Anaerobic digestion process monitoring system and anaerobic digestion process monitoring method

Abstract

Provided is an anaerobic digestion process monitoring system that predicts the amount of biogas generation with high accuracy even when the activity of anaerobic microorganisms in a digestion tank changes. The present invention is provided with: a first sensor (4) for measuring the amount of organic waste fed into a digestion tank (2) and outputting load data as the measurement result; a second sensor (5) for measuring the property of the fermentation broth inside the digestion tank (2) and outputting fermentation broth property data as the measurement result; a preprocessing unit (12) that uses the first estimation model to obtain estimated activity index data indicating the degree of activity of the anaerobic microorganisms from the load data and the fermentation broth property data; and a gas generation amount prediction unit (13) that uses the second estimation model to obtain a biogas generation amount predicted value from the load data, the fermentation broth property data, and the estimated activity index data.

Description

Technical Field

[0001] The present application relates to an anaerobic digestion process monitoring system and an anaerobic digestion process monitoring method. Background Art

[0002] In order to reuse organic waste such as sludge, food waste, and factory wastewater as resources, the organic waste is put into a digestion tank where anaerobic microorganisms are cultivated to extract biogas by methane fermentation. This process is called anaerobic digestion, and the biogas extracted from the organic waste is used and converted into heat energy through a boiler or into electricity through a generator, thereby contributing to the resource utilization of organic waste or the reduction of greenhouse gases through the reduction of fuel or electricity usage.

[0003] In the energy industry, the transformation from a method of supplying energy by concentrated power generation in existing large-scale power plants to a method of building a regionally distributed power supply network that flexibly utilizes renewable energy has gained attention for the purpose of reducing greenhouse gases or countering power outages during large-scale disasters (enhancing resilience), which is called the 3D (decarbonization, decentralization, and digitalization) of the energy industry. By building microgrids using distributed power sources (distribution) that flexibly utilize renewable energy (decarbonization), it is possible to reduce greenhouse gases or counter power outages during large-scale disasters (enhancing resilience). On the other hand, in the construction of microgrids, it is important to achieve a balanced adjustment of power demand and supply, reduce imbalances, and avoid suppressing the output of renewable energy power generation. Therefore, in order for operators such as integrators to be able to grasp the power adjustment capabilities based on distributed power sources, it is necessary to model (digitize) the amount of power that can be generated and make it predictable.

[0004] In biogas power generation using biogas obtained through the anaerobic digestion process, not only is electricity extracted from organic waste that is semi-permanently generated through human activities, but also, unlike sunlight or wind power, the amount of electricity that can be generated is not affected by the natural environment. Therefore, it has the potential to become a distributed power source that is the most promising renewable energy source for flexible use in realizing the 3Dization of the energy industry.

[0005] On the other hand, there are two issues in biogas power generation. The first is digitization. The amount of biogas produced depends on (1) the amount and properties of organic matter fed into the digester, (2) the mixing ratio of each organic matter fed into the digester, and (3) the activity of anaerobic microorganisms in the digester. Therefore, the organic waste fed into the digester is sometimes diverse, and the process of converting organic waste into biogas is very complicated. Practical modeling (digitalization) of the anaerobic digestion process has not yet been achieved. The second is the increase in operation and management load. When biogas power generation equipment is installed in various places, the number of machines will increase dramatically, so the operation and management load will increase significantly. However, the population decline or the shortage of experienced and technically capable operators is becoming increasingly serious, and the increase in operation and management load may become a major obstacle to installing biogas power generation as a distributed power source in real society. Therefore, in order to flexibly utilize biogas power generation using biogas obtained in the anaerobic digestion process as a distributed power source, it is necessary to be able to construct a practical model of the anaerobic digestion process, to predict the biogas power generation and even the biogas production with high accuracy, and to provide an automatic operation monitoring system and an automatic operation monitoring method for the anaerobic digestion process that can reduce the operation management load.

[0006] As a system for monitoring the anaerobic digestion process, a system for comparing the actual value of the biogas production rate generated from the digester and the predicted value of the biogas production rate is proposed. As a method for predicting the amount of biogas produced, the following system is proposed: the biogas production rate or decomposition rate is calculated in advance for each type of organic waste input, and the biogas production amount is predicted based on the actual amount of organic waste input or the mixing ratio (for example, refer to patent document 1).

[0007] Prior art literature

[0008] Patent Document 1: Japanese Patent Application Publication No. 2005-111338 Summary of the invention

[0009] In the anaerobic digestion process monitoring system shown in Patent Document 1, the biogas production rate is calculated in advance for each type of organic waste input, and the biogas production amount is predicted based on the actual amount of each organic waste input. However, the activity of anaerobic microorganisms may be promoted or hindered depending on the combination or amount of organic waste input, so there is a possibility that the activity of anaerobic microorganisms in the digester may change. There is a problem that when the biogas production rate changes due to changes in the activity of anaerobic microorganisms in the digester, the prediction accuracy of the biogas production amount decreases.

[0010] The present application has been made to solve the above-mentioned problems, and an object of the present application is to provide an anaerobic digestion process monitoring system and an anaerobic digestion process monitoring method that can accurately predict the amount of biogas production even when the activity of anaerobic microorganisms in a digestion tank changes.

[0011] The anaerobic digestion process monitoring system disclosed in the present application is an anaerobic digestion process monitoring system that monitors the state of the anaerobic digestion process by predicting the amount of biogas produced from the fermentation liquid in which anaerobic microorganisms are cultivated inside the digester by feeding organic waste into the digester. The system comprises: a first sensor that measures the amount of organic waste fed into the digester and outputs the measurement result as load data; a second sensor that measures the property of the fermentation liquid inside the digester and outputs the measurement result as fermentation liquid property data; a process observation unit that stores the load data and the fermentation liquid property data; a pretreatment unit that uses a first estimation model to obtain estimated activity index data indicating the activity of anaerobic microorganisms based on the load data and the fermentation liquid property data; and a gas generation amount prediction unit that uses a second estimation model to obtain a predicted value of biogas generation based on the load data, the fermentation liquid property data, and the estimated activity index data.

[0012] The anaerobic digestion process monitoring system disclosed in the present application comprises: a first sensor for measuring the input amount of organic waste into a digester and outputting the measurement result as load data; a second sensor for measuring the property of the fermentation liquid inside the digester and outputting the measurement result as fermentation liquid property data; a process observation unit for storing the load data and the fermentation liquid property data; a pretreatment unit for using a first estimation model to obtain estimated activity index data representing the activity of anaerobic microorganisms based on the load data and the fermentation liquid property data; and a gas generation amount prediction unit for using a second estimation model to obtain a biogas generation amount prediction value based on the load data, the fermentation liquid property data and the estimated activity index data, so that the biogas generation amount can be predicted with high accuracy even when the activity of the anaerobic microorganisms in the digester changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 This is a diagram showing the configuration of the anaerobic digestion process monitoring system according to the first embodiment.

[0014] Figure 2 This is a flowchart showing details of the processing in the anaerobic digestion process monitoring system according to the first embodiment.

[0015] Figure 3 This is a diagram showing the configuration of an anaerobic digestion process monitoring system according to the second embodiment.

[0016] Figure 4This is a diagram showing the configuration of an anaerobic digestion process monitoring system according to the third embodiment. DETAILED DESCRIPTION

[0017] Hereinafter, the anaerobic digestion process monitoring system and the anaerobic digestion process monitoring method according to the embodiment of the present application will be described in detail with reference to the accompanying drawings. In addition, the same reference numerals in the drawings represent the same or corresponding parts.

[0018] Implementation method 1.

[0019] Figure 1 2 is a diagram showing the structure of the anaerobic digestion process monitoring system according to Embodiment 1. Figure 1 In the figure, the solid arrows represent the piping, and the dotted arrows represent the lines for exchanging information, for example, signal lines. The anaerobic digestion process monitoring system includes a monitoring device 10, a first sensor 4, a second sensor 5, and a third sensor 7. The monitoring device 10 includes a process observation unit 11, a pretreatment unit 12, a gas generation amount prediction unit 13, and a process monitoring unit 14. Organic waste such as sludge, food residues, and factory wastewater is crushed by a slag crusher or the like as needed and sent to a buffer tank 1. The buffer tank 1 stores the organic waste fed into the digestion tank 2. The buffer tank 1 and the digestion tank 2 are connected via a feed pipe 31. The organic waste inside the buffer tank 1 is fed into the digestion tank 2 through a feed unit 3. The feed unit 3 is, for example, a pump. The organic waste fed into the digestion tank 2 is assimilated by the anaerobic microorganisms cultivated inside the digestion tank 2 to generate biogas. The first sensor 4 for measuring the amount of organic waste fed into the digestion tank 2 is provided in the feed pipe 31. The first sensor 4 outputs information on the amount of organic waste fed as a measurement result as load data to the process observation unit 11 of the monitoring device 10. The process observation unit 11 stores the acquired load data.

[0020] The biogas generated in the digester 2 is sent to, for example, a biogas generator via a gas pipe 32. The gas pipe 32 is provided with a third sensor 7 for measuring the amount of biogas generated in the digester 2. The third sensor 7 outputs information of an actual value of the biogas generation amount as a measurement result to the process monitoring unit 14 of the monitoring device 10.

[0021] The second sensor 5 provided in the digestion tank 2 measures the property of the fermentation liquid which is a suspension of organic waste and anaerobic microorganisms in the digestion tank 2, and outputs the information of the property of the fermentation liquid as the measurement result to the process observation unit 11 of the monitoring device 10 as fermentation liquid property data. The process observation unit 11 stores the obtained fermentation liquid property data. The property of the fermentation liquid measured by the second sensor 5 is correlated with the physical property value which is an index of the activity of the anaerobic microorganism, that is, the estimated activity index data. That is, the condition is that the property of the fermentation liquid measured by the second sensor 5 is, for example, an automatically measurable physical property value presented by an intermediate product (organic acid, hydrogen, carbon dioxide, etc.) in the process of converting organic waste into biogas by anaerobic microorganisms, for example, any property value among pH, TDS (Total Dissolved Solids) and carbonic acid gas ion concentration can be used. Alternatively, the condition is that the property of the fermentation liquid measured by the second sensor 5 is a physical property value that can be automatically measured, such as a substance that interferes with the activity of anaerobic microorganisms (ammonia nitrogen, organic acid, nutrient salt, etc.), and any property value among pH, TDS and conductivity can be used. That is, the property of the fermentation liquid measured by the second sensor 5 includes, for example, at least one of pH, TDS, carbon dioxide ion concentration and conductivity.

[0022] When organic waste is fed into the digestion tank 2, the discharge unit 6 discharges the residue of the organic waste that is no longer needed, anaerobic microorganisms, and not converted into biogas as fermentation residue to the outside of the digestion tank 2 through the discharge pipe 33. The discharge unit 6 is, for example, a pump.

[0023] In addition, the transfer of information from the first sensor 4 to the process observation unit 11, the transfer of information from the second sensor 5 to the process observation unit 11, and the transfer of information from the third sensor 7 to the process monitoring unit 14 can be carried out through wired or wireless communication or via a network such as the Internet.

[0024] Next, the operation of the monitoring device 10 will be described. The process observation unit 11 obtains the load data from the first sensor 4, obtains the fermentation liquid property data from the second sensor 5, stores the obtained load data and fermentation liquid property data, and outputs the load data and fermentation liquid property data to the preprocessing unit 12 and the gas generation amount prediction unit 13. The preprocessing unit 12 obtains the load data and fermentation liquid property data from the process observation unit 11, obtains the estimated activity index data indicating the activity degree of the anaerobic microorganisms based on the load data and the fermentation liquid property data using the first estimation model, stores the obtained estimated activity index data, and outputs it to the gas generation amount prediction unit 13.

[0025] The gas generation amount prediction unit 13 obtains the load data and the fermentation liquid property data from the process observation unit 11, obtains the estimated activity index data from the pretreatment unit 12, and uses the second estimation model to obtain the biogas generation amount prediction value based on the load data, the fermentation liquid property data, and the estimated activity index data. The obtained biogas generation amount prediction value is output to the process monitoring unit 14 and is also output to the outside of the monitoring device 10. For example, when the gas piping 32 is connected to the biogas power generation equipment, the biogas generation amount prediction value output from the gas generation amount prediction unit 13 is used to predict the biogas power generation in the biogas power generation equipment.

[0026] The process monitoring unit 14 obtains the actual value of the biogas production from the third sensor 7, obtains the predicted value of the biogas production from the gas production prediction unit 13, calculates the error rate based on the actual value of the biogas production and the predicted value of the biogas production, and monitors whether the error rate is a value included in the predetermined range. In addition, the process monitoring unit 14 can output the error rate to the outside of the monitoring device 10, and can also output the result obtained by determining whether the error rate is a value included in the predetermined range to the outside of the monitoring device 10. For example, the error rate can be obtained by (predicted value of biogas production-actual value of biogas production) / actual value of biogas production*100.

[0027] Here, the first inference model and the second inference model are described. The first inference model is, for example, an operation formula for obtaining inferred activity index data indicating the activity of anaerobic microorganisms based on load data and fermentation liquid property data, and is a regression formula as follows: inferred activity index data such as the concentration of intermediate products (organic acids, hydrogen, carbon dioxide, etc.) or the concentration of substances that interfere with the activity of anaerobic microorganisms (ammonia nitrogen, organic acids, nutrients, etc.) are set as target variables, and for each inferred activity index data, load data and fermentation liquid property data are set as multiple explanatory variables. The second inference model is, for example, an operation formula for obtaining a predicted value of biogas production based on load data, fermentation liquid property data, and inferred activity index data, and is a regression formula as follows: biogas production is set as the target variable, and load data, fermentation liquid property data, and inferred activity index data are set as multiple explanatory variables. By setting a learning period after the introduction of the system and performing multivariate analysis in the preprocessing unit 12, necessary explanatory variables are extracted from the measured load data and fermentation liquid property data for each estimated activity index data, and coefficients related to the necessary explanatory variables are obtained, thereby constructing a regression formula of the first estimation model. By performing multivariate analysis in the gas production prediction unit 13 after the first estimation model is constructed, necessary explanatory variables are extracted from the measured load data, fermentation liquid property data, and estimated activity index data obtained by the first estimation model for the actual value of biogas production, and coefficients related to the necessary explanatory variables are obtained, thereby constructing a regression formula of the second estimation model. In addition, the calculation formula of the first estimation model and the calculation formula of the second estimation model can also be obtained by a preliminary experiment before the introduction of the system, and the method is shown below.

[0028] In the experiment of putting organic waste into a digestion tank for laboratory test, the input amount of organic waste put into the digestion tank is measured as load data, and the index of the amount of organic matter in the organic waste put into the digestion tank, namely TVS (Total Volatile Solids), is measured. In the case of multiple types of organic waste, the input amount of organic waste and TVS are measured for each type of organic waste. The digestion tank for laboratory test into which the organic waste is put is maintained at a certain temperature, and the amount of biogas produced is measured after 20 days, for example. Thus, data on the amount of biogas produced per TVS input corresponding to the load data of the input amount of organic waste put into the digestion tank can be obtained. In addition, at this time, the conditions of the input amount of organic waste (load data) into the digestion tank are allocated, and the values ​​of pH, TDS, carbon dioxide ion concentration and conductivity of the fermentation liquid inside the digestion tank are measured as fermentation liquid property data. In addition, the concentrations of ammonia nitrogen, organic acids (acetic acid, valeric acid, butyric acid, propionic acid, etc.) and nutrient salts (nickel, iron, cobalt, etc.) are measured as physical property values ​​that become indicators of the activity of anaerobic microorganisms, that is, estimated activity index data.

[0029] When the input amount (load data) of organic waste to the digester changes, the concentration of organic acids (acetic acid, valeric acid, butyric acid, propionic acid, etc.) increases or the amount of nutrient salts is insufficient, thereby reducing the pH of the fermentation liquid in the digester and reducing the activity of anaerobic microorganisms. Therefore, as the properties of the fermentation liquid in the digester 2, the pH, TDS or carbon dioxide ion concentration of the fermentation liquid are measured by the second sensor 5, and the values ​​of pH, TDS or carbon dioxide ion concentration are used as fermentation liquid property data, thereby being able to infer the estimated activity index data, that is, the organic acid concentration or nutrient salt concentration, which is an index of the activity of anaerobic microorganisms, based on the values ​​of pH, TDS or carbon dioxide ion concentration as fermentation liquid property data. For example, by gradually increasing the amount of organic waste fed into a digester used for laboratory experiments, the pH, TDS or carbon dioxide ion concentration and organic acid concentration as property data of the fermentation liquid are measured while measuring the load amount, thereby obtaining information on the correlation between the pH, TDS or carbon dioxide ion concentration and the organic acid concentration as property data of the fermentation liquid, thereby obtaining a first estimation model for calculating the organic acid concentration or nutrient salt concentration as estimated activity indicator data representing the activity of anaerobic microorganisms based on the load data and the pH, TDS or carbon dioxide ion concentration as property data of the fermentation liquid.

[0030] In addition, when the input amount (load data) of organic waste input into the digester changes, the ammonia nitrogen concentration increases and the activity of anaerobic microorganisms decreases. Therefore, as the property of the fermentation liquid in the digester 2, the conductivity of the fermentation liquid is measured by the second sensor 5 and the value of the conductivity is used as the fermentation liquid property data, thereby being able to infer the inferred activity index data, i.e., the ammonia nitrogen concentration, which is an index of the activity of anaerobic microorganisms, based on the value of the conductivity as the fermentation liquid property data. For example, by gradually increasing the input amount of organic waste input into the digester for laboratory tests, the conductivity and ammonia nitrogen concentration of the fermentation liquid are measured while measuring the load amount, thereby being able to obtain information on the correlation between the conductivity of the fermentation liquid and the ammonia nitrogen concentration, thereby being able to obtain the first inference model for the ammonia nitrogen concentration as the inferred activity index data representing the activity of anaerobic microorganisms based on the load data and the conductivity as the fermentation liquid property data.

[0031] Furthermore, by performing multivariate analysis on the amount of biogas generated per TVS input, the load amount as the input amount of organic waste, the pH of the fermentation liquid, TDS or carbonic acid ion concentration, and the organic acid concentration or nutrient salt concentration obtained based on the pH, TDS or carbonic acid ion concentration, it is possible to obtain information on the correlation between the load data, pH, TDS or carbonic acid ion concentration, and the organic acid concentration or nutrient salt concentration and the predicted value of biogas generation, and to obtain a second estimation model for obtaining the predicted value of biogas generation based on the load amount and the pH, TDS or carbonic acid ion concentration of the fermentation liquid. Alternatively, by performing multivariate analysis on the amount of biogas generated per TVS input, the load amount as the input amount of organic waste, the conductivity of the fermentation liquid, and the ammonia nitrogen concentration obtained based on the conductivity, it is possible to obtain information on the correlation between the load data, the conductivity, and the predicted value of biogas generation, and to obtain a second estimation model for obtaining the predicted value of biogas generation based on the load amount and the conductivity of the fermentation liquid. Furthermore, by performing multivariate analysis on the amount of biogas produced per TVS input, the load amount as the input amount of organic waste, the pH of the fermentation liquid, the TDS or carbon dioxide ion concentration, the organic acid concentration calculated based on the pH, TDS or carbon dioxide ion concentration, the electrical conductivity of the fermentation liquid, and the ammonia nitrogen concentration calculated based on the electrical conductivity, it is possible to obtain information on the correlation between the load data, pH, TDS or carbon dioxide ion concentration, electrical conductivity, organic acid concentration and ammonia nitrogen concentration, and the predicted value of biogas production, and to obtain a second estimation model for calculating the predicted value of biogas production based on the load amount, the pH of the fermentation liquid, the TDS or carbon dioxide ion concentration, and the electrical conductivity of the fermentation liquid.

[0032] According to the above, in the first estimation model, based on the load amount of the input amount of organic waste and the fermentation liquid property data as information on the properties of the fermentation liquid of the digester, the estimated activity index data that is an index of the activity of the anaerobic microorganisms in the fermentation liquid can be calculated based on the information of the correlation obtained in the learning period after the system is introduced or in the laboratory test before the system is introduced. In the case where the fermentation liquid property data is the pH, TDS or carbon dioxide ion concentration of the fermentation liquid, the estimated activity index data is the organic acid concentration or nutrient salt concentration of the fermentation liquid, and in the case where the fermentation liquid property data is the conductivity of the fermentation liquid, the estimated activity index data is the ammonia nitrogen concentration of the fermentation liquid. In addition, in the second estimation model, based on the load amount of the input amount of organic waste, the fermentation liquid property data as information on the properties of the fermentation liquid of the digester, and the estimated activity index data calculated using the first estimation model, the predicted value of the amount of biogas generated per input TVS can be calculated based on the information of the correlation obtained in the learning period after the system is introduced or in the laboratory test before the system is introduced.

[0033] Although it is not possible to automatically measure by sensors, the first inference model can be used to infer the physical property values ​​that are indicators of the activity of anaerobic microorganisms required for accurately predicting the amount of biogas produced, as inferred activity index data, and the inferred activity index data, load data, and fermentation liquid property data are input into the calculation formula of the second inference model to obtain the predicted value of biogas production, so that the amount of biogas produced can be predicted with high accuracy while suppressing the increase in operating load. In addition, the fermentation liquid property data is not limited to pH, TDS, carbon dioxide ion concentration, or conductivity, but can also be acetate ion concentration, COD (Chemical Oxygen Demand) concentration, etc. In the test of putting organic waste into a digester for laboratory testing, it is sufficient to confirm the correlation with the physical property values ​​that are indicators of the activity of anaerobic microorganisms, such as the organic acid concentration, nutrient salt concentration, or ammonia nitrogen concentration of the fermentation liquid. The more types of fermentation liquid property data there are, the higher the estimation accuracy of the activity index data can be. However, when the types of fermentation liquid property data increase, the number of sensors required increases, and the initial cost and the load of operation and management increase. Therefore, the number of types of fermentation liquid property data can be determined according to the required estimation accuracy.

[0034] The process monitoring unit 14 may also obtain the predicted value of biogas production from the gas production prediction unit 13 and the actual value of biogas production from the third sensor 7, and when the error rate between the actual value of biogas production and the predicted value of biogas production is outside the predetermined normal range of error rate and the actual value of biogas production is included in the predetermined normal range of gas production, it is determined that there is a possibility that the estimated activity index data obtained by using the first estimation model is out of the normal value range due to a failure of at least one of the first sensor 4 and the second sensor 5, and a signal urging maintenance of the first sensor 4 and the second sensor 5 is output. Through the above processing, the maintenance response of the first sensor 4 and the second sensor 5 can be smoothly performed.

[0035] In addition, the gas production prediction unit 13 may output the estimated activity index data together with the biogas production prediction value to the process monitoring unit 14. At this time, the process monitoring unit 14 may determine that there is a possibility of failure of the third sensor 7 when the value of the error rate between the actual measured value of biogas production obtained from the third sensor 7 and the predicted value of biogas production obtained from the gas production prediction unit 13 is outside the predetermined normal range of error rate, and the actual measured value of biogas production is outside the predetermined normal range of gas production, and the estimated activity index data obtained from the gas production prediction unit 13 is included in the predetermined normal range of activity index, and output a signal urging maintenance of the third sensor 7. Through the above processing, the maintenance response of the third sensor 7 can be smoothly performed. Furthermore, the process monitoring unit 14 may determine that the activity of anaerobic microorganisms is reduced due to the influence of organic waste, and output a signal urging the stop of the biogas generation system and the confirmation of the nature of the organic waste introduced into the digestion tank 2 inside the buffer tank 1 when the error rate value of the actual measurement value of the biogas generation obtained from the third sensor 7 and the predicted value of the biogas generation obtained from the gas generation prediction unit 13 is included in the predetermined normal error rate range, and the actual measurement value of the biogas generation is outside the predetermined normal gas generation range, and the estimated activity index data is outside the predetermined normal activity index range. By the above processing, the biogas generation system can be stopped and the nature of the organic waste inside the buffer tank 1 can be confirmed smoothly. In addition, the process monitoring unit 14 may also obtain the estimated activity index data from the pre-processing unit 12.

[0036] When biogas power generation using biogas obtained through anaerobic digestion is flexibly used as a distributed power source, operators such as integrators can appropriately set the normal range of error rate according to the variation tolerance of the power adjustment force required based on the power of biogas power generation and the total power of the microgrid including the biogas power generation. The normal range of error rate is preferably within 10%, and more preferably within 5%. In addition, when there are seasonal changes in the properties of organic waste, it is necessary to consider seasonal changes and change the rated value. Therefore, when there are seasonal changes in the properties of organic waste, it is preferred to implement the test of putting organic waste into the digestion tank for laboratory tests in the seasons of spring, summer, autumn and winter. According to the data obtained in the test of putting organic waste into the digestion tank for laboratory tests using the input amount of organic waste as a parameter, the normal range of activity indicators can be appropriately set. For example, the organic acid concentration is preferably less than 2000 mg / L, and more preferably less than 100 mg / L. In addition, for example, the ammonia nitrogen concentration is preferably less than 5000 mg / L, and more preferably less than 1000 mg / L.

[0037] From the viewpoint of improving the prediction accuracy of the amount of biogas generated, it is preferred that the first sensor 4 measures the input amount of organic waste in real time at a predetermined measurement time interval and outputs it to the process observation unit 11 as load data, and the process observation unit 11 stores the load data as time series data of the measurement time interval. In addition, it is preferred that the second sensor 5 measures the properties of the fermentation liquid inside the digester 2 in real time at the same measurement time interval as the first sensor 4 and outputs it to the process observation unit 11 as fermentation liquid property data, and the process observation unit 11 stores the fermentation liquid property data as time series data of the same measurement time interval as the first sensor 4. Furthermore, it is preferred that the third sensor 7 measures the amount of biogas generated in the digester 2 in real time at the same measurement time interval as the first sensor 4 and outputs it to the process monitoring unit 14 as a biogas generation prediction value. The measurement time interval of the measurements performed by the first sensor 4, the second sensor 5 and the third sensor 7 is not particularly limited. However, when the measurement time interval is large, the prediction accuracy of the amount of biogas production may be reduced. In addition, when the measurement time interval is small, the amount of data stored in the process observation unit 11 or the process monitoring unit 14 increases. Therefore, the measurement time interval is preferably greater than 1 minute and less than 60 minutes, and more preferably greater than 5 minutes and less than 10 minutes.

[0038] The measurement time (measurement timing) of the load data of the time series measured by the first sensor 4 for measuring the input amount of organic waste, the measurement time (measurement timing) of the fermentation liquid property data of the time series measured by the second sensor 5 for measuring the property of the fermentation liquid inside the digester 2, and the measurement time (measurement timing) of the actual value of the biogas generation amount measured by the third sensor 7 for measuring the actual value of the biogas generation amount are preferably the same, that is, the second sensor 5 preferably measures the property of the fermentation liquid at the time when the first sensor 4 measures the input amount of organic waste, and the third sensor 7 preferably measures the actual value of the biogas generation amount at the time when the first sensor 4 measures the input amount of organic waste. Furthermore, when one of the times when the first sensor 4 measures the input amount of organic waste is set as a specific time, it is preferred that the pre-processing unit 12 obtains the estimated activity index data at the specific time using the first estimation model based on the load data measured at the specific time and the fermentation liquid property data measured at the specific time, and stores the estimated activity index data at each time as the time series data of the measurement time interval. Similarly, when the gas generation prediction unit 13 uses the load data, the fermentation liquid property data, and the estimated activity index data as inputs and uses the second estimation model to obtain the biogas generation prediction value, it is also preferred to obtain the biogas generation prediction value based on the load data measured at a specific time, the fermentation liquid property data measured at a specific time, and the estimated activity index data at the same specific time. Through the above processing, it is possible to suppress the increase in the operating load and perform a high-precision prediction of the biogas generation corresponding to the activity change of the anaerobic microorganisms that may change moment by moment.

[0039] Figure 2 Detailed flowchart showing the processing of the anaerobic digestion process monitoring system according to Embodiment 1. Figure 2 In the process, step S01 is a load measurement step, step S02 is a fermentation liquid measurement step, step S03 is a gas volume measurement step, step S04 is a process observation step, step S05 is a pretreatment step, step S06 is a gas generation volume prediction step, step S07 is a process monitoring step, and step S08 is a fault determination step.

[0040] First, organic waste such as sludge, food waste, and factory wastewater is crushed by a crusher or the like as needed and sent to the buffer tank 1. Figure 2In step S01, the organic waste inside the buffer tank 1 is fed into the digestion tank 2 through the feeding unit 3, the first sensor 4 measures the amount of organic waste fed into the digestion tank 2, outputs the load data as the measurement result to the process observation unit 11, and proceeds to step S02. In step S02, the second sensor 5 measures the properties of the fermentation liquid which is a suspension of organic waste and anaerobic microorganisms inside the digestion tank 2, outputs the fermentation liquid property data as the measurement result to the process observation unit 11, and proceeds to step S03. In step S03, the third sensor 7 measures the amount of biogas generated by the anaerobic microorganisms cultivated inside the digestion tank 2, outputs the actual value of the biogas generation as the measurement result to the process monitoring unit 14, and proceeds to step S04. In addition, in Figure 2 2 shows that the processing is performed in the order of step S01, step S02, and step S03, but in step S01, step S02, and step S03, it is preferable that each value is measured at the same measurement time at a predetermined measurement time interval.

[0041] In step S04, the process observation unit 11 stores the load data obtained from the first sensor 4 as time series data, stores the fermentation liquid property data obtained from the second sensor 5 as time series data, and outputs the load data and the fermentation liquid property data to the preprocessing unit 12 and the gas generation amount prediction unit 13, and then proceeds to step S05. In step S05, the preprocessing unit 12 obtains the load data and the fermentation liquid property data from the process observation unit 11, obtains and stores the estimated activity index data indicating the activity degree of the anaerobic microorganisms based on the load data and the fermentation liquid property data obtained using the first estimation model, and outputs the estimated activity index data to the gas generation amount prediction unit 13, and then proceeds to step S06. In step S06, the gas production prediction unit 13 obtains the load data and fermentation liquid property data from the process observation unit 11, obtains the estimated activity index data from the pretreatment unit 12, uses the second estimation model and obtains the biogas production prediction value based on the load data, fermentation liquid property data and estimated activity index data, outputs the biogas production prediction value to the process monitoring unit 14 and the outside of the monitoring device 10, outputs the estimated activity index data to the process monitoring unit 14, and enters step S07. In step S07, the process monitoring unit 14 obtains the actual biogas production value from the third sensor 7, obtains the biogas production prediction value from the gas production prediction unit 13, calculates the error rate based on the actual biogas production value and the biogas production prediction value, outputs the error rate to the outside of the monitoring device 10, and enters step S08.

[0042] In step S08, the process monitoring unit 14 obtains the estimated activity index data from the gas generation amount prediction unit 13. Furthermore, when the value of the error rate between the actual measurement value of the methane generation amount and the predicted value of the methane generation amount is outside the predetermined error rate normal range, and the actual measurement value of the methane generation amount is included in the predetermined gas generation amount normal range, the process monitoring unit 14 outputs a signal urging maintenance of the first sensor 4 and the second sensor 5. Furthermore, when the value of the error rate between the actual measurement value of the methane generation amount and the predicted value of the methane generation amount is outside the predetermined error rate normal range, the actual measurement value of the methane generation amount is outside the predetermined gas generation amount normal range, and the estimated activity index data is included in the predetermined activity index normal range, the process monitoring unit 14 outputs a signal urging maintenance of the third sensor 7. Moreover, when the error rate between the actual value of biogas production and the predicted value of biogas production is included in the predetermined normal range of error rate and the estimated activity index data is outside the predetermined normal range of activity index, the process monitoring unit 14 outputs a signal urging the stop of the biogas production system and urging the confirmation of the nature of the organic waste inside the buffer tank 1 fed into the digester 2, and ends the processing of the anaerobic digestion process monitoring system.

[0043] As described above, the anaerobic digestion process monitoring system involved in embodiment 1 is an anaerobic digestion process monitoring system that monitors the state of the anaerobic digestion process by predicting the amount of biogas generated from the fermentation liquid in which anaerobic microorganisms are cultivated inside the digester 2 by feeding organic waste into the digester 2, and comprises: a first sensor 4 that measures the amount of organic waste fed into the digester 2 and outputs the measurement result as load data; a second sensor 5 that measures the property of the fermentation liquid inside the digester 2 and outputs the measurement result as fermentation liquid property data; a pretreatment unit 12 that uses the first estimation model to obtain estimated activity index data indicating the activity of the anaerobic microorganism based on the load data and the fermentation liquid property data; and a gas generation amount prediction unit 13 that uses the second estimation model to obtain a biogas generation amount prediction value based on the load data, the fermentation liquid property data and the estimated activity index data, so that even when the activity of the anaerobic microorganisms inside the digester 2 changes, the biogas generation amount can be predicted with high accuracy.

[0044] In addition, it is known that the synergistic effect of increasing (changing) the biogas production rate or decomposition rate can be obtained by the combination of the organic wastes input. When the activity of the anaerobic microorganisms in the digester cannot be grasped due to the diversification trend of the organic wastes input in recent years, the prediction of the biogas production amount is more difficult. Although a method of performing a prediction operation of the biogas production amount by measuring the number of anaerobic microorganisms in the digester in addition to the information related to the input amount or the mixing ratio of the organic wastes is proposed, although the prediction accuracy of the biogas production amount is improved in this method, a great amount of work is required when measuring the number of anaerobic microorganisms inside the digester 2, which leads to an increase in the load of operation and management. The anaerobic digestion process monitoring system involved in embodiment 1 can simultaneously achieve the suppression of the increase of the operation and management load and the high-precision prediction of biogas.

[0045] Implementation method 2.

[0046] Figure 3 2 is a diagram showing the structure of the anaerobic digestion process monitoring system according to Embodiment 2. Figure 3 The anaerobic digestion process monitoring system according to the second embodiment shown in FIG. Figure 1 When compared with the anaerobic digestion process monitoring system according to the embodiment 1 shown in the figure, the monitoring device 10 is changed to the monitoring device 10a, the gas generation amount prediction unit 13 is changed to the gas generation amount prediction unit 13a, and the process monitoring unit 14 is changed to the process monitoring unit 14a. The gas generation amount prediction unit 13a has the same function as the gas generation amount prediction unit 13, and also has the function described below. Similarly, the process monitoring unit 14a has the same function as the process monitoring unit 14, and also has the function described below. The other structures of the anaerobic digestion process monitoring system according to the embodiment 2 are the same as those of the anaerobic digestion process monitoring system according to the embodiment 1.

[0047] The process monitoring unit 14a obtains the load data, fermentation liquid property data, estimated activity index data, and biogas production prediction value from the gas production prediction unit 13a, obtains the biogas production actual measurement value from the third sensor 7, and when the error rate value of the biogas production actual measurement value and the biogas production prediction value is included in the predetermined error rate normal range, the biogas production actual measurement value is included in the predetermined gas production normal range, and the estimated activity index data is included in the predetermined activity index normal range, the load data, the fermentation liquid property data, the estimated activity index data, and the biogas production actual measurement value, i.e., updated correlation data, are obtained, and the obtained updated correlation data are output to the gas production prediction unit 13a. The gas production prediction unit 13a updates the calculation formula of the second estimation model using the updated correlation data obtained from the process monitoring unit 14a. That is, the second estimation model can be updated by adding the load data, fermentation liquid property data, and estimated activity index data obtained in actual operation obtained from the process monitoring unit 14a to the load amount of organic waste input, the fermentation liquid property data, and estimated activity index data, and the correlation data of the biogas production amount obtained during the initial learning period set after the introduction of the system or in the test of inputting organic waste into the digester for laboratory test, and the fermentation liquid property data as information on the properties of the fermentation liquid of the digester, and the estimated activity index data, i.e., updating the related data. For example, the updating is performed by correcting the relevant coefficient for each of the multiple explanatory variables (load data, fermentation liquid property data, and estimated activity index data) extracted from the regression formula of the second estimation model. By updating the second inference model, even if the load amount of organic waste input obtained in the test of inputting organic waste into the digester for laboratory test, the fermentation liquid property data as information on the properties of the fermentation liquid in the digester, and the correlation between the inferred activity index data and the biogas production amount change in actual operation due to the influence of the synergistic effect of increasing (changing) the biogas production rate or decomposition rate due to the combination of organic waste input, the second inference model can be updated by using the updated relevant data obtained in the actual operation, thereby predicting the biogas production amount corresponding to the change in the correlation without increasing the operation management load.

[0048] In addition, the second inference model is updated only by using the update-related data when it is confirmed that the error rate between the actual value of biogas production and the predicted value of biogas production is included in the predetermined normal range of error rate, the actual value of biogas production is included in the predetermined normal range of gas production, and the inferred activity index data is included in the predetermined normal range of activity index. Therefore, the second inference model can be updated using the data when the first sensor 4, the second sensor 5 and the third sensor 7 all output normal values, and the second inference model will not be updated when any of the first sensor 4, the second sensor 5 and the third sensor 7 outputs an abnormal value due to a malfunction.

[0049] Furthermore, the process monitoring unit 14a is assumed to obtain the load data, the fermentation liquid property data, and the estimated activity index data from the gas generation amount prediction unit 13a, but the load data and the fermentation liquid property data may be obtained from the process observation unit 11 and the estimated activity index data may be obtained from the pretreatment unit 12.

[0050] As described above, in the anaerobic digestion process monitoring system involved in embodiment 2, when the value of the error rate is included in the predetermined normal range of the error rate, the actual value of the biogas production is included in the predetermined normal range of the gas production, and the estimated activity index data is included in the predetermined normal range of the activity index, the process monitoring unit 14a obtains the correlation data between the load data, the fermentation liquid property data, and the estimated activity index data and the actual value of the biogas production, that is, the updated correlation data, and outputs the updated correlation data to the gas production prediction unit 13a. The gas production prediction unit 13a uses the updated correlation data to update the calculation formula of the second estimation model. Therefore, even if the correlation between the load amount, the fermentation liquid property data, the estimated activity index data and the biogas production obtained in the experiment of feeding organic waste into the digester for laboratory experiments changes in actual operation, by using the updated correlation data obtained in the actual operation to update the second estimation model, it is possible to predict the biogas production corresponding to the change in the correlation without increasing the operation management load.

[0051] Implementation method 3.

[0052] Figure 4 2 is a diagram showing the structure of the anaerobic digestion process monitoring system according to Embodiment 3. Figure 4 The anaerobic digestion process monitoring system according to the third embodiment shown in FIG. Figure 1Compared with the anaerobic digestion process monitoring system involved in the embodiment 1 shown, an input amount calculation unit 15, a fourth sensor 21, a gas storage tank 22 and a fifth sensor 23 are added, the pretreatment unit 12 becomes a pretreatment unit 12b, the gas generation amount prediction unit 13 becomes a gas generation amount prediction unit 13b, and the monitoring device 10b includes a process observation unit 11, a pretreatment unit 12b, a gas generation amount prediction unit 13b, a process monitoring unit 14 and an input amount calculation unit 15. The pretreatment unit 12b has the same function as the pretreatment unit 12, and also has the function described below. Similarly, the gas generation amount prediction unit 13b has the same function as the gas generation amount prediction unit 13, and also has the function described below. The other structures of the anaerobic digestion process monitoring system involved in the embodiment 3 are the same as the structures of the anaerobic digestion process monitoring system involved in the embodiment 1.

[0053] The fourth sensor 21 measures the amount of organic waste stored in the buffer tank 1, and outputs the measurement result as the waste storage amount to the pre-treatment unit 12b. The gas storage tank 22 stores the biogas generated in the digestion tank 2. The fifth sensor 23 measures the amount of biogas stored in the gas storage tank 22, and outputs the measurement result as the gas storage amount to the input amount calculation unit 15. In addition, the information from the fifth sensor 23 to the input amount calculation unit 15 can be delivered by wired or wireless communication, or through a network such as the Internet.

[0054] Next, the operation of the monitoring device 10b will be described. The preprocessing unit 12b obtains the load data and the fermentation liquid property data from the process observation unit 11, and obtains the waste storage amount from the fourth sensor 21. Next, the preprocessing unit 12b uses the first estimation model to obtain the estimated activity index data indicating the activity of the anaerobic microorganisms based on the load data and the fermentation liquid property data, stores the obtained estimated activity index data, and outputs it to the gas generation amount prediction unit 13b. Moreover, the preprocessing unit 12b uses the first estimation model to obtain the maximum value of the amount of organic waste to be input into the digestion tank 2, i.e., the input allowable amount, so that the estimated activity index data becomes a value included in the predetermined activity index reference range based on the waste storage amount and the fermentation liquid property data, and outputs the information of the input allowable amount to the gas generation amount prediction unit 13b. In addition, the activity index reference range may also be the same range as the activity index normal range shown in Embodiments 1 and 2.

[0055] The gas generation prediction unit 13b obtains the load data and the fermentation liquid property data from the process observation unit 11, obtains the estimated activity index data from the pretreatment unit 12b, and uses the second estimation model to obtain the biogas generation prediction value based on the load data, the fermentation liquid property data and the estimated activity index data, and outputs it. Furthermore, the gas generation prediction unit 13b uses the second estimation model to obtain the predicted value of the biogas generation at each input amount when the input amount of organic waste to the digester 2 is changed within the range from zero to the input allowable amount, that is, the gas generation change predicted value, and generates gas generation characteristic information indicating the relationship between the input amount and the gas generation change predicted value when the input amount of organic waste is changed within the range from zero to the input allowable amount, and outputs it to the input amount calculation unit 15.

[0056] The input amount calculation unit 15 obtains the gas generation amount characteristic information from the gas generation amount prediction unit 13b, obtains the gas storage amount from the fifth sensor 23, obtains the biogas generation amount requirement instruction, i.e., the biogas requirement, from the outside, and uses the gas generation amount characteristic information and the gas storage amount to obtain the input amount of organic waste to be input into the digestion tank 2, i.e., the input amount designated value, which is required for delivering the biogas of the required biogas amount from the gas storage tank 22, and sends an input amount control signal for inputting the input amount of organic waste indicated by the input amount designated value into the digestion tank 2 to the input unit 3. The input unit 3 obtains the input amount control signal from the input amount calculation unit 15, and inputs the input amount of organic waste indicated by the input amount designated value into the digestion tank 2.

[0057] The input unit 3 is, for example, a pump, which can transport organic waste from the buffer tank 1 to the digestion tank 2, and can change the liquid delivery amount according to the input amount control signal from the input amount calculation unit 15. The fourth sensor 21 is a sensor for measuring the storage amount of organic waste inside the buffer tank 1, for example, a water level sensor for measuring the water level of the buffer tank 1, for example, a weight sensor for measuring the weight of the buffer tank 1 containing organic waste. The fifth sensor 23 is a sensor for measuring the storage amount of biogas inside the gas storage tank 22, for example, a gas pressure gauge for measuring the pressure inside the gas storage tank 22.

[0058] As described above, the anaerobic digestion process monitoring system involved in the third embodiment includes: a buffer tank 1 for storing organic waste fed into the digestion tank 2; a fourth sensor 21 for measuring the storage amount of organic waste in the buffer tank 1 and outputting the measurement result as the waste storage amount; a gas storage tank 22 for storing biogas generated in the digestion tank 2; a fifth sensor 23 for measuring the storage amount of biogas in the gas storage tank 22 and outputting the measurement result as the gas storage amount; an input amount calculation unit 15 for outputting the input amount of organic waste indicated by the input amount specified value. The input amount control signal of the input into the digestion tank 2; and the input unit 3, which obtains the input amount control signal, inputs the input amount of organic waste indicated by the input amount specified value into the digestion tank 2, wherein the pretreatment unit 12b uses the first estimation model and, based on the waste accumulation amount and the fermentation liquid property data, obtains the maximum value of the input amount of organic waste into the digestion tank 2, that is, the input allowable amount, so that the estimated activity index data becomes a value included in the predetermined activity index reference range, and outputs it, and the gas generation amount prediction unit 13b uses the second estimation model and, based on the fermentation liquid property data and the estimated activity index data, obtains the maximum value of the input amount of organic waste into the digestion tank 2, that is, the input allowable amount, and outputs it. The activity index data is measured, and the predicted value of the amount of biogas produced at each input amount when the input amount of organic waste to the digester 2 is changed within the range from zero to the input allowable amount, that is, the predicted value of the gas production change, is calculated, and gas production characteristic information representing the relationship between the input amount and the predicted value of the gas production change when the input amount of organic waste is changed within the range from zero to the input allowable amount is generated and outputted, and the input amount calculation unit 15 obtains the required instruction of the biogas production amount, that is, the required biogas amount, and uses the gas production characteristic information and the gas storage amount to calculate the required instruction for converting the biogas into the biogas. The required amount of biogas is sent from the gas storage tank 22, and the input amount of organic waste input into the digester 2, that is, the input amount specified value, generates an input amount control signal for inputting the input amount of organic waste indicated by the input amount specified value. Therefore, when the biogas obtained through the anaerobic digestion process is used for power generation in the generator (not shown) installed at the rear stage of the gas storage tank 22 and flexibly used as a distributed power source, the increase in the management load of the biogas power generation required by the generator, that is, the increase in the management load of the biogas production required for the operation of the anaerobic digestion process can be suppressed.

[0059] The present application describes various exemplary embodiments, but various features, forms, and functions described in one or more embodiments are not limited to application in specific embodiments, and can be applied to the embodiments alone or in various combinations.

[0060] Therefore, numerous modifications not shown in the examples are conceivable within the technical scope disclosed in the present application, including, for example, the case where at least one component is modified, the case where at least one component is added, or the case where at least one component is omitted, and the case where at least one component is extracted and combined with components of other embodiments.

[0061] Explanation of symbols

[0062] 1: buffer tank; 2: digestion tank; 3: input unit; 4: first sensor; 5: second sensor; 6: discharge unit; 7: third sensor; 10, 10a, 10b: monitoring device; 11: process observation unit; 12, 12b: pretreatment unit; 13, 13a, 13b: gas generation amount prediction unit; 14, 14a: process monitoring unit; 15: input amount calculation unit; 21: fourth sensor; 22: gas storage tank; 23: fifth sensor; 31: input piping; 32: gas piping; 33: discharge piping.

Claims

1. An anaerobic digestion process monitoring system for predicting the amount of biogas generated from a fermentation liquid in which anaerobic microorganisms are cultivated in a digestion tank by feeding organic waste into the digestion tank, and monitoring the state of the anaerobic digestion process, wherein the anaerobic digestion process monitoring system comprises: a first sensor for measuring the amount of the organic waste fed into the digestion tank and outputting the measurement result as load data; a second sensor for measuring the property of the fermentation liquid in the digestion tank and outputting the measurement result as fermentation liquid property data; A process observation unit, storing the load data and the fermentation liquid property data; a preprocessing unit that uses a first estimation model to obtain estimated activity index data indicating the activity of anaerobic microorganisms based on the load data and the fermentation liquid property data; as well as The gas generation amount prediction unit obtains a biogas generation amount prediction value based on the load data, the fermentation liquid property data, and the estimated activity index data using a second estimation model.

2. The anaerobic digestion process monitoring system according to claim 1, It is characterized in that The property of the fermentation liquid measured by the second sensor includes at least one of pH, TDS, carbon dioxide ion concentration, and conductivity.

3. The anaerobic digestion process monitoring system according to claim 1 or 2, It is characterized in that have: a third sensor for measuring the amount of biogas generated in the digestion tank and outputting the measurement result as a biogas production amount actual measurement value; and The process monitoring unit calculates an error rate based on the actual value of the methane generation amount and the predicted value of the methane generation amount.

4. The anaerobic digestion process monitoring system according to claim 3, It is characterized in that The first sensor measures the input amount of the organic waste at a predetermined measurement time interval and outputs the measured amount as the load data. The second sensor measures the property of the fermentation liquid at the measurement time interval when the first sensor measures the input amount of the organic waste and outputs the property data of the fermentation liquid. The third sensor measures the amount of biogas generated in the digester at the measurement time interval when the first sensor measures the amount of organic waste input, and outputs it as the actual value of biogas generation. The process observation unit stores the load data and the fermentation liquid property data as time series data at the measurement time intervals, When one of the times when the first sensor measures the input amount of organic waste is set as the specific time, The preprocessing unit obtains the estimated activity index data at the specific time based on the load data measured at the specific time and the fermentation liquid property data measured at the specific time, and stores the estimated activity index data at each time as time series data of measurement time intervals. The gas generation amount prediction unit obtains the biogas generation amount prediction value at the specific time based on the load data measured at the specific time, the fermentation liquid property data measured at the specific time, and the estimated activity index data at the specific time.

5. The anaerobic digestion process monitoring system according to claim 3 or 4, It is characterized in that The process monitoring unit obtains correlation data between the load data, the fermentation liquid property data, and the estimated activity index data and the actual value of the biogas production, i.e., updated correlation data, when the error rate value is within a predetermined normal range of the error rate, the actual value of the biogas production is within a predetermined normal range of the gas production, and the estimated activity index data is within a predetermined normal range of the activity index, and outputs the updated correlation data to the gas production prediction unit. The gas generation amount prediction unit updates the calculation formula of the second estimation model using the update correlation data.

6. The anaerobic digestion process monitoring system according to claim 3 or 4, It is characterized in that The gas generation amount prediction unit outputs the estimated activity index data to the process monitoring unit, The process monitoring unit outputs a signal urging maintenance of the third sensor when the error rate value is outside a predetermined normal error rate range, the actual measured value of the biogas production amount is outside a predetermined normal gas production amount range, and the estimated activity index data is included in a predetermined normal activity index range.

7. The anaerobic digestion process monitoring system according to claim 3 or 4, It is characterized in that The process monitoring unit outputs a signal urging maintenance of the first sensor and the second sensor when the error rate value is outside a predetermined normal error rate range and the actual methane generation amount measurement value is within a predetermined normal gas generation amount range.

8. The anaerobic digestion process monitoring system according to claim 3 or 4, It is characterized in that The gas generation amount prediction unit outputs the estimated activity index data to the process monitoring unit, The process monitoring unit outputs a signal urging the stop of the biogas production system and the confirmation of the nature of the organic waste fed into the digester when the value of the error rate is within a predetermined normal range of the error rate, the actual value of the biogas production is outside a predetermined normal range of the gas production, and the estimated activity index data is outside a predetermined normal range of the activity index.

9. The anaerobic digestion process monitoring system according to any one of claims 1 to 8, It is characterized in that have: A buffer tank for storing organic waste fed into the digestion tank; a fourth sensor for measuring the amount of organic waste stored in the buffer tank and outputting the measurement result as the amount of waste stored; A gas storage tank for storing the biogas generated in the digester; a fifth sensor for measuring the amount of biogas stored in the gas storage tank and outputting the measurement result as the amount of gas stored; an input amount calculation unit that outputs an input amount control signal for inputting an input amount of organic waste indicated by an input amount designated value into the digestion tank; and The input unit obtains the input amount control signal and inputs the input amount of organic waste indicated by the input amount designated value into the digestion tank, The pretreatment unit uses the first estimation model to obtain, based on the waste accumulation amount and the fermentation liquid property data, a maximum value of the amount of organic waste to be input into the digestion tank, i.e., an allowable input amount, which is a value included in a predetermined activity index reference range, and outputs the value. The gas generation prediction unit uses the second estimation model to obtain, based on the fermentation liquid property data and the estimated activity index data, a predicted value of the amount of biogas generated at each input amount when the input amount of organic waste to the digester is changed within the range from zero to the input allowable amount, i.e., a predicted value of gas generation change, and generates and outputs gas generation characteristic information indicating a relationship between the input amount and the predicted value of gas generation change when the input amount of organic waste is changed within the range from zero to the input allowable amount. The input amount calculation unit obtains a required instruction for the amount of biogas produced, i.e., the required amount of biogas, and uses the gas production characteristic information and the gas storage amount to calculate the input amount of organic waste to be input into the digester for delivering the required amount of biogas from the gas storage tank, i.e., the designated input amount value, and generates the input amount control signal for inputting the input amount of organic waste indicated by the designated input amount value.

10. A method for monitoring an anaerobic digestion process, wherein the amount of biogas generated from a fermentation liquid containing anaerobic microorganisms cultured in a digestion tank is predicted by feeding organic waste into the digestion tank, and the state of the anaerobic digestion process is monitored, wherein the method is characterized in that ， include: A load measurement step of measuring the amount of the organic waste input into the digestion tank and outputting the measurement result as load data; a fermentation liquid measuring step of measuring the properties of the fermentation liquid inside the digestion tank and outputting the measurement results as fermentation liquid property data; a process observation step, storing the load data and the fermentation liquid property data; A pre-processing step of obtaining estimated activity index data indicating the activity of anaerobic microorganisms based on the load data and the fermentation liquid property data using a first estimation model; as well as The gas generation amount prediction step uses a second estimation model to obtain a biogas generation amount prediction value based on the load data, the fermentation liquid property data, and the estimated activity index data.

11. The method for monitoring the anaerobic digestion process according to claim 10, It is characterized in that include: A gas amount measuring step, measuring the amount of biogas generated in the digester, and outputting the measurement result as a measured value of biogas production; as well as The process monitoring step is to calculate the error rate according to the measured value of the biogas production and the predicted value of the biogas production.

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