JP2005007326A - Apparatus for measuring concentration of organic matter - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To obtain an apparatus for measuring the concentration of organic matter by which the concentration of organic matter components in waste water can automatically and continuously measured using a neutral network without requiring artificial measurement work. <P>SOLUTION: The apparatus for measuring the concentration of organic matter is provided with: an oxidation-reduction electrometer 2 measuring the oxidation-reduction potential of a bioreaction tank 1; a data collector 3 collecting the measured value in the oxidation-reduction electrometer 2; and an analyzer 4 predicting the concentration of organic matter from the data in the data collector 3 by a neural network. <P>COPYRIGHT: (C)2005,JPO&NCIPI

Description

[0001]
BACKGROUND OF THE INVENTION
The present invention relates to an organic matter concentration measuring apparatus for measuring an organic matter concentration in wastewater.
[0002]
[Prior art]
With the recent strengthening of nitrogen and phosphorus regulations in public water areas, biological nitrogen and phosphorus removal processes have been provided in the treatment of wastewater such as sewage and industrial wastewater. For example, a circulation type nitrification denitrification method, an anaerobic / aerobic activated sludge method, an anaerobic / anoxic / aerobic method, and the like are known.
Here, in the biological nitrogen removal process, an organic substance is required as an electron donor for the denitrification process, and in the phosphorus removal process, as a PHA source for phosphorus accumulating bacteria.
Until now, the total amount of organic carbon and BOD have been used as indicators of the organic matter concentration. However, it has been reported that these indicators alone are insufficient to grasp the nitrogen / phosphorus removal performance.
[0003]
On the other hand, the activated water sludge mathematical model (hereinafter referred to as ASM) proposed by the task group of the International Water Association (IWA) introduces a concept that classifies BOD in more detail as a biodegradable organic substance. . That is, it is an easily decomposable organic substance S _F , a fermentation product S _A , and a slow decomposable organic substance X _S. It has already been shown in practical use in Japan, especially in Europe, that it is possible to predict the concentration of nitrogen and phosphorus in treated water by grasping the properties of wastewater including these organic matter concentrations.
[0004]
A method of Guger et al. Is known as a method for measuring organic components. This method estimates the concentration of organic components from the respiration rate of microorganisms by batch experiments. For example, this method can be used when continuous measurement is not necessary, such as when measuring the daily average organic component concentration using a composite sample.
[0005]
The above prior art is a technique generally known to those skilled in the art, and does not relate to a known document invention.
[0006]
[Problems to be solved by the invention]
However, in the method for measuring organic components by Guger et al. As described above, organic substances in wastewater are used for the purpose of performing unsteady control for the purpose of further improvement of processing performance such as removal of nitrogen and phosphorus at a stable high level and energy saving. When measuring the component concentration, it takes time and labor to measure, and therefore, the organic component measurement method by Guger et al. Is not suitable.
Further, in order to measure organic components by the method of Gujer et al., The oxygen consumption rate must be measured manually, and a certain amount of technology is required for the measurement / analysis. There is a problem that it is difficult to implement in a small-scale treatment plant where there is a shortage of workers.
Therefore, there is a demand for a measuring device that measures the concentration of organic components in wastewater with an easy device, but to date, no such measuring device has been proposed.
On the other hand, instead of measuring the concentration of organic components in wastewater, these concentrations are treated as parameters, and methods are being developed for determination using methods such as genetic algorithms and optimal regulator methods. However, these methods are not determined based on factors that are affected by the concentration of organic matter in wastewater, but are determined using mathematical methods, so parameters other than the concentration of organic matter components are biologically impossible values. In addition, there is a large influence on other parts, such as work for obtaining parameters for each season and each treatment plant.
[0007]
The present invention has been made in order to solve the above-described problems, and does not require an artificial measurement / disassembly operation, and an organic substance that can automatically measure the concentration of organic components in wastewater using a neural network. An object is to provide a concentration measuring device.
[0008]
[Means for Solving the Problems]
The organic substance concentration measuring apparatus according to the present invention includes an oxidation-reduction potentiometer that measures the oxidation-reduction potential of a biological reaction tank, a data collector that collects the measurement value of the oxidation-reduction potentiometer, and data from the data collector by a neural network. And an analyzer for predicting the organic substance concentration.
[0009]
The organic matter concentration measuring apparatus according to the present invention collects the redox potential meter that measures the redox potential of the biological reaction tank, the pH meter that measures the pH of the biological reaction tank, and the measured values of the redox potential meter and the pH meter. And an analyzer for predicting the organic substance concentration from the data of the data collector using a neural network.
[0010]
DETAILED DESCRIPTION OF THE INVENTION
Hereinafter, an embodiment of the present invention will be described.
Embodiment 1 FIG.
FIG. 1 is a flowchart for explaining a basic configuration of an organic substance concentration measuring apparatus according to Embodiment 1 of the present invention.
In FIG. 1, reference numeral 1 denotes a biological reaction tank. As the biological reaction tank 1, an anaerobic tank (a tank for anaerobically treating wastewater, and a phosphorus release tank in which phosphorus accumulating bacteria release phosphorus for ingestion of organic substances. ), An oxygen-free tank (such as a denitrification tank that denitrifies oxidized nitrogen by denitrifying bacteria), and an aeration tank. In addition, the present invention can be applied to a floating biological treatment tank, a fixed biological treatment tank, and a carrier input type biological treatment tank.
[0011]
Reference numeral 2 denotes an oxidation-reduction potentiometer that measures the oxidation-reduction potential in the biological reaction tank 1. An ORP electrode is used as the oxidation-reduction potentiometer 2. This ORP electrode measures the oxidation reduction potential in the biological reaction tank 1. Such an ORP electrode can be replaced with a pH electrode or shared with a pH electrode. The pH electrode measures a pH value in order to obtain an index indicating whether the inside of the biological reaction tank 1 is acidic or alkaline. These ORP electrodes and pH electrodes are permanently installed in a biological reaction tank at a sewage treatment plant, and largely depend on biological reactions, and the biological reactions depend on the quality and amount of wastewater. Therefore, it is possible to reversely estimate the quality and quantity of wastewater from the measurement data obtained by the ORP electrode and the pH electrode.
[0012]
Reference numeral 3 denotes a data collector that collects the measured value based on the oxidation-reduction potential 2, and reference numeral 4 denotes an analyzer (neural network analyzer) that predicts the organic component concentration from the data collector 3 using a neural network. Here, the neural network is an information processing method that simulates a human brain (neuron), and is particularly excellent in pattern recognition, and is used for so-called inverse estimation in which “cause” is predicted from “result”. Many.
[0013]
Therefore, in the organic matter concentration measuring apparatus according to the present invention, the measured value of the ORP electrode and / or the pH electrode is used as the “result”, and the organic component concentration of waste water is used as the “cause”. The analyzer 4 predicts the organic component concentration of wastewater based on the measurement data from the ORP electrode and / or pH electrode by learning parameters (weights, threshold values) in the neural network.
[0014]
Example 1.
The results of the experiment according to the first embodiment will be described below.
As the bioreactor 1 in FIG. 1, a batch-type bioreactor having an effective capacity of 100 L was used. As waste water, artificial sewage composed of acetic acid / glucose / peptone substrate shown in the table of FIG. 2 was used. On ASM, acetic acid is classified as a fermentation product S _A , glucose is classified as a readily decomposable organic substance S _F , and peptone is classified as a slowly decomposable organic substance X _S. Artificial sewage was introduced at 25 L in the initial 60 minutes of the cycle. Including this inflow time, the anaerobic / aerobic operation was performed for a total of 360 minutes, including a stirring process of 120 minutes, an aeration process of 180 minutes, and a precipitation drainage process of 60 minutes. HRT is one day. In the aeration process, the dissolved oxygen concentration in the biological reaction tank was controlled to 2.5 mg / L.
SRT was set on the 20th, and excess sludge was extracted immediately before the end of the aeration process in the biological reaction tank 1. The water temperature was controlled at 20 ° C. throughout the cycle. After the treatment was stabilized, a response experiment was conducted in which artificial sewage having different acetic acid, glucose, and peptone concentrations was introduced for one cycle to measure the values of ORP and pH. The response experiment was carried out on nine patterns of artificial sewage with acetic acid, glucose and peptone concentrations shown in FIG.
[0015]
As an example of the measurement result, ORP and pH electrode data for a stirring process of 120 minutes when artificial sewage of fermentation product S _A = 0, readily degradable organic substance S _F = 320 mg / L, slow degradable organic substance X _S = 0 flows in Is shown in FIG.
ORP continued to decrease from the start of the cycle and an inflection point was observed at about 30 minutes. After that, it continued to decrease. The nitrate nitrogen remaining at 2.8 mg-N / L at the start of the cycle had already reached zero after 30 minutes. From this, it is considered that the bending point corresponds to the end of denitrification. The decrease after the inflection point is considered to be related to fermentation.
On the other hand, the pH increased until the same time as the ORP inflection point and then decreased. This increase / decrease in pH is considered to be related to the production of OH ⁻ accompanying denitrification and the production of organic acid accompanying fermentation.
Therefore, it was demonstrated that changes in ORP and pH in the stirring process are affected by the organic components and the concentration of inflowing sewage.
[0016]
Here, the basic concept of analysis using a neural network will be described.
FIG. 5 shows the concept of a neural network for inversely estimating the inflow organic substance component concentration from ORP and pH electrode data. The neural network has an input layer for inputting ORP time-series data and / or pH time-series data, an intermediate layer, and an output layer. As teacher data, ORP and pH time-series data are input to the input layer. Are learned by giving the fermentation product S _A , the readily decomposable organic substance S _F and the slow decomposable organic substance X _S as the inflow organic substance component concentration. When the unknown ORP and pH time-series data are given to the input layer and recalled after the weights and thresholds are determined, S _A , S _F and X _S can be estimated.
[0017]
Next, the structure of the neural network and the learning method will be described.
The input layer is ORP time series data and / or pH time series data. In addition to these data, by adding factors that affect biological reactions such as temperature and influent nitric acid concentration to the input layer, more stable analysis can be performed.
Further, the ORP time series data and / or pH time series data may be used as they are, or may be used in a form in which some data processing such as averaging or standardization is performed.
By selecting appropriate numbers of intermediate layers, neurons, and transfer functions, it is possible to improve estimation accuracy and shorten calculation time.
Learning is generally performed by the error back propagation method, but is not limited to this method, and a better method may be used as appropriate.
[0018]
As an example of the network structure, the structure used this time will be described below. The neural network has a three-layer structure in which the number of neurons in the input layer is 80, the intermediate layer is 80, and the output layer is 3. ORP and pH electrode data were measured at 1 minute intervals for 120 minutes immediately after the start of the cycle. For this reason, there are 120 data each, and an average value at intervals of 3 minutes was obtained for each 40 data. The 40 data were standardized using the following formula as a standardization function.
_{Z t = (P t -P min} ) / (P max -P min)
Here, Z _t is a value obtained by standardizing ORP or pH at cycle time t, P _t is a value of ORP or pH at cycle time t, P _max is a maximum value of ORP or pH, and P _min is a minimum value of ORP or pH. It is.
ORP 40 data and then pH 40 data were placed in order on 80 neurons in the input layer. As the transfer function at this time, a sigmoid function was used in the intermediate layer, and a linear function was used in the output layer. Nine cases of teacher data were given to this neural network, and learning was performed by the error back propagation method.
[0019]
Next, the recall result will be described. After the treatment was stabilized under the same experimental conditions as the response experiment used for learning, a response experiment was conducted in which artificial sewage with different concentrations of acetic acid, glucose, and peptone was flowed in to measure the values of ORP and pH. This response experiment was conducted on 6 patterns of artificial sewage. In addition, this organic substance component is a composition different from the inflow organic substance component used for learning.
The table of FIG. 6 shows the results of recalling the ORP and pH time-series data similarly applied to the input layer of the learned neural network after being averaged and standardized. In Example 1, organic components of artificial sewage are readily decomposable organic matter S _F , fermentation product S _A , and slowly decomposable organic matter X _S. As can be seen from FIG. 6, the estimated value showed a numerical value comparable to the preparation concentration, and the organic substance concentration could be measured by the apparatus of the present invention.
[0020]
Example 2
Next, an experiment was carried out using actual sewage (actual sewage) as waste water in the same experimental apparatus and conditions as described above. The learned neural network was recalled by applying the ORP and pH time-series data to the input layer after averaging and standardizing in the same manner. At this time, the organic component concentration was simultaneously measured by the method of Gujer et al. The results estimated by the device of the present invention and the results measured by the method of Gujer et al. Are shown in the table of FIG.
The estimated value of the device of the present invention was the same as the result measured by the method of Guger et al., And the organic substance concentration could be measured by the device of the present invention even in actual sewage.
[0021]
Embodiment 2. FIG.
FIG. 8 shows an actual plant that performs a circulation type nitrification denitrification treatment. In FIG. 8, 10 is an anaerobic tank, 11 is an aerobic tank, 12 is a sedimentation tank, 13 is a sludge return pipe from the sedimentation tank 12 to the anoxic tank 10, and 14 is an anaerobic tank 10 from the aerobic tank 11. This is a circulation pipe to which the actual sewage is subjected to a circulation nitrification denitrification treatment. 21 is a plurality of ORP electrodes and / or pH electrodes arranged in the oxygen-free tank 10, and a plurality of these ORP electrodes and / or pH electrodes 21 are installed in the flow direction of waste water in the oxygen-free tank 10. Has been.
Also in the second embodiment, as in the first embodiment, the data collector 3 that inputs and collects measurement values by the ORP electrode and / or the pH electrode 21 and the data collector 3 by a neural network. And an analyzer 4 for predicting the organic component concentration.
[0022]
Example 3 FIG.
The results of the experiment according to the second embodiment will be described below.
Circulating nitrification denitrification actual plant in Fig. 8, anoxic tank 1140M ^3, the aerobic tank 1720M ^3, the average daily sewage volume is 6800m ³ / d. The circulating water volume is about 1.5 times the inflow water volume and the return sludge volume is about 0.5 times the same.
In the oxygen-free tank of this plant, four ORP electrodes and four pH electrodes 21 were installed in the flow direction. Simultaneously with measuring the values of ORP and pH, the organic component concentration was measured by the method of Gujer et al. This ORP, pH measurement, and organic component concentration measurement by the method of Gujer et al. Were carried out 25 times.
Here, organic components "(sum of easily decomposable organic matter S _F and fermentation products S _A) microorganisms directly ingestible organic Ss" were two types of "slow degradable organic substances X _S".
The network structure is a four-layer structure in which the number of neurons in the input layer is 50, the intermediate layer 1 is 50, the intermediate layer 2 is 50, and the output layer is 2. The values obtained by averaging and standardizing the measured values of ORP and pH, the water temperature, and the amount of water were placed in the input layer, and the organic substance concentration measured by the method of Gujar et al. Was given to the output layer. At this time, as a transfer function, a sigmoid function was used in the intermediate layer, and a linear function was used in the output layer. 20 cases of data were given to this neural network, and learning was performed by the error back propagation method. FIG. 9 shows the results of recalling the ORP, the average of the pH measurement values, the standardized value, the water temperature, and the water amount of the remaining five cases not used for learning to the neural network after learning.
[0023]
As a result of the above experiments, it is possible to estimate the organic substance concentration and slow-decomposable organic substance concentration that can be ingested directly by the microorganism from the ORP and pH electrode data, and measure the organic substance concentration with the device of the present invention. I was able to.
[0024]
Incidentally, "(sum of the easily degradable organic substances S _F fermentation product S _A) microorganisms directly ingestible organic Ss" as organic component as described above, be a two "slow degradable organic substances X _S" Thus, estimation can be performed more easily. In nitrogen removal, the concentration of treated water nitrogen can be predicted by grasping these two types of organic components.
[0025]
Since the apparatus of the present invention uses pH and ORP electrode data that are commonly used in existing sewage treatment plants, it has flexibility and versatility that can be applied to sewage treatment plants that do not have analytical equipment. ing.
[0026]
【The invention's effect】
As described above, according to the present invention, it is not necessary to manually measure the oxygen consumption rate as in the prior art, and it is possible to automatically measure the concentration of organic substances in wastewater using a neural network. There is.
In addition, since automatic measurement is possible, there is an effect that a specific engineer is not required, maintenance is not complicated, and the quality of the influent wastewater can be grasped.
Furthermore, the organic substance concentration measuring apparatus of the present invention is used to automatically measure the concentration of organic substance components, and it is expected that the operation management of the wastewater treatment facility can be automatically and remotely controlled based on this measurement data. it can.
[Brief description of the drawings]
FIG. 1 is a flow sheet for explaining a basic configuration of an organic substance concentration measuring apparatus according to Embodiment 1 of the present invention.
FIG. 2 is a table showing components and water quality of artificial sewage applied to an experiment of an organic matter concentration measuring apparatus according to the present invention.
FIG. 3 is a table showing organic component concentrations of artificial sewage used for learning in Example 1 of the present invention.
FIG. 4 is a diagram illustrating ORP and pH time-series data in an experiment with artificial sewage performed by the organic substance concentration measurement apparatus according to Example 1 of the present invention.
FIG. 5 is a conceptual diagram of a neural network applied to the organic matter concentration measuring apparatus of the present invention.
FIG. 6 is a comparison table showing the organic substance component concentration estimated by the experiment with artificial sewage of the organic substance concentration measuring apparatus according to Example 1 of the present invention and the organic substance component concentration of the created artificial sewage.
FIG. 7 is a comparison table showing the organic component concentration estimated by an experiment in actual sewage of the organic concentration measuring apparatus according to Example 2 of the present invention and the organic component concentration measured by the method of Gujer et al.
FIG. 8 is a flow sheet diagram showing a circulation type nitrification denitrification treatment plant in which an organic substance concentration measuring apparatus according to Embodiment 2 of the present invention is installed.
FIG. 9 is a comparative table showing the organic component concentration estimated by an experiment in actual sewage of the organic concentration measuring apparatus according to Example 3 of the present invention and the organic component concentration measured by the method of Gujar et al.
[Explanation of symbols]
1 biological reaction tank 2 ORP (redox potential meter), pH meter 3 data collector 4 analyzer (neural network analyzer)
10 Anoxic tank 11 Aerobic tank 12 Sedimentation tank 13 Sludge return pipe 14 Circulation pipe 21 ORP meter and / or pH meter

Claims (2)

An oxidation-reduction potentiometer that measures the oxidation-reduction potential of the biological reaction tank;
A data collector for collecting the measured values of the oxidation-reduction potentiometer;
An organic matter concentration measuring apparatus comprising: an analyzer for predicting an organic matter concentration from data of a data collector by a neural network. An oxidation-reduction potentiometer that measures the oxidation-reduction potential of the biological reaction tank;
A pH meter for measuring the pH of the biological reaction tank;
A data collector for collecting the measured values of the oxidation-reduction potentiometer and the pH meter;
An organic matter concentration measuring apparatus comprising: an analyzer for predicting an organic matter concentration from data of a data collector by a neural network.

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