A screw compressor internal volume ratio self-adaptive intelligent adjusting method

Abstract

The application provides a screw compressor content volume ratio self-adaptive intelligent adjusting method, and relates to the technical field of intelligent control of positive displacement compressors. The application establishes a compressor dynamic characteristic matching model by incorporating the mechanical inertia hysteresis characteristic of the content volume ratio adjusting mechanism and the nonlinearity of the execution dead zone, performs multi-cycle working condition prediction with a time span not less than the mechanical inertia hysteresis time, completes constraint optimization with the minimization of the compressor operating power as the optimization target and the matching of the internal and external pressure ratios as the constraint condition, and adaptively corrects the model parameters based on the deviation between the measured operating parameters and the calculated values, so as to realize the real-time intelligent adjustment of the content volume ratio. The application solves the core problems in the prior art, such as the mismatch of the internal and external pressure ratios under variable working conditions, the control oscillation caused by the lag of the execution mechanism, and the insufficient computing power of the industrial controller, has significant energy-saving effect and equipment life extension effect, and can be widely applied to the energy efficiency optimization control of various screw compressors.

Description

Technical Field

[0001] This invention relates to the technical field of intelligent control of positive displacement compressors, and more particularly to an adaptive intelligent adjustment method for the internal volume ratio of a screw compressor. Background Technology

[0002] Screw compressors, as the mainstream core equipment of positive displacement compressors, have been widely used in many key industrial fields such as industrial refrigeration, chemical process gas compression, centralized gas supply in factories, and food cold chain due to their outstanding advantages such as compact structure, stable operation, and strong adaptability to changing operating conditions. At the same time, screw compressors are high-energy-consuming core equipment in industrial production, accounting for more than 10% of the total electricity consumption of the national industry. Their operating energy efficiency directly determines the production costs of enterprises and the overall energy consumption level of the industrial sector. Therefore, optimizing screw compressor control technology and reducing energy loss are currently key research directions in this field.

[0003] The internal volume ratio (Vi) is a key parameter directly affecting the energy efficiency of a screw compressor. It is defined as the ratio of the compressor's cavity volume at the end of intake to the cavity volume at the beginning of exhaust. The corresponding regulating mechanism is the internal volume ratio regulating slide valve (Vi slide valve). From the perspective of compression principles, under ideal operating conditions, when the compressor's internal volume ratio perfectly matches the actual internal and external pressure ratio, the unit can achieve isentropic compression, completely avoiding over-compression and under-compression problems. If the internal volume ratio deviates from the pressure ratio parameter, irreversible compression efficiency loss will occur, potentially causing an overall unit efficiency decrease of over 15% under extreme conditions. Therefore, achieving high-precision, real-time adjustment of the internal volume ratio is the core breakthrough for improving the energy efficiency of screw compressors under all operating conditions.

[0004] Currently, the industry has developed various control schemes for adjusting the internal volume ratio of screw compressors, covering multiple technical directions such as traditional closed-loop control, single slide valve composite structure adjustment, offline optimal control, and general intelligent control. However, all existing schemes have inherent technical defects and cannot meet the diversified operational needs of industrial sites. The specific technical solutions and their defects are as follows: This paper presents an offline optimal control scheme based on the Pontryagin Maximum Principle (PMP). This scheme uses the PMP algorithm to solve for the optimal control sequence under specific fixed operating conditions, thereby planning the volume ratio adjustment trajectory. It belongs to an open-loop offline control mode. However, this scheme has limited applicability, only suitable for preset design operating points. If there are deviations in operating conditions such as fluctuations in cooling water temperature or abrupt changes in gas load, the pre-calculated optimal adjustment trajectory becomes useless. Furthermore, this scheme does not consider equipment aging issues such as rotor wear and volumetric efficiency degradation during long-term compressor operation, resulting in poor system robustness and failing to meet the basic requirements for long-term continuous and stable operation of industrial equipment.

[0005] In summary, the industry currently lacks an integrated adaptive adjustment solution that is compatible with low-end computing power industrial controllers, can compensate for actuator lag characteristics, suppress mechanical wear, and adapt to equipment aging and operating condition fluctuations. This makes it difficult to fundamentally solve industry pain points such as high energy efficiency losses and poor control stability during variable operating conditions of screw compressors. Therefore, developing an intelligent volume ratio adjustment solution for screw compressors that balances energy efficiency optimization, equipment lifespan, and industrial applicability has significant engineering application value and market promotion implications. Summary of the Invention

[0006] Based on the aforementioned technical issues mentioned in the background, the industry currently lacks an integrated adaptive adjustment scheme that is compatible with low-end computing power industrial controllers, can compensate for the hysteresis characteristics of actuators, suppress mechanical wear, and adapt to equipment aging and operating condition fluctuations. Therefore, this invention provides an adaptive intelligent adjustment method for the internal volume ratio of a screw compressor. This invention overcomes the energy efficiency losses caused by over-compression / under-compression due to hysteresis correction in traditional closed-loop control, solves the control failure and adjustment oscillation problems caused by the 200ms~1000ms mechanical inertial hysteresis and nonlinearity of the execution dead zone of the internal volume ratio adjustment mechanism, and achieves real-time and accurate matching of the internal and external pressure ratios under all varying operating conditions. It breaks through the computing power bottleneck of general intelligent control methods, which suffer from excessive computational load and cannot be implemented in real time on mainstream industrial PLCs, achieving real-time control under low computing power requirements and adapting to the needs of existing equipment upgrades. It balances energy efficiency optimization and equipment mechanical lifespan, significantly reducing the operation frequency of the internal volume ratio adjustment mechanism while minimizing operating power consumption, thus extending the equipment maintenance cycle and service life. It addresses performance drift problems such as rotor wear and volumetric efficiency decay caused by long-term compressor operation, achieving adaptive correction of the model and ensuring control accuracy and stability throughout the equipment's entire life cycle. Solving capacity regulation under partial load and Caused by regulating coupling Control misalignment issues and achieve precise matching of internal and external pressure ratios under all operating conditions (25%-100%).

[0007] The technical means employed in this invention are as follows: A method for adaptive intelligent adjustment of the internal volume ratio of a screw compressor includes the following steps: Step 1: At the beginning of each control cycle, acquire the operating status parameters of the screw compressor; the operating status parameters include: discharge pressure, suction pressure, operating power, and current internal volume ratio; Step 2: Establish an offline compressor dynamic characteristic matching model by introducing mechanical inertial hysteresis characteristics and execution dead zone nonlinear characteristics; the established offline compressor dynamic characteristic matching model is as follows: ; in, Indicates the compressor discharge pressure; Indicates the compressor suction pressure; This indicates the actual internal volume ratio of the compressor; Indicates the compressor rotor speed; Indicates the compressor's volumetric efficiency; This indicates the content area ratio setting value; Represents the state matrix; Represents the input matrix; Indicates the sequence number of the discrete control cycle; They represent the first , The state vector of each control cycle; Step 3: Set the working condition prediction cycle and optimization execution cycle Based on the offline compressor dynamic characteristic matching model, the compressor operating trajectory is calculated over multiple future control cycles. Step 4: Taking the minimization of compressor operating power as the optimization objective, and the matching of internal and external pressure ratios as a hard constraint, a soft constraint of the smoothness of the internal product ratio adjustment action is introduced. The optimization problem is constructed by combining offline operating condition partitioning with online table lookup. The constrained finite-time optimal control problem is as follows: ; in, This represents the objective function to be optimized. Indicates the working condition prediction cycle; Indicates the first The compressor operating power per cycle; This indicates the amount of change in the content-to-volume ratio adjustment command, i.e., the magnitude of the action. Indicates the weight of the power optimization term; This indicates the weight of the motion smoothness penalty term; Step 5: Use the recursive least squares method with a forgetting factor to estimate the compressor volumetric efficiency online. The key model parameters for leakage coefficient are determined, and projection constraints are applied based on the physically reasonable range of these parameters.

[0008] Furthermore, in step 2, the state matrix for Square matrix, the state matrix The rows and columns correspond to the state vectors respectively. The five dimensions are: diagonal coefficients represent the self-holding coefficients of each state variable, and off-diagonal coefficients represent the coupling influence coefficients between state variables. The input matrix for Column vectors, the input matrix Corresponding content ratio adjustment command The weights of the influence on each state variable.

[0009] Furthermore, the mechanical inertial hysteresis characteristic is a first-order inertial characteristic, corresponding to... The transfer function for the mechanical response lag time is: ; in, Represents the Laplace operator; The feedforward compensation amount for the execution dead-zone nonlinearity characteristic is: ; in, Instructions that represent the product ratio after compensation; Indicates the original content product ratio instruction; This indicates the current actual content product ratio.

[0010] Furthermore, the operating condition prediction cycle It consists of 10 to 25 steps, corresponding to a time span of... Furthermore, the response time corresponding to the mechanical inertia hysteresis characteristic of the volume ratio adjustment mechanism.

[0011] Furthermore, in step 4, during the offline stage, a state space polyhedral partition is constructed using intake pressure, exhaust pressure, rotor speed, and current internal volume ratio as partition dimensions. For each working condition partition, a multi-parameter quadratic programming algorithm is used to solve the constrained optimal control problem, and the optimal state feedback control law in each partition is obtained and stored in a lookup table. During each control cycle in the online phase, the current state vector is read, the corresponding working condition zone is located, the control command is obtained by looking up the table, and the control command is output to the spool valve actuator. Within the overlapping buffer of adjacent partitions, control rules are smoothly switched using a convex combination interpolation method based on normalized distance.

[0012] Furthermore, the offline operating condition partitioning includes the following steps: using inhalation pressure Exhaust pressure Rotor speed and current content ratio As four partition dimensions; the inhalation pressure is respectively... Exhaust pressure Rotor speed and current content ratio The space is divided into four sub-intervals; 256 state space polyhedral partitions are obtained by combining the Cartesian products of the intervals in each dimension; the boundaries of each partition are defined by inequalities. Definition; where, Represents a 4×4 identity matrix; This indicates the upper limit of the dimensions for the corresponding partition.

[0013] Furthermore, the hard constraint includes: Internal and external pressure ratio matching constraints ;in Represents the isentropic index of the refrigerant; Content product ratio adjustment range constraint ; Adjustment rate constraint cycle; The soft constraints include: exhaust temperature constraints. .

[0014] Furthermore, the smooth switching control rule includes the following steps: Determine the state point Does it fall into an adjacent partition? and Overlapping buffers; when state point Fall into adjacent partition and When there is an overlapping buffer, a smooth switch is performed; when the state point Not falling into adjacent partitions and When there are overlapping buffers, perform offline or online processing on them; Set up partitions With partitions The boundary threshold is State point To partition The normalized distance of the center is To partition The normalized distance of the center is The calculation formula is: ; in, Representing state points To partition The normalized distance at the center; Indicates partition The center value vector; A vector representing the upper limit of the range for each state dimension; A vector representing the lower limit of the range for each state dimension; Representing state points To partition The normalized distance at the center; Convex combination weight allocation; partitioning The weight is partition weight ; Smooth control quantity calculation enables smooth transition of control commands: ; in, Indicates the first The final control command after a smooth switching of each control cycle; Indicates partition Convex combination weights; Indicates partition The optimal state feedback gain matrix; Indicates partition Control bias terms; Indicates partition Convex combination weights; Indicates partition The optimal state feedback gain matrix; Indicates partition The control bias term.

[0015] Furthermore, the projection constraint processing formula is as follows: ; in, ; ; ; Indicates the first The estimated volumetric efficiency of the step; Indicates the first The estimated volumetric efficiency of the step; Indicates the first The recursive gain vector of the step; Indicates the first The measured operating power value of the step; Indicates the first The regression vector of the step; Indicates the first The covariance matrix of the step; Indicates the first The covariance matrix of the step; Indicates the forgetting factor; Represents the identity matrix.

[0016] Furthermore, it also includes boundary conditions and anomaly tolerance processing; the boundary conditions and anomaly tolerance processing includes: sensor fault detection and optimization anomaly detection; The sensor fault detection is achieved through a dual-sensor cross-validation mechanism. If the deviation between the detection values ​​of the two sensors exceeds 5% and persists for three control cycles, a sensor fault is determined, and the system automatically switches to a fixed volume ratio. The backup operation mode is activated, and a fault alarm is triggered simultaneously. The optimization anomaly detection means that if an anomaly occurs during online table lookup where the state exceeds all partitions, the system maintains the effective control output of the previous cycle and triggers an alarm.

[0017] Compared with the prior art, the present invention has the following advantages: This invention reduces the online calculation time per cycle from over 200ms to less than 5ms by using an offline pre-optimization and online table lookup strategy. It is fully compatible with the computing power limitations of mainstream PLCs in industrial settings and can achieve industrial-grade real-time control without the need for high-end hardware investment, thus completely solving the industry pain point that general intelligent control solutions cannot be implemented.

[0018] This invention solves the control oscillations and partial load issues in large time-delay systems by incorporating a dedicated model that includes hysteresis characteristics and the influence of capacity valve position, along with full-cycle operating condition prediction. Regarding the issue of adjustment misalignment, the adjustment time was reduced by 62% under the condition of a 50% load jump to 100%; under the full load condition of 25%-100%, the internal and external pressure ratio matching deviation was controlled within [a certain range]. Within the range of the rated operating conditions, the matching deviation The core energy efficiency indicator, the unit's specific power, is as low as Compared to the industry's conventional PID closed-loop control scheme, the measured comprehensive energy saving rate under rated operating conditions reaches 10.7%, far exceeding the energy saving level of the industry's conventional scheme.

[0019] This invention reduces the frequency of operation of the volume ratio adjustment mechanism by 65% ​​under the same control precision through the smoothness constraint of dynamic weight adjustment, which greatly reduces the risk of wear of the slide valve sealing ring and valve body jamming, and significantly extends the mechanical life and maintenance cycle.

[0020] This invention, by setting an adaptive correction mechanism with physical hard constraints, can automatically track the slow change process of the compressor's performance parameters during long-term operation, always maintaining the model's prediction accuracy. It eliminates the need for manual on-site recalibration, completely solving the problem of inaccuracy in fixed-parameter models during long-term operation and meeting the requirements for continuous and stable operation in industrial settings. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart illustrating the adaptive intelligent adjustment method of the present invention. Detailed Implementation

[0023] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0024] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0025] In screw compressors, energy level (capacity) regulation and internal volume ratio regulation are two functionally independent and physically isolated control loops. Energy level regulation changes the effective suction volume by moving the suction-side capacity slide valve, and its control objective is to make the compressor's cooling capacity / gas delivery volume follow the terminal load demand, focusing on supply and demand balance. On the other hand, internal volume ratio regulation changes the geometric volume ratio of the compression chamber by moving the discharge-side Vi slide valve, and its control objective is to match the compressor's internal compression ratio with the external pressure ratio determined by the condensing pressure and evaporating pressure, focusing on the efficiency of the compression process and eliminating over-compression or under-compression losses. The controlled objects, dynamic characteristics, optimization objectives, and constraints of the two are completely different. The control model, state variables (such as load demand and number of operating units), and optimization objective (minimizing cooling capacity deviation) of energy level regulation cannot be directly transferred to the internal volume ratio regulation scenario.

[0026] like Figure 1 As shown, this invention provides an adaptive intelligent adjustment method for the internal volume ratio of a screw compressor, comprising the following steps: Step 1: At the beginning of each control cycle, acquire the operating status parameters of the screw compressor; the operating status parameters include: discharge pressure, suction pressure, operating power, and current internal volume ratio; Step 2: Establish an offline compressor dynamic characteristic matching model by introducing mechanical inertial hysteresis characteristics and execution dead zone nonlinear characteristics; the established offline compressor dynamic characteristic matching model is as follows: ; in, Indicates the compressor discharge pressure; Indicates the compressor suction pressure; This indicates the actual internal volume ratio of the compressor; Indicates the compressor rotor speed; Indicates the compressor's volumetric efficiency; This indicates the content area ratio setting value; Represents the state matrix; Represents the input matrix; Indicates the sequence number of the discrete control cycle; They represent the first , The state vector of each control cycle.

[0027] Preferably, in step 2, the state matrix for Square matrix, the state matrix The rows and columns correspond to the state vectors respectively. The five dimensions are: diagonal coefficients represent the self-holding coefficients of each state variable, and off-diagonal coefficients represent the coupling influence coefficients between state variables. The input matrix for Column vectors, the input matrix Corresponding content ratio adjustment command The weights of the influence on each state variable.

[0028] In a preferred embodiment, the mechanical inertial hysteresis characteristic is a first-order inertial characteristic, corresponding to... The transfer function for the mechanical response lag time is: ; in, Represents the Laplace operator; The feedforward compensation amount for the execution dead-zone nonlinearity characteristic is: ; in, Instructions that represent the product ratio after compensation; Indicates the original content product ratio instruction; This indicates the current actual content product ratio.

[0029] Step 3: Set the working condition prediction cycle and optimization execution cycle Based on the offline compressor dynamic characteristic matching model, the compressor operating trajectory is calculated over multiple future control cycles. Step 4: Taking the minimization of compressor operating power as the optimization objective, and the matching of internal and external pressure ratios as a hard constraint, a soft constraint of the smoothness of the internal product ratio adjustment action is introduced. The optimization problem is constructed by combining offline operating condition partitioning with online table lookup. The constrained finite-time optimal control problem is as follows: ; in, This represents the objective function to be optimized. Indicates the working condition prediction cycle (10~25 steps); Indicates the first The compressor operating power per cycle; This indicates the amount of change in the content-to-volume ratio adjustment command, i.e., the magnitude of the action. Indicates the weight of the power optimization term; This represents the weight of the action smoothness penalty term; the corresponding time span is... Furthermore, the response time corresponding to the mechanical inertia hysteresis characteristic of the volume ratio adjustment mechanism.

[0030] In this application, in step 4, the offline stage constructs a state space polyhedron partition using intake pressure, exhaust pressure, rotor speed and current internal volume ratio as partition dimensions. For each working condition partition, a multi-parameter quadratic programming algorithm is used to solve the constrained optimal control problem, and the optimal state feedback control law in each partition is obtained and stored in a lookup table. During each control cycle in the online phase, the current state vector is read, the corresponding working condition zone is located, the control command is obtained by looking up the table, and the control command is output to the spool valve actuator. Within the overlapping buffer of adjacent partitions, control rules are smoothly switched using a convex combination interpolation method based on normalized distance.

[0031] Preferably, the offline operating condition partitioning includes the following steps: using inhalation pressure Exhaust pressure Rotor speed and current content ratio As four partition dimensions; the inhalation pressure is respectively... Exhaust pressure Rotor speed and current content ratio The space is divided into four sub-intervals; 256 state space polyhedral partitions are obtained by combining the Cartesian products of the intervals in each dimension; the boundaries of each partition are defined by inequalities. Definition; where, Represents a 4×4 identity matrix; This indicates the upper limit of the dimensions for the corresponding partition.

[0032] In this application, the hard constraints include: Internal and external pressure ratio matching constraints ;in Represents the isentropic index of the refrigerant; Content product ratio adjustment range constraint ; Adjustment rate constraint cycle; The soft constraints include: exhaust temperature constraints. .

[0033] Preferably, the smooth switching control rule includes the following steps: Determine the state point Does it fall into an adjacent partition? and Overlapping buffers; when state point Fall into adjacent partition and When there is an overlapping buffer, a smooth switch is performed; when the state point Not falling into adjacent partitions and When there are overlapping buffers, perform offline or online processing on them; Set up partitions With partitions The boundary threshold is State point To partition The normalized distance of the center is To partition The normalized distance of the center is The calculation formula is: ; in, Representing state points To partition The normalized distance at the center; Indicates partition The center value vector; A vector representing the upper limit of the range for each state dimension; A vector representing the lower limit of the range for each state dimension; Representing state points To partition The normalized distance at the center; Convex combination weight allocation; partitioning The weight is partition weight ; Smooth control quantity calculation enables smooth transition of control commands: ; in, Indicates the first The final control command after a smooth switching of each control cycle; Indicates partition Convex combination weights; Indicates partition The optimal state feedback gain matrix; Indicates partition Control bias terms; Indicates partition Convex combination weights; Indicates partition The optimal state feedback gain matrix; Indicates partition The control bias term.

[0034] Step 5: Use the recursive least squares method with a forgetting factor to estimate the compressor volumetric efficiency online. The key model parameters for leakage coefficient are determined, and projection constraints are applied based on the physically reasonable range of these parameters.

[0035] The projection constraint processing formula is as follows: ; in, ; ; ; Indicates the first The estimated volumetric efficiency of the step; Indicates the first The estimated volumetric efficiency of the step; Indicates the first The recursive gain vector of the step; Indicates the first The measured operating power value of the step; Indicates the first The regression vector of the step; Indicates the first The covariance matrix of the step; Indicates the first The covariance matrix of the step; Indicates the forgetting factor; Represents the identity matrix.

[0036] As a preferred adjustment method, the method of this application further includes boundary conditions and anomaly tolerance processing; the boundary conditions and anomaly tolerance processing includes: sensor fault detection and optimization anomaly detection; The sensor fault detection is achieved through a dual-sensor cross-validation mechanism. If the deviation between the detection values ​​of the two sensors exceeds 5% and persists for three control cycles, a sensor fault is determined, and the system automatically switches to a fixed volume ratio. The backup operation mode is activated, and a fault alarm is triggered simultaneously. The optimization anomaly detection means that if an anomaly occurs during online table lookup where the state exceeds all partitions, the system maintains the effective control output of the previous cycle and triggers an alarm.

[0037] Example 1 This embodiment applies to an oil-injected twin-screw refrigeration compressor for industrial refrigeration, employing the mainstream dual independent slide valve structure for medium and large industrial units. The capacity regulating slide valve, located on the suction side, is responsible for load regulation. The slide valve, located on the exhaust side, is responsible for independent adjustment of the internal volume ratio; the control functions of the two are decoupled; the unit's rated power... Theoretical displacement , Adjustment range 2.2~5.0, hydraulic drive system response time approximately Position sensor accuracy .

[0038] The control system uses a Siemens S7-1516 PLC, and the control cycle is set to... (Preferred value, reasonable range) ); equipped with a high-precision exhaust pressure sensor (range) Precision Inhalation pressure sensor (range) Precision ), motor power transmitter (real-time calculation of three-phase current and voltage, accuracy ±1%), and absolute encoder for slide valve position.

[0039] The control method of the screw compressor internal volume ratio adaptive intelligent adjustment method of this application includes the following steps: Step 1: At the beginning of each 50ms control cycle, collect the operating status parameters of the screw compressor, including discharge pressure, suction pressure, operating power, and current internal volume ratio (calculated by feedback from the slide valve position encoder).

[0040] Step 2: Establish an offline compressor dynamic characteristic matching model by introducing mechanical inertial hysteresis characteristics and execution dead zone nonlinear characteristics; the established offline compressor dynamic characteristic matching model is as follows: ; in, Indicates the compressor discharge pressure; Indicates the compressor suction pressure; This indicates the actual internal volume ratio of the compressor; Indicates the compressor rotor speed; Indicates the compressor's volumetric efficiency; This indicates the content area ratio setting value; Represents the state matrix; Represents the input matrix; ; ; State matrix It is a 5×5 square matrix, with rows and columns corresponding to state vectors. The five dimensions are: the diagonal coefficients are the self-holding coefficients of each state variable, and the off-diagonal coefficients are the coupling influence coefficients between state variables. The coefficient 0.90 in the third row and third column corresponds to the first-order inertial hysteresis characteristic of the spool valve hydraulic drive system, which is 500ms. The calculation yielded, where To control the cycle, The time constant of the actuator; input matrix This is a 5×1 column vector, corresponding to the content product ratio adjustment command. The influence weights of each state variable are as follows: the coefficient 0.10 in the third row is the influence coefficient of the Vi adjustment command on the actual volume ratio, which is identified by the correspondence between the measured displacement and volume ratio of the actuator; all matrix parameters are based on the 110kW twin-screw refrigeration compressor of this embodiment, and are systematically identified by the step response method under rated operating conditions (condensing temperature 40℃, evaporating temperature -5℃, full load), and are adapted to the R134a refrigerant operating conditions.

[0041] Control input That is, the content-volume ratio setting; sampling time. It is consistent with the PLC control cycle.

[0042] In this embodiment, the hydraulic drive system of the spool valve exhibits first-order inertial characteristics, and its transfer function is: The mechanical response lag time of 500ms is fully incorporated into the discrete state equation; the measured control dead zone corresponding to the static friction force of the slide valve is ±0.05, and the feedforward compensation amount is preset in the model: ; The compensation amount is fixed at 0.05, consistent with the measured dead zone size. The compensated command is output to the actuator after being limited by the hard constraint of the volume ratio adjustment range, ensuring that it does not exceed the physical boundary of 2.2-5.0.

[0043] Step 3: Set the working condition prediction cycle This corresponds to a 750ms time span, fully covering the 500ms mechanical lag time of the actuator; optimizing the execution cycle. Based on the offline compressor dynamic characteristic matching model, the compressor operating trajectory within the next 15 control cycles is calculated, including the changing trends of discharge pressure, operating power, and internal / external pressure ratio deviation.

[0044] Step 4: With minimizing compressor operating power as the optimization objective and matching internal and external pressure ratio as a hard constraint, a soft constraint on the smoothness of internal volume ratio adjustment is introduced. The optimization problem is constructed by combining offline operating condition partitioning with online table lookup.

[0045] Four real-time acquireable operating parameters are used as the state dimensions for partitioning, namely: inhalation pressure Exhaust pressure Rotor speed Current content product ratio The four dimensions constitute a complete state space, covering all operating conditions of the compressor.

[0046] The parameter range for each dimension is divided into a uniform grid, where the inhalation pressure... (range) The exhaust pressure is divided into 4 sections. (Measuring range 0-2.5MPa) is divided into 4 intervals, rotor speed (Range 300-3000rpm) is divided into 4 intervals, current volume ratio (Range 2.2-5.0) is divided into 4 intervals (total 256 intervals, for higher control precision); the Cartesian product of each interval is combined to obtain... The state space is divided into polyhedral partitions, each corresponding to a working condition interval; the boundary of each partition is defined by the inequalities. Definition, where It is a 4×4 identity matrix. This refers to the upper limit of the dimension for the corresponding partition. For example, a partition with an intake pressure of 0-0.25MPa and an exhaust pressure of 0-0.625MPa corresponds to... .

[0047] The selection criteria for 256 partitions: The number of partitions is positively correlated with control accuracy and negatively correlated with online table lookup computing power. 256 partitions can... Internal table lookup and location are completed, fully adapting to PLC computing power constraints; 256 partitions can be completely covered. Under full load conditions, the impact of operating condition fluctuations within a zone on the control effect is less than 1%, satisfying the pressure ratio matching deviation requirement. The control requirements are met; the number of partitions is a power of 2, which is compatible with the binary addressing logic of the PLC and has the best table lookup efficiency.

[0048] With the core objective of minimizing the compressor's single-cycle operating power, the constrained finite-time optimal control problem is constructed as follows: ; in, This represents the objective function to be optimized. Indicates the working condition prediction cycle; Indicates the first The compressor operating power per cycle; This indicates the amount of change in the content-to-volume ratio adjustment command, i.e., the magnitude of the action. Indicates the weight of the power optimization term; This indicates the weight of the motion smoothness penalty term; Simultaneously set both hard and soft constraints: Among them, hard constraints include: internal and external pressure ratio matching constraints. ,in The isentropic index is the value of R134a refrigerant under the condition of rated condensing temperature of 40℃ and evaporating temperature of -5℃; under other operating conditions or compressed media, the isentropic index is taken in real time according to the thermodynamic characteristic parameters of the corresponding medium; internal volume ratio adjustment range is constrained. Adjustment rate constraint cycle.

[0049] Soft constraints include: exhaust temperature constraints ; For each operating condition zone, a multi-parameter quadratic programming algorithm is used to solve the constrained optimal control problem, yielding the optimal state feedback control law for that zone. ,in It is a 4×1 state feedback gain matrix, corresponding to the weights of the four state dimensions. For the control bias items of this partition, adapt the operating condition baseline of the partition; for all partitions The parameter set is stored in the PLC's lookup table, completing the construction of the offline rule base.

[0050] Within each control cycle, the PLC reads the current state vector, locates the corresponding operating condition zone, obtains control commands through table lookup and interpolation, and sets the control quantity changes. After the rate is limited by the cycle, the output is sent to the spool valve actuator.

[0051] In the overlapping buffer of adjacent partitions (the buffer width is 8% of the state range), a convex combination interpolation method based on normalized distance is used to smoothly switch control rules. The specific implementation steps are as follows: When the state point Fall into adjacent partition and When there are overlapping buffers, perform a smooth switch; Normalized distance calculation: Set up partitions With partitions The boundary threshold is State point To partition The normalized distance of the center is To partition The normalized distance of the center is The calculation formula is: ;in , This represents the center value of the two partitions. , These are the upper and lower limits of the range for the state dimension; Convex Combination Weight Allocation: Partitioning weight partition weight ; Smooth control quantity calculation: The final control command is a convex combination of two partition control rules: ; To achieve a smooth transition of control commands and avoid abrupt changes in valve action and mechanical shock.

[0052] When the load change rate of two adjacent cycles When this occurs, it is determined to be a load change condition; where the load... Calculated from the ratio of current gas delivery volume to rated gas delivery volume; steady-state operating condition ( When %), the weight of the smoothness penalty term Prioritize limiting the movement range of the spool valve to reduce mechanical wear; under sudden load changes ( When ), the weight of the smoothness penalty term Prioritize response speed to quickly match changes in operating conditions; use first-order inertial filtering for weight switching to avoid sudden changes in control quantity caused by weight jumps.

[0053] In this embodiment, the online calculation time for a single cycle is stable within 4.2ms, which fully meets the 50ms control cycle requirement of the PLC.

[0054] Step 5: Use recursive least squares (RLS) with a forgetting factor to estimate the compressor volumetric efficiency online. Key model parameters such as leakage coefficient are updated every 50 control cycles (2.5s), and the forgetting factor is also included. The online estimation and constraint processing logic for the leakage coefficient is completely consistent with the above, with a constraint interval of [0.01, 0.05] to prevent model mismatch and control divergence caused by parameter drift. The basic principle of soft sensing is to construct a sensitivity matrix of power prediction, volumetric efficiency, and leakage coefficient, and then recursively estimate the volumetric efficiency and leakage coefficient combination that minimizes the power prediction error using the residual between the measured power value and the model calculated value.

[0055] Example 2 All test data in this embodiment are based on the following standard operating conditions: refrigerant R134a, rated condensing temperature 40℃, rated evaporating temperature -5℃, suction superheat 5K, ambient temperature 25℃, rated power supply voltage 380V / 50Hz, and test load range 25%~100% of rated load.

[0056] Under the condition of a step change from 50% load to 100% load, the settling time is only 3 seconds with no overshoot; under the condition of 25% low load, the internal and external pressure ratio matching deviation is controlled within... Within this range, the frequency of volume ratio adjustment is 42 times / hour; under 30% load conditions, the internal and external pressure ratio matching deviation... Unit specific power Under 75% load conditions, the internal and external pressure ratio matching deviation is ≤ ±1.6%, and the unit's specific power... The deviation of the internal and external pressure ratio matching under all operating conditions is stably controlled within... Within the range of the rated operating conditions, the matching deviation The measured specific power of the unit under rated operating conditions was as low as Compared with traditional PID control, the measured comprehensive energy saving rate reaches 10.7%, and the frequency of volume ratio adjustment actions is reduced by 65%.

[0057] Example 3 This embodiment is for a 250kW process gas screw compressor used in chemical processes. The application scenario is high temperature and high pressure. The design exhaust pressure is 2.5MPa, the upper limit of exhaust temperature is 110℃, and it is equipped with a plunger-type internal volume ratio adjustment mechanism with a hydraulic drive response time of about 800ms.

[0058] The difference from Example 2 is as follows: Adjusting the operating condition prediction cycle This corresponds to a 1000ms time span, fully covering the 800ms lag time of the plunger-type regulating mechanism; it also corresponds to adjusting the correlation coefficient of the actuator in the discrete state matrix to match a longer mechanical lag characteristic; and it adds exhaust temperature to the optimization target. The hard constraints ensure the safe operation of the equipment under high temperature and high pressure conditions; the adaptive correction mechanism adds online estimation of the leakage coefficient to adapt to the drift of compression characteristics caused by changes in process gas composition.

[0059] In continuous operation tests at the chemical plant site, this embodiment achieved a comprehensive energy saving effect of 12.3% compared to the original equipment's PID control scheme, and reduced the frequency of plunger adjustment mechanism operation by 58%, fully meeting the requirements for long-term stable operation of the chemical process.

[0060] Example 4 This embodiment, based on embodiment 2, increases the compressor's variable frequency rotor speed. Ratio of content area As a joint control input, a dual-input collaborative optimization framework is constructed to adapt to the application scenarios of variable frequency drive screw compressors.

[0061] Control input extended to Simultaneously optimize the internal volume ratio and rotor speed; The optimization objective function is expanded to:

[0062] in This is the penalty coefficient for smoothness of speed regulation. for Adjust the smoothness penalty coefficient; the offline pre-optimization rule base is synchronously expanded into dual-input control rules to adapt to the control requirements of collaborative optimization.

[0063] Under partial load conditions, the compressor is first operated within the motor's highest efficiency range by speed regulation. Then, the internal volume ratio is fine-tuned to match the internal and external pressure ratio at the current speed. This solves the pressure ratio mismatch problem caused by changes in compression characteristics after frequency conversion, thus achieving speed and... The synergistic energy efficiency is optimal.

[0064] All test data in this embodiment are obtained under the same standard operating conditions as in Embodiment 1, tested under a 75% partial load condition. This is in contrast to the single test data in Embodiment 1. According to the adjustment scheme, this collaborative optimization scheme can improve the motor operating efficiency by 3.2% and further improve the overall energy saving rate of the unit to 15%, with significant optimization effect.

[0065] Comparative Example This comparative counterexample uses the exact same hardware platform, compressor model, and operating conditions as Example 2. The difference is that it does not use the offline pre-optimization + online table lookup strategy of the present invention, but directly uses the online quadratic programming solution of the general intelligent control scheme, and does not have the exclusive dynamic characteristic matching model of the present invention.

[0066] Test results: On the Siemens S7-1516 PLC, the single-cycle calculation time exceeded 200ms, failing to meet the 50ms control cycle requirement. This forced an extension of the control cycle to 200ms, ultimately leading to deteriorated control performance: a settling time of 6s, oscillations occurring over 3-4 cycles, a steady-state pressure ratio matching deviation of ±5%, an internal volume ratio adjustment frequency of 180 times / hour, and a unit specific power... The counterexample has optimized the operating parameters of the general online optimization scheme on the S7-1516 PLC to the best of its ability; 200ms is the shortest control cycle for achieving stable operation of this scheme on this hardware platform. However, under the exact same hardware platform and test conditions, the specific power of the unit in this invention is only... Compared to the control example, the power consumption per unit volume of gas is reduced by 5.1%, and the control performance and energy efficiency are improved by orders of magnitude, which is far superior to the control effect of the general online optimization scheme.

[0067] Simultaneously, a conventional PID closed-loop control scheme in the industry was tested under the same operating conditions. The results showed: 8s settling time under load step conditions, 15% overshoot, ±8% steady-state pressure ratio matching deviation, 120 volume ratio regulation frequencies per hour, and unit specific power. The solution of this invention achieves an overall energy saving rate of 10.7% compared to traditional PID control, reduces the frequency of volume ratio adjustment actions by 65%, and has significant advantages in control performance and energy efficiency.

[0068] This comparative test uses industry-standard PID parameters: proportional coefficient. Integral time Differential time The PID parameter is derived from the default factory settings of mainstream screw compressors in the industry and is widely used in industrial settings.

[0069] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments. In the above embodiments of the present invention, the descriptions of each embodiment have their own emphasis; parts not described in detail in a certain embodiment can be referred to in the relevant descriptions of other embodiments. It should be understood that the disclosed technical content in the several embodiments provided in this application can be implemented in other ways.

[0070] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for adaptive intelligent adjustment of the internal volume ratio of a screw compressor, characterized in that, Includes the following steps: Step 1: At the beginning of each control cycle, acquire the operating status parameters of the screw compressor; The operating status parameters include: exhaust pressure, intake pressure, operating power, and current internal volume ratio; Step 2: Establish an offline compressor dynamic characteristic matching model by introducing mechanical inertial hysteresis characteristics and execution dead zone nonlinear characteristics; the established offline compressor dynamic characteristic matching model is as follows: ； in, Indicates the compressor discharge pressure; Indicates the compressor suction pressure; This indicates the actual internal volume ratio of the compressor; Indicates the compressor rotor speed; Indicates the compressor's volumetric efficiency; This indicates the content area ratio setting value; Represents the state matrix; Represents the input matrix; Indicates the sequence number of the discrete control cycle; They represent the first , The state vector of each control cycle; Step 3: Set the working condition prediction cycle and optimization execution cycle Based on the offline compressor dynamic characteristic matching model, the compressor operating trajectory is calculated for multiple future control cycles. Step 4: Taking the minimization of compressor operating power as the optimization objective, and the matching of internal and external pressure ratios as a hard constraint, a soft constraint of the smoothness of the internal product ratio adjustment action is introduced. The optimization problem is constructed by combining offline operating condition partitioning with online table lookup. The constrained finite-time optimal control problem is as follows: ； in, This represents the objective function to be optimized. Indicates the working condition prediction cycle; Indicates the first The compressor operating power per cycle; This indicates the amount of change in the content-to-volume ratio adjustment command, i.e., the magnitude of the action. Indicates the weight of the power optimization term; This indicates the weight of the motion smoothness penalty term; Step 5: Use the recursive least squares method with a forgetting factor to estimate the compressor volumetric efficiency online. The key model parameters for leakage coefficient are determined, and projection constraints are applied based on the physically reasonable range of these parameters.

2. The adaptive intelligent adjustment method for the internal volume ratio of a screw compressor according to claim 1, characterized in that, In step 2, the state matrix for Square matrix, the state matrix The rows and columns correspond to the state vectors respectively. The five dimensions are: diagonal coefficients represent the self-holding coefficients of each state variable, and off-diagonal coefficients represent the coupling influence coefficients between state variables. The input matrix for Column vectors, the input matrix Corresponding content ratio adjustment command The weights of the influence on each state variable.

3. The adaptive intelligent adjustment method for the internal volume ratio of a screw compressor according to claim 1, characterized in that, The mechanical inertial hysteresis characteristic is a first-order inertial characteristic, corresponding to The transfer function for the mechanical response lag time is: ； in, Represents the Laplace operator; The feedforward compensation amount for the execution dead-zone nonlinearity characteristic is: ； in, Instructions that represent the product ratio after compensation; Indicates the original content product ratio instruction; This indicates the current actual content product ratio.

4. The adaptive intelligent adjustment method for the internal volume ratio of a screw compressor according to claim 1, characterized in that, The working condition prediction cycle It consists of 10 to 25 steps, corresponding to a time span of... Furthermore, the time span is not less than the response time corresponding to the mechanical inertia hysteresis characteristic of the volume ratio adjustment mechanism.

5. The adaptive intelligent adjustment method for the internal volume ratio of a screw compressor according to claim 1, characterized in that, In step 4, the offline stage constructs a state space polyhedron partition using intake pressure, exhaust pressure, rotor speed and current internal volume ratio as partition dimensions. For each working condition partition, a multi-parameter quadratic programming algorithm is used to solve the constrained optimal control problem, and the optimal state feedback control law in each partition is obtained and stored in a lookup table. During each control cycle in the online phase, the current state vector is read, the corresponding working condition zone is located, the control command is obtained by looking up the table, and the control command is output to the spool valve actuator. Within the overlapping buffer of adjacent partitions, control rules are smoothly switched using a convex combination interpolation method based on normalized distance.

6. The adaptive intelligent adjustment method for the internal volume ratio of a screw compressor according to claim 1, characterized in that, The offline operating condition partitioning includes the following steps: using inhalation pressure Exhaust pressure Rotor speed and current content ratio As four partition dimensions; the inhalation pressure is respectively... Exhaust pressure Rotor speed and current content ratio The space is divided into four sub-intervals; 256 state space polyhedral partitions are obtained by combining the Cartesian products of the intervals in each dimension; the boundaries of each partition are defined by inequalities. Definition; where, Represents a 4×4 identity matrix; This indicates the upper limit of the dimensions for the corresponding partition.

7. The adaptive intelligent adjustment method for the internal volume ratio of a screw compressor according to claim 1, characterized in that, The hard constraints include: Internal and external pressure ratio matching constraints ;in Represents the isentropic index of the refrigerant; Content product ratio adjustment range constraint ; Adjustment rate constraint cycle; The soft constraints include: exhaust temperature constraints. .

8. The adaptive intelligent adjustment method for the internal volume ratio of a screw compressor according to claim 5, characterized in that, The smooth switching control rule includes the following steps: Determine the state point Does it fall into an adjacent partition? and Overlapping buffers; when state point Fall into adjacent partition and When there is an overlapping buffer, a smooth switch is performed; when the state point Not falling into adjacent partitions and When there are overlapping buffers, perform offline or online processing on them; Set up partitions With partitions The boundary threshold is State point To partition The normalized distance of the center is To partition The normalized distance of the center is The calculation formula is: ； in, Representing state points To partition The normalized distance at the center; Indicates partition The center value vector; A vector representing the upper limit of the range for each state dimension; A vector representing the lower limit of the range for each state dimension; Representing state points To partition The normalized distance at the center; Convex combination weight allocation; partitioning The weight is partition weight ; Smooth control quantity calculation enables a smooth transition of control commands: ； in, Indicates the first The final control command after a smooth switching of each control cycle; Indicates partition Convex combination weights; Indicates partition The optimal state feedback gain matrix; Indicates partition Control bias terms; Indicates partition Convex combination weights; Indicates partition The optimal state feedback gain matrix; Indicates partition The control bias term.

9. The adaptive intelligent adjustment method for the internal volume ratio of a screw compressor according to claim 1, characterized in that, The projection constraint processing formula is as follows: ； in, ; ; ; Indicates the first The estimated volumetric efficiency of the step; Indicates the first The estimated volumetric efficiency of the step; Indicates the first The recursive gain vector of the step; Indicates the first The measured operating power value of the step; Indicates the first The regression vector of the step; Indicates the first The covariance matrix of the step; Indicates the first The covariance matrix of the step; Indicates the forgetting factor; Represents the identity matrix.

10. A method for adaptive intelligent adjustment of the internal volume ratio of a screw compressor according to any one of claims 1-9, characterized in that, It also includes boundary conditions and exception handling; The boundary conditions and anomaly tolerance processing include: sensor fault detection and optimization anomaly detection; The sensor fault detection is achieved through a dual-sensor cross-validation mechanism. If the deviation between the detection values ​​of the two sensors exceeds 5% and persists for three control cycles, a sensor fault is determined, and the system automatically switches to a fixed volume ratio. The backup operation mode is activated, and a fault alarm is triggered simultaneously. The optimization anomaly detection means that if an anomaly occurs during online table lookup where the state exceeds all partitions, the system maintains the effective control output of the previous cycle and triggers an alarm.

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