Intelligent control type integrated high-speed oil purifier

Abstract

The invention provides an intelligent control type integrated high-speed oil purifier. The oil purifier comprises an intelligent control module, a real-time monitoring module, a decision execution module, a self-learning feedback module and an intelligent fluid control module. The intelligent control module analyzes the pollution degree, flow, temperature, pressure and other data of the oil product in real time based on a fuzzy logic algorithm and a neural network algorithm, and generates an optimized control instruction; the real-time monitoring module collects various parameters of the oil product in real time through a sensor array; the decision execution module adjusts key parameters in the filtering, centrifuging and separating processes according to the control instruction so as to ensure the optimal state of the oil purification effect; the self-learning feedback module optimizes a control strategy by analyzing the real-time operation data and the historical data, and feeds back the control strategy to the intelligent control module; and the intelligent fluid control module accurately controls the flowing speed and direction of the oil product so as to improve the fluid dynamic characteristics and the purification efficiency.

Description

Technical Field

[0001] The invention relates to an intelligent control type integrated high-speed oil purifier. Background Art

[0002] With the rapid development of industrial production, the widespread use of mechanical equipment has led to an increasing demand for lubricating oil. Lubricating oil plays an important role in lubrication, cooling, sealing and cleaning during the operation of equipment. However, with the long-term operation of the equipment, the lubricating oil will inevitably be contaminated. The main sources of contamination include particulate impurities, moisture, oxidation products and metal debris. If these pollutants are not removed in time, the performance of the lubricating oil will be reduced, which will accelerate equipment wear, reduce equipment efficiency, and even cause equipment failure. Therefore, how to effectively purify the lubricating oil and maintain its efficient working state has become a key issue to ensure the long-term and stable operation of the equipment.

[0003] Existing oil purification technologies usually include physical filtration, centrifugal separation, vacuum dehydration, heating evaporation and chemical adsorption. Although these traditional oil purification methods can remove impurities and moisture in oil to a certain extent, there are several major problems: Traditional oil purification equipment mostly adopts fixed filtering structures or simple mechanical separation methods, which cannot be dynamically adjusted according to the pollution status of the oil, resulting in low purification efficiency and limited purification effect. Centrifugal, heating and vacuum treatment methods in the existing technology often consume a lot of energy, increase the operating cost of the equipment, and long-term use will have a negative impact on the environment. Most traditional oil purifiers rely on manual setting of operating parameters, lack intelligent control means, and cannot make adaptive adjustments according to the real-time oil status, which easily leads to unstable oil purification effect. In addition, traditional equipment cannot monitor the oil status in real time during operation, and lacks effective self-learning and feedback mechanisms to optimize operating parameters.

[0004] Therefore, the problem that technicians in this field urgently need to solve is how to automatically adjust the oil purification operation parameters according to the oil pollution status, optimize the oil purification process, improve the oil purification efficiency, and reduce energy consumption. Summary of the invention

[0005] The purpose of the present invention is to solve the deficiencies of the above prior art and provide an intelligent control type integrated high-speed oil purifier, which integrates an intelligent control module, a real-time monitoring module, a decision execution module, a self-learning feedback module and an intelligent fluid control module to realize closed-loop control from data acquisition, intelligent analysis, decision execution to self-learning feedback, and can achieve efficient and stable oil purification effects in complex industrial environments. The following is a specific technical solution:

[0006] On the one hand, an intelligent control type integrated high-speed oil purifier includes an intelligent control module, a real-time monitoring module, a decision-making execution module, a self-learning feedback module and an intelligent fluid control module. After receiving the input data from the real-time monitoring module, the intelligent control module analyzes the pollution degree, flow rate, temperature and pressure of the oil in real time through a neural network algorithm; the real-time detection module collects the data of the oil in real time through a plurality of sensor arrays, converts the collected data into digital signals, and sends them to the intelligent control module regularly; the decision-making execution module receives the control instructions from the intelligent control module, and uses the fuzzy reasoning algorithm and dynamic control strategy to adjust the filtering parameters, centrifugal parameters and separation parameters in the oil purification process in real time, continuously monitors the oil purification effect during operation, and sends feedback information back to the intelligent control module; the self-learning feedback module performs pattern recognition based on real-time operation data and historical data, automatically updates the prediction model by analyzing the oil purification effect, generates optimization suggestions, and transmits them to the intelligent control module; the intelligent fluid control module is used to adjust the flow speed and direction of the oil in real time, and optimize the fluid dynamics in the oil purification process through precise valve control and pump speed adjustment.

[0007] Furthermore, the intelligent control module further includes a multi-layer neural network unit, which performs a dynamic assessment of the degree of oil contamination based on the following mathematical model:

[0008] P = σ(W·X+b), where P is the output pollution level, W is the weight matrix, X is the input feature vector, b is the bias term, and σ is the activation function.

[0009] Furthermore, the multiple sensor arrays of the real-time monitoring module are configured as a distributed sensor network, and data fusion is performed based on the following formula: Among them, D fused is the fused monitoring data, D i is the output data of the i-th sensor, α i is the weighting coefficient, and N is the number of sensors.

[0010] Furthermore, the decision execution module adopts a fuzzy reasoning system, and the system performs real-time control through the following fuzzy rules: Among them, C is the control instruction, R j is the jth fuzzy rule, μ j (D fused ) is the membership function corresponding to the fused data, and M is the number of fuzzy rules.

[0011] Furthermore, the self-learning feedback module updates the prediction model through an adaptive gain algorithm based on the following formula: new =M old +γ·(EM old), where M new : is the updated model, M old is the old model, E is the expected output, γ is the learning rate. Through the self-learning feedback module, combined with real-time monitoring data and historical data, it can autonomously optimize the control strategy in the oil purification process, avoiding the problem of traditional oil purification equipment still using high energy consumption mode when processing low-pollutant oil products.

[0012] Furthermore, the flow regulation function of the intelligent fluid control module is optimized in real time based on the following fluid dynamics equation: Where Q is the flow rate, A is the pipe cross-sectional area, v is the flow velocity, P in and P out are the inlet and outlet pressures, respectively, and ρ is the fluid density. The intelligent fluid control module can accurately control the oil flow speed and direction, and by optimizing the fluid dynamics, reduce the ineffective energy consumption of pump speed and valve adjustment, thereby significantly reducing the overall energy consumption of the equipment. Under the same purification capacity, the energy consumption of the present invention is reduced by about 25%-40%, effectively reducing the operating cost.

[0013] Furthermore, the intelligent fluid control module is connected to the intelligent control module via a feedback loop to form the following control system: Among them, S control : is the overall control strategy, C k is the kth control instruction, μ k (Q) is the membership function of the flow rate, and K is the number of control instructions.

[0014] On the other hand, a method for implementing an intelligent control type integrated high-speed oil purifier: S1: collecting oil pollution degree, flow rate, temperature and pressure data through a plurality of sensor arrays of a real-time monitoring module, and converting the data into digital signals;

[0015] S2: The intelligent control module analyzes the data sent by the real-time monitoring module through fuzzy logic algorithms and neural network algorithms to determine the contamination of the oil and the optimization parameters required for purification;

[0016] S3: The decision-making execution module adjusts the filtering, centrifugation and separation parameters in the oil purification process in real time according to the control instructions generated by the intelligent control module;

[0017] S4: The intelligent fluid control module dynamically adjusts the flow speed and direction of the oil according to the changes in various parameters during the oil purification process, and optimizes the fluid dynamics characteristics during the oil purification process through valve control and pump speed adjustment;

[0018] S5: The self-learning feedback module performs pattern recognition and effect analysis based on real-time operation data and historical data, automatically updates the control strategy, and feeds back optimization suggestions to the intelligent control module to further adjust the operating parameters of the oil purification process.

[0019] Beneficial effects: The present invention can monitor and dynamically adjust multiple parameters such as the degree of oil pollution, flow, temperature and pressure in real time during the oil purification process. By using fuzzy logic algorithms and neural network algorithms, the system can accurately calculate and automatically adjust key oil purification parameters such as filtration, centrifugation and separation to ensure that the oil purifier always works in the best state, greatly improving the oil purification efficiency. Compared with traditional fixed parameter oil purifiers, the present invention can perform adaptive adjustments according to real-time operating conditions, so that the purification efficiency is improved by more than 30%;

[0020] The self-learning feedback module can continuously update the prediction model and optimize the control strategy based on the real-time feedback data of the oil purification effect, so that the oil purifier has the ability to learn autonomously and adapt to complex working conditions. The equipment can not only meet the purification needs of oil products with different pollution levels, but also gradually improve the purification effect, reduce manual intervention, and achieve highly automated operation;

[0021] The intelligent fluid control module effectively optimizes the flow characteristics of the fluid, avoiding system blockage or component loss caused by too fast or too slow oil flow, and further reducing the maintenance frequency and downtime of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a schematic diagram of the module structure of an intelligently controlled integrated high-speed oil purifier;

[0023] Figure 2 It is a flow chart of an intelligent controlled integrated high-speed oil purifier. DETAILED DESCRIPTION

[0024] In order to deepen the understanding of the present invention, the present invention will be further described in detail below in conjunction with embodiments and drawings. The embodiments are only used to explain the present invention and do not constitute a limitation on the protection scope of the present invention.

[0025] Please see Figure 1-2The invention discloses an intelligent control type integrated high-speed oil purifier, comprising an intelligent control module, a real-time monitoring module, a decision-making execution module, a self-learning feedback module and an intelligent fluid control module. After receiving the input data from the real-time monitoring module, the intelligent control module analyzes the pollution degree, flow rate, temperature and pressure of the oil in real time through a neural network algorithm. The real-time detection module collects the data of the oil in real time through a plurality of sensor arrays, converts the collected data into digital signals, and sends them to the intelligent control module regularly. The decision-making execution module receives the control instructions from the intelligent control module, and adjusts the filtering parameters, centrifugal parameters and separation parameters in the oil purification process in real time by using a fuzzy reasoning algorithm and a dynamic control strategy. During the operation, the oil purification effect is continuously monitored, and feedback information is sent back to the intelligent control module. The self-learning feedback module performs pattern recognition based on real-time operation data and historical data, automatically updates the prediction model by analyzing the oil purification effect, generates optimization suggestions, and transmits them to the intelligent control module. The intelligent fluid control module is used to adjust the flow speed and direction of the oil in real time, and optimizes the fluid dynamics in the oil purification process through precise valve control and pump speed adjustment.

[0026] The intelligent control module further includes a multi-layer neural network unit, which performs a dynamic assessment of the degree of oil contamination based on the following mathematical model: P = σ(W·X+b), wherein P is the output contamination degree, W is the weight matrix, X is the input feature vector, b is the bias term, and σ is the activation function.

[0027] The multiple sensor arrays of the real-time monitoring module are configured as a distributed sensor network, and data fusion is performed based on the following formula: Among them, D fused is the fused monitoring data, D i is the output data of the i-th sensor, α i is the weighting coefficient, and N is the number of sensors.

[0028] The decision-making execution module adopts a fuzzy reasoning system, and the system performs real-time control through the following fuzzy rules: Among them, C is the control instruction, R j is the jth fuzzy rule, μ j (D fused ) is the membership function corresponding to the fused data, and M is the number of fuzzy rules.

[0029] The self-learning feedback module updates the prediction model through an adaptive gain algorithm based on the following formula:

[0030] M new =M old +γ·(EM old ), where M new : is the updated model, M oldis the old model, E is the expected output, and γ is the learning rate.

[0031] The flow regulation function of the intelligent fluid control module is optimized in real time based on the following fluid dynamics equations: Where Q is the flow rate, A is the pipe cross-sectional area, v is the flow velocity, P in and P out are the inlet and outlet pressures respectively, and ρ is the fluid density.

[0032] The intelligent fluid control module is connected to the intelligent control module through a feedback loop to form the following control system: Among them, S control : is the overall control strategy, C k is the kth control instruction, μ k (Q) is the membership function of the flow rate, and K is the number of control instructions.

[0033] A method for implementing an intelligent control type integrated high-speed oil purifier: S1: collecting oil pollution degree, flow rate, temperature and pressure data through a plurality of sensor arrays of a real-time monitoring module, and converting the data into digital signals;

[0034] S2: The intelligent control module analyzes the data sent by the real-time monitoring module through fuzzy logic algorithms and neural network algorithms to determine the contamination of the oil and the optimization parameters required for purification;

[0035] S3: The decision-making execution module adjusts the filtering, centrifugation and separation parameters in the oil purification process in real time according to the control instructions generated by the intelligent control module;

[0036] S4: The intelligent fluid control module dynamically adjusts the flow speed and direction of the oil according to the changes in various parameters during the oil purification process, and optimizes the fluid dynamics characteristics during the oil purification process through valve control and pump speed adjustment;

[0037] S5: The self-learning feedback module performs pattern recognition and effect analysis based on real-time operation data and historical data, automatically updates the control strategy, and feeds back optimization suggestions to the intelligent control module to further adjust the operating parameters of the oil purification process.

[0038] In a specific embodiment, the sensor array in the real-time monitoring module collects data such as the degree of oil pollution, flow, temperature and pressure in real time, with a collection frequency of once per second, and converts the data into digital signals through the signal processing unit and sends them to the intelligent control module; the intelligent control module analyzes the received data through fuzzy logic algorithm and neural network algorithm, calculates the current pollution status of the oil, and according to the analysis results, the intelligent control module automatically generates control parameters of the purification process, including filtration parameters, centrifugal parameters and separation parameters. The system uses the formula: Popt =α1f(Q)+α2g(T)+α3h(C), where, P opt is the optimal filtration pressure, Q is the oil flow rate, T is the oil temperature, C is the oil pollution concentration, and α1, α2, and α3 are the control parameter weights.

[0039] In a specific embodiment: the decision execution module adjusts the filtration pressure and flow rate of the filter device, the rotation speed of the centrifugal separation device, and the separation time of the separation device in real time according to the control instructions generated by the intelligent control module to ensure the optimal state of the oil purification process. During the execution process, the filtration pressure is maintained at 0.6-0.8MPa, and the rotation speed of the centrifugal separation is 3000 rpm; the intelligent fluid control module adjusts the oil flow speed and direction according to the real-time monitoring data to ensure that the flow rate of the oil in the equipment is maintained within the range of 1-2 meters per second, and by automatically adjusting the valve opening and pump speed, the smooth flow of the fluid is guaranteed, and the influence of turbulence on the separation effect is reduced.

[0040] In a specific embodiment: the self-learning feedback module uses machine learning technology to analyze the operating data generated during the oil purification process and gradually optimize the prediction model. The system can identify the best operating parameter combination based on historical data during the purification process and feed back these optimization suggestions to the intelligent control module so that the system can further optimize the oil purification effect. After each purification cycle, the oil purification efficiency is improved by about 2% and the energy consumption is reduced by about 1.5%.

[0041] In actual use, the intelligent controlled high-speed oil purifier purified a batch of oil products containing 10% water and 3% solid particles. After purification, the water content of the oil products dropped to 0.05%, and the solid particle content dropped to 0.01%. The entire purification process took about 30 minutes, and the overall energy consumption of the system was 5kWh. Compared with traditional oil purification equipment, the purification efficiency was increased by 35% and the energy consumption was reduced by 25%.

[0042] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

Claims

1. An intelligent control integrated high-speed oil purifier, characterized in that: It includes an intelligent control module, a real-time monitoring module, a decision-making execution module, a self-learning feedback module and an intelligent fluid control module. After receiving the input data from the real-time monitoring module, the intelligent control module analyzes the pollution degree, flow rate, temperature and pressure of the oil in real time through a neural network algorithm; the real-time detection module collects the data of the oil in real time through a plurality of sensor arrays, converts the collected data into digital signals, and sends them to the intelligent control module regularly; the decision-making execution module receives the control instructions from the intelligent control module, and uses the fuzzy reasoning algorithm and dynamic control strategy to adjust the filtering parameters, centrifugal parameters and separation parameters in the oil purification process in real time, continuously monitors the oil purification effect during operation, and sends feedback information back to the intelligent control module; the self-learning feedback module performs pattern recognition based on real-time operation data and historical data, automatically updates the prediction model by analyzing the oil purification effect, generates optimization suggestions, and transmits them to the intelligent control module; the intelligent fluid control module is used to adjust the flow speed and direction of the oil in real time, and optimize the fluid dynamics in the oil purification process through precise valve control and pump speed adjustment.

2. The intelligent control integrated high-speed oil purifier according to claim 1, characterized in that: The intelligent control module further includes a multi-layer neural network unit, which performs a dynamic assessment of the degree of oil contamination based on the following mathematical model: P = σ(W·X+b), wherein P is the output contamination degree, W is the weight matrix, X is the input feature vector, b is the bias term, and σ is the activation function.

3. The intelligent control integrated high-speed oil purifier according to claim 1, characterized in that: The multiple sensor arrays of the real-time monitoring module are configured as a distributed sensor network, and data fusion is performed based on the following formula: Among them, D fused is the fused monitoring data, D i is the output data of the i-th sensor, α i is the weighting coefficient, and N is the number of sensors.

4. The intelligent control integrated high-speed oil purifier according to claim 1, characterized in that: The decision execution module adopts a fuzzy inference system, and the system performs real-time control through the following fuzzy rules: Among them, C is the control instruction, R j is the jth fuzzy rule, μ j (D fused ) is the membership function corresponding to the fused data, and M is the number of fuzzy rules.

5. The intelligent control integrated high-speed oil purifier according to claim 1, characterized in that: The self-learning feedback module updates the prediction model through an adaptive gain algorithm based on the following formula: new =M old +γ·(EM old ), where M new is the updated model, M old is the old model, E is the expected output, and γ is the learning rate.

6. The intelligent control integrated high-speed oil purifier according to claim 1, characterized in that: The flow regulation function of the intelligent fluid control module is optimized in real time based on the following fluid dynamics equations: Where Q is the flow rate, A is the pipe cross-sectional area, v is the flow velocity, P in and P out are the inlet and outlet pressures respectively, and ρ is the fluid density.

7. The intelligent control integrated high-speed oil purifier according to claim 1, characterized in that: The intelligent fluid control module is connected to the intelligent control module via a feedback loop to form the following control system: Among them, S control is the overall control strategy, C k is the kth control instruction, μ k (Q) is the membership function of the flow rate, and K is the number of control instructions.

8. A method for implementing the intelligent control type integrated high-speed oil purifier according to claim 1: S1: collecting the pollution degree, flow rate, temperature and pressure data of the oil product through multiple sensor arrays of the real-time monitoring module, and converting these data into digital signals; S2: The intelligent control module analyzes the data sent by the real-time monitoring module through fuzzy logic algorithms and neural network algorithms to determine the contamination of the oil and the optimization parameters required for purification; S3: The decision-making execution module adjusts the filtering, centrifugation and separation parameters in the oil purification process in real time according to the control instructions generated by the intelligent control module; S4: The intelligent fluid control module dynamically adjusts the flow speed and direction of the oil according to the changes in various parameters during the oil purification process, and optimizes the fluid dynamics characteristics during the oil purification process through valve control and pump speed adjustment; S5: The self-learning feedback module performs pattern recognition and effect analysis based on real-time operation data and historical data, automatically updates the control strategy, and feeds back optimization suggestions to the intelligent control module to further adjust the operating parameters of the oil purification process.

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