification and positioning of abnormal events such as leakage, third-party damage and the like are realized.

The invention relates to the technical field of intelligent heat supply, in particular to a multi-source energy fusion agricultural greenhouse intelligent heat supply and regulation and control system, which comprises a multi-source energy acquisition module for acquiring energy output parameters of a solar heat collector, a geothermal well and a biomass boiler in real time and monitoring external meteorological data of a greenhouse; the energy dynamic matching module is used for generating an energy scheduling strategy through a dynamic priority algorithm and outputting a target energy type and supplied energy; the energy storage buffer module is used for storing or releasing the supplied energy and outputting the temperature and the residual capacity of an energy storage medium; the multi-parameter coupling regulation and control module is used for generating a regulation and control instruction through an environment parameter coupling model; the actuating mechanism driving module is used for converting the regulation and control instruction into control signals of a heater, a fan and a humidifier; the self-learning optimization module is used for dynamically correcting the dynamic priority algorithm and the environment parameter coupling model; according to the invention, through multi-parameter cooperative regulation and dynamic optimization, crops are ensured to be in the optimal environmental conditions in different growth stages.

The invention discloses a heat supply system multi-stage emergency disposal scheduling method containing a large model. The method comprises the steps of forming a heat supply system emergency disposal scheduling large model; in the emergency prevention preparation stage, a large model is used for building a heat supply system multi-type emergency event question and answer database, and emergency knowledge intelligent question and answer, emergency event reason analysis, risk hidden danger query and emergency disposal scheduling measure recommendation are conducted; in the emergency response stage, the large model is guided to output an optimal feature combination representing various types of emergency events, and machine learning is adopted to identify the various types of emergency events; heat supply loss caused by an emergency event is evaluated by using the large model, a multi-level emergency disposal scheduling strategy is set, and an optimal emergency disposal scheduling scheme is obtained; and in an emergency disposal post-stage, analyzing the emergency disposal scheduling process by using the large model, establishing an emergency disposal scheduling plan library, and performing plan simulation, identifying potential vulnerabilities and strengthening the emergency disposal scheduling process by using the intelligent agent.

The embodiment of the invention provides an energy storage peak regulation system and method coupling waste heat recovery and water body heat storage. The system comprises a thermal power generation subsystem, a circulating water waste heat utilization subsystem, a water body heat storage subsystem and a heating subsystem. By recycling waste heat of circulating water, the heat efficiency of the unit can be improved under the same combined heat and power generation capacity, the coal consumption rate is reduced, and energy-saving and carbon-reducing transformation is assisted to be achieved. After the water body heat storage technology is coupled, the system can participate in peak regulation and peak service all the year round, and flexible transformation is assisted to be achieved; the diversified heat supply modes of steam extraction heat supply, circulating water waste heat recovery heat supply and heat storage water body heat supply assist in achieving heat supply transformation. The circulating water waste heat utilization technology and the water heat storage technology are deeply coupled with an existing combined heat and power generation unit, and energy-saving and carbon-reducing transformation, flexible transformation and heat supply transformation of the combined heat and power generation unit can be achieved at the same time.

The invention discloses an analogue simulation system for operation of a ground source heat pump and a gas boiler in a severe cold area. The analogue simulation system comprises a heat supply correction model building module, a ground source / gas heat supply estimation module, a heat supply dispatching distribution module and a correction model optimization module. According to historical operation data of a ground source heat pump subsystem, heat supply correction models associated with soil geology and environmental climate are constructed based on a single variable principle, a heat supply amount estimated value of the ground source heat pump subsystem is dynamically corrected, heat supply amount of a gas boiler subsystem and heat load required by a building group are estimated, heat supply output of the two subsystems is intelligently distributed, and heat supply efficiency is improved. And the heat supply amount correction model is optimized through the actual heat supply amount deviation. The method can effectively overcome the problem of ground source heat pump heat supply amount deviation caused by soil heat unbalance and climate fluctuation in a severe cold area, further improves the energy efficiency estimation precision of a heat supply system, facilitates optimization of a scheduling strategy, has dynamic compensation and model self-evolution capabilities, and can adapt to complex environmental changes.

A clothes drying device, including an air channel, an air path switching assembly, multiple clothes drying apparatuses, and multiple heating apparatuses. The air path switching assembly includes a first multi-position switching component and a second multi-position switching component. The first multi-position switching component and the second multi-position switching component are respectively provided between a hot air input section and intermediate heat source sections and between a hot air output section and the intermediate heat source sections; the first multi-position switching component and the second multi-position switching component have the same number of switching positions and are configured to work in conjunction to each other so that at least one clothes drying apparatus is selectively communicated with the corresponding hot air input section, the hot air output section, and the intermediate heat source sections so as to form a circulation loop.

The invention relates to the technical field of data processing, and discloses a heat supply network heat supply system control method based on a prediction model, and the method comprises the steps: S1, collecting historical operation data and real-time operation data of a heat supply network heat supply system, S2, carrying out the preprocessing of the historical operation data and the real-time operation data, and generating a standardized heat supply data set; by constructing a prediction model based on historical data and real-time data, the thermal load demand change in a future time period can be dynamically predicted, the problem of adjustment lag caused by dependence on a static model in a traditional control method is solved, the response ability of a heat supply system to extreme weather and user demand sudden change is improved, and the heat supply efficiency is improved. The stability and reliability of heat supply quality are guaranteed, the time hysteresis between a regulation and control instruction and the actual heat supply effect is reduced by analyzing the heat transmission characteristic and the delay effect of each node of the pipe network in real time, the phenomena of insufficient heat supply and energy waste caused by untimely regulation of the system are avoided, and the energy consumption is reduced. And the operation efficiency and the energy utilization rate of the heat supply system are improved.

The invention discloses a heat supply whole network collaborative dynamic optimization regulation and control method, system and equipment and a storage medium, relates to the technical field of heat supply control, and aims at solving the technical problems that in the prior art, thermal load prediction data is inaccurate, and heat transfer distribution cannot be optimized by a heat supply system, so that operation energy consumption is large. According to the technical scheme, the method mainly comprises the steps that building load prediction data and heating station load prediction data are acquired, and a load prediction model is constructed; training the load prediction model, and optimizing the model and pipe network data by using an ARX active regression algorithm; a heat supply whole network physical simulation model is constructed, and heat supply whole network thermal load data is obtained through calculation by using an MILP mixed integer linear programming algorithm; an optimal control strategy is obtained by optimizing a heat supply whole network physical simulation model and introducing a real-time feedback mechanism and an overall cooperative control module, optimization parameters of the heat supply whole network physical simulation model are automatically adjusted by using the optimal control strategy, and the heat supply whole network is continuously in an optimal operation state through automatic repeated iteration.

Methods and apparatus for anti-ice heat supply from waste heat recovery systems are disclosed. An apparatus for an aircraft, the apparatus comprising a fuel heat exchange system, an anti-ice heat exchanger, a waste heat recovery heat exchanger, and a conduit coupled to the fuel heat exchange system, the anti-ice heat exchanger, and the waste heat recovery heat exchanger, the conduit including a first portion and a second portion distinct from the first portion, wherein the first portion of the conduit carries a first portion of a thermal transfer fluid from the waste heat recovery heat exchanger to the anti-ice heat exchanger in which the thermal transfer fluid supplies anti-ice heat to a portion of the aircraft, wherein the second portion of the conduit carries a second portion of the thermal transfer fluid from the waste heat recovery heat exchanger to the fuel heat exchange system.

The invention discloses an offshore multi-energy combined supply system based on constant-pressure compressed carbon dioxide energy storage and an operation method of the offshore multi-energy combined supply system. The system comprises a working medium storage device, a heat energy storage device, a charging process module, a gas-liquid conversion loop, a discharging process module, a cold energy supply module and a heat energy supply module. By improving the energy release process structure of the compressed carbon dioxide energy storage system, the pressure of high-pressure carbon dioxide is reduced to normal pressure after primary expansion, so that low-temperature cold energy is generated while power is generated, consumption of stored heat is saved, residual stored heat is used for heat supply, the comprehensive utilization rate of energy is increased, and combined supply of cooling, heating and power is achieved; the system further comprises an integrated thermally-driven combined cooling and power supply subsystem capable of switching power generation / refrigeration modes, organic Rankine cycle and jet refrigeration cycle are highly integrated, investment cost and space occupation are reduced, redundant heat energy is flexibly and efficiently converted into electric energy or cold energy, and the flexibility of multi-energy combined supply of the system is improved.