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TRANSCRIPT
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From a black-box to a glass-box system: the attempt
towards a plant-wide automation concept for full-scale
biogas plants
J. Wiese and R. Konig
ABSTRACT
J. Wiese
GKUGesellschaft fur kommunale Umwelttechnik
mbH,
Heinrichstrasse 17/19,
36037 Fulda,
Germany
E-mail: [email protected]
R. Konig
HACH LANGE GmbH,
Willstatterstr. 1,
40549 Dusseldorf,
Germany
E-mail: [email protected]
Biogas plants gain worldwide increasing importance due to several advantages. However,
concerning the equipment most of the existing biogas plants are low-tech plants. E.g., from
the point of view of instrumentation, control and automation (ICA) most plants are black-box
systems. Consequently, practice shows that many biogas plants are operated sub-optimally
and/or in critical (load) ranges. To solve these problems, some new biogas plants have been
equipped with modern machines and ICA equipment. In this paper, the authors will show details
and discuss operational results of a modern agricultural biogas plant and the resultant
opportunities for the implementation of a plant-wide automation.
Key words | anaerobic, automation, biogas plants, control, instrumentation, renewable energy
INTRODUCTION
Agricultural biogas plants based on energy crops win more
and more importance, because of numerous energetic,
environmental and agricultural benefits. In these biogas
plants (BP) biogas can be produced by using numerous
different farm products: cattle and pork liquid manure/
dung, poultry excrements, wheat, green rye, corn/maize,
rape, sunflowers, sugar beets et cetera. General information
about biogas plants and their potential can be found in Lens
et al. (2004) or Trogisch & Baaske (2004). Concerning the
equipment (e.g., machines, automation), most of the biogas
plants are still low-tech plants. That is, from the point of
view of ICA most biogas plants are black-box systems
because only few on-line process data are available, which
could be used for optimization and decision support.
Consequently, practice shows that many plants run sub-
optimally and/or are operated in critical (load) ranges.
Another effect is a low average plant efficiency of older
plants (e.g., in Germany
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service building with a centralized pumping-station, a
control room, a switch cabinet, a dosage system for
biosolids, and a combined heat and power generation unit
(CHP). On this plant biogas is produced using farm
products: cattle manure, cattle dung, ensiled maize/green
rye/Sudanese grass, poultry excrements and sugar/fodder
beets. The biogas plant was designed according to following
procedural principles:
Two-stage process with digester and post-digester toincrease operational safety.
Simultaneous wet fermentation: 7.37.8 pH, 59% totalsuspended solids (TSS).
Mesophilic conditions: 408C (or 313 K). Hydraulic retention time (HRT): .60 days for efficient
use of ensiled maize.
Automatic dosage system for biosolids: container withload cells, push-rod discharger and several vertical resp.
horizontal screw-conveyors.
Centralized pumping station (1 pump, 1 cutter, 9pneumatic slides).
Digester tanks are covered with 2-layer membranes forcollection and storage of biogas.
High level instrumentation and automation (see below). Internal aerobic hydrogen sulfide removal.
ICA equipment
BP Lelbach is equipped with numerous on-line measure-
ments (Table 1), powerful programmable logic controllers
(PLC) and a modern PC-based industrial supervisory
control and data acquisition (SCADA) system (investment
costs:
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electric generator (power: 530 kWel, efficiency: 35.6%) with
an upstream gas cooling. The electricity is fed into the local
electricity network. The heat is used to heat both digesters
and the machine hall (
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PREDICTIVE MAINTENANCE CONCEPTS
The aim of predictive maintenance concepts is to reduce
the down times of pumps, engines et cetera as well as to
reduce operational costs. One bottleneck in the process is
the rotary-piston pump in the centralized pumping station.
Abrasion of the rotary-pistons leads to a reduction of the
flow rate and an increase energy costs. So, according to
the conventional maintenance concept, approximately
every 4 months the rotary-pistons have to be changed to
avoid a breakdown. But, an analysis of the curves of the
flow rate and the energy consumption has shown that
because of the high energy prices it is economically more
reasonable to replace the rotary-pistons every 80 days.
That is, it is possible to create a simple tool, which is able
to calculate the cost-effective moment of maintenance.
Other possible applications are the continuously obser-
vation of temperature curves of the 16 cylinder heads of
the gas engine to predict the cost-effective moment of the
changing of the ignition plugs. In combination with the
H2S sensor it is also promising to create a soft sensor,
which is able to predict the optimal moment for the oil
change (today: fixed periods of 1,000 operating hours): the
practice shows that in case of low H2S concentrations and
a low water content in biogas it is possible to use the
engine oil longer than 1,500 hours.
OPTIMAL FEEDING STRATEGIES
The (organic) dry matter content (oDM/DM), the methane
concentration and biogas yield of the different input
substrates can vary, which can influence the energy pro-
duction. A good example for this typical problem is shown in
Figure 3 (left): a periodic sampling of the ensiled maize has
shown dry matter contents between 22.8 and 37.5% DM.
That is, a dosage strategy for biosolids, which is only based
on weighing (e.g., 30 tonnes/d), is suboptimal, because
in case of a high DM content more biogas is produced than
necessary and in case of a low DM content not enough biogas
can be produced to use the full CHP capacity. Two different
feeding strategies are reasonable to solve this problem:
Using a mixture of different substrates
Sugar/fodder beets are an ideal supplement of slowly
biodegradable substrates like maize, because beets are
easily/quickly biodegradable. Figure 3 (right) shows an
example: the SCADA recognized a lack of biogas in the gas
storage of the post-digester. By pumping 3 tonnes of
smashed fodder beets into the post-digester, it was possible
to produce biogas within a few hours.
Feeding strategy based on the energy content of
biosolids
The first results of the NIRS system are very promising
(Wiese et al. 2008): by using this technology it seems to be
Table 2 | Operational key data of BP Lelbach in comparison with benchmarking values of FNR (2005) and Hesse (2006) (a average value, m median value, r min/max range)
Operational key data Unit BP Lelbach Benchmark
Electricity production to input ratio kWh/tonneinput 249 53570 r, 150 m
Electricity demand (biogas plant) % 7.7 314 r, 8 a
Electricity production to biogas ratio kWh/m3Biogas 1.81 1.42.4 r
CH4 production to effective reactor volume m3CH4/(m
3R d) 1.33 0.31.1 r, 0.74 a
Degree of engine utilization (530 kW) % 97.2 62 a
Degree of degradation % of oDM 77.3 61.5 a
Figure 2 | Electricity productions (monthly average) of BP Lelbach since startingoperation.
324 J. Wiese and R. Konig | Black-box to glass-box: automation concept for biogas plant Water Science & TechnologyWST | 60.2 | 2009
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possible to measure on-line the concentrations of (organic)
dry matter (Figure 3), proteins, nitrogen (TN, NH4-N),
volatile fatty acids and acetic acid. That is, it is not only
useful to use NIRS for a detection of process disturbances
but also to create a feeding strategy based on the weighing
machine of the dosage system and the on-line measured
oDM content of the different biosolids (e.g., 8.5 tonnes
oDM per day).
BENCHMARKING
Benchmarking tools/studies, which are widely used in
numerous industries to evaluate plant performance, are
used up to now only rarely in the biogas branch. But, in
order to operate BPs more efficiently, it is necessary to use
such tools. Therefore, the use of SCADA systems in
combination with on-line measurements and additional
lab analysis is indispensable for a technical/economical
controlling. In case of BP Lelbach it was possible to set up
an almost complete closed material flow balance (Table 3).
Based on such (automatically calculated) mass balances a
reliable calculation of important operational values (see
Table 2) is possible.
OPTIMISING ORGANIC LOAD RATE AND A COST-
EFFECTIVE SUBSTRATE MIXTURES
The prices for biosolids are the most important cost factor
for an agricultural BP (500 kWel: input costs
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TSS it is easier to detect overload situations (optimal
values in digester/post-digester: pH 7.27.7, ORP ,2350 mV, EC 1622 mS/cm). The data can also beused to identify substances (see below).
The digester was equipped with an explosion-proof videosupervision system in order to detect scum and foam
problems, which are typical for biogas plants using maize
or grass. Furthermore, the video system can be used to
optimize the control of the stirring devices.
Digester, post-digester and the gas-tight slurry storagetank are equipped with gas pressure (^) sensors
(resolution: 0.1 mbar). By using these sensors and a
PID controller it is easier to operate the gas engine than
by using only gas level meters.
The data of the O2 and H2S sensors are used to controlthe aerobic hydrogen sulfide removal process by adapt-
ing the amount of air, which is injected into the gas
storages of digester, post-digester and slurry storage tank.
The pneumatic slides are equipped with flushing valves.Due to the fact that the end positions as well as the
closing time of the slides are monitored, an automatic
flushing of the slides is possible to avoid clogging.
Every submersible stirring device is equipped with a levelmeter, so it is possible to monitor the level of the height-
adjustable stirring devices continuously and thus to
reduce the risks of sedimentation and scum/foam
problems.
The gas flow meter was equipped with a gas temperatureand gas pressure compensation to increase the validity of
the gas flow rate.
PATTERN RECOGNITION FOR THE DETECTION OF
(HAZARDOUS) SUBSTANCES
On agricultural biogas plants the unknowingly dosage of
some (hazardous) substances can cause serious process
disturbances. By using a mixture of different measurements
(e.g., pH, ORP, EC, TSS) some hazardous substances
(e.g., manure contaminated with detergents: pH @ 8,
heavily polluted stormwater run-off from silo areas:
pH , 4, ORP . 0 mV, EC < 8 mS/cm, TSS , 1%) canbe segregated from normal farm products (e.g., cattle/pig
manure:pH 78,ORP , 2300mV,EC 1215mS/cm,TSS 25%).
COSTBENEFIT CALCULATION
It is very difficult to measure the economic benefit of the use
of ICA equipment on biogas plants because the overall
performance of a biogas plant also depends on other factors
(e.g., design of the plant, quality of the operator). Never-
theless, the authors will try to show the benefits with the
help of several examples:
The annual turnover of a German 530 kWel biogas plant(based on renewable energy crops) with a capacity
utilization of 90% and a suitable heat using concept is
approx. 1 million e. In case of BP Lelbach the on-line
measurements detected a serious overload situation in
December 2006, which was caused by a handling error of
the operator. The biological disturbance was detected
early enough to start counter measures (Wiese et al.
2008) to avoid a breakdown (and a restart) of the
system. Consequently, a financial loss of more than
80.000 e could be avoided. That means, in this example
the payback period of the ICA equipment was less
than a year.
The practice shows that on biogas plants, which areequipped with numerous online measurements and a
powerful SCADA system, the start-up period can be
reduced to a minimum. E.g., the duration needed to
achieve a stable full-load operation was reduced on BP
Nordholz (type: BP Lelbach ) significantly (regu-lar: 12 to 16 weeks, practice: 6 to 7 weeks), which
resulted in higher revenues of more than 60,000 e.
Taking into account actual investment and operatingcosts for biogas plants, the break-even point is reached
when the plant efficiency is between 7580%. On the
other hand, the owner of a biogas plant with an efficiency
of 90 to 95% can earn much money. In some cases equity
yield rates from more than 20% p.a. can be reached.
Some financing companies (e.g., banking corporations,insurance and leasing companies) have already built up
specialized teams to assess the technical design of biogas
projects (e.g., ICA equipment, machines). Depending on
the assessment results, the interest rates as well as the
326 J. Wiese and R. Konig | Black-box to glass-box: automation concept for biogas plant Water Science & TechnologyWST | 60.2 | 2009
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insurance rates can vary significantly. This means that
the biogas plant owner has convincing financial reasons
to reach a high efficiency and a stable process.
CONCLUSION AND OUTLOOK
The ICA level on existing biogas plants is still relatively low,
which leads often to a poor performance of these black-box
systems. During the last years, a few modern biogas plants
have been built, which are equipped with modern ICA
equipment and reliable machines. In case of these grey-box
systems, numerous process data are available which can be
used to realize a high-level automation. This new generation
of biogas plants can reach very high plant efficiencies.
Nevertheless, there is still potential for further improve-
ments, because common control strategies are still almost
exclusively based on conventional controllers (e.g., time-
based controller, PID); mostly manual intervention by the
plant operators is also necessary. But, because of the
complex dynamics/structures of anaerobic processes and
biogas plants these controllers are often overstressed. If
biogas plants should be operated close to the capacity limit,
while at the same time minimizing operating costs and the
amount of output/input substances, the consideration of
these boundary conditions in the controller strategy is
essential. In these cases, it is necessary to use complex
controllers, which could be based on model predictive
control and artificial intelligence. On the basis of the
economic benefits of on-line monitoring and control, the
authors draw the conclusion that the use of ICA on biogas
plants is only at the beginning. Nevertheless, it will last at
least several years and further research and development to
convert a biogas plant into a real glass-box system.
REFERENCES
FNR (eds) 2005 Ergebnisse des Biogasmessprogramms (Results of a
biogas plant measuring study), Fachagentur Nachwachsende
Rohstoffe (FNR) (Agency for renewable resources), Germany:
http://www.fnr-server.de/pdf/literatur/pdf_223ergebnisse_
biogas_messprogramm.pdf
Hesse (eds) 2006 Biogas Hessen (Biogas in Hesse), Hesse Ministry
for Environment, Germany, ISBN 3-89274-249-9.
Lens, P., Hamelers, B., Hoitink, H. & Bidlingmaier, W. (eds) 2004
Resource Recovery and Reuse in Organic Solid Waste
Management, Integrated Environmental Technology Series,
ISBN 1-84339-054-X, IWA Publishing, UK.
Trogisch, S. & Baaske, W. E. (eds) 2004 Biogas Powered Fuel
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626 2) Trauner Verlag, Austria.
Wiese, J., Kujawski, O., Konig, R., Dickmann, K. & Andree, H. 2008
Applying Instrumentation, Control and Automation for Biogas
PlantsResults of Full-scale Applications. Proceedings, World
Bioenergy Congress, Sweden.
327 J. Wiese and R. Konig | Black-box to glass-box: automation concept for biogas plant Water Science & TechnologyWST | 60.2 | 2009