CN113759702A - Internet of things-automatic control system and method for anaerobic treatment of garbage - Google Patents
Internet of things-automatic control system and method for anaerobic treatment of garbage Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 29
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- 230000001105 regulatory effect Effects 0.000 claims abstract description 20
- 238000012545 processing Methods 0.000 claims abstract description 13
- 239000007789 gas Substances 0.000 claims description 66
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 claims description 25
- 238000001514 detection method Methods 0.000 claims description 14
- RWSOTUBLDIXVET-UHFFFAOYSA-N Dihydrogen sulfide Chemical compound S RWSOTUBLDIXVET-UHFFFAOYSA-N 0.000 claims description 12
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims description 12
- 229910000037 hydrogen sulfide Inorganic materials 0.000 claims description 12
- 239000001301 oxygen Substances 0.000 claims description 12
- 229910052760 oxygen Inorganic materials 0.000 claims description 12
- 238000004364 calculation method Methods 0.000 claims description 7
- 239000002699 waste material Substances 0.000 claims description 5
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- 230000000694 effects Effects 0.000 abstract 1
- 238000004519 manufacturing process Methods 0.000 description 4
- 239000000523 sample Substances 0.000 description 4
- 238000012544 monitoring process Methods 0.000 description 3
- 238000000855 fermentation Methods 0.000 description 2
- 238000001914 filtration Methods 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 239000000126 substance Substances 0.000 description 2
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- G05B11/00—Automatic controllers
- G05B11/01—Automatic controllers electric
- G05B11/36—Automatic controllers electric with provision for obtaining particular characteristics, e.g. proportional, integral, differential
- G05B11/42—Automatic controllers electric with provision for obtaining particular characteristics, e.g. proportional, integral, differential for obtaining a characteristic which is both proportional and time-dependent, e.g. P. I., P. I. D.
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Abstract
The invention provides an Internet of things-automatic control system and method for anaerobic garbage treatment, wherein the system comprises an intelligent electric meter, a gas concentration monitoring device and a data center arranged on an upper computer, the data center comprises a database and an operation terminal, the intelligent electric meter is arranged at the inlet end of a workshop section general electric control cabinet and is connected with the data center, the gas concentration monitoring device is arranged on corresponding workshop section equipment for collecting gas content and transmitting the gas content to the database for processing and storage, the data collection, transmission and storage are realized based on the Internet of things equipment, the upper computer control method is based on a fuzzy self-tuning PID algorithm, a fuzzy relation is established through the change relation between gas content data and regulating variable generated by the equipment, the PID parameter is tuned through the fuzzy relation, the regulating variable is output by the PID algorithm according to the obtained PID parameter to realize the Internet of things-automatic control, the intelligent effect of the factory is improved, and the high efficiency and stability of the system operation are guaranteed.
Description
Technical Field
The invention relates to the technical field of kitchen waste treatment, in particular to an Internet of things-automatic control system and method for waste anaerobic treatment.
Background
At present, generally, anaerobic fermentation treatment is carried out in the field of garbage treatment, anaerobic fermentation sections of most treatment plants comprise a buffer tank, a homogenizing tank, an anaerobic tank and a methane gas storage cabinet, the traditional control method is that each part obtains each index in the tank through an instrument of a manual observation tank table, then manual or automatic adjustment is carried out to complete control of the equipment, so that output methane and materials reach the standard, the traditional observation method uses workers to observe equipment or a simple instrument extending out of the tank body through naked eyes before the workers stand on each equipment, color, consistency or instrument indexes observed by human eyes are taken as equipment parameters and recorded on a paper record book, and the experience of the workers is taken as the basis for adjusting the equipment parameters, but in the actual situation, the viscosity, the flora density and the like of the feeding cannot be effectively quantified, and each gas in the tank cannot collect data in time, the control has lag, thereby influencing the gas production rate and the gas production purity and reducing the gas production stability.
With the development of modern industry, industrial sensor equipment such as gas on-line monitoring systems, smart electric meters and the like has been developed and applied in industries such as chemical industry in a large quantity, and a complete and mature industrial chain is formed, so that the target equipment is accurately detected, is not easy to damage, and is convenient to install and transform.
However, in the processing working section, the control of the equipment still needs manual parameter adjustment control equipment, the automation degree is low, the labor cost is high, the working environment of workers is extremely severe, the product output quality and efficiency can be influenced by repeated adjustment, the advantages of the data monitoring equipment cannot be better exerted, and the problems that data cannot be uploaded in time, the high-quality output gas production is unstable and the like still exist.
Disclosure of Invention
The invention provides an Internet of things-automatic control system and method for garbage anaerobic treatment, which are characterized in that data information acquired by sensor acquisition equipment is subjected to summarizing processing to realize timely acquisition and uploading of annual data, acquired data is subjected to fuzzy setting of PID parameters based on a fuzzy self-setting PID algorithm to obtain fuzzy decision, then de-fuzzification is carried out to obtain actual PID parameters, and a PID algorithm model is utilized to output regulating quantity to realize automatic control of processing equipment.
The invention provides an Internet of things-automatic control system for anaerobic treatment of garbage, which comprises an intelligent electric meter, a gas concentration monitoring device and a data center, wherein the data center comprises a database and an operation terminal, the intelligent electric meter is arranged at a wire inlet end of a workshop section general electric control cabinet, the gas concentration monitoring device is arranged on corresponding workshop section equipment, and the intelligent electric meter and the gas concentration monitoring device are respectively connected with the data center to process and store acquired data in the database.
The intelligent electric meter is used for collecting indexes such as electricity consumption, voltage, current, power factor and the like and providing a data basis for later energy management; the gas concentration monitoring device adopts a sensor detection module to collect actually required gas content, and is provided with a data center to receive collected data information.
Furthermore, the database comprises a time sequence database and a relational database, collected data are transmitted to the time sequence database through a modbus communication protocol, and the data are stored in the relational database after being filtered.
The time sequence database uses influxdb to store the collected and transmitted data in real time, so as to realize quick writing and query.
Further, the gas concentration monitoring device comprises an oxygen content detection device, a hydrogen sulfide content detection device and a methane content detection device.
The gas concentration monitoring device acquires data through each sensor, and adopts an industrial-grade trace gas probe to acquire oxygen, hydrogen sulfide and methane and perform online measurement on the contents of the oxygen, the hydrogen sulfide, the methane and other gases.
The invention also provides an Internet of things-automatic control method for anaerobic treatment of garbage, which comprises the following specific steps:
step S1: the gas concentration monitoring device collects gas data and transmits the gas data to the data center for processing and storing;
step S2: acquiring stored gas content data in the anaerobic treatment process;
step S3: performing PID parameter setting on the obtained gas content data through a fuzzy algorithm in the upper computer;
calculating the deviation and deviation change rate of the gas parameters according to the acquired data, and establishing a fuzzy table corresponding to each gas content deviation and outputting a corresponding fuzzy table;
acquiring parameter standard values of all sections in the anaerobic treatment process, and combining the acquired fuzzy table to construct a fuzzy control table and acquire a fuzzy relation;
step S4: acquiring PID parameter fuzzy regulating quantity output by a fuzzy controller according to the established fuzzy relation, and performing defuzzification to obtain actual PID parameter regulating quantity;
step S5: and taking the obtained actual PID parameter adjustment quantity as input, calculating by a PID algorithm to obtain actual control output, and downloading to each device by an upper computer.
Further, the PID parameter tuning process is as follows:
firstly, fuzzifying sampled data, and calculating deviation and deviation change rate corresponding to each gas parameter;
the deviation calculation formula is as follows:
e(k)＝h0(k)-h(k)
wherein e (k) represents the deviation of the gas parameter corresponding to the time k, h0(k) The input quantity standard value at the moment corresponding to the gas parameter k is represented, and h (k) represents the actual measured value of the gas parameter at the moment k;
the deviation change rate calculation formula is as follows:
ec(k)＝e(k)-e(k-1)
wherein e isc(k) The deviation change rate of the corresponding gas parameter at the time k is shown, and e (k-1) shows the deviation of the corresponding gas parameter at the time k-1;
then setting a change grade and a fuzzy set, and constructing a deviation fuzzy table according to the deviation corresponding to different gas parameters as observed quantity;
and finally, combining the parameter standards in the process of working section treatment to obtain a deviation fuzzy table, constructing a fuzzy control table of each output control quantity and obtaining a fuzzy control relation.
Furthermore, in the fuzzy relation of the deviation, the variation grade is divided into 7 grades which are respectively-3, -2, -1, 0, 1, 2 and 3, in the fuzzy relation of the control quantity, the variation grade is divided into 9 grades which are respectively-4, -3, -2, -1, 0, 1, 2, 3 and 4, and in the fuzzy relation of the deviation and the fuzzy relation of the control quantity, the fuzzy set is divided into five grades of negative large, negative small, zero, positive small and positive large.
Further, the output of the fuzzy controller is synthesized by combining the deviation and the fuzzy relation, and the formula is as follows:
ukp＝e·Rukp
uki＝e·Ruki
ukd＝e·Rukd
wherein ukp, uki and ukd respectively represent fuzzy regulating quantity corresponding to each parameter of PID, e represents deviation, R representsukp、Ruki、RukdRespectively representing fuzzy relations corresponding to the PID parameters.
The invention has the following beneficial effects:
1. in the existing engineering system, the detection equipment required by installation is directly configured and additionally arranged on the corresponding equipment, so that data collection and collection can be realized, the system equipment can timely and accurately acquire parameter data and upload the parameter data in work, the system is convenient to additionally install and implement, and the cost is reduced.
2. The fuzzy algorithm and the PID algorithm are combined, a corresponding relation table is established according to the collected data quantity and the control rule, the fuzzy relation is established to obtain the fuzzy regulating quantity, then the actual regulating quantity is obtained through defuzzification, and the actual regulating quantity is input into the PID algorithm model to obtain the output control quantity, so that the automatic control system is more stable and efficient.
3. The system is additionally provided with the Internet of things equipment, the data are acquired and acquired by the sensors and are stored in the database in a centralized manner, real-time monitoring, storage and control of the equipment in the whole workshop section are realized, the database adopts the time sequence database to store the data acquired by the sensors in real time, and the data processing rate and the bearing capacity of the system are improved.
Drawings
FIG. 1 is a flow chart of the overall architecture of the system of the present invention;
FIG. 2 is a schematic flow diagram of the process of the present invention.
Detailed Description
In the following description, technical solutions in the embodiments of the present invention are clearly and completely described, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
The embodiment 1 of the invention provides an internet of things-automatic control system for anaerobic treatment of garbage, and as shown in fig. 1, the system comprises an intelligent electric meter, a gas concentration monitoring device and a data center, wherein the internet of things-automatic control system is additionally arranged on workshop processing equipment, the processing equipment comprises a cache tank, a homogenizing tank, an anaerobic tank and a biogas storage tank, the cache tank, the homogenizing tank and the biogas storage tank are connected through pipelines, and in the embodiment, a main electric control cabinet is arranged to be respectively connected with and control each processing equipment through power and control cables;
the intelligent electric meter is connected with the inlet end of the workshop section main electric control cabinet through a data acquisition cable and is used for acquiring indexes such as power consumption, voltage, current, power factor and the like, and the intelligent electric meter is connected with the data center through a data transmission cable and uploads the acquired data;
the gas concentration monitoring device comprises an oxygen content detection device, a hydrogen sulfide content detection device and a methane content detection device, wherein the oxygen content detection device adopts a chemical oxygen measurement probe, the hydrogen sulfide content detection device adopts an electrochemical trace H2S probe, the methane content detection device adopts a non-dispersive infrared high-range probe and is arranged on the anaerobic tank to detect the content of oxygen, hydrogen sulfide and methane, meanwhile, a vibration sensor and a temperature sensor are also arranged on the workshop section processing equipment to monitor the temperature data and the vibration state of the equipment, the gas concentration monitoring device is connected with the data center through a data transmission cable, the data information which is uploaded and collected in real time is stored in a time sequence database, and the data information is stored in a relation database after preliminary cleaning.
Data are called from the relational database, processing and analysis are carried out according to the collected data, and the automatic adjustment of the rotating speeds of the homogenizing tank and the anaerobic tank and the temperature of the anaerobic tank is realized through the control cabinet; the data center comprises a database and an operation terminal, the database comprises a time sequence database and a relational database, acquired data are transmitted to the time sequence database through a modbus communication protocol, the data are stored in the relational database after being filtered, in the embodiment, the data stored in the time sequence database are the data acquired and transmitted by the gas concentration monitoring device in real time, due to the fact that miscellaneous numbers and messy codes exist, the data are stored in the relational database after being processed through filtering, and a filtering formula is as follows:
yt×10％×tΔ·δ+yt>yt+1>yt-yt×10％×tΔ·δ
wherein, ytSensor data for time t, yt+1Sensor data for time t +1, tΔδ is a specific time coefficient for the time difference from t to t + 1.
Example 2
The embodiment 2 of the invention provides an internet of things-automatic control method for anaerobic treatment of garbage, and as shown in fig. 2, the method specifically comprises the following steps:
step S1: the gas concentration monitoring device collects gas data and transmits the gas data to the data center for processing and storing;
in this embodiment, the content of oxygen, hydrogen sulfide and methane is collected by the gas concentration monitoring device and transmitted to the time sequence database, and then the collected data is filtered and stored in the relational database.
Step S2: acquiring gas content data in the anaerobic treatment process;
gas content data is obtained from the relational database, with the contents of oxygen, hydrogen sulfide and methane as inputs, here denoted as Oh0, HSh0 and CHh0, respectively.
Step S3: PID parameter setting is carried out on the obtained gas content data through a fuzzy algorithm;
the PID adjusting parameters comprise kp, ki and kd, and respectively represent a proportional adjusting coefficient, an integral adjusting coefficient and a differential adjusting coefficient;
s301: firstly, calculating the deviation and deviation change rate of gas parameters according to acquired data, and establishing a fuzzy table corresponding to each gas content deviation and outputting a corresponding fuzzy table;
the deviation calculation formula is as follows:
e(k)＝h0(k)-h(k)
wherein e (k) represents the deviation of the gas parameter corresponding to the time k, h0(k) The input quantity standard value at the moment corresponding to the gas parameter k is represented, and h (k) represents the actual measured value of the gas parameter at the moment k;
deviations oe (k), hse (k), and che (k) corresponding to oxygen, hydrogen sulfide, and methane are obtained, respectively.
S302: acquiring parameter standard values of all sections in the anaerobic treatment process, and combining the acquired fuzzy table to construct a fuzzy control table and acquire a fuzzy relation;
according to the obtained deviation corresponding to each gas parameter, taking the current deviation e as an observed quantity, if e is negative, the current quantity is lower than a standard value, and e is positive, the current value is higher than the standard value, and establishing a fuzzy table corresponding to the deviation e, wherein in the embodiment, the change grades are set to be-3, -2, -1, 0, 1, 2 and 3 respectively, the fuzzy sets are set to be 5, NB, NS, ZO, PS and PB respectively represent negative large, negative small, zero, positive small and positive large, and the table relationship is as follows:
table 1: fuzzy table corresponding to deviation e
The deviation change rate calculation formula is as follows:
ec(k)＝e(k)-e(k-1)
wherein e isc(k) The deviation change rate of the corresponding gas parameter at the time k is shown, and e (k-1) shows the deviation of the corresponding gas parameter at the time k-1;
respectively obtaining deviation change rates Oe corresponding to oxygen, hydrogen sulfide and methanec(k)、HSec(k) And CHEc(k)。
According to the deviation change rate corresponding to each gas parameter, using the current deviation change rate ecAs an observed quantity, if ecNegative means that the current quantity is below the standard value, ecIn this embodiment, the change levels of the controlled variable u are set to 9 as-4, -3, -2, -1, 0, 1, 2, 3, and 4, respectively, the fuzzy sets are set to 5 as NB, NS, ZO, PS, and PB, respectively, which represent negative, zero, positive, and positive values, and taking the controlled variable ukp corresponding to kp as an example, the table relationship is as follows:
table 2: fuzzy relation table corresponding to control quantity
According to the relationship between the difference value condition of each parameter error compared with the standard value and the temperature change regulation and control, a fuzzy control table is constructed according to actual needs, and the following steps are shown:
table 3: fuzzy control table
| IF (IF) | NBe | NSe | ZOe | PSe | PBe |
| THEN (THEN) | NBukp | NSukp | ZOukp | PSukp | PBukp |
| AND (AND) | PBuki | PSuki | ZOuki | NSuki | NBuki |
| AND (AND) | PBukd | ZOukd | NBukd | ZOukd | PBukd |
That is, in the PID control, when e is large, kp is large to increase the response speed, ki is small to avoid large overshoot, and kd is small to prevent ec instantaneous value from being excessively large.
If e is medium, to reduce overshoot, kp takes an appropriate median, ki takes an appropriate median, and kd takes a larger value.
If e is smaller, kp is smaller in order to improve stability, ki is larger in order to reduce static error, and kd is smaller;
constructing fuzzy relation R corresponding to kp, ki and kd according to a fuzzy control tablekp、Rki、RkdConcrete relation tableShown below:
Rukp＝(NBe×NBukp)∪(NSe
×NSukp)∪(ZOe×ZOukp)∪(PSe×PSukp)∪(PBe×PBukp)
Ruki＝(NBe×PBuki)∪(NSe
×PSuki)∪(ZOe×ZOuki)∪(PSe×NSuki)∪(PBe×NBuki)
Rukd＝(NBe×PBukd)∪(NSe
×ZOukd)∪(ZOe×NBukd)∪(PSe×ZOukd)∪(PBe×PBukd)
step S4: acquiring PID parameter fuzzy regulating quantity output by a fuzzy controller according to the established fuzzy relation, and performing defuzzification to obtain actual PID parameter regulating quantity;
synthesizing the obtained fuzzy relation and the current deviation vector to obtain PID parameter fuzzy adjustment quantity, wherein the calculation formula is as follows:
ukp＝e·Rukp
uki＝e·Ruki
ukd＝e·Rukd
for example, when the deviation e is NB, e is [1, 0.5, 0, 0, 0, 0, 0], and the output ukp of the fuzzy controller is:
and performing defuzzification according to the obtained fuzzy regulating quantity of each parameter of the PID, and respectively adding the fuzzy regulating quantity and the corresponding initial regulating quantity to obtain the final actual regulating quantity kp, ki and kd of each parameter.
Step S5: and taking the obtained actual PID parameter regulating quantity as input, and calculating by a PID algorithm to obtain the output of the corresponding equipment rotating speed and temperature regulating actual control quantity.
The invention is not limited to the foregoing embodiments. The invention extends to any novel feature or any novel combination of features disclosed in this specification and any novel method or process steps or any novel combination of features disclosed.
Claims (7)
1. The Internet of things-automatic control system for anaerobic treatment of garbage is characterized by comprising an intelligent electric meter, a gas concentration monitoring device and a data center, wherein the data center comprises a database and an upper computer, the intelligent electric meter is arranged at a wire inlet end of a workshop section main electric control cabinet, the gas concentration monitoring device is arranged on corresponding workshop section equipment, and the intelligent electric meter and the gas concentration monitoring device are respectively connected with the data center to process collected data and store the processed data in the data center.
2. The internet of things-automated control system for anaerobic waste treatment according to claim 1, wherein the database comprises a time sequence database and a relational database, data collected by devices based on the internet of things are transmitted to the time sequence database through a modbus communication protocol, and are stored in the relational database after being filtered.
3. The internet of things-automated control system for anaerobic waste treatment according to claim 1, wherein the gas concentration monitoring device comprises an oxygen content detection device, a hydrogen sulfide content detection device and a methane content detection device.
4. An Internet of things-automatic control method for anaerobic treatment of garbage is characterized by comprising the following steps:
step S1: the gas concentration monitoring device collects gas data and transmits the gas data to the data center for processing and storing;
step S2: acquiring stored gas content data in the anaerobic treatment process;
step S3: PID parameter setting is carried out on the obtained gas content data through a fuzzy algorithm;
step S4: acquiring PID parameter fuzzy regulating quantity output by a fuzzy controller according to the established fuzzy relation, and performing defuzzification to obtain actual PID parameter regulating quantity;
step S5: and taking the obtained actual PID parameter adjustment quantity as input, calculating by a PID algorithm to obtain an actual control output parameter, and downloading to each device by an upper computer.
5. The Internet of things-automatic control method for anaerobic garbage treatment according to claim 4, wherein the PID parameter setting process is as follows:
firstly, fuzzifying sampled data, and calculating deviation and deviation change rate corresponding to each gas parameter;
the deviation calculation formula is as follows:
e(k)＝h0(k)-h(k)
wherein e (k) represents the deviation of the gas parameter corresponding to the time k, h0(k) The input quantity standard value at the moment corresponding to the gas parameter k is represented, and h (k) represents the actual measured value of the gas parameter at the moment k;
the deviation change rate calculation formula is as follows:
ec(k)＝e(k)-e(k-1)
wherein e isc(k) The deviation change rate of the corresponding gas parameter at the time k is shown, and e (k-1) shows the deviation of the corresponding gas parameter at the time k-1;
then setting a change grade and a fuzzy set, and constructing a deviation fuzzy table according to the deviation corresponding to different gas parameters as observed quantity;
and finally, combining the parameter standards in the process of working section treatment to obtain a deviation fuzzy table, constructing a fuzzy control table of each output control quantity and obtaining a fuzzy control relation.
6. The Internet of things-automatic control method for anaerobic waste treatment according to claim 4, wherein in the fuzzy relation of the deviation, the variation grade is divided into 7 grades, which are-3, -2, -1, 0, 1, 2 and 3 respectively, in the fuzzy relation of the control quantity, the variation grade is divided into 9 grades, which are-4, -3, -2, -1, 0, 1, 2, 3 and 4 respectively, and in the fuzzy relation of the deviation and the fuzzy relation of the control quantity, fuzzy sets are divided into five grades of negative large, negative small, zero, positive small and positive large.
7. The internet of things-automated control method for anaerobic waste treatment according to claim 4, wherein the output of the fuzzy controller is obtained by synthesizing the deviation and the fuzzy relation, and the formula is as follows:
ukp＝e·Rukp
uki＝e·Ruki
ukd＝e·Rukd
wherein ukp, uki and ukd respectively represent fuzzy regulating quantity corresponding to each parameter of PID, e represents deviation, R representsukp、Ruki、RukdRespectively representing fuzzy relations corresponding to the PID parameters.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115793557A (en) * | 2022-11-18 | 2023-03-14 | 瑞邦环境治理(广东)有限公司 | Control method of stock garbage in-situ humus drying system |
| CN117568150A (en) * | 2023-11-21 | 2024-02-20 | 国润生物质能源(山东)有限公司 | Biomass natural gas management system based on fermentation tank |
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