Final Report Optimisation of Small Scale AD With an exponential increase in the uptake of Anaerobic Digestion Units for biogas production, there is a renewed need to ensure the overall operating efficiency is maximised for small scales systems. This study seeks to improve the sensitive economic balance between capital expenditure and operational benefits, by ensuring the operational efficiency is maximised whenever possible using well established optimisation techniques from other industrial sectors. Project code: OIN001-020 Research date: 2015 Date: 12 May 2015
Transcript
Final Report
Optimisation of Small Scale AD
With an exponential increase in the uptake of Anaerobic Digestion Units for biogas production, there is a renewed need to ensure the overall operating efficiency is maximised for small scales systems. This study seeks to improve the sensitive economic balance between capital expenditure and operational benefits, by ensuring the operational efficiency is maximised whenever possible using well established optimisation techniques from other industrial sectors.
Project code: OIN001-020 Research date: 2015 Date: 12 May 2015
WRAP’s vision is a world without waste, where resources are used sustainably. We work with businesses, individuals and communities to help them reap the benefits of reducing waste, developing sustainable products and using resources in an efficient way. Find out more at www.wrap.org.uk This report was commissioned and financed as part of WRAP’s Driving Innovation in AD programme. The report remains entirely the responsibility of the author and WRAP accepts no liability for the contents of the report howsoever used. Publication of the report does not imply that WRAP endorses the views, data, opinions or other content contained herein and parties should not seek to rely on it without satisfying themselves of its accuracy.
Written by: David Lovett
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Over the past 10 years, much progress has been made in anaerobic digestion technology, however the development of better monitoring and control strategies has been lacking, particularly in the UK. If anaerobic treatment processes are to reach full potential, particularly for the small scale/multi-feedstock applications, this situation needs to change. Model based control and predictive analytics have been applied in the oil and petrochemical industry with great success for many years, however very little of this technology has transferred into the area of waste management, where automation is still often via simple regulatory loops, or even manual control. Phase II of this WRAP funded project has enabled the development of a commercial data acquisition, monitoring and optimisation system, from the concept and early prototype developed during WRAP DIAD Phase I. This project has demonstrated the potential for optimising AD operation using a minimal number of sensors, and standard weekly samples to ensure that flexible operating modes are possible for a typical farm (small scale) operation. One prime challenge has been the presentation of real time information of the key parameters to the user in a simple intuitive format and the development of a robust real time alarming mechanism capable of early detection of abnormal operating conditions which can be used to enhance availability as well as performance of the AD. The conversion of the “potential opportunity” (identified in Phase I) into a viable commercial solution has meant overcoming the following problems:
1. Fragility of measuring equipment, 2. Amalgamation of manual and real time data, 3. Avoidance of “alarm overload”, 4. Automatic management of faulty sensors, 5. Combination of process knowledge with a simple data driven model to optimise
performance. 6. Constraining the cost of the system to provide a complete solution that would be
attractive to the small scale AD market. The solution has been commissioned on a 3000 tonne, 350kWe unit with pig slurry as a primary feedstock and co-digestion from a variety of other feedstock’s from local sources. The system developed, called the ADvisorMVTM, collects real time process data from the standard sensors and automatically amalgamates the daily operator sample entries. By using a simple process model, real time data and an economic calculator for the whole system, the ADvisorMVTM has been able to estimate the expected biogas and methane yield from the units. This information has been used to inform the operator of the likely impact that different feedstock’s have on performance. Underpinning the ADvisorMVTM module is a Data Quality Monitor that uses Statistical Process Monitoring techniques to generate Operator Alarms, with accompanying email/sms alerts to avoid abnormal operation. The system generates an Event log for the Operator and a notepad feature with timestamped entries to keep a record of any events that are not recorded automatically, such as the addition of anti-foaming chemicals. To account for the interaction between many of the process parameters, a multivariate monitor has been devised to assess the overall health of the AD. The details of the multivariate monitor are proprietary; however, it is the tool used to identify the majority of the abnormal Events.
Additionally, the system calculates the estimated headroom to increase OLR and provides a target feed profile for the Operator. Overload conditions are monitored using the existing standard sensors, defined within the body of this report, and, critically, a new ORP sensor positioned to indicate digester REDOX activity. The system has been operational for 3 months, detecting a variety of process abnormalities and identifying a gap between current and optimal performance for the digester. The Overload system has just been commissioned and is under evaluation for the next 4 months. A supplementary addendum will be submitted to WRAP once the final results are collated. A key criteria and major constraint on this solution has been restricting the commercial cost below £25,000. This has been achieved subject to the availability of the initial base layer of automation and standard sensors. The incremental benefits of the system are estimated to be 5% on yield and 10% increase in Organic Loading Rate, which together deliver an ROI of 77% and a payback period of less than 1 year on the system under evaluation.
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Contents
1.0 Introduction ................................................................................................. 7 1.1 Perceptive Engineering ............................................................................... 9 1.3 Overview of the Anaerobic Digestion Process .............................................. 10 1.4 Operational Management of Anaerobic Digesters ........................................ 10
2.0 Anaerobic Digestion – Monitoring, Control and Optimisation..................... 12 2.1 Monitoring Control Optimisation – Key Process Parameters .......................... 12 2.2 Monitoring Control Optimisation – Conceptual Design .................................. 14 2.3 AD Model incorporating process data and sampled data .............................. 15
2.3.1 Feed Material Calculations ............................................................... 15 2.3.2 Dry Matter and Organic Dry Matter .................................................. 17 2.3.3 Hydraulic Retention Time ................................................................ 18 2.3.4 Solids Retention Time ..................................................................... 18 2.3.5 Organic Loading Rate ..................................................................... 19 2.3.6 Volatile Solids Destruction ............................................................... 19 2.3.7 Biogas Yield Estimation ................................................................... 20 2.3.8 Biogas Production Estimation .......................................................... 21 2.3.9 Methane Production Rate ................................................................ 21 2.3.10 Electricity and Heat Generation ....................................................... 21
3.0 Work Package 2: Development and Trial of ADvisorMVTM Platform at CPI . 22 3.1 Lab Sample Entry ..................................................................................... 24 3.2 Event Viewer ........................................................................................... 25 3.3 Operational Trends ................................................................................... 26 3.4 Operator Dials (Dashboard) ...................................................................... 27 3.5 Statistical Process Control charts ............................................................... 28
4.0 Work Package 3: Site Commissioning of ADvisorMVTM Platform ................ 29 4.1 CSTR Mesophilic Digester Overview ........................................................... 31 4.2 Site Configuration..................................................................................... 32 4.3 System Validation ..................................................................................... 34
5.0 Work Package 4: Performance Assessment of ADvisorMVTM Platform ........ 35 5.1 Fault Diagnostics ...................................................................................... 37
6.0 WP 6 System Improvement and Documentation for ADvisorMVTM ............. 40 6.1 Update of the AD Economic Spreadsheet .................................................... 40 6.2 Amendments to the AD Optimiser HMI based on experience of 6 months operation ........................................................................................................... 41 6.3 System Performance Review using “before” and “after” data ....................... 41 6.4 Develop system User Guide and Datasheet for the AD Optimiser. ................. 43
Figure 1: Essential Operational Parameters suggested for AD Optimisation Figure 2: Infrastructure of the proposed small scale AD Optimisation Supervisor Figure 3: Manual Sample Data Entry Mechanism Figure 4: Comprehensive User Guide available Figure 5: Operator Event Viewer with Diagnostic Advisor Figure 6: Easily Configurable Operational Trends Figure 7: AD Performance Dashboard Figure 8: SPC charts for the Advanced User Figure 9: AD System and ORP Probe Figure 10: Overview of the configuration of the trial Farm Scale Anaerobic Digester Figure 11: DQM Data Quality Summary Figure 12: Validation of System Configuration Figure 13: Biogas Production vs feed type to the digester Figure 14: Validation of process measurements, sample entries and calculations Figure 15: Simple Fault Detection Figure 16: Multivariate Monitoring of the Anaerobic Digestion Unit Figure 17: Multivariate Monitoring revealed Biogas engine performance problem Figure 18: Multivariate Monitoring highlights unexpected increase in % methane Figure 19: Changing Feedstock mix impacts the Methane % Figure 20: Statistical Monitoring of the Anaerobic Digestion Unit Figure 21: Intuitive Browsable Platform for small scale AD Optimisation Supervisor Figure 22: Performance Assessment using ADvisorMVTM Figure 23: Insight into Co-digestion using ADvisorMVTM Figure 24: Marketing literature Figure 25: AD Market Status and Projected Growth Figure 26: UK AD Market Figure 27: Key Market Participants
Glossary
The following terminology may be employed in this document: AD - Anaerobic Digestion APC – Advanced Process Control - loosely defined as any control strategy more complicated than PID. CSTR – Continuously Stirred Tank Reactor CUSUM - 'Cumulative Sum' - a form of statistical control charting technique. DS - Dry Solids EWMA – Exponentially Weighted Moving Average KPI - Key Performance Indicator LIMS - Laboratory Information Management Systems MAD - Mesophillic Anaerobic Digester MPC – Model Predictive Control - a form of APC utilising an explicit model of the process. HMI – Human Machine Interface OLR – Organic Loading Rate ORP – Oxidation Reduction Potential OP – process controller output PID - Proportional Integral Derivative - a basic form of closed loop control algorithm PLC – Programmable Logic Controller PV – process value of a control loop SCADA – Supervisory Control And Data Acquisition Shewhart Chart - a statistical control charting technique SP – Set point value of a control loop TS – Total Solids VS - Volatile Solids
Acknowledgements
The author acknowledges input from the team at Glebe Farm and WRAP personnel for support and advice.
1.0 Introduction Over the past 10 years, much progress has been made in anaerobic digestion technology, however the development of better monitoring and control strategies has been lacking, particularly in the UK. For the anaerobic treatment process to reach its full potential, particularly for small scale/multi-feedstock applications, this situation needs to change. Model based control and predictive analytics have been applied in the oil and petrochemical industry with great success for many years, however very little has been transferred into the area of waste management, where automation is still often via simple regulatory loops, or even manual control. Phase I of this WRAP funded project reviewed the current market and near to market technologies used to monitor and then optimise the operational efficiency of biogas production. A summary of the significant academic and industrial articles are referenced, rather than repeating within the body of this report. Within this introduction, a very brief summary of the basic biology and chemistry of anaerobic digestion is provided to aid the reader understand and recognise the potential complexity of the optimisation challenge. Section 2 describes the terminology, and essential operating parameters required for the consistent and flexible operation of Anaerobic Digesters. The design of the monitoring system and first principle calculations used to model the behaviour of the AD are detailed and described. Section 3 describes the design of the ADvisorMVTM User Interface. Section 4 describes the installation and configuration of the field based trial system. The system overview, instrumentation and validation of the model/monitoring system are discussed. Section 5 describes the operational performance of ADvisorMVTM. Fault conditions are detected using univariate and multivariate statistical techniques, highlighting the onset of potential digester failure, deterioration in methane yield, increasing H2S levels, influence of different co-digestion feedstocks. Section 6 describes the improvements made to the systems after 6 months of operator feedback. Specifically, improvements to the economic calculations, Lab sample data entry and Event Manager. Additionally, this section details the documentation associated with ADvisorMVTM. Section 7 describes the economic viability of the ADvisorMVTM system. The benefits due to increasing availability of the AD, through early fault detection are as important, if not more so, than the ability to increase biogas and methane yield or Organic Loading Rate. simplifying operation. Since the monitoring system required for increasing availability can be delivered at a much lower cost than the complete optimising solution, it has been possible to offer an ADvisorMV “Lite” at a much lower price point. Section 8 details an independent survey into the marketability of ADvisorMVTM. It highlights the opportunities across the EU and delivers a strategic plan to market the system. Section 9 Conclusions from this project.
1.1 Perceptive Engineering
Perceptive Engineering was formed in 2003 as a spin out from the University of Manchester. It provides software, consultancy and support relating to Advanced Process Control and Multivariate Monitoring applications in the process industries. The staff profile includes people with backgrounds in electrical engineering, chemical engineering, control systems, time series analysis, chemometrics, software development and mechatronics. Within the company we have more than 200 man years of APC and Multivariate monitoring experience, and a software tool that is the culmination of 80 man years of development. Perceptive Engineering has a proven track record of delivering solutions to the waste water, pharmaceuticals, speciality chemicals, nutritionals and pulp and paper industries. Perceptive Engineering’s customer base is spread across 21 countries and 5 continents and includes a mix of national, multinational and blue chip companies. Perceptive Engineering has successfully implemented its Model Based Optimisation technologies in the challenging environment of waste treatment plants for municipal water companies (United Utilities, Yorkshire Water, Welsh Water, Northumbrian Water, Thames water), and remains one of the few companies who have successfully deployed and sustained the benefits generated by its systems. On activated sludge processes, our systems regularly achieve in excess of 25% aeration energy savings. The company has won awards for innovation in energy management, and recently won the UK Water Industry Achievement Award in 2015 for “most innovative use of existing technology”.
1.3 Overview of the Anaerobic Digestion Process Anaerobic digestion is a multistep process in which organic matter is degraded into a gas mixture of methane and carbon dioxide by microorganisms. It reduces the Chemical Oxygen Demand (COD) of the influent and produces valuable energy (methane). The biological scheme involves several multi-substrate multi-organism reactions that are performed both in series and in parallel. It has been experimentally demonstrated that the anaerobic digestion process is particularly adapted for concentrated wastes such as agricultural (e.g. plant residues, animal wastes) urban and industry wastewaters. In addition, this process is able to operate under severe conditions: high-strength effluents and short hydraulic retention times. Last but not least, anaerobic digestion is also often used as a sludge treatment for the volume reduction and the stabilization of primary and secondary sludge at waste water treatment sites. 1.4 Operational Management of Anaerobic Digesters The microbial population in the anaerobic digesters are very complex. The physicochemical parameter values in the bioreactor drive the metabolic pathways, the kinetics, and the microbial diversity. Much literature exists related to the unpredictability of AD process behaviour, due to the complex biochemical pathways and imbalances that occur during operation, however whilst this is true, it is important to put this in context; many plants operate reliably and efficiently by keeping regular monitoring and automatic adjustment systems in place, just as we keep our own bodies operational by making occasional adjustments to our diet to improve our own “performance”. Not everything can be predicted, but there is a balance between doing sufficient to keep our performance levels nearer an optimum. Of course shock events can always occur, which can be extremely difficult to predict, but with the appropriate monitoring systems in place, early onset of problems can be seen and dealt with.
The performance of biogas plants can be controlled by studying and monitoring the variation in parameters like pH, temperature, loading rate, agitation, gas volume, methane and carbon dioxide etc. Any drastic change in these can adversely affect the biogas production. So these parameters should only be varied within a desirable range to operate the biogas plant efficiently. As shown in this report the biological ecosystem has a semi stable state provided adjustments are made to the operating parameters, such as temperature, mixing and feedrate to keep the organic loading rate in tune with the treatment capacity and prevailing condition of the system. Phase I of the DIAD confirmed that the operational parameters that warrant monitoring include variations in the feed characteristics, temperature, pH, VFA’s, biogas properties and where possible the entrance of toxic matter. Evidence from numerous studies reveal that the microbiological system can easily be overloaded or more often under loaded. In the first case, the overload can generate the acidification of the system and it can stop the microbiological conversion. In the second case, the system is working well under its design capacity and therefore inefficiently. The challenge is to operate consistently in the “sweetspot”. This extremely brief synopsis of the Anaerobic Digestion process and its operation simply sets the scene for the following work. For an in depth description of AD and Biogas production, please refer to References 1-3. From these literature reviews and subsequent discussions, we recognise that extensive research has allowed designers to determine the many different parameters involved in the Anaerobic Digestion process. We also recognise that there are
established values, or at least relative values that can be used to ensure operational efficiency is high; the caveat being provided they can be reliably measured and appropriately controlled!
2.0 Anaerobic Digestion – Monitoring, Control and Optimisation 2.1 Monitoring Control Optimisation – Key Process Parameters From the increasing body of research undertaken on pilot, commercial scale and operational Anaerobic Digestion Plants there are established parameters that are known to have the greatest influence of performance; these are summarised in this section. Organic loading rate (OLR) Gas production rate is highly dependent on loading rate. Methane yield was found to increase with reduction in loading rate. In a study carried out in Pennsylvania on a 100 m3 biogas plant operating on manure, when OLR was varied from 346 kg VS/day to 1030 kg VS/day, gas yield increased from 67 to 202 m3/day. There is considered to be an optimum feed rate for a particular size of plant, which will produce maximum gas and beyond which further increase in the quantity of substrate will not proportionately produce more gas. The hydraulic retention time must be chosen such that constant replacement of the reactor contents does not flush out more microorganisms than can be replenished by new growth during that time. Total Solid (and Volatile Solids) concentration The amount of fermentable/digestible material of feed in a unit volume of slurry is defined as solid concentration. Ordinarily 7–9% solids concentration is appropriate for small scale AD’s. Temperature Temperature inside the digester has a major effect on the biogas production process. There are different temperature ranges during which anaerobic fermentation can be carried out: psychrophilic (<30oC), mesophilic (30–40oC) and thermophilic (50–60oC). However, anaerobes are most active in the mesophilic and thermophilic temperature range. The length of the fermentation period is dependent on temperature and temperature stability. Studies have shown biogas yield increases with temperature; examples have shown biogas reaching 0.46 m3/(m3 day) at 37oC and 0.68 m3/(m3 day) at 55oC respectively. pH pH is an important parameter affecting the growth of microbes during anaerobic fermentation. pH of the digester should be kept within a desired range of 6.8–7.2 by feeding it at an appropriate loading rate. The amount of carbon dioxide and volatile fatty acids produced during the anaerobic process affects the pH of the digester contents. For an anaerobic fermentation to proceed normally, the concentration of volatile fatty acids, (acetic acid in particular) should be below 2000 mg/l. The pH is of limited use in controlling the plant because of its inertia in representing the state of the plant. Volatile fatty acids (VFA) VFAs are some of the most important intermediates in the anaerobic biogas process; it is the conversion from VFA into methane and carbon dioxide which is important. The increase of VFA concentration in the biogas process is well known, as a result of process imbalance. Thus, it has been commonly suggested as an indicator in the anaerobic digester ref 5,6 and 7. Nutrients Efficient biodegradation requires nutrients and sufficient nutrients are therefore important to microbial cell growth. Macro-nutrients such as carbon, nitrogen, potassium phosphorus, sulphur and micro-nutrients such as Fe, Ni, Zn and Co in smaller amount are required for optimal anaerobic microbial growth. From the economical point of view, in the farm scale
industrial operation, the need for supplements according to different waste characteristics should be considered in order to reduce the operational cost. Agitation Stirring of digester contents needs to be done to ensure intimate contact between microorganisms and substrate which ultimately results in improved digestion process. Agitation of digester contents can be carried out in a number of ways. For instance regular daily feeding of slurry can have the desired mixing effect. Stirring can also be carried out by installing mixing devices like scrapers, impellers, material re-circulation etc. in the plant. From the above information a readily available set of measurement parameters were chosen to be used to optimise AD operation. These are shown in Figure 1.
Figure 1: Essential Operational Parameters suggested for AD Optimisation
2.2 Monitoring Control Optimisation – Conceptual Design Whilst the extensive available literature, ref 5 and 6, indicates there is an opportunity to improve the performance of the Anaerobic Digestion units, we realised the opportunity extends beyond the Anaerobic Digester in isolation. It is typical for the whole biogas production facility to lend itself to optimisation including the feed pre-processing, CHP/AD heat balance and dewatering systems, as well as the transportation costs and government incentive schemes. All these aspects form part of the economic optimisation problem. The scope of this Phase II demonstrator covers monitoring, control and optimisation of only the AD & CHP units, primarily because the operational data available was limited to these units. At the conceptual design stage in Phase I we raised the question “What do we mean by Optimisation of Small Scale Anaerobic Digesters?” We understood that an optimal solution would change depending upon the prevailing operational limits and feed/digestate/energy prices at a given time. For Anaerobic Digesters, it was originally considered that an optimal operating mode would be one where the amount of usable energy produced was maximised. The actual value would of course depend upon the Biomethane Potential (BMP) of the feedstock and the particular efficiency of the Reactor. The complexity of the “Optimisation” criteria has been contained by choosing four particular operational modes; where each could be considered optimal in terms of financial return and dependent upon a variety of prevailing conditions, such as Gate fees, Sludge availability, CHP system etc. These Operational options would be made available to the Plant Supervisor.
Operational Modes
1. Maximise Organic Loading Rate (active when ample substrate available) 2. Maximise Methane Yield (in parallel with biogas flow) 3. Maximise Overall System Efficiency (Heat/Electricity) 4. Stabilisation i.e. Operate in “Tick Over” mode if little slurry available,
maintain plentiful buffer in anticipation of new variable load. With these modes in mind and a view of the operational parameters that were of importance we could begin the data analysis and Optimisation investigation.
2.3 AD Model incorporating process data and sampled data
Determining the performance of the AD and CHP units required real time Key Performance Indicators. These were developed from a combination of real time process measurements, operator sampled data and first principle equations. Some of the parameters for the first principle equations were obtained from experiments (the sources are referenced). This section summarises all the real time calculations that are operating within the ADvisorMVTM system.
2.3.1 Feed Material Calculations Mass and Volume Flows
Solid feed materials are measured in mass flow, whereas liquid feed materials are measured in volumetric flows. Both mass and volumetric flows are used in the calculations, with the conversion from mass flow to volume flow shown below:
ii i vm Equation 1
Where:
im = Mass flow rate of feed material i, [kg/day]
i = Density of feed material i, [kg/m3]
iv = Volume flow rate of feed material i, [m3/day]
The data available from the demonstrator farm does not have information relating to the density of different feeds, and so literature values were used instead. See Table 1 below.
Table 1 – Feed Material Densities used for calculations for Glebe Farm AD
Calculations for the mass flow rate of dry matter and organic dry matter are given in Equation 2 and Equation 3:
ii DM i, m%DMm Equation 2
DMi,i ODM i, m%ODMm Equation 3
Where:
im = Mass flow rate of material i, [kg/day]
DM i,m = Mass flow rate of the dry matter of material i, [kg/day]
ODM i,m = Mass flow rate of the organic dry matter of material i, [kg/day]
i %DM = % of the material i that is dry matter, [%]
i %ODM = % of the dry matter of material i that is organic, [%]
Feed Material [%DM] [%ODM] (of DM)
Pig Slurry 5% 90%
Chicken Litter 72% 80%
Sludge 35% 90%
Grass Silage 35% 90%
Maize Silage 30% 90%
Soup 10% 88%
Table 2 - Measured [%DM] and [%ODM] for the feed materials from the demonstrator Farm
Measured [%DM] and [%ODM] for the feed materials from the demonstrator farm are shown in Table 2.
2.3.3 Hydraulic Retention Time
The hydraulic retention time (HRT) is the average length of time that the feed material would stay in the digester tank (and be processed by the microorganisms). It is calculated as a ratio of the active volume of the digester and the volume flow of feed material.
Feed
*Digester
v
v[HRT]
Equation 4
Where:
[HRT] = Hydraulic Retention Time [days]
*Digesterv = Active volume of the digester tank [m3]
Feedv = Total flow rate of feed material [m3/day]
2.3.4 Solids Retention Time
Solids retention time (SRT) is another way to infer how long the feed material remains in the digester tank. This is defined as:
Feed DM,
Digester DM,
m
m[SRT]
Equation 5
Where:
[SRT] = Solids Retention Time [days]
Digester DM,m = Mass of dry matter in the digester tank [kg]
Feed DM,m = Mass of dry matter in the digester feed [kg/day]
The difference between SRT and HRT could be assumed as negligible for simple digester designs. More complex tanks however may have solid retention devices to trap the solids in the tank. This would allow the same amount of sludge to be processed by a smaller tank (and reduce capital cost). With these designs the HRT would be less than the SRT. The demonstrator farm is a simple CSTR without recirculation of solids, and HRT is equivalent to SRT.
2.3.5 Organic Loading Rate
The organic loading rate is a measure of how much organic (digestable) material goes into the digester relative to its size (and by inference its active biomass). This is calculated as:
*Digester
ODM Feed,
v
m[OLR]
Equation 6
Where:
[OLR] = Organic Loading Rate [kg ODM/m3 day]
ODM Feed,m = Total organic dry matter mass flow of feed [kg/day]
2.3.6 Volatile Solids Destruction
Theoretically, VSD would be calculated as the difference in ODM mass flow between the feed and digestate, divided by the ODM mass flow of the feed:
100%
m
m1 or 100%
m
mm[VSD]
ODMF,
ODMD,
ODMF,
ODMD,ODMF,
Equation 7
However, it has been noted that the organic dry matter of the digestate is not always measured. So an alternative approach was required.
When ODM is not measured a surrogate approach is taken. Volatile Solids Destruction (VSD) was calculated as a function of the hydraulic retention time and the operating temperature. Both factors affect the activity of the microorganisms that process the sludge, and subsequently the volatile solids destroyed. The equation 8 structure was taken from the works from Bolzonella et al. (2005), with a temperature factor taken from the works of Kim et al. (2006).
HRTCB1
HRTCBAVSD
Equation 8
Where:
VSD = Volatile Solids Destruction; [%]
A = Model Parameter [dimensionless]
Initial value used: 0.87 (based on average equivalent to the actual calculation used in the economic calculator*)
B = Model Parameter [dimensionless]
Initial value used: 0.5 (Bolzonella, et al., 2005)
C = Temperature factor [dimensionless]
Initial value used: subject to temperature
For CSTRs, the mixing profile causes variation in the actual HRT of sludge in the tank. AquaEnviro summarised that for a CSTR operating at a given HRT:
39% of the material would leave the tank at about half the expected HRT
13% would remain in the tank for over double the expected HRT
HRT variations affect the VSD, and the calculation was modified to account for this. So for a given HRT, the VSD calculation is repeated three times: at half HRT, at HRT and at double HRT. A weighted average is then taken, based on the proportions specified in AquaEnviro (2010):
TVSD = Volatile Solids Destruction for a retention time of T; [%]
2.3.7 Biogas Yield Estimation
Biogas Production was estimated based on the VSD and the theoretical maximum of biogas that could be produced for the given amount of feed material.
potiBG,
estiBG, yVSDy Equation 10
Where:
estiBG,y = Estimated biogas yield of feed material i (m3 biogas / kg feed material)
potiBG,y = Potential biogas yield of feed material i (m3 biogas / kg feed material)
Ideally, the potential biogas yield would be measured based on the feed type used in the farm. But measurement of this is often not available. Literature reported maximums were used instead.
A major assumption from this calculation is that all organic dry matter, regardless of source, is the same. This would mean that the amount destroyed is directly correlated with the VSD. This is not true.
Organic dry matter is a mixture of various organic compounds, such as fats, cellulose, proteins etc. The breakdown (and thus the conversion to biogas) of these compounds are actually different. Burton and Turner (2003) conducted tests on the main organic compound groups, and gave the follow order from most difficult to easiest to break down:
For example: Pig manure (which contain relatively high fat content) would breakdown to produce methane more readily than cattle manure (which contain a relatively high cellulose content) (Burton & Turner, 2003).
2.3.8 Biogas Production Estimation
The biogas production is estimated by multiplying the estimated biogas yield with the corresponding feed mass flow.
estiBG,i
estiBG, ymv Equation 11
Where:
estiBG,v = Estimated biogas volume flow associated with feed material i (m3/day
biogas)
A literature review has indicated that many sources (and existing AD calculators) estimate the biogas production using the yield also. But those sources gave a yield figure that is based on a fixed condition (such as 30°C & 30 days HRT). From speaking with AD unit owners, it appears that these conditions are not always used. Some owners prefer a much longer retention time to extract more biogas from the same amount of feed, whereas others have to process as much as possible due to large supplies and limited inventory space.
2.3.9 Methane Production Rate
Methane concentration within the biogas and the flow rate of the biogas are both measured
BGCH4CH4 vCv Equation 12
Where:
CH4v = Volume flow rate of methane [kg/day]
CH4C = Concentration of methane within the biogas stream [%]
BGv = Volume flow rate of biogas [m3/day]
2.3.10 Electricity and Heat Generation
The methane in the biogas is the key component used to generate heat and electricity in the CHP (through combustion). The energy potential in the biogas is estimated from the amount of methane produced from digestion, and the calorific value of methane:
CH4CH4
PotCH4 vCalQ Equation 13
Where:
PotCH4Q = Potential energy from the biogas [kWh/day]
CH4Cal = Calorific value of methane (9.97kWh/m3 methane (FNR, 2010))
In a CHP unit, the energy from the biogas is used to generate heat and electricity. The conversion is reported to be around 50% heat and 30% electricity (The Andersons Centre, 2010).
3.0 Work Package 2: Development and Trial of ADvisorMVTM Platform at CPI The ADvisorMVTM system connects directly to the existing regulatory automation system (PLC), and records all the process data, including laboratory sampled information to be monitored in real time. The “Normal” process operating parameters are calculated from the process data, typically 2 months of data, and configured in the system. This rapid setup and supervisory system will significantly simplify the operation and maintenance of small (and large) AD’s. Whilst the system is equally applicable to large AD’s, it is likely that the larger AD’s would have a more frequent operator supervision and therefore this system is directed at the smaller scale AD’s. .
Figure 2: Infrastructure of the proposed small scale AD Optimisation Supervisor The ADvisorMVTM platform consists of all the components required to measure and monitor expected vs actual performance of each Anaerobic Digester.
The main features provided by the ADvisorMVTM platform are:
Lab Sample This is the data entry window for samples that are manually recorded (e.g. lab samples).
Event Viewer This is a summary of the most recent alarms and events. It provides an intuitive summary of the most frequent events and enables the operator to identify when an event occurred, record what action was taken and build up a history of problematic events and solutions over time. The system is preconfigured with 10 most frequent operational issues and appropriate actions to recover.
Data Trend: This shows the trend of a signal over time.
o The time range can be adjusted by the operator
Operator comments are entered and displayed here
Dial Plot: This shows the current value of a signal.
o Control limits can be configured to warn an operator of abnormal operation
SPC Monitoring:
SPC Monitoring is a form of quality control using statistical methods.
o Options include Shewart, EWMA, Fixed and CUSUM
3.1 Lab Sample Entry To ease the operational management of the Anaerobic Digester, a data entry faceplate has been developed to assist with the correct recording of any sampled measurements. The sample entry mechanism, records the time the sample is taken, checks the sample is within expected limits and prompts the Operator to retake if there is a problem. All the measured samples are automatically aligned and stored with the digital process data recorded within the industrial PC.
Figure 3: Manual Sample Data Entry Mechanism A comprehensive User Guide explains each of the Operator faceplates.
Figure 4: Comprehensive User Guide available
3.2 Event Viewer A combination of single variable and multivariable monitors run in the background to assess the operational behaviour of the process, such that automatic detection of “abnormal” behaviour is brought to the attention of the operator through the “Event Viewer”. Each “Event” is typically a combination of things that are relevant to the safe and efficient operation of the AD. Each “Event” may be selected, and acknowledged once the remedial/recovery “Action” has been completed. If the “Event” warrants an “Alarm” then an associated Alarm Tag is set and emails/SMS sent to the Operator, otherwise the “Event” simply remains standing until acknowledge during routine checks.
Figure 5: Operator Event Viewer with Diagnostic Advisor
Each row in the Event Overview represents an active/standing event. Information for each event includes:
Timestamp: The time at which the event was raised.
User: The user logged in when the event was raised.
Message: The message assigned to each specific Event.
Ack: The acknowledgement tab. Click on the button would indicate that the event raised has been acknowledge by the user. The orange highlight would be removed from that particular event.
Action: The list of recommended corrective actions associated with the raised event.
3.3 Operational Trends The amalgamation of time aligned laboratory sampled data with real time process data and simple statistical monitoring provides much greater insight into process behaviour, enabling the Operator and the ADvisorMVTM algorithms to understand the process. The data can be discrete sampled data points, for example feed properties, or continuous process data recorded from the online sensors, such as digester temperature, biogas volume, methane. Multiple trends can be configured from each area of the process and combined with the real time calculated values generated within ADvisorMVTM.
Figure 6: Easily Configurable Operational Trends
3.4 Operator Dials (Dashboard) Current operational performance is presented in a simple, intuitive dashboard. The needle should rest within the green zone displayed on the dial. The zones change automatically to represent acceptable operating parameters as the mode of operation changes.
Figure 7: AD Performance Dashboard
3.5 Statistical Process Control charts Through the use of statistical process monitoring techniques, it is straightforward to detect shifts in operational states, detection of outlying measurements and keep tight control over key operational parameters. These techniques are available to the advanced operator and form one aspect of the detection mechanism used to trigger the Events/Alarms.
Figure 8: SPC charts for the Advanced User This completes the overview of the ADvisorMVTM system developed for the DIAD Phase II with feedback from the operational staff at the Centre of Process Innovation. This system is in operation at the Anaerobic Digestion facility at the Centre for Process Innovation.
4.0 Work Package 3: Site Commissioning of ADvisorMVTM Platform In order to implement a commercially viable system it was necessary to collaborate with a fully operational Farm managed Anaerobic Digestion system. The trial was conducted on a 3000m3 CSTR Mesophilic AD with 350kWe biogas generator. The AD is operated locally, fed daily with feed material from a variety of sources, including pig slurry, maize, grass, sewage sludge and food waste. ADvisorMVTM connected directly to the site PLC, automatically collecting all real time process measurements every 5 seconds. All operator sampled values are entered into the system database via the ADvisorMVTM User Interface. A retractable ORP probe was installed at the base of the AD to provide insight into the status of the digestion reactions based on information discussed in Ref 5, 7, & 9.
3000m 3 Anaerobic Digestion Unit
Hopper and Macerator
Figure 9: AD System and ORP Probe
Retractable ORP Probe
350kW Biogas Engine & Generator
4.1 CSTR Mesophilic Digester Overview The Anaerobic Digester is fed daily with a mix of pig slurry, chicken litter, farmyard manure and maize silage, with an overall SRT of approximately 100 days. Whilst the unit is oversized for the associated 300kW biogas engine, it provides a substantial buffer to accept a wide variety of feed material available at the site. The co-digestion capability of the AD, helps to increase the flexibility in feed material sources, however it also requires careful monitoring to maintain at an acceptable biogas and methane levels.
Figure 10: Overview of the configuration of the trial Farm Scale Anaerobic Digester The primary process measurements recorded at the site are:
Analytical Measurements
VS and TS in
VS and TS out COD in and out Calculated Organic Loading Rate
Anaerobic Biogasification Potential (Need to understand the components of the organic material)
(VOA/TIC) pH
Online Process Parameters
Feeding Rate
Biogas Flow Rate
% Methane, % CO2
Reactor Temperature
Agitator On duration
4.2 Site Configuration Prior to using any of the online process parameters, the signals were evaluated in real-time using the DQM (Data Quality Monitoring) toolset within ADvisorMVTM. For each signal, the DQM provides an estimate of the quality of the process data, by performing simple logical and statistical tests. Figure 11 shows the data quality summary pie charts over a historical data set. This view indicates which signals are likely to be problematic and or unreliable, and thus not appropriate for use in high-level fault detection.
Figure 11: DQM Data Quality Summary
The pre-processed information has been used directly in the process performance calculations and monitoring module, providing confidence that the values calculated remain valid and robust.
4.3 System Validation The system was configured for the particular site with the sample entries and analytical measurements entered and amalgamated as described previously. The operational calculations described in section 2.3, were verified using some historical data from the site. Figure 12 below shows a validation trend which compares the estimated biogas vs the actual biogas over a 6-month period. Whilst there are differences between the two values, the correlation is sufficiently strong to satisfy the team that the configuration was correct. The differences are most likely attributed to the inaccuracies of the biogas potential parameters, particularly with respect to the “Soup”. The “Soup” is a blend of food waste material from local sources.
Figure 12: Validation of System Configuration
5.0 Work Package 4: Performance Assessment of ADvisorMVTM Platform The ADvisorMVTM monitoring system was initially evaluated against historical process operating data from the Farm AD to determine a set of operational parameters for the real time monitoring system. Using the data from the farm scale AD it was straightforward to develop appropriate operating limits for the key process parameters, which were subsequently used in the real time monitor. Using the ADvisorMVTM Rule Configurator the operator incrementally developed a Rule Base to include “Events” seen in historical operating process data, which combined individual parameter information with statistical techniques to improve the robustness of the “Event” generation. Due to the long dynamic response and process delays, monitoring of trends using Statistical monitoring charts/detectors has proved effective generating robust alarms without over sensitivity to the typical fluctuations seen in day to day operation. By using simple statistical detectors, it was possible to detect a shift in operating conditions, which, as a minimum, raised an operator Event Message & Alarm. The system can be configured to automatically adjust the feed rate, digester temperature or mixing appropriately using a fuzzy rule base.
Figure 13: Biogas Production vs feed type to the digester Figure 13 shows how the biogas potential of the different feed types varies. This is overlaid with the actual measured biogas production (Red). If all of the feed types produced gas exactly inline with their biogas potentials, then we would expect to see a correlation between the total potential (the upper edge of the green region) and the actual biogas production. This trend highlights how challenging it is to model and predict the AD process behaviour, even when individual biogas potentials, process delays, operating conditions are taken into
account on a “live” unit. In particular, the trend shows that the addition of grass sludge (the yellow region) is not always met with the expected increase in gas. Furthermore, the addition of sewage sludge has a negligibly small biogas potential addition, but a significantly larger ODM addition. There are approximate correlations between the addition of the sludge and the creation of biogas which suggest that the biogas potential may be better than the theoretical figure used. A set of pre-configured trends are available within ADvisorMVTM with all the input signals checked to verify that the process measurements, sample data entries and internal calculations report sensible, accurate values. Fig 14 shows how the biogas model estimates biogas and more importantly when it doesn’t.
Figure 14: Validation of biogas estimate with actual measurements
5.1 Fault Diagnostics Whilst it proved straightforward to identify and record obvious operational problems, such as start-up problems with the CHP, Gas instrument failures as revealed in Figure 15, it is much more informative to visualise the operation of the process plant as a whole, compare the process behaviour relative to a model and reveal relative changes in operation that indicate the onset of failures.
Figure 15: Simple Fault Detection This was achieved by inclusion of the multivariate monitor within the ADvisorMVTM platform to provide greater insight into the operation of the AD. Eight months of operational data provided the baseline for the system model. Figure 16 shows the how the multivariate model was visualised during development. A period of operation was selected to represent “Normal” (or acceptable operation) and by using a Principal Component data reduction technique it was possible to generate an “envelope” of operation for the whole Digester. This “envelope” and associated statistical metrics were used to detect any changes around “Normal” operation.
Figure 16: Multivariate Monitoring of the Anaerobic Digestion Unit Multivariate Statistical Process Control (MSPC) extends the analysis beyond consideration of each variable; it considers a variable in relation to other variables. For example: 3 process signals may typically be highly correlated, so when any of them fail to follow the usual
behaviour, the MSPC monitor indicates a fault and shows which of the signals have broken the relationship. Using the MSPC techniques the following process faults have been highlighted to the operators during the performance assessment period.
Figure 17: Multivariate Monitoring revealed Biogas engine performance problem In this example, Fig 17, electricity generation is observed to have dipped. Contribution analysis pointed towards an issue primarily with the biogas engine performance. Electricity/Biogas ratio was 2.03 kWhe/m3, compared to an average of 2.24 kWhe/m3 in the period. No major change in methane content was observed. In this second example, Fig 18, the contribution analysis shows that adjustments in the pig slurry affects the Methane content, overall biogas production and the CHP Electricity output in a manner that isn’t consistent with previous fluctuations in Pig Slurry.
Figure 18: Multivariate Monitoring highlights unexpected increase in % methane Whilst there isn’t an immediate, obvious explanation, it is still worthwhile being aware that the addition of Pig Slurry has caused an unusual increase in methane within the biogas.
Figure 19: Changing Feedstock mix impacts the Methane % In Fig 19 the Contribution analysis pointed towards unusual changes in the methane content and feed profile. Dip in the biogas methane content observed. Change in the feed profile observed, most notably with a decrease in pig slurry and an increase in sludge.
Figure 20: Statistical Monitoring of the Anaerobic Digestion Unit In Fig 20, a simple statistical chart (EWMA) is used to show the fluctuation in Organic Dry Matter. Ideally the operator should aim to keep the mix within limits to avoid unstable operation of the digester.
6.0 WP 6 System Improvement and Documentation for ADvisorMVTM
This section covers the systems improvements made following feedback from the “live” implementation of the ADvisorMVTM system. The implementation of Operational Dashboards, underpinned by pre-processed and validated data, has been web-enabled. The implementation on a fully browsable platform (Figure 21) will make the accessibility and hence regularity of operational checks much easier and consequently more effective.
Figure 21: Intuitive Browsable Platform for small scale AD Optimisation Supervisor 6.1 Update of the AD Economic Spreadsheet Within AdvisorMVTM there is a practical economic calculator for estimating the biogas production and the operational revenue and costs for UK-based medium to small scale (up to 1000kW) anaerobic digestion (AD) units. It became evident during the commissioning period that for any given AD unit, the operation may change, nearby facilities may offer opportunities for new feed material, and changes in government support may necessitate the way a unit is operated. So to assess the financial implications of these alternative configurations the “live” calculator estimates the revenue and costs of each particular configuration and hopefully enables the operator to make better informed decisions about optimal operation of their particular system.
6.2 Amendments to the AD Optimiser HMI based on experience of 6 months operation During 6 months of operation the system has closely monitored performance at the trial site and improvements have been made to the Sampled Data Entry system. The Operator may now enter a specific sample entry time for the sample (in case they forgot to press the “Sample” button before taking the sample!). To aid operation, all comments may now be added with each sample, displayed on the active trend and recorded in the Notebook. All events may now be viewed on a Pareto Chart, with a direct selection option to trend each Event and Operator Acknowledgement. Emails can now be triggered with each Event, if required. 6.3 System Performance Review using “before” and “after” data
Figure 22: Performance Assessment using ADvisorMVTM One of the goals of this project has been to achieve both stability of operation and optimal biogas output whenever possible. During the operational runs it was possible to see the AD was able to generate a consistent 20%-30% increase in biogas and commensurate CHP Output following a start-up and stabilisation of feed material during the latter 30 days in Figure 22. Through inspections of the model output overlaid with both real time process data and the daily discrete sampled data it can be verified that there is a “sweet spot” of operation. The actual biogas flow to the CHP (top blue line) is plotted with the “expected” biogas (Brown dots) calculated from a time delayed superposition of the mass of individual Total Solids multiplied by their respective BMP values from the variety of feedstocks. It’s possible to see when there is a deviation between estimated and actual. It is important to note that the monitoring and fault diagnosis is primarily a function of the measured process parameters, however once the system is running consistently an advanced monitor may be activated which uses the model residuals (i.e. the difference between the actual measured values and the
estimated values) to detect abnormalities. This advanced system requires the initial model to be parameterised to each particular AD and can be done gradually by the operator, rather than at the onset as a “research” project. Fig 23 provides information linking the co-digestion feed profile with the actual biogas generated. The time adjusted feed profiles enables the operator to understand the relative effect of different feed stock on biogas generation.
Figure 23: Insight into Co-digestion using ADvisorMVTM
6.4 Develop system User Guide and Datasheet for the AD Optimiser. A comprehensive User Guide and Datasheet have been produced. These have been used successfully in marketing campaigns. The system is being used on new AD sites in Asia. Whilst the uptake in the UK has been low, this may be indicative of the uncertainty currently prevailing in the financial subsidies available for AD.
Figure 24: Marketing literature The User Guide provides the basic installation and configuration settings and the Operational Instructions for the AD manager. The system is intuitive to navigate, and readily configurable for OEM’s choosing to use the platform to complement their AD units.
7.0 Economic Viability The economic viability of Anaerobic Digesters is a function of many factors, which are thoroughly detailed in reference 10 (Economic Viability of Farm Scale AD Biogas Generation Across Cheshire and Warrington). Whichever AD system is chosen, the financial viability is a complex calculation based on input material costs, capital and revenue costs and transport requirements. Set against this will be the income from energy sales which will be set at a market rate for equivalent energy derived from other sources together with renewable incentives offered by the Government.
Although AD is a relatively straightforward technology, the complexities come in running efficiently and consistently – this can be a challenge and can have a major impact on the economics. From the information described in the excellent economic assessments, ref 3, 16 and 17, ensuring Methane content remains 5% higher than the normal operation performance and OLR is on average 10% higher, would return a rapid payback on even the smallest farm systems. The overall efficiency of a typical AD/CHP combination has not been evaluated in this project, however the author has evidence from municipal WWTP that optimisation of the inventory and energy balance across a site can deliver over 15% improvements to the system efficiency. If a conservative estimate of 5% improvement in overall AD/CHP energy efficiency is added to the yield and OLR performance improvement, then economic viability looks encouraging. Using the Unit Models defined in Ref 3, Chapter 17 Sensitivity Analysis, a 10% increase in Organic Loading Rate provides a ~£35,000 benefit and a 5% increase in conversion efficiency creates an additional £28,000 benefit.
Table 3 - Profit Sensitivity Analysis of Feedstock Capacity Change
Table 4: Profit Sensitivity of Operational Efficiency With a combined benefit of £62,000 per annum the ROI is 77% and Payback period <7 months (i.e. Simple ROI=£62,000/£35,000). Through numerous discussions with end users, the Perceptive team recognise that the economic benefits are dominated by increasing the availability of the AD and aiding flexibility to co-digest when the opportunity arises. This has become the prime objective of the ADvisorMVTM solution. The simplification of data management, with real time calculation of the financial performance for the AD and basic Event Management has been offered as a low entry “lite” version of the platform.
8.0 Commercialisation of technology 8.1 The Market Drivers, market potential and exploitation plan Energy produced from digester biogas is categorised as “green energy”. The processing of waste streams, including source separated organic waste or mixed municipal waste in anaerobic digesters contributes to sustainable and renewable energy targets set across the developed nations.
8.1.1 The Market Drivers Treatment of organic wastes is currently of particular importance in the European Union (EU) and particularly in the UK with the following targets already in place: 1. EU Climate Change Act. Reduce greenhouse gas emissions by at least 80% by 2050 and to reduce
CO2 emissions by at least 26% by 2020, against a 1990 baseline. 2. EU target to source 20% of the EU’s total final energy demand from renewable sources by 2020.
The UK’s contribution to this target will require us to increase our share of renewables in our energy mix from around 1.5% in 2006 to 15% by 2020.
3. EU Landfill Directive for diversion of biodegradable municipal waste from landfill. By 2020 the volume of biodegradable municipal waste sent to landfill will be cut to 35% of the 1995 level.
The UK Government has established numerous programmes that will dramatically increase the use of Anaerobic Digestion within the next 5 years; these include: Water Companies to ensure at least 20% of all energy used by UK water industry comes from renewable sources by 2020. Anaerobic digestion is to make an important contribution to this. NFU vision for anaerobic digestion -1,000 farm-based anaerobic digestion plants by 2020. This legislation, and guideline targets, combined with the obvious benefits of generating energy from otherwise discarded waste material, are the current market drivers. Our challenge has been to develop the necessary “Plug & Play” AD Monitoring and Optimisation Units that can be sold or licensed to all AD users and ensure that operation is simplified, optimised and sustained.
8.1.2 The market potential From an independent survey completed on behalf of Perceptive Engineering during 2015 the following information was obtained: Solar, Wind and Biomass are technologies which are growing at the fastest rate. Biomass is dominant for the heating sector, however solar and wind power developments were occurring for electricity production. In 2013, there were 14,653 biogas plants in Europe, with a total installed capacity of 7857 MWel, representing a growth of 10.2%. In 2013 the primary energy produced from biogas in Europe was 52.3TWh; this amounts to 13.4 million tonnes of oil equivalent. Germany was found to operate the largest number of AD plants (9035).
Figure 25: AD Market Status and Projected Growth The UK is the E.U.’s second-largest generator of primary Energy from biogas, in 2012 an estimated 2,000 ktoe of biogas was produced. 85% of the biogas used in 2012 was derived from more than 350 land fill sites. There are 184 anaerobic digestion sites across the UK and Ireland. In 2015 it was found that 5% of Farmers process waste by anaerobic digestion, which shows an increase of 3.5% from 2014. As can be seen in figure 2, the largest segment is the Agricultural CHP anaerobic digester market, with 44.6% of the market share (82 plants). Of
the 184 plants, it was found that 83 of the plants were small scale, operating at 500kW or less.
Market Restraints
• Barriers to growth include the lack of confidence in the industry and in the stability of financial incentives.
• There are high costs involved and the requirements to connect to the grid are stringent.
• Lack of UK sourced AD expertise and infrastructure has increased reliance on imported technology, which has brought some challenges and difficulties - including high costs of supply, repair and maintenance.
• Lack of timely, co-ordinated collection of biodegradable wastes of a suitable quality from
households.
Figure 26: UK AD Market 8.1.3 Market Structure The European market is a highly fragmented market. As the market has been traditionally focusing on small-scale farm systems, many national/regional participants are installing small, systems. For example, in Germany, about 400 companies supply biogas systems. Most of these are small, local companies that work within their communities. German biogas companies control the European farm and centralised digester market, because they have the required technology and microbiological expertise. In 2012 some exceptions included Valorga, Ros Roca from Spain and Kompogas that have a strong international presence and are targeting the centralised plant segment of the market.
Figure 27: Key Market Participants 8.1.4 Exploitation Plan
re Ultimately the commercial success of this project will be driven by 4 factors:
1. The technical success; that is the ability of ADvisorMVTM to be a versatile, configurable, robust AD optimiser, providing real operational advantages capable of stabilising the operation of AD’s and maximising the biogas yield when required to do so. Evidence within this report suggests that technical success is well within reach.
2. The uptake of the AD processes in line with EU and UK targets. In particular the
expansion of AD’s in Farm and small industrial centres to deliver a self-sustainable energy source using food and animal slurry.
3. Securing partnerships with AD Original Equipment Manufacturers (OEMs), for example Anaergia, Envitec Biogas
4. Delivering at a price point that provides a compelling value proposition for all of the Tier 1-3 Market participants.
8.1.5 Competitive Edge Our market research shows that there are just over 30 sizable manufacturers of Anaerobic Digesters currently in the market place yet there is no dedicated product in the marketplace that is used to monitor the quality and performance of Anaerobic digesters and CHP’s in a simple and intuitive way. There have been academic developments in this field, and particularly in Sweden who have been active for at least 10 years. One commercial product directed at Biogas Optimisation is available, although it does not provide the real time monitoring of analytical instruments which we believe to be the key to achieving a robust self-monitoring system. From our discussions with the UK (and Dutch) water companies there appears to be little incentive for the individual AD manufacturers to provide a generic monitoring system; they would prefer to sell individual diagnostics for their proprietary design. There is however a great desire from the end users to have a system that enables robust self-monitoring Anaerobic Digesters capable of optimum performance at minimum operational cost!
Our challenge is to engage with the OEM’s and embed our solution at a price point that creates a win-win for both parties. We have leveraged our real time instrument monitoring system and statistical monitoring software to provide a robust platform for AD monitoring. The base system is downloadable and affordable by the vast majority of small scale AD Operators (< 500kWe). The advanced monitor and optimiser can be added as a modular unit on top of the base system. This is targeted at AD’s with output greater than 750kWe.
9.0 Conclusions Irrespective of the design of Anaerobic Reactor (Digester) being considered, the phrase “What you can’t measure you can’t control” remains valid and crucial. If you can’t measure and control the performance of the digester and CHP, you are clearly unable to optimise performance. The challenges explored and discussed within this report are directed at finding the simplest mechanism to optimise reactor performance when faced with limited real time measurements of uncertain quality. Whilst the variety of reactors increases, from the simplest MAD system to advanced reactors like UASB, fluidized bed, and up flow anaerobic filters, there remains a common desire to squeeze more out of the existing asset simply by making timely operational adjustments to give the micro-organisms the best chance of performing when faced with the varying feed stocks presented to them. In section 3 of this report we have demonstrated that real time process data can be processed to give a better chance of diagnosing process upsets at their onset, and adjustments can be made to remedy the problem quickly. Applying appropriate statistical analysis to the process measurements, a robust operational picture was developed to guide the Supervisory system to enable adjustments to reach optimum operation. It was recognised that “Optimum” operation is likely to be different at each plant and dependent upon the prevailing conditions (Constraints e.g. heating, feedstock,) which therefore require different operational modes for the Supervisory system. One key constraint to reach high Organic Loading Rate is the detection of imminent “overload” condition in the reactor, and whilst insufficient data or suitable measurements were available to the author in this feasibility, the literature has many suggestions of interesting approaches. It may well be possible to use ORP, with pH and knowledge of the other fundamental parameters, e.g. (CO2, Methane, BMP) to provide a good indication of overload. The gentle iterative adjustment to feedrate proposed by Steyer et al appears to be a pragmatic approach, particularly when combined with the statistical monitors detailed within Section 3. To verify whether the approaches described within this report are commercially viable, (and to a much lesser extent technically viable), we propose that a full installation of the AD Supervisor/Optimiser is installed on a working AD plant for a period of 1 year. The incremental benefits delivered by the system will be assessed and compared with the additional costs for the complete solution. On the basis that the majority of AD’s are operated at 65% of their design rating and we estimate volume manufacture of the Supervisory system could retail at ~£25,000 including wireless connectivity, we consider the opportunity worthwhile. Ensuring Methane content remains 5% higher than the normal operation performance and OLR is on average 10% higher, would return a rapid payback on even the smallest farm systems. The overall efficiency of a typical AD/CHP combination has not been evaluated in this study, however the author has evidence from municipal WWTP that optimisation of the inventory and energy balance across a site can deliver over 15% improvements to the system efficiency. If a conservative estimate of 5% improvement in overall AD/CHP energy efficiency is added to the yield and OLR performance improvement, then economic viability looks encouraging. With a return on Investment of ~77% and a Payback period of ~ 7 months we recommend that the system is commissioned for the Phase II Project.
10.0 References
1. Schon Michael, Numerical Modelling of Anaerobic Digestion Processes in Agricultural Biogas Plants, Innsbruk University Press
2. Guide to biogas: From Production to Use; Fachagentur Nachwachsende Rohstoffe e. V. (FNR) www.fnr.de.
3. A Detailed Economic Assessment of Anaerobic Digestion Technology and its Suitability
to UK Farming and Waste Systems 2nd Edition. The Anderson Centre. March 2010.
4. Monitoring of Anaerobic Digestion Process to Optimise Performance and Prevent System Failure, Labatut and Gooch, Department of Biological and Environmental Engineering, Cornell University, Ithaca, NY. 2012.
5. Boe, K., Steyer, J.P. et al. (2008). “Monitoring and control of the biogas process based
on propionate concentration using online VFA measurement”. Water Science & Technology 57(5): 661
6. A contribution to the Optimisation of Biogas Digesters using Design of Experiments F Koudache; A.Ait Yala; Journal of International Environmental Application & Science; July 2008.
7. Kanokwan Boe Ph.D. Thesis May 2006 “Online monitoring and control of the biogas process”; Institute of Environment & Resources Technical University of Denmark
8. Principles and potential of the anaerobic digestion of waste-activated sludge Lise
Appels
9. Relationship between Oxidation Reduction Potential (ORP) and Volatile Fatty Acid (VFA) Production in the Acid-Phase Anaerobic Digestion Process
10. Simple and rapid methods to evaluate methane potential and biomass yield for a range
of mixed solids; P Shanmugam, N.J. Horan; Bioresource Technology June 2008
11. Advanced Control of Anaerobic Digestion Processes through disturbance monitoring; Jean-Philippe Steyer, Rene Moletta et al.Water Res Vol 33 No 9 pp2059-2068 1999
12. A Simple Fuzzy Logic Management Support System for Farm Biogas Plants; A Finzi, G
Cocolo, F.Perazzolo. Dipartimento di Ingegneria Agraria, University degli Studi di Milano.
13. A review on production of biogas, fundamentals, applications & its recent enhancing
techniques K.Vijay Kumar et al.
14. Spanjers, H. and J. B. v. Lier (2006). “Instrumentation in anaerobic treatment – research and practice.” Water Science & Technology 53 (4-5): 63
15. Steyer, J. P., O. Bernard, et al. (2006). “Lessons learnt from 15 years of ICA in
anaerobic digesters.” Water Science and Technology 53(4): 25-33
16. Economic Viability of Farm Scale AD Biogas Generation across Cheshire and Warrington 2010
17. Economic Analysis of Anaerobic Digestion Systems and the Financial Incentives
provided by the New York State Renewable Portfolio Standard (RPS) Customer-Sited Tier (CST) Anaerobic Digester Gas (ADG)-to-Electricity Program. By D Enaharo and B Gloy, Agricultural and Finance management at Cornell. April 2008
Appendix 1
To be updated with a performance description of the ADvisorMV system driving OLR and yield through the model based Optimiser with ORP and “soft sensor” overload detector. Anticipated results within 6 months.
