Control system for biogas power generation facilities and control method for biogas power generation facilities

Abstract

To provide a control system for biogas power generation facilities and a control method for biogas power generation facilities that enable efficient operation of generators attached to the biogas generation facilities.SOLUTION: A control system for biogas power generation facilities has: an acquisition unit 11 that acquires operational information of a biogas generation facility 2; a gas generation amount prediction unit 12 that predicts an amount of biogas or methane gas generated from the biogas generation facility 2 based on the operational information; a gas consumption calculation unit 13 that calculates a gas consumption target value to be consumed by generators 4a, 4b, ... 4x attached to the biogas generation facility 2 from the gas generation amount prediction value predicted by the gas generation amount prediction unit 12; a number determining unit 14 that determines the number of operating generators 4a, 4b,...4x based on the gas consumption target value; and a power generation control unit 15 that controls starting or stopping of the generators 4a, 4b, ... 4x based on a determination result of the number determining unit 14.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to a control system for a biogas power generation facility and a control method for a biogas power generation facility, and more particularly, to a control system for a biogas power generation facility and a control method for a biogas power generation facility suitable for treating biogas generated in methane fermentation of sludge.

Background Art

[0002] Biogas generated from a methane fermentation facility is mainly converted into electric power by a power generation facility, sold, and profitized. Efficient operation of a biogas power generation facility and effective utilization of biogas lead to increased profits. However, since the amount of biogas generated during methane fermentation greatly depends on the properties of the input raw materials and the active state of microorganisms, it is difficult to predict. In addition, the management method of the biogas generation amount and the operation method of the power generation facility also largely depend on the knowledge and experience of engineers. Therefore, research on management methods and operation methods that do not depend on the proficiency of engineers is underway.

[0003] Japanese Patent Application Laid-Open No. 2020-6291 (Patent Document 1) describes using a function including flora data related to two or more types of bacteria and data on the amount of biogas generated as variables as prediction data for predicting the amount of biogas generated.

[0004] Japanese Patent Application Laid-Open No. 2019-141756 (Patent Document 2) describes an example of a waste treatment system that analyzes a photographed image of waste, identifies the type of waste based on the color, density, and unevenness of the waste, and predicts the amount of methane gas or biogas generated using the identified type and the amount of waste input into the methane fermentation tank.

[0005] Japanese Patent Application Laid-Open No. 2009-33906 (Patent Document 3) describes an example of a control device for a gas power generation facility equipped with a plurality of generators. In this facility, when the stored gas volume exceeds the reference gas volume, the master generator is operated at its rated capacity, and when it exceeds the set gas volume, the slave generator is operated at its rated capacity. Non-Patent Document 1 describes a technology for supporting the electricity sales business of a biomass power plant using AI.

Prior Art Documents

Patent Documents

[0006]

Patent Document 1

Patent Document 2

Patent Document 3

Non-Patent Documents

[0007]

Non-Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0008] However, in Patent Document 1, since the gas generation amount is predicted based on the flora data of the fermentation tank, an analysis device or the like for acquiring the flora data is required, which poses problems of time and cost. Also, in Patent Document 1, since it takes a certain amount of time to acquire the flora data, it is not suitable for near-real-time prediction. Patent Document 2 also requires a waste imaging device, thus necessitating a separate device for predicting the methane gas generation amount.

[0009] In Patent Document 3, the operation of a plurality of generators is controlled based on the amount of gas stored in the gas tank. However, energy loss occurs when the generators start and stop. Therefore, if the variation in the stored gas amount is large, the start / stop frequency of the generators increases, and conversely, the power generation efficiency may decrease. Further, in Non-Patent Document 1, it is described that machine learning is used to predict the amount of gas generated 1 to 3 days later. However, the algorithm is ensemble learning, and time-series analysis is not performed. Also, no specific consideration has been made regarding how to operate the generators based on the prediction result of the gas generation amount.

[0010] In view of the above problems, the present invention provides a control system and a control method for biogas power generation equipment that can efficiently operate a generator attached to a biogas generation facility.

Means for Solving the Problems

[0011] As a result of intensive studies by the inventors of the present invention to solve the above problems, it has been found useful to predict the amount of biogas generated from a biogas generation facility, calculate a target value for the gas consumption of a generator based on the predicted gas generation amount value, and further control the number of operating generators based on the target value for the gas consumption of the generator.

[0012] Based on the above findings, the present invention, in one aspect, is a control system for biogas power generation equipment including an acquisition unit that acquires operation information of a biogas generation facility, a gas generation amount prediction unit that predicts the amount of biogas or methane gas generated from the biogas generation facility based on the operation information, a gas consumption amount calculation unit that calculates a target value for the gas consumption to be consumed by a generator attached to the biogas generation facility from the predicted gas generation amount value predicted by the gas generation amount prediction unit, a number determination unit that determines the number of operating generators based on the target value for the gas consumption amount, and a power generation control unit that controls the start or stop of the generator based on the determination result of the number determination unit.

[0013] In one embodiment, the control system of the biogas power generation facility according to the embodiment of the present invention includes a gas generation amount prediction unit that predicts a gas generation amount prediction value based on a learned model obtained by machine learning using, as learning data, information including at least any one of the amount of biomass input to the biogas generation facility, the input biomass concentration, and the processing temperature as operation information.

[0014] In another embodiment, the control system of the biogas power generation facility according to the embodiment of the present invention further includes the actually measured gas generation amount of the biogas generation facility in the past as input data of the learned model.

[0015] In yet another embodiment, the control system of the biogas power generation facility according to the embodiment of the present invention includes a gas consumption amount calculation unit that calculates, as a gas consumption amount target value, an average value of the gas generation amount prediction values for a future predetermined period predicted by the gas generation amount prediction unit.

[0016] In yet another embodiment, the control system of the biogas power generation facility according to the embodiment of the present invention includes a power generation control unit that controls the start or stop of the generator so that the number of start and stop times of the generator is minimized based on the determination result of the number of operating units of the generator.

[0017] In yet another embodiment, the control system of the biogas power generation facility according to the embodiment of the present invention further includes a gas tank for storing biogas and a level meter for measuring the level of the gas tank, and the number determination unit further determines the number of operating units of the generator based on the measurement result of the level.

[0018] In yet another embodiment, the control system of the biogas power generation facility according to the embodiment of the present invention further includes a correction unit that corrects the number of operating units of the generator determined by the number determination unit.

[0019] In another aspect, the present invention uses the operation information of a biogas generation facility to predict the future gas generation amount of biogas generated from the biogas generation facility, calculates a target gas consumption amount to be consumed by a generator attached to the biogas generation facility from the predicted value of the biogas generation amount, determines the number of operating generators based on the target gas consumption amount, and starts or stops the generator based on the determination result of the number of operating generators. This is a control method for biogas power generation equipment.

Effects of the Invention

[0020] According to the present invention, it is possible to provide a control system for biogas power generation equipment and a control method for biogas power generation equipment that can efficiently operate a generator attached to a biogas generation facility.

Brief Description of the Drawings

[0021]

Figure 1

Figure 2

Embodiments for Carrying Out the Invention

[0022] Hereinafter, embodiments of the present invention will be described with reference to the drawings. The embodiments shown below illustrate devices and methods for embodying the technical idea of this invention, and the technical idea of this invention does not specify the structure, arrangement, etc. of the components as follows.

[0023] (Biogas Power Generation Equipment) As shown in FIG. 1, the biogas power generation equipment according to an embodiment of the present invention includes a biogas generation facility 2 that generates biogas, a gas tank 3 that stores the biogas generated by the biogas generation facility 2, a plurality of generators 4a, 4b,... 4x that generate electricity using the biogas, and a control device 10 that controls the start and stop of the generators 4a, 4b,... 4x.

[0024] A pretreatment facility 1 for pretreating the raw water flowing into the biogas generation facility 2 may be provided upstream of the biogas generation facility 2. Also, a post-treatment facility (not shown) for post-treating the treated sludge obtained in the biogas generation facility 2 may be provided downstream of the biogas generation facility 2.

[0025] As the raw water to be treated, organic wastewater or organic waste containing organic substances is preferably used. For example, organic sludge such as sewage sludge, feces and urine, septic tank sludge, drainage sludge, or organic waste such as kitchen waste and food waste discharged from various factories is preferably used.

[0026] The specific device configuration of the pretreatment facility 1 is not particularly limited. For example, for the raw water, pretreatment such as solubilization treatment, coagulation sedimentation treatment, aerobic treatment, anaerobic treatment, solid-liquid separation treatment (concentration, dehydration), etc. is performed to obtain sludge or biomass with properties suitable for the fermentation treatment in the biogas generation facility 2. It is preferable to use a treatment device as the pretreatment facility 1. For example, a treatment device that performs gravity concentration using gravity sedimentation, mechanical concentration using a screen, filter cloth, or centrifugation, etc. can typically be used.

[0027] The biogas generation facility 2 is a device that ferments the sludge or biomass flowing into the biogas generation facility 2 to obtain treated sludge and generates biogas. As the biogas generation facility 2, for example, a digester that anaerobically treats the input sludge with anaerobic microorganisms can be used. The treated sludge treated in the biogas generation facility 2 is typically carried out of the system after post-treatment using a dehydrator, dryer, etc. The biogas generated in the biogas generation facility 2 contains hydrogen sulfide etc. in addition to methane gas, carbon dioxide, etc. that make up the biogas, and after purification treatment such as desulfurization treatment, it is stored in the gas tank 3.

[0028] The gas tank 3 stores the biogas generated in the biogas generation facility 2. The gas tank 3 is not limited to only one as illustrated in FIG. 1, and for example, one or a plurality of gas tanks may be provided adjacent to each of the plurality of generators 4a, 4b, ··· 4x. The gas tank 3 is provided with a level gauge 31 capable of measuring the level (remaining amount) of the biogas stored in the gas tank 3 within the gas tank 3.

[0029] The measurement result of the level gauge 31 is configured to be output to a control device 10 connected to the level gauge 31. The generators 4a, 4b, ··· 4x are each connected to the control device 10, and via the control device 10, the activation and stop of each of the generators 4a, 4b, ··· 4x can be controlled.

[0030] The control device 10 is composed of a general-purpose computer or the like. The control device 10 includes an acquisition unit 11, a gas generation amount prediction unit 12, a gas consumption amount calculation unit 13, a number determination unit 14, a power generation control unit 15, and a correction unit 16. Further, a storage unit 20 that stores various information necessary for the processing of the control device 10 and a learning unit 17 that stores various information necessary for predictive analysis using machine learning may be connected to the control device 10.

[0031] The control device 10 may be connected to other biogas power generation facilities 30 via a network 50, and may be configured to be able to mutually exchange control information between the other biogas power generation facilities 30 and the generators 4a, 4b, ··· 4x. The control device 10 may be connected to an operation management facility 40 or the like capable of aggregating and managing a plurality of facilities including the biogas power generation facility 30 according to the embodiment of the present invention, and thereby, the control information of the generators 4a, 4b, ··· 4x, the learned model, past performance data, etc. may be configured to be able to mutually exchange.

[0032] The acquisition unit 11 acquires the operation information of the biogas generation facility 2 that generates biogas. The operation information includes, for example, the amount of input biomass input to the biogas generation facility 2, the input biomass concentration (TS, VS), COD, pH, treatment temperature, volumetric load, hydraulic retention time, the effective capacity of the treatment tank of the biogas generation facility 2, stirring speed, the generation rate of biogas or methane gas (sludge decomposition rate), the TS, VS, pH, and sludge withdrawal amount of the treated sludge treated in the biogas generation facility 2, etc. The acquisition unit 11 may further acquire the pretreatment information of the pretreatment facility 1. The pretreatment information includes the treatment conditions of the pretreatment facility 1, the sludge concentration (TS, VS) of the raw water, the coagulant injection rate, the concentration (TS, VS) of the biomass obtained by pretreatment, the suspended solid concentration (SS), the biomass withdrawal amount and transfer amount, etc.

[0033] Based on the operation information, the gas generation amount prediction unit 12 predicts the gas generation amount of biogas or methane gas contained in the biogas. For example, the gas generation amount prediction unit 12 has a database that accumulates at least any one of the amount of input biomass input to the biogas generation facility 2, the input biomass concentration, the treatment temperature, the gas generation amount of biogas or methane gas corresponding to the type of input biomass, or the conversion rate to methane gas with respect to the input biomass. Based on the operation information, statistical analysis and simulation are performed to calculate the predicted value of the gas generation amount of biogas or methane gas in the current or a future predetermined period. The predicted value of the gas generation amount of biogas may be obtained by referring to the past database and obtaining the average value of the actually measured gas generation amounts in a past predetermined period, or may be obtained by performing a simulation using analysis software or the like for analyzing the operation information.

[0034] The prediction of the amount of biogas or methane gas generated can be improved in prediction accuracy by using machine learning. For example, the gas generation amount prediction unit 12 predicts the predicted value of the gas generation amount of biogas or methane gas based on a learned model obtained by machine learning using, as learning data, operation information including at least any one of the amount of biomass input to the biogas generation facility 2, the input biomass concentration, and the processing temperature. By using machine learning, the operation information of the biogas generation facility 2 can be analyzed in time series, so that the amount of biogas or methane gas generated in a future predetermined period can be predicted more accurately. Note that the creation of the learned model may be performed by a computer separate from the control device 10.

[0035] As explanatory variables of the learned model used in the prediction analysis by the gas generation amount prediction unit 12, the operation information of the biogas generation facility 2 (for example, fermentation treatment conditions, input raw materials, information on treatment sludge, etc.) and the pretreatment information of the pretreatment facility 1 are used. For example, when a sewage sludge digestion tank is arranged as the biogas generation facility 2, as explanatory variables, for example, the processing temperature of the digestion tank, the amount of sludge input to the digestion tank, the input sludge concentration, etc. can be appropriately selected and used.

[0036] As machine learning algorithms, various known analysis tools using random forest, neural network (ANN, RNN), etc. can be appropriately selected and used. In particular, for the prediction of the amount of biogas generated according to this embodiment, LSTM or GRU in RNN is preferable, and more preferably LSTM, but it is not limited thereto.

[0037] In the case of RNN, the time step, which is a hyperparameter, is preferably within 90 days, and more preferably within the fermentation days (hydraulic retention time) of the biogas generation facility 2 from the viewpoint of improving accuracy. When there are a plurality of biogas generation facilities 2, the gas generation amount prediction unit 12 may predict the predicted value of the gas generation amount for each of the biogas generation facilities 2, or may predict the total generation amount.

[0038] When predicting the amount of biogas or methane gas generated, it is more preferable to further include the measured values of the past gas generation amounts of the biogas generation facility 2 as input data for the learned model. By including the measured values of the past gas generation amounts as input data other than the above-described explanatory variables, it is possible to perform prediction considering the measured values of the past gas generation amounts, so that the accuracy of the prediction is enhanced.

[0039] If the period for predicting the biogas generation amount is set too far into the future, the difference between the current gas generation amount and the predicted value of the future gas generation amount will increase. As a result, the speed at which the biogas stored in the gas tank 3 reaches a predetermined level may increase. The period for predicting the biogas generation amount is preferably determined in consideration of the variation in the predicted value of the gas generation amount and the effective capacity of the gas tank 3.

[0040] For example, regarding the period for predicting the biogas generation amount, for example, regarding how many days in the future to predict, it is preferable to perform a verification on the plane in advance and determine it so that the number of operating units and the number of start-stop times of the generators 4a, 4b, ··· 4x are optimized based on the measured values of the gas generation amounts in the past one month. Although not limited to the following, in one embodiment, the prediction period of the biogas generation amount is preferably within 10 days, more preferably 1 to 7 days, and even more preferably 1 to 3 days. For example, when the prediction period of the biogas generation amount is 3 days, the gas generation amount prediction unit 12 predicts the biogas generation amounts 1 day later, 2 days later, and 3 days later.

[0041] The gas consumption calculation unit 13 calculates a target gas consumption value to be consumed by the generators 4a, 4b, ··· 4x attached to the biogas generation facility 2 from the predicted gas generation amount predicted by the gas generation amount prediction unit 12. For example, the gas consumption calculation unit 13 calculates the target gas consumption value so that power generation is appropriately performed within a range where the power generation efficiency of the generators 4a, 4b, ··· 4x achieves a predetermined target efficiency based on the predicted gas generation amount predicted by the gas generation amount prediction unit 12, a predetermined rated power generation efficiency, and an actual power generation efficiency.

[0042] Furthermore, it is preferable that the gas consumption calculation unit 13 calculates the gas consumption target value so that the number of start / stop operations of the generators 4a, 4b, ···, 4x is minimized. For example, even if it is predicted that there will be a rapid change in the predicted gas generation amount predicted by the gas generation amount prediction unit 12 in the future within an extremely short time, if the gas generation amount tends to stabilize within a certain range thereafter, the gas consumption target value is calculated by smoothing the prediction result of the gas generation amount in the extremely short time when the rapid change in the gas generation amount occurs. As a result, it is possible to prevent the generators 4a, 4b, ···, 4x from starting and stopping due to a rapid change in the gas generation amount, and thus the number of start / stop operations of the generators 4a, 4b, ···, 4x can be reduced.

[0043] When the generators 4a, 4b, ···, 4x start or stop, a large amount of energy is consumed, so the amount of energy consumed also increases as the number of start / stop operations increases. According to the present embodiment, since the gas consumption target value is calculated so that the number of start / stop operations of the generators 4a, 4b, ···, 4x is minimized, it is possible to perform power generation with the energy loss of the generators 4a, 4b, ···, 4x reduced as much as possible. The "number of start / stop operations" basically refers to the number of start or stop operations of the generators 4a, 4b, ···, 4x. For example, when the generator 4a changes from the stopped state to the operating state, it is counted as 1 time, and when it changes from the operating state to the stopped state, it is counted as 1 time. Regarding the number of start / stop operations, more simply, when changing the number of operating generators, it may be counted as 1 time. In this case, for example, when changing the number of operating units from 8 to 10, it is counted as 1 time, and when changing from 10 to 13, it is counted as 1 time.

[0044] The biogas generated from the biogas generation facility 2 contains, in addition to methane gas, carbon dioxide, hydrogen sulfide, etc., and its composition varies depending on the properties of the sludge introduced into the biogas generation facility 2, the treatment conditions, etc. Therefore, it is preferable that the gas consumption calculation unit 13 calculates the average value of the predicted gas generation amounts of biogas or methane gas in a future predetermined period predicted by the gas generation amount prediction unit 12, and determines this as the gas consumption target value. In this way, regarding the biogas generation amount, by calculating the gas consumption target value in consideration of the time series of a certain period from the present to the future, the influence of the extremely short-term fluctuations in the biogas generation amount on the start-up or stop operation of the generators 4a, 4b, ··· 4x can be reduced. As a result, the number of start-stop times of the generators 4a, 4b, ··· 4x can be reduced, and efficient power generation can be realized.

[0045] In addition, when the predicted gas generation amount is stable over a long period and there is no need to consider the fluctuations in the gas generation amount, etc., the gas consumption calculation unit 13 may directly determine the predicted gas generation amount at an arbitrary specific time in the future as the gas consumption target value.

[0046] The number determination unit 14 determines the number of operating units of the generators 4a, 4b, ··· 4x based on the gas consumption target value calculated by the gas consumption calculation unit 13. For example, the number determination unit 14 uses the following formula: (Number of operating units) = (Gas consumption target value) ÷ (Gas consumption per generator) ··· (1) to determine the number of operating units of the generators 4a, 4b, ··· 4x.

[0047] Furthermore, it is more preferable that the number determination unit 14 determines the number of operating units of the generators 4a, 4b, ··· 4x based on the measurement result of the level of the gas tank 3 by the level meter 31. By determining the number of operating units based on the level of the gas tank 3, processing according to the level of the biogas in the gas tank 3 can be performed, so that efficient operation of the generators becomes possible.

[0048] For example, when the measurement result of the level of the gas tank 3 exceeds the upper limit value, the number of operating generators 4a, 4b, ··· 4x can be determined to be more than the value obtained by dividing the gas consumption target value by the gas consumption per generator. On the other hand, when the measurement result of the level of the gas tank 3 is less than or equal to the lower limit value, the number of operating generators can be set to be less than the value obtained by dividing the gas consumption target value by the gas consumption per generator.

[0049] For example, in the above formula (1), when the level of the gas tank 3 exceeds the upper limit value, the number of operating generators is determined to be an integer value obtained by rounding up the numerical value after the decimal point of the value obtained by the formula (1). When the level of the gas tank 3 exceeds the lower limit value, the number of operating generators is determined to be an integer obtained by rounding down the numerical value after the decimal point of the value obtained by the formula (1).

[0050] The number determination unit 14 may correct the calculation formula of the formula (1) by giving a predetermined parameter. For example, when it is desired to increase the level of the gas tank 3, the number determination unit 14 can also be determined to subtract the correction parameter a (integer) from the calculation result of the formula (1). In the above example using the formula (1), the case where the gas consumption of the generators 4a, 4b, ··· 4x is the same is taken as an example for explanation, but it is not limited to this example. Of course, the number determination unit 14 can appropriately optimize the number of operating generators according to the magnitude of the gas consumption of each of the generators 4a, 4b, ··· 4x.

[0051] The timing of measuring the level of the gas tank 3 is not particularly limited. For example, after the unit determination unit 14 determines the number of operating units based on the target value of gas consumption, before the determined number of operating units is reflected in the operation control of the generators by the power generation control unit 15, the level of the gas tank 3 is measured. And when the level of the gas tank 3 exceeds the upper limit value or the lower limit value, the unit determination unit 14 may recalculate the number of operating units based on the formula (1). Alternatively, when it is predicted that the level of the gas tank 3 will exceed the upper limit value or the lower limit value based on the prediction result of the gas generation amount prediction unit 12, the unit determination unit 14 may determine the number of operating units using the formula (1) before reaching the upper limit value or the lower limit value of the level.

[0052] The control device 10 can further include a correction unit 16. The correction unit 16 corrects the number of operating units of the generators 4a, 4b, ··· 4x determined by the unit determination unit 14. For example, when the result of measuring the level of the gas tank 3 by the level meter 31 shows that the rising or falling trend of the level does not change even after a certain period of time, the correction unit 16 may correct the number of operating units. For example, even though the level of the gas tank 3 reaches the upper limit value and it is desired to lower the level of the gas tank 3, if the level continues to rise even after the number of operating units is set, the correction unit 16 corrects the number of operating units so that it is one more than the determination result of the unit determination unit 14, so that the level of the gas tank 3 can be switched to decrease earlier.

[0053] Even when the level of the gas tank 3 exceeds the upper limit value or the lower limit value, depending on the prediction result of the gas generation amount, there may be cases where the correction unit 16 does not need to correct the number of operating units of the generators 4a, 4b, ··· 4x. Specifically, for example, even when the predicted value of the gas generation amount temporarily decreases, and it is predicted that the predicted value of the gas generation amount will increase immediately afterwards. In such cases, if the number of operating units is determined based on the temporary predicted value of the gas generation amount, the number of start-stop times of the generators 4a, 4b, ··· 4x may increase, resulting in energy loss.

[0054] When the level of the gas tank 3 reaches the upper limit value, for example, if the target value of gas consumption at that time is smaller than the actual gas consumption ((gas consumption per generator) × (number of operating units)), the correction unit 16 does not perform the correction of the number of operating units. Thereby, while reducing the number of start-stop times of the generators 4a, 4b, ··· 4x as much as possible and minimizing the energy loss, power generation can be performed efficiently.

[0055] Based on the determination result of the unit number determination unit 14, the power generation control unit 15 controls the start or stop of the generators 4a, 4b, ··· 4x so that the number of start-stop times of the generators 4a, 4b, ··· 4x is minimized. For example, the power generation control unit 15 can control the start or stop of the generators 4a, 4b, ··· 4x only when there is a need to newly start or stop the generators 4a, 4b, ··· 4x based on the determination result of the unit number determination unit 14 and the operating status of the generators 4a, 4b, ··· 4x.

[0056] According to the control system of the biogas power generation facility according to the embodiment of the present invention, the gas consumption calculation unit 13 calculates the target value of gas consumption based on the predicted value of gas generation amount of the gas generation amount prediction unit 12, and based on this target value of gas consumption, the number of operating units is determined so that the number of start-stop times of the generators 4a, 4b, ··· 4x is minimized as much as possible. Thereby, a control system of a biogas power generation facility capable of efficiently operating the generators attached to the biogas generation facility can be provided.

[0057] (Control Method and Power Generation Method of Biogas Power Generation Facility) The control method and power generation method of the biogas power generation facility according to the embodiment of the present invention, as shown in FIG. 2, include a step S1 of acquiring operation information of the biogas generation facility 2, a step S2 of predicting the future gas generation amount of the biogas generated from the biogas generation facility 2 using the operation information, a step S3 of calculating a target gas consumption amount to be consumed by the generators attached to the biogas generation facility from the predicted gas generation amount value of the biogas, a step S4 of determining the number of operating units of the generators 4a, 4b, ··· 4x based on the target gas consumption amount, a step S5 of determining whether to correct the number of operating units of the determined generators 4a, 4b, ··· 4x, a step S6 of correcting the number of operating units of the generators 4a, 4b, ··· 4x, and a step S7 of starting or stopping the generators 4a, 4b, ··· 4x based on the determination result of the number of operating units.

[0058] In step S1 of FIG. 2, the acquisition unit 11 of FIG. 1 acquires various information necessary for predicting the generation amount of the biogas generated from the biogas generation facility 2. For example, the acquisition unit 11 acquires the operation information of the biogas generation facility 2 and the pretreatment information of the pretreatment facility 1 as necessary. The acquisition unit 11 acquires the actually measured value of the past gas generation amount of the biogas generation facility 2 from the storage unit 20 or the operation management facility 40 via the network 50, and uses this as input data, thereby improving the prediction accuracy.

[0059] In step S2 of FIG. 2, the gas generation amount prediction unit 12 of FIG. 1 predicts the current and future gas generation amounts of the biogas generated from the biogas generation facility 2 during a predetermined period based on the operation information acquired by the acquisition unit 11. The prediction of the gas generation amount may be calculated based on the actually measured values of the past gas generation amounts stored in the storage unit 20, or may be predicted using the learned model obtained by executing machine learning as described above.

[0060] In step S3 of FIG. 2, the gas consumption calculation unit 13 in FIG. 1 calculates a target gas consumption value based on the predicted gas generation amount of biogas for a predetermined period predicted in step S2 so that the power generation efficiencies of the generators 4a, 4b, ··· 4x are within an appropriate range. In step S4 of FIG. 2, the unit number determination unit 14 in FIG. 1 determines the number of operating units of the generators 4a, 4b, ··· 4x based on the target gas consumption value so that the power generation efficiencies of the generators 4a, 4b, ··· 4x are operated within an appropriate range.

[0061] In step S5 of FIG. 2, the correction unit 16 in FIG. 1 determines whether it is necessary to correct the number of operating units of the generators 4a, 4b, ··· 4x determined by the unit number determination unit 14. For example, based on the measurement result of the level meter 31 provided in the gas tank 3 in FIG. 1, when the correction unit 16 determines that it is necessary to correct the number of operating units because the upper limit value of the future level of the gas tank 3 will be exceeded, in step S6, the correction is performed to increase the number of operating units. After the correction, the process proceeds to step S7. In step S5, when the correction unit 16 determines that it is not necessary to correct the number of operating units of the generators 4a, 4b, ··· 4x determined by the unit number determination unit 14, the process proceeds to step S7. In step S7, the power generation control unit 15 controls the start and stop of the generators 4a, 4b, ··· 4x based on the determination result of the number of operating units of the generators 4a, 4b, ··· 4x.

[0062] As described above, according to the control system of the biogas power generation facility according to the embodiment of the present invention, it is possible to predict the gas generation amount of the biogas generation facility and, based on this, determine and correct the number of operating units of the generators 4a, 4b, ··· 4x. Therefore, even without the experience of a skilled technician, it is possible to continuously and stably operate the generators 4a, 4b, ··· 4x with good efficiency.

Example

[0063] Examples of the present invention are shown below together with comparative examples. These examples are provided to better understand the present invention and its advantages and are not intended to limit the invention.

[0064] Anaerobic digestion tank for anaerobic digestion of sewage sludge, a gas tank for storing the digestion gas generated from the digestion tank, and a biogas power generation facility equipped with a plurality of generators for generating electricity using the digestion gas stored in the gas tank were set as the control targets. A dataset including the digestion gas generation amount, the amount of digestion tank sludge input, the digestion tank sludge concentration, and the concentrated sludge concentration treated by gravity concentration or mechanical concentration in the pretreatment before the digestion tank input was used as learning data. Using LSTM as the machine learning algorithm, a model for predicting the gas generation amount of digestion gas was constructed. The digestion tank was divided into three tanks, and a model was constructed for each tank, and the total value of the predicted values of each tank was used as the predicted value of the gas generation amount of the gas generation facility.

[0065] The number of operating generators was set when the level of the gas tank reached the upper limit line or the lower limit line. When the level of the gas tank reached the upper limit line, the integer value obtained by rounding up the decimal point of the value calculated using Equation (1) was used as the number of operating generators for the number of operating units. When the level of the gas tank reached the lower limit line, the integer value obtained by rounding down the decimal point of the value calculated using Equation (1) was used as the number of operating generators for the number of operating units.

[0066] By performing machine learning using LSTM, the average value of the predicted gas generation amounts for the next 3 days, 5 days, and 7 days was set as the gas consumption target value. Based on this gas consumption target value, the number of operating generators was determined, and the start-up and stop of the generators were controlled. The number of start-stop times was counted as 1 time when the number of operating units changed. As a result, the number of start-stop times was the least when the average value was 3 days. Therefore, in this embodiment, for the prediction of the gas generation amount, the predicted gas generation amounts for 1 day later, 2 days later, and 3 days later were obtained respectively, and the average value of these predicted values was used as the gas consumption target value.

[0067] As a result of verification for 20 days under the above conditions, the number of start-stop times of the generator in this embodiment was 6 times. When the same biogas power generation facility as in this embodiment was set by the operator, the number of start-stop times was 14 times in 20 days. That is, according to this embodiment, it was found that the effect of reducing the number of start-stop times can be obtained.

Explanation of symbols

[0068] 1…Pretreatment facility 2…Biogas generation facility 3…Gas tank 4a, 4b, ··· 4x…Generators 10…Control device 11…Acquisition unit 12…Gas generation amount prediction unit 13…Gas consumption calculation unit 14…Number determination unit 15…Power generation control unit 16…Correction unit 17…Learning unit 20…Memory unit 30…Biogas power generation equipment 31…Level meter 40…Operation management facility 50…Network

Claims

1. An acquisition unit that acquires operation information of a biogas generation facility; A gas generation amount prediction unit that predicts the gas generation amount of biogas or methane gas generated from the biogas generation facility based on the operation information; A gas consumption amount calculation unit that calculates a target gas consumption amount to be consumed by a generator attached to the biogas generation facility from the predicted gas generation amount predicted by the gas generation amount prediction unit; A number determination unit that determines the number of operating units of the generator based on the target gas consumption amount; A power generation control unit that controls the start or stop of the generator based on the determination result of the number determination unit A control system for biogas power generation equipment comprising the above.

2. The control system for biogas power generation equipment according to claim 1, wherein the gas generation amount prediction unit predicts the gas generation amount prediction value based on a learned model obtained by machine learning using, as learning data, information including at least any one of the amount of biomass input to the biogas generation facility, the biomass concentration, and the processing temperature as the operation information.

3. The control system for biogas power generation equipment according to claim 2, further including the past measured gas generation amount of the biogas generation facility as input data of the learned model.

4. The control system for biogas power generation equipment according to any one of claims 1 to 3, wherein the gas consumption amount calculation unit calculates the average value of the predicted gas generation amount predicted by the gas generation amount prediction unit over a predetermined future period as the target gas consumption amount.

5. The control system for biogas power generation equipment according to any one of claims 1 to 4, wherein the power generation control unit controls the start or stop of the generator so that the number of start / stop times of the generator is minimized based on the determination result of the number of operating units of the generator.

6. A gas tank for storing the biogas; A level gauge for measuring the level of the gas tank Further comprising, The control system for biogas power generation equipment according to any one of claims 1 to 5, wherein the number determination unit further determines the number of operating units of the generator based on the measurement result of the level.

7. The control system for biogas power generation equipment according to any one of claims 1 to 6, further comprising a correction unit that corrects the number of operating units of the generator determined by the number determination unit.

8. Using the operation information of the biogas generation facility, predict the future gas generation amount of the biogas generated from the biogas generation facility, Calculate a target gas consumption value to be consumed by a generator attached to the biogas generation facility from the predicted gas generation amount of the biogas, Based on the target gas consumption value, determine the number of operating units of the generator, Based on the determination result of the number of operating units, start or stop the generator A control method for biogas power generation equipment, characterized by comprising the above steps.

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