1410 results about "Plant" patented technology

The invention belongs to the technical field of robot control, particularly provides a force feedback teleoperation control system and method for the tail end of a mechanical arm of a quadruped robot, and aims at the key challenges that control errors are caused by communication time delay and soft obstacles are difficult to recognize in a dynamic environment. Modeling and judgment are conducted on the contact state of the tail end of the mechanical arm in advance, and feedforward control and buffer adjustment oriented to communication time delay are achieved. And meanwhile, a dynamic semantic map is constructed in combination with multi-source sensing information, and soft obstacle reasoning and path optimization are performed by fusing a tail end force sense change trend, so that the recognition and avoidance capabilities of the system in a complex and invisible obstacle environment are remarkably improved. The system has good perspectiveness, self-adaptability and high redundancy safety characteristics, is suitable for multi-task inspection operation of industrial sites such as a thermal power plant, and is especially suitable for a remote man-machine cooperative operation scene in a narrow space.

The invention provides a thermal power plant flexibility transformation and deep peak regulation optimization control method and system, and relates to the technical field of control engineering, and the method comprises the steps: collecting the omnibearing operation data of a thermal power plant to construct a multi-level thermal power plant dynamic nonlinear model, carrying out the real-time identification of model parameters, generating an identification result, and building a multi-dimensional thermal power plant operation characteristic database; based on the thermal power plant operation characteristic database, designing a transformation scheme, and performing visual simulation and optimization to obtain a flexible transformation result; constructing a multi-objective optimization function, formulating an adaptive deep peak regulation optimization control strategy, carrying out short-term and medium-term prediction on a power grid load, generating an initial control strategy based on a prediction result, carrying out online optimization and adjustment, and continuously evaluating and optimizing the control strategy.

The invention relates to a coal conveying control system and method for a thermal power plant. The method comprises the following steps that S1, operation data of a coal conveying system are collected in real time through a preset multi-source sensor network; s2, constructing a time sequence prediction model based on an attention mechanism, and dynamically predicting the operation state of the coal conveying system; s3, executing real-time optimization calculation at the edge calculation node, and generating a control instruction set including the belt speed, the coal feeder opening degree and the transfer station cooperation strategy; s4, the control instruction is issued to an execution mechanism through a 5G-MEC network, and meanwhile key data is uploaded to a cloud end for model iteration updating; and S5, dynamically adjusting a safety protection threshold value based on an equipment vibration spectrum analysis result, and triggering a hierarchical protection mechanism when a coal blockage risk is detected. The method has the effects of effectively improving the performance of the coal conveying system and being green and energy-saving.

The invention provides a thermal control loop quality monitoring method for a thermal power plant, and aims to improve the thermal control loop performance of the thermal power plant. According to the method, firstly, relevant operation parameters of a PID control loop are collected online in real time and normalized, then performance is evaluated by using a minimum variance or a generalized minimum variance, and actual, ideal and historical optimal indexes are compared. And aiming at the condition that the performance is influenced by unreasonable PID parameters, data-driven modeling and an advanced control technology are utilized to optimize the parameters. And setting a threshold range, and respectively triggering alarm and carrying out trend prediction and early warning on the performance index deviation and long-term performance degradation problems. A control strategy is adjusted according to evaluation and detection information, the effect is continuously monitored, key factors are found in combination with multi-dimensional data fusion and big data analysis, and a rule is excavated. Performance reports are generated regularly, key indexes are displayed, iterative optimization is carried out based on results of all links, it is guaranteed that the loop is in the optimal state, and the reliability and efficiency of thermal power plant operation are improved.

The invention relates to a power plant temperature control system safety protection device and system. The power plant temperature control system safety protection device comprises a redundant temperature acquisition module, a temperature control safety processing module, an actuator control protection module, an intelligent strategy recovery module and a communication and alarm module. Through multi-point temperature data acquisition, abnormal trend intelligent identification and local linkage control, the device realizes quick response and protection control of temperature control abnormity of a key area of a boiler thermodynamic system. The system supports data interconnection with a DCS platform and an alarm analysis platform, has local independent operation and remote centralized management capabilities, and is suitable for temperature control safety protection of high heat load areas such as boilers of coal-fired power plants.

The invention discloses an intelligent regulation and control method and system for a dust removal system of an iron and steel enterprise, and the method comprises the following steps: collecting real-time data according to a set frequency, including the operation parameters of each dust production point, the opening degree of a valve, and the operation parameters and exhaust condition parameters of a dust remover, dust conveying equipment and a fan, and dividing the data into operation variables and regulation variables; a hybrid neural network model is constructed, the hybrid neural network model comprises an LSTM-GPT-Neo backbone network, a DenseNet branch network and a meta learning feature fusion correction layer, and the LSTM-GPT-Neo backbone network fuses an LSTM architecture and a GPT-Neo architecture; inputting time sequence data to carry out model training and optimization; and inputting the data into the trained hybrid neural network model for calculation and analysis, and outputting the opening degree of the valve and the optimal operation parameters of the fan. Under the condition that the dust removal efficiency and stability are guaranteed, the electricity utilization cost of the steel plant can be remarkably reduced, and the problem of equipment loss caused by excessive dust emission or excessive air volume due to insufficient air volume is avoided.

The invention relates to the technical field of boiler control, and discloses a gas-fired boiler starting control method and system. The method comprises the following steps: acquiring initial operation parameters such as hearth pressure, water inlet temperature, flue gas oxygen content and fuel valve opening of the gas-fired boiler, establishing a thermodynamic state model in a starting stage according to the initial operation parameters, and dividing dynamic response thresholds of different load intervals; and calculating a theoretical fuel supply rate of the combustor based on the model, generating and dynamically updating a fuel supply correction coefficient in combination with a threshold value, synchronously marking key nodes according to abnormal distribution of hearth temperature, and adjusting a spatial air distribution strategy of the combustor. And after the correction coefficient is subjected to multi-stage buffer smoothing, a fuel control instruction is output to an executing mechanism. And switching to steady state control after the boiler reaches the preset load. The method adapts to the characteristics of a power plant unit, so that the starting process is more stable, the damage to a heated surface and pollutant emission are reduced, and rapid grid connection is assisted.

The invention relates to the technical field of power plant intelligent adjustment, and discloses a power plant load intelligent adjustment method and system. The method comprises the following steps: distributing and deploying sensors according to field equipment of a power plant, and constructing a distributed sensor network based on the sensors; establishing a single time scale control framework according to the distributed sensor network; a heterogeneous delay compensation mechanism is constructed based on a distributed sensor network and a single time scale control framework; constructing a local linear matrix inequality constraint condition of each node, obtaining an optimal solution of the local linear matrix inequality constraint condition according to a heterogeneous delay compensation mechanism, and then generating an optimal control strategy of each node; the method comprises the following steps: constructing a global Lyapunov function based on a distributed sensor network and a single time scale control framework; and the change rate of the global Lyapunov function is monitored in real time, and corresponding equipment is intelligently and dynamically adjusted according to the optimal control strategy. The method achieves the intelligent regulation and control of the power plant load, is good in stability, is high in adaptability, and is short in regulation and control period.

A computer-implemented method (100) for operating a plurality of devices (21-25) in an industrial plant (1), comprising the steps of: —obtaining (110) at least the location (21a) of a to-be-controlled device (21) within the industrial plant (1); —obtaining (120) at least the locations (22a-25a) of other devices (22-25) in the vicinity of the to-be-controlled device (21); —determining (130), based at least in part on a ruleset (3) with rules that are dependent at least on the locations (22a-25a) of the other devices (22-25), at least one action (4) that may be performed by the to-be-controlled device (21); and —causing (140) the to-be-controlled device (21) to perform the at least one action (4).

The invention discloses a thermal power plant inspection robot path planning method, device and equipment and a storage medium, and the method comprises the steps: carrying out the semantic analysis and information extraction of an inspection task instruction, an equipment document and a historical inspection log through a large language model; obtaining a semantic importance score, a perception modal demand, recommended residence time and a shooting angle of each inspection target; establishing a multi-target path optimization model based on an improved ant colony optimization algorithm, and inputting the semantic importance score, the perception modal demand, the recommended residence time and the shooting angle into the multi-target path optimization model to obtain a joint scheduling sequence; the thermal power plant inspection robot is controlled to execute the joint scheduling sequence to perform autonomous navigation and perception tasks, the current path planning is adjusted according to the task completion condition, the intelligence, autonomy and adaptability level of the inspection robot can be remarkably improved, and the speed and efficiency of path planning of the thermal power plant inspection robot are improved.

The invention discloses a hydraulic power plant operation remote monitoring method and system based on artificial intelligence, and relates to the technical field of hydraulic power plant operation remote monitoring, and the method comprises the steps: collecting data in real time, transmitting the collected data to an edge calculation node, carrying out the preprocessing, and carrying out the feature extraction of the preprocessed data, and carrying out data fusion by combining a multi-modal data fusion algorithm, carrying out real-time analysis on the extracted features, predicting the operation trend and detecting abnormity, adjusting a prediction strategy according to a detection result, automatically adjusting a fault early warning threshold according to historical data of the equipment and the current working condition, and adjusting equipment operation parameters according to fault diagnosis and early warning results. The long-short-term memory network is adopted for data preprocessing and feature extraction, the network training process is optimized, data fusion, running trend prediction, anomaly detection, fault diagnosis and equipment running parameter adjustment are achieved, and the intelligent level, fault early warning accuracy and remote control capacity of a monitoring system are improved.

A control device (100) for controlling a number N, with N ≥ 1, of actuators (200) for actuating N optical elements of an optical system, comprising a control node (K2) coupling to the actuator (200) for providing a control voltage (V2) for the actuator (200), an amplifier (110) configured to receive a supply voltage (V1) provided at an input node (K1) and to provide an output voltage (V3) at an output node (K3), wherein the amplifier (110) comprises a plurality M, with M ≥ 2, of current sources (111, 112) comprising at least one charge-controlled current source (112) and a holding capacitor (113) for maintaining the level of the provided output voltage (V3), and a buffer coupled between the output node (K3) and the control node (K2). (120), which is designed to,to decouple the control node (K2) from the output node (K3) and to provide the control voltage (V2) for the actuator (200) based on the level of the output voltage (V3) of the holding capacitor (113).

The embodiment of the invention provides a state monitoring method and system for a hydraulic power plant computer monitoring system, and the method comprises the steps: carrying out the semantic embedded coding and global significance fusion of each state evaluation index and each assembly working condition data of the hydraulic power plant computer monitoring system through employing an artificial intelligence technology based on deep learning; according to the method, the global semantic feature representation of the state evaluation index and the component working condition is obtained, and then fine-grained semantic interaction matching is carried out on the state evaluation index and the component working condition, so that the working state of the hydraulic power plant computer monitoring system is comprehensively understood, and the state grade is intelligently evaluated. Therefore, potential faults of the hydraulic power plant computer monitoring system can be found in time, corresponding measures can be taken in time to prevent further expansion of the faults, and reliability and safety of the hydraulic power plant computer monitoring system are improved.

To provide a control device for a magnetic bearing that enables mode separation of a natural vibration mode with high precision and can design a stable controller with the degree of the controller made small.SOLUTION: With respect to a model for a rotary body controlled by radial magnetic bearings 1, 2, a status space model is structured by excluding a controller which controls a plant from a mode composition model having parameters of a mass matrix, a rigidity matrix, and an attenuation matrix identified and adjusted by a parameter estimation part, and a mode separation part 61 on the input side of controllers 1, 2 inputs a measurement signal of a displacement sensor from the radial magnetic bearings 1, 2 to perform mode separation with a mode separation coefficient. A mode composition part 62 on the output side of the controllers 1, 2 performs mode composition with a mode composition coefficient, and outputs a drive command signal (current, voltage) to electric magnets of the radial magnetic bearings 1, 2.SELECTED DRAWING: Figure 14

The invention relates to the technical field of information management, in particular to an informatization concrete mixing plant management system. In the invention, through periodically distributing the state labels of the inventory remaining amount of the concrete materials and generating the time sequence, a dynamic label mapping relation can be established for inventory management of the concrete materials, and the coupling degree between the inventory state and the consumption trend is enhanced. In a process of deducing a future state change path by using a state label sequence, frequency statistics and transition probability modeling between label pairs are adopted, so that inventory change trend prediction has a data-driven evolution capability, and the adaptability and foresight of prediction are improved. In the aspect of analysis of the running state of the conveying belt, running tracks of continuous time periods are generated in a potential energy space by means of a normalized vector form of running parameter tension, belt speed and motor load, so that the running state can specifically express and monitor the jamming trend in a multi-dimensional space, and the accuracy of abnormal state recognition is enhanced.

The present invention relates to systems and methods for managing plant irrigation schedules. The system includes an outer housing and an inner housing disposed in the outer housing. The inner housing receives growth media for supporting a plant. The system includes a first fluid reservoir and a second fluid reservoir. The second fluid reservoir is fluidically coupled to the first fluid reservoir via a control valve. The system further includes a plurality of sensors configured to detect ambient parameters of the growth environment and one or more parameters of the growth media. The system includes a control circuitry operatively coupled to the control valve, the plurality of sensors, and a terminal device. The control circuitry determines a fluid dispensing schedule from predefined plant profiles stored in the terminal device and operates the control valve to dispense the fluid to the growth media based on the fluid dispensing schedule.

Systems and methods for controlling industrial process automation and control systems can automatically, through the use of machine learning (ML) models and algorithms, extract plant assets from engineering diagrams and other plant engineering data sources. The systems and methods can establish asset relationships to create a plant asset registry and build an asset hierarchy from the plant assets. The systems and methods can generate an ontological knowledge base from the plant asset hierarchy, and provide an HMI for controlling the industrial process based on the plant asset hierarchy and the ontological knowledge base.

The invention provides an intelligent fan running state control system based on multi-source data, and relates to the field of wind power generation, and the intelligent fan running state control system comprises the steps: firstly, collecting a time sequence set of running parameter data of a target fan object and a time sequence set of electric field outlet power data, and then constructing a field domain-equipment two-stage information fusion mechanism; and performing mutual information guided deep interaction on the global time sequence characteristics of the electric field outlet power and the local time sequence characteristics of the single machine operation parameters, thereby optimizing the expression of the self-information of the fan operation state by using the time sequence characteristics of the electric field outlet power, determining the detection result of the fan operation state, and determining the early warning level and the control strategy. The method is used for breaking through the technical bottleneck of decoupling of global context information and local equipment state of wind power plant operation in a traditional method, and is beneficial to more accurate detection and control of the operation state of the fan.

The invention relates to a multi-modal oscillation suppression method and system for a network construction type power supply grid-connected system, belongs to the technical field of power system control, and solves the problem that multi-modal oscillation of a large-scale inversion type power supply grid-connected system cannot be cooperatively and rapidly suppressed. The method comprises the following steps: when a system generates broadband oscillation, collecting grid-connected point voltage and current signals of each power station to extract a dominant oscillation mode of each power station; constructing a modal coupling matrix based on the dominant oscillation mode to extract modal coupling strength; identifying a key path of oscillation propagation in the dominant oscillation mode, and extracting a path weight and a power station participation degree; calculating the comprehensive power regulation and control weight of each power generation station in multiple modes so as to determine the power regulation quantity; and distributing the power regulation quantity to an energy storage device, a static var generator (SVG) and each power generation station according to oscillation characteristics of different frequency bands so as to cooperatively suppress multi-modal oscillation. The active / reactive oscillation energy is cooperatively adjusted, so that the oscillation energy of each frequency band is effectively suppressed.

The invention discloses a regulation and control method for stably improving the water quality of water produced by a reverse osmosis system of a power plant, and relates to the field of water quality regulation and control, which comprises the following steps: configuring a water inlet flow regulation device, performing initial setting, configuring the position of an online sensor, performing data acquisition, setting judgment rules of turbidity, differential pressure and transmembrane pressure difference, and determining the water quality. And configuring a flocculant adding device and adding control logic, configuring a backwashing and adding linkage control state machine, and carrying out data smoothing, trend analysis and self-adaptive threshold adjustment. When the water quality of the raw water fluctuates for a short time or the pollution load is increased, dynamically adjusting the water inlet flow, the backwashing intensity and the flocculant adding concentration based on parameters such as turbidity, pollution index, conductivity and pressure difference which are acquired by an online sensor in real time; and meanwhile, the concentrated water reflux proportion and the high-pressure pump frequency are automatically corrected by continuously calculating the desalting rates of the first-stage and second-stage reverse osmosis devices, so that the running state of the membrane component is kept in an optimal interval.

The embodiment of the invention provides a supercritical steam turbine rotor temperature and stress state real-time monitoring method and device, and the method comprises the steps: obtaining the operation parameters of a current steam turbine generator unit rotor from a DCS system of a current power plant, and inputting the operation parameters into a pre-trained graph convolution model, the graph convolution model outputs corresponding reconstruction results of a peak-shaving rotor temperature field and a stress field, and according to the reconstruction results of the peak-shaving rotor temperature field and the stress field, the temperature state and the stress state of the current steam turbine generator unit rotor in the peak-shaving operation state are determined; the temperature and stress states of the steam turbine rotor can be monitored in real time under the background of depth peak regulation normalization, and safe and reliable operation of a generator set is guaranteed.

The invention discloses a wind power plant inertia damping adaptive regulation and control method and system fused with a deep Q network, and belongs to the technical field of power systems. The method comprises the following steps: establishing a doubly-fed wind turbine generator, a converter mathematical model and an interaction model of a wind power system connected to a power grid, and analyzing the grid-connected stability of the wind power system; the dynamic response characteristic of providing inertia support by the VSG system under different conditions is researched, and the primary frequency modulation capability is improved; a depth Q network is adopted to select rotational inertia and a damping coefficient, a damping inertia adaptive control strategy is improved, cooperation of inertia support and frequency response capability improvement of a wind turbine generator and continuous and smooth adjustment of active power is realized, the stability of a power grid is improved, and the operation performance of a wind power plant is optimized. According to the method, dynamic optimal matching of inertia response, primary frequency modulation and the running state of a unit is achieved, auxiliary services are provided, meanwhile, the running safety and service life of a draught fan are guaranteed, and smooth and impact-free adjustment of active power is achieved.

State of the art systems used for industrial plant monitoring have the disadvantage that they fail to correctly assess reason for dip in performance of the plant and in turn trigger appropriate corrective measures. The disclosure herein generally relates to industrial plant monitoring, and, more particularly, to a system and method for development and deployment of self-organizing cyber-physical systems for manufacturing industries. The system monitors and collects data with respect to various parameters, from the industrial plant. If any performance dip is detected, the system determines corresponding cause, and also triggers one or more corrective actions to improve performance of the plant and different plant components to a desired performance level.

The invention discloses a creep fatigue state evaluation method and system for high-temperature nuclear power station equipment. The method comprises the following steps: acquiring operation state parameter data of the high-temperature nuclear power station equipment; determining a total strain range of the equipment based on the operation state parameter data; according to the total strain range, the allowable cycle index and the creep initial stress of the equipment are determined; determining the fatigue damage and the creep damage of the equipment by using the allowable cycle index and the creep initial stress respectively; and based on the fatigue damage and the creep damage, evaluating the creep fatigue state of the equipment. Creep-fatigue comprehensive damage full-period safety and service life analysis management of key components of the high-temperature power station can be realized, when equipment has risks, an evaluation result is fed back to a main control room in time to remind a user of focusing on attention and monitoring, and a corresponding technical scheme is provided for a health monitoring and service life prediction system of a digital nuclear power station and an in-service power station.

The invention relates to the technical field of coal-fired power plant environmental protection, in particular to a coal-fired power plant intelligent environmental protection island control system based on multi-source data fusion. Comprising a multi-source data acquisition module, a data transmission module, a data preprocessing module, a multi-source data fusion module, an operation state analysis module, an intelligent control decision module, a control instruction execution module and a man-machine interaction module. According to the coal-fired power plant intelligent environmental protection island control system based on multi-source data fusion, equipment operation, environmental monitoring, power grid, fuel and meteorological data are widely acquired through the multi-source data acquisition module, the limitation of a single data source of a traditional system is broken through, a distributed Kalman filtering algorithm is combined with a neural network algorithm for data fusion, and the data fusion efficiency is improved. According to the method, the incidence relation between data can be deeply mined, temperature and pressure parameters of desulfurization equipment and flue gas sulfur dioxide concentration data are subjected to fusion analysis, the reason of desulfurization efficiency reduction can be accurately positioned, and compared with traditional single data source judgment, the fault diagnosis accuracy is improved.

The invention relates to the technical field of space life science, in particular to a multi-axis control and acoustic levitation combined microgravity environment simulation closed cultivation device and a control system.The multi-axis control and acoustic levitation combined microgravity environment simulation closed cultivation device comprises two vertical sliding rail mechanisms, a first rotating servo motor is arranged on each vertical sliding rail mechanism in a sliding mode, and an outer rotating frame is driven to rotate in the first direction; the second rotating servo motor is fixed to the outer rotating frame and drives the cultivation chamber connected with the inner rotating frame to rotate in the second direction, and the acoustic levitation device is installed in the cultivation chamber. According to the invention, random gravity disturbance is introduced through random sliding on the sliding rail, and a microgravity environment with coupled random gravity and rotating speed double factors is constructed in combination with random rotation of double shafts; according to the method, the influence of microgravity on plant leaf respiration under the condition of no heat convection and air shear force can be deeply researched, the influence of air heat convection and shear force can be inhibited, the space microgravity cultivation condition can be truly simulated, and a reliable platform is provided for ground plant space cultivation research.

The invention provides an analysis method and device for flow, efficiency and water consumption rate of a hydraulic power plant generator set. A distributed flow measurement unit based on a domestic PLC controller and a domestic operating system is constructed, and each unit is independently configured with the flow measurement unit to realize decentralization of data acquisition and processing; active power, guide vane opening, water head difference, instantaneous flow and volute inlet pressure data are collected in real time through a distributed flow measurement unit, and a unit efficiency and water consumption rate calculation model is dynamically fitted based on the data; sending an output result of the calculation model to a monitoring system through an anti-interference communication network, wherein the communication network adopts layered wiring and a double-shielded twisted pair to reduce the signal bit error rate; and the IMC integrated management and control platform is used for remote monitoring and diagnosis of the flow measurement unit, and seamless connection with a smart power plant data medium station is realized. The system reliability and the maintenance flexibility are improved, and the autonomous controllable requirements of domestic substitution and intelligent power plants are comprehensively met.

The embodiment of the invention provides a fault diagnosis method and system for a thermal power plant auxiliary engine system based on a neural network, and the method comprises the steps: firstly extracting pressure data and flow data of an outlet pipeline of a water feed pump system from an edge side, and removing noise interference in the pressure data and the flow data through data filtering processing; a data processing technology based on deep learning is introduced to carry out multi-scale time sequence analysis on pressure data and flow data of the feed pump system so as to capture time sequence change characteristics of the pressure data and the flow data, and then bidirectional interaction analysis is carried out on the pressure time sequence characteristics and the flow time sequence characteristics so as to obtain the time sequence change characteristics of the feed pump system. The dynamic response relation of the outlet pipeline pressure relative to the flow change is excavated, and whether the feed pump system breaks down or not is intelligently recognized according to the dynamic response relation. Therefore, real-time monitoring and accurate diagnosis of the feed pump system can be realized, the accuracy and timeliness of fault diagnosis are improved, and meanwhile, the intelligent level of fault identification is improved.

The invention relates to a PAC adding control method, system and device for an intelligent water plant and a medium. The method comprises the steps that multiple anti-interference processing and standardized feature extraction are conducted on historical operation data, and a temperature flow coupling data set is constructed by implementing double barrel separation processing on the basis of the water inlet temperature and flow; in each subset, establishing a steady-state prediction equation of the PAC dosage and the inflow and outflow water turbidity through a dynamic balance equation and logarithmic space mapping; carrying out online parameter calibration by adopting a recursive mean square error minimization algorithm, and forming a self-adaptive control model in combination with offline simulation residual error correction; and deploying the adaptive control model to a PAC dosing control end, dynamically adjusting control parameters through error back propagation of a real-time data stream and a prediction result, and generating an optimal dosing instruction. The technical problems that in a traditional scheme, due to dynamic changes of water inlet conditions, model adaptability is poor, precision is poor, and adjustment depends on artificial experience are solved, and accurate automatic control over coagulant adding under complex working conditions is achieved.

The invention relates to a method for the temporary fixing of micro-electro-mechanically movable elements (100), in particular those that can be used in equipment for semiconductor technology. In the method for temporarily fixing an element (100) that is micro-electro-mechanically movable relative to a basic structure (101) in at least one degree of freedom by at least one actuator (102), the fixing is carried out by filling at least one cavity (103) provided for the movement of the element, wherein - the material (202) intended for filling the at least one cavity (103) is introduced into the at least one cavity (103) in gaseous form in such a way that the material (202) is adsorbed and fills the at least one cavity (103) in layers, and - wherein at least one surface (200) of the movable element (100) to be kept free of the material (202) intended for filling is heated to a temperature above the sublimation temperature of the material (202) in order to prevent adsorption of the material (202) on the at least one surface (200) to be kept free. The invention also relates to a micro-electro-mechanically movable element (100) which is or was fixed by a method according to the invention.