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1. Nb redox potential in mV and EC in mS 3 4 Volatile fatty acids VFAs Volatile fatty acids VFAs have long been recognised as the most important intermediates in the biogas production process and have been proposed as a control parameter AD processes are sensitive to hydraulic or organic overloading due to imbalanced or insufficiently controlled feeding The relative concentrations of the volatile fatty acids consisting of acetic propionic butyric and iso butyric acids are important process monitoring parameters The ratio between propionic acid and acetic acid can be determined using an offline instrument and can be used to identify effective methanogenesis Different approaches were developed for on line monitoring of VFAs J von Sachs et al 2003 Feitkenhauer et al 2002 Pind et al 2003 Boe et al 2005 These on line approaches include spectrophotometer methods and titration methods respectively A new online method for VFAs in digester samples has been developed through gas phase extraction i e online headspace chromatographic method for measuring VFA in biogas reactor Boe et al 2005 This method needs no sample filtration which is of advantage for samples with high solids Nielsen et al 2007 demonstrated an online VFA sensor in order to study VFA dynamics during stable and unstable operation of the biogas plant They identified propionate concentration as a key parameter for optimizing the biogas process By on line moni2. af e agrobiogas r J of Project no 513949 Project acronym EU AGRO BIOGAS Project title European Biogas Initiative to improve the yield of agricultural biogas plants Instrument Specific targeted research or innovation project Thematic Priority Priority 6 Sustainable Energy Systems Deliverable 18 User manual for the automatic monitoring management and early warning system Due date of deliverable 2009 11 30 Actual submission date 2009 12 12 Start date of project 2007 01 15 Duration 36 months 2007 2009 Organisation name of lead contractor for this deliverable Partner N 2 North Wyke Research Rothamsted Research p e agrobiogas lt ae Deliverable 18 User manual for the automatic monitoring management and early warning system Abstract The rationale for monitoring biogas production is that biogas plants that operate without online monitoring may be underperforming Furthermore for economic reasons more plants are operating in a critical load range where there is a risk of a digester failure that results in financial deficits In the case of larger plants the cost of online monitoring is only a small fraction of the total costs Controlling the biogas production process can be difficult often because different feedstocks are used that have different process requirements and or produce different responses with the measuring equipment Therefore the intention of this manual is to determine thos
3. Portable Electronic Nose 7 1 Hardware Configuration In this instance the Portable Electronic Nose PEN can be used for the quantitative identification of propionic acid in biogas A high level of standardisation is needed to get comparable results from PEN measurements which includes the configuration of the used hardware Therefore a specimen holder as well as a stamp formed tube and cannula carrier was developed For the measurements it was furthermore determined to purge the needed fresh air by an activated charcoal filter and the delivery rate of the pump was fixed at 250 ml min during measurement 16 Figure 4 Measuring adapter with specimen holder An easy to operate stamp device was built from a mobile inner acrylic glass tube and a fixed outer guide tube see figure 4 At the bottom of this device a silicon made plug was fixed with two cannulas for the supply and exhaust air These were diametrically opposed with a distance of 10 mm from the centre and a penetration depth of 15 mm see figure 5 To attach the stamp device on the septum of the measuring tube the device handle is pulled down with one hand and while removing the measuring tube is fixed with the other hand Between two measurements and for zero point calibration a blank sample is used Figure 5 fixed outer guide tube The determination of the concentration of acetic and propanoic acids in the headspace can be determined However there are two further
4. be a good parameter for process monitoring and also to control the feed that influences VFA formation 20 9 Further Reading Information Alastair Ward PhD Thesis Optimisation of biogas production by advanced process monitoring and the effect of mixing frequency on biogas reactors with and without microbial support media Gonzalo Ruiz Filippi Advanced monitoring and control of anaerobic reactor Department of Chemical Engineering Universidade de Santiago de Compostela Spain Defended on March 3rd 2005 Electronic version in pdf can be obtained by sending an e mail to gonzalo ruiz ucv cl note that the document was written in Spanish Modelling and monitoring the anaerobic digestion process in view of optimisation and smooth operation of WWTPs Usama El sayed Zaher BIOMATH Ghent University Belgium defended 14 June 2005 Download http biomath ugent be publications download zaherusama_phd pdf IWA Specialist Group on Instrumentation Control and Automation 21 10 References Boe K Batstone D J and Angelidaki I 2007 An innovative online VFA monitoring system for the anerobic process based on headspace gas chromatography Biotechnol Bioeng 96 712 721 Boe K Batstone D J and Angelidaki 2005 Online headspace chromatographic method for measuring VFA in biogas reactor Water Science and Technology 52 473 478 Fabian Jacobi H Moschner C R and Hartung E 2009 Use of near infrared spectroscopy
5. in monitoring of volatile fatty acids in anaerobic digestion Water Science and Technology 60 339 346 Feitkenhauer H von Sachs J Meyer U 2002 On line titration of volatile fatty acids for the process control of anaerobic digestion plants Water Research 36 212 218 Guwy A J Hawkes F R Hawkes D L Rozzi A G 1997 Hydrogen production in a high rate fluidized bed anaerobic digester Water Research 31 6 1291 1298 Holm Nielsen J B Dahl C K Esbensen K H 2006 Representative sampling for process analytical characterization of heterogeneous bioslurry systems a reference study of issues in PAT Chemometrics and Intelligent Laboratory Systems 83 114 126 J von Sachs Meyer U Rys P Feitkenhauer H 2003 New approach to control the methanogenic reactor of a two phase anaerobic digestion system Water Research 37 973 982 Nielsen H B Uellendahl H and Ahring B K 2007 Regulation and optimization of the biogas process Propionate as a key parameter Biomass and Bioenergy 31 820 830 Pind P F Angelidaki and Ahring B K 2003 Dynamics of the anaerobic process effects of volatile fatty acids Biotechnol Bioeng 82 791 801 Mathiot S Escoffier Y Ehlinger F Couderc J P Leyris J P Moletta R 1992 Control parameter variations in an anaerobic fluidized bed reactor subjected to organic shock loads Water Science and Technology 25 93 101 Zhang Y Zhang Z Sugiura N an
6. in those changes in both the quantity and composition of the substrate produces rapid peaks in H partial pressure Guwy et al 1997 but this did not necessarily mean process failure In addition the absolute concentration of biogas hydrogen was not constant following similar process overload situations Guwy et al 1997 4 General indicator trends in biogas production During a stable fermentation of biogas production the pH alkalinity conductivity and methane concentration either remain stable or increase with time During a period of instability these may decline Such parameters could be used in several different process control approaches to maintain a stable fermentation process Important biogas plant fermentation parameters with functional range pH hydrolysis 4 0 6 5 pH methanogenesis 6 8 7 4 Redox potential 330 and lower Alkalinity or buffering capacity over 4000 mg bicarbonate Mesophilic temperatures 37 to 39 C VEFA TIC ratio lt 0 3 Thermophilic temperatures 50 to 55 C HRT energy crops 60 120 days HRT manure amp food wastes 10 to 25 days 5 Hardware and software Below are the examples of different hardware and software components for automatic monitoring Components Details use Source WaterWatch The This system can www partech co uk 2620 WaterWatch2620 measure up to system seven parameters Multi parameter provides a compact Including
7. min depending on the washing duration and sensor response is 10 min This is appropriate for full scale reactors since dynamics and feeding intervals of most biogas reactors are of the order of several hours For further information please read Boe et al 2007 3 6 Redox potential Strictly anaerobic bacteria such as methanogens need a strongly reducing environment with redox potentials below 330 mV to perform well If the feedstock inputs are stable then redox potential enables detection of disruptions to the process even earlier than the VFA TIC ratio A difference of 10mV in 500mV can indicate that changes are about to occur The redox potential change can identify problems earlier than the VFA TIC ratio In addition the redox sensor is more stable that the pH sensor During the biogas fermentation process the hydrogen gas concentration can change Generally for energy crops the hydrogen concentration is less than 200 ppm v v and for organic wastes less than 500ppm v v In experiments at North Wyke the hydrogen gas would suddenly fall and then increase before digester failure So a trend could be identified and integrated into the process control software Gas phase He concentration has previously been measured in anaerobic digesters to determine the effectiveness of this parameter for process state determination Guwy et al 1997 Mathiot et al 1992 It has been shown that the partial pressure of H is an important disturbance indicator
8. pH Monitor cost effective Redox Dip Probe Sonde package for Conductivity Configuration monitoring a suite of Temperature standard parameters DO SS and in either Turbidity discrete dip probe or combined sonde configuration pH Redox pH sensor 199660 BNC plug in type www partech co uk Conductivity Redox electrode Temperature 201300 probes Conductivity Sensor 220501 10 Temperature 220500 RS232 Legacy 16 port Data acquisition and www ni cim RS232 interface for control NI PCI 232 16 Windows Me 9x NT RS232 LabVIEW Real Time SCSI 100 Standard baud rates connector up to 115 2 kb s 1 converter cable Mb s with NI PCI and breakout box 8430 16 SCSI 100 to 16 ante DESM 64 B transmit and DB9 receive FIFOs 128 B with PCI 8340 16 Computers Windows 98 Me Please make sure www ni cim 2000 and XP that the computer compatible on which you plan to The computer you install Lab VIEW or use for your DAQ any other software system can to control plant and drastically affect the for data acquisition maximum speeds at that meets the which you can minimum system continuously acquire requirements for the data program to run Computer Hard The data transfer The limiting factor Disk capabilities of your for acquiring large computer can amounts of data significantly affect often is the hard the performance of drive Disk access your DAQ system time
9. required and it is non destructive and may allow the simultaneous analysis of several components in complex matrices NIRS can observe any sample in virtually any state liquids solutions pastes powders films fibres gases and surfaces can all be examined to study biological systems such as proteins peptides lipids biomembranes carbohydrates pharmaceuticals foods and both plant and animal tissues Parameters such as alkalinity total and ammonical nitrogen dry and organic matter and total and individual VFA concentrations can be determined The magnitude of some of these parameters could be inputs to a control program to determine optimal digester OLRs or be used as a source of advice for biogas plant managers NIRS has been used to monitor VFAs Fabian et al 2009 COD total organic carbon TOC and partial and total alkalinity on line in the liquid phase 2 4 Electronic Nose An electronic nose is a device intended to detect volatile compounds and is associated with odour emissions from foods pharmaceuticals and other sources Over the last decade electronic sensing or e sensing technologies have undergone important developments from a technical and commercial point of view The expression electronic sensing refers to the capability of reproducing human senses using sensor arrays and pattern recognition systems The stages of the recognition process are similar to human olfaction and are performed for identificat
10. variables to 17 consider before we can determine the impact on the fermentation process these are 1 The relationship of headspace concentration to that in the digestate 2 The levels at which the concentration of these two VFAs act as indicators for process control to be changed The latter two points highlight the need for further research for the PEN device to be effective for process control 8 Near Infrared reflectance spectroscopy 8 1 Determination of VFA in the digester by NIRS Before we can determine the acetic or propanoic acid concentration in the digester we need to calibrate the NIRS system with known concentrations of acetic and propanoic acids in the digester liquid Here we describe the method to calibrate a NIRS system to monitor VFAs in a digester One litre samples were collected five times a week for a five month period and VFA species were measured by gas chromatography for NIRS calibration The samples were prepared for NIRS analysis by heating them to the reactor temperature in one litre beakers The beakers were then placed on rotary shakers at approximately 100 rpm with the probe fixed above and immersed to a depth of 20 mm NIRS spectra were acquired with a Bomen QFA Flex Fourier Transform spectrometer with an optical fibre interface and diffuse reflectance probe Spectra were collected using Q Interline INFRAquant software Each measurement was an average of 256 scans The advantage of this arrangement was
11. IRS probe had little effect on model quality therefore an on line NIRS system can be as effective as a more labour intensive at line NIRS analysis A single model for estimating propanoic acid in different manures can be made by combining the data from all those manures However samples from all combinations of manure and or waste types with a wide range of VFA values are necessary for calibration of a true generic model Collecting this many samples would be an immense task NIRS is suitable as a rapid and low maintenance method of determining VFA in digesters with a single unchanging feedstock but is not suitable for biogas plants with a changing input At North Wyke we monitor biogas fermentation in a pilot plant using NIRS The FT NIRS instrument from Bruker was configured with a fibre optic Reflector NIR 12S 300 051220 1 probe to monitor inside the digester Near infrared spectroscopy NIR was used for real time monitoring of different parameters using the Bruker Matrix F FT NIRS with optic cable Near Infra Red Spectrometer Bruker Optics Limited Banner Lane Coventry CV4 9GH Tel 02476 855200 www brukeroptics com This technique is a promising on line monitoring technique and calibration of the NIRS was possible with data from the HPLC analysis for VFAs This approach was integrated into a pilot scale plant for on line monitoring of VFAs and other parameters simultaneously to develop an early warning system Alkalinity proved to
12. and hard drive LabVIEW 8 2 graphical Use full www ni cim programming development of test measurement and control applications Near Infra Red Bruker Matrix F FT Reflector NIR 12S www brukeroptics com Spectrometer NIRS with optic 300 051220 1 cable QIA 2050 ABB Bomen Q www q interline com Interline QFA Flex with diffuse reflectance probe VFAs headspace gas gas Biotechnol Bioeng chromatography chromatography 2007 96 712 721 HSGC flame ionization detection GC FID Table 1 Hardware and software components 5 1 Computer The computer you use for your DAQ system can drastically affect the maximum speeds at which you can continuously acquire data For remote DAQ applications communication that 11 use RS 232 or RS 485 serial Your data throughput is usually limited by the serial communication rates When choosing a DAQ device and bus architecture keep in mind the data transfer methods supported by your chosen device and bus Fragmentation can significantly reduce the maximum rate at which data can be acquired and streamed to disk For systems that must acquire high frequency signals select a high speed hard drive for your PC and ensure that there is enough contiguous unfragmented free disk space to hold the data In addition dedicate a hard drive to the acquisition and run the operating system OS on a separate disk when streaming data to disk Applications requiring real time
13. d Maekawa T 2002 Monitoring of methanogen density using near infrared spectroscopy Biomass and Bioenergy 22 489 495 22
14. e generic process control parameters that can be considered for all biogas plants The need to prevent digester failure or acidification is a real possibility as we approach an optimum biogas output Identification of key parameters for process monitoring is the first stage to prevent methanogenesis failure Second automatic monitoring will also help to develop the most appropriate process control approaches using these key parameters and prevent the real possibility of plant breakdown and aid process diagnosis Third automatic monitoring will help to identify the dynamics of change and give early warning of several important changes Here we provide a manual for a softsensor approach the use of near infrared reflectance spectroscopy an electronic nose and volatile fatty acid analysis Contents 1 2 7 8 9 10 Document Description Introduction 2 1 Soft sensors 2 2 Sensors 2 3 Near Infrared Reflectance Spectroscopy 2 4 Electronic Nose Important biogas plant fermentation parameters to monitor 3 1 pH 3 2 Alkalinity 3 3 Electrode sensors real time alkalinity determination 3 4 Volatile fatty acids VFAs 3 5 On line measurement of volatile fatty acids VFA 3 6 Redox potential General indicator trends in biogas production Hardware and software 5 1 Computer Softsensor early warning system 6 1 Software 6 2 Development of softsensor to measure alkalinity 6 3 Storage of data Portable Electronic No
15. ion comparison quantification and other applications These devices have undergone much development and are now used to fulfil industrial needs Electronic Noses include three major parts a sample delivery system a detection system a computing system The sample delivery system enables the generation of the headspace volatile compounds of a sample which is the fraction analyzed The detection system which consists of a sensor set is the reactive part of the instrument When in contact with volatile compounds the sensors react which means they experience a change of electrical properties Each sensor is sensitive to all volatile molecules but each in their specific way Most electronic noses use sensor arrays that react to volatile compounds on contact the adsorption of volatile compounds on the sensor surface causes a physical change of the sensor The computing system works to combine the responses of all of the sensors which represents the input for the data treatment 3 Important biogas plant fermentation parameters to monitor Parameters can be monitored using hard sensors like pH redox potential conductivity and temperature sensors The alkalinity value is predicted soft sensor using an algorithm based on the magnitude of the conductivity pH and redox potential measurements In addition near infra red spectroscopy NIRS probes installation allows other parameters such as volatile fatty acids or alkalini
16. pling timing controls organic loading rate g vs l d ibc pre mix on time ENEJ pump 1 on time feed pre delay ibc mixer _ Eo flow rate pump 1 _ jf l min tank 1 mi pre delay tank 1 mix loop time tank 1 mix on time tank mixer iam Feed loop time sample pump sample loop time svi sve a t2 gas outlet pump tl gas outlet pump B10 Emax volatile solids Bp s ve fa Pump 2 controller type heater controls temperature tank setpoint control HG ores c tank 2 setpoint JEE degrees c flow rate 2 pump 2 on time JE l min gas mix loop time gas mix on time oas mber A 4 Lal start sgo I CAilot scale digester d Unicenter Remote Control JSF Pilot plant T1 amp 12g Li Pilot plant T1 amp T2 gas VADEDE Y AOA 15 38 Figure 1 LabVIEW vi front panel showing the controls tab where you set various parameters such as OLR total solids amp volatile solids content feed loop time tank mixing times and frequency of mixing 13 Previous Redox potential probe Conductivity probe Temperature probe Previous alkalinity OLR Alkalinity Proportional ints increase decrease igh si se gt yhigh po ve oe townie Le Lew Calculate digestate transfer pump on NextOLR times based on DM amp VS contents of digestate and individual pump flow rates x derivative t weighting 1000 Communicate to compute
17. processing of high frequency signals need a high speed 32 bit processor with its accompanying coprocessor or a dedicated plug in processor such as a digital signal processing DSP board If the application only acquires and scales a reading once or twice a second however a low end PC can be satisfactory 6 Softsensor early warning system 6 1 Software There is a selection of instrument control software available Here we describe just one LabVIEW to highlight necessary features and requirements We configured the LabVIEW software includes a graphical interface and a data acquisition system simple level sensor calibration procedure real time displays of measurements and results facilities for remote diagnostics plus backup and restore of mainstream configuration The LabVIEW software was used to control feedstock and digestate pumping and mixing events and also acquires displays and saves real time data from the probes installed in the tanks Soft sensors can also be developed allowing LabVIEW to predict the value of parameters such as alkalinity HCO3 from measuring parameters such as pH redox potential and conductivity Automatic process control decisions such as the organic loading rate can then be made by LabVIEW depending on the magnitude of these parameter values 12 Pilot plant T1 amp T2 gas collection 26 08 08 sondeT1 vi Qperate Tools Window Help pump and mixer timing controls gas sam
18. r serial portto switch on relaysto operate digesate pumps for required time Figure 2 Flow diagram showing hardware and software steps required to calculate the predicted alkalinity next organic loading rate and digestate transfer pump operation times Note the sensors that are used include redox pH conductivity and temperature probes These are interfaced to the RS232 via the Waterwatch series from Partech Ltd that converts the four signals into RS232 compatible mode Previous predicted alkalinity Current predicted alkalinity previous predicted alkalinity from previous loop iteration 7 Derivative calculation gr Egy OP 2 gt gt pe Tal False vp ak gt y high Tank 2 alkalinity 34 DELH OLR rule based OLR rule based D E gt 3 ng o Prevents negative OLRs Takes the Previous Current Different settable pacers OLR increment predicted alkalinity ranges set on ae re i He ement or or decrement then alkalinity the Front Panel Rule Sele CEpEndInE on adds the result Based control tab in where the current predicted from the derivative Fig 5 alkalinity falls in the settable calculation to give ranges the new OLR for the next feed event 15 Figure 3 Part of the Block Diagram code explaining how the OLR is calculated based on the current and previous predicted alkalinity This algorithm considers the difference in magnitude between the current and previou
19. s alkalinity together with comparing the current predicted alkalinity with the set points and the previous OLR 6 2 Development of softsensor to measure alkalinity To develop the softsensor to measure alkalinity requires that we collate the data from the sensors and perform a multi linear regression analysis against the alkalinity as measured in the laboratory Alkalinity can be determined by titration against a acid solution normally a dilution of sulphuric acid Also there are a range of auto titrators available that titrate acid to the digestate as well as a manual titration procedure to determine the alkalinity Most statistical packages will perform a multi linear regression here we used a Genstat statistical package The software identifies the most relevant factors and weightings to provide an equation to determine the alkalinity It is important not to over model the alkalinity in the softsensor as erroneous predictions can result 6 3 Storage of data The LabVIEW vi can be used for data acquisition Data from sensors like temperature pH redox potential conductivity and other data like biogas volume tally and hourly rate of biogas production can be acquired and stored The operation of this method of early warning system proved effective for cattle slurry and a mixture of cattle slurry and grass silage for a pilot plant system This system of process control was not validated for a larger commercial scale biogas plant 7
20. se 7 1 Hardware Configuration Near Infrared reflectance spectroscopy 8 1 Determination of VFA in the digester by NIRS Further Reading Information References List of tables and Figures Table 1 Hardware and software components Table 2 Statistical data summary as an example for different models to assess the analysis of acetic and propanoic acids by NIRS RMSEV root mean square error of validation average error lower is better RPD residual prediction deviation ratio of RMSEV to standard deviation greater than three is a good model Figure 1 LabVIEW vi front panel showing the controls tab where you set various parameters such as OLR total solids amp volatile solids content feed loop time tank mixing times and frequency of mixing Figure 2 Flow diagram showing hardware and software steps required to calculate the predicted alkalinity next organic loading rate and digestate transfer pump operation times Figure 3 Part of the Block Diagram code explaining how the OLR is calculated based on the current and previous predicted alkalinity This algorithm considers the difference in magnitude between the current and previous alkalinity together with comparing the current predicted alkalinity with the set points and the previous OLR Figure 4 Measuring adapter with specimen holder Figure 5 fixed outer guide tube Figure 6 On line fitting of reflectance probe to a digester Figure 7 On line fitting of reflectance probe
21. tage which is mostly an equilibrium of carbon dioxide and bicarbonate ions that provides resistance to significant and rapid changes in pH Buffer alkalinity is a more reliable method of measuring digester imbalance than pH An accumulation of short chain fatty acids will reduce the buffering capacity significantly before the pH decreases Several monitoring systems have been investigated Automatic flow titrator for monitoring alkalinity rule based algorithms will help to measure estimated alkalinity from the measurements using other sensors 3 3 Electrode sensors real time alkalinity determination Monitoring was primarily in the liquid phase by pH redox and conductivity probes mounted in each of the two vessels pilot plant Tank 1 and tank 2 the data from these sensors was used as inputs to an algorithm or software sensor for prediction of the bicarbonate alkalinity All actuators were controlled via software with the organic loading rate modulated by rules based process control approach Alkalinity was predicted using the same algorithm but with different factors as in Eqn1 and Eqn2 The factors for the equations were determined using multiple linear regression of the real alkalinity as determined by autotitration and the pH Redox and conductivity values as measured by the sensors Algorithm 1 predicted alk 8906 1678 x pH 1 998 x redox 384 2 x EC Algorithm 2 predicted alk 4876 22 x pH 0 16 x redox 223 x EC
22. to 30 cubic metre cattle manure digester outlet 1 Document Description This user manual was produced as a requirement of Task 6 2 of work package 6 in the EU AGRO BIOGAS project entitled European Biogas Initiative to improve the yield of agricultural biogas plants Proposal Contract no 019884 Task 6 2 is defined as Demonstration of automatic monitoring management and early warning system Months 8 15 This deliverable provides information by developing reliable automatic monitoring management and early warning system in the form of user manual All project partners except for Partner 14 RTDs were involved as shown in Table 1 Table 1 Project partners Participant 1 2 3 l4 5 6 7 8 9 10 11 12 13 id Person month 0 per participant 0 5 10 0 5 3 0 5 0 0 5 0 5 0 5 0 5 0 5 0 5 0 5 0 5 2 Introduction A description of the different means of monitoring are described in this section This includes _ soft sensors near infrared reflectance spectroscopy NIRS and an electronic nose These were previously identified as the best means of automatic monitoring of biogas plants in real time so that adaptive process control could be used to avert fermentation failure and optimum biogas production 2 1 Soft sensors Soft sensor or virtual sensor is a common name for the adaptation of several measurements that are processed together to give a prediction of a more rele
23. toring of VFAs loading rate feed in to the anaerobic digester can be controlled 3 5 On line measurement of volatile fatty acids VFA There are a range of methods for the online measurement of VFA in anaerobic digesters this one was developed based on headspace gas chromatography HSGC The method applies ex situ VFA stripping with variable headspace volume and gas analysis by gas chromatography flame ionization detection GC FID In each extraction a digester sample was acidified with H3PO4 and NaHSQ and then heated to strip the VFA into the gas phase The gas was sampled in a low friction glass syringe before injected into the GC for measurement The system has been tested for online monitoring of a lab scale CSTR reactor treating manure for more than 6 months and has shown good agreement with off line analysis The system is capable of measuring individual VFA components This is of advantage since specific VFA components such as propionic and butyric acid can give extra information about the process status Another important advantage of this sensor is that there is no filtration which makes possible application in high solids environments The system can thus be easily applied in a full scale biogas reactor by connecting the system to the liquid circulation loop to obtain fresh sample from the reactor Local calibration is needed but is automatic Calibration is also possible using a standard addition method Sampling duration is 25 40
24. twofold in that the motion caused the sample to be mixed slightly to reduce sedimentation and also that the sample was moved in relation to the probe tip over an elliptical path of approximately 40 mm by 20 mm thus providing a more representative measurement Calibration models were constructed with PLSplus IQ software The software determines the most relevant areas of the spectra and performs spectral correction and a range of regression options before determining the calibration curve 18 Figure 7 On line fitting of reflectance probe to 30 cubic metre cattle manure digester outlet Table 2 Statistical data summary as an example for different models to assess the analysis of acetic and propanoic acids by NIRS RMSEV root mean square error of validation average error lower is better RPD residual prediction deviation ratio of RMSEV to standard deviation greater than three is a good model 19 Acetic acid Propanoic acid R R S D RPD R eg S D RPD Experiment Exp 1 Pig slurry 0 879 309 1165 3 77 0 919 129 710 5 50 silage Exp 2 Chicken 0 817 555 1350 2 43 0 971 135 788 5 84 manure Exp 3 Cattle 0 234 312 345 1 11 0 659 110 264 2 40 manure Exp 1 2 3 0 768 722 1479 2 05 0 919 218 912 4 18 Exp 1 2 0 743 742 1401 1 89 0 952 169 808 4 78 Different methods of presenting the material to the N
25. ty to be measured on line using a predetermined calibration model Details on installing different probes can be found in suppliers user manuals 3 1 pH For the hydrolysis stage an acidic pH 5 to 6 5 encourages hydrolysis auto hydrolysis and is therefore important There are robust sensors that are used to monitor the hydrolysis stage Generally a low pH is indicative of effective hydrolysis Conductivity also can measure the degree to which natural polymers have been broken into their smaller parts Analysis of the volatile fatty acids from the hydrolysis stage can be made off line with a liquid chromatography system There are off line sampling devices that can provide such a sample The ratio and concentrations of the volatile fatty acids consisting of acetic propionic butyric and iso butyric acids are important process monitoring parameters The methanogenesis stage is neutral pH during good operation The same sensors and electrodes used to monitor this stage as the hydrolysis stage The primary conditions for good biogas production involve providing the best environment for the Archaea that produce methane These conditions include a pH 6 8 to 8 access to volatile fatty acids and not too rigorous stirring The redox potential should be more negative than 250mV The ideal pH range for methanogenesis is very narrow pH 6 8 7 2 3 2 Alkalinity Buffer capacity is often referred to as alkalinity mq COs l in the methanogenesis s
26. vant parameter There may be dozens or even hundreds of measurements The interaction of the signals can be used for calculating new quantities that are otherwise difficult to measure Soft sensors are especially useful in data fusion where measurements of different characteristics and dynamics are combined It can be used for fault diagnosis as well as control applications To implement soft sensors for use in process control often requires the use of neural networks or fuzzy computing 2 2 Sensors For this manual were are concerned with electrode sensors that measure pH Redox conductivity and temperature These were supplied by Partech instruments St Austell Cornwall UK supplied the above electrodes to monitor pH redox conductivity and temperature for the soft sensor development Data from the probes can be downloaded into a file via instrument operating software such as LabView 2 3 Near Infrared Reflectance Spectroscopy The IR region of the electromagnetic spectrum runs from lt 400 cm to 14285 cm and can be split into three parts far IR mid IR and near IR The near infra red region of the electromagnetic spectrum spans from 14285 4000 cm Infra red spectroscopy is a powerful tool for studying a number of applications regarding biological systems NIRS does not suffer unduly from water band adsorption like mid range IR and is also fast in that data is delivered within 1 2 minutes often sample pre treatment is not
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