Layered crude extraction system and method for waste animal and plant oil

Abstract

A waste animal and plant oil layering crude extraction system comprises a heater, a mixer, a separation tank, a filter, a storage tank, a data monitoring assembly, a data analysis assembly, a control assembly and other modules. The heater heats the waste grease through the flow channel and conveys the waste grease to the mixer, the mixer mixes the waste grease with auxiliaries and then conveys the mixture to the separation tank for separation of the grease and impurities, and the separated grease enters the storage tank after impurities are removed through the filter. The data monitoring assembly collects various parameters in the refining process in real time through a sensor and transmits data to the data analysis assembly for processing. The data analysis component generates optimized operation data by using real-time parameters, and the control component automatically adjusts the working state of each component of the system according to the data, so that the refining efficiency and the grease quality are improved. And by integrating data monitoring, analysis and control functions, the environmental pollution is reduced through the extraction efficiency of the waste grease and the grease quality.

Description

Technical Field

[0001] The invention relates to the field of oil recovery, and in particular to a system and method for rough refining of waste animal and plant oils by stratification. Background Art

[0002] Waste edible oils and fats include animal oils and vegetable oils, which are a series of oil wastes that have lost their edible value when humans consume natural vegetable oils and animal fats and deep processing of oils. These waste oils are rich in carbon-containing organic matter such as fatty acids, which not only poses a risk of environmental pollution, but also has the potential for recycling. Traditional recycling methods are often inefficient and difficult to achieve precise control, resulting in low extraction efficiency and unstable oil quality. If these waste oils are consumed again, it may cause serious environmental pollution and food safety problems. Therefore, there is an urgent need for an efficient and automated system for the stratified crude refining of waste animal and plant oils and fats, so as to monitor and optimize the refining process in real time to achieve efficient utilization of resources and minimize the impact on the environment. In order to solve the above problems, a system and method for the stratified crude refining of waste animal and plant oils and fats is needed. Summary of the invention

[0003] In view of the deficiencies in the prior art, the present invention provides a system and method for crudely refining waste animal and vegetable oils by stratification.

[0004] Among them, a specific technical solution of a system for rough extraction of waste animal and vegetable oils and fats is as follows:

[0005] A system for rough refining of waste animal and vegetable oils by stratification, characterized by:

[0006] It includes data monitoring components, data analysis components, control components, heaters, mixers, separation tanks, filters, extraction towers and storage tanks;

[0007] The heater is connected to the mixer through a flow channel, and the heater is used to heat the waste grease. The mixer is connected to the separation tank through a flow channel, and the mixer is used to mix the waste grease with the additive.

[0008] The separation tank is used to separate grease from impurities, the separated grease enters the filter through the flow channel, the filter is used to filter the impurities to obtain grease, and the filter transports the extracted grease to the storage tank through the flow channel;

[0009] The data monitoring component collects various parameters in the refining process in real time through a sensor network, and the data monitoring component transmits the collected data to the data analysis component for analysis;

[0010] The data analysis component generates optimized operation data according to real-time parameters, and the control component automatically adjusts the working status of each component of the system according to the optimized operation data.

[0011] To better implement the present invention, it can be further provided that: the data monitoring component includes a temperature sensor, a pressure sensor, a flow meter, a viscosity sensor, a mass sensor and a pH sensor, and the data monitoring component transmits the collected data to the parameter optimization component in real time through a communication interface;

[0012] The temperature sensor is used to monitor the temperature in the heater and the separation tank, the pressure sensor is used to monitor the pressure in the separation tank and the extraction tower, the flow meter is used to measure the flow rate of the oil, the viscosity sensor is used to monitor the viscosity of the oil, the quality sensor is used to detect the quality parameters of the separated oil, and the pH sensor is used to monitor the pH of the solvent and the additive.

[0013] Furthermore: the data analysis component includes a random forest model and a particle swarm optimization model, the random forest model is used to predict the extraction rate and oil quality, and the particle swarm optimization model is used to generate the best operating parameter combination according to the prediction results of the random forest model.

[0014] The specific technical scheme of a working method of a system for rough extraction of waste animal and vegetable oils and fats is as follows:

[0015] A working method of a system for rough extraction of waste animal and vegetable oils by stratification, characterized in that:

[0016] S1: The heater heats the waste grease, and the heater transports the heated waste grease to the mixer through the flow channel;

[0017] S2: The mixer mixes the waste oil and the additives, and transports the mixed raw materials to a separation tank;

[0018] S3: The separation tank is used to separate grease and impurities, and the separated grease enters the filter through the flow channel;

[0019] S4: The filter removes impurities and finally transports the extracted oil to a storage tank to achieve crude refining of waste animal and plant oils;

[0020] S5: The data monitoring component collects various parameters in the refining process in real time, and the data monitoring component transmits the collected data to the data analysis component;

[0021] S6: the data analysis component receives real-time parameters, the data analysis component includes a random forest model and a particle swarm optimization model, the random forest model uses real-time monitoring data to predict extraction rate and oil quality, and the particle swarm optimization model is used to generate optimization operation data according to the prediction results of the random forest model;

[0022] S7: the particle swarm optimization model transmits the generated optimization operation data to the control component;

[0023] S8: The control component automatically adjusts the working status of the components of the heater, mixer, separation tank and filter system according to the optimized operation data.

[0024] Further: S4: the data monitoring component collects various parameters in the refining process in real time through the sensor network to obtain an input vector x(t), and the data monitoring component transmits the input vector x(t) to the data analysis component:

[0025] x(t)=[x1(t),x2(t),...,x n (t)];

[0026] Among them, x i (t) represents the i-th eigenvalue at time t.

[0027] Further: S6 comprises the following steps:

[0028] S6-1: The data analysis component preprocesses the input vector x(t) according to the following formula:

[0029]

[0030] in, is the mean of the eigenvector;

[0031] is the standard deviation of the eigenvector;

[0032] max(x) and min(x) are the maximum and minimum values ​​in the eigenvector, respectively;

[0033] S6-2: The random forest model obtains the feature vector x after preprocessing p The prediction function f is input to each tree m The predicted values ​​of extraction rate and oil quality for each tree were obtained. Next, the prediction results of all trees are averaged to get the final prediction value

[0034]

[0035] S6-3: A standard value m is set, and the data analysis component converts the predicted value If the predicted result does not meet the standard value, the data analysis component sets the predicted value Send to the particle swarm optimization model;

[0036] S6-4: The particle swarm optimization model is used to predict the value For the objective function, generate the optimization operation parameter p:

[0037]

[0038] Wherein, p′ is the operating parameter combination to be optimized; the particle swarm optimization model seeks the best parameter combination that minimizes the objective function by exploring different p′.

[0039] The beneficial effects of the present invention are as follows: the overall structure is simple, and the present invention realizes efficient treatment of waste grease by integrating multiple functional components such as data monitoring components, data analysis components, and control components. The waste grease is heated by a heater and then mixed with an additive to improve the subsequent separation efficiency. The grease is effectively separated from impurities by the design of the separation tank. The set filter further removes impurities to ensure the purity of the extracted grease. The system collects various parameters in the refining process in real time through a sensor network, and transmits these data to the data analysis component, which uses a random forest model to predict the extraction rate and grease quality. The particle swarm optimization model generates specific optimization operation data based on the prediction results, so that the control component automatically adjusts the working status of each component of the system. Greatly improve the extraction efficiency and grease quality, and reduce the errors caused by manual adjustment. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 The overall block diagram of the present invention;

[0041] Figure 2 Workflow diagram of the present invention. DETAILED DESCRIPTION

[0042] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] like Figure 1 As shown: A system for rough extraction of waste animal and vegetable oils by stratification, comprising a data monitoring component, a data analysis component, a control component, a heater, a mixer, a separation tank, a filter, an extraction tower and a storage tank;

[0044] The heater is connected to the mixer through a flow channel, and the heater is used to heat the waste grease. The mixer is connected to the separation tank through a flow channel, and the mixer is used to mix the waste grease with the additive.

[0045] The separation tank is used to separate grease from impurities, the separated grease enters the filter through the flow channel, the filter is used to filter the impurities to obtain grease, and the filter transports the extracted grease to the storage tank through the flow channel;

[0046] The data monitoring component collects various parameters in the refining process in real time through a sensor network, and the data monitoring component transmits the collected data to the data analysis component for analysis;

[0047] The data analysis component generates optimized operation data according to real-time parameters, and the control component automatically adjusts the working status of each component of the system according to the optimized operation data.

[0048] To better implement the present invention, it can be further provided that: the data monitoring component includes a temperature sensor, a pressure sensor, a flow meter, a viscosity sensor, a mass sensor and a pH sensor, and the data monitoring component transmits the collected data to the parameter optimization component in real time through a communication interface;

[0049] The temperature sensor is used to monitor the temperature in the heater and the separation tank, the pressure sensor is used to monitor the pressure in the separation tank and the extraction tower, the flow meter is used to measure the flow rate of the oil, the viscosity sensor is used to monitor the viscosity of the oil, the quality sensor is used to detect the quality parameters of the separated oil, and the pH sensor is used to monitor the pH of the solvent and the additive.

[0050] Furthermore: the data analysis component includes a random forest model and a particle swarm optimization model, the random forest model is used to predict the extraction rate and oil quality, and the particle swarm optimization model is used to generate the best operating parameter combination according to the prediction results of the random forest model.

[0051] like Figure 2 As shown, the specific technical scheme of a working method of a system for rough extraction of waste animal and vegetable oils and fats is as follows:

[0052] A working method of a system for rough extraction of waste animal and vegetable oils and fats by stratification, comprising the following steps:

[0053] S1: The heater heats the waste grease, and the heater transports the heated waste grease to the mixer through the flow channel;

[0054] S2: The mixer mixes the waste oil and the additives, and transports the mixed raw materials to a separation tank;

[0055] S3: The separation tank is used to separate grease and impurities, and the separated grease enters the filter through the flow channel;

[0056] S4: The filter removes impurities and finally transports the extracted oil to a storage tank to achieve crude refining of waste animal and plant oils;

[0057] S5: The data monitoring component collects various parameters in the refining process in real time, and the data monitoring component transmits the collected data to the data analysis component;

[0058] Specifically: S5: The data monitoring component collects various parameters in the refining process in real time through the sensor network to obtain an input vector x(t), and the data monitoring component transmits the input vector x(t) to the data analysis component:

[0059] x(t)=[x1(t),x2(t),...,x n (t)];

[0060] Among them, x i (t) represents the i-th eigenvalue at time t;

[0061] S6: the data analysis component receives real-time parameters, the data analysis component includes a random forest model and a particle swarm optimization model, the random forest model uses real-time monitoring data to predict extraction rate and oil quality, and the particle swarm optimization model is used to generate optimization operation data according to the prediction results of the random forest model;

[0062] Specifically, the S6 comprises the following steps:

[0063] S6-1: The data analysis component preprocesses the input vector x(t) according to the following formula:

[0064]

[0065] in, is the mean of the eigenvector;

[0066] is the standard deviation of the eigenvector;

[0067] max(x) and min(x) are the maximum and minimum values ​​in the eigenvector, respectively;

[0068] S6-2: The random forest model obtains the feature vector x after preprocessing p The prediction function f is input to each tree m The predicted values ​​of extraction rate and oil quality for each tree were obtained. Next, the prediction results of all trees are averaged to get the final prediction value

[0069]

[0070] S6-3: A standard value m is set, and the data analysis component converts the predicted value If the predicted result does not meet the standard value, the data analysis component sets the predicted value Send to the particle swarm optimization model;

[0071] S6-4: The particle swarm optimization model is used to predict the value For the objective function, generate the optimization operation parameter p:

[0072]

[0073] Wherein, p′ is the operating parameter combination to be optimized, and the particle swarm optimization model seeks the best parameter combination that minimizes the objective function by exploring different p′.

[0074] S7: the particle swarm optimization model transmits the generated optimization operation data to the control component;

[0075] S8: The control component automatically adjusts the working status of the components of the heater, mixer, separation tank and filter system according to the optimized operation data.

[0076] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

[0077] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A system for rough refining of waste animal and vegetable oils by stratification, characterized by: It includes data monitoring components, data analysis components, control components, heaters, mixers, separation tanks, filters, extraction towers and storage tanks; The heater is connected to the mixer through a flow channel, and the heater is used to heat the waste grease. The mixer is connected to the separation tank through a flow channel, and the mixer is used to mix the waste grease with the additive. The separation tank is used to separate grease from impurities, the separated grease enters the filter through the flow channel, the filter is used to filter the impurities to obtain grease, and the filter transports the extracted grease to the storage tank through the flow channel; The data monitoring component collects various parameters in the refining process in real time through a sensor network, and the data monitoring component transmits the collected data to the data analysis component for analysis; The data analysis component generates optimized operation data according to real-time parameters, and the control component automatically adjusts the working status of each component of the system according to the optimized operation data.

2. A system for rough extraction of waste animal and vegetable oils and fats by stratification according to claim 1, characterized in that: The data monitoring component includes a temperature sensor, a pressure sensor, a flow meter, a viscosity sensor, a mass sensor and a pH sensor, and the data monitoring component transmits the collected data to the parameter optimization component in real time through a communication interface; The temperature sensor is used to monitor the temperature in the heater and the separation tank, the pressure sensor is used to monitor the pressure in the separation tank and the extraction tower, the flow meter is used to measure the flow rate of the oil, the viscosity sensor is used to monitor the viscosity of the oil, the quality sensor is used to detect the quality parameters of the separated oil, and the pH sensor is used to monitor the pH of the solvent and the additive.

3. A system for rough extraction of waste animal and vegetable oils by stratification according to claim 2, characterized in that: The data analysis component includes a random forest model and a particle swarm optimization model. The random forest model is used to predict the extraction rate and oil quality, and the particle swarm optimization model is used to generate an optimal operating parameter combination according to the prediction results of the random forest model.

4. A working method of the system for rough extraction of waste animal and vegetable oils and fats by stratification according to claim 3, characterized in that: S1: The heater heats the waste grease, and the heater transports the heated waste grease to the mixer through the flow channel; S2: The mixer mixes the waste oil and the additives, and transports the mixed raw materials to a separation tank; S3: The separation tank is used to separate grease and impurities, and the separated grease enters the filter through the flow channel; S4: The filter removes impurities and finally transports the extracted oil to a storage tank to achieve crude refining of waste animal and plant oils; S5: The data monitoring component collects various parameters in the refining process in real time, and the data monitoring component transmits the collected data to the data analysis component; S6: the data analysis component receives real-time parameters, the data analysis component includes a random forest model and a particle swarm optimization model, the random forest model uses real-time monitoring data to predict extraction rate and oil quality, and the particle swarm optimization model is used to generate optimization operation data according to the prediction results of the random forest model; S7: the particle swarm optimization model transmits the generated optimization operation data to the control component; S8: The control component automatically adjusts the working status of the components of the heater, mixer, separation tank and filter system according to the optimized operation data.

5. The working method of the system for crudely refining waste animal and vegetable oils and fats according to claim 4, characterized in that: S5: The data monitoring component collects various parameters in the refining process in real time through the sensor network to obtain an input vector x(t), and the data monitoring component transmits the input vector x(t) to the data analysis component: x(t)=[x1(t),x2(t),...,x n (t)]; Among them, x i (t) represents the i-th eigenvalue at time t.

6. A system for rough extraction of waste animal and vegetable oils by stratification according to claim 5, characterized in that: The S6 comprises the following steps: S6-1: The data analysis component preprocesses the input vector x(t) according to the following formula: in, is the mean of the eigenvector; is the standard deviation of the eigenvector; max(x) and min(x) are the maximum and minimum values ​​in the eigenvector, respectively; S6-2: The random forest model obtains the feature vector x after preprocessing p The prediction function f is input to each tree m The predicted values ​​of extraction rate and oil quality for each tree were obtained. Next, the prediction results of all trees are averaged to get the final prediction value S6-3: A standard value m is set, and the data analysis component converts the predicted value If the predicted result does not meet the standard value, the data analysis component sets the predicted value Send to the particle swarm optimization model; S6-4: The particle swarm optimization model is used to predict the value For the objective function, generate the optimization operation parameter p: Wherein, p′ is the operating parameter combination to be optimized.

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