KR101830150B1 - Model predictive control system and method for improving gas production performance of anaerobic digestion plant - Google Patents

Abstract

The present invention relates to a process model predicting and controlling system for promoting anaerobic digestion gas generation and a method thereof. The process model predicting and controlling system for promoting anaerobic digestion gas generation, according to the present invention, applies an ADM1 model provided by International Water Association (IWA), as an anaerobic digestion model for predicting an amount of digestion gases, and comprises an optimizing module which has a function of correcting errors between a real measuring value and a predicted value for improving a predicting reliability of the digestion model, and a scenario analyzing function which diagnoses whether the generation amount of anaerobic digestion gas is increased or decreased, and figures out a quantitatively operating condition for an amount of inflow and application of microorganism promoters at a satisfactory level of the generation amount of anaerobic digestion gas. The present invention draws operating conditions necessary for stable operation of anaerobic digestion facilities, a reference value of an amount of gas generation set by an operator, or maintaining a generation amount of digestion gas satisfying history information of an amount of gas generation during a predetermined period in the past, and provides the same for an unskilled operator or an operator necessary for objective information for determination in real time. Therefore, the present invention is greatly useful for operation of anaerobic digestion facilities using organic waste resources.

Description

Technical Field [0001] The present invention relates to a process control system for anaerobic digestion gas production,

The present invention relates to a process model predictive control system and method for promoting anaerobic digestion gas production, and more particularly, to an anaerobic digestion model capable of predicting the amount of anaerobic digestion gas generated, The present invention relates to a process model predictive control system and method for promoting the production of anaerobic digestion gas that can provide operating conditions based on an inflow amount and an accelerator application.

Because of the growth of the biogasification industry using organic waste, many anaerobic digestion facilities such as food biogasification facility, drinking water biogasification facility, sewage sludge solid fueling facility, and merged biogasification facility are operated.

However, most of the anaerobic digestion facilities in operation have difficulty in stable operation due to the low digestion efficiency due to the irregular supply of the influent source, accumulation of byproducts in the digestion tank, and low gas generation amount.

In the anaerobic digestion facility, anaerobic digestion of the influent source generates digestion gas. Since the time and condition of the anaerobic digestion reaction vary depending on the characteristics of the influent source and the operating conditions, the amount of digestion gas is different, This is because the digestion process is a slow process, and the anaerobic digestion facility is managed by monitoring the digestion tank at a predetermined interval rather than checking the water quality of the source and outlet of the digester daily.

Despite the limitations of stable operation of anaerobic digestion facilities, it is hoped that the stable generation of digestive gases will be maintained in the case of energy use schemes utilizing digestive gases.

The amount of digestion gas production depends on the condition of the incoming source. In addition, the digestion gas generation rate can be increased by the pH, temperature, injection of microorganism promoter and the like. However, it is difficult for the operator to judge and forecast such information and artificially control it.

On the other hand, the mathematical model predicts the anaerobic digestion reaction and can confirm the increase / decrease of the digestion gas amount due to the change of the operation condition in advance. Thus, it is useful in the optimization and control of the process.

The ADM1 model (anaerobic digestion model no.1) proposed by the Task Force Group of the International Water Association (IWA) is a biological reaction required for the anaerobic digestion process. Hydrolysis, anaerobic microbial growth and death, This can be a representative example of a mathematical model of anaerobic digestion considered together.

However, the existing ADM1 digestion model is useful as a tool for predicting the time-dependent thermal changes in the digester with respect to various state variables. However, in order to provide digestion tank operating conditions that can improve the digestion gas generation, It is necessary to further develop a process model predictive control system and its predictive control method capable of evaluating the anaerobic digestion process and predicting and managing digestive facilities.

Korean Registered Patent No. 10-0683477 (Feb. 9, 2007) Korean Patent Publication No. 10-2010-0109172 (Oct. 8, 2010) Korean Patent Publication No. 10-2015-0111675 (Oct. 10, 2015)

The present invention relates to a process model predictive control system for predicting the generation of anaerobic digestion gas based on the anaerobic digestion model ADM1 model and promoting the production of anaerobic digestion gas, and a predictive control method for the anaerobic digesting facility, It is intended to provide operating conditions for accelerator application.

In order to achieve the above object, the present invention adopts the ADM1 model proposed by the International Water Association (IWA) as an anaerobic digestion model for predicting the generation amount of anaerobic digestion gas; A function of correcting an error between an actual value and a predicted value in order to improve the predictive reliability of the extinguishing model; a function of diagnosing itself whether the amount of generated anaerobic digestion gas is increased or decreased and determining a gas generation amount reference value set by the operator, An optimization module having a scenario analysis function for deriving an influent flow rate at an anaerobic digestion gas generation level satisfying the following equation and a quantitative operation condition for application of a microbial promoter; Thereby improving the reliability of the predicted value and improving the amount of generated digestion gas. The present invention also provides a process model predictive control system for promoting anaerobic digestion gas production.

Wherein the error correction function is performed by an error feedback process, wherein the optimization module receives a cumulative record of the difference between the measured value and the predicted value from the anaerobic digestion process database to derive a linear function formula, Calculating an error value, and estimating a value obtained by adding the predicted value and the error value to the corrected gas generation amount.

Here, the scenario analysis is performed through the digestion model on the basis of the scenario priority of the scenario database in which the influent flow rate, the default value for the microbial accelerator, and the scenario priority are set.

Here, the average value of the past driving history is used as the flow rate of the scenario analysis and the default value for the microbial accelerator.

Where the influent flow is set to precede the microbial promoter at the scenario priority.

Here, the above scenario analysis is performed by setting the range based on the design value information by 25% up to the default value, but applying the ratio in the range of 25 ~ 300%.

Further, the present invention applies the ADM1 model proposed by the International Water Association (IWA) as an anaerobic digestion model for predicting the generation amount of anaerobic digestion gas; A measuring instrument assembly installed in the anaerobic digestion tank for acquiring information on the measurement parameters required for the digestion model; Data switching means for switching information on the measurement parameters to input variables necessary for the digestion model; An anaerobic digestion process database storing information on input parameters and measurement parameters derived through the data conversion means; An extinguishing gas generation amount predicting means for predicting an amount of generated anaerobic digestion gas by inputting the input variable into the extinguishing model; A fixed control means for maintaining the pH and the temperature of the anaerobic digestion tank at a predetermined level and a variable control means for determining an inflow flow rate of the organic waste flowing into the anaerobic digestion tank and the injection of the microbial promoter, A process model predictive control system is provided.

The anaerobic digestion tank is composed of an ideal anaerobic digestion tank divided into an acid fermentation tank and a methane fermentation tank. The boiler is capable of maintaining the temperature of the digestion tank through the fixed control means. The buffer tank is further provided with a buffer tank for maintaining the pH of the digestion tank. A sensor and a thermometer are installed in the digester and a buffer tank. The pH and temperature values of the digester and buffer measured by the pH measuring sensor and the thermometer are stored in the memory and monitored in real time by an external monitor.

Here, the pH of the acid fermentation tank is maintained at 5 to 6, and the pH of the methane fermentation tank is maintained at 7 to 7.5.

Wherein the temperature of the anaerobic digester is maintained at a mid-temperature of 33-38 ° C.

The fixed control means for maintaining the pH of the anaerobic digestion tank checks the pH value collected by the sensor for pH measurement of the digestion tank in real time and compares the pH value with the reference pH value to be maintained, And a control algorithm for determining whether to operate the inflow pump.

Further, the present invention applies the ADM1 model proposed by the International Water Association (IWA) as an anaerobic digestion model for estimating the amount of generated digestion gas; Obtaining a measurement variable required for the digestion model through a measurement device assembly; When the actual gas generation amount acquired in the measurement parameter acquisition step does not reach the gas generation amount reference value set by the operator or the gas generation amount satisfying the gas generation amount history information in the past certain period, A data conversion step of converting the data into a data; Estimating a gas generation amount by applying the data converted input variable to the fire extinguishing model and estimating a gas generation amount corrected by an error feedback process of the optimization module to improve reliability of the fire extinguishing model; If the calibrated gas generation predicted value does not reach the gas generation amount reference value set by the operator or the gas generation amount satisfying the gas generation amount history information in the past period, the scenario database is operated on the fire extinguishing model to set the gas generation amount reference value And deriving an operating condition for reaching a gas generation amount satisfying the gas generation history information during a certain period of time in the past.

The process model predictive control system and method for promoting the production of anaerobic digestion gas according to the present invention derives the operating conditions necessary for stable operation of the anaerobic digestion facility and maintenance of the standard digestion gas generation amount so that it is necessary to provide objective information to the unskilled driver or decision- And it is very useful for driving the anaerobic digestion facility using organic waste resources because it can provide to the driver in real time.

FIG. 1 is a diagram illustrating a system according to an embodiment of the present invention. Referring to FIG. 1, when the actual gas generation amount measured through the measurement device assembly does not reach the gas generation amount reference value set by the operator or the gas generation amount satisfying the gas generation amount history information during the past predetermined period And converting the measured variables into input parameters for application to the anaerobic digestion model.
FIG. 2 is a logic diagram for predicting the gas generation amount corrected by the error feedback of the optimization module in order to improve the reliability of the anaerobic digestion model according to an embodiment of the present invention.
FIG. 3 illustrates an example of a system according to an embodiment of the present invention. In an embodiment of the present invention, when the calibrated gas generation predicted value does not reach the gas generation reference value set by the operator or the gas generation amount satisfying the gas generation history information in the past period, A logic configuration diagram of an optimization module for operating a database to derive an operating condition that can reach a reference gas generation amount.
4 is a control algorithm of the fixed control means for maintaining the pH of the anaerobic digester according to an embodiment of the present invention.
5 is a control algorithm of the fixed control means for maintaining the temperature of the anaerobic digester according to an embodiment of the present invention.
6 is a schematic block diagram of a process model predictive control system according to an embodiment of the present invention.
7 is a schematic flowchart of a process model predictive control according to an embodiment of the present invention.
FIG. 8 is a chart of COD components measured according to an embodiment of the present invention, according to solubility, inertness, particleity, and the like.
FIG. 9 is a graph comparing the amount of generated gas after the improvement of the amount of gas generated by the optimizing module when the concentration of the influent organic material changes, according to an experimental example of the present invention.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. In the drawings, the same reference numerals denote the same or similar parts throughout the several views of the drawings.

In the following description, well-known functions or constructions are not described in detail since they would obscure the invention in unnecessary detail.

In addition, the present invention applies the ADM1 model proposed by the International Water Association (IWA) as an anaerobic digestion model for predicting the amount of digestion gas generated, and the anaerobic digestion model, the digestion model, the process model, Are all ADM1 digestion models.

FIG. 1 is a graph showing the relationship between the actual gas generation amount measured by the measurement device assembly and the gas generation amount reference value set by the operator or the gas generation amount satisfying the gas generation amount history information in the past period, And is configured to include a measurement device assembly S110, an anaerobic digestion process database S210 for storing collected information, an actual gas generation amount diagnosis S220, and a data switching means S230.

The measurement parameters of the anaerobic digestion facility are first measured and recorded through the measuring instrument assembly (S110), and the information about the measurement parameters through the measuring instrument assembly (S110) And stored in the digestion process database (S210).

When the actual gas generation amount does not reach the gas generation amount reference value set by the operator or the gas generation amount history information in the past period, the information on the measured variables is sent to the data conversion means S230 And switches to an input variable necessary for the anaerobic digestion model (S310).

Alternatively, when the actual gas generation amount exceeds the gas generation amount reference value set by the operator or the gas generation amount history information in the past period, the current information is stored in the anaerobic digestion process database S210 as it is.

The measuring instrument assembly (S110) is installed in the anaerobic digestion tank and measures the water quality items (measurement variables) necessary for the anaerobic digestion model. The total COD (CODt), dissolved COD (CODs), volatile sludge It is also possible to use acetate, propionate, and butyrate as the kjeldal nitrogen (TKN), ammonia nitrogen (HH4 ⁺ -N), normal hexane extract (HEM), carbohydrate, protein, lipid, butyrate, valerate and biological methane potential test (BMP test), sensor and experiment information on the amount of digestion gas is obtained.

Data conversion means (S230) S _I is the measured variable (Soluble inerts, soluble inert material), X _I (Particular inerts, the inert particulate material) in accordance with the diagnosis result (S220) of the actual generation amount, S _su (Soluble sugar), S _aa (Soluble amino acid), S _fa (soluble LCFA), S _va (soluble valerate), S _bu (Dissolved butyrate), S _pro (dissolved propionate), S _ac (dissolved acetate), S _h2 (dissolved hydrogen), S _ch4 (dissolved methane), S _IC (inorganic carbon), S _IN X _c (particulate matter), X _ch (particulate carbohydrates), X _pro (particulate proteins), X _li (particulate lipids), X _su (particulate sugar), X _aa (particulate amino acid) , X _fa (particulate LCFA), X _c4 (particulate valerate and butyrate), X _pro (particulate propionate), X _ac (particulate acetate), X _h2 (particulate hydrogen) .

FIG. 8 is a diagram showing the COD component subdivided according to solubility, inertness, particulate property, etc. The state variables (input variables) directly applicable to the anaerobic digestion model are total COD (CODt), dissolved COD (CODs), normal hexane extraction (HEM), organic acid (VFAt), and the like.

As shown in FIG. 8, based on the subdivision ratio of the synthetic parameters such as COD and the biodegradability derived from the biological methane potential test (BMP test), the synthesis parameters are further subdivided as shown in Table 1 below, The input variable is derived. The calculation method of each variable is shown in Table 1, and data conversion (S230) and anaerobic digestion model (S310) are performed.

The information obtained through the measurement device assembly (S110) and the data conversion (S230) is stored in the anaerobic digestion process database (S210).

division formula

Composite variable

COD _p = COD _t - COD _s COD _pb = COD _p x biodegradation (%) COD _pi = COD _p x (100 - biodegradation (%)) COD _sb = COD _s x biodegradation (%) COD _si = COD _s x (100 - biodegradation (%))









Input variable X _ch = COD _pb x Carbohydrates (%) X _pr = COD _pb x Proteins (%) X _li = COD _pb x Lipids (%) S _water = COD _sb x Carbohydrates (%) S _aa = COD _sb x Proteins (%) S _fa = COD _sb x Lipids (%) S _I = COD _si X _I = COD _p xf _xi X _C = COD _p xf _xc X _water = Effluent COD _t x m _water X _aa = Effluent COD _t x 탆 _aa X _fa = Effluent COD _t x 탆 _fa X _c4 = Effluent COD _t x 占 퐉 _c4 X _pro = Effluent COD _t x 占 퐉 _pro X _ac = Effluent COD _t x ㎛ _ac X _h2 = Effluent COD _t x 탆 _h2 S _IC = (α _no · S _no + α _nh · S _nh + α _alk · S _alk ) / αω ₂
- (a _va S _{v a} + a _bu S _bu + a _pro s _pro + a _ac S _ac + a _IN s _IN ) / a ₂ S _IN = S _nh / 14

FIG. 2 is a logic diagram for predicting the amount of gas generation corrected by the error feedback of the optimization module in order to improve the reliability of the anaerobic digestion model. The input parameters required for the anaerobic digestion model S310 according to the order of progress are as follows: ) Or from the anaerobic digestion process database S210 in which information is stored.

In order to increase the reliability of the model while the input variable is inputted to the anaerobic digestion model S310 and the gas generation amount is predicted, the optimization module S320 performs an error feedback process that minimizes the error between the actual measured value and the predicted value, The amount of gas generation is predicted and the operating conditions are derived. The predicted information is stored in the anaerobic digestion process database (S210) and simultaneously transmitted to the monitor so that quantitative numerical information is delivered to the operator.

Since the process model predictive control by the optimization module S320 proceeds through an error feedback process, there is a difference between the gas amount actually generated and the predicted gas amount through the anaerobic digestion tank, S320) receives the cumulative record of the difference between the measured value and the predicted value from the anaerobic digestion process database (S210) and derives a linear function formula or corrects the linear function formula to calculate an error value.

Accordingly, the optimization module (S320) predicts a value obtained by adding the predicted value and the error value to the corrected gas generation amount, and derives an operation condition capable of improving the gas generation amount as described later (S330) do.

In addition, information on the predicted gas generation amount and the operation condition is visualized on a monitor so as to be transmitted to an external user, and the estimated gas generation amount and the operation condition history are stored in the anaerobic digestion process database S210.

FIG. 3 is a graph showing the relationship between the gas generation amount set by the operator and the gas generation amount set by the operator by operating the scenario database in the anaerobic digestion model when the calibrated gas generation predicted value does not reach the gas generation reference value set by the operator or the gas generation amount satisfying the gas generation history information in the past period. A reference value or an operating condition that can reach a gas generation amount satisfying the gas generation amount history information in the past certain period.

If the corrected gas generation amount is predicted, the gas generation amount diagnosis process (S321) is performed to compare the gas generation amount reference value set by the operator or the gas generation amount history information in the past period. If the diagnosis result is not satisfactory, the scenario priority set in the scenario database (S322) The scenario analysis is performed through the digestion model until satisfactory driving conditions are derived.

Subsequently, when a satisfactory level of driving condition is derived (S323), it is presented to the operator (S330), stored in the anaerobic digestion process database (S210), and then the control is terminated.

The scenario analysis is a step in which quantitative values of inflow flow rate and microbial promoter injection amount related to hydraulic retention time (HRT) are determined. Therefore, the default value for the inflow flow rate and the accelerator is set in the scenario database (S322), and the average value of the past driving history is applied as a default value.

By setting up the range based on the design information by adjusting it up by 25% from the default value, the scenario is analyzed by applying the ratio differently in the range of 25 ~ 300%, and the operating condition is derived. Finally, I will present it.

1 to 3, the process model predictive control method for promoting anaerobic digestion gas production according to the present invention is applied to the ADM1 model proposed by the International Water Association (IWA) as an anaerobic digestion model for predicting the amount of generated digestion gas; Obtaining a measurement variable required for the digestion model through a measurement device assembly; When the actual gas generation amount acquired in the measurement parameter acquisition step does not reach the gas generation amount reference value set by the operator or the gas generation amount satisfying the gas generation amount history information in the past certain period, A data conversion step of converting the data into a data; Estimating a gas generation amount by applying the data converted input variable to the fire extinguishing model and estimating a gas generation amount corrected by an error feedback process of the optimization module to improve reliability of the fire extinguishing model; If the calibrated gas generation predicted value does not reach the gas generation amount reference value set by the operator or the gas generation amount satisfying the gas generation amount history information in the past period, the operation of the scenario database to improve the gas generation amount by operating the scenario database And deriving the condition.

4 is a control algorithm of the fixed control means for maintaining the pH of the anaerobic digestion tank. Acid fermentation tank and methane fermentation tank, and in case of single phase anaerobic digestion tank, the algorithm for methane fermentation tank can be applied. Each algorithm allows real time or intermittent checking of the pH value collected from the sensor, comparing it with the reference pH value to be maintained, and determining whether to operate the inlet pump of the drug reservoir to maintain the reference pH value.

The pH of the acid fermentation tank is maintained at 5 to 6, and the pH of the methane fermentation tank is maintained at 7 to 7.5. On / off type fixed control means is provided. When the pH of the acid fermentation tank is out of the range, Acid fermentation is not effective and the pH of the methane fermentation tank is out of the range, it affects the activity of the methanogenic microorganism group and the methane gas production is not efficiently performed.

FIG. 5 is a control algorithm of the fixed control means for maintaining the temperature of the anaerobic digestion tank, and the presence or absence of the boiler operation is determined according to the set value of the operator.

The boiler on / off type fixed control means is provided so that the temperature of the digester can be maintained at the mid-temperature extinguishing temperature of 33 to 38 ° C.

The pH and temperature of the anaerobic digester are maintained at a constant level because pH and temperature are environmental factors that can significantly affect the anaerobic microbial behavior. In order to ensure the stable prediction performance of the anaerobic digestion model, It is preferable that the digester is operated at a temperature.

6 is a schematic block diagram of a process model prediction control system according to the present invention.

(Not shown) capable of maintaining the temperature of the digestion tank through a fixed control means and a buffer tank for maintaining the pH of the anaerobic digestion tank, wherein the anaerobic digestion tank is divided into an acid fermentation tank and a methane fermentation tank. And pH and temperature values of the digester and buffer measured by the pH measuring sensor and the thermometer are stored in a memory and monitored in real time by an external monitor.

The buffer tank is connected to a storage tank (not shown) of an acid or alkaline chemical, so that the pH can be applied to the buffer tank so that the pH value measured by the pH sensor of the digester can satisfy the reference pH value.

As shown in FIG. 6, when the gas flow of the acid fermenter and the methane fermenter is diagnosed and determined as a target of the process model predictive control, the optimization module is applied to the acid fermenter and the methane fermenter Scenario analysis is performed and the finally derived operating conditions are applied. If the gas generation amount is not improved, the variable control means is driven to inject the microorganism promoting agent to improve the gas generation amount.

FIG. 7 is a schematic flow chart of the process model predictive control according to the present invention. The operating conditions (influent flow rate, microorganism promoter) derived through the optimization module, which is a main technical element of the present invention, are transmitted to the operator to support the operation of the anaerobic digestion facility A series of processes are shown.

As a driving condition to be controlled in the present invention, the selection of the influent flow rate and the amount of the microbial promoter injection amount can not be individually controlled for various variables such as influent flow rate, temperature, inflow sample, etc., In order to maintain the condition, there are limitations on the choice of parameters that can be easily controlled because of the closed structure. Injection flow rate and microbial promoter injection are relatively easy to control before the raw material enters the digester.

[Experimental Example]

Hereinafter, the present invention will be described with reference to a laboratory-scale anaerobic digester.

First, the anaerobic digestion model (ADM1) applied in this experiment was also developed based on the results obtained in the laboratory. Therefore, it is assumed that the prediction will be performed under the conditions with the minimum error, The feedback process was omitted.

In addition, the present experimental example is an example of confirming that the gas generation amount is promoted by controlling only the inflow flow rate, and therefore, the microorganism promoter, which is the second order control factor, is not applied, so the description thereof is not described.

The volume of the digester and the water quality information of the sample applied to the digester are shown in Table 2 below.

Digestion tank volume (㎥) 0.03 Time of stay (day) 26.3 Inflow flow rate (L / d) 1.14 Inflow CODt (kg COD / m3) 55.04 Incoming CODs (kg COD / m3) 0.04 Gas production reference value (L / d) 20.25

While the influent flow remains the same during the operation of the digester, there may be a situation in which the incoming COD, which is normally influenced by seasonality and the generation characteristics of the waste (substrate), is reduced. In this case, the organic matter becomes insufficient and the gas production amount by the anaerobic microorganism is changed.

9 is a graph comparing the amount of gas generated when the amount of gas generated is improved through the optimizing module when the concentration of the influent organic material changes in a normal manner.

Referring to FIG. 9, in the normal case, a sample having a COD value of 20.25 L / d is introduced into a digester, which corresponds to a reference value of gas generation amount, and the amount of gas is decreased to 10.830 L / .

Accordingly, it is necessary to improve the gas generation amount by the process model predictive control of the present invention, and it is necessary to present the operating conditions through the optimization module of FIG.

In order to apply the optimization module, the gas generation amount estimated by the anaerobic digestion model (IWA1) is 13.28 L / d, which is also lower than the reference gas volume of 20.25 L / d. (25%) to estimate the amount of gas generated by the hydraulic retention time (HRT).

 First, the amount of gas flowing into the digester was increased by 25% from the basic flow rate to predict the amount of gas generation. However, since the reference gas volume has not yet reached 20.25 L / d, the scenario flow rate is gradually increased by 25% through the scenario database to 2.28 L / d, which is twice the conventional flow rate (1.14 L / d) The amount of gas generated is close to the reference gas amount.

As a result of operating the digester under these operating conditions, the reference gas volume (20.25 L / d)

0.25 L / d. Thus, it can be confirmed that the process model predictive control system and method according to the present invention are useful for promoting the production of anaerobic digestion gas.

S110: Measuring device assembly
S210: Anaerobic digestion process database
S220: Diagnosis of actual gas generation
S230: Data switching means
S310: Anaerobic digestion model
S320: Optimization module
S321: Diagnosis of predicted gas volume
S322: scenario database
S323: Derivation of operating conditions
S330: Present driving conditions

Claims (12)

The ADM1 model proposed by the International Water Association (IWA) is applied as an anaerobic digestion model to estimate the amount of anaerobic digestion gas generated;
In order to improve the predictive reliability of the digestion model, a cumulative record of the difference between the measured value and the predicted value is received from the anaerobic digestion process database, and a linear function expression is derived or an error value is calculated by correcting the linear function expression, And a function of correcting an error between an actual value and a predicted value by predicting a value obtained by summing up the amount of gas generated in the past period and a function of correcting an error between the actual value and the predicted value by automatically estimating whether or not the amount of generated anaerobic digestion gas is increased, And an optimization module having a scenario analysis function for deriving a flow rate at an anaerobic digestion gas generation level satisfying the history information and a quantitative operation condition for application of the microbial promoter;
Wherein the scenario analysis is performed through the digestion model based on scenario priorities of a scenario database in which default values and scenario priorities for influent flow rates and microbial accelerators are set, and process model predictive control for promoting anaerobic digestion gas production system. delete delete The method according to claim 1,
Wherein the average value of past operating histories is applied to the flow rate of the scenario analysis and the default value for the microbial accelerator, and the process model predictive control system for promoting anaerobic digestion gas production. 5. The method of claim 4,
Wherein the influent flow rate is set prior to the microbiological promoter in the scenario priority. 5. The method of claim 4,
The scenario analysis is performed by setting the range based on the design value information by 25% up to the default value, and performing the analysis by applying the ratio in the range of 25 to 300%. The process model for promoting anaerobic digestion gas production Predictive control system. The ADM1 model proposed by the International Water Association (IWA) is applied as an anaerobic digestion model to estimate the amount of anaerobic digestion gas generated;
A measuring instrument assembly installed in the anaerobic digestion tank for acquiring information on the measurement parameters required for the digestion model;
Data switching means for switching information on the measurement parameters to input variables necessary for the digestion model;
An anaerobic digestion process database storing information on input parameters and measurement parameters derived through the data conversion means;
And an error value is calculated by correcting the linear function formula, and a value obtained by adding the predicted value and the error value is converted into a corrected gas generation amount And a function of diagnosing itself whether or not the amount of generated anaerobic digestion gas is increased or decreased and calculating a gas generation amount reference value set by the operator or an anaerobic digesting gas generation amount satisfying the gas generation amount history information in the past period An optimization module having a scenario analysis function for deriving a quantitative operation condition for application of a flow rate at a level and a microorganism promoter application through the digestion model;
A fixed control means for maintaining pH and temperature of the anaerobic digestion tank at a predetermined level;
And a variable control means for determining an inflow flow rate of the organic waste flowing into the anaerobic digestion tank and the infusion of the microorganism promoter into the anaerobic digestion tank. 8. The method of claim 7,
The anaerobic digestion tank is composed of an ideal anaerobic digestion tank divided into an acid fermentation tank and a methane fermentation tank, a boiler capable of maintaining the temperature of the digestion tank through a fixed control means, and a buffer tank for maintaining the pH of the digestion tank. And a thermometer are installed in the digester and a buffer tank. The pH and temperature values of the digester and buffer measured by the pH measuring sensor and the thermometer are stored in a memory and monitored in real time by an external monitor. Process Model Predictive Control System for Production Promotion. 9. The method of claim 8,
Wherein the pH of the acid fermentation tank is maintained at 5 to 6, and the pH of the methane fermentation tank is maintained at 7 to 7.5. The method of claim 8, wherein
Wherein the temperature of the anaerobic digestion tank is maintained at a mid-temperature of 33 to 38 ° C. 10. The method of claim 9,
The fixed control means for maintaining the pH of the anaerobic digestion tank checks the pH value collected by the sensor for pH measurement of the digestion tank in real time and compares the pH value with the pH value to be maintained so that the inflow pump of the medicine storage tank connected to the buffer tank And a control algorithm for determining whether to operate the anaerobic digestion gas. delete

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