43451446.pdf

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

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:

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 (

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

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

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


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

CellsCase Studies for Their Implementation. (ISBN 3 85487

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.


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