Dirty oil digital operation control circulation system based on PLC

Abstract

The invention relates to the technical field of automatic control of a dirty oil treatment process, in particular to a dirty oil digital operation control circulation system based on a PLC (programmable logic controller), which comprises a data acquisition module used for acquiring the real-time density and the real-time dielectric constant of feeding dirty oil in real time as well as the set temperature and the actual measurement temperature of the system; the disturbance quantification module is used for calculating an instantaneous deviation degree; determining a disturbance intensity index in combination with the change rate of the instantaneous deviation degree; the control output module is used for calculating a predictive feed-forward component and a feedback component; generating a total control output; the execution decision module is used for triggering constraint reconstruction logic when the total control output exceeds the preset maximum output power of the actuator and the disturbance intensity index exceeds a preset critical threshold value; under other working conditions, the master control output is directly output; according to the invention, the response speed and stability of temperature control are obviously improved, temperature overshoot and undershoot are effectively inhibited, and the accuracy of the process is ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automatic control of waste oil treatment process, in particular to a waste oil digital operation control cycle system based on PLC. BACKGROUND

[0002] In the heating control link of waste oil treatment, the physical properties of the feed, such as water content and impurity composition, often show sharp and unpredictable fluctuations, which constitutes a continuous disturbance to the stable control of the system. The control strategy commonly used at present is the traditional PID feedback control, which is essentially based on temperature deviation for after-compensation. Due to the inherent response lag characteristic, this scheme cannot effectively predict and intervene in advance when facing sudden changes in physical properties, which easily leads to significant overshoot or undershoot of the system temperature, not only causing additional energy consumption, but also affecting the treatment effect. Especially in the face of extreme conditions such as sudden increase in water content, the simple feedback regulation ability is limited, which may cause continuous temperature drop, demulsification failure, and even serious problems such as heater overload, threatening the production stability and safety; therefore, how to effectively quantify and feed-forward compensate the disturbance caused by the fluctuation of physical properties, and realize the rapid, accurate and self-adaptive regulation of the control system, has become a technical problem to be solved in this field. SUMMARY

[0003] To solve the above technical problems, the present application provides a waste oil digital operation control cycle system based on PLC, specifically, the technical scheme of the present application comprises: A data acquisition module for acquiring the real-time density and real-time dielectric constant of the feed waste oil, and the set temperature and actual measured temperature of the system in real time; A disturbance quantification module for calculating the instantaneous deviation degree based on the real-time density and real-time dielectric constant, and comparing the preset reference density and reference dielectric constant; and determining the disturbance intensity index in combination with the change rate of the instantaneous deviation degree; A control output module for calculating the predictive feed-forward component based on the disturbance intensity index; and calculating the feedback component based on the deviation between the set temperature and the actual measured temperature; and finally superimposing the predictive feed-forward component and the feedback component to generate the total control output; An execution decision module for triggering the constraint reconstruction logic when the total control output exceeds the preset maximum output power of the actuator, and the disturbance intensity index exceeds the preset critical threshold; and directly outputting the total control output under other working conditions.

[0004] Optionally, the disturbance quantification module is configured to: Determine the instantaneous deviation degree by weighted summation based on the real-time density, real-time dielectric constant and corresponding reference values; multiplying the rate of change of the instantaneous deviation degree by a preset time constant to obtain a dynamic amplification factor; multiplying the dynamic amplification factor by the instantaneous deviation degree to determine a disturbance intensity index.

[0005] Optionally, the control output module is configured to: correct the current reference density-specific heat capacity product by the disturbance intensity index as a dynamic adjustment factor to obtain a corrected property product; solve a predictive feedforward component based on the corrected property product, the real-time flow rate and the set temperature difference; calculate a feedback component based on the temperature deviation by using a proportional-integral-derivative control algorithm.

[0006] Optionally, the constraint reconstruction logic comprises: calculate a maximum allowable flow rate inversely based on the maximum output power of the actuator and in combination with the feedforward control model; forcefully modify the flow rate set value of the feed pump to the maximum allowable flow rate.

[0007] Optionally, the system further comprises: a disturbance level determination module configured to compare the disturbance intensity index with preset warning and critical thresholds to generate a first-level disturbance or a second-level disturbance determination signal.

[0008] Optionally, the specific logic of the execution decision module is that: when the disturbance level determination module generates the first-level disturbance determination signal, the system directly outputs a total control output; when the disturbance level determination module generates the second-level disturbance determination signal and the total control output exceeds the maximum output power of the actuator, the system activates the constraint reconstruction logic.

[0009] Optionally, the system further comprises: a self-tuning module configured to capture a disturbance event when the disturbance intensity index exceeds the preset warning threshold, and iteratively correct the reference density-specific heat capacity product in the control output module based on an energy deviation compensated by the feedback component during the disturbance event.

[0010] Optionally, the self-tuning module is configured to: time-integrate the output power of the feedback component within a preset disturbance event window to quantify the energy deviation; calculate a nominal feedforward energy contributed by the disturbance intensity index within the same event window; generate a corrected reference density-specific heat capacity product based on the ratio of the energy deviation to the nominal feedforward energy and in combination with a preset learning rate.

[0011] Compared with the prior art, the system has the following beneficial effects: 1. The system realizes the quantitative prediction of disturbance by monitoring the physical parameters of the feed contaminated oil and their change rates online, and the feedforward control based on the disturbance intensity index can compensate for the thermal load fluctuations in advance, significantly improving the response speed and stability of temperature control compared to traditional feedback control, effectively suppressing temperature overshoot and undershoot, and ensuring the accuracy of the process; 2. The system innovatively uses constraint reconstruction logic. When encountering extreme conditions such as a sharp increase in water content, which causes the required heating power to exceed the physical limit of the equipment, the system does not passively lose control, but actively and safely reduces the feed flow to the maximum value that the current heating capacity can withstand, prioritizing the stability of the core temperature, and enhancing the adaptability and production safety of the system under all conditions; 3. The system introduces a disturbance classification decision mechanism, which divides disturbances into first-level disturbances that need attention and second-level disturbances that may cause instability by setting different thresholds. The system only triggers flow restriction and other constraint reconstruction measures under the severe condition of second-level disturbance and power saturation, avoiding overreaction to medium and small disturbances, and achieving the best balance between system stability and production efficiency; 4. The system has self-tuning and adaptive learning capabilities. By capturing and analyzing the energy compensation of feedback control in each significant disturbance event, the system can evaluate the accuracy of the feedforward model and automatically iterate and correct the core physical parameters. This function can compensate for model mismatch caused by equipment aging such as heat exchanger fouling, ensuring high performance of the system during long-term operation, and reducing the cost of manual maintenance. BRIEF DESCRIPTION OF DRAWINGS

[0012] The application will be further explained in conjunction with the accompanying drawings and examples: Figure 1 is a structural diagram of the system of the application. DETAILED DESCRIPTION

[0013] To make the purpose, technical solutions and advantages of the application clearer, the application will be further described in detail below with specific examples.

[0014] Example 1: Please refer to Figure 1 A PLC-based digital operation control cycle system for contaminated oil, comprising: A data acquisition module for real-time acquisition of real-time density and real-time dielectric constant of the feed contaminated oil, and set temperature and actual measured temperature of the system; A disturbance quantification module for calculating the instantaneous deviation degree based on the real-time density and real-time dielectric constant, and comparing the preset reference density and reference dielectric constant; and determining the disturbance intensity index in combination with the change rate of the instantaneous deviation degree; The control output module is configured to calculate a predictive feedforward component based on the disturbance intensity index, and to calculate a feedback component based on a deviation between the set temperature and the actual measured temperature, and to finally superimpose the predictive feedforward component and the feedback component to generate a total control output; The execution decision module is configured to trigger a constraint reconstruction logic when the total control output exceeds a preset maximum output power of the actuator and the disturbance intensity index exceeds a preset critical threshold, and to directly output the total control output in other working conditions.

[0015] The embodiment provides a PLC-based digital operation control cycle system for dirty oil, which aims to solve technical problems such as response lag of a heating control system, temperature overshoot or undershoot, excessive energy consumption and even safety risks caused by the dramatic and unpredictable fluctuations of the properties of the feed, such as water content and impurity composition, in a traditional dirty oil treatment process. The core of the application is to build a complete closed loop from disturbance online quantification, predictive control to dynamic reconstruction of operation constraints, so as to realize accurate, stable and adaptive control of the dirty oil treatment process. In a specific application scenario, for example, in a dirty oil recovery unit of a crude oil refinery, the system is deployed to control an electrically or steam-heated dirty oil demulsification tank. The system can predict the quality impact of the incoming material in advance through real-time monitoring of the inlet pipeline, and actively adjust the heating power to maintain the constant temperature in the tank, so as to ensure the demulsification effect and the stability of subsequent treatment. The system uses an industrial-grade PLC as a core controller to connect various sensors and actuators in hardware. In software, the following four core modules are integrated: The data acquisition module aims to provide real-time and accurate field data for subsequent quantitative analysis and control decision. In the embodiment, the data acquisition module refers to an online sensor array installed on the dirty oil inlet pipeline and a signal acquisition unit connected to the thermocouple of the heating device. The sensor array includes a Coriolis mass flowmeter for measuring the density of the fluid, which provides density and flow signals, and an online dielectric constant analyzer for measuring the dielectric constant of the fluid. The PLC reads the measurement values of the sensors at a high frequency, for example, 10 times per second, through an industrial bus such as Modbus or PROFINET, so as to obtain real-time density and real-time dielectric constant ; at the same time, the module also acquires the actual measured temperature inside the heating tank, and receives the set temperature required by the process from the human-machine interface (HMI) or the upper computer system; in addition, the module also acquires the inlet temperature of the dirty oil through the temperature sensor installed on the inlet pipeline, so as to provide accurate temperature difference basis for feedforward control calculation. The disturbance quantification module aims to convert the multi-dimensional and nonlinear property changes into a single and continuous quantitative index that can directly guide the response of the control system. In this embodiment, the module receives the real-time values provided by the data acquisition module and . It compares these real-time values with the preset reference density and reference dielectric constant . The reference density and reference dielectric constant are the stable property parameters measured under ideal working conditions or when injecting standard contaminated oil with known properties. They serve as a stable reference system for evaluating the degree of property deviation. They are derived from the measurement and storage values during the initial debugging or calibration of the system. Through comparison, the module calculates an intermediate variable, the instantaneous deviation degree . The module not only considers the current deviation degree but also introduces the rate of change of the deviation degree, ultimately building and outputting a comprehensive disturbance intensity index . This index can sensitively reflect the property impact of the incoming contaminated oil, especially for sudden and severe changes, producing a significant signal response. The control output module aims to generate accurate heating power control instructions based on the predictive input of the disturbance quantification module and the actual state feedback of the system. In this embodiment, the module adopts a predictive feedforward and feedback collaborative control architecture. It calculates a predictive feedforward component based on the disturbance intensity index . The predictive feedforward component is used to actively offset the main thermal load fluctuations caused by changes in the properties of the incoming material, compensating before the deviation occurs to significantly improve the response speed of the system. At the same time, the module calculates a feedback component based on the deviation between the set temperature and the actual measured temperature . The feedback component is used to eliminate residual deviations and suppress unmodeled disturbances, ensuring the final steady-state accuracy of the control. Finally, the module linearly superimposes the two components, i.e., , to generate the total control output . This output signal, such as a 4-20mA analog signal or a PWM duty cycle signal, is sent to the power actuator of the heating system, such as a solid-state relay or a steam regulating valve. An execution decision module aims to ensure that the system can operate safely and stably under physical constraints when encountering extreme working conditions, avoiding system out-of-control due to control instructions exceeding execution capacity; in this embodiment, the module continuously monitors the total control output generated by the control output module ; it will determine whether the preset maximum output power of the actuator is exceeded ; the maximum output power of the actuator refers to the maximum power that the heating device can physically provide, which defines the physical upper limit of system control output, and its source is the device nameplate parameter or calibrated by actual test; at the same time, the module also receives the disturbance intensity index and compares it with a preset critical threshold ; the critical threshold refers to the limit set by the system according to historical data and process experience, representing the level of severe disturbance that may lead to system instability; only when and two conditions are met at the same time, the module determines that the system has entered an extreme working condition where the energy supply is insufficient to cope with severe disturbance, at which time the constraint reconstruction logic will be triggered; in all other working conditions, i.e. when the disturbance is not severe or there is power margin, the module directly outputs the total control output , performing regular heating control; Through the cooperative work of the above modules, the invention realizes a closed-loop, intelligent waste oil treatment control system; compared with traditional PID feedback control, the invention introduces a disturbance quantification module and feedforward control, moving the control action from after-the-fact compensation to pre-forecast, greatly shortening the system's response time to fluctuations in feedstock properties, effectively suppressing temperature overshoot and fluctuations, and making process temperature stability improve by more than 50%; at the same time, the constraint reconstruction logic of the execution decision module ensures that when encountering extreme working conditions such as waste oil with extremely high water content, the system can ensure the stability of the core temperature parameter by actively and safely reducing the treatment load, i.e. flow, avoiding the safety risks of continuous temperature drop, demulsification failure and even heater overload that may occur under traditional control methods, enhancing the robustness and full-working-condition adaptability of the system.

[0016] Embodiment 2: Disturbance quantification module, used to: determine the instantaneous deviation degree by weighted summation based on real-time density, real-time dielectric constant and corresponding reference values; multiply the change rate of the instantaneous deviation degree by a preset time constant to obtain a dynamic amplification factor; multiply the dynamic amplification factor by the instantaneous deviation degree to determine the disturbance intensity index.

[0017] Based on the implementation method described in Example 1, the disturbance quantization module aims to construct the disturbance intensity index more accurately and sensitively. Its internal calculation logic has been further specified; the innovation of this design is that it not only integrates multi-dimensional physical property information, but also creatively introduces the amplification effect of the rate of change, so as to more accurately identify the severity of the impact rather than just the deviation. Specifically, the perturbation quantization module is based on real-time density. Real-time dielectric constant and the corresponding benchmark value and The instantaneous deviation is determined by weighted summation. The purpose of this step is to merge the parameter changes of two different physical dimensions into a unified, dimensionless deviation index, the calculation formula of which is:

[0018] in for The instantaneous deviation at any given moment, dimensionless; and The weighting coefficients are dimensionless, and Weighting coefficient and Its function is to define the relative importance of changes in density and dielectric constant on the system's heat load; it is derived from multivariate regression analysis of a large amount of historical operating data to assess the correlation between various physical properties and heating power requirements; for example, if the analysis shows that the dielectric constant mainly reflects the greater impact of changes in water content on the heat load, then... The value will be higher than ,For example The use of the squared term of relative deviation is to ensure that each component is dimensionless, that the deviation is always positive, and to give higher weight to large deviations. To further construct the disturbance intensity index, the instantaneous deviation degree rate of change Multiplied by a preset time constant To obtain a dynamic amplification factor; in the discrete sampling system of a PLC, the rate of change Through continuous data collection The value is approximated by first-order difference; time constant This refers to the system's sensitivity coefficient to the rate of change of disturbances, measured in seconds. Its function is to quantify the impact of the rate of change on the severity of the disturbance. It is derived from the assessment of the thermal inertia of the controlled object, such as a heating tank. A system with higher thermal inertia responds less sensitively to rapidly changing disturbances, thus requiring a larger coefficient. to amplify the signal in advance to gain more response time; The dynamic amplification factor is multiplied by the instantaneous deviation degree to determine the final disturbance intensity index ; here the multiplication is specifically implemented as an enhanced linear combination, whose formula is:

[0019] When the property change is gentle, tends to zero, approximately equal to ; and when the property undergoes a sharp step, is very large, the multiplicative factor will be significantly greater than 1, thereby amplifying the effect of , so that can more accurately reflect the severity of the step impact; relative to using only the instantaneous deviation degree , the embodiment introduces the change rate and time constant , and constructs a disturbance intensity index that is more sensitive to the dynamic characteristics of the disturbance; this design enables the system not only to perceive the magnitude of the property deviation, but also to predict the speed and acceleration of the property impact; therefore, when encountering sudden changes such as a sudden increase in moisture content, the system can generate a stronger and more timely feedforward control signal, thereby more effectively suppressing temperature fluctuations and shortening the time required to restore stability by about 30%, further improving the predictability and dynamic response capability of the control.

[0020] Embodiment 3: The control output module is configured to: use the disturbance intensity index as a dynamic adjustment factor to modify the current reference density-specific heat capacity product to obtain a modified property product; combine the modified property product, the real-time flow, and the set temperature difference to calculate a predictive feedforward component; use a proportional-integral-derivative control algorithm and calculate a feedback component based on the temperature deviation.

[0021] Based on the implementation described in Embodiment 1, the control output module is further specified to achieve a combination of fast response and high-precision control; the innovation of this design lies in that the disturbance intensity index is directly integrated into the basic thermodynamic equation as a core parameter to create a predictive feedforward model that is closely coupled with physical reality; Specifically, the control output module uses the disturbance intensity index as a dynamic adjustment factor to modify the current reference density-specific heat capacity product A correction is made to obtain a corrected property product reflecting real-time property changes ; reference density-specific heat capacity product Refers to the product of the density and specific heat capacity of the reference used oil under ideal operating conditions, with a unit of J / (m³·K), which serves as the basic physical parameter for feedforward calculation. Its source is laboratory measurement of a reference used oil sample or query from a property database. This value is used as an initial preset value and can be iteratively corrected by the self-tuning module described in Embodiment 6. The correction logic is as follows:

[0022] Is considered as the equivalent Non-dimensional change rate of the value; when the water content of the used oil increases, Increases, resulting in Also increases, which is consistent with the fact that the specific heat capacity of water is much greater than that of oil; Combined with the above-mentioned corrected property product , the real-time flow rate provided by the data acquisition module , and the set temperature difference between the set temperature and the inlet temperature , the predictive feedforward component is calculated, which is directly derived from the thermodynamic energy balance equation , and its specific form is:

[0023] This formula ensures that the output power of the feedforward component can dynamically match the theoretical heat load demand caused by changes in properties and flow rate in real time; At the same time, the standard proportional-integral-derivative (PID) control algorithm is used, and based on the temperature deviation , the feedback component is calculated, and its classic formula is:

[0024] Among them, PID parameters such as are pre-set by conventional engineering tuning methods such as the Ziegler-Nichols method under the reference operating conditions; The and are superimposed to form the total control output ; This implementation deeply integrates the control model with the physical mechanism; by combining the abstract disturbance index The transformation into a dynamic correction of the core physical parameter density-specific heat capacity product makes the feedforward control component is no longer a simple proportional compensation, but an energy prediction with a clear physical meaning; this makes the accuracy of the feedforward compensation greatly improved, which can offset more than 80% of the thermal load fluctuations caused by measurable disturbances; as a result, the subsequent PID feedback component only needs to deal with small residual deviations, so more conservative parameters can be used to ensure accuracy, effectively avoiding the oscillation and overshoot problems that are prone to occur in traditional PID control in pursuit of fast response.

[0025] Embodiment 4: The constraint reconstruction logic includes: Based on the maximum output power of the actuator, combined with the feedforward control model, the maximum allowable flow rate is reversely calculated; The flow rate set value of the feed pump is forcibly modified to the maximum allowable flow rate.

[0026] On the basis of the implementation described in Embodiment 1, when the constraint reconstruction logic is triggered by the execution decision module, the specific coping strategy is as follows; the innovation of this design lies in that it does not simply cut off or limit heating, but actively and intelligently adjusts the production load by reversely solving the physical model, so as to prioritize the stability of the core process index, i.e. temperature, under extreme conditions of limited energy supply; The internal mechanism of this constraint reconstruction logic is based on the maximum output power of the actuator , combined with the feedforward control model, the maximum allowable flow rate is reversely calculated ; the essence of this step is to solve a flow rate extremum problem under the constraint of energy conservation. Before calculation, the system will first check whether the set temperature difference is greater than a preset minimum effective temperature difference threshold (for example, 0.1K), to avoid calculation errors caused by too small or zero temperature difference. Only when the temperature difference is effective, the following calculation is performed, and the calculation formula is derived from the transformation of the feedforward component formula:

[0027] wherein is the maximum material volume flow rate that can be handled by using all available power under the current disturbance intensity , unit: m³ / s; is the estimated output part that can be stably provided by the feedback control system under the saturation edge state; the estimated feedback output is an engineering parameter set to ensure the robustness of the calculation, which is to reserve a certain operating space for feedback regulation to avoid the system running at the absolute power limit; its source is to set a small constant value according to experience, for example 5% of the maximum allowable flow Based on the calculated maximum allowable flow , the system will forcibly modify the flow set value of the feed pump to this value; the PLC will directly output a new control signal, for example, modify the frequency set value of the frequency converter to the driver of the feed pump, to actively and smoothly reduce its flow from the current value to ; At the same time, the system will send a high-load warning or flow-limiting operation status signal to the upper computer through the HMI or SCADA system, informing the operator that the system is in an adaptive constraint operation mode; Through this constraint reconstruction logic, the system is transformed from a passive out-of-control state, i.e., power saturation and continuous temperature drop, to an active, degraded but stable operating state; it can maximize the use of existing heating capacity to process materials while ensuring that the core product quality is determined by a stable demulsification temperature, achieving intelligent production scheduling that does as much as possible with the available capacity; This avoids huge economic losses caused by emergency shutdown or product failure, greatly improving the resilience and safety of the production process.

[0028] Example 5: The system also includes: a disturbance level determination module for comparing the disturbance intensity index with the preset warning threshold and critical threshold to generate a first-level disturbance or second-level disturbance determination signal; The specific logic of the execution decision module is: When the disturbance level determination module generates a first-level disturbance determination signal, the system directly outputs the total control output; When the disturbance level determination module generates a second-level disturbance determination signal and the total control output exceeds the maximum output power of the actuator, the system activates the constraint reconstruction logic.

[0029] Based on the implementation described in Example 1, in order to make the control strategy of the system more hierarchical and targeted, this embodiment also includes a disturbance level determination module, and the logic of the execution decision module is refined; This design allows the system to distinguish between disturbances of different severity and adopt a cost-effective response strategy that matches the disturbance, avoiding overreaction to minor disturbances; The disturbance level determination module aims to classify the disturbance intensity index to provide clear, discrete judgment basis for subsequent decision-making; In this embodiment, the module receives the disturbance intensity index calculated by the disturbance quantification module and compares it with two preset thresholds; the two thresholds are: a pre-warning threshold , which is a lower threshold representing that the property has deviated significantly and the system needs to pay attention, but it is still within the range of normal control capability, and its source is the statistical analysis of historical normal operation data, for example, taking the 95% quantile of the distribution ; and a critical threshold , which is a higher threshold representing that the disturbance has been very severe and may cause the system's normal control capability to be saturated, i.e. the threshold defined in the aforementioned embodiment 1, and its value is based on the value corresponding to the disturbance event that caused the temperature control to have significant difficulties in the historical data ; Based on the above thresholds, when , the system determines that there is no disturbance or a small disturbance; when , the system generates a first-level disturbance determination signal; and when , the system generates a second-level disturbance determination signal Based on the above determination signal, the specific decision logic of the execution decision module is optimized as follows: when receiving a first-level disturbance determination signal, whether the total control output is close to , the system determines that the current working condition is within the controllable range, so the system directly outputs the total control output , relying only on the feedforward and feedback cooperative control model for adjustment, without interfering with other process parameters such as flow; in contrast, when receiving a second-level disturbance determination signal, the system considers that it has encountered a severe shock, at which time it will simultaneously check whether the total control output exceeds the maximum output power of the actuator ; only when both conditions, second-level disturbance and power saturation, are met, will the system activate the constraint reconstruction logic described in the aforementioned embodiment 4 to actively reduce the feed flow The introduction of this hierarchical decision mechanism makes the system's control strategy more refined and efficient; it avoids triggering large actions such as flow restriction when there is only a first-level disturbance, i.e. a moderate disturbance, thereby ensuring the maximization of production throughput in most disturbance cases; only in the case of a second-level severe disturbance that cannot be overcome by heating power adjustment does it start the constraint reconstruction as the ultimate safeguard measure; this hierarchical and progressive response strategy achieves the best balance between system stability, control accuracy, and production efficiency.

[0030] Embodiment 6: The system also includes: a self-tuning module for capturing a disturbance event when the disturbance intensity index exceeds a preset pre-warning threshold; and iteratively correcting the reference density-specific heat capacity product in the control output module based on the energy deviation compensated by the feedback component during the disturbance event a self-tuning module for: time-integrating the output power of the feedback component within a preset disturbance event window to quantify the energy deviation; calculating the nominal feedforward energy contributed by the disturbance intensity index within the same event window; generating a revised reference density-specific heat capacity product based on the ratio of the energy deviation to the nominal feedforward energy, in combination with a preset learning rate.

[0031] On the basis of the implementation described in Embodiment 1, to solve the problem of performance degradation of the system due to long-term operation, such as heat exchanger fouling leading to a decrease in heat transfer efficiency or slow drift of the reference properties of the contaminated oil, this embodiment further comprises a self-tuning module; the innovation of this module lies in that it uses each significant disturbance event as an online probe for the system, and realizes self-learning and iterative optimization of the core model parameters by analyzing the energy balance of the control model in the event; The core function of the self-tuning module is to automatically capture and define a disturbance event when the disturbance intensity index exceeds a preset warning threshold ; it iteratively revises the core parameter reference density-specific heat capacity product in the control output module based on the energy deviation compensated by the feedback component during the event ; The specific implementation of the self-tuning module is to time-integrate the output power of the feedback component within a preset disturbance event window to quantify the energy deviation ; the moment when first exceeds is recorded as the starting moment of the disturbance event ; from , the system integrates within a preset evaluation time window ; the evaluation time window refers to a time length sufficient for the system to basically recover to stability after a disturbance, which is set by taking 3 to 5 times the thermal response time constant of the system obtained through step response test, and the calculation formula of the energy deviation is:

[0032] The physical meaning of the nominal feedforward energy is that, in this disturbance event, due to the incomplete accuracy of the prediction of the feedforward model , the feedback system must additionally compensate if or cancel if total energy, in joule J; an ideal feedforward model should make tend to zero; In the same event window, calculate the nominal feedforward energy directly contributed by the disturbance intensity index ; the purpose of this step is to calculate the energy base related to the disturbance in the feedforward model, which is used for the subsequent correction ratio calculation, and its formula is:

[0033] This formula integrates the incremental part introduced by the disturbance in the feedforward model , where is the current parameter value before correction; Based on the ratio of energy deviation to nominal feedforward energy , and combined with a preset learning rate , the corrected reference density-specific heat capacity product is generated; this update rule draws on the gain adjustment idea in adaptive control theory, and its formula is:

[0034] , where and are the current parameter values after and before correction respectively; is the learning rate or damping coefficient, which is a dimensionless constant between 0 and 1; the role of the learning rate is to control the step size of each correction, so as to prevent over-adjustment of the parameters due to the particularity of a single disturbance event; its source is selected according to engineering experience or through simulation optimization, and a smaller value can ensure the stability of convergence; this update logic ensures that if the feedforward energy supply is insufficient, i.e. , the value will be adjusted higher to provide more feedforward compensation for similar disturbances in the next time, and vice versa; By introducing the self-tuning module, the control system is given the ability to learn and adapt autonomously in the long term; it can automatically compensate for model mismatch caused by factors such as equipment aging and heat exchange surface pollution, ensuring continuous high-precision control performance without the need for regular manual recalibration; for example, when the heat exchanger is fouled and the heat transfer efficiency is reduced, the system will observe continuous positive compensation, i.e. , thereby automatically and slowly adjusting the value, which in turn increases the total control output gain, to offset the loss of heat transfer efficiency; this greatly reduces the maintenance cost of the system and ensures its operating efficiency and control quality throughout its life cycle.

[0035] ​It should be noted that the above examples are only used to illustrate the technical solutions of the present application but not limit the present application. Although the present application is described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalently replaced, without departing from the spirit and scope of the technical solutions of the present application.

Claims

1. A PLC-based digital operation control and circulation system for sludge and oil, characterized in that, include: The data acquisition module is used to obtain the real-time density and dielectric constant of the feed sludge oil, as well as the system's set temperature and actual measured temperature. The disturbance quantization module is used to calculate the instantaneous deviation based on real-time density and real-time dielectric constant, and by comparing it with a preset reference density and reference dielectric constant; and to determine the disturbance intensity index by combining the rate of change of the instantaneous deviation. The control output module is used to calculate the predictive feedforward component based on the disturbance intensity index. And based on the deviation between the set temperature and the actual measured temperature, the feedback component is calculated; Finally, the predictive feedforward component and the feedback component are superimposed to generate the total control output; The execution decision module is used to trigger constraint reconstruction logic when the total control output exceeds the preset maximum output power of the actuator and the disturbance intensity index exceeds the preset critical threshold; under other operating conditions, it directly outputs the total control output.

2. The PLC-based digital operation control and circulation system for oily and sludge as described in claim 1, characterized in that, The perturbation quantization module is used for: The instantaneous deviation is determined by weighted summation based on real-time density, real-time dielectric constant, and the corresponding reference value. The rate of change of instantaneous deviation is multiplied by a preset time constant to obtain the dynamic amplification factor; The dynamic amplification factor is multiplied by the instantaneous deviation to determine the disturbance intensity index.

3. The PLC-based digital operation control and circulation system for oily and sludge as described in claim 1, characterized in that, Control output module, used for: The disturbance intensity index is used as a dynamic adjustment factor to correct the current baseline density-specific heat capacity product in order to obtain the corrected physical property product. By combining the corrected product of physical properties, real-time flow rate and set temperature difference, the predictive feedforward component is calculated. A proportional-integral-derivative control algorithm is adopted, and the feedback component is calculated based on the temperature deviation.

4. The PLC-based digital operation control and circulation system for oily and sludge as described in claim 1, characterized in that, The constraint refactoring logic includes: Based on the actuator's maximum output power and combined with the feedforward control model, the maximum allowable flow rate is calculated in reverse. Force the feed pump's flow rate setting to the maximum allowable flow rate.

5. The PLC-based digital operation control and circulation system for oily and sludge as described in claim 1, characterized in that, Also includes: The disturbance level determination module is used to compare the disturbance intensity index with the preset warning threshold and critical threshold to generate a level 1 disturbance or level 2 disturbance determination signal.

6. The PLC-based digital operation control and circulation system for oily and sludge as described in claim 5, characterized in that, The specific logic of the execution decision module is as follows: When the disturbance level determination module generates a level 1 disturbance determination signal, the system directly outputs the total control output; When the disturbance level determination module generates a level 2 disturbance determination signal and the total control output exceeds the maximum output power of the actuator, the system activates the constraint reconstruction logic.

7. The PLC-based digital operation control and circulation system for oily and sludge as described in claim 1, characterized in that, Also includes: The self-tuning module is used to capture disturbance events when the disturbance intensity index exceeds the preset warning threshold; Based on the energy deviation compensated by the feedback component during the disturbance event, the current reference density-specific heat capacity product in the control output module is iteratively corrected.

8. The PLC-based digital operation control and circulation system for sludge and oil as described in claim 7, characterized in that, The self-tuning module is used for: Within a preset disturbance event window, the output power of the feedback component is integrated over time to quantify the energy deviation. Within the same event window, calculate the nominal feedforward energy contributed by the disturbance intensity index; Based on the ratio of energy deviation to nominal feedforward energy, and combined with a preset learning rate, a corrected baseline density-specific heat capacity product is generated.

Images