Classifications

• • C—CHEMISTRY; METALLURGY
  • C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
  • C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
  • C02F11/00—Treatment of sludge; Devices therefor
  • C02F11/02—Biological treatment
  • C02F11/04—Anaerobic treatment; Production of methane by such processes
• • C—CHEMISTRY; METALLURGY
  • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
  • C12M—APPARATUS FOR ENZYMOLOGY OR MICROBIOLOGY; APPARATUS FOR CULTURING MICROORGANISMS FOR PRODUCING BIOMASS, FOR GROWING CELLS OR FOR OBTAINING FERMENTATION OR METABOLIC PRODUCTS, i.e. BIOREACTORS OR FERMENTERS
  • C12M21/00—Bioreactors or fermenters specially adapted for specific uses
  • C12M21/04—Bioreactors or fermenters specially adapted for specific uses for producing gas, e.g. biogas
• • C—CHEMISTRY; METALLURGY
  • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
  • C12M—APPARATUS FOR ENZYMOLOGY OR MICROBIOLOGY; APPARATUS FOR CULTURING MICROORGANISMS FOR PRODUCING BIOMASS, FOR GROWING CELLS OR FOR OBTAINING FERMENTATION OR METABOLIC PRODUCTS, i.e. BIOREACTORS OR FERMENTERS
  • C12M41/00—Means for regulation, monitoring, measurement or control, e.g. flow regulation
  • C12M41/12—Means for regulation, monitoring, measurement or control, e.g. flow regulation of temperature
• • C—CHEMISTRY; METALLURGY
  • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
  • C12M—APPARATUS FOR ENZYMOLOGY OR MICROBIOLOGY; APPARATUS FOR CULTURING MICROORGANISMS FOR PRODUCING BIOMASS, FOR GROWING CELLS OR FOR OBTAINING FERMENTATION OR METABOLIC PRODUCTS, i.e. BIOREACTORS OR FERMENTERS
  • C12M41/00—Means for regulation, monitoring, measurement or control, e.g. flow regulation
  • C12M41/26—Means for regulation, monitoring, measurement or control, e.g. flow regulation of pH
• • C—CHEMISTRY; METALLURGY
  • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
  • C12M—APPARATUS FOR ENZYMOLOGY OR MICROBIOLOGY; APPARATUS FOR CULTURING MICROORGANISMS FOR PRODUCING BIOMASS, FOR GROWING CELLS OR FOR OBTAINING FERMENTATION OR METABOLIC PRODUCTS, i.e. BIOREACTORS OR FERMENTERS
  • C12M41/00—Means for regulation, monitoring, measurement or control, e.g. flow regulation
  • C12M41/28—Means for regulation, monitoring, measurement or control, e.g. flow regulation of redox potential
• • C—CHEMISTRY; METALLURGY
  • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
  • C12M—APPARATUS FOR ENZYMOLOGY OR MICROBIOLOGY; APPARATUS FOR CULTURING MICROORGANISMS FOR PRODUCING BIOMASS, FOR GROWING CELLS OR FOR OBTAINING FERMENTATION OR METABOLIC PRODUCTS, i.e. BIOREACTORS OR FERMENTERS
  • C12M41/00—Means for regulation, monitoring, measurement or control, e.g. flow regulation
  • C12M41/30—Means for regulation, monitoring, measurement or control, e.g. flow regulation of concentration
• • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
  • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
  • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
  • Y02E50/00—Technologies for the production of fuel of non-fossil origin
  • Y02E50/30—Fuel from waste, e.g. synthetic alcohol or diesel

Definitions

• the invention relates to the biological conversion, such as digestion, of biomass such as industrial sludges, municipal sludge, slurried refuse and agricultural crops and residues, such as manure, to biogas e.g. comprising methane.
• biomass such as industrial sludges, municipal sludge, slurried refuse and agricultural crops and residues, such as manure.
• Anaerobic digestion is a technology having three main disadvantages that result in it not being considered for energy production as frequently as possible.
• the principal disadvantage stems from the fact that the reduction in volatile organic solid material is frequently far from complete. The results depend on the substrate, but incomplete conversion is typical of systems in which water is the sole plasticizing agent, hydrolytic pretreatment is not employed and/or when multiple substrates are codigested.
• the second disadvantage is the long hydraulic residence time or the length of time the liquid must stay in the digestion system to complete the transformation of the slowest metabolizing materials.
• the residence time in the digester can be very long, as much as 40 to 60 days, depending on the type of biomass.
• the third disadvantage is that conventional digestion is prone to failure caused by three types of overloads-organic, hydraulic, and toxic-that can result in the disruption of gas production.
• the digestion system must be taken out of service, the digester tank(s) cleaned out, and the system restarted.
• the environmental effects of such failures can be serious, particularly when other facilities for treatment are unavailable and raw sewage or untreated sludge must be disposed directly to the environment.
• a specific variety of digestion is known as codigestion, wherein a plurality of input biomass streams are fed into an apparatus for simultaneous digestion. Codigestion may be more attractive than digestion of one single biomass feed stream since the codigestion process is less dependent on the supply of one biomass component.
• the composition of the combined biomass feed may fluctuate due to fluctuations in the ratio in which the separate biomass streams are supplied to the process and/or to fluctuations of parameters within each individual biomass component stream.
• This known apparatus comprises a digestion unit operating at a controlled temperature and having a concentrator component and a pressure swing component each containing anaerobic bacteria and means for conveying aqueous slurried biomass from a biomass source to the concentrator component, and for removing the biogas from the concentrator component.
• the apparatus also includes means for conveying concentrated aqueous biomass from the concentrator component to the pressure swing component and for conveying digested aqueous biomass from the pressure swing component to the concentrator component, thereby forming a closed loop between the concentrator and pressure swing components.
• the apparatus further includes means for removing waste solids from the pressure swing component and a pressure swing pump for controlling the pressure within the pressure swing component in a cycle comprising a first phase having a first time duration at a sub-atmospheric first pressure and a second phase having a second time duration and a second pressure at or above atmospheric pressure.
• the apparatus further comprises a programmable computer provided with a database comprising data relating previously determined biomass-biogas conversion for biomass materials of varying compositions to values for the first and second phases, respectively, pressures and time durations of the first and second phases, wherein the computer operates to continuously monitor the pressure of biogas in the pressure swing component and adjust the cycle of the pressure swing pump to optimize biogas conversion of biomass from the biomass source.
• a control method for a biogas plant includes metering biomass substrate by a substrate metering device and regulating a process parameter as control variable of the anaerobic digestion process by a control mechanism with an automatic controller.
• a parameter nominal value is assigned to the controller to which an actual value transmitter is attached.
• the transmitter delivers an adjusting signal according to an assignable control algorithm for a subordinate control member of the control system after comparison of the actual value with the nominal value of an observed offset.
• the offset relative to the process parameter takes place through a control metering of an assigned control biomass, which influences the respective process parameter during the anaerobic digestion process.
• the assigned control biomasses are held back in assigned control storage vessels for the control metering by dosing according to the adjusting signal.
• the purpose of this known control method is to realize a more stable anaerobic digestion process with a higher biogas yield. Summary of the invention The inventor realized that, although the above known apparatus is complex, it may improve the speed of digestion of a single biomass feed and completeness of conversion from biomass to biogas. The inventor also realized, however, that these parameters may not result in actual optimal performance of the apparatus.
• the computer of the known apparatus controls the digestion process only on the basis of process physical variables, such as biogas pressure.
• the inventor also realized that, although the above known control method is complex, it may improve the conversion of biomass to biogas.
• the inventor also realized, however, that the control method may not result in actual optimal performance of the apparatus.
• the known method is aimed at counteracting an offset, which may not take away the cause of the offset. This may result in suboptimal performance of the biogas plant.
• the invention provides a method for digestion of biomass for the production of biogas, comprising, feeding biomass into a reactor comprising bacteriae for conversion of the biomass into biogas and digestate, monitoring at least one process physical variable by means of a processing unit, providing at least one process external variable to the processing unit, and on the basis of the at least one process physical variable and the at least one process external variable determining a desired feed rate for feeding the biomass into the reactor.
• This provides the advantage that the digestion process, e.g. the feed rate of biomass into the digestion process, can be controlled to any desired optimum value and not just to an optimum in view of process physical variables.
• a method for digestion of biomass for the production of biogas comprising, feeding biomass into a reactor comprising bacteriae for conversion of the biomass into biogas and digestate, monitoring, by means of a processing unit, process physical variables, including at least biogas yield, such as biogas production rate, and biomass feed, such as biomass feed rate, into the reactor, determining, using adaptive modelling, a relationship forecasting biogas yield as a function of biomass feed, providing at least one process external variable to the processing unit, determining, on the basis of the relationship and the at least one process external variable, a desired biogas yield, and determining a desired biomass feed on the basis of the determined desired biogas yield and the relationship.
• the determined desired biogas yield may be the optimum biogas yield in view of the at least one process external variable. It is also possible that the determined desired biogas yield is chosen to be closer to the optimum biogas yield in view of the at least one process external variable than the present biogas yield. The latter may be of benefit in case the optimum biogas yield in view of the at least one process external variable differs too much from the present biogas yield to be corrected at once, e.g. because this would entail the risk of upsetting the process in the reactor.
• the determined desired biomass feed is such that the forecast biogas yield associated with the determined desired biomass feed is substantially equal to the determined desired biogas yield.
• the biomass feed input into the reactor may be adjusted according to the determined desired biomass feed.
• the determined desired biomass feed may be used as a setpoint for a control controlling the actual biomass feed.
• the process physical variable is chosen from biogas production rate, reactor temperature, pH in the reactor, biogas composition, biomass composition, biomass feed rate, biomass quantity in the reactor, fatty acid composition in the reactor, fatty acid composition of the biomass, or redox. It has been found that these process physical variables, taken alone or in any combination of two or more thereof, provide useful information on the efficiency of the digestion process.
• the process physical variable(s) may be monitored continuously so as to provide information on the instantaneous efficiency of the digestion process.
• the at least one process external variable is one or more from biomass availability, biomass cost price, environmental load, environmental tax, digestate price, refuse costs, desired biogas production rate, energy prices, and financial balance of the reactor. It has been found that these process external parameters may influence the desired settings of the digestion process. These process external variables, taken alone or in any combination of two or more thereof, thus provide important information for determining the desired feed rate of biomass into the reactor.
• the desired feed rate is determined using adaptive modelling.
• adaptive modelling allows to, instantaneously, estimate the relationship between the biomass feed rate and the biogas yield.
• the biogas yield may be forecast.
• a relationship forecasting biogas yield as a function of biomass input may be determined.
• the desired optimum biogas yield may be determined, by the processing unit, from the process external variable and the relationship between the biogas yield and the biomass feed.
• the desired biomass feed associated with the desired optimum biogas yield may be determined, by the processing unit, from the process external variable and the relationship between the biogas yield and the biomass feed.
• the desired biogas yield in view of the process external variable may be determined taking into account the forecast biogas yield as a function of biomass input. Hence, the digestion process may be even more accurately adapted to the process external variable.
• Bayesian dynamic modelling with the system of prior and posterior is very flexible and robust, and thereby highly suitable for instantaneously estimating the relationship between the biomass feed rate and the biogas yield.
• dynamic linear modelling as a species of Bayesian dynamic modelling using linear models may provide a very robust and simple framework.
• the method includes, if the measured actual biogas yield differs from the determined desired biogas yield, e.g. by more than a predetermined amount or ratio, e.g. automatically, re-evaluating the relationship.
• the method includes, if the measured actual biogas yield differs from the determined desired biogas yield, e.g. by more than a predetermined amount or ratio, e.g. automatically, adjusting the biomass feed.
• the method includes, if the measured actual biogas yield differs from the determined desired biogas yield, e.g. by more than a predetermined amount or ratio, e.g. automatically, generating a warning signal to an operator of the reactor.
• the biomass fed into the reactor comprises a first biomass component fed into the reactor via a first feed channel and a second biomass component fed into the reactor via a different second feed channel, also known in the art as codigestion.
• the step of determining a desired biomass feed rate comprises determining a desired first feed rate for the first biomass component and determining a desired second feed rate for the second biomass component
• the step of adjusting comprises adjusting a feed rate of the first biomass component to conform to the desired first feed rate and adjusting a feed rate of the second biomass component to conform to the desired second feed rate.
• the biomass feed comprises at least one substrate or a group of substrates.
• the biomass material fed into the reactor may be a selected substrate or may be a mix of several substrates.
• the substrates may e.g. be chosen from industrial sludges, municipal sludges, slurried refuse and agricultural crops and residues, such as manure.
• the biomass feed comprises at least two substrates and/or groups of substrates.
• the at least two substrates may be combined, or mixed, to provide n efficient biogass yield.
• a feed interval of the biomass feed of each individual substrate or group of substrates is adjusted separately.
• the optimum feed interval for each individual substrate or group of substrates may be determined on the basis of the determined desired biogas yield and individual relationships forecasting the biogas yield as a function of the individual substrate feed, for each individual substrate or group of substrates.
• the optimum feed interval for each individual substrate or group of substrates may be determined on the basis of the determined desired biogas yield and a relationship forecasting the biogas yield as a function of the individual substrate feeds.
• the method according to the invention comprises feeding the biomass into a plurality of reactors, determining relationships forecasting biogas yield as a function of biomass input for each separate reactor, determining, on the basis of the at least one process external variable, at least a desired biogas yield for the totality of the plurality of reactors, determining desired biomass feeds for each of the reactors such that a forecast biogas yield for the totality of the plurality of reactors is substantially equal to the desired biogas yield, and adjusting the biomass input into each of the reactors according to the determined desired biomass feeds.
• the desired biomass feeds for each of the reactors are determined such that for each of the reactors the desired biomass feed is associated with a forecast optimum biogas yield of that reactor.
• each reactor is adjusted to perform optimally, while the totality of reactors is adjusted to deliver the desired biogas yield.
• Adjusting the input to conform to the determined desired biomass feed (setpoint) may then include adjusting an amount to be inputted per batch, and/or a timing of input of a batch into the reactor.
• the biomass is inputted into the reactor continuously. Adjusting the input to conform to the setpoint may then include adjusting a feed rate of the biomass.
• the relationship comprises a static model, with fixed coefficients.
• the relationship may be updated at certain points in time, e.g. every hour or every day. Alternatively, the relationship may be updated continuously or quasi- continuously (e.g. every second). The relationship may be updated on the basis of continuously measured process physical variables, on the basis of time-averaged process physical variables, on the basis of real-time measured process physical variables, on the basis of inline measured process physical variables, and/or on the basis of off-line measured process physical variables.
• the method includes monitoring, by means of a processing unit, at least one mixing property of the reactor, determining, using adaptive modelling, a relation forecasting biogas yield as a function of the at least one mixing property, determining at least one desired mixing property on the basis of the desired biogas yield and the relation, and adjusting the at least one mixing property of the reactor according to the at least one determined desired mixing property.
• the at least one mixing property may include one or more of mixing duration, mixing time interval, and/or mixing intensity.
• the invention also relates to an apparatus for digestion of biomass for the production of biogas.
• a method for digestion of biomass for the production of biogas comprising feeding, at a feed rate, biomass into a reactor comprising bacteriae for conversion of the biomass into biogas and digestate, monitoring at least one process physical variable by means of a processing unit, on the basis of the at least one process physical variable determining, using adaptive modelling, preferably a Bayesian approach for dynamic modelling or dynamic linear modelling (DLM), a desired feed rate for feeding the biomass into the reactor, adjusting the feed rate of the biomass to conform to the desired feed rate.
• adaptive modelling preferably a Bayesian approach for dynamic modelling or dynamic linear modelling (DLM)
• the biomass fed into the reactor comprises a first biomass component fed into the reactor via a first feed channel and a second biomass component fed into the reactor via a different second feed channel
• the step of determining comprises determining a desired first feed rate for the first biomass component and/or determining a desired second feed rate for the second biomass component
• the step of adjusting comprises adjusting a feed rate of the first biomass component to conform to the desired first feed rate and/or adjusting a feed rate of the second biomass component to conform to the desired second feed rate.
• the codigestion process can efficiently be controlled using the adaptive modelling, preferably Bayesian dynamic modelling or dynamic linear modelling.
• Fig. 1 shows a schematic representation of an apparatus for digestion of biomass according to the invention
• Fig. 2 shows an exemplary graph of how the biogas production, changes as a function of biomass feed rate
• Fig. 3 shows a schematic representation of an alternative apparatus for digestion of biomass according to the invention
• Fig. 4 shows an exemplary graph of how the biogas production, changes as a function of biomass coproduct feed rate
• Fig. 5a shows an exemplary graph of how the biogas production, changes as a function of biomass feed rate in relation to a plurality of process external variables
• Fig. 5b shows an exemplary graph of how the biogas production, changes as a function of a plurality of biomass feeds.
• Fig. 1 shows a schematic representation of an apparatus 1 for digestion of biomass according to the invention.
• the apparatus 1 comprises a reactor 2.
• the apparatus 1 further comprises a biomass feed channel 4 for feeding the biomass into the reactor 2.
• the apparatus further comprises a biogas outlet channel 6 for removing biogas from the reactor and a digestate outlet channel 8 for removing digestate from the reactor 2.
• the apparatus 1 further comprises a processing unit 10.
• the processing unit 10 is communicatively coupled to at least one sensor 12.
• the sensor 12 is a flow meter for measuring the flow of biogas through the biogas outlet channel 6.
• the processing unit 10 is also communicatively coupled to a valve 14 in the feed channel 4 for controlling the feed rate of the biomass.
• the apparatus as described thus far can be used in a method for producing biogas from biomass as follows.
• the reactor 2 comprises an amount of biomass and bacteriae.
• the biomass may comprise industrial sludges, municipal sludge, slurried refuse and/or agricultural crops and/or residues, such as manure or vegetal components. It will be appreciated that the biomass may also comprise additives such as nutrients, for the bacteriae, or enzymes, e.g. for enhancing the conversion of the biomass.
• the bacteriae cause the conversion of biomass to biogas. The bacteriae may for instance cause hydrolysis and methanogenesis of the biomass. It will be appreciated that the biogas may contain methane.
• the biogas may contain water vapour, ammonia (NH4), hydrogen sulfide (H2S) and/or other trace gases. The remnants of the biomass not converted to biogas form the digestate.
• the reactor 1 is designed for continuous operation. That is, in use (semi-)continuously biomass is fed into the reactor 2, while also (semi-)continuously biogas and digestate is drained from the reactor 2.
• biomass in use (semi-)continuously biomass is fed into the reactor 2, while also (semi-)continuously biogas and digestate is drained from the reactor 2.
• (semi-)continuously is understood to at least comprise truly continuously, or continuously during a certain period of time with intervals of no feed in between intervals of continuous feed.
• the residence time of the biomass in the reactor 2 is an important process physical variable and co-determines the efficiency of the conversion of biomass into biogas. It will be appreciated that the residence time will be affected by the feed rate of the biomass into the reactor 2 via the feed channel 4.
• the processing unit 10 determines the realized biogas yield (e.g. in m 3 /day) by continuously measuring the biogas flow rate at the biogas outlet channel 6.
• the processing unit 10 also continuously measures the actual biomass input by measuring the flow in the biomass feed channel 4 during the period that the control valve 14 is open.
• a response curve of the influence of the biomass input on biogas yield can be evaluated. It will be appreciated that the response curve can be based on instantaneously measured biomass input and instantaneously measured biogas yield. It is also possible to base the response curve on time- averaged values.
• the response curve may be determined on the basis of an average biomass input during a predetermined period of time, such as an hour or a day, and an average biogas yield during a predetermined period of time such as an hour or a day.
• the curve C shows an example of how the biogas production changes as a function of the biomass feed rate.
• the curve C gives the relationship between the biogas yield and the biomass feed.
• the instantaneous correlation between biomass feed rate and biogas production is calculated using a Bayesian approach for dynamic modelling. It will be appreciated that thanks to the dynamic modelling the curve C will be re- determined or adapted after a certain period of time, on the basis of measurements of the biomass input and biogas yield during, and possibly before (historic data), that period. In this example, each day the response curve is re-evaluated based on the new and historic data by using the Bayesian approach for dynamic modelling.
• the processing unit 10 continuously determines the production rate of biogas (e.g. in m 3 /hr) by measuring the biogas flow rate at the biogas outlet channel 6.
• the processing unit 10 determines the biomass input by recording the amount (mass) of biomass input and the time at which this amount was input.
• the desired biomass feed rate associated with the desired optimum biogas production rate may be determined by the processing unit 10 from the curve C.
• the valve 14 may be adjusted under control of the processing unit 10 such that the feed rate conforms to the determined desired feed rate.
• the point A is associated with the feed rate TA at which the highest biogas yield VA is achieved. At higher feed rates the biogas production rate may be lower, e.g. due to "washing" bacteriae out of the reactor 2. Hence, from a perspective of process physical variables, the point A represents the optimum point of operation of the apparatus 1.
• the process external variable is the environmental load.
• the environmental load is considered to be proportional to the amount of digestate to be dispensed, so that the environmental load is proportional to the biomass feed rate.
• the environmental load is represented by line L.
• the processing unit 10 determines from the environmental load line
• the processing unit 10 determines a parallel projection L' of the line L which is tangent to the curve C.
• the tangent point B defines the point of optimum operation with respect to environmental load.
• the yield of biogas VB represents the highest yield to environmental load ratio.
• the desired feed rate ⁇ B associated with the desired optimum biogas production rate VB may be determined by the processing unit 10 from the curve C and the process external variable, here the environmental load.
• the valve 14 may be adjusted under control of the processing unit 10 such that the feed rate conforms to the determined desired feed rate ⁇ B.
• the optimum biogas production rate VB differs too much from the present biogas production rate to be corrected at once, e.g. because this would entail the risk of upsetting the process in the reactor.
• the desired biogas production rate may (temporarily) be set at a value between the optimum biogas production rate VB and the present biogas production rate.
• the process control exerted by the processing unit 10 is a control wherein the , here point B, is determined from the curve C.
• the curve C can be updated as described hereinabove. It will also be appreciated that if updating of the response curve C results in a change of the setpoint for the desired feed rate and/or a change in the desired optimum biogas yield, the setpoint for the valve 14 may be adjusted. It will be appreciated that if the measured actual biogas yield differs from the determined desired biogas yield, e.g. by more than a predetermined amount or ratio, the processing unit 10 may, e.g. automatically, re-evaluate the relationship between the biogas feed and biogas yield.
• the processing unit 10 may, e.g. automatically, adjust the biomass feed. Also, if the measured actual biogas yield differs from the determined desired biogas yield, e.g. by more than a predetermined amount or ratio, the processing unit 10 may, e.g. automatically, generate a warning signal to an operator of the reactor.
• Fig. 3 shows a schematic representation of an alternative apparatus
• the apparatus 1 of Fig. 3 is similar to the apparatus shown in Fig. 1.
• the apparatus 1 comprises a first feed channel 4A for feeding a first biomass component, e.g. a main biomass product such as manure, into the reactor 2, and a second feed channel 4B for feeding a second biomass component, e.g. a coproduct such as a vegetal additive, into the reactor 2.
• a first biomass component e.g. a main biomass product such as manure
• a second feed channel 4B for feeding a second biomass component, e.g. a coproduct such as a vegetal additive
• the processing unit 10 is in this example communicatively coupled to valve 14A and 14B in the first and second feed channel 4A,4B, respectively.
• the apparatus of Fig. 3 can be used for producing biogas from biomass as follows.
• biomass is (semi-)continuously fed into the reactor
• the curve C shows an example of how the biogas production, changes as a function of the biomass coproduct second feed rate.
• the instantaneous correlation between biomass coproduct second feed rate and biogas production is calculated using Bayesian dynamic modelling.
• the processing unit 10 continuously determines the production rate of biogas (e.g. in m 3 /hr) by measuring the biogas flow rate at the biogas outlet channel 6. It will be appreciated that it is now possible to determine whether or not the biogas production rate is optimum. If the biogas production rate is determined not to be optimum, the desired second feed rate associated with the desired optimum biogas production rate may be determined by the processing unit 10 from the curve C. The valve 14B may be adjusted under control of the processing unit 10 such that the second feed rate conforms to the determined desired second feed rate. It will be appreciated that the process control exerted by the processing unit 10 is a control wherein the setpoint, here point B', is determined from the curve C. The curve C can be updated as described hereinabove.
• the process external variable provided to the processing unit 10 is the cost of the added biomass coproduct.
• the cost is proportional to the amount of coproduct added, i.e. to the second feed rate, as represented by line L c in Fig. 4.
• the processing unit 10 determines from the cost line L c the optimum point of operation of the apparatus 1.
• the processing unit 10 determines at what feed rate a first derivative of the cost line L c is equal to a first derivative of the curve C, e.g. determining a parallel projection L c ' of the line L c which is tangent to the curve C.
• This feed rate, represented at tangent point B' defines the point of optimum operation with respect to coproduct cost.
• the yield of biogas VB' represents the highest yield to coproduct cost ratio.
• the desired feed r& rate associated with the desired optimum biogas production rate VB' may be determined by the processing unit 10 from the curve C and the process external variable, here the coproduct cost.
• the desired optimum biogas production rate VB' and the associated desired feed rate TB may be determined by the processing unit 10 from the process external variable and the relationship between the biogas yield and the biomass feed.
• the valve 14B may be adjusted under control of the processing unit 10 such that the second feed rate conforms to the determined desired second feed rate TB'.
• the relationship between the biomass feed rate and the biogas production yield is instantaneously determined by an adaptive model. Most preferably, said relationship is determined by Bayesian dynamic modelling. Bayesian dynamic modelling is known in the art and will not be discussed in detail here for conciseness. It will be appreciated that Bayesian dynamic modelling provides the advantage that at any moment in time the relationship between the biomass feed rate and the biogas production yield may be calculated and preferably updated on the basis of, e.g. continuous, in-line measurements of the biomass feed rate and the biogas yield.
• the Bayesian dynamic model comprises a plurality of input parameters, wherein one or more of the aforementioned process physical variables may be used as the input parameters.
• the digestion process output i.e. the biogas yield
• the Bayesian dynamic model may, thus, use in-line, preferably real-time, information from the digestion process to accurately estimate the instantaneous relationship between the biogas yield and the biomass feed rate. This estimation can be performed recursively during the modelling process. It is also possible that the Bayesian dynamic model uses time-averaged input values.
• the Bayesian dynamic model may use an average biomass input during a predetermined period of time, such as an hour or a day, and an average biogas yield during a predetermined period of time such as an hour or a day.
• a predetermined period of time such as an hour or a day
• an average biogas yield during a predetermined period of time such as an hour or a day.
• the Bayesian dynamic model uses individual model coefficients and model variables that are, at least partly, time-dependent.
• the digestion process takes place on time scales of hours or even days, it may be very efficient to forecast the biogas yield beforehand on the basis of past, present and optionally future biomass feed rates. Hence it is possible to even more accurately adapt the digestion process to the process external variables.
• the Bayesian dynamic model allows forecasting of the future biogas yield.
• the Bayesian dynamic model then provides the relationship forecasting biogas yield as a function of biomass feed.
• the response curve is daily re-evaluated based on the most recent data, so that the forecast is based on the response curve that is highly accurate due to the fact that it has been determined with data that have been updated daily up until the present day.
• the process external variable includes a desired biogas yield VB, such as a daily desired biogas yield VB. If the actually realized biogas yield does not correspond to the desired biogas yield, a new setpoint for the biomass input can be determined as a new biomass input. This setpoint can be close to or equal to ⁇ B, since this will lead to a forecast biogas yield close to or equal to the desired biogas yield VB.
• the adjustment of biomass input can be done through an expert judgement of the operator but could also be done by the processing unit on the basis of an algorithm.
• the coproduct second feed rate is adjusted by the processing unit.
• the main product first feed rate may be adjusted by the processing unit, e.g. on the basis of main product cost or another process external variable.
• codigestion is achieved by feeding the main product and the coproduct into the reactor via separate feed channels.
• the main product and the coproduct are premixed before being fed into the reactor.
• the feed rate of the main product and/or the coproduct prior to mixing e.g. upstream of a mixer, may be adjusted by the processing unit.
• Such premixing may take place in a premix container, in which the main product and coproduct, e.g. liquids, sludges or solids, are mixed.
• the premixed main product and coproduct may than be transported from the premix container to the reactor, e.g. via a pipe, conveyor or the like.
• the biomass feed channel and the further biomass feed channel are in fluid communication with the reactor.
• the feed rate of the biomass may e.g. be controlled on the basis of mass reduction of the remaining biomass in the premix container or volume flow rate from the premix container to the reactor.
• the feed rate of the biomass is controlled by means of a valve. This may work very well, especially with biomass materials that can flow such as sludges.
• the biomass feed rate may also be controlled by means of a pump, conveyor, auger or similar. Also batch- wise feed of the biomass, wherein the processing unit controls the amount of biomass in the batch and/or the timing of batch addition is conceivable.
• the feed rate is adjusted on the basis of the process external variable and the biogas yield as the process physical variable.
• the process physical variable may also be chosen to be one or more of the reactor temperature, pH in the reactor, biogas composition, biomass composition, biomass quantity in the reactor, fatty acid composition of the biomass, fatty acid composition in the reactor, or redox. These variables have been found to influence biogas yield and thus form well measurable process physical parameters to be used in the method according to the invention. Additional, or alternative, sensors, e.g. a temperature sensor 12A, may thereto be incorporated in the apparatus.
• the feed rate is adjusted on the basis of one process external variable. It will be appreciated that it is also possible to adjust the feed rate on the basis of a plurality of process external variables, e.g. on the basis of a joint effect of such plurality of process external variables.
• Fig. 5a shows an example where the line L, represents such joint effect.
• the joint effect may e.g. be a joint cost and/or joint environmental loads.
• the line L is subdivided into segments I, II and III. For each segment it is assessed whether a local optimum feed rate can be determinded with respect to the local effect line segment, in this example, the segments II and III each yield a local optimum feed rate.
• the highest optimum feed rate rBi ⁇ may now be selected as overall desired feed rate with respect to the joint effect of the plurality of process external variables.
• the optimum feed rate is influenced by a desired biogas production yield. It is for instance possible that delivery of a certain biogas rate to a gas network is contractually limited to a maximum and/or minimum delivery rate. Such maximum and or minimum rate may also influence the desired feed rate. It will be appreciated that the processing unit may also use such maximum and/or minimum rate as process external variable. In the examples, one biomass feed is adjusted. It will be appreciated that it is also possible, for instance in the embodiment shown in Fig. 3, to adjust a plurality of biomass feeds, or to adjust the input of a plurality of substrates or groups of substrates within a single biomass feed. Fig.
• Ci (and C 1 ' and Ci") denotes the response curve of the biogas yield as a function of a first biomass feed and where C2 (and C2' and C2") denotes the response curve of a second biomass feed.
• the response curves Ci, Ci', Ci", C2, C2', C2" are all part of a response surface.
• the line L c i represents the cost line for the first biomass feed and L C 2 denotes the cost line for the second biomass feed.
• the optimum biogas yield y 0 is in Fig.
• the processing unit is arranged for determining the desired feed rate and adjusting the feed rate accordingly on the basis of the at least one process physical variable and the at least one process external variable. It is also possible that the processing unit is in a similar fashion arranged for determining a desired value of another process physical variable, such as reactor temperature, and adjusting said process physical variable accordingly, e.g. by heating or cooling the reactor.
• the desired biomass feed is determined on the basis of the desired biogas yield and the relationship. It will be appreciated that it is also possible to determine a desired feed biomass composition on the basis of the desired biogas yield and the relationship.
• Such desired feed biomass composition may include parameters such as biomass material nature and/or additive content.
• additives may e.g. be nutrients and/or enzymes.
• the additives are chosen for their known ability to promote biogas production and/or improve biogas quality, e.g. reducing the hydrogen sulphide content of the biogas.
• the desired biomass feed is determined on the basis of the relationship between biomass feed and biogas yield. It will be appreciated that it is also possible to determine desired mixing properties of a mixer of the reactor on the basis of a relation between at least one mixing property and biogas yield. This may be done instead of or additionally to determining the relationship between biomass feed and biogas yield.
• the method according to the invention may include monitoring, by means of a processing unit, at least one mixing property of the reactor, determining, using adaptive modelling (e.g.
• Bayesian dynamic modelling a relation forecasting biogas yield as a function of the at least one mixing property, determining at least one desired mixing property on the basis of the desired biogas yield and the relation, and adjusting the at least one mixing property of the reactor according to the at least one determined desired mixing property.
• Possible mixing properties are e.g, mixing duration, mixing time interval (frequency), and/or mixing intensity (firm/gentle).
• any reference signs placed between parentheses shall not be construed as limiting the claim.
• the word 'comprising' does not exclude the presence of other features or steps then those listed in a claim.
• the words 'a' and 'an' shall not be construed as limited to 'only one', but instead are used to mean 'at least one', and do not exclude a plurality.
• the mere fact that certain measures are recited in mutually different claims does not indicate that a combination of these measures cannot be used to advantage.

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