A PLC-based digital operation control and circulation system for sludge and oil.

Abstract

The application relates to the technical field of automatic control of a dirty oil treatment process, in particular to a dirty oil digital operation control circulating system based on PLC, which comprises a data acquisition module, which is used for acquiring the real-time density and real-time dielectric constant of feeding dirty oil, and the set temperature and the actually measured temperature of the system; a disturbance quantification module, which is used for calculating an instantaneous deviation degree; and combining the change rate of the instantaneous deviation degree, determining a disturbance intensity index; a control output module, which is used for calculating a predictive feedforward component and a feedback component; generating a total control output; and an execution decision module, which is used for triggering a constraint reconstruction logic when the total control output exceeds the preset maximum output power of an actuator and the disturbance intensity index exceeds a preset critical threshold; and directly outputting the total control output under other working conditions; the application significantly improves the response speed and stability of temperature control, effectively suppresses temperature overshoot and undershoot, and guarantees the accuracy of the process.

Description

Technical Field

[0001] This invention relates to the field of automated control technology for waste oil treatment processes, specifically a PLC-based digital operation control loop system for waste oil treatment. Background Technology

[0002] In the heating control stage of sludge and oil treatment, the physical properties of the feed, such as moisture content and impurity composition, often exhibit drastic and unpredictable fluctuations, which pose a continuous disturbance to the stable control of the system.

[0003] The currently prevalent control strategy is traditional PID feedback control, which essentially compensates for temperature deviations after the fact. Due to its inherent response lag, this approach cannot effectively predict or intervene in the face of sudden changes in physical properties, easily leading to significant overshoot or undershoot in system temperature. This not only results in additional energy consumption but also affects processing efficiency. Especially under extreme conditions such as a surge in moisture content, simple feedback regulation has limited capacity and may cause serious problems such as continuous temperature drop, demulsification failure, or even heater overload, threatening production stability and safety. Therefore, how to effectively quantify and feedforward compensate for disturbances caused by fluctuations in physical properties to achieve rapid, accurate, and adaptive adjustment of the control system has become a pressing technical problem to be solved in this field. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a PLC-based digital operation control and circulation system for sludge and oil. Specifically, the technical solution of this invention includes:

[0005] 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.

[0006] 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.

[0007] The control output module is used to calculate the predictive feedforward component based on the disturbance intensity index; and to calculate the feedback component based on the deviation between the set temperature and the actual measured temperature; finally, the predictive feedforward component and the feedback component are superimposed to generate the total control output.

[0008] 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.

[0009] Optional, a perturbation quantization module is used for:

[0010] The instantaneous deviation is determined by weighted summation based on real-time density, real-time dielectric constant, and the corresponding reference value.

[0011] The rate of change of instantaneous deviation is multiplied by a preset time constant to obtain the dynamic amplification factor;

[0012] The dynamic amplification factor is multiplied by the instantaneous deviation to determine the disturbance intensity index.

[0013] Optional, control output module, used for:

[0014] 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.

[0015] By combining the corrected product of physical properties, real-time flow rate and set temperature difference, the predictive feedforward component is calculated.

[0016] A proportional-integral-derivative control algorithm is adopted, and the feedback component is calculated based on the temperature deviation.

[0017] Optionally, the constraint refactoring logic includes:

[0018] Based on the actuator's maximum output power and combined with a feedforward control model, the maximum allowable flow rate is calculated in reverse.

[0019] Force the feed pump's flow rate setting to the maximum allowable flow rate.

[0020] Optional, also includes:

[0021] 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 one or level two disturbance determination signal.

[0022] Optionally, the specific logic of the execution decision module is as follows:

[0023] When the disturbance level determination module generates a level 1 disturbance determination signal, the system directly outputs the total control output;

[0024] 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.

[0025] Optional, also includes:

[0026] The self-tuning module is used to capture disturbance events when the disturbance intensity index exceeds a preset warning threshold; and iteratively corrects 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.

[0027] Optional, self-tuning module, used for:

[0028] Within a preset disturbance event window, the output power of the feedback component is integrated over time to quantify the energy deviation.

[0029] Within the same event window, calculate the nominal feedforward energy contributed by the disturbance intensity index;

[0030] 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.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] 1. This system constructs a disturbance intensity index by monitoring the physical properties and change rate of the feed sludge oil online, thereby realizing the quantitative prediction of disturbances. Based on this index, the feedforward control can compensate for heat load fluctuations in advance. Compared with traditional feedback control, it significantly improves the response speed and stability of temperature control, effectively suppresses temperature overshoot and undershoot, and ensures the accuracy of the process.

[0033] 2. This system features a unique constraint reconfiguration logic. When encountering extreme conditions such as a surge in moisture content, causing the required heating power to exceed the physical limits of the equipment, the system does not passively lose control. Instead, it actively and safely reduces the feed flow rate to the maximum value that the current heating capacity can withstand through inverse model calculations, prioritizing the stability of the core temperature and enhancing its adaptability to all operating conditions and production safety.

[0034] 3. This system introduces a disturbance classification decision mechanism. By setting different thresholds, disturbances are divided into primary disturbances that require attention and secondary disturbances that may lead to instability. The system only triggers constraint reconfiguration measures such as flow limiting under the stringent conditions of secondary disturbances and power saturation, thus avoiding overreaction to medium and small disturbances and achieving the best balance between system stability and production efficiency.

[0035] 4. This system has self-tuning and adaptive learning capabilities. By capturing and analyzing the energy compensation amount of feedback control in each significant disturbance event, the system can evaluate the accuracy of the feedforward model and automatically iteratively correct the core physical parameters. This function can compensate for model mismatch caused by equipment aging such as heat exchanger fouling, ensure the high performance of the system in long-term operation, and reduce manual maintenance costs. Attached Figure Description

[0036] The present invention will be further explained below with reference to the accompanying drawings and embodiments:

[0037] Figure 1 This is a structural diagram of the system of the present invention. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0039] Example 1:

[0040] Please see Figure 1 A PLC-based digital operation control and circulation system for oily and sludge, comprising:

[0041] 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.

[0042] 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.

[0043] The control output module is used to calculate the predictive feedforward component based on the disturbance intensity index; and to calculate the feedback component based on the deviation between the set temperature and the actual measured temperature; finally, the predictive feedforward component and the feedback component are superimposed to generate the total control output.

[0044] 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.

[0045] This embodiment provides a PLC-based digital operation control loop system for waste oil treatment. This system aims to solve technical problems in traditional waste oil treatment processes, such as delayed response of the heating control system, temperature overshoot or undershoot, excessive energy consumption, and even safety risks caused by drastic and unpredictable fluctuations in feed properties like moisture content and impurity composition. The core of this invention lies in constructing a complete closed loop from online disturbance quantification and predictive control to dynamic reconstruction of operational constraints, achieving precise, stable, and adaptive control of the waste oil treatment process.

[0046] In a specific application scenario, such as in the sludge recovery unit of a crude oil refinery, the system is deployed to control an electrically heated or steam-heated sludge demulsifying tank. By monitoring the inlet pipeline in real time, the system can predict the quality impact of the incoming material in advance and actively adjust the heating power to maintain a constant temperature inside the tank, ensuring the demulsification effect and the stability of subsequent processing.

[0047] The system uses an industrial-grade PLC as its core controller in terms of hardware, connecting various sensors and actuators; in terms of software, it integrates the following four core modules:

[0048] The data acquisition module aims to provide real-time and accurate field data for subsequent quantitative analysis and control decisions. In this embodiment, the data acquisition module refers to the online sensor array installed on the feed sludge pipeline and the signal acquisition unit connected to the thermocouple of the heating equipment. The sensor array includes a Coriolis mass flow meter for measuring fluid density, which provides both density and flow signals, and an online dielectric constant analyzer for measuring the dielectric constant of the fluid.

[0049] The PLC reads the measurements from these sensors at a high frequency, such as 10 cycles per second, via industrial buses like Modbus or PROFINET to obtain real-time density data. and real-time dielectric constant Simultaneously, the module also collects the actual measured temperature inside the heating tank. It receives the set temperature required by the process from the human-machine interface (HMI) or the host computer system. In addition, the module also collects the inlet temperature of the feed sludge in real time through a temperature sensor installed on the inlet pipeline. This provides accurate temperature difference data for feedforward control calculations;

[0050] The disturbance quantization module aims to transform multi-dimensional, nonlinear changes in physical properties into a single, continuous quantifiable index that can directly guide the response of the control system. In this embodiment, this module receives data from the data acquisition module. and It compares these real-time values ​​with a preset baseline density. and reference dielectric constant For comparison; baseline density With reference dielectric constant This refers to stable physical property parameters measured under ideal operating conditions or when injecting standard waste oil with known properties. Its function is to provide a stable reference system for assessing the degree of deviation in physical properties. These parameters originate from measured and stored values ​​during the initial system commissioning or calibration phase. By comparison, this module calculates an intermediate variable, the instantaneous deviation. This module not only considers the current degree of deviation but also incorporates information on the rate of change of that deviation, ultimately constructing and outputting a comprehensive disturbance intensity index. This index can sensitively reflect the physical impact of feed sludge and oil, especially for step-like drastic changes, and can produce a significant signal response.

[0051] The control output module aims to generate precise heating power control commands based on the predictive input from the disturbance quantization module and the actual state feedback of the system. In this embodiment, the module employs a predictive feedforward and feedback coordinated control architecture; it is based on the disturbance intensity index. Calculate the predictive feedforward components Predictive feedforward components This refers to the control section used to actively offset the main heat load fluctuations caused by changes in feed properties. Its function is to compensate before deviations occur, thereby significantly improving the system's response speed; simultaneously, this module is based on a set temperature. Compared with the actual measured temperature Deviation between Calculate the feedback component Feedback components This refers to the fine-tuning section used to eliminate residual bias and suppress unmodeled disturbances, its function being to ensure the final steady-state accuracy of the control; ultimately, this module linearly superimposes these two components, i.e. Generate total control output The 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.

[0052] The execution decision module aims to ensure that the system can operate safely and stably under physical constraints when encountering extreme operating conditions, avoiding system loss of control due to control commands exceeding the execution capability. In this embodiment, this module continuously monitors the total control output generated by the control output module. It will judge Does it exceed the preset maximum output power of the actuator? Maximum output power of the actuator This refers to the maximum power that the heating equipment can physically provide. Its function is to define the physical upper limit of the system's control output, and its source is the equipment's nameplate parameters or calibration through actual testing. Simultaneously, this module also receives the disturbance intensity index. And compare it with a preset critical threshold. Comparison; critical threshold This refers to the limits set by the system based on historical data and process experience, representing the level of severe disturbances that could lead to system instability; if and only if and When both conditions are met simultaneously, the module determines that the system has entered an extreme operating condition where the energy supply is insufficient to cope with severe disturbances, and will trigger the constraint reconfiguration logic. Under all other operating conditions, i.e., when the disturbance is not severe or there is a power margin, the module directly outputs the total control output. Perform standard heating control;

[0053] Through the collaborative work of the above modules, this invention realizes a closed-loop, intelligent sludge and oil treatment control system. Compared with traditional PID feedback control, this invention introduces a disturbance quantization module and feedforward control, advancing the control action from post-event compensation to pre-event prediction, greatly shortening the system's response time to fluctuations in feed properties, effectively suppressing temperature overshoot and fluctuations, and improving process temperature stability by more than 50%. At the same time, the constraint reconstruction logic of the execution decision module ensures that when encountering extreme conditions such as sludge and oil with extremely high water content, the system can actively and safely reduce the processing load, i.e., the flow rate, to maintain the stability of core temperature parameters. This avoids the safety risks of continuous temperature drop, demulsification failure, or even heater overload that may occur under traditional control methods, and enhances the system's robustness and adaptability to all operating conditions.

[0054] Example 2:

[0055] The perturbation quantization module is used for:

[0056] The instantaneous deviation is determined by weighted summation based on real-time density, real-time dielectric constant, and the corresponding reference value.

[0057] The rate of change of instantaneous deviation is multiplied by a preset time constant to obtain the dynamic amplification factor;

[0058] The dynamic amplification factor is multiplied by the instantaneous deviation to determine the disturbance intensity index.

[0059] 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.

[0060] 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:

[0061]

[0062] in for The instantaneous deviation at any given moment is 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.

[0063] 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. The value is used to amplify the signal in advance in order to gain a longer response time;

[0064] The dynamic amplification factor and instantaneous deviation Multiply to determine the final disturbance intensity index. The multiplication here is specifically implemented as an enhanced linear combination, and its formula is:

[0065]

[0066] When the change in physical properties is gradual Approaching zero Approximately equal to When the physical properties undergo a drastic step change, Large, multiplicative factor It will be significantly greater than 1, thus making The effect is amplified, making It can more accurately reflect the severity of a step impact;

[0067] Compared to using only instantaneous deviation This embodiment introduces the rate of change and the time constant. A disturbance intensity index that is more sensitive to the dynamic characteristics of disturbances was constructed. This design enables the system not only to sense the magnitude of material property deviations but also to predict the speed and acceleration of material property impacts. Therefore, when encountering sudden changes in operating conditions such as a sudden increase in moisture content, the system can generate a stronger and more timely feedforward control signal, thereby more effectively suppressing drastic temperature fluctuations and shortening the time required to restore stability by about 30%, further improving the predictability and dynamic response capability of the control.

[0068] Example 3:

[0069] Control output module, used for:

[0070] 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.

[0071] By combining the corrected product of physical properties, real-time flow rate and set temperature difference, the predictive feedforward component is calculated.

[0072] A proportional-integral-derivative control algorithm is adopted, and the feedback component is calculated based on the temperature deviation.

[0073] Based on the implementation method described in Example 1, the internal derivation and calculation logic of the control output module is further clarified to achieve an organic combination of fast response and high-precision control; the innovation of this design lies in using the disturbance intensity index... As a core parameter, it is directly integrated into the fundamental thermodynamic equations, creating a predictive feedforward model that is closely coupled with physical reality;

[0074] Specifically, the control output module will output the disturbance intensity index. As a dynamic adjustment factor, the current baseline density-specific heat capacity product... Make corrections to obtain a corrected product of physical properties that reflects real-time changes in physical properties. Reference density - specific heat capacity product This refers to the product of the density and specific heat capacity of the benchmark sludge under ideal operating conditions, expressed in J / (m³·K). It serves as the fundamental physical parameter for feedforward calculations. Its source is either laboratory measurement of the benchmark sludge sample or a query from a property database. This value serves as an initial preset value and can be iteratively corrected by the self-tuning module described in Example 6. The correction logic is as follows:

[0075]

[0076] Considered as an equivalent caused by changes in physical properties The dimensionless rate of change of the value; as the water content of the sludge increases... Enlargement, leading to This also increases accordingly, which is consistent with the physical fact that the specific heat capacity of water is much greater than that of oil;

[0077] Combining the above-corrected product of physical properties Real-time traffic provided by the data acquisition module And the set temperature difference between the set temperature and the inlet temperature. The predictive feedforward components are calculated. This calculation is directly derived from the thermodynamic energy balance equation. Its specific form is:

[0078]

[0079] This formula ensures the feedforward component The output power can be dynamically matched to the physical properties in real time. and traffic Theoretical heat load demand caused by the change;

[0080] At the same time, a standard proportional-integral-derivative PID control algorithm is adopted, and based on temperature deviation... Calculate the feedback components Its classic formula is:

[0081]

[0082] in, PID parameters are under the baseline operating conditions, i.e. It is pre-set using conventional engineering tuning methods such as the Ziegler-Nichols method;

[0083] Will and Superimposed to form the total control output ;

[0084] This implementation method deeply integrates the control model with the physical mechanism; by abstracting the perturbation index This is transformed into a dynamic correction of the product of the core physical parameter density and specific heat capacity, enabling the feedforward control component to... Instead of simple proportional compensation, it involves energy prediction with explicit physical meaning; this significantly improves the accuracy of feedforward compensation, enabling it to offset over 80% of heat load fluctuations caused by measurable disturbances; as a result, the subsequent PID feedback components... It only needs to handle small residual deviations, thus allowing for the use of more conservative parameters while ensuring accuracy, effectively avoiding the oscillation and overshoot problems that are easily caused by pursuing fast response in traditional PID control.

[0085] Example 4:

[0086] The constraint refactoring logic includes:

[0087] Based on the actuator's maximum output power and combined with a feedforward control model, the maximum allowable flow rate is calculated in reverse.

[0088] Force the feed pump's flow rate setting to the maximum allowable flow rate.

[0089] Based on the implementation method described in Example 1, when the execution decision module triggers the constraint reconstruction logic, its specific response strategy is as follows; the innovation of this design is that it does not simply cut off or limit heating, but actively and intelligently adjusts the production load by solving the physical model in reverse, so as to prioritize the stability of the core process indicator, namely temperature, under the extreme condition of limited energy supply.

[0090] The underlying mechanism of this constraint reconstruction logic lies in the fact that it is based on the actuator's maximum output power. And by combining the feedforward control model, the maximum allowable flow rate is calculated in reverse. The essence of this step is to solve a flow extremum problem under an energy conservation constraint. Before the calculation, the system will first check the set temperature difference. Whether the temperature difference exceeds a preset minimum effective temperature difference threshold (e.g., 0.1K) is checked to avoid calculation errors caused by a denominator of zero due to an excessively small or zero temperature difference. The following calculation is performed only when the temperature difference is valid; its formula is derived from the feedforward component. Formula transformation:

[0091]

[0092] in In order to the current disturbance intensity Next, use all available power. The maximum volumetric flow rate of material that can be processed, in m³ / s; It is the portion of the output that the feedback control system can stably provide under the saturation edge state; the predicted feedback output. This is an engineering parameter set to ensure computational robustness. Its purpose is to reserve a certain amount of operating space for feedback adjustment, preventing the system from operating at its absolute power limit. It is derived from setting a small, constant value based on experience, for example... 5%;

[0093] Based on the calculated maximum allowable flow rate The system will forcibly modify the feed pump's flow rate setpoint to that value; the PLC will directly output a new control signal, such as modifying the frequency setpoint of the frequency converter, to the feed pump's driver, actively and smoothly reducing its flow rate from the current value to the setpoint. At the same time, the system will send a high load warning or flow restriction status signal to the host computer through the HMI or SCADA system to inform the operator that the system is in adaptive constraint operation mode.

[0094] Through this constraint reconstruction logic, the present invention transforms the system from a passive, uncontrolled state of power saturation and continuous temperature drop into an active, degraded but stable operating state. It can maximize the use of existing heating capacity to process materials while ensuring that the quality of core products is determined by a stable demulsification temperature, thus achieving intelligent production scheduling that allows for the processing of materials within the limits of available capacity. This avoids huge economic losses caused by emergency shutdowns or product defects and greatly enhances the resilience and safety of the production process.

[0095] Example 5:

[0096] This system also includes:

[0097] The disturbance level determination module is used to compare the disturbance intensity index with the preset warning threshold and critical threshold to generate a first-level disturbance or second-level disturbance determination signal.

[0098] The specific logic of the execution decision module is as follows:

[0099] When the disturbance level determination module generates a level 1 disturbance determination signal, the system directly outputs the total control output;

[0100] 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.

[0101] Based on the implementation method described in Example 1, in order to make the system's control strategy more hierarchical and targeted, this embodiment also includes a disturbance level determination module and refines the logic of the execution decision module; this design enables the system to distinguish disturbances of different severity and take a matching, cost-effective response strategy, avoiding overreaction to minor disturbances.

[0102] The disturbance level determination module aims to classify the disturbance intensity index, providing a clear and discrete basis for subsequent decision-making. In this embodiment, the module receives the disturbance intensity index calculated by the disturbance quantification module. It is then compared with two preset thresholds; these two thresholds are: warning thresholds. This threshold is relatively low, indicating a significant deviation in physical properties that requires close monitoring by the system, but remains within the range of normal control capabilities. It is derived from statistical analysis of historical normal operating data, for example, by taking... The 95th percentile of the distribution; and the critical threshold. This threshold is a relatively high threshold, indicating that the disturbance is very severe and may cause the system's normal control capability to saturate. That is, the threshold defined in Example 1 above, whose value is based on the disturbance events in historical data that caused significant difficulties in temperature control. value;

[0103] Based on the above threshold, when When the system determines that there is no disturbance or a small disturbance, then... When, the system generates a first-level disturbance judgment signal; when At that time, the system generates a secondary disturbance judgment signal;

[0104] Based on the aforementioned judgment signals, the specific decision logic of the execution decision module is optimized as follows: when a first-level disturbance judgment signal is received, regardless of the total control output... Is it close? The system determines that the current operating condition is within a controllable range, therefore the system directly outputs the total control output. The system relies solely on a feedforward and feedback coordinated control model for adjustment, without interfering with other process parameters such as flow rate. Conversely, when a secondary disturbance judgment signal is received, the system considers it to have encountered a severe impact, at which point it will simultaneously check the total control output. Does it exceed the actuator's maximum output power? Only when both conditions of secondary disturbance and power saturation are met simultaneously will the system activate the constraint reconstruction logic described in Example 4 above and actively reduce the feed flow rate.

[0105] The introduction of this hierarchical decision-making 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, thus ensuring the maximization of production capacity in most disturbance situations. Only in the case of a second-level severe disturbance that cannot be overcome by adjusting the heating power will the constraint reconstruction, the final safeguard, be initiated. This hierarchical and progressive response strategy achieves the best balance between system stability, control precision, and production efficiency.

[0106] Example 6:

[0107] This system also includes:

[0108] The self-tuning module is used to capture disturbance events when the disturbance intensity index exceeds the preset warning threshold; and iteratively corrects 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.

[0109] The self-tuning module is used for:

[0110] Within a preset disturbance event window, the output power of the feedback component is integrated over time to quantify the energy deviation.

[0111] Within the same event window, calculate the nominal feedforward energy contributed by the disturbance intensity index;

[0112] 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.

[0113] Based on the implementation method described in Example 1, in order to solve the problems that may occur in the system due to long-term operation, such as heat exchanger fouling leading to decreased heat transfer efficiency or slow drift of the reference properties of sludge oil, this embodiment further includes a self-tuning module; the innovation of this module is that it uses each significant disturbance event as an online probe of the system, and by analyzing the energy balance of the control model in the event, it realizes autonomous learning and iterative optimization of the core model parameters;

[0114] The core function of the self-tuning module is to adjust the disturbance intensity index. Exceeding the preset warning threshold At that time, a disturbance event is automatically captured and defined; it is based on the feedback components during that event. The compensated energy deviation affects the core parameter in the control output module: the product of reference density and specific heat capacity. Perform iterative corrections;

[0115] The self-tuning module is implemented by adjusting the feedback components within a preset disturbance event window. The output power is integrated over time to quantify the energy deviation. ;when First time exceeding The moment is recorded as the start time of the disturbance event. ;from Initially, the system operates within a preset evaluation time window. Integrating within the timeframe; evaluation time window This refers to a timeframe sufficient for the system to essentially recover stability after a disturbance. It is derived by performing a step response test on the system, obtaining its thermal response time constant, and setting it as 3 to 5 times that constant. The formula for calculating the energy deviation is:

[0116]

[0117] The physical meaning is that, in this disturbance event, due to the feedforward model The predictions are not entirely accurate, leading to problems with the feedback system. Additional compensation is required. Or cancel if The total energy, expressed in joules (J); an ideal feedforward model should make... Approaching zero;

[0118] Within the same event window, calculate the disturbance intensity index. Nominal feedforward energy directly contributed The purpose of this step is to calculate the energy base related to the perturbation in the feedforward model, which is used for subsequent correction ratio calculations. The formula is as follows:

[0119]

[0120] This formula integrates the feedforward model. Zhongyou The introduced incremental part, in which This is the current parameter value before the correction;

[0121] Based on energy deviation With nominal feedforward energy The ratio, combined with a preset learning rate. Generate the corrected baseline density-specific heat capacity product. This update rule draws on the gain adjustment concept from adaptive control theory, and its formula is as follows:

[0122]

[0123] in and These are the current parameter values ​​before and after the correction, respectively. The learning rate, or damping coefficient, is a dimensionless constant between 0 and 1; Its function is to control the step size of each correction to prevent over-adjustment of parameters due to the peculiarities of a single disturbance event; its value is selected based on engineering experience or through simulation optimization, and a smaller value ensures convergence stability; this update logic ensures that if the feedforward energy supply is insufficient, i.e. ,but The value will be increased to provide more feedforward compensation for the next similar disturbance, and vice versa;

[0124] By introducing a self-tuning module, this invention endows the control system with the ability to learn autonomously and adapt over a long period. It can automatically compensate for model mismatches caused by factors such as equipment aging and heat exchanger fouling, ensuring consistently high-precision control performance without the need for periodic manual recalibration. For example, when heat exchanger fouling leads to a decrease in heat transfer efficiency, the system will observe… Continuous positive compensation, i.e. This will automatically and slowly increase the [adjustment / reduction]. This value indirectly increases the total control output gain to offset the loss of heat transfer efficiency; this greatly reduces the system's maintenance costs and ensures its operating efficiency and control quality throughout its entire life cycle.

[0125] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A PLC-based digital operation control cycle system for sludge, characterized by, Comprise: A data acquisition module for acquiring real-time density and real-time dielectric constant of the incoming waste oil, and set temperature and actual measured temperature of the system in real time; A disturbance quantification module for calculating an instantaneous deviation degree based on the real-time density and the real-time dielectric constant, and comparing the preset reference density and the reference dielectric constant, and determining a disturbance intensity index in combination with a change rate of the instantaneous deviation degree; A control output module for calculating a predictive feedforward component based on the disturbance intensity index; And calculating a feedback component based on a deviation of the set temperature and the actual measured temperature; Finally, superimposing the predictive feedforward component and the feedback component to generate a total control output; An execution decision module for triggering a constraint reconstruction logic when the total control output exceeds a preset maximum output power of an actuator, and the disturbance intensity index exceeds a preset critical threshold; otherwise, directly outputting the total control output; The disturbance quantification module is used for: Determining the instantaneous deviation degree by weighted summation based on the real-time density, the real-time dielectric constant and the corresponding reference values; Multiplying the change rate of the instantaneous deviation degree by a preset time constant to obtain a dynamic amplification factor; Multiplying the dynamic amplification factor and the instantaneous deviation degree to determine the disturbance intensity index; 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: ； wherein is the instantaneous deviation at time t, dimensionless; and is a weight coefficient, dimensionless, and ; The control output module is used for: Taking the disturbance intensity index as a dynamic adjustment factor to correct the current reference density-specific heat capacity product to obtain a corrected physical property product; Combining the corrected physical property product, the real-time flow and the set temperature difference to solve the predictive feedforward component; Using a proportional-integral-derivative control algorithm and calculating the feedback component based on the temperature deviation; The constraint reconstruction logic comprises: Based on the maximum output power of the actuator, and combining the feedforward control model, reversely calculating the maximum allowed flow; Forcing to modify the flow set value of the feed pump to the maximum allowed flow.

2. The PLC-based digital operation control cycle system for sludge according to claim 1, characterized in that, Further comprise: A disturbance level determination module for comparing the disturbance intensity index with a preset warning threshold and a critical threshold to generate a first-level disturbance or a second-level disturbance determination signal.

3. The PLC-based digital operation control cycle system for sludge according to claim 2, characterized in that, 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.

4. The PLC-based digital operation control cycle system for sludge according to claim 1, characterized in that, Further comprise: A self-tuning module for capturing a disturbance event when the disturbance intensity index exceeds a preset warning threshold; And based on the energy deviation compensated by the feedback component during the disturbance event, iteratively correcting the current reference density-specific heat capacity product in the control output module.

5. The PLC-based digital operation control cycle system for sludge according to claim 4, characterized in that, The self-tuning module is used for: Time-integrating the output power of the feedback component within a preset disturbance event window to quantify the energy deviation; Within the same event window, calculating a nominal feedforward energy contributed by the disturbance intensity index; Based on the ratio of the energy deviation and the nominal feedforward energy, and combining a preset learning rate, generating a corrected reference density-specific heat capacity product.

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