Anaerobic digestion process monitoring system and anaerobic digestion process monitoring method

Abstract

An anaerobic digestion process monitoring system can accurately predict the amount of biogas produced even when the activity of anaerobic microorganisms in a digester fluctuates. The system comprises: a first sensor (4) for measuring the amount of organic waste fed into a digester (2) and outputting load data as a measurement result; a second sensor (5) for measuring the properties of a fermentation liquid inside the digester (2) and outputting fermentation liquid property data as a measurement result; a preprocessing unit (12) for calculating 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; and a gas production prediction unit (13) for calculating a predicted value of biogas production based on the load data, the fermentation liquid property data, and the estimated activity index data using a second estimation model.

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] To recycle organic waste such as sludge, food scraps, and factory wastewater as a resource, the waste is fed into digesters where anaerobic microorganisms are cultivated, where methane fermentation occurs and biogas is extracted. This process, known as anaerobic digestion, uses the biogas extracted from the organic waste and converts it into heat energy in a boiler or electricity in a generator. This contributes to the resource utilization of organic waste and the reduction of greenhouse gases by reducing fuel or electricity usage.

[0003] In the energy industry, with the goal of reducing greenhouse gases or countering power outages during large-scale disasters (enhancing resilience), the shift from a system that relies on concentrated power generation at existing large-scale power plants to a system that builds a regionally distributed power supply network that flexibly utilizes renewable energy is gaining attention. This shift is referred to as the 3D transformation (decarbonization, decentralization, and digitalization) of the energy industry. By building microgrids using distributed power sources (distribution) that flexibly utilize renewable energy (decarbonization), greenhouse gas reduction or countering power outages during large-scale disasters (enhancing resilience) can be achieved. 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 generation. Therefore, in order for operators such as integrators to understand the power adjustment capabilities of distributed power sources, it is necessary to model (digitize) the available power generation and make it predictable.

[0004] Biogas power generation, which uses biogas obtained through anaerobic digestion, not only extracts electricity 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 with the most promising renewable energy potential for flexible use in achieving the 3D transformation of the energy industry.

[0005] On the other hand, there are two issues with biogas power generation. The first is digitalization. The amount of biogas produced depends on (1) the amount and properties of organic matter fed into the digester, (2) the mixing ratio of the 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, with the population decline or the increasing shortage of experienced and skilled operators, the increase in operation and management load may become a major obstacle to the installation of 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, accurately predict the biogas power generation and even the biogas production, and 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 has been proposed that compares the actual value of the biogas production rate generated from the digester with the predicted value of the biogas production rate. As a method for predicting the amount of biogas produced, the following system has been proposed: the biogas production rate or decomposition rate is pre-calculated 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, see Patent Document 1).

[0007] Prior art literature

[0008] Patent Document 1: Japanese Patent Application Laid-Open No. 2005-111338 Summary of the Invention

[0009] In the anaerobic digestion process monitoring system disclosed in Patent Document 1, the biogas production rate is precalculated 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. Therefore, there is a possibility that the activity of anaerobic microorganisms in the digester may fluctuate. As a result, there is a problem that when the biogas production rate fluctuates due to fluctuations 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 digester fluctuates.

[0011] The anaerobic digestion process monitoring system disclosed in the present application is an anaerobic digestion process monitoring system that monitors the status of the anaerobic digestion process by predicting the amount of biogas produced from fermentation liquid containing anaerobic microorganisms cultivated in a digester when organic waste is fed 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 properties of the fermentation liquid in 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 preprocessing unit that uses a first estimation model to determine estimated activity index data indicating the activity of the anaerobic microorganisms based on the load data and the fermentation liquid property data; and a gas production amount prediction unit that uses a second estimation model to determine a predicted value of biogas production 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 amount of organic waste fed into a digester and outputting the measurement result as load data; a second sensor for measuring the properties 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 determine 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 prediction unit for using a second estimation model to determine a predicted value for biogas generation based on the load data, the fermentation liquid property data, and the estimated activity index data. Therefore, even when the activity of the anaerobic microorganisms in the digester fluctuates, the biogas generation can be predicted with high accuracy. 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 a 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.

[0018] Implementation method 1.

[0019] Figure 1 : is a diagram showing the structure of the anaerobic digestion process monitoring system involved in embodiment 1. Figure 1 In the figure, solid arrows represent piping, and dotted arrows represent lines for exchanging information, such as 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 waste, and factory wastewater is crushed as needed using a slag crusher or the like and sent to a buffer tank 1. The buffer tank 1 stores the organic waste to be fed into the digester 2. The buffer tank 1 and the digester 2 are connected by a feed pipe 31. The organic waste in the buffer tank 1 is fed into the digester 2 via a feed unit 3. The feed unit 3 is, for example, a pump. The organic waste fed into the digester 2 is assimilated by the anaerobic microorganisms cultivated within the digester 2, generating biogas. A first sensor 4 for measuring the amount of organic waste fed into the digester 2 is provided on the feed pipe 31. The first sensor 4 outputs information on the amount of organic waste fed, as a result of the measurement, 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] Biogas generated in digester 2 is sent to, for example, a biogas generator via gas piping 32. A third sensor 7 for measuring the amount of biogas generated in digester 2 is provided on gas piping 32. Third sensor 7 outputs information on the actual value of biogas generation, which is a measurement result, to process monitoring unit 14 of monitoring device 10.

[0021] The second sensor 5 installed in the digester 2 measures the properties of the fermentation liquid, which is a suspension of organic waste and anaerobic microorganisms within the digester 2, and outputs information on the properties of the fermentation liquid, which is the measurement result, as fermentation liquid property data to the process observation unit 11 of the monitoring device 10. The process observation unit 11 stores the obtained fermentation liquid property data. The properties of the fermentation liquid measured by the second sensor 5 are correlated with physical property values ​​that serve as indicators of the activity of anaerobic microorganisms, namely, estimated activity index data. In other words, the property of the fermentation liquid measured by the second sensor 5 is, for example, an automatically measurable physical property value exhibited 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 of pH, TDS (Total Dissolved Solids), and carbon dioxide ion concentration can be used. Alternatively, the property of the fermentation liquid measured by the second sensor 5 is an automatically measurable physical property value exhibited by a substance that interferes with or inhibits the activity of anaerobic microorganisms (ammonia nitrogen, organic acids, nutrients, etc.). For example, any of pH, TDS, and conductivity can be used. Specifically, 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, such as 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 handover of information from the first sensor 4 to the process observation unit 11, the handover of information from the second sensor 5 to the process observation unit 11, and the handover 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 acquires load data from the first sensor 4 and fermentation liquid property data from the second sensor 5, stores the acquired load data and fermentation liquid property data, and outputs the acquired load data and fermentation liquid property data to the preprocessing unit 12 and the gas generation amount prediction unit 13. The preprocessing unit 12 acquires the load data and fermentation liquid property data from the process observation unit 11, uses the first estimation model to calculate estimated activity index data representing the activity level of the anaerobic microorganisms based on the load data and fermentation liquid property data, stores the calculated estimated activity index data, and outputs the calculated estimated activity index data to the gas generation amount prediction unit 13.

[0025] The gas production prediction unit 13 obtains load data and fermentation liquid property data from the process observation unit 11 and estimated activity index data from the pretreatment unit 12. Using the second estimation model, it calculates a biogas production prediction value based on the load data, fermentation liquid property data, and estimated activity index data. The calculated biogas production prediction value is output to the process monitoring unit 14 and also to the outside of the monitoring device 10. For example, if the gas piping 32 is connected to a biogas power generation facility, the biogas production prediction value output from the gas production prediction unit 13 is used to predict the biogas power generation at the biogas power generation facility.

[0026] The process monitoring unit 14 obtains the actual measured value of biogas production from the third sensor 7 and the predicted value of biogas production from the gas production prediction unit 13. It calculates an error rate based on the actual measured value and the predicted value of biogas production, and monitors whether the error rate is within a predetermined range. Alternatively, the process monitoring unit 14 can output the error rate to the outside of the monitoring device 10 or output the result of determining whether the error rate is within the predetermined range to the outside of the monitoring device 10. For example, the error rate can be calculated as (predicted value of biogas production - actual measured value of biogas production) / actual measured value of biogas production * 100.

[0027] Here, the first and second inference models are described. The first inference model, for example, is a computational formula for calculating estimated activity index data representing the activity of anaerobic microorganisms based on load data and fermentation broth property data. This is a regression formula that sets estimated 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.), as target variables. For each estimated activity index data, load data and fermentation broth property data are set as multiple explanatory variables. The second inference model, for example, is a computational formula for calculating a predicted value for biogas production based on load data, fermentation broth property data, and estimated activity index data. This is a regression formula that sets biogas production as the target variable and load data, fermentation broth property data, and estimated activity index data as multiple explanatory variables. After the system is implemented, a learning period is set up, and multivariate analysis is performed in the preprocessing unit 12. For each estimated activity index data set, necessary explanatory variables are extracted from the measured load data and fermentation liquid property data. Coefficients associated with the necessary explanatory variables are obtained, thereby constructing a regression formula for the first estimation model. After the first estimation model is constructed, multivariate analysis is performed in the gas production prediction unit 13. For the actual measured value of biogas production, necessary explanatory variables are extracted from the measured load data, fermentation liquid property data, and estimated activity index data obtained using the first estimation model. Coefficients associated with the necessary explanatory variables are obtained, thereby constructing a regression formula for the second estimation model. Furthermore, the computational formulas for the first and second estimation models can be determined through preliminary experiments before the system is implemented. The following describes a method for this.

[0028] In a test involving the addition of organic waste to a digester for laboratory testing, the amount of organic waste added to the digester was measured as load data, and TVS (Total Volatile Solids), an indicator of the amount of organic matter in the organic waste added, was measured. In the case of multiple types of organic waste, the amount of organic waste added and the TVS were measured for each type of organic waste. The digester for laboratory testing into which the organic waste was added was maintained at a constant temperature, and the amount of biogas produced was measured after, for example, 20 days. This allowed data on the amount of biogas produced per TVS input corresponding to the load data for the amount of organic waste added. Furthermore, at this time, the conditions for the amount of organic waste added to the digester (load data) were assigned, and values ​​such as pH, TDS, carbon dioxide ion concentration, and conductivity of the fermentation liquid inside the digester were measured as fermentation liquid property data. Furthermore, 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 serve as indicators of the activity of anaerobic microorganisms, i.e., estimated activity index data.

[0029] When the amount of organic waste fed into the digester (load data) is changed, the concentration of organic acids (such as acetic acid, valeric acid, butyric acid, and propionic acid) increases or the amount of nutrient salts becomes insufficient, thereby lowering the pH of the fermentation liquid in the digester and reducing the activity of anaerobic microorganisms. Therefore, as properties of the fermentation liquid in digester 2, the second sensor 5 measures the pH, TDS, or carbon dioxide ion concentration of the fermentation liquid, and uses the pH, TDS, or carbon dioxide ion concentration as fermentation liquid property data. This allows the estimated activity indicator data, namely, the organic acid concentration or nutrient salt concentration, which serves as an indicator of the activity of anaerobic microorganisms, to be estimated based on the 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 of the fermentation liquid 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 of the fermentation liquid as property data of the fermentation liquid. Based on this, a first estimation model can be obtained for calculating the organic acid concentration or nutrient salt concentration as estimated activity indicator data representing the activity degree 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 amount of organic waste added to the digester (load data) is changed, the ammonia nitrogen concentration increases and the activity of anaerobic microorganisms decreases. Therefore, as a property of the fermentation liquid in the digester 2, the conductivity of the fermentation liquid is measured using the second sensor 5, and the conductivity value is used as the fermentation liquid property data. Thus, it is possible to infer the ammonia nitrogen concentration, which is the inferred activity index data that is an indicator of the activity of anaerobic microorganisms, based on the conductivity value as the fermentation liquid property data. For example, by gradually increasing the amount of organic waste added to the digester for laboratory testing, the conductivity and ammonia nitrogen concentration of the fermentation liquid are measured while measuring the load amount. This allows information on the correlation between the conductivity of the fermentation liquid and the ammonia nitrogen concentration to be obtained. Based on this, it is possible to obtain a first inference model for the ammonia nitrogen concentration, which is inferred activity index data that represents 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 biogas production per TVS input, the loading amount as the input amount of organic waste, the pH, TDS or carbon dioxide ion concentration of the fermentation liquid, and the organic acid concentration or nutrient salt concentration calculated based on the pH, TDS or carbon dioxide ion concentration, information on the correlation between the loading data, pH, TDS or carbon dioxide ion concentration, and the organic acid concentration or nutrient salt concentration and the predicted biogas production can be obtained, thereby obtaining a second estimation model for calculating the predicted biogas production value based on the loading amount and the pH, TDS or carbon dioxide ion concentration of the fermentation liquid. Alternatively, by performing multivariate analysis on the biogas production per TVS input, the loading amount as the input amount of organic waste, the electrical conductivity of the fermentation liquid, and the ammonia nitrogen concentration calculated based on the electrical conductivity, information on the correlation between the loading data, electrical conductivity, and ammonia nitrogen concentration and the predicted biogas production value can be obtained, thereby obtaining a second estimation model for calculating the predicted biogas production value based on the loading amount and the electrical conductivity of the fermentation liquid. Moreover, 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 carbonic acid ion concentration, the organic acid concentration calculated based on the pH, TDS or carbonic acid 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 carbonic acid ion concentration, electrical conductivity, organic acid concentration and ammonia nitrogen concentration, and the predicted value of the biogas production amount, and obtain a second inference model for calculating the predicted value of the biogas production amount based on the load amount, the pH, TDS or carbonic acid ion concentration of the fermentation liquid, and the electrical conductivity of the fermentation liquid.

[0032] As described above, the first estimation model calculates estimated activity index data, which serves as an indicator of the activity of anaerobic microorganisms in the fermentation broth, based on the load of organic waste input and fermentation broth property data, which serves as information on the properties of the fermentation broth in the digester, based on correlation information obtained during a pre-set learning period after system implementation or laboratory testing before system implementation. If the fermentation broth property data is the pH, TDS, or carbon dioxide ion concentration of the fermentation broth, the estimated activity index data is the organic acid concentration or nutrient salt concentration of the fermentation broth; if the fermentation broth property data is the electrical conductivity of the fermentation broth, the estimated activity index data is the ammonia nitrogen concentration of the fermentation broth. Furthermore, the second estimation model calculates a predicted value of biogas production per TVS input based on the load of organic waste input, the fermentation broth property data, which serves as information on the properties of the fermentation broth in the digester, and the estimated activity index data calculated using the first estimation model, based on correlation information obtained during a pre-set learning period after system implementation or laboratory testing before system implementation.

[0033] While automatic measurement using sensors and the like is not possible, the first estimation model can be used to estimate the physical property values ​​that serve as indicators of anaerobic microbial activity, which are required for accurately predicting biogas production. This is used as estimated activity index data. This estimated activity index data, along with load data and fermentation broth property data, is then input into the calculation formula of the second estimation model to obtain a predicted biogas production value. This allows for highly accurate prediction of biogas production while suppressing increases in operating load. Furthermore, fermentation broth property data is not limited to pH, TDS, carbon dioxide ion concentration, or conductivity; it can also include acetate ion concentration, COD (Chemical Oxygen Demand) concentration, and other such values. By testing organic waste in laboratory digesters, correlations with physical property values ​​that serve as indicators of anaerobic microbial activity, such as the fermentation broth's organic acid concentration, nutrient salt concentration, or ammonia nitrogen concentration, can be confirmed. 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 number of types of fermentation liquid property data increases, 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 biogas production value from the gas production prediction unit 13 and the actually measured biogas production value from the third sensor 7. If the error rate between the actually measured biogas production value and the predicted biogas production value is outside a predetermined normal error rate range and the actually measured biogas production value is within the predetermined normal gas production range, the process monitoring unit 14 may determine that there is a possibility that the estimated activity index data obtained using the first estimation model is outside the normal value range due to a failure in at least one of the first sensor 4 and the second sensor 5, and output a signal urging maintenance of the first sensor 4 and the second sensor 5. Through the above processing, maintenance of the first sensor 4 and the second sensor 5 can be smoothly performed.

[0035] Furthermore, the gas production prediction unit 13 may output the estimated activity index data along with the predicted methane production value to the process monitoring unit 14. In this case, if the error rate between the actual methane production value obtained from the third sensor 7 and the predicted methane production value obtained from the gas production prediction unit 13 is outside a predetermined normal error rate range, the actual methane production value is outside the predetermined normal gas production range, and the estimated activity index data obtained from the gas production prediction unit 13 is within the predetermined normal activity index range, the process monitoring unit 14 may determine that there is a possibility of a failure in the third sensor 7 and output a signal urging maintenance of the third sensor 7. The above process enables smooth maintenance of the third sensor 7. Furthermore, if the error rate between the actual methane production value obtained from the third sensor 7 and the predicted methane production value obtained from the gas production prediction unit 13 is within a predetermined normal error rate range, the actual methane production value is outside the predetermined normal gas production range, and the estimated activity index data is outside the predetermined normal activity index range, the process monitoring unit 14 may determine that the activity of anaerobic microorganisms has decreased due to the influence of organic waste and output a signal urging the shutdown of the methane production system and confirmation of the nature of the organic waste contained in the buffer tank 1 and fed into the digester 2. The above process allows for smooth shutdown of the methane production system and confirmation of the nature of the organic waste contained in the buffer tank 1. Furthermore, the process monitoring unit 14 may also obtain estimated activity index data from the pre-processing unit 12.

[0036] When utilizing biogas power generation using biogas generated through anaerobic digestion as a distributed power source, operators such as integrators can appropriately set a normal error rate range based on the permissible fluctuation in power regulation required by the amount of biogas-generated electricity and the total amount of electricity in the microgrid that includes it. The normal error rate range is preferably within 10%, and more preferably within 5%. Furthermore, if the properties of organic waste fluctuate seasonally, it is necessary to adjust the rated value to account for seasonal fluctuations. Therefore, if the properties of organic waste fluctuate seasonally, it is preferable to conduct tests in which organic waste is fed into laboratory test digesters using the input amount as a parameter in each of the four seasons: spring, summer, autumn, and winter. The normal activity index range can be appropriately set based on data obtained from these tests in which organic waste is fed into laboratory test digesters using the input amount as a parameter. For example, the organic acid concentration is preferably 2000 mg / L or less, and more preferably 100 mg / L or less. Furthermore, for example, the ammonia nitrogen concentration is preferably 5000 mg / L or less, and more preferably 1000 mg / L or less.

[0037] From the perspective of improving the accuracy of predicting the amount of biogas production, it is preferred that the first sensor 4 measures the amount of organic waste input in real time at a predetermined measurement time interval and outputs the measured amount as load data to the process monitoring unit 11. The process monitoring unit 11 stores the load data as time-series data at the measurement time intervals. Furthermore, it is preferred that the second sensor 5 measures the properties of the fermentation liquid within the digester 2 in real time at the same measurement time intervals as the first sensor 4 and outputs the measured amount as fermentation liquid property data to the process monitoring unit 11. The process monitoring unit 11 stores the measured amount as time-series data at the same measurement time intervals 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 intervals as the first sensor 4 and outputs the measured amount as a predicted value of biogas production to the process monitoring unit 14. 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 biogas production amount may decrease. 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 times (measurement timings) of the time-series load data of the organic waste input amount measured by the first sensor 4, the measurement times (measurement timings) of the time-series fermentation liquid property data of the fermentation liquid inside the digester 2 measured by the second sensor 5, and the measurement times (measurement timings) of the time-series actual values ​​of the biogas production amount measured by the third sensor 7 are preferably the same. That is, the second sensor 5 preferably measures the fermentation liquid property at the time when the first sensor 4 measures the organic waste input amount, and the third sensor 7 preferably measures the actual value of the biogas production amount at the time when the first sensor 4 measures the organic waste input amount. Furthermore, when one of the times when the first sensor 4 measures the organic waste input amount is defined as a specific time, the preprocessing unit 12 preferably calculates estimated activity index data at the specific time using the first estimation model based on the load data 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 at the measurement time interval. Similarly, when the gas production prediction unit 13 uses the load data, fermentation liquid property data, and estimated activity index data as input and calculates the biogas production prediction value using the second estimation model, it is also preferable to calculate the biogas production prediction value based on the load data and fermentation liquid property data measured at a specific time, and the estimated activity index data at the same specific time. This process allows for highly accurate prediction of biogas production in response to changes in the activity of anaerobic microorganisms, which may fluctuate moment by moment, while suppressing increases in the operating load.

[0039] Figure 2 : is a flowchart showing the details of the processing of the anaerobic digestion process monitoring system involved in 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 amount measurement step, step S04 is a process observation step, step S05 is a pretreatment step, step S06 is a gas generation amount prediction step, step S07 is a process monitoring step, and step S08 is a fault judgment 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, and 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, and 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 produced by the anaerobic microorganisms cultivated inside the digestion tank 2, and outputs the actual value of the biogas production as the measurement result to the process monitoring unit 14, and proceeds to step S04. In addition, in Figure 2 2 and 3 , the processing is performed in the order of step S01 , step S02 , and step S03 . However, it is preferable that each value is measured at the same measurement time at a predetermined measurement time interval in step S01 , step S02 , and step S03 .

[0041] In step S04, the process observation unit 11 stores the load data acquired from the first sensor 4 as time-series data and the fermentation liquid property data acquired from the second sensor 5 as time-series data. It then outputs the load data and fermentation liquid property data to the preprocessing unit 12 and the gas production prediction unit 13, and the process proceeds to step S05. In step S05, the preprocessing unit 12 acquires the load data and fermentation liquid property data from the process observation unit 11, calculates and stores estimated activity index data indicating the activity level of the anaerobic microorganisms based on the load data and fermentation liquid property data acquired using the first estimation model, and then outputs the estimated activity index data to the gas production prediction unit 13, and the process proceeds to step S06. In step S06, the gas production prediction unit 13 obtains load data and fermentation liquid property data from the process observation unit 11 and estimated activity index data from the preprocessing unit 12. Using the second estimation model, the load data, fermentation liquid property data, and estimated activity index data are used to calculate a biogas production prediction value. The predicted biogas production value is output to the process monitoring unit 14 and to the outside of the monitoring device 10. The estimated activity index data is also output to the process monitoring unit 14, and the process proceeds to step S07. In step S07, the process monitoring unit 14 obtains the actual biogas production value from the third sensor 7 and the predicted biogas production value from the gas production prediction unit 13. An error rate is calculated based on the actual biogas production value and the predicted biogas production value, and the error rate is output to the outside of the monitoring device 10. The process then proceeds to step S08.

[0042] In step S08, the process monitoring unit 14 obtains the estimated activity index data from the gas production prediction unit 13. Furthermore, if the error rate between the actual and predicted methane production values ​​is outside the predetermined error rate normal range, and the actual methane production value is within the predetermined gas production normal range, the process monitoring unit 14 outputs a signal urging maintenance of the first and second sensors 4 and 5. Furthermore, if the error rate between the actual and predicted methane production values ​​is outside the predetermined error rate normal range, the actual measured methane production value is outside the predetermined gas production normal range, and the estimated activity index data is within 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 the biogas production amount and the predicted value of the biogas production amount is included in the predetermined normal range of the error rate and the estimated activity index data is outside the predetermined normal range of the 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 according to the first embodiment is an anaerobic digestion process monitoring system that monitors the state of the anaerobic digestion process by predicting the amount of biogas generated from a fermentation liquid containing anaerobic microorganisms grown in the digester 2 when organic waste is fed into the digester 2. The system 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 properties of the fermentation liquid in the digester 2 and outputs the measurement result as fermentation liquid property data; a preprocessing unit 12 that uses a first estimation model to determine estimated activity index data indicating the activity of the anaerobic microorganisms based on the load data and the fermentation liquid property data; and a gas generation amount prediction unit 13 that uses a second estimation model to determine a predicted value of biogas generation based on the load data, the fermentation liquid property data, and the estimated activity index data. Therefore, even when the activity of the anaerobic microorganisms in the digester 2 fluctuates, the biogas generation amount can be predicted with high accuracy.

[0044] Furthermore, it is known that the combination of organic waste input can produce a synergistic effect of increasing (changing) the biogas production rate or decomposition rate. However, due to the recent trend of diversifying the input of organic waste, it is difficult to grasp the activity of anaerobic microorganisms in the digester, making it more difficult to predict the amount of biogas produced. Although a method has been proposed that uses information related to the amount of organic waste input or the mixing ratio to measure the number of anaerobic microorganisms in the digester to perform a calculation to predict the amount of biogas produced, while this method improves the prediction accuracy of the amount of biogas produced, it requires a lot of work to measure the number of anaerobic microorganisms inside the digester 2, resulting in an increase in the load of operational management. The anaerobic digestion process monitoring system according to Embodiment 1 can simultaneously suppress the increase in operational management load and achieve high-precision prediction of biogas.

[0045] Implementation method 2.

[0046] Figure 3 This is a diagram showing the structure of the anaerobic digestion process monitoring system according to the second embodiment. Figure 3 The anaerobic digestion process monitoring system according to the second embodiment shown in FIG. Figure 1 Compared to the anaerobic digestion process monitoring system according to the first embodiment shown, the monitoring device 10 is replaced by the monitoring device 10a, the gas generation prediction unit 13 is replaced by the gas generation prediction unit 13a, and the process monitoring unit 14 is replaced by the process monitoring unit 14a. The gas generation prediction unit 13a has the same functions as the gas generation prediction unit 13, and also has the functions described below. Similarly, the process monitoring unit 14a has the same functions as the process monitoring unit 14, and also has the functions described below. The remaining structure of the anaerobic digestion process monitoring system according to the second embodiment is the same as that of the anaerobic digestion process monitoring system according to the first embodiment.

[0047] The process monitoring unit 14a obtains the load data, fermentation liquid property data, estimated activity index data, and the predicted biogas production value from the gas production prediction unit 13a, and obtains the actual biogas production value from the third sensor 7. If the error rate between the actual biogas production value and the predicted biogas production value is within a predetermined normal error rate range, the actual biogas production value is within a predetermined normal gas production range, and the estimated activity index data is within a predetermined normal activity index range, the process monitoring unit 14a calculates updated correlation data, which is correlation data between the load data, fermentation liquid property data, estimated activity index data, and the actual biogas production value, and outputs the calculated updated correlation data to the gas production prediction unit 13a. The gas production prediction unit 13a uses the updated correlation data obtained from the process monitoring unit 14a to update the calculation formula of the second estimation model. Specifically, the second estimation model can be updated by adding the correlation data between the load data, fermentation liquid property data, and estimated activity index data obtained during actual operation and the actual measured value of biogas production, obtained from the process monitoring unit 14a, to the correlation data between the load data, fermentation liquid property data, and estimated activity index data obtained during the initial learning period after the system is introduced or during a test in which organic waste is fed into a digester for laboratory testing, which represents the amount of organic waste fed, and the fermentation liquid property data, which represents information on the properties of the fermentation liquid in the digester, and the estimated activity index data, and the biogas production. For example, the updating is performed by correcting the coefficients associated with 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 the input organic waste, the second inference model can be updated using the updated relevant data obtained in actual operation, thereby not increasing the operation management load and predicting the biogas production amount corresponding to the change in the correlation.

[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 sensor among 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 load data, fermentation liquid property data, and estimated activity index data from the gas generation amount prediction unit 13a. However, the load data and 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 according to the second embodiment, when the error rate value is within the predetermined normal error rate range, the actual measured biogas production value is within the predetermined normal gas production range, and the estimated activity index data is within the predetermined normal activity index range, the process monitoring unit 14a calculates updated correlation data, which is correlation data between the load data, the fermentation liquid property data, and the estimated activity index data and the actual measured biogas production value, 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 data, the fermentation liquid property data, the estimated activity index data, and the biogas production obtained in the test of feeding organic waste into a digester for laboratory tests changes during actual operation, by updating the second estimation model using the updated correlation data obtained during actual operation, it is possible to predict the biogas production corresponding to the change in the correlation without increasing the operation and management load.

[0051] Implementation method 3.

[0052] Figure 4 This is a diagram showing the structure of the anaerobic digestion process monitoring system according to the third embodiment. Figure 4 The anaerobic digestion process monitoring system according to the third embodiment shown in FIG. Figure 1Compared to the anaerobic digestion process monitoring system according to the first embodiment shown, the system has an additional input amount calculation unit 15, a fourth sensor 21, a gas storage tank 22, and a fifth sensor 23. The pretreatment unit 12 has been changed to a pretreatment unit 12b, and the gas generation amount prediction unit 13 has been changed to a gas generation amount prediction unit 13b. 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 functions as the pretreatment unit 12, and also has the functions described below. Similarly, the gas generation amount prediction unit 13b has the same functions as the gas generation amount prediction unit 13, and also has the functions described below. The remaining configuration of the anaerobic digestion process monitoring system according to the third embodiment is the same as that of the anaerobic digestion process monitoring system according to the first embodiment.

[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 digester 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. Information exchange between the fifth sensor 23 and the input amount calculation unit 15 can be performed via wired or wireless communication, or via a network such as the Internet.

[0054] Next, the operation of the monitoring device 10b will be described. The preprocessing unit 12b obtains load data and fermentation liquid property data from the process observation unit 11 and obtains the waste accumulation amount from the fourth sensor 21. Next, the preprocessing unit 12b uses the first estimation model to determine estimated activity index data representing the activity level of the anaerobic microorganisms based on the load data and fermentation liquid property data. The preprocessing unit 12b stores the determined estimated activity index data and outputs it to the gas generation prediction unit 13b. Furthermore, the preprocessing unit 12b uses the first estimation model to determine the maximum amount of organic waste that can be fed into the digester 2, or the allowable input amount, for the estimated activity index data to fall within a predetermined activity index reference range, based on the waste accumulation amount and fermentation liquid property data. Information on the allowable input amount is then output to the gas generation prediction unit 13b. The activity index reference range may also be the same range as the normal activity index range described in Embodiments 1 and 2.

[0055] The gas production prediction unit 13b obtains load data and fermentation liquid property data from the process observation unit 11 and estimated activity index data from the pretreatment unit 12b. Using the second estimation model, the gas production prediction unit 13b calculates and outputs a predicted biogas production value based on the load data, fermentation liquid property data, and estimated activity index data. Furthermore, using the second estimation model, the gas production prediction unit 13b calculates predicted biogas production values ​​for each input amount of organic waste into the digester 2 when the input amount is varied within a range from zero to the permissible input amount, i.e., predicted gas production change values. The unit generates gas production characteristic information representing the relationship between the input amount and the predicted gas production change values ​​when the input amount of organic waste is varied within a range from zero to the permissible input amount, and outputs the information to the input amount calculation unit 15.

[0056] The input amount calculation unit 15 obtains gas generation characteristic information from the gas generation amount prediction unit 13b, obtains the gas storage amount from the fifth sensor 23, and obtains a biogas demand instruction indicating the biogas generation amount from the outside. Using the gas generation characteristic information and the gas storage amount, the unit 15 calculates the input amount of organic waste required to be fed into the digester 2, i.e., the input amount designated value, in order to deliver the biogas demanded amount from the gas storage tank 22. The unit 15 then transmits an input amount control signal to the input unit 3 for inputting the input amount of organic waste indicated by the input amount designated value into the digester 2. The input unit 3 receives 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 digester 2.

[0057] The feeding unit 3 is, for example, a pump, capable of feeding organic waste from the buffer tank 1 to the digestion tank 2, and can change the feeding rate based on an input rate control signal from the input rate calculation unit 15. The fourth sensor 21 is a sensor for measuring the amount of organic waste stored in the buffer tank 1. For example, it is a water level sensor for measuring the water level in the buffer tank 1, or a weight sensor for measuring the weight of the buffer tank 1 containing the organic waste. The fifth sensor 23 is a sensor for measuring the amount of biogas stored in the gas tank 22. For example, it is a gas pressure gauge for measuring the pressure inside the gas tank 22.

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

[0059] This 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, but 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 this application, including, for example, modifying at least one component, adding at least one component, omitting at least one component, and extracting at least one component and combining it with components from other embodiments.

[0061] Explanation of symbols

[0062] 1: Buffer tank; 2: Digestion tank; 3: Feed 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 prediction unit; 14, 14a: Process monitoring unit; 15: Feed amount calculation unit; 21: Fourth sensor; 22: Gas storage tank; 23: Fifth sensor; 31: Feed piping; 32: Gas piping; 33: Discharge piping.

Claims

1. An anaerobic digestion process monitoring system for predicting the amount of methane produced from a fermentation liquid containing anaerobic microorganisms grown in a digestion tank by feeding organic waste into the digestion tank, thereby monitoring the state of the anaerobic digestion process, the anaerobic digestion process monitoring system comprising: a first sensor for measuring the amount of the organic waste fed into the digester and outputting the measurement result as load data; a second sensor for measuring at least pH and conductivity of the fermentation liquid inside the digestion tank as properties and outputting the measurement results; a process observation unit storing the load data and at least the pH and the conductivity; a preprocessing unit that uses a first estimation model composed of at least the load data acquired during a preset learning period after system introduction or before system introduction, a correlation expression between the pH and the concentration of organic acids in the fermentation liquid inside the digester, and a correlation expression between the load data, the conductivity, and the concentration of ammonia nitrogen in the fermentation liquid inside the digester, to calculate at least the concentration of the organic acids and the concentration of the ammonia nitrogen based on the load data, the pH, and the conductivity; as well as The gas production prediction unit uses a second estimation model composed of at least the load data, the pH, the correlation equation between the concentration of the organic acid in the fermentation liquid inside the digester and the biogas production amount, and the correlation equation between the load data, the electrical conductivity, the concentration of ammonia nitrogen in the fermentation liquid inside the digester and the biogas production amount, acquired during a preset learning period after system introduction or before system introduction. The prediction unit uses at least the load data, the pH, the electrical conductivity, and the concentration of the organic acid and the concentration of ammonia nitrogen calculated by the preprocessing unit to predict a biogas production value. The state of the anaerobic digestion process is monitored using the concentration of the organic acid and the concentration of the ammonia nitrogen calculated by the pre-processing unit and the predicted value of the biogas production amount predicted by the gas production amount prediction unit.

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

3. The anaerobic digestion process monitoring system according to claim 2, characterized in that: The first sensor measures the amount of organic waste input 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 measurement result. 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 the measured value of the biogas amount. The process observation unit stores the load data, the pH, and the conductivity as time series data at the measurement time intervals. When one of the times when the first sensor measures the amount of organic waste input is set as the specific time, The preprocessing unit calculates the concentration of the organic acid and the concentration of the ammonia nitrogen at the specific time based on the load data measured at the specific time and the pH and the conductivity measured at the specific time, and stores the concentration of the organic acid and the concentration of the ammonia nitrogen 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 pH and the conductivity measured at the specific time, and the organic acid concentration and the ammonia nitrogen concentration at the specific time.

4. The anaerobic digestion process monitoring system according to claim 2, characterized in that: When the error rate value is within a predetermined normal error rate range, the actual methane production amount measurement value is within a predetermined normal gas production amount range, and the organic acid concentration and the ammonia nitrogen concentration are within a predetermined normal activity index range, the process monitoring unit obtains updated correlation data, which are correlation data between the load data, the pH, the electrical conductivity, the organic acid concentration, the ammonia nitrogen concentration, and the actual methane production amount measurement value, and outputs the updated correlation data to the gas production amount prediction unit. The gas generation amount prediction unit updates the calculation formula of the second estimation model using the update correlation data.

5. The anaerobic digestion process monitoring system according to claim 2, characterized in that: The gas generation amount prediction unit outputs the concentration of the organic acid and the concentration of the ammonia nitrogen 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 range of the error rate, the actual value of the biogas production amount is outside a predetermined normal range of the gas production amount, and the concentration of the organic acid and the concentration of the ammonia nitrogen are within a predetermined normal range of the activity index.

6. The anaerobic digestion process monitoring system according to claim 2, 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 production amount value is within a predetermined normal gas production amount range.

7. The anaerobic digestion process monitoring system according to claim 2, characterized in that: The gas generation amount prediction unit outputs the concentration of the organic acid and the concentration of the ammonia nitrogen 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 error rate value is within a predetermined normal range of the error rate, the actual value of the biogas production amount is outside a predetermined normal range of the gas production amount, and the concentration of the organic acid and the concentration of the ammonia nitrogen are outside a predetermined normal range of the activity index.

8. The anaerobic digestion process monitoring system according to claim 1, 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 designation value into the digestion tank; and The feeding unit obtains the feeding amount control signal and feeds the organic waste of the feeding amount indicated by the feeding amount specified value into the digestion tank. The pretreatment unit uses the first estimation model to determine, based on the waste accumulation amount, the pH, and the conductivity, a maximum allowable input amount of organic waste to be fed into the digester at which the concentration of the organic acid and the concentration of the ammonia nitrogen fall within a predetermined activity index reference range, and outputs the result. The gas generation amount prediction unit uses the second estimation model to determine predicted values ​​of biogas generation at various input amounts of organic waste to the digester when the input amount of organic waste to the digester is varied within a range from zero to the allowable input amount, i.e., predicted gas generation amount change values, based on the pH, the electrical conductivity, the concentration of the organic acid, and the concentration of ammonia nitrogen. The unit generates and outputs gas generation amount characteristic information indicating a relationship between the input amount of organic waste and the predicted gas generation amount change values ​​when the input amount of organic waste is varied within a range from zero to the allowable input amount. The input amount calculation unit obtains a required instruction for the amount of biogas produced, namely, the required biogas amount, and uses the gas production amount 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, namely, the input amount specified value, and generates the input amount control signal for inputting the input amount of organic waste indicated by the input amount specified value.

9. A method for monitoring an anaerobic digestion process, wherein the method predicts the amount of biogas generated from a fermentation liquid containing anaerobic microorganisms grown in a digestion tank by feeding organic waste into the digestion tank, and monitors the state of the anaerobic digestion process, wherein: include: a load measurement step of measuring the amount of the organic waste fed into the digester and outputting the measurement result as load data; a fermentation liquid measurement step of measuring at least pH and conductivity of the fermentation liquid inside the digestion tank as properties and outputting the measurement results; a process observation step of storing the load data and at least the pH and the conductivity; a preprocessing step of calculating at least the organic acid concentration and the ammonia nitrogen concentration based on the load data, the pH, and the conductivity using a first estimation model composed of at least the load data acquired during a preset learning period after system introduction or before system introduction, a correlation expression between the pH and the concentration of organic acids in the fermentation liquid inside the digester, and a correlation expression between the load data, the conductivity, and the concentration of ammonia nitrogen in the fermentation liquid inside the digester; as well as a gas production prediction step using a second estimation model composed of at least the load data, the pH, the correlation equation between the concentration of the organic acid in the fermentation liquid inside the digester and the biogas production amount, and the correlation equation between the load data, the electrical conductivity, the concentration of ammonia nitrogen in the fermentation liquid inside the digester and the biogas production amount, obtained during a preset learning period after system introduction or before system introduction, to predict a biogas production prediction value using at least the load data, the pH, the electrical conductivity, and the concentration of the organic acid and the concentration of ammonia nitrogen calculated in the pretreatment step; The state of the anaerobic digestion process is monitored using the concentration of the organic acid and the concentration of the ammonia nitrogen calculated in the pretreatment step and the predicted value of the biogas production amount predicted in the gas production amount prediction step.

10. The anaerobic digestion process monitoring method according to claim 9, characterized in that: include: a gas amount measuring step, measuring the amount of biogas generated in the digester, and outputting the measurement result as an actual value of biogas production; as well as The process monitoring step is to calculate an error rate based on the actual value of the biogas production and the predicted value of the biogas production.

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