Multi-air-compressor system energy-saving optimization method based on segmented modeling

Abstract

The invention discloses an energy-saving optimization method for a multi-air-compressor system based on segmented modeling. The energy-saving optimization method comprises the steps that 1, instantaneous electric power and instantaneous flow of all air compressors in an air compressor control system at all moments are collected; calculating a load rate according to the instantaneous electric power of each air compressor; 2, based on the load rate and the instantaneous flow, a piecewise function is adopted to establish a flow characteristic model for each air compressor, and based on the load rate, the piecewise function is adopted to establish an energy consumption characteristic model for each air compressor; 3, estimating model parameters of the flow characteristic model and the energy consumption characteristic model by adopting a least square method; 4, solving the optimal load rate of each air compressor by taking the minimum total energy consumption of the air compressor control system as a target function and taking the condition of meeting the total air supply demand as a constraint; compared with the prior art, the method has the advantages that the crossing from simple start-stop control to intelligent optimized operation is realized, and the air compressor group system can be always maintained to operate in the optimal energy efficiency state.

Description

Technical Field

[0001] This invention belongs to the field of industrial energy conservation and automatic control technology, and in particular relates to an energy-saving optimization method for multi-air compressor systems based on segmented modeling. Background Technology

[0002] Air compressors are key power equipment in industrial production, and their energy consumption accounts for a significant proportion of a factory's total electricity consumption. To meet the constantly fluctuating demand for air in production, most manufacturing enterprises adopt a parallel operation mode for multiple air compressors. However, the currently widely used air compressor group control strategies are still at a relatively rudimentary level, mainly suffering from the following two problems: Existing air compressor systems mostly employ simple sequential control or fixed priority control methods. These methods mechanically execute the start and stop operations of the air compressors based solely on the upper and lower thresholds of the main pipe pressure signal, neglecting both the differences in the operating characteristics of individual air compressors and the overall energy efficiency optimization of the system. In reality, the energy consumption characteristics of air compressors exhibit significant nonlinearity, and their operating efficiency fluctuates dramatically with changes in load rate. Most industrial frequency air compressors maintain optimal energy efficiency only within the 70%-100% load range; when the load rate drops below 40%, efficiency decreases significantly. Traditional control methods often lead to air compressors operating in the inefficient range for extended periods, resulting in severe energy waste. More importantly, for a given total air supply demand, there are theoretically multiple equipment combinations and load allocation schemes, with vastly different schemes resulting in significantly different total system energy consumption. For example, achieving the same air supply volume can be accomplished by three medium-load air compressors or by two high-load air compressors working together; the latter typically saves considerable energy. Existing control strategies lack the ability to compare and optimize multiple options, and cannot achieve energy consumption minimization at the system level. Summary of the Invention

[0003] The purpose of this invention is to provide an energy-saving optimization method for multi-air compressor systems based on segmented modeling, in order to solve the problem that traditional control methods often lead to air compressors operating in the inefficient zone for a long time, resulting in serious energy waste.

[0004] This invention adopts the following technical solution: an energy-saving optimization method for multi-air compressor systems based on segmented modeling, comprising: Step 1: Collect the instantaneous electrical power and instantaneous flow rate of each air compressor in the air compressor control system at each moment; calculate the load rate based on the instantaneous electrical power of each air compressor; Step 2: Based on the load rate and instantaneous flow rate, establish a flow characteristic model for each air compressor using a piecewise function, and establish an energy consumption characteristic model for each air compressor using a piecewise function based on the load rate; Step 3: Estimate the model parameters of the flow characteristic model and the energy consumption characteristic model using the least squares method; Step 4: Using the minimum total energy consumption of the air compressor control system as the objective function and meeting the total air supply demand as the constraint, solve for the optimal load rate of each air compressor.

[0005] The beneficial effects of this invention are: This invention establishes a model of the correspondence between load rate and flow rate, and load rate and energy consumption for each air compressor by collecting historical operating data. Addressing the nonlinear operating characteristics of air compressors in different load ranges, a piecewise modeling method is employed to accurately describe the energy efficiency characteristics of the equipment under different operating conditions such as low load and high load, providing an accurate mathematical model foundation for subsequent optimization calculations.

[0006] To address real-time gas supply demands, this invention employs a two-stage optimization strategy combining combinatorial optimization and mathematical programming. First, based on the maximum gas production capacity of each air compressor, all possible equipment operation combinations that can meet the current demand are generated. Then, for each feasible combination, the optimal load allocation scheme under the gas supply balance constraint is solved, ensuring that all possible operating modes are explored at the system level, thereby finding the globally optimal solution.

[0007] This invention employs an improved optimization algorithm to solve the load distribution problem. This algorithm can effectively handle various constraints during equipment operation, including upper and lower limits of load rate and flow balance, and ensures the stability and convergence of the calculation process, making it suitable for practical application needs in industrial settings.

[0008] Compared with existing technologies, this invention achieves a leap from simple start-stop control to intelligent optimized operation, enabling the air compressor group system to always operate in the optimal energy efficiency state. Detailed Implementation

[0009] The present invention will now be described in detail with reference to specific embodiments.

[0010] This invention discloses an energy-saving optimization method for multi-air compressor systems based on segmented modeling, comprising four steps.

[0011] Step 1: Collect the instantaneous electrical power and instantaneous flow rate of each air compressor in the air compressor control system at each moment; calculate the load rate based on the instantaneous electrical power of each air compressor.

[0012] First, data acquisition is required. The parameters to be collected from the air compressor control system should include: instantaneous electrical power (kW) of each air compressor, instantaneous outlet flow rate (m³ / min) of each air compressor, total pipe pressure (MPa), and timestamp information. The data acquisition cycle should be determined based on the process characteristics, and is generally recommended to be between 1 second and 5 minutes. To ensure data representativeness, long-term operating data covering different seasons and production conditions should be collected.

[0013] Based on the collected power data, the real-time load rate of each air compressor is calculated. Load rate at time t. The calculation formula is: in, Let be the instantaneous electrical power of the i-th air compressor at time t. This represents the rated power or historical maximum power value of the i-th air compressor.

[0014] Since the raw data usually contains outliers and invalid data, it is necessary to remove constant numerical data caused by sensor failure, remove zero or Nan value data segments caused by communication interruption, and remove data points that obviously do not conform to physical laws based on process knowledge.

[0015] After data preparation and processing are completed, an accurate air compressor operating characteristic model needs to be established. The air compressor operating characteristic model includes two parts: flow characteristic model and energy consumption characteristic model. The operating law of the air compressor is accurately described through mathematical methods.

[0016] Step 2: Based on the load rate and instantaneous flow rate, establish a flow characteristic model for each air compressor using a piecewise function, and establish an energy consumption characteristic model for each air compressor using a piecewise function based on the load rate.

[0017] The flow characteristic model describes the quantitative relationship between air compressor load rate and flow rate through flow characteristics. Based on data analysis, it was found that the flow rate-load rate relationship of air compressors usually exhibits non-linear characteristics; therefore, a function fitting method is used to establish the flow characteristic model.

[0018] For air compressors with continuous characteristics, a quadratic function is used for fitting: in, This indicates that the i-th air compressor is at the load rate The traffic below, , , These are all parameters of the flow characteristic model to be solved.

[0019] For air compressors exhibiting a clear inflection point in their operating characteristics, piecewise function modeling is employed. Let the inflection point be... Then the piecewise function is expressed as: in, The parameters of the flow characteristic model to be solved are: This marks a turning point in the flow characteristic model. The linear slope in the low-load region. The parameters of the flow characteristic model to be solved are: Determined by the continuity condition: The energy consumption characteristic model describes the mapping relationship between air compressor load rate and energy consumption through energy consumption characteristics. Considering the piecewise linear characteristics of air compressor energy consumption characteristics, a piecewise linear function is used for modeling, specifically: in, This indicates that the i-th air compressor is at the load rate The energy consumption is as follows , These are all parameters of the energy consumption characteristic model to be solved.

[0020] For air compressors with different operating modes (such as loading / unloading), a piecewise linear model is used: in, , All are energy consumption characteristic coefficients under loading mode. , This is the energy consumption characteristic coefficient during unloaded mode; , , , These are all parameters of the energy consumption characteristic model to be solved.

[0021] Step 3: Estimate the model parameters of the flow characteristic model and the energy consumption characteristic model using the least squares method.

[0022] The least squares method is used to estimate the model parameters. Taking flow characteristics as an example, the following optimization problem is solved to determine the parameters of the flow characteristic model: in, Let N be the actual flow observation value of the j-th sample, where N is the number of samples and j is the sample index. For the i-th air compressor in the j-th sample, at the load rate The traffic volume.

[0023] After the model is built, it needs to be validated to ensure accuracy. This is done by calculating the coefficient of determination. Assess the goodness of fit: in, This is the average of the observed values. It is usually required that... A value of >=0.85 is used to ensure the reliability of the model.

[0024] Step 4: Using the minimum total energy consumption of the air compressor control system as the objective function and meeting the total air supply demand as the constraint, solve for the optimal load rate of each air compressor.

[0025] Before solving in step 4, the feasibility of the solution is checked by filtering the equipment combinations d. Let D be the set of all non-empty equipment subsets. For each combination... Check the feasibility and obtain : Maximum gas production capacity check This ensures that the maximum gas production capacity of combination d can meet production needs.

[0026] Minimum gas production capacity check The minimum gas production capacity of combination d shall not exceed the total demand; where d is the equipment combination of air compressors.

[0027] The goal of the optimization solution is to determine the optimal load rate allocation for each air compressor while meeting the total air supply demand, thereby minimizing the total system energy consumption. Therefore, a hierarchical optimization strategy based on equipment combination enumeration is adopted, with the specific steps as follows: Suppose there are n air compressors in the system, for a given total air supply demand... The optimization problem can be described as: Objective function: ; in, This refers to the number of air compressors.

[0028] Constraints: , in, For the i-th air compressor at the load rate The energy consumption is as follows For the i-th air compressor at the load rate The traffic below, Let be the lower limit of the load rate of the i-th air compressor. Let be the upper limit of the load rate of the i-th air compressor; This represents the total gas supply demand.

[0029] Next, an optimization algorithm based on the Lagrange multiplier method is used to solve the problem, specifically: in, For Lagrange multipliers, the optimal solution satisfies the KKT conditions: , The above system of equations is solved using a numerical iterative method, with the following iterative format: , in, and All values ​​represent the learning rate, which is adaptively adjusted to ensure algorithm convergence.

[0030] For each feasible combination Calculate the total energy consumption corresponding to its optimal solution: ; in, This represents the total energy consumption corresponding to the optimal solution. The optimal solution for the load rate of each air compressor; For each air compressor, find the optimal solution at the load rate. Total energy consumption per hour.

[0031] The final optimal load allocation combination is selected as the final solution: Through the above optimization process, the system can automatically determine the optimal equipment operation combination and the load rate setting value of each air compressor, so as to minimize energy consumption while ensuring air supply demand.

[0032] Example: In the centralized air supply system of an industrial production workshop, an optimized load distribution operation test was conducted on 4 air compressors under continuous load production conditions. The specific data is shown in Table 1.

[0033] Table 1 Through application verification of this embodiment, it can reduce the total system energy consumption by 8.6%, and has the characteristics of strong adaptability and good practicality. It can automatically adjust and optimize strategies according to equipment aging, changes in operating conditions, etc.

Claims

1. An energy-saving optimization method for a multi-air compressor system based on segmented modeling, characterized in that, include: Step 1: Collect the instantaneous electrical power and instantaneous flow rate of each air compressor in the air compressor control system at each moment; The load rate is calculated based on the instantaneous electrical power of each air compressor. Step 2: Based on the load rate and instantaneous flow rate, establish a flow characteristic model for each air compressor using a piecewise function, and establish an energy consumption characteristic model for each air compressor using a piecewise function based on the load rate; Step 3: Estimate the model parameters of the flow characteristic model and the energy consumption characteristic model using the least squares method; Step 4: Using the minimum total energy consumption of the air compressor control system as the objective function and meeting the total air supply demand as the constraint, solve for the optimal load rate of each air compressor.

2. The energy-saving optimization method for a multi-air compressor system based on segmented modeling according to claim 1, characterized in that, The flow characteristic model in step 2 is as follows: ； in, For the i-th air compressor at the load rate The instantaneous flow rate below , , , These are all parameters of the flow characteristic model to be solved; This marks a turning point in the flow characteristic model. The linear slope in the low-load region. The calculation method is as follows: 。 3. The energy-saving optimization method for a multi-air compressor system based on segmented modeling according to claim 2, characterized in that, The energy consumption characteristic model in step 2 is as follows: ； in, This indicates that the i-th air compressor is at the load rate The energy consumption is as follows , All are energy consumption characteristic coefficients under loading mode. , This is the energy consumption characteristic coefficient during unloaded mode; , , , These are all parameters of the energy consumption characteristic model to be solved.

4. The energy-saving optimization method for a multi-air compressor system based on segmented modeling according to claim 3, characterized in that, The objective function in step 4 is: ； in, This refers to the number of air compressors; The constraints in step 4 are: ， in, For the i-th air compressor at the load rate The energy consumption is as follows For the i-th air compressor at the load rate The traffic below, Let be the lower limit of the load rate of the i-th air compressor. Let be the upper limit of the load rate of the i-th air compressor; This represents the total gas supply demand.

5. The energy-saving optimization method for a multi-air compressor system based on segmented modeling according to claim 4, characterized in that, Before solving in step 4, check the feasibility of the solution. The method for checking is as follows: ; Where d represents the equipment combination of the air compressor.

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