Activated sludge process is one of the main processes for urban sewage treatment. Its mechanism is to obtain enough oxygen through aeration to make activated sludge and sewage fully contacted. Soluble organic pollutants in water are adsorbed by activated sludge. The microorganisms that are living on the activated sludge are decomposed and the sewage is purified. Because the internal mechanism of the sewage treatment process is very complex and cannot be described by an accurate mathematical model, it is difficult to obtain satisfactory control results using traditional control strategies (such as typical PID control). As one of the important branches of intelligent control, the fuzzy control does not depend on the precise mathematical model of the controlled object. It can control the controlled variable according to the variation of error and error. It has strong robustness and the parameters of the controlled object. The influence of change on fuzzy control is not obvious. It can be used for the control of nonlinear, time-varying and time-delay systems. The real-time control performance is good. The control mechanism is in line with people's intuitive description of process control and thinking logic [1].
At the same time, the use of adjustable fuzzy control rules in the design of the fuzzy controller can significantly improve and improve the stability and self-adaptive ability of the fuzzy controller. Domestic Peng Yongxuan et al [2] proposed to use DO as the fuzzy control parameter of SBR method to realize the control of aeration volume. Wang Xianlu et al. [3] took the COD deviation and deviation variation of the effluent as input, and the pump opening degree designed a fuzzy controller for the output to realize the fuzzy control of COD. However, there is a biggest problem in the process of sewage treatment using a fuzzy control system, that is, the stability and self-adaptability of the control system. Due to the PD control effect of fuzzy control and the continuous change of the water environment, the control effect of most systems oscillates significantly, and the stability and self-adaptability are not good.
Based on the sequential batch activated sludge process (SBR), this paper focuses on the influence of the change of dissolved oxygen concentration (DO) in wastewater treatment on the wastewater treatment process, and designs a self-adaptive fuzzy control system for wastewater treatment DO with the introduction of adjustment factors. By adjusting the blower frequency, the DO is stably controlled in an ideal position, the efficiency of sewage treatment is improved, and the control system has a better stability and self-adaptability.
1.SBR introduction and control strategy
Sequencing Batch Reactor Activated Sludge Process (SBR) is an intermittently operating wastewater biological treatment process that has been rapidly developed since the last century, and is very suitable for small and medium-sized sewage treatment plants. Its operation process includes five stages of influent, reaction (aeration), precipitation, drowning, and idle. Compared with the activated sludge method, there is no need for a sinking basin and a secondary settling pond. All five stages are in the same reaction pool. get on. At present, the control method of SBR is mainly time program control. The five stages of the processing process are performed in sequence according to the time sequence set in advance. Automatic control can be conveniently realized by using a programmable controller (PLC), which is also widely used and popularized. One of the reasons.
In the SBR process, the bio-oxidation process (aeration process) is a key part of the process. In this process, micro-organisms that use aerobic bacteria process the organic pollutants in the wastewater through biochemical reactions and determine their disposal. One of the key factors for the effect is the dissolved oxygen concentration (DO) in biochemical pools [4]. As the quality of raw water is constantly changing, it may fluctuate violently under certain circumstances. This makes it very difficult to control according to the traditional time-program control method. Long time of aeration or large volume of energy caused by aeration Waste, short aeration time or small amount of aeration may cause the effluent water quality to fluctuate greatly or even fail to reach the standard. Therefore, using a fixed amount of fixed air volume for aeration is a departure from the actual process of sewage reaction. According to domestic scholars' research, DO is kept at about 2mg/L, and the treatment effect of activated sludge is the best [5]. Moreover, using the DO value as the fuzzy control parameter of the SBR method can save operating costs as much as possible under the premise of guaranteeing the quality of the effluent, and can avoid the sludge bloat caused by insufficient aeration amount or excessively long reaction time. Therefore, how to control the DO under ideal conditions becomes the key to improve the processing efficiency.
2.DO adaptive fuzzy control system
The fan speed of this system is regulated by the inverter. The control principle is to first compare the set value with the detection value to obtain the precise amount E and EC, and then transform it into a fuzzy quantity through fuzzification, and then according to a large amount of experimental data and expert experience. The fuzzy knowledge base makes the fuzzy input quantity fuzzy inference to obtain the corresponding fuzzy control quantity. After the fuzzy decision, the fuzzy control quantity is converted into the output of the precise control quantity so as to realize the control of the aeration quantity and then adjust the DO concentration in the pool.
2.1 Structure Selection of Fuzzy Controller
The so-called fuzzy controller structure selection is to determine the fuzzy controller input and output variables. The structure of the fuzzy controller has a great influence on the performance of the entire system, and must be reasonably selected according to the specific conditions of the controlled object. The structure of the fuzzy controller is mainly divided into a single input-single output (SISO) structure and a multiple input-multiple output (MIMO) structure. According to the actual process of sewage treatment, the system adopts a typical two-input single-output two-dimensional fuzzy controller. The input variables are the deviation E of the DO and the rate of change of the deviation EC, and the output variable U is the frequency VRI of the variable frequency fan.
2.2 The definition and fuzzification of fuzzy linguistic variables and universe
Fuzzy rules are fuzzy conditional sentences made up of several linguistic variables, which reflect a certain kind of human thinking. When determining a fuzzy variable, first determine its basic language value, and then generate a number of language sub-values ​​as needed. In general, the more linguistic values ​​of a linguistic variable, the more accurate the description of the thing and the better the control that may be obtained. However, the subdivision will make the control rules complicated and difficult to implement. Therefore, it should be based on the specific circumstances.
In this system, the fuzzy subsets for E, EC, and U are defined as:
E=EC=U={Negative Large (NB), Negative Middle (NM), Negative Small (NS), Zero (ZO), Positive Small (PS), Positive Center (OM), Positive Large (PB)}
The basic domain of E is: (-0.6, 0.6), and the linguistic variables are: {-6,-5,-4,-3,-2,-1,-0,0,1,2,3,4, 5,6};
The basic domain of EC is: (-0.15, 0.15), and the linguistic variables are: {-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6 };
The basic domain of U is: (-4,4), and the linguistic variables are: {-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4, 5,6,7};
2.3 Determination of fuzzy control rules and establishment of fuzzy control tables
In the fuzzy control system, the performance of the fuzzy controller has a great influence on the control characteristics of the system, and the performance of the fuzzy controller depends to a large extent on the establishment and adjustability of the fuzzy control rules. We use experience induction to change the experience of DO by manually adjusting the blower frequency, summing up the forty-nine rules in the form "IF E = â”… and IF EC = â”… then U = â”…." All the control rule bases are shown in Table 1.
Table 1 Fuzzy Control Rule Table
EC ENBNMNSZ0PSPMPB
NBPBPBPBPMPSPSZ0
NMPBPBPMPMPSZONS
NSPBPMPSPSZONSNM
ZOPBPMPSZONSNMNB
PSPMPSZONSNSNMNB
PMPSZONSNMNMNBNB
PBZONSPSNMNBNBNB
According to the fuzzy control rule table, the maximum degree of membership method is used to defuzzify the input variables, and the fuzzy controller output of each state is obtained through off-line calculation, and the fuzzy control table shown in Table 2 is made.
Table 2 Fuzzy Control Table
E
EC-6-5-4-3-2-10123456
-67777777765420
-57777777654310
-4777766654320-1
-377666554321-1-2
-26655544310-1-2-3
-1654432210-1-2-3-4
-1-1-2-3-4-5
14320-1-2-2-3-3-4-5-6-6
2321-1-2-3-4-5-6-6-6-7-7
321-1-2-4-5-6-7-7-7-7-7-7
410-2-4-6-7-7-7-7-7-7-7-7
50-1-3-5-7-7-7-7-7-7-7-7-7
60-2-4-6-7-7-7-7-7-7-7-7-7
In the conventional two-dimensional fuzzy control, the output variable value is determined by the input quantities E and EC, and their weight coefficients are each 0.5. Once the design is completed, the control rules are also determined. In the water treatment process, the water quality is constant. This change is obviously not conducive to the stability of the control system. In view of this, we introduce adjustment factors to adjust the control rules to make them adaptive to changing water environments. The control rule with the adjustment factor can be expressed as: u=-[αE+(1-α)C], 0<α<1. α in the equation is the adjustment factor, also called the weighting factor. We adjust the size of α according to the COD value measured by the on-line monitor. The range of variation (0,1) of α varies linearly with the range of COD (0,1000) (mg/L). Each change of COD is 100 mg/L. α changes by 0.1, so that the system adaptively changes the weighting of the error E and the error change EC.
3 fuzzy control system operation results
3.1 experimental system device
The experimental system is a set of small SBR sewage treatment system, which is divided into upper computer and lower computer. The lower computer adopts PLC control. The upper computer is industrial computer and Kingview. It has a good man-machine interface and adaptive fuzzy control algorithm. Embedded in Kingview's scripting program. The treatment system has a SBR depth of 1.8M, a width of 0.8M, a length of 1.5M, and an effective volume of 2 M3. Blower aeration, air distribution system using Φ70*500 microporous aerator, oxygen utilization rate of 18 to 28%; air resistance ≦ 150 mm water column; dissolved oxygen control range of up to 0.5mg/L - 10.0mg/ L. The system runs for 2 hours per cycle, influent for 10 minutes, aerates for 1 hour, precipitates for 30 minutes, dipped in water for 20 minutes, idles for 30 minutes, drains 1M3 each time, and is very close to the actual engineering environment.
3.2 Experimental Results and Analysis
The experiment used the sewage from the living area of ​​our school, and a certain amount of glucose was added to adjust the COD concentration required for the experiment. The experimental influent COD was 860 mg/L, the BOD was 620 mg/L, the sludge concentration MLSS was 4200 mg/L, the set DO was 3 mg/L, and the fan frequency adjustment time interval was 60 seconds. To test the adaptive capacity of the fuzzy control system we designed, in the 40th minute of the running process, we added glucose equivalent to 300mg/LCOD to the pool to simulate the water quality mutation. The operating results are shown in Fig. 2. The effluent COD was 37 mg/L, the BOD was 29 mg/L, the COD removal rate was 95.7%, and the BOD removal rate was 95.3%. From the figure we can see that in the case of load changes, the control system can better adapt to changes in water quality, the process is stable, and has good self-adaptive and anti-impact capabilities.
4 Conclusion
It has become the consensus of the industry to adopt intelligent control methods including fuzzy control to control the sewage treatment process. However, there are not many intelligent control technologies actually applied to actual production so far. The main reason is that the huge changes in the sewage environment are The stability and adaptability of the control system are very demanding. There are many factors that affect the control performance and it is difficult to fully consider it. This article aims at this situation, how to improve the stability and self-adaptability of the control system, and comprehensively studies the influencing factors of various aspects and proposes an adaptive fuzzy control system that introduces adjustment factors. After experimental verification, the system has a very good The stability and adaptability are suitable for practical engineering applications. In addition, the establishment of DO adaptive fuzzy control system must be targeted, different working conditions, the establishment of the rules table, the setting of time parameters are not exactly the same, with the influent organic concentration, sludge concentration, hydraulic load and reaction The size of the pool is all related. The relationship between the determination of adjustment factors and various influencing factors remains to be further studied.
references:
[1] Feng Dongqing, Xie Songhe. Fuzzy intelligent control [M]. Beijing: Chemical Industry Press, 2001.75-93.
[2] Peng Yongji, etc., Research, application and development of intelligent control of sewage treatment [J], China Water Supply and Drainage, 2002, 6
[3] Wang Xianlu et al., Research on COD Fuzzy Control in Wastewater Treatment [J], Technical Perspectives, 2002, 2
[4] Zhang Zijie, Lin Rongzhen, Jin Rulin. Biological treatment of sewage [M], Drainage Engineering, 2000.
[5] Zeng Wei, Peng Yongxuan, Zhang Dongli et al. The fuzzy control of aeration rate of SBR method [J], Journal of Harbin University of Civil Engineering and Architecture, 2002, 35 (1), 53-57.
At the same time, the use of adjustable fuzzy control rules in the design of the fuzzy controller can significantly improve and improve the stability and self-adaptive ability of the fuzzy controller. Domestic Peng Yongxuan et al [2] proposed to use DO as the fuzzy control parameter of SBR method to realize the control of aeration volume. Wang Xianlu et al. [3] took the COD deviation and deviation variation of the effluent as input, and the pump opening degree designed a fuzzy controller for the output to realize the fuzzy control of COD. However, there is a biggest problem in the process of sewage treatment using a fuzzy control system, that is, the stability and self-adaptability of the control system. Due to the PD control effect of fuzzy control and the continuous change of the water environment, the control effect of most systems oscillates significantly, and the stability and self-adaptability are not good.
Based on the sequential batch activated sludge process (SBR), this paper focuses on the influence of the change of dissolved oxygen concentration (DO) in wastewater treatment on the wastewater treatment process, and designs a self-adaptive fuzzy control system for wastewater treatment DO with the introduction of adjustment factors. By adjusting the blower frequency, the DO is stably controlled in an ideal position, the efficiency of sewage treatment is improved, and the control system has a better stability and self-adaptability.
1.SBR introduction and control strategy
Sequencing Batch Reactor Activated Sludge Process (SBR) is an intermittently operating wastewater biological treatment process that has been rapidly developed since the last century, and is very suitable for small and medium-sized sewage treatment plants. Its operation process includes five stages of influent, reaction (aeration), precipitation, drowning, and idle. Compared with the activated sludge method, there is no need for a sinking basin and a secondary settling pond. All five stages are in the same reaction pool. get on. At present, the control method of SBR is mainly time program control. The five stages of the processing process are performed in sequence according to the time sequence set in advance. Automatic control can be conveniently realized by using a programmable controller (PLC), which is also widely used and popularized. One of the reasons.
In the SBR process, the bio-oxidation process (aeration process) is a key part of the process. In this process, micro-organisms that use aerobic bacteria process the organic pollutants in the wastewater through biochemical reactions and determine their disposal. One of the key factors for the effect is the dissolved oxygen concentration (DO) in biochemical pools [4]. As the quality of raw water is constantly changing, it may fluctuate violently under certain circumstances. This makes it very difficult to control according to the traditional time-program control method. Long time of aeration or large volume of energy caused by aeration Waste, short aeration time or small amount of aeration may cause the effluent water quality to fluctuate greatly or even fail to reach the standard. Therefore, using a fixed amount of fixed air volume for aeration is a departure from the actual process of sewage reaction. According to domestic scholars' research, DO is kept at about 2mg/L, and the treatment effect of activated sludge is the best [5]. Moreover, using the DO value as the fuzzy control parameter of the SBR method can save operating costs as much as possible under the premise of guaranteeing the quality of the effluent, and can avoid the sludge bloat caused by insufficient aeration amount or excessively long reaction time. Therefore, how to control the DO under ideal conditions becomes the key to improve the processing efficiency.
2.DO adaptive fuzzy control system
The fan speed of this system is regulated by the inverter. The control principle is to first compare the set value with the detection value to obtain the precise amount E and EC, and then transform it into a fuzzy quantity through fuzzification, and then according to a large amount of experimental data and expert experience. The fuzzy knowledge base makes the fuzzy input quantity fuzzy inference to obtain the corresponding fuzzy control quantity. After the fuzzy decision, the fuzzy control quantity is converted into the output of the precise control quantity so as to realize the control of the aeration quantity and then adjust the DO concentration in the pool.
2.1 Structure Selection of Fuzzy Controller
The so-called fuzzy controller structure selection is to determine the fuzzy controller input and output variables. The structure of the fuzzy controller has a great influence on the performance of the entire system, and must be reasonably selected according to the specific conditions of the controlled object. The structure of the fuzzy controller is mainly divided into a single input-single output (SISO) structure and a multiple input-multiple output (MIMO) structure. According to the actual process of sewage treatment, the system adopts a typical two-input single-output two-dimensional fuzzy controller. The input variables are the deviation E of the DO and the rate of change of the deviation EC, and the output variable U is the frequency VRI of the variable frequency fan.
2.2 The definition and fuzzification of fuzzy linguistic variables and universe
Fuzzy rules are fuzzy conditional sentences made up of several linguistic variables, which reflect a certain kind of human thinking. When determining a fuzzy variable, first determine its basic language value, and then generate a number of language sub-values ​​as needed. In general, the more linguistic values ​​of a linguistic variable, the more accurate the description of the thing and the better the control that may be obtained. However, the subdivision will make the control rules complicated and difficult to implement. Therefore, it should be based on the specific circumstances.
In this system, the fuzzy subsets for E, EC, and U are defined as:
E=EC=U={Negative Large (NB), Negative Middle (NM), Negative Small (NS), Zero (ZO), Positive Small (PS), Positive Center (OM), Positive Large (PB)}
The basic domain of E is: (-0.6, 0.6), and the linguistic variables are: {-6,-5,-4,-3,-2,-1,-0,0,1,2,3,4, 5,6};
The basic domain of EC is: (-0.15, 0.15), and the linguistic variables are: {-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6 };
The basic domain of U is: (-4,4), and the linguistic variables are: {-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4, 5,6,7};
2.3 Determination of fuzzy control rules and establishment of fuzzy control tables
In the fuzzy control system, the performance of the fuzzy controller has a great influence on the control characteristics of the system, and the performance of the fuzzy controller depends to a large extent on the establishment and adjustability of the fuzzy control rules. We use experience induction to change the experience of DO by manually adjusting the blower frequency, summing up the forty-nine rules in the form "IF E = â”… and IF EC = â”… then U = â”…." All the control rule bases are shown in Table 1.
Table 1 Fuzzy Control Rule Table
EC ENBNMNSZ0PSPMPB
NBPBPBPBPMPSPSZ0
NMPBPBPMPMPSZONS
NSPBPMPSPSZONSNM
ZOPBPMPSZONSNMNB
PSPMPSZONSNSNMNB
PMPSZONSNMNMNBNB
PBZONSPSNMNBNBNB
According to the fuzzy control rule table, the maximum degree of membership method is used to defuzzify the input variables, and the fuzzy controller output of each state is obtained through off-line calculation, and the fuzzy control table shown in Table 2 is made.
Table 2 Fuzzy Control Table
E
EC-6-5-4-3-2-10123456
-67777777765420
-57777777654310
-4777766654320-1
-377666554321-1-2
-26655544310-1-2-3
-1654432210-1-2-3-4
-1-1-2-3-4-5
14320-1-2-2-3-3-4-5-6-6
2321-1-2-3-4-5-6-6-6-7-7
321-1-2-4-5-6-7-7-7-7-7-7
410-2-4-6-7-7-7-7-7-7-7-7
50-1-3-5-7-7-7-7-7-7-7-7-7
60-2-4-6-7-7-7-7-7-7-7-7-7
In the conventional two-dimensional fuzzy control, the output variable value is determined by the input quantities E and EC, and their weight coefficients are each 0.5. Once the design is completed, the control rules are also determined. In the water treatment process, the water quality is constant. This change is obviously not conducive to the stability of the control system. In view of this, we introduce adjustment factors to adjust the control rules to make them adaptive to changing water environments. The control rule with the adjustment factor can be expressed as: u=-[αE+(1-α)C], 0<α<1. α in the equation is the adjustment factor, also called the weighting factor. We adjust the size of α according to the COD value measured by the on-line monitor. The range of variation (0,1) of α varies linearly with the range of COD (0,1000) (mg/L). Each change of COD is 100 mg/L. α changes by 0.1, so that the system adaptively changes the weighting of the error E and the error change EC.
3 fuzzy control system operation results
3.1 experimental system device
The experimental system is a set of small SBR sewage treatment system, which is divided into upper computer and lower computer. The lower computer adopts PLC control. The upper computer is industrial computer and Kingview. It has a good man-machine interface and adaptive fuzzy control algorithm. Embedded in Kingview's scripting program. The treatment system has a SBR depth of 1.8M, a width of 0.8M, a length of 1.5M, and an effective volume of 2 M3. Blower aeration, air distribution system using Φ70*500 microporous aerator, oxygen utilization rate of 18 to 28%; air resistance ≦ 150 mm water column; dissolved oxygen control range of up to 0.5mg/L - 10.0mg/ L. The system runs for 2 hours per cycle, influent for 10 minutes, aerates for 1 hour, precipitates for 30 minutes, dipped in water for 20 minutes, idles for 30 minutes, drains 1M3 each time, and is very close to the actual engineering environment.
3.2 Experimental Results and Analysis
The experiment used the sewage from the living area of ​​our school, and a certain amount of glucose was added to adjust the COD concentration required for the experiment. The experimental influent COD was 860 mg/L, the BOD was 620 mg/L, the sludge concentration MLSS was 4200 mg/L, the set DO was 3 mg/L, and the fan frequency adjustment time interval was 60 seconds. To test the adaptive capacity of the fuzzy control system we designed, in the 40th minute of the running process, we added glucose equivalent to 300mg/LCOD to the pool to simulate the water quality mutation. The operating results are shown in Fig. 2. The effluent COD was 37 mg/L, the BOD was 29 mg/L, the COD removal rate was 95.7%, and the BOD removal rate was 95.3%. From the figure we can see that in the case of load changes, the control system can better adapt to changes in water quality, the process is stable, and has good self-adaptive and anti-impact capabilities.
4 Conclusion
It has become the consensus of the industry to adopt intelligent control methods including fuzzy control to control the sewage treatment process. However, there are not many intelligent control technologies actually applied to actual production so far. The main reason is that the huge changes in the sewage environment are The stability and adaptability of the control system are very demanding. There are many factors that affect the control performance and it is difficult to fully consider it. This article aims at this situation, how to improve the stability and self-adaptability of the control system, and comprehensively studies the influencing factors of various aspects and proposes an adaptive fuzzy control system that introduces adjustment factors. After experimental verification, the system has a very good The stability and adaptability are suitable for practical engineering applications. In addition, the establishment of DO adaptive fuzzy control system must be targeted, different working conditions, the establishment of the rules table, the setting of time parameters are not exactly the same, with the influent organic concentration, sludge concentration, hydraulic load and reaction The size of the pool is all related. The relationship between the determination of adjustment factors and various influencing factors remains to be further studied.
references:
[1] Feng Dongqing, Xie Songhe. Fuzzy intelligent control [M]. Beijing: Chemical Industry Press, 2001.75-93.
[2] Peng Yongji, etc., Research, application and development of intelligent control of sewage treatment [J], China Water Supply and Drainage, 2002, 6
[3] Wang Xianlu et al., Research on COD Fuzzy Control in Wastewater Treatment [J], Technical Perspectives, 2002, 2
[4] Zhang Zijie, Lin Rongzhen, Jin Rulin. Biological treatment of sewage [M], Drainage Engineering, 2000.
[5] Zeng Wei, Peng Yongxuan, Zhang Dongli et al. The fuzzy control of aeration rate of SBR method [J], Journal of Harbin University of Civil Engineering and Architecture, 2002, 35 (1), 53-57.