CN115557600A - Artificial neural network intelligent aeration device for biochemical reaction and control method thereof - Google Patents
Artificial neural network intelligent aeration device for biochemical reaction and control method thereof Download PDFInfo
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- 238000005273 aeration Methods 0.000 title claims abstract description 46
- 238000005842 biochemical reaction Methods 0.000 title claims abstract description 31
- 238000000034 method Methods 0.000 title claims abstract description 31
- 238000013528 artificial neural network Methods 0.000 title claims abstract description 19
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 86
- 238000012544 monitoring process Methods 0.000 claims description 38
- 210000002569 neuron Anatomy 0.000 claims description 34
- 238000003062 neural network model Methods 0.000 claims description 27
- 238000005276 aerator Methods 0.000 claims description 15
- 239000010865 sewage Substances 0.000 claims description 15
- 230000001105 regulatory effect Effects 0.000 claims description 13
- 239000010802 sludge Substances 0.000 claims description 13
- 238000000926 separation method Methods 0.000 claims description 12
- 230000001276 controlling effect Effects 0.000 claims description 9
- 239000012528 membrane Substances 0.000 claims description 9
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims description 6
- 239000006185 dispersion Substances 0.000 claims description 6
- 239000001301 oxygen Substances 0.000 claims description 6
- 229910052760 oxygen Inorganic materials 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 4
- 238000005086 pumping Methods 0.000 claims description 3
- 230000000694 effects Effects 0.000 description 3
- XKMRRTOUMJRJIA-UHFFFAOYSA-N ammonia nh3 Chemical compound N.N XKMRRTOUMJRJIA-UHFFFAOYSA-N 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 244000005700 microbiome Species 0.000 description 2
- 239000002351 wastewater Substances 0.000 description 2
- 230000002411 adverse Effects 0.000 description 1
- 241001148470 aerobic bacillus Species 0.000 description 1
- 230000032683 aging Effects 0.000 description 1
- 230000003851 biochemical process Effects 0.000 description 1
- 230000015556 catabolic process Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000000354 decomposition reaction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000006731 degradation reaction Methods 0.000 description 1
- 230000000593 degrading effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 239000003344 environmental pollutant Substances 0.000 description 1
- 238000005189 flocculation Methods 0.000 description 1
- 230000016615 flocculation Effects 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 231100000719 pollutant Toxicity 0.000 description 1
- 238000012163 sequencing technique Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
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- C—CHEMISTRY; METALLURGY
- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F3/00—Biological treatment of water, waste water, or sewage
- C02F3/02—Aerobic processes
- C02F3/12—Activated sludge processes
- C02F3/1236—Particular type of activated sludge installations
- C02F3/1263—Sequencing batch reactors [SBR]
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- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F3/00—Biological treatment of water, waste water, or sewage
- C02F3/02—Aerobic processes
- C02F3/12—Activated sludge processes
- C02F3/1236—Particular type of activated sludge installations
- C02F3/1268—Membrane bioreactor systems
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- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F7/00—Aeration of stretches of water
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
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- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F2209/00—Controlling or monitoring parameters in water treatment
- C02F2209/02—Temperature
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- C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F2209/00—Controlling or monitoring parameters in water treatment
- C02F2209/04—Oxidation reduction potential [ORP]
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- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
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- C02F2209/06—Controlling or monitoring parameters in water treatment pH
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- C02F2209/00—Controlling or monitoring parameters in water treatment
- C02F2209/08—Chemical Oxygen Demand [COD]; Biological Oxygen Demand [BOD]
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- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F2209/00—Controlling or monitoring parameters in water treatment
- C02F2209/40—Liquid flow rate
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Abstract
The invention discloses an artificial neural network intelligent aeration device for biochemical reaction and a control method thereof.
Description
Technical Field
The invention relates to the technical field of sewage treatment, in particular to an artificial neural network intelligent aeration device for biochemical reaction and a control method thereof.
Background
SBR water treatment process is a sequencing batch activated sludge process, also called batch activated sludge process sewage treatment process. The SBR process is based on a process of biologically treating activated sludge in wastewater by degrading organic matters, ammonia nitrogen and other pollutants in the wastewater by using suspended microorganisms under aerobic-anoxic-anaerobic conditions. In order to provide aerobic conditions, aeration is needed in the SBR biochemical reaction tank and is often provided in the form of an aerator; in the anoxic-anaerobic phase, aeration is stopped.
The aeration amount directly determines the growth activity of aerobic microorganisms, and plays an important role in biochemical reaction stages such as degradation of organic matters, nitrification and the like; if the aeration quantity is insufficient, the dissolved oxygen in water is insufficient, and aerobic bacteria can not normally survive, so that the sludge activity is inhibited, the nitrification is insufficient, the chemical oxygen demand and ammonia nitrogen of effluent exceed the standards, and the effluent quality is influenced; if the aeration rate is excessive, namely the aeration rate exceeds the oxygen amount actually required by activated sludge treatment sewage, the activated sludge is oxidized by itself, adverse consequences such as sludge aging and flocculation decomposition, sludge bulking and the like are caused, and excessive aeration not only has negative effects on the process, but also increases unnecessary energy consumption.
The existing aeration technology can not be adjusted and optimized according to water quality, water quantity and external environment, so that the aeration rate is not accurate.
Disclosure of Invention
Aiming at the defects in the prior art, the artificial neural network intelligent aeration device for biochemical reaction and the control method thereof provided by the invention solve the problem that the aeration quantity is inaccurate because the conventional aeration technology cannot be adjusted and optimized according to the water quality, the water quantity and the external environment.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that: an artificial neural network intelligent aeration device for biochemical reaction, comprising: the device comprises a controller, an aerator, a regulating tank, an SBR biochemical tank, an MBR membrane separation tank, a water outlet tank, an inlet water quality monitoring assembly and an outlet water quality monitoring assembly;
a lift pump is arranged in the regulating tank and is used for pumping the sewage converged into the regulating tank into the SBR biochemical tank; the SBR biochemical tank is used for carrying out biochemical reaction on the sewage and converging the sewage subjected to biochemical reaction into the MBR membrane separation tank; the MBR membrane separation tank is used for carrying out sludge-water separation on the sewage subjected to the biochemical reaction to obtain filtered water; the water outlet pool is used for containing filtered water; the water inlet quality monitoring assembly is inserted into the regulating tank, is used for acquiring water inlet data and sending the water inlet data to the controller; the effluent water quality monitoring component is inserted into the SBR biochemical pool, is used for collecting water quality data in the aeration process and sending the water quality data to the controller; the controller is used for controlling the aeration rate of the aerator according to the water inlet data and the water quality data; the aerator is used for inputting oxygen into the SBR biochemical tank.
Further, the quality of water monitoring subassembly of intaking includes: the device comprises a flowmeter, a COD detector, a TP detector, a TN detector, a DO detector, a temperature detector and a pH detector; the water quality monitoring subassembly of giving out water includes: the device comprises a COD detector, a TP detector, a TN detector, a DO detector, a sludge concentration detector, a pH detector and an ORP detector.
A control method of an artificial neural network intelligent aeration device for biochemical reaction comprises the following steps:
s1, training a neural network model according to data collected by a water inlet quality monitoring assembly and a water outlet quality monitoring assembly to obtain a preliminarily trained neural network model;
s2, deploying the preliminarily trained neural network model in a controller;
s3, collecting data of the inlet water quality monitoring assembly and the outlet water quality monitoring assembly in real time, inputting the data into a controller, and adjusting network parameters of a preliminarily trained neural network model;
and S4, calculating according to the adjusted neural network model to obtain the aeration amount of the aerator.
Further, the neural network model in step S1 includes: an input layer, a hidden layer and an output layer;
the relationship between the input layer and the output layer is as follows:
wherein Y is the output of the output layer, X k Is the kth input quantity of the input layer, K is the number of input quantities,
actual output for kth input quantity, C jk Radial basis center, σ, for the kth input for the jth hidden layer neuron j Is the radial base width of the jth hidden layer neuron; d jk Weight of kth input quantity for jth hidden layer neuron, L number of hidden layer neurons, w j Is the weight of the jth hidden layer neuron, and N is the number of hidden layer neurons.
Further, the acquiring the radial basis center comprises the following steps:
a1, calculating distances among all input quantities to obtain a sample distance set;
a2, selecting a plurality of different sample distances from the sample distance set as initial centers;
a3, calculating the distance from a plurality of input quantities to each initial center;
a4, judging whether the distance is smaller than a distance threshold value, if so, adding the input quantity into the corresponding initial center to obtain a plurality of similar sets, and if not, taking the distance as a new initial center and skipping to the step A3 until all the input quantity is distributed to the similar sets;
and A5, obtaining a radial basis center according to all input quantities in the similar set.
The beneficial effects of the above further scheme are: selecting a plurality of different sample distances from a sample distance set as initial centers to obtain a plurality of initial centers, finding input quantities close to the initial centers so as to classify the input quantities into a similar set, wherein the input quantities in the similar set are all lower than a distance threshold, and when the input quantities are all far away from the existing initial centers, namely the distance threshold is not met, the distances are taken as new initial centers, so that each input quantity can be classified into the respective similar set to find radial basis centers in a self-adaptive manner.
Further, the calculation formula of the radial base center in step A5 is:
wherein, C jk Is a radial basis center corresponding to the kth input quantity of the jth hidden layer neuron in a similar set, P is the quantity of the input quantities in the similar set, X k Is the kth input quantity of the input layer.
Further, the weight update formula of the jth hidden layer neuron is as follows:
w j i+1 ＝w j i -△w j i
wherein, w j i+1 Weight of the jth hidden layer neuron for the (i + 1) th iteration, w j i Weight of the jth hidden layer neuron for the ith iteration,. DELTA.w j i The weight variance for the jth hidden layer neuron for the ith iteration.
Further, the weight change amount Δ w j i The formula of (1) is:
wherein eta is the moving step length,
mean estimate of gradient for the i-1 iteration, θ i-1 Estimation of the statistical dispersion of the gradients, lambda, for the i-1 st iteration i-1 Attenuation factor, λ, for the i-1 th iteration i-2 For the attenuation factor of the (i-2) th iteration,
gradient mean estimator for i-2 iterations, θ i-2 Gradient statistical dispersion estimator for i-2 iterations, Y i ε is a constant for the output of the output layer of the ith iteration.
In conclusion, the beneficial effects of the invention are as follows: according to the invention, the neural network model is deployed in the controller, and network parameters are adjusted by the neural network model according to data collected by the water inlet quality monitoring assembly and the water outlet quality monitoring assembly, so that the calculation process can be optimized according to water quality, water quantity and external environment, and aeration quantity can be accurately controlled.
Drawings
FIG. 1 is a schematic diagram of an artificial neural network intelligent aeration apparatus for biochemical reaction;
FIG. 2 is a flow chart of a control method of an artificial neural network intelligent aeration apparatus for biochemical reaction.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined by the appended claims, and all changes that can be made by the invention using the inventive concept are intended to be protected.
As shown in fig. 1, an artificial neural network intelligent aeration apparatus for biochemical reaction comprises: the device comprises a controller, an aerator, a regulating tank, an SBR biochemical tank, an MBR membrane separation tank, a water outlet tank, an inlet water quality monitoring assembly and an outlet water quality monitoring assembly;
a lifting pump is arranged in the regulating tank and is used for pumping the sewage which is converged into the regulating tank into the SBR biochemical tank; the SBR biochemical tank is used for carrying out biochemical reaction on the sewage and converging the sewage subjected to biochemical reaction into the MBR membrane separation tank; the MBR membrane separation tank is used for carrying out sludge-water separation on the sewage subjected to the biochemical reaction to obtain filtered water; the water outlet pool is used for containing filtered water; the water quality monitoring assembly is inserted into the regulating tank, is used for acquiring water inlet data and sending the water inlet data to the controller; the effluent water quality monitoring assembly is inserted into the SBR biochemical pool, is used for collecting water quality data in the aeration process and sending the water quality data to the controller; the controller is used for controlling the aeration rate of the aerator according to the water inlet data and the water quality data; the aerator is used for inputting oxygen into the SBR biochemical tank.
The quality of water monitoring subassembly of intaking includes: the device comprises a flowmeter, a COD detector, a TP detector, a TN detector, a DO detector, a temperature detector, a pH detector and a sludge concentration detector; the effluent water quality monitoring assembly comprises: a COD detector, a TP detector, a TN detector, a DO detector, a sludge concentration detector, a pH detector and an ORP detector.
As shown in FIG. 2, a control method of an artificial neural network intelligent aeration apparatus for biochemical reaction comprises the following steps:
s1, training a neural network model according to data collected by a water inlet quality monitoring assembly and a water outlet quality monitoring assembly to obtain a preliminarily trained neural network model;
s2, deploying the preliminarily trained neural network model in a controller;
s3, collecting data of the inlet water quality monitoring assembly and the outlet water quality monitoring assembly in real time, inputting the data into a controller, and adjusting network parameters of a preliminarily trained neural network model;
and S4, calculating according to the adjusted neural network model to obtain the aeration amount of the aerator.
Data through biochemical reaction front and back collection obtain next operation cycle's reference aeration rate through machine learning, along with the operating duration of system increases, the aeration rate process data of storage increases, and the aeration rate data that give follow-up referable are more, and the aeration rate is also more and more accurate, and it is also more stable to go out water quality, reaches intelligence, high efficiency and energy-conserving purpose.
In step S1, firstly, data acquired by the water inlet quality monitoring assembly and data acquired by the water outlet quality monitoring assembly are used, the data corresponding relation between inlet water and outlet water after aeration by the aerator is found preliminarily, a preliminarily trained neural network model is obtained, the preliminarily trained neural network model is applied to the biochemical process of the biochemical pool, in the actual operation process, the outlet water data after aeration is used for continuously fine-tuning the network parameters of the neural network model again, which is equivalent to that in the actual operation process, the neural network model is always trained, so that the network parameters are changed to adapt to the external environment.
The data collected by the inlet water quality monitoring assembly is input data of the neural network model, and the actual data of the data collected by the outlet water quality monitoring assembly after passing through the aerator can adjust network parameters of the neural network model according to the input data and the actual data.
The neural network model in step S1 includes: an input layer, a hidden layer and an output layer;
the relationship between the input layer and the output layer is as follows:
wherein Y is the output of the output layer, X k Is the kth input quantity of the input layer, K is the number of input quantities,
actual output for kth input quantity, C jk For the radial basis center, σ, corresponding to the kth input for the jth hidden layer neuron j Is the radial base width of the jth hidden layer neuron; d jk Is the weight of the kth input quantity of the jth hidden layer neuron, L is the number of hidden layer neurons, w j Is the weight of the jth hidden layer neuron, and N is the number of hidden layer neurons.
Kth input quantity X of input layer k The data of (2) is derived from the data collected by the water inlet quality monitoring assembly, and the actual output corresponding to the kth input quantity is derived from the data collected by the water outlet quality monitoring assembly.
The method for acquiring the radial basic center comprises the following steps:
a1, calculating distances among all input quantities to obtain a sample distance set;
a2, selecting a plurality of different sample distances from the sample distance set as initial centers;
a3, calculating the distance from a plurality of input quantities to each initial center;
a4, judging whether the distance is smaller than a distance threshold value, if so, adding the input quantity into the corresponding initial center to obtain a plurality of similar sets, and if not, taking the distance as a new initial center and skipping to the step A3 until all the input quantity is distributed to the similar sets;
and A5, obtaining the radial basis center according to all input quantities in the similar set.
In the present embodiment, the types of input amounts include: reaction water temperature, sludge concentration, inflow rate, pH value of a regulating tank, COD value, NH3-N value, ORP value and TP value.
The calculation formula of the radial base center in the step A5 is as follows:
wherein, C jk Is the radial base center corresponding to the kth input quantity of the jth hidden layer neuron in a similar set, P is the quantity of the input quantities in the similar set, X k Is the kth input quantity of the input layer.
The weight updating formula of the jth hidden layer neuron is as follows:
w j i+1 ＝w j i -Δw j i
wherein w j i+1 Weight of the jth hidden layer neuron for the (i + 1) th iteration, w j i Weight of the jth hidden layer neuron for the ith iteration, Δ w j i The weight variance for the jth hidden layer neuron for the ith iteration.
The weight change amount Δ w j i The formula of (1) is:
wherein eta is the moving step length,
gradient mean estimator for i-1 iteration, theta i-1 Estimate of the statistical dispersion of the gradients, λ, for the i-1 th iteration i-1 Attenuation factor, λ, for the i-1 th iteration i-2 For the attenuation factor of the i-2 th iteration,
gradient mean estimator for i-2 iterations, θ i-2 Gradient statistical dispersion estimator for i-2 iterations, Y i Is the output of the output layer of the ith iteration, and epsilon is a constant。
In this embodiment, the relational formula between the input layer and the output layer, the process of obtaining the radial basis center, the formula of the radial basis center, the weight update formula of the neuron in the hidden layer, and the formula of the weight variation are applicable to all the training processes of the neural network model of the present invention.
Claims (8)
1. The utility model provides a biochemical reaction's artificial neural network intelligence aeration equipment which characterized in that includes: the device comprises a controller, an aerator, a regulating tank, an SBR biochemical tank, an MBR membrane separation tank, a water outlet tank, a water inlet quality monitoring component and a water outlet quality monitoring component;
a lifting pump is arranged in the regulating tank and is used for pumping the sewage which is converged into the regulating tank into the SBR biochemical tank; the SBR biochemical tank is used for carrying out biochemical reaction on the sewage and converging the sewage subjected to biochemical reaction into the MBR membrane separation tank; the MBR membrane separation tank is used for carrying out sludge-water separation on the sewage subjected to the biochemical reaction to obtain filtered water; the water outlet pool is used for containing filtered water; the water inlet quality monitoring assembly is inserted into the regulating tank, is used for acquiring water inlet data and sending the water inlet data to the controller; the effluent water quality monitoring component is inserted into the SBR biochemical pool, is used for collecting water quality data in the aeration process and sending the water quality data to the controller; the controller is used for controlling the aeration rate of the aerator according to the water inlet data and the water quality data; the aerator is used for inputting oxygen into the SBR biochemical tank.
2. The biochemical reaction artificial neural network intelligent aeration device according to claim 1, wherein the influent water quality monitoring assembly includes: the device comprises a flowmeter, a COD detector, a TP detector, a TN detector, a DO detector, a temperature detector and a pH detector; the water quality monitoring subassembly of giving out water includes: the device comprises a COD detector, a TP detector, a TN detector, a DO detector, a sludge concentration detector, a pH detector and an ORP detector.
3. The method for controlling an artificial neural network intelligent aeration apparatus for biochemical reactions according to claim 1, comprising the steps of:
s1, training a neural network model according to data collected by a water inlet quality monitoring assembly and a water outlet quality monitoring assembly to obtain a preliminarily trained neural network model;
s2, deploying the preliminarily trained neural network model in a controller;
s3, collecting data of the water inlet quality monitoring assembly and the water outlet quality monitoring assembly in real time, inputting the data into a controller, and adjusting network parameters of a preliminarily trained neural network model;
and S4, calculating according to the adjusted neural network model to obtain the aeration amount of the aerator.
4. The method for controlling an artificial neural network intelligent aeration apparatus for biochemical reactions according to claim 3, wherein the neural network model in the step S1 includes: an input layer, a hidden layer and an output layer;
the relationship between the input layer and the output layer is as follows:
wherein Y is the output of the output layer, X k Is the kth input quantity of the input layer, K is the number of input quantities,
actual output for kth input quantity, C jk Radial basis center, σ, for the kth input for the jth hidden layer neuron j Is the radial base width of the jth hidden layer neuron; d jk Weight of kth input quantity for jth hidden layer neuron, L number of hidden layer neurons, w j Is the weight of the jth hidden layer neuron, and N is the number of hidden layer neurons.
5. The method for controlling an artificial neural network intelligent aeration apparatus for biochemical reactions according to claim 4, wherein the obtaining of radial basis center comprises the steps of:
a1, calculating distances among all input quantities to obtain a sample distance set;
a2, selecting a plurality of different sample distances from the sample distance set as initial centers;
a3, calculating the distance from a plurality of input quantities to each initial center;
a4, judging whether the distance is smaller than a distance threshold value, if so, adding the input quantity into the corresponding initial center to obtain a plurality of similar sets, and if not, taking the distance as a new initial center and skipping to the step A3 until all the input quantity is distributed to the similar sets;
and A5, obtaining the radial basis center according to all input quantities in the similar set.
6. The method for controlling an artificial neural network intelligent aeration apparatus for biochemical reactions according to claim 5, wherein the calculation formula of the radial basis center in step A5 is:
wherein, C jk Is a radial basis center corresponding to the kth input quantity of the jth hidden layer neuron in a similar set, P is the quantity of the input quantities in the similar set, X k Is the kth input quantity of the input layer.
7. The method for controlling an artificial neural network intelligent aeration apparatus for biochemical reactions according to claim 4, wherein the weight update formula of the jth hidden layer neuron is:
w j i+1 ＝w j i -Δw j i
wherein, w j i+1 Weight of the jth hidden layer neuron for the (i + 1) th iteration, w j i Weight of the jth hidden layer neuron for the ith iteration, Δ w j i The weight variance for the jth hidden layer neuron for the ith iteration.
8. The method for controlling an artificial neural network intelligent aeration apparatus for biochemical reactions according to claim 7, wherein the weight change Δ w j i The formula of (1) is:
wherein eta is the moving step length,
gradient mean estimator for i-1 iteration, theta i-1 Estimate of the statistical dispersion of the gradients, λ, for the i-1 th iteration i-1 Attenuation factor, λ, for the i-1 th iteration i-2 For the attenuation factor of the i-2 th iteration,
mean estimate of gradient for the i-2 iteration, θ i-2 Estimation of the statistical dispersion of the gradients for the i-2 iterations, Y i ε is a constant, the output of the output layer for the ith iteration.
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| CN116863465A (en) * | 2023-09-01 | 2023-10-10 | 四川省每文环保科技有限公司 | Sewage intelligent operation monitoring system |
| CN117808216A (en) * | 2024-03-01 | 2024-04-02 | 四川省铁路建设有限公司 | Energy saving and emission reduction effect evaluation method for sewage treatment |
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