Pharmaceutical enterprise clean air conditioning energy-saving control system and method

Abstract

The present invention discloses a clean air-conditioning energy-saving control system and method for pharmaceutical enterprises, which relates to the technical field of clean air-conditioning energy-saving control. The clean air-conditioning energy-saving control system and method for pharmaceutical enterprises can, by monitoring the particulate matter concentration and microbial load in the clean room in real time, dynamically adjust the operating efficiency of the air-conditioning filter through fuzzy control to ensure cleanliness and achieve energy conservation. By monitoring the exhaust heat and analyzing the heat recovery potential and energy efficiency ratio, the operating mode of the heat recovery device is dynamically adjusted by using an adaptive control algorithm to improve the system energy efficiency and reduce energy consumption. The pressure difference between the clean room and the external area is monitored, and the air supply volume is adjusted through the PID control algorithm to ensure the maintenance of a positive pressure environment in the clean room and prevent the intrusion of external pollutants. By setting the threshold of the working state index and comprehensively analyzing the cleanliness index, heat recovery trigger index, and differential pressure control index, each functional module of the air-conditioning system is accurately controlled, thereby optimizing the overall operating state.

Description

Technical Field

[0001] The present invention relates to the technical field of clean air-conditioning energy-saving control, and specifically to a clean air-conditioning energy-saving control system and method for pharmaceutical enterprises. Background Art

[0002] With the intensification of global climate change issues, governments and enterprises around the world are paying more and more attention to reducing carbon emissions and saving energy. As an energy-intensive industry, the pharmaceutical production uses a large number of air-conditioning systems in its production environment. Traditional air-conditioning systems have high energy consumption, especially clean room air-conditioning systems, because they need to maintain constant temperature and humidity and highly clean air quality. Through an energy-saving control system, pharmaceutical enterprises can significantly reduce energy consumption and carbon footprint while maintaining production standards, meeting the requirements of global sustainable development.

[0003] With the development of Internet of Things, artificial intelligence and big data analysis technologies, the application of intelligent control systems is becoming more and more widespread in various industries. The intelligent upgrade of the clean air-conditioning system in pharmaceutical enterprises can optimize the system operation through real-time data collection, analysis and feedback, achieving the purpose of refined management. This technological progress not only improves the energy-saving efficiency of the system, but also reduces manual intervention and improves the degree of production automation, conforming to the trend of modern industry.

[0004] For example, an air purification control system for a pharmaceutical clean room with the publication number of CN118328489A includes an air purification host computer for pharmaceutical production, a clean room and a fresh air unit installed outside the clean room. Uniformly distributed air supply outlets and air return outlets are fixedly installed inside the clean room, where the air supply outlets are located above the clean room and the air return outlets are located below the clean room. The air supply outlets and air return outlets are respectively connected to the air outlet and air inlet of the fresh air unit through air ducts, and an air supply valve and an air return valve are respectively installed on the air supply outlets and air return outlets.

[0005] However, the traditional system mainly relies on preset control parameters and cannot be flexibly adjusted according to the real-time changes of the internal and external environments of the clean room. This method is prone to low system operation efficiency and serious energy waste. Especially in the case of large load changes, it cannot respond in time and is difficult to maintain the best air quality and energy efficiency. The traditional system lacks in-depth analysis and intelligent regulation of the heat recovery potential and energy efficiency ratio, resulting in waste of heat energy resources, further increasing energy consumption and operating costs. At the same time, when maintaining the pressure difference between the clean room and the external area, the control accuracy is low and fluctuations are likely to occur. These fluctuations will affect the air flow state in the clean room, resulting in unstable air cleanliness, and further affecting the quality and safety of pharmaceutical production.

[0006] Therefore, there is still much room for improvement in the energy-saving control of existing clean air-conditioning systems. There is an urgent need to develop an energy-saving control system that can monitor in real time, adjust dynamically, and has a high degree of intelligence to better meet the production needs and sustainable development goals of pharmaceutical enterprises. Summary of the Invention

[0007] In view of the deficiencies of the prior art, the present invention provides an energy-saving control system and method for the clean air-conditioning of pharmaceutical enterprises, which solves the problems in the above-mentioned background technology.

[0008] To achieve the above objectives, the present invention is realized through the following technical solutions: An energy-saving control system for the clean air-conditioning of pharmaceutical enterprises includes the following modules: an air treatment module, a heat recovery module, a differential pressure control module, and an equipment control module; the air treatment module is used to monitor in real time the particulate matter concentration and microbial load in the air of the clean room in a pharmaceutical enterprise, analyze the clean air index of the clean room, and dynamically adjust the operating efficiency of the clean air-conditioning filter through fuzzy control; the heat recovery module is used to monitor the heat in the exhaust air of the clean air-conditioning system, analyze the heat recovery potential and energy efficiency ratio, obtain a heat recovery trigger index, and dynamically adjust the operating mode of the clean air-conditioning heat recovery device through adaptive control based on the heat recovery trigger index; the differential pressure control module is used to monitor the pressure difference between the clean room in a pharmaceutical enterprise and other areas, analyze the stability of the pressure difference and the air flow state, and obtain a differential pressure control index; based on the differential pressure control index, dynamically adjust the air supply volume of the clean air-conditioning air supply system through the PID control algorithm; the equipment control module is used to set the threshold of the working state index, analyze the clean air index of the clean room, the heat recovery trigger index, and the differential pressure control index, and control the working states of the clean air-conditioning filter, the clean air-conditioning heat recovery device, and the clean air-conditioning air supply system.

[0009] Furthermore, the specific process of analyzing the clean air index of the clean room is as follows: By monitoring the particulate matter concentration and microbial load in the air of the clean room in a pharmaceutical enterprise, the concentration of particulate matter and the microbial load are obtained; the concentration of particulate matter and the microbial load are respectively subjected to weighted arithmetic processing with the set concentration threshold of particulate matter and the microbial load threshold to obtain the clean air index of the clean room.

[0010] Furthermore, the specific process of dynamically adjusting the operating efficiency of the air-conditioning filter through fuzzy control is as follows: The clean air index of the clean room is set as the input variable and divided into different fuzzy sets respectively; the operating efficiency of the air-conditioning filter is set as the output variable and divided into fuzzy sets; according to the preset fuzzy rules, combined with the influence of the fuzzy states of the particulate matter concentration and the microbial load on the operating efficiency of the air-conditioning filter, fuzzy reasoning is carried out to determine the adjustment of the operating efficiency of the air-conditioning filter.

[0011] Further, the specific process of analyzing the heat recovery potential and energy efficiency ratio to obtain the heat recovery trigger index is as follows: Monitor the exhaust air temperature and return air temperature of the clean air conditioning system. By comparing the temperature difference between the exhaust air and the return air, obtain the heat recovery amount. Evaluate the energy efficiency ratio of the clean air conditioning heat recovery system by taking the ratio of the heat recovery amount to the input power of the heat recovery device; Perform comprehensive arithmetic processing on the heat recovery amount and the energy efficiency ratio of the clean air conditioning heat recovery system to obtain the heat recovery trigger index.

[0012] Further, the specific process of dynamically adjusting the operation mode of the clean air conditioning heat recovery device based on the heat recovery trigger index through adaptive control is as follows: Compare the heat recovery trigger index with the operation mode threshold; When the heat recovery trigger index is greater than the first mode threshold, adjust the clean air conditioning heat recovery device to the first operation mode; When the heat recovery trigger index is greater than the second mode threshold and less than the first mode threshold, adjust the clean air conditioning heat recovery device to the second operation mode; When the heat recovery trigger index is less than the second mode threshold, adjust the clean air conditioning heat recovery device to the third operation mode.

[0013] Further, the specific process of analyzing the pressure difference stability and air flow state to obtain the pressure difference control index is as follows: Continuously monitor the pressure difference between the inside and outside of the clean room in real time to obtain the pressure difference between the inside and outside of the clean room. Analyze the air flow state inside the clean room and evaluate the air flow state index; Perform coupled analysis on the air flow state index and the pressure difference between the inside and outside of the clean room, and comprehensively evaluate to obtain the pressure difference control index.

[0014] Further, the specific process of dynamically adjusting the air supply volume of the clean air conditioning air supply system based on the pressure difference control index through the PID control algorithm is as follows: Use the pressure difference control index as the input variable of the PID control algorithm, and calculate the deviation value between the pressure difference control index and the set target pressure difference control index; Perform integral processing and differential processing on the deviation value, and obtain the adjustment of the air supply volume of the clean air conditioning system through the PID controller according to the deviation value.

[0015] Further, the specific process of the control logic for controlling the working states of the clean air conditioning filter, the clean air conditioning heat recovery device, and the clean air conditioning air supply system is as follows: Compare the clean room air cleanliness index, the heat recovery trigger index, and the pressure difference control index with their corresponding working state index thresholds respectively; When the clean room air cleanliness index, the heat recovery trigger index, and the pressure difference control index are greater than their corresponding working state index thresholds, adjust the equipment to the working state.

[0016] Energy-saving control method for clean air conditioning in pharmaceutical enterprises, including the following steps: S1. Real-time monitor the particulate matter concentration and microbial level in the air of the clean room in the pharmaceutical enterprise, analyze the air cleanliness index of the clean room, and dynamically adjust the operation efficiency of the clean air conditioning filter through fuzzy control; S2. Real-time monitor the heat in the exhaust air of the clean air conditioning system, analyze the heat recovery potential and energy efficiency ratio, obtain the heat recovery trigger index, and dynamically adjust the operation mode of the clean air conditioning heat recovery device through adaptive control based on the heat recovery trigger index; S3. Real-time monitor the pressure difference between the clean room in the pharmaceutical enterprise and other areas, analyze the pressure difference stability and air flow state, and obtain the pressure difference control index; Based on the pressure difference control index, dynamically adjust the air supply volume of the clean air conditioning air supply system through the PID control algorithm; S4. Set the working state index threshold, analyze the air cleanliness index, heat recovery trigger index and pressure difference control index of the clean room, and control the working states of the clean air conditioning filter, clean air conditioning heat recovery device and clean air conditioning air supply system.

[0017] The present invention has the following beneficial effects:

[0018] (1). The energy-saving control system for clean air conditioning in the pharmaceutical enterprise ensures the compliance of the air quality in the clean room through real-time monitoring of the particulate matter concentration and microbial level in the air. By dynamically adjusting the operation efficiency of the filter through fuzzy control, the air conditioning system can be intelligently adjusted under different load conditions, ensuring both cleanliness and energy-saving effects. By monitoring and analyzing the heat in the exhaust air, the operation potential and efficiency of the heat recovery device can be accurately evaluated. The adaptive control algorithm enables the heat recovery device to dynamically respond to environmental changes, improving the energy efficiency of the system, reducing energy waste and lowering the operation cost.

[0019] (2). The energy-saving control method for clean air conditioning in the pharmaceutical enterprise ensures the positive pressure environment in the clean room by monitoring and controlling the pressure difference inside and outside the clean room, effectively preventing external pollutants from entering. The PID control algorithm enables the air supply system to automatically adjust the air supply volume according to the real-time pressure difference, maintaining the system stability and operation efficiency. By comprehensively analyzing various indexes and setting reasonable working state thresholds, it is ensured that the clean air conditioning system automatically switches to the optimal operation mode under different working conditions. This strategy not only improves the operation stability and reliability of the system, but also significantly reduces energy consumption and extends the service life of the equipment.

[0020] Of course, it is not necessary for any product implementing the present invention to achieve all the above-mentioned advantages simultaneously. Description of the Drawings

[0021] Figure 1 It is a flowchart of the energy-saving control system for clean air conditioning in the pharmaceutical enterprise of the present invention.

[0022] Figure 2This is the flow chart of the energy-saving control method for the clean air-conditioning system of pharmaceutical enterprises in the present invention. Specific embodiments

[0023] Through the energy-saving control system and method for the clean air-conditioning system of pharmaceutical enterprises in the embodiments of the present application, the problems of high energy consumption, lack of real-time monitoring and dynamic adjustment capabilities, low heat recovery efficiency, unstable pressure difference control, and low intelligence level in the traditional clean air-conditioning system are solved.

[0024] The general idea for the problems in the embodiments of the present application is as follows:

[0025] Real-time monitor the particulate matter concentration and microbial level in the clean indoor air of pharmaceutical enterprises, analyze the clean air index of the clean room, and dynamically adjust the operation efficiency of the clean air-conditioning filter through fuzzy control.

[0026] Real-time monitor the heat in the exhaust air of the clean air-conditioning system, analyze the heat recovery potential and energy efficiency ratio, obtain the heat recovery trigger index, and dynamically adjust the operation mode of the clean air-conditioning heat recovery device through adaptive control based on the heat recovery trigger index.

[0027] Real-time monitor the pressure difference between the clean room of pharmaceutical enterprises and other areas, analyze the stability of the pressure difference and the air flow state, obtain the pressure difference control index; based on the pressure difference control index, dynamically adjust the air supply volume of the clean air-conditioning air supply system through the PID control algorithm.

[0028] Set the threshold of the working state index, analyze the clean air index of the clean room, the heat recovery trigger index, and the pressure difference control index, and control the working states of the clean air-conditioning filter, the clean air-conditioning heat recovery device, and the clean air-conditioning air supply system.

[0029] Please refer to Figure 1, an embodiment of the present invention provides a technical solution: a clean air conditioning energy saving control system for pharmaceutical enterprises, including the following modules: an air treatment module, a heat recovery module, a differential pressure control module, and an equipment control module; the air treatment module is used to monitor the particulate matter concentration and microbial load in the air of the clean room in the pharmaceutical enterprise in real time, analyze the clean air index of the clean room, and dynamically adjust the operation efficiency of the clean air conditioner filter through fuzzy control; the heat recovery module is used to monitor the heat in the exhaust air of the clean air conditioning system, analyze the heat recovery potential and energy efficiency ratio, obtain the heat recovery trigger index, and dynamically adjust the operation mode of the clean air conditioner heat recovery device through adaptive control based on the heat recovery trigger index; the differential pressure control module is used to monitor the pressure difference between the clean room in the pharmaceutical enterprise and other areas, analyze the stability of the pressure difference and the air flow state, and obtain the differential pressure control index; based on the differential pressure control index, dynamically adjust the air supply volume of the clean air conditioning air supply system through the PID control algorithm; the equipment control module is used to set the threshold of the working state index, analyze the clean air index of the clean room, the heat recovery trigger index and the differential pressure control index, and control the working states of the clean air conditioner filter, the clean air conditioner heat recovery device and the clean air conditioning air supply system.

[0030] In this implementation plan, the particulate matter concentration refers to the number of suspended particulate matters in the air of the cleanroom. In the cleanroom environment, controlling the particulate matter concentration is an important indicator to ensure air cleanliness. The microbial load refers to the number and types of microorganisms in the air. These microorganisms may be transmitted through the air and affect the quality and safety of drugs. Therefore, in the cleanrooms of pharmaceutical enterprises, it is necessary to strictly control the microbial load. The air cleanliness index is used to quantify the cleanliness of the air in the cleanroom. This index is calculated based on parameters such as particulate matter concentration and microbial load and is used to evaluate whether the cleanroom meets specific cleanliness standards. The fuzzy control algorithm makes the input variables fuzzy and then makes decisions according to empirical rules to dynamically adjust the operating parameters of the system. In the clean air-conditioning system, the fuzzy control algorithm is used to adjust the operating efficiency of the filter according to the air cleanliness index. The heat recovery potential refers to the potential ability of the heat that can be recovered and utilized in the clean air-conditioning system. In the air-conditioning system, the exhaust air often contains a large amount of heat that can be utilized. By recovering this heat, the overall energy efficiency of the system can be improved and energy consumption can be reduced. The energy efficiency ratio is an indicator used to measure the efficiency of the air-conditioning system and is the ratio of the output cooling capacity to the input power. The higher the energy efficiency ratio, the higher the energy utilization efficiency of the system. The heat recovery trigger index is a comprehensive index used to determine whether to activate the heat recovery device. This index is based on the analysis results of heat recovery potential and energy efficiency ratio, and dynamically adjusts the operating mode of the heat recovery device through an adaptive control algorithm. The adaptive control algorithm is an intelligent control method that can automatically adjust the control strategy according to environmental changes to maintain the optimal operating state of the system. In the clean air-conditioning system, the adaptive control algorithm is used to dynamically adjust the working mode of the heat recovery device to achieve the highest energy utilization efficiency. The pressure difference refers to the air pressure difference between the cleanroom and the external environment. Maintaining an appropriate pressure difference is an important means to prevent external pollutants from entering the cleanroom. Usually, the air pressure in the cleanroom is maintained at a state higher than the external environment, that is, positive pressure, to ensure that air only flows out of the cleanroom and does not flow into the outside. The PID control algorithm is a classic control system design method that achieves stable control of the system by adjusting three parameters of the system. In the clean air-conditioning system, the PID control algorithm is used to accurately adjust the air volume of the air supply system and maintain an appropriate pressure difference. The working state index threshold is a preset numerical standard used to judge whether the working state of each part of the system meets the requirements. When the air cleanliness index, heat recovery trigger index or pressure difference control index of the cleanroom reaches or exceeds this threshold, the system will automatically adjust the working state of relevant equipment to ensure the normal operation of the air-conditioning system.

[0031] Furthermore, the specific process of analyzing the air cleanliness index of the cleanroom is as follows: By monitoring the particulate matter concentration and microbial load in the air of the cleanroom in a pharmaceutical enterprise, the concentration of particulate matter and the microbial load are obtained; the concentration of particulate matter and the microbial load are respectively subjected to weighted arithmetic processing with the set concentration threshold of particulate matter and the microbial load threshold to obtain the air cleanliness index of the cleanroom.

[0032] In this implementation plan, the particulate matter concentration and the microbial load are monitored: Special sensors and detection equipment are used in the cleanroom to measure the particulate matter concentration and the microbial load in the air in real time. These data reflect the actual cleanliness of the air in the cleanroom, and the measured particulate matter concentration and microbial load data are used for subsequent calculations. These data are the basis for cleanliness evaluation. The actually measured particulate matter concentration and microbial load are compared with the preset thresholds, and the air cleanliness index of the cleanroom is calculated through weighted arithmetic processing. The thresholds are set based on industry standards or specific requirements and are used to determine whether the air quality meets the regulations. Through weighted arithmetic processing, the data of the particulate matter concentration and the microbial load are converted into a comprehensive index, and the air cleanliness index of the cleanroom is obtained, which is used to quantify the cleanliness of the air and help determine whether it is necessary to adjust the air treatment equipment. The calculation formula for the air cleanliness index of the cleanroom is as follows: Among them, J index represents the air cleanliness index of the cleanroom, C actual represents the actually measured particulate matter concentration, C threshold represents the set concentration threshold of particulate matter, M actual represents the actually measured microbial load, M threshold represents the set microbial load threshold, w 1 represents the weight coefficient of the particulate matter concentration, w 2 represents the weight coefficient of the microbial load.

[0033] Furthermore, the specific process of dynamically adjusting the operating efficiency of the air conditioner filter through fuzzy control is as follows: The air cleanliness index of the cleanroom is set as the input variable and divided into different fuzzy sets respectively; the operating efficiency of the air conditioner filter is set as the output variable and divided into fuzzy sets; according to the preset fuzzy rules, combined with the influence of the fuzzy states of the particulate matter concentration and the microbial load on the operating efficiency of the air conditioner filter, fuzzy reasoning is carried out to determine the adjustment of the operating efficiency of the air conditioner filter.

[0034] In this implementation, the air cleanliness index of the cleanroom is defined as the input variable of the fuzzy control system. This variable represents the overall quality of the air, which is obtained by evaluating the particulate matter concentration and microbial load. The cleanliness index will be divided into several fuzzy sets to represent different air quality states. For example: Low: The cleanliness index is between 0 and 30; Medium: The cleanliness index is between 25 and 75; High: The cleanliness index is between 60 and 100. The operating efficiency of the air-conditioning filter is defined as the output variable of the fuzzy control system. This variable represents the actual working efficiency of the filter and is also divided into fuzzy sets, such as: Low Efficiency: 0% to 30%; Medium Efficiency: 20% to 70%; High Efficiency: 60% to 100%. The relationship between the input variable and the output variable is defined as a set of fuzzy rules. These rules are used to describe the relationship between the cleanliness index and the filter efficiency. If the cleanliness index is low, the filter efficiency should be high;

[0035] If the cleanliness index is medium, the filter efficiency should be medium; if the cleanliness index is high, the filter efficiency should be low. The fuzzy sets of the cleanliness index are combined with the preset rules using the fuzzy inference method to determine the fuzzy sets of the filter efficiency. The fuzzy inference process includes the following steps: Convert the cleanliness index into fuzzy sets; Apply the rule base, input the fuzzy sets into the inference engine to generate a fuzzy output. Process the fuzzy output and calculate the membership degree of each output fuzzy set. Convert the fuzzy inference result into a specific control value to adjust the actual operating efficiency of the air-conditioning filter. The defuzzification calculation formula is as follows: where E i represents the corresponding filter efficiency value. Membership i represents the membership degree of the fuzzy set, and E efficiency represents the operating efficiency of the air-conditioning filter.

[0036] Furthermore, the specific process of analyzing the heat recovery potential and the energy efficiency ratio to obtain the heat recovery trigger index is as follows: Monitor the exhaust air temperature and return air temperature of the clean air-conditioning system, obtain the heat recovery amount by comparing the temperature difference between the exhaust air and the return air, and evaluate the energy efficiency ratio of the clean air-conditioning heat recovery system by taking the ratio of the heat recovery amount to the input power of the heat recovery device; Perform a comprehensive arithmetic process on the heat recovery amount and the energy efficiency ratio of the clean air-conditioning heat recovery system to obtain the heat recovery trigger index.

[0037] In this implementation, the calculation formula of the heat recovery trigger index is as follows, Among them, TRI represents the heat recovery trigger index, and α and β respectively represent the weight coefficient of the heat recovery amount and the weight coefficient of the energy efficiency ratio of the clean air-conditioning heat recovery system, which are used to balance the contributions of the heat recovery amount and the energy efficiency ratio, C p represents the specific heat capacity of air, ρ represents the air density, V represents the flow rate, T out and T in are the exhaust air temperature and the return air temperature respectively, and P input is the input power of the heat recovery device.

[0038] Furthermore, the specific process of dynamically adjusting the operation mode of the clean air-conditioning heat recovery device through adaptive control based on the heat recovery trigger index is as follows: compare the heat recovery trigger index with the operation mode threshold; when the heat recovery trigger index is greater than the first mode threshold, adjust the clean air-conditioning heat recovery device to the first operation mode; when the heat recovery trigger index is greater than the second mode threshold and less than the first mode threshold, adjust the clean air-conditioning heat recovery device to the second operation mode; when the heat recovery trigger index is less than the second mode threshold, adjust the clean air-conditioning heat recovery device to the third operation mode.

[0039] In this implementation plan, the heat recovery trigger index is calculated in real time, and this index reflects the current performance of the heat recovery system. Thresholds are set to determine different operation modes. Specifically, it includes the first mode threshold and the second mode threshold, which are used to guide the operation adjustment of the heat recovery device. When the TRI is higher than the first mode threshold, it indicates that the heat recovery potential of the system is strong, and at this time, the device is adjusted to the first operation mode to optimize its performance for efficient recovery. When the TRI is between the second mode threshold and the first mode threshold, it shows that the heat recovery potential is at a medium level, and the device is adjusted to the second operation mode to maintain reasonable energy efficiency and equipment load. When the TR is lower than the second mode threshold, it indicates that the heat recovery potential of the system is insufficient, and at this time, the device is adjusted to the third operation mode, and the heat recovery operation may be reduced to save energy. The adaptive control algorithm adjusts the operation mode of the device according to the real-time heat recovery trigger index to ensure that the system can maintain the best performance under various working conditions. This dynamic adjustment can improve energy efficiency and optimize the overall operation state of the system.

[0040] Furthermore, the specific process of analyzing the pressure difference stability and the air flow state to obtain the pressure difference control index is as follows: monitor the pressure difference between the inside and outside of the clean room in real time to obtain the pressure difference between the inside and outside of the clean room, analyze the air flow state inside the clean room, and evaluate the air flow state index; perform a coupled analysis of the air flow state index and the pressure difference between the inside and outside of the clean room, and comprehensively evaluate to obtain the pressure difference control index.

[0041] In this implementation scheme, the pressure difference between the inside and outside of the clean room is monitored, that is, the difference between the pressure inside the clean room and the pressure of the external environment. This difference is crucial for maintaining the air flow and clean environment in the clean room. Analyze the air flow state inside the clean room, which includes the flow direction, speed, and distribution of air in the room. These factors affect the air flow efficiency and cleanliness. According to the monitoring data and analysis results, calculate an index representing the air flow state. This index reflects the effectiveness and uniformity of the air flow inside the clean room. Perform a coupling analysis of the air flow state index and the pressure difference between the inside and outside of the clean room. This means considering these two factors comprehensively to evaluate their mutual influence and the impact on the overall air flow. Based on the results of the coupling analysis, calculate the pressure difference control index. This index reflects the comprehensive performance of the pressure difference and the air flow state under the current conditions and is used to guide how to adjust the system to maintain a stable pressure difference and optimize the air flow. The calculation formula of the pressure difference control index is as follows: The non-linear weighting function of the pressure difference control index is expressed as where PCI represents the pressure difference control index, ΔP represents the pressure difference between the inside and outside of the clean room, I A represents the air flow state index, W 1 represents the weight coefficient of the pressure difference, W 2 represents the weight coefficient of the air flow state, and e represents the natural constant.

[0042] Furthermore, based on the pressure difference control index, the specific process of dynamically adjusting the air supply volume of the clean air conditioning system through the PID control algorithm is as follows: Take the pressure difference control index as the input variable of the PID control algorithm, and calculate the deviation value between the pressure difference control index and the set target pressure difference control index; Perform integral processing and differential processing on the deviation value, and obtain the air supply volume for adjusting the clean air conditioning system through the PID controller according to the deviation value.

[0043] In this implementation scheme, the pressure difference control index is a comprehensive index that reflects the pressure difference between the inside and outside of the clean room and the air flow state. It is used as the input variable of the PID control algorithm. Calculation of the deviation value, the system calculates the difference between the real-time pressure difference control index and the set target pressure difference control index in real time. This difference is called the deviation value, E(t) = X actual (t) - X target (t) where E(t) is the deviation value, X actual (t) is the real-time pressure difference control index, and X target (t) is the set target value, U(t) represents the control quantity, which is the adjustment signal calculated by the PID controller according to the deviation value and is used to adjust the operating variable of the system, the air supply volume, K pK represents the proportional gain coefficient, which controls the response intensity of the proportional part and determines the influence degree of the current deviation value on the control quantity. The proportional term provides an immediate response but may introduce a steady-state deviation. i K represents the integral gain coefficient, which controls the response intensity of the integral part, accumulates past deviations, and eliminates long-term steady-state errors. The integral term helps to eliminate deviations but may introduce a delay effect. d K represents the derivative gain coefficient, which controls the response intensity of the derivative part, cancels future deviations by predicting the change trend of the deviation. The derivative term can reduce the volatility of the response but may amplify noise. τ represents the integral of the deviation value, which is the cumulative sum of the deviation value over time and is used to eliminate the long-term deviation of the system. represents the derivative of the deviation value, which is the rate of change of the deviation value with respect to time and is used to predict the future trend of the deviation and reduce overshoot and fluctuations. These parameters work together to achieve dynamic control of the system through the adjustment of the PID controller, ensuring that the differential pressure control index is as close as possible to the target value. The PID controller processes the deviation value: Proportional (P): That is, the deviation value itself, which is used to immediately respond to the current error. The proportional control output is P out (t) = K p ×E(t) where K p is the proportional gain coefficient. The integral accumulates past deviations and is used to eliminate the steady-state error of the system. The integral control output is: where K i is the integral gain coefficient. Derivative (D) predicts the future change trend of the deviation and is used to suppress the rapid change of the deviation. The derivative control output is where K d is the derivative gain coefficient.

[0044] Further, the specific process of the control logic for controlling the working states of the clean air-conditioning filter, the clean air-conditioning heat recovery device, and the clean air-conditioning air supply system is as follows: Compare the clean room air cleanliness index, the heat recovery trigger index, and the differential pressure control index with the corresponding working state index thresholds respectively; when the clean room air cleanliness index, the heat recovery trigger index, and the differential pressure control index are greater than the corresponding working state index thresholds, adjust the equipment to the working state.

[0045] In this implementation plan, the air cleanliness index of the clean room represents the cleanliness of the air in the clean room, which is usually determined by monitoring the particulate matter concentration and microbial load. The heat recovery trigger index measures the operating efficiency of the heat recovery system and is calculated by analyzing the heat recovery amount and the energy efficiency ratio. The pressure difference control index reflects the comprehensive situation of the pressure difference and air flow state inside and outside the clean room. The system will compare these three key indexes with the corresponding working state index thresholds respectively. If the air cleanliness index of the clean room is greater than the set threshold, it means that the air quality does not meet the expectation, and the operating state of the air conditioner filter needs to be adjusted to improve the air cleanliness. If the heat recovery trigger index is greater than the set threshold, it means that the operating efficiency of the heat recovery system is low, and the operating mode of the heat recovery device needs to be adjusted to improve the energy efficiency. If the pressure difference control index is greater than the set threshold, it means that the pressure difference between indoors and outdoors is abnormal or the air flow state is poor, and the air supply volume of the air supply system needs to be adjusted. When the system detects that any one or more indexes exceed their corresponding thresholds, it will start to adjust the working state of the relevant equipment: Air conditioner filter: Adjust its operating efficiency and enhance the filtering ability to improve the air cleanliness. Heat recovery device: Change the operating mode to improve the heat recovery efficiency and reduce energy waste. Air supply system: Adjust the air supply volume to ensure that the pressure difference and air flow state are within the ideal range. This control logic ensures that each device in the clean air-conditioning system can be adjusted according to actual needs to reach the best operating state by monitoring and analyzing the three key indexes. By comparing the indexes with the thresholds, the system can automatically adjust the working states of the air conditioner filter, heat recovery device and air supply system, and maintain the air quality, energy efficiency and pressure stability in the clean room. This control method improves the automation level of the system and ensures the performance of the clean air-conditioning system in terms of energy conservation and efficiency.

[0046] Please refer to Figure 2 , the energy-saving control method for the clean air-conditioning in pharmaceutical enterprises, includes the following steps: S1. Monitor the particulate matter concentration and microbial level in the air of the clean room in the pharmaceutical enterprise in real time, analyze the air cleanliness index of the clean room, and dynamically adjust the operating efficiency of the clean air-conditioner filter through fuzzy control; S2. Monitor the heat in the exhaust air of the clean air-conditioning system in real time, analyze the heat recovery potential and the energy efficiency ratio, obtain the heat recovery trigger index, and dynamically adjust the operating mode of the clean air-conditioning heat recovery device through adaptive control based on the heat recovery trigger index; S3. Monitor the pressure difference between the clean room in the pharmaceutical enterprise and other areas in real time, analyze the pressure difference stability and air flow state, obtain the pressure difference control index; based on the pressure difference control index, dynamically adjust the air supply volume of the clean air-conditioning air supply system through the PID control algorithm; S4. Set the working state index threshold, analyze the air cleanliness index of the clean room, the heat recovery trigger index and the pressure difference control index, and control the working states of the clean air-conditioner filter, the clean air-conditioning heat recovery device and the clean air-conditioning air supply system.

[0047] In summary, the present application has at least the following effects:

[0048] The energy-saving control system for clean air conditioning in pharmaceutical enterprises ensures the compliance of the air quality in the clean room by real-time monitoring of the particulate matter concentration and microbial level in the air. By dynamically adjusting the operating efficiency of the filter through fuzzy control, the air conditioning system can be intelligently adjusted under different load conditions, ensuring both cleanliness and energy-saving effects. By monitoring and analyzing the heat in the exhaust air, the operating potential and efficiency of the heat recovery device can be accurately evaluated. The adaptive control algorithm enables the heat recovery device to dynamically respond to environmental changes, improving the energy efficiency of the system, reducing energy waste, and lowering the operating cost. By monitoring and controlling the pressure difference inside and outside the clean room, a positive pressure environment in the clean room is ensured, effectively preventing external pollutants from entering. The PID control algorithm enables the air supply system to automatically adjust the air supply volume according to the real-time pressure difference, maintaining the system stability and operating efficiency. By comprehensively analyzing various indexes and setting reasonable working state thresholds, the clean air conditioning system can be automatically switched to the optimal operating mode under different working conditions. This strategy not only improves the operating stability and reliability of the system, but also significantly reduces energy consumption and extends the service life of the equipment.

[0049] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can be implemented in the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can be implemented in the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0050] The present invention is described with reference to the flowcharts and / or block diagrams of systems, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one or more flows or multiple flows and / or blocks Figure 1 one or more blocks or multiple blocks.

[0051] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions in the flowFigure 1 one process or multiple processes and / or blocks Figure 1 the functions specified in one block or multiple blocks.

[0052] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0053] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications to these embodiments once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed as including the preferred embodiments and all changes and modifications falling within the scope of the present invention.

[0054] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention also intends to include these changes and modifications.

Claims

1. The clean air conditioning energy-saving control system of pharmaceutical enterprises is characterized by: Includes the following modules: air treatment module, heat recovery module, pressure difference control module, equipment control module; The air treatment module is used to monitor the particle concentration and microbial load in the clean room air of the pharmaceutical enterprise in real time, analyze the clean room air cleanliness index, and dynamically adjust the operating efficiency of the clean air conditioning filter through fuzzy control; The heat recovery module is used to monitor the heat in the exhaust air of the clean air conditioning system, analyze the heat recovery potential and energy efficiency ratio, obtain the heat recovery trigger index, and dynamically adjust the operation mode of the clean air conditioning heat recovery device through adaptive control based on the heat recovery trigger index; The pressure difference control module is used to monitor the pressure difference between the clean room and other areas of the pharmaceutical enterprise, analyze the pressure difference stability and air flow state, and obtain the pressure difference control index; based on the pressure difference control index, the air supply volume of the clean air conditioning air supply system is dynamically adjusted through the PID control algorithm; The equipment control module is used to set the working status index threshold, analyze the clean room air cleanliness index, heat recovery trigger index and pressure difference control index, and control the working status of the clean air conditioning filter, the clean air conditioning heat recovery device and the clean air conditioning air supply system; The specific process of analyzing the heat recovery potential and energy efficiency ratio and obtaining the heat recovery trigger index is as follows: Monitor the exhaust and return air temperatures of the clean air conditioning system, obtain the heat recovery amount by comparing the temperature difference between the exhaust and return air, and evaluate the energy efficiency ratio of the clean air conditioning heat recovery system by comparing the heat recovery amount to the input power of the heat recovery device; The heat recovery amount and the energy efficiency ratio of the clean air conditioning heat recovery system are comprehensively calculated to obtain the heat recovery trigger index; The calculation formula of heat recovery trigger index is as follows, ; Among them, TRI represents the heat recovery trigger index, and They represent the weight coefficient of heat recovery and the weight coefficient of energy efficiency ratio of clean air conditioning heat recovery system, respectively, and are used to balance the contribution of heat recovery and energy efficiency ratio. is the specific heat capacity of air, represents the air density, Indicates flow rate, and are the exhaust and return air temperatures, is the input power of the heat recovery device; The specific process of dynamically adjusting the operation mode of the clean air conditioning heat recovery device through adaptive control based on the heat recovery trigger index is as follows: comparing a heat recovery trigger index with an operation mode threshold; When the heat recovery trigger index is greater than the first mode threshold, the clean air conditioner heat recovery device is adjusted to the first operation mode; When the heat recovery trigger index is greater than the second mode threshold and less than the first mode threshold, the clean air conditioner heat recovery device is adjusted to the second operation mode; When the heat recovery trigger index is less than the second mode threshold, the clean air conditioning heat recovery device is adjusted to the third operation mode; The specific process of analyzing the pressure difference stability and air flow state and obtaining the pressure difference control index is as follows: Monitor the pressure difference between inside and outside the clean room in real time, obtain the pressure difference between inside and outside the clean room, analyze the air flow status in the clean room, and evaluate the air flow status index; The air flow state index is coupled with the pressure difference between the inside and outside of the clean room for analysis, and the pressure difference control index is obtained through comprehensive evaluation; The calculation formula of the pressure difference control index is as follows: ; Where PCI represents the pressure difference control index, Indicates the pressure difference between the inside and outside of the clean room. Indicates the air flow state index, represents the weight coefficient of the pressure difference, It represents the weight coefficient of air flow state, and e represents a natural constant.

2. The pharmaceutical enterprise clean air conditioning energy-saving control system according to claim 1 is characterized by: The specific process of analyzing the clean room air cleanliness index is as follows: By monitoring the concentration of particulate matter and microbial load in the air of the clean room of the pharmaceutical enterprise, the concentration of particulate matter and microbial load can be obtained; The concentration of particulate matter and the microbial load are weightedly calculated with the set particulate matter concentration threshold and microbial load threshold respectively to obtain the clean room air cleanliness index.

3. The pharmaceutical enterprise clean air conditioning energy-saving control system according to claim 2 is characterized by: The specific process of dynamically adjusting the operating efficiency of the air conditioning filter through fuzzy control is as follows: The clean room air cleanliness index is set as the input variable and divided into different fuzzy sets; Taking the air conditioner filter operating efficiency as the output variable, divide it into fuzzy sets; According to the preset fuzzy rules, combined with the influence of the fuzzy state of particle concentration and microbial load on the operating efficiency of the air conditioning filter, fuzzy reasoning is performed to determine the operating efficiency of the air conditioning filter and adjust it.

4. The pharmaceutical enterprise clean air conditioning energy-saving control system according to claim 3 is characterized by: Based on the pressure difference control index, the specific process of dynamically adjusting the air supply volume of the clean air conditioning air supply system through the PID control algorithm is as follows: The pressure difference control index is used as the input variable of the PID control algorithm, and the deviation value between the pressure difference control index and the set target pressure difference control index is calculated; The deviation value is processed by integration and differentiation, and the air supply volume of the clean air conditioning system is adjusted according to the deviation value obtained by the PID controller.

5. The pharmaceutical enterprise clean air conditioning energy-saving control system according to claim 4 is characterized by: The specific process of the control logic for controlling the working status of the clean air conditioning filter, the clean air conditioning heat recovery device and the clean air conditioning air supply system is as follows: The clean room air cleanliness index, heat recovery trigger index and pressure difference control index are respectively compared with the corresponding working status index thresholds; When the clean room air cleanliness index, heat recovery trigger index and pressure difference control index exceed the corresponding working state index threshold, the equipment is adjusted to the working state.

6. A method for controlling energy-saving of clean air conditioners in pharmaceutical enterprises, applied to the energy-saving control system for clean air conditioners in pharmaceutical enterprises according to any one of claims 1 to 5, characterized in that: The following steps are involved: S1. Real-time monitoring of the particle concentration and microbial level in the clean room air of pharmaceutical enterprises, analysis of the clean room air cleanliness index, and dynamic adjustment of the operating efficiency of the clean air conditioning filter through fuzzy control; S2. Real-time monitoring of the heat in the exhaust air of the clean air conditioning system, and analysis of the heat recovery potential and energy efficiency ratio, to obtain the heat recovery trigger index, and dynamically adjust the operation mode of the clean air conditioning heat recovery device through adaptive control based on the heat recovery trigger index; S3. Real-time monitoring of the pressure difference between the clean room and other areas of the pharmaceutical enterprise, analysis of the pressure difference stability and air flow status, and acquisition of the pressure difference control index; based on the pressure difference control index, dynamically adjust the air supply volume of the clean air conditioning air supply system through the PID control algorithm; S4. Set the working status index threshold, analyze the clean room air cleanliness index, heat recovery trigger index and pressure difference control index, and control the working status of the clean air conditioning filter, clean air conditioning heat recovery device and clean air conditioning air supply system.

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