Efficient pineapple leaf cellulose extraction method based on three-gradient microwave-biological enzyme synergistic enhancement

Abstract

The invention provides an efficient pineapple leaf cellulose extraction method based on three-gradient microwave-biological enzyme synergistic enhancement, and relates to the technical field of high-value utilization of natural plant resources. The cellulose content is obtained in real time through the near infrared spectrum, the content deviation is calculated in the control system, and the microwave power is automatically adjusted; an integrated pH value and temperature sensor is combined to realize environment monitoring, and when parameters are abnormal, cellulase is accurately supplemented; meanwhile, energy efficiency indexes are calculated according to energy input and saccharide product conversion results, control parameters are dynamically adjusted, a content, environment and energy efficiency triple closed loop is constructed, and the cellulose extraction efficiency and the energy utilization rate are remarkably improved.

Description

Technical Field

[0001] The invention relates to the technical field of high-value utilization of natural plant resources, and in particular to a method for efficiently extracting pineapple leaf cellulose based on three-gradient microwave-bioenzyme synergistic enhancement. Background Art

[0002] Cellulose enzymatic hydrolysis and subsequent fermentation production often rely on an intermittent sampling and offline monitoring process: substrate concentration is measured using a near-infrared spectrometer, while pH and temperature probes monitor environmental conditions. Heating, enzyme addition, and agitation are then manually regulated or controlled by a single controller. This approach provides basic monitoring, but relies on manual experience, lacks real-time performance, and makes it difficult to simultaneously record energy consumption information.

[0003] The process industry is evolving toward online multi-source sensing, deep integration of programmable logic controllers, and refined energy efficiency management. The industry is gradually integrating microwave heating, automated enzyme compensation, and data-driven optimization algorithms to achieve continuous closed-loop control through a single control core. Near-infrared spectroscopy is also moving from the laboratory to the production site, leveraging chemometric models to provide real-time insights into substrate conversion.

[0004] In existing technologies, the sensing, decision-making and execution systems lack a unified main control logic, and the lengthy feedback chain leads to response delays; there is a lack of coupling strategies between microwave power regulation and pH, temperature, and enzyme concentration, which easily leads to heat energy waste; and traditional technologies are unable to perform online evaluation and dynamic optimization of the core indicator of "energy consumption and yield", making it difficult to quantify energy-saving effects; in addition, enzyme compensation usually adopts a fixed ratio or manual addition, which cannot correct environmental fluctuations in real time with high metering accuracy, and historical operating data is not effectively utilized. Summary of the Invention

[0005] In order to overcome the shortcomings of the existing technology, the purpose of the present invention is to provide a method for efficiently extracting pineapple leaf cellulose based on three-gradient microwave-bioenzyme synergistic enhancement. By constructing a triple closed-loop control system of content, environment and energy efficiency, the problems of slow response, low energy efficiency and inaccurate enzyme supplementation in the existing technology are overcome, and the high efficiency, low consumption and adaptive regulation of the pineapple leaf cellulose extraction process are achieved.

[0006] To achieve the above object, the present invention provides the following solutions:

[0007] A method for efficiently extracting pineapple leaf cellulose based on triple-gradient microwave-bioenzyme synergistic enhancement, comprising:

[0008] The spectral signal of the target material is continuously collected by a near-infrared spectrometer, and the spectral signal is input into a chemometric model to calculate the real-time cellulose content value C(t) in the PLC, and C(t) is stored as content data in the PLC memory;

[0009] Compare C(t) with the target content C0 inside the PLC to obtain the content deviation ΔC. Based on ΔC, the PLC's built-in PID algorithm is called to generate the power setting value P*, which is used as the control parameter for this cycle.

[0010] Controlling the microwave generator to output corresponding power according to P* to heat the target material;

[0011] The pH value p(t) and temperature T(t) are measured in real time by an integrated pH-temperature sensor, and p(t) and T(t) are recorded as environmental data and written synchronously into the PLC memory;

[0012] When p(t) or T(t) deviates from the respective set intervals, the PLC controls the automatic enzyme replenishment actuator to add cellulase with a metering accuracy of δV until p(t) and T(t) return to the target interval at the same time;

[0013] Calculate the effective conversion energy E based on the microwave real-time power and sugar conversion rate e , and E e With input power E total Calculate the energy efficiency ratio η=E e / E total ;

[0014] When η is lower than the set threshold η min When the PLC automatically adjusts the PID parameters Kp, Ki, and Kd to form a new set of control parameters to improve energy efficiency.

[0015] Preferably, it also includes:

[0016] Write the content data C(t), environmental data [p(t), T(t)] and the new control parameter set into the historical database;

[0017] The chemometric model and PID parameters are adaptively updated regularly using the historical database through machine learning algorithms for the next batch to call.

[0018] Preferably, the spectral signal of the target material is continuously collected by a near-infrared spectrometer, and the spectral signal is input into a chemometric model, the real-time cellulose content value C(t) is calculated in a PLC, and C(t) is stored as content data in the memory of the PLC, including:

[0019] Before the batch is started, the white plate and dark plate benchmarks of the near-infrared spectrometer are called to generate reference spectra under the current temperature and humidity conditions and store them in the PLC memory;

[0020] The original spectrum data S of the target material is obtained by a fixed optical fiber probe at an interval of no more than thirty seconds. raw (t), and S raw(t) Real-time transmission to PLC via Ethernet;

[0021] In the PLC, S raw (t) Perform scatter correction, smoothing filtering and second-order derivative enhancement in sequence to obtain the preprocessed spectrum line S pre (t);

[0022] S pre (t) is vector multiplied with the coefficient matrix B of the chemometric model to calculate the real-time cellulose content value C(t)=S pre (t) × B;

[0023] If C(t) exceeds the applicable range of the model, an alarm is triggered and the last valid value is maintained. Otherwise, the verified C(t) is written into the PLC memory according to the timestamp.

[0024] Preferably, C(t) is compared with the target content C0 in the PLC to obtain the content deviation ΔC, and based on ΔC, the PLC built-in PID algorithm is called to generate the power setting value P*, and P* is used as the control parameter of this cycle, including:

[0025] In the real-time control loop of the PLC, the real-time cellulose content value C(t) of the current cycle is read and the target content value set for the corresponding batch is called. , and according to the formula Calculated content deviation ;

[0026] The content deviation ΔC and the previous cycle deviation ΔC prev Enter the proportional-integral-derivative control model and first calculate the integral term , It is the sum of all deviations within the sampling window multiplied by the sampling period Δt. The formula is: ;

[0027] Calculate the initial power output value by using proportional, integral and differential terms , the initial power output value The calculation formula is: ;

[0028] The initial power output value , after correction by the nonlinear gain factor α, the corrected power value is calculated ; Corrected power value The calculation formula is: ;

[0029] The corrected power value Input limit function, constraint Get the final power setting value within the allowable range [Pmin, Pmax] ; The final power setting value The calculation formula is: ;

[0030] The final power setting value Write the control register of the microwave heating unit and set the deviation value ΔC of the current cycle and the initial output value , correction value And the final power setting value Written into the historical database.

[0031] Preferably, the pH value p(t) and temperature T(t) are measured in real time by an integrated pH-temperature sensor, and p(t) and T(t) are recorded as environmental data and synchronously written into the memory of the PLC, including:

[0032] An integrated pH-temperature sensor is provided in the reactor; the integrated pH-temperature sensor is equipped with both an electrochemical pH measurement probe and a thermistor temperature sensing element, and establishes a data connection with the PLC via an industrial signal conversion interface;

[0033] Based on the integrated pH-temperature sensor, the fermentation liquid is continuously tested with a sampling period of no more than 1 second, and the pH value at the current moment is output respectively. and temperature value , and is the real-time environment state variable;

[0034] The pH value After the accuracy calibration within the range of ±0.1, the temperature value After being calibrated to an accuracy of ±0.5 degrees Celsius, they are encoded into 16-bit data words;

[0035] Transmitting the encoded 16-bit data word to the environmental data storage area of ​​the PLC system via the Modbus industrial bus protocol;

[0036] The control program periodically reads the environmental data storage area and stores the corresponding and Synchronously write to the PLC memory.

[0037] Preferably, when p(t) or T(t) deviates from the respective set intervals, the PLC controls the automatic enzyme supplementation actuator to add cellulase with a metering accuracy of δV until p(t) and T(t) return to the target interval at the same time, including:

[0038] a) The PLC continuously receives the current pH value output by the integrated pH-temperature sensor and temperature value , and respectively with the set target interval and For comparison, and The target lower and upper control limits for pH value; and are the target lower and upper control limits of temperature respectively;

[0039] b) When or When any of the conditions are met, the PLC starts the enzyme compensation judgment logic;

[0040] c) Determine whether the current enzyme compensation status is in the "uncompensated" sign. If so, send an enzyme solution injection instruction to the automatic enzyme replenishment actuator, and the injection amount is the preset metering accuracy. ,in Total volume of the reaction system;

[0041] d) After the actuator completes the compensation action, it sets the "compensated" flag and waits for a set response time. Wait for the system to stabilize;

[0042] e) Re-read after the system stabilizes and If both still do not return to the target range at the same time, repeat sub-steps c) and d) until and When the set range is met at the same time, the enzyme supplement action is terminated;

[0043] f) After termination, the PLC writes the number of enzyme additions, total addition amount and response time of this enzyme supplement process into the historical database, and clears the compensation status flag, preparing to enter the next cycle judgment.

[0044] Preferably, the effective conversion energy E is calculated based on the microwave real-time power and sugar conversion rate. e , and E e The energy efficiency ratio η=E is obtained by comparing it with the input power Etotal e / Etotal, including:

[0045] During the microwave heating process, collect the real-time power output value of the microwave generator System operation time period , according to the formula Calculate the total input power during the system operation period ;

[0046] The mass concentration of sugar products per unit volume in the reaction system was obtained by near-infrared spectrometry , combined with the total volume of the system and sugar conversion enthalpy constant , calculate the effective conversion energy during the system operation period, satisfying the formula ;in and represent the sugar concentration at the end and starting time, respectively;

[0047] The effective conversion energy With input power Perform ratio calculation to obtain the energy efficiency ratio of the system at the current stage , satisfying the formula

[0048] If the calculated energy efficiency ratio Below the set minimum energy efficiency threshold , then activate the control parameter update trigger signal in the PLC to automatically correct the PID parameters.

[0049] Preferably, when η is lower than the set threshold η min When the PLC automatically adjusts the PID parameters Kp, Ki, and Kd to form a new set of control parameters, including:

[0050] Energy efficiency ratio at the completion of the current stage After calculation, PLC will With preset threshold Compare, if satisfied , then the control parameter adaptive adjustment subroutine is triggered;

[0051] The PLC calls the PID parameter set corresponding to the operating cycle with high energy efficiency ratio in the past period from the historical database, and builds the parameter optimization reference range based on the PID parameter set; the constraints for high energy efficiency ratio are: ;in, The energy efficiency ratio level achieved during a typical operating cycle known to perform well in historical data;

[0052] Combined with the current control response lag and content deviation, The fluctuation range and integral accumulation trend of the modified formula are used to calculate the new PID parameter set; the modified formula is:

[0053]

[0054]

[0055]

[0056] in, 、 、 It is the adaptive adjustment coefficient set in the system, which is used to limit the parameter adjustment range to prevent system oscillation; 、 、 They are respectively the proportional coefficient, integral coefficient and differential coefficient in the new PID parameter set;

[0057] The newly generated PID parameter set Write into the PLC's PID control unit, overwrite the original parameters, and record the update timestamp and corresponding energy efficiency background data;

[0058] Closed-loop control will continue to be performed in the next control cycle after the parameter adjustment, and the response effect of the updated parameters will be evaluated. If the energy efficiency ratio is still lower than the threshold for multiple consecutive cycles, a maintenance prompt will be issued or the system will switch to manual intervention mode.

[0059] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0060] The present invention couples near-infrared online quantification, three-gradient microwave heating, and an automated enzyme supplementation closed-loop. Through PLC real-time updates of content deviations, environmental parameters, and energy efficiency ratios, it is able to dynamically adjust enzyme supplementation at the microsecond level and adaptively adjust the PID coefficient, thereby optimizing the cellulose conversion rate and unit energy consumption, avoiding overheating inactivation, enzyme waste, and energy efficiency fluctuations, thereby improving extraction efficiency and reducing energy consumption overall. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0062] Figure 1 A flowchart of a method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0063] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0064] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0065] Figure 1 A flow chart of the method provided in the embodiment of the present invention is shown in FIG. Figure 1 As shown, the present invention provides a method for efficiently extracting pineapple leaf cellulose based on three-gradient microwave-biological enzyme synergistic enhancement, comprising:

[0066] Step 100: Continuously collect spectral signals of the target material through a near-infrared spectrometer, input the spectral signals into a chemometric model, calculate the real-time cellulose content value C(t) in the PLC, and store C(t) as content data in the PLC memory;

[0067] Step 200: Compare C(t) with the target content C0 within the PLC to obtain the content deviation ΔC. Based on ΔC, the PLC's built-in PID algorithm is called to generate the power setting value P*, which is used as the control parameter for this cycle.

[0068] Step 300: Control the microwave generator to output corresponding power according to P* to heat the target material;

[0069] Step 400: The pH value p(t) and temperature T(t) are measured in real time by an integrated pH-temperature sensor, and p(t) and T(t) are recorded as environmental data and synchronously written into the memory of the PLC;

[0070] Step 500: When p(t) or T(t) deviates from the respective set ranges, the PLC controls the automatic enzyme replenishment actuator to add cellulase with a metering accuracy of δV until p(t) and T(t) return to the target range at the same time;

[0071] Step 600: Calculate the effective conversion energy E based on the microwave real-time power and sugar conversion rate e , and E e With input power E total Calculate the energy efficiency ratio η=E e / E total ;

[0072] Step 700: When η is lower than the set threshold η min When the PLC automatically adjusts the PID parameters Kp, Ki, and Kd to form a new set of control parameters to improve energy efficiency.

[0073] Preferably, it also includes:

[0074] Step 800: writing the content data C(t), environmental data [p(t), T(t)] and the new control parameter set into the history database;

[0075] Step 900: Regularly use the historical database through machine learning algorithms to adaptively update the chemometric model and PID parameters for the next batch.

[0076] Specifically, in steps 800 through 900 of this embodiment, after each control cycle, the current real-time cellulose content C(t), environmental parameters p(t) and T(t), and control parameters P*, Kp, Ki, and Kd are all written to the historical database. To enable rapid subsequent retrieval based on both time and batch dimensions, this embodiment employs a data structure with a dual primary key consisting of a batch unique identifier and a cycle index. Each field is stored with floating-point precision and a millisecond-level timestamp to prevent truncation errors from impacting subsequent model training accuracy.

[0077] After completing each batch of extraction process, this embodiment automatically triggers the model training sub-process to analyze the data of nearly n batches with excellent energy efficiency performance in the historical database. First, the Bayesian optimization combined with the partial least squares method is used to fit the chemometric model to improve the robustness of content prediction under complex background noise; then, based on the control deviation, response time and historical energy efficiency ratio, the original PID parameters are adaptively adjusted to form an optimized parameter set, and written into the control system as a candidate parameter after the simulation verification. Different from the traditional static control strategy, this embodiment links offline training with closed-loop verification to build a dynamic evolution mechanism of control parameters in the production process, which is a key technical path to improve energy efficiency.

[0078] This embodiment assigns a unique version identifier and performance tag to the updated chemometric model and PID parameters before deploying them to the next batch. During actual operation, the system evaluates content prediction deviation and real-time energy efficiency performance at preset intervals. If the evaluation indicators fail to meet the set thresholds for three consecutive cycles, a safety rollback is automatically executed, restoring the previous stable parameter set and recording the abnormal period data in a rejection list.

[0079] In step 100 of this embodiment, before each batch is started, the white plate and dark plate reference of the near-infrared spectrometer are called, and the reference spectrum is generated under the current workshop temperature and humidity conditions and written into the PLC memory; then, with a fixed sampling period of no more than 30 seconds, the original spectrum data S of the material is obtained through the optical fiber probe installed on the side wall of the reactor. raw (t) and transmit it to PLC in real time via Gigabit Ethernet to ensure the synchronization of spectral information and control loop. raw (t) performs scattering correction, smoothing filtering and second-order derivative operation in sequence to obtain the preprocessed spectrum line Spre(t); then S pre The cellulose content C(t) is calculated in real time by performing vector multiplication of C(t) with the chemometric model coefficient matrix B. If C(t) exceeds the upper or lower limit of the model, the PLC immediately triggers an alarm and retains the last valid value. If it is within the range, it is written to the memory with a timestamp, providing reliable content data for subsequent closed-loop control and historical database archiving.

[0080] Furthermore, in step 200 of this embodiment, during each control cycle, the programmable logic controller first reads the real-time cellulose content and calls the preset target content value for the corresponding batch. The difference between the two is the content deviation for the current cycle. This deviation serves as the core basis for adjustments during this cycle, ensuring that the control strategy responds to real-time changes in the material conversion state.

[0081] The controller feeds the current cycle's content deviation and the previous cycle's deviation into a proportional-integral-derivative control algorithm. The integral term is calculated as the product of all deviations over several consecutive sampling cycles, depicting the long-term cumulative error trend. The differential term reflects the rate of change of the current deviation, thereby suppressing system oscillations and improving regulatory stability.

[0082] This embodiment uses proportional, integral, and differential terms to calculate the initial power output value for the current cycle, and then adjusts this power value through a nonlinear gain correction strategy. This correction uses the relative ratio of the current content deviation to the target value as an amplification factor, enhancing the response strength when the deviation is significant, and improving the sensitivity and adaptability of heating control.

[0083] Before inputting the heating control command, the corrected power output value must be limited to the minimum and maximum power allowed by the device through a limiting function to ensure that power regulation is carried out within a safe and controllable range. The resulting power setting is written to the control register of the microwave heating unit. The deviation, initial output value, corrected value, and final setting value for this cycle are simultaneously written to a historical database for subsequent energy efficiency evaluation and parameter optimization.

[0084] Furthermore, in step 300, this embodiment uses the current power setting output by the proportional-integral-differential algorithm as a control parameter within the control cycle and writes it in real time to the control register of the microwave heating unit. The microwave generator dynamically adjusts the RF output based on this power setting, precisely controlling the heating intensity within the device's permitted power range. Because cellulase is temperature-sensitive, this embodiment employs a three-gradient power curve to achieve a staged temperature increase, preventing imbalances in enzyme activity during the initial reaction phase and improving thermal energy efficiency.

[0085] In step 400, to monitor the environmental conditions within the reaction system, this embodiment installs an integrated pH-temperature sensor within the reactor. This sensor integrates an electrochemical pH measurement probe and a thermistor temperature sensing element, providing dual-channel independent sampling capabilities. It also establishes a stable data connection with the PLC via an industrial signal conversion module. This structure enables simultaneous sensing of pH and temperature without adding additional interfaces, facilitating subsequent environmental compensation and process correction.

[0086] This embodiment sets a sensor sampling period of no more than one second to continuously monitor the reaction solution. The collected pH and temperature values ​​are calibrated to an accuracy of no more than ±0.1°C for pH and ±0.5°C for temperature. The data are then encoded into a 16-bit data word format. This data is transmitted in real time to the PLC's environmental data buffer via the Modbus industrial bus protocol, ensuring data integrity and transmission speed to meet process control requirements.

[0087] During each control cycle, the PLC control program automatically reads the latest pH and temperature data from the storage area and writes the environmental status value corresponding to that sampling point into a memory variable, which serves as the environmental input parameter for the current cycle and participates in the comprehensive judgment. This operation provides a direct basis for subsequent judgments on whether to execute enzyme supplementation control actions and also serves as a source for synchronously writing environmental data to the historical database, forming an important foundation for the "content-environment-energy efficiency" triple feedback loop in this embodiment.

[0088] Furthermore, in step 500 of this embodiment, during each control cycle, the programmable logic controller receives the latest readings from the pH and temperature sensors in real time and compares them against the preset pH and temperature tolerances, respectively. If either reading falls below the lower limit or rises above the upper limit, the reaction environment is considered to have deviated from the target state. Once a deviation is detected, the controller immediately enters the enzyme replenishment determination process. If the current state is marked as "uncompensated," an enzyme injection command is issued to the automatic enzyme replenishment actuator, which adds cellulase solution to the system according to the preset metering accuracy. The single dosage does not exceed one thousandth of the total volume to ensure enzyme replenishment accuracy and avoid localized excessive concentrations. After the enzyme replenishment is completed, the controller switches the flag to "compensated" and enters a ten-second response waiting phase to allow the system to fully mix. After the waiting period, the pH and temperature are read again. If both indicators still do not return to the tolerance range simultaneously, the waiting flag is cleared and the "add-wait" cycle is repeated until both indicators simultaneously meet the tolerances, at which point the enzyme replenishment process automatically terminates. After the enzyme replenishment process is complete, the controller writes the number of enzyme replenishments, cumulative dosage, and total response time to a historical database, clears the compensation status flag, and begins the next cycle of deviation monitoring. This closed-loop strategy promptly suppresses environmental fluctuations, avoids overdosing and energy waste, and ensures long-term stability and efficiency of the reaction process.

[0089] In step 600 of this embodiment, during the microwave heating process, the power monitoring module collects the microwave generator's output power in real time and simultaneously records the start and end times of heating. The controller integrates the power and time data during this period to determine the total input electrical energy consumed during this phase, providing baseline data for subsequent energy efficiency assessment. The conversion of sugar products in the reaction system is determined by online near-infrared spectroscopy. The difference between the measured initial and endpoint concentrations, multiplied by the total volume of the reaction system, is combined with a pre-calibrated sugar conversion enthalpy constant to calculate the effective conversion energy for that phase. This value reflects the energy actually used to generate the product through the combined effects of microwave heating and enzymatic hydrolysis. The controller divides this effective conversion energy by the total input electrical energy to determine the energy efficiency ratio. If this ratio falls below a set threshold, the system immediately triggers a parameter self-optimization process, automatically correcting the proportional, integral, and differential control coefficients to improve energy utilization in the subsequent heating phase.

[0090] Optionally, in step 700, after this embodiment completes calculating the energy efficiency ratio for a particular stage, the programmable logic controller first compares the energy efficiency ratio with a pre-set minimum energy efficiency threshold. If the current energy efficiency ratio is lower than the threshold, the controller immediately triggers the adaptive parameter adjustment subroutine. This subroutine uses the energy consumption and output performance of the current batch as input signals, marks the start time of this parameter adjustment, and locks the running closed-loop logic to ensure that subsequent parameter switching processes do not interrupt data collection and safety protection.

[0091] After the parameter adjustment subroutine is started, the controller automatically searches the historical database, selects the operating cycles with energy efficiency levels higher than the reference standard, and loads the proportional coefficients, integral coefficients, and differential coefficients used in these cycles together with the corresponding environmental variables into the cache. On this basis, this embodiment constructs a reference parameter range to define the "allowable gain" and "safety upper limit" to prevent the parameters generated by subsequent adaptive calculations from deviating from the reasonable physical range. This step is equivalent to providing data samples and boundary constraints for real-time parameter adjustment, which is the key to avoiding over-adjustment and maintaining system stability.

[0092] Subsequently, the controller comprehensively analyzes the response lag of the current process, the fluctuation amplitude of the cellulose content error, and the integral accumulation trend, and assigns adjustment factors to the proportional coefficient, integral coefficient, and differential coefficient respectively. The adjustment factor is adjusted proportionally or inversely proportionally to the real-time deviation based on the aforementioned reference interval to increase the proportional effect, accelerate the convergence of the integral, or weaken the differential suppression, so as to achieve the purpose of quickly suppressing the decline in energy efficiency. In addition, to prevent violent oscillations, the system also sets a gradually increasing or decreasing transition curve to ensure a smooth transition between the old and new parameters within several sampling cycles, avoiding instantaneous jumps that cause equipment overload or temperature peaks to exceed the limit.

[0093] Once a new set of proportional, integral, and differential coefficients is generated, the controller writes them to the current closed-loop control unit and automatically records the parameter version number, generation time, and corresponding energy efficiency background data, forming a complete change log for easy traceability and performance comparison. Once the write is complete, the controller unlocks the parameter adjustment, and the system enters the next control cycle and begins using the new parameters. This entire process is completed in milliseconds without affecting microwave power output or enzyme feeding time, thus ensuring the continuity and safety of the reaction process.

[0094] Over several cycles of adopting the new parameters, the controller continuously monitors the energy efficiency ratio, content error, and environmental stability. If the efficiency remains below the minimum threshold for multiple consecutive cycles, this embodiment automatically issues a maintenance reminder and switches to manual intervention or conservative control mode, resuming automatic parameter adjustment after the operator verifies the device status. This fault-tolerant and fallback mechanism ensures that adaptive parameter adjustment does not lead to continued inefficiency or loss of control in the face of unexpected operating conditions or hardware aging, achieving long-term stable, efficient, and traceable parameter optimization.

[0095] The beneficial effects of the present invention are as follows:

[0096] (1) The present invention uses "online near-infrared quantification + three-gradient microwave rapid heating" synergistic enzymatic hydrolysis to complete cellulose release in a shorter time. The overall production cycle is significantly shortened compared to traditional constant power heating and artificial enzyme supplementation schemes, and the single batch output is improved.

[0097] (2) The present invention adopts real-time power monitoring and energy conversion feedback, combined with proportional-integral-differential adaptive control, which can dynamically compress invalid energy input while ensuring the conversion rate, thereby achieving a significant reduction in power consumption under the same output conditions.

[0098] (3) The integrated pH-temperature sensor of the present invention forms a closed loop with the precise metering enzyme supplementation actuator, which can correct environmental fluctuations in seconds, avoid enzyme inactivation and by-product formation, and ensure the consistency of cellulose quality and subsequent processes.

[0099] (4) The present invention regularly updates the chemometric model and control parameters through batch data archiving and machine learning algorithms. The system can automatically maintain the optimal operating conditions when raw materials fluctuate or equipment ages, reducing the frequency of manual parameter adjustment and improving the level of factory intelligence.

[0100] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0101] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only intended to help understand the method and core concept of the present invention. At the same time, those skilled in the art will find that the specific implementation methods and application scopes may vary based on the concept of the present invention. In summary, the contents of this specification should not be construed as limiting the present invention.

Claims

1. A method for efficiently extracting pineapple leaf cellulose based on triple-gradient microwave-enzyme synergistic enhancement, characterized in that: include: The spectral signal of the target material is continuously collected by a near-infrared spectrometer, and the spectral signal is input into a chemometric model to calculate the real-time cellulose content value C(t) in the PLC, and C(t) is stored as content data in the PLC memory; Compare C(t) with the target content C0 inside the PLC to obtain the content deviation ΔC. Based on ΔC, the PLC's built-in PID algorithm is called to generate the power setting value P*, which is used as the control parameter for this cycle. Controlling the microwave generator to output corresponding power according to P* to heat the target material; The pH value p(t) and temperature T(t) are measured in real time by an integrated pH-temperature sensor, and p(t) and T(t) are recorded as environmental data and written synchronously into the PLC memory; When p(t) or T(t) deviates from the respective set intervals, the PLC controls the automatic enzyme replenishment actuator to add cellulase with a metering accuracy of δV until p(t) and T(t) return to the target interval at the same time; Calculate the effective conversion energy E based on the microwave real-time power and sugar conversion rate e , and E e With input power E total Calculate the energy efficiency ratio η=E e / E total ; When η is lower than the set threshold η min When the PLC automatically adjusts the PID parameters Kp, Ki, and Kd to form a new set of control parameters to improve energy efficiency.

2. The pineapple leaf cellulose efficient extraction method based on triple gradient microwave-biological enzyme synergistic enhancement according to claim 1, is characterized in that, Also includes: Write the content data C(t), environmental data [p(t), T(t)] and the new control parameter set into the historical database; The chemometric model and PID parameters are adaptively updated regularly using the historical database through machine learning algorithms for the next batch to call.

3. The pineapple leaf cellulose efficient extraction method based on triple gradient microwave-biological enzyme synergistic enhancement according to claim 1, is characterized in that, The target material's spectral signal is continuously collected by a near-infrared spectrometer, and the spectral signal is input into the chemometric model. The real-time cellulose content value C(t) is calculated in the PLC, and C(t) is stored as content data in the PLC memory, including: Before the batch is started, the white plate and dark plate benchmarks of the near-infrared spectrometer are called to generate reference spectra under the current temperature and humidity conditions and store them in the PLC memory; The original spectrum data S of the target material is obtained by a fixed optical fiber probe at an interval of no more than thirty seconds. raw (t), and S raw (t) Real-time transmission to PLC via Ethernet; In the PLC, S raw (t) Perform scatter correction, smoothing filtering and second-order derivative enhancement in sequence to obtain the preprocessed spectrum line S pre (t); S pre (t) is vector multiplied with the coefficient matrix B of the chemometric model to calculate the real-time cellulose content value C(t)=S pre (t) × B; If C(t) exceeds the applicable range of the model, an alarm is triggered and the last valid value is maintained. Otherwise, the verified C(t) is written into the PLC memory according to the timestamp.

4. The pineapple leaf cellulose efficient extraction method based on triple gradient microwave-biological enzyme synergistic enhancement according to claim 1, is characterized in that, Compare C(t) with the target content C0 within the PLC to obtain the content deviation ΔC. Based on ΔC, the PLC's built-in PID algorithm is called to generate the power setting value P*, which is used as the control parameter for this cycle, including: In the real-time control loop of the PLC, the real-time cellulose content value C(t) of the current cycle is read and the target content value set for the corresponding batch is called. , and according to the formula Calculated content deviation ; The content deviation ΔC and the previous cycle deviation ΔC prev Enter the proportional-integral-derivative control model and first calculate the integral term , It is the sum of all deviations within the sampling window multiplied by the sampling period Δt. The formula is: ; Calculate the initial power output value by using proportional, integral and differential terms , the initial power output value The calculation formula is: ; The initial power output value , after correction by the nonlinear gain factor α, the corrected power value is calculated ; Corrected power value The calculation formula is: ; The corrected power value Input limit function, constraint Get the final power setting value within the allowable range [Pmin, Pmax] ; The final power setting value The calculation formula is: ; The final power setting value Write the control register of the microwave heating unit and set the deviation value ΔC of the current cycle and the initial output value , correction value And the final power setting value Written into the historical database.

5. The pineapple leaf cellulose efficient extraction method based on triple gradient microwave-biological enzyme synergistic enhancement according to claim 1 is characterized in that, The integrated pH-temperature sensor measures the pH value p(t) and temperature T(t) in real time, records p(t) and T(t) as environmental data, and writes them synchronously to the PLC memory, including: An integrated pH-temperature sensor is provided in the reactor; the integrated pH-temperature sensor is equipped with both an electrochemical pH measurement probe and a thermistor temperature sensing element, and establishes a data connection with the PLC via an industrial signal conversion interface; Based on the integrated pH-temperature sensor, the fermentation liquid is continuously tested with a sampling period of no more than 1 second, and the pH value at the current moment is output respectively. and temperature value , and is the real-time environment state variable; The pH value After the accuracy calibration within the range of ±0.1, the temperature value After being calibrated to an accuracy of ±0.5 degrees Celsius, they are encoded into 16-bit data words; Transmitting the encoded 16-bit data word to the environmental data storage area of ​​the PLC system via the Modbus industrial bus protocol; The control program periodically reads the environmental data storage area and stores the corresponding and Synchronously write to the PLC memory.

6. The method for efficiently extracting pineapple leaf cellulose based on triple-gradient microwave-biological enzyme synergistic enhancement according to claim 1, wherein When p(t) or T(t) deviates from the respective set ranges, the PLC controls the automatic enzyme supplementation actuator to add cellulase with a metering accuracy of δV until p(t) and T(t) return to the target range at the same time, including: a) The PLC continuously receives the current pH value output by the integrated pH-temperature sensor and temperature value , and respectively with the set target interval and For comparison, and The target lower and upper control limits for pH value; and are the target lower and upper control limits of temperature respectively; b) When or When any of the conditions are met, the PLC starts the enzyme compensation judgment logic; c) Determine whether the current enzyme compensation status is in the "uncompensated" sign. If so, send an enzyme solution injection instruction to the automatic enzyme replenishment actuator, and the injection amount is the preset metering accuracy. ,in Total volume of the reaction system; d) After the actuator completes the compensation action, it sets the "compensated" flag and waits for a set response time. Wait for the system to stabilize; e) Re-read after the system stabilizes and If both still do not return to the target range at the same time, repeat sub-steps c) and d) until and When the set range is met at the same time, the enzyme supplement action is terminated; f) After termination, the PLC writes the number of enzyme additions, total addition amount and response time of this enzyme supplement process into the historical database, and clears the compensation status flag, preparing to enter the next cycle judgment.

7. The method for efficiently extracting pineapple leaf cellulose based on triple-gradient microwave-biological enzyme synergistic enhancement according to claim 1, wherein Calculate the effective conversion energy E based on the microwave real-time power and sugar conversion rate e , and E e The energy efficiency ratio η=E is obtained by comparing it with the input power Etotal e / Etotal, including: During the microwave heating process, collect the real-time power output value of the microwave generator System operation time period , according to the formula Calculate the total input power during the system operation period ; The mass concentration of sugar products per unit volume in the reaction system was obtained by near-infrared spectrometry , combined with the total volume of the system and sugar conversion enthalpy constant , calculate the effective conversion energy during the system operation period, satisfying the formula ;in and represent the sugar concentration at the end and starting time, respectively; The effective conversion energy With input power Perform ratio calculation to obtain the energy efficiency ratio of the system at the current stage , satisfying the formula ; If the calculated energy efficiency ratio Below the set minimum energy efficiency threshold , then activate the control parameter update trigger signal in the PLC to automatically correct the PID parameters.

8. The method for efficiently extracting pineapple leaf cellulose based on triple-gradient microwave-biological enzyme synergistic enhancement according to claim 1, wherein When η is lower than the set threshold η min When the PLC automatically adjusts the PID parameters Kp, Ki, and Kd to form a new set of control parameters, including: Energy efficiency ratio at the completion of the current stage After calculation, PLC will With preset threshold Compare, if satisfied , then the control parameter adaptive adjustment subroutine is triggered; The PLC calls the PID parameter set corresponding to the operating cycle with high energy efficiency ratio in the past period from the historical database, and builds the parameter optimization reference range based on the PID parameter set; the constraints for high energy efficiency ratio are: ;in, The energy efficiency ratio level achieved during a typical operating cycle known to perform well in historical data; Combined with the current control response lag and content deviation, The fluctuation range and integral accumulation trend of the modified formula are used to calculate the new PID parameter set; the modified formula is: ； ； ； in, 、 、 It is the adaptive adjustment coefficient set in the system, which is used to limit the parameter adjustment range to prevent system oscillation; 、 、 They are respectively the proportional coefficient, integral coefficient and differential coefficient in the new PID parameter set; The newly generated PID parameter set Write into the PLC's PID control unit, overwrite the original parameters, and record the update timestamp and corresponding energy efficiency background data; Closed-loop control will continue to be performed in the next control cycle after the parameter adjustment, and the response effect of the updated parameters will be evaluated. If the energy efficiency ratio is still lower than the threshold for multiple consecutive cycles, a maintenance prompt will be issued or the system will switch to manual intervention mode.

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