Neural network and on-off control of bicarbonate alkalinity in a fluidised-bed anaerobic digester

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Pergamon

PIh S0043-1354(97)00016-X

Wat. Res. Vol. 31, No. 8, pp. 2019-2025, 1997 © 1997 ElsevierScienceLtd. All rights reserved Printed in Great Britain 0043-1354/97 $17.00 + 0.00

N E U R A L NETWORK A N D ON-OFF CONTROL OF BICARBONATE ALKALINITY IN A FLUIDISED-BED ANAEROBIC DIGESTER A. J. G U W Y @t, F. R. H A W K E S ®2, S. J. W I L C O X 1 and D. L. H A W K E S @1. ~Department of Mechanical and Manufacturing Engineering and 2School of Applied Sciences, University of Glamorgan, Pontypridd, Mid-Glamorgan, CF37 1DL, U.K. (Received May 1996; accepted in revised form January 1997)

Abstract--Alaboratory-scale fluidised-bed

anaerobic digester with a sintered glass carrier, Siran ~, was operated for 8 months on a simulated baker's yeast wastewater (12,000 mg soluble COD 1 L)at a loading rate of 27 kg COD m -3 d -~, giving 75% removal of soluble COD. Percentage CO_,, H2 concentration, gas flow rate and pH were measured continuously. An on-line bicarbonate alkalinity (BA) monitor was used in experiments comparing two control strategies, adjusting digester buffering by addition of NaHCO3 solution during organic overloads. The first, an on-off controller with a set point at the steady-state level (2700 mg CaCO31-~), maintained BA concentration but resulted in levels above the upper set point. Thus, to avoid consuming excess NaHCO3 the rate of delivery and solution strength must be carefully adjusted. The second was a controller developed from a neural network trained on BA data from an anaerobic filter operating on ice-cream processing wastewater (alkalinity around 1400 mg CaCO3 1-1). Without re-training, despite the different steady-state BA levels and reactor type, the neural network based controller was capable of maintaining stable BA levels during overload without overshoot. Control of BA during overloads did not prevent changes in gaseous CO2 and H2 concentrations and gas flow rate. © 1997 Elsevier Science Ltd Key words--anaerobic digestion, neural network, control, bicarbonate alkalinity

INTRODUCTION Effluents from the pharmaceutical, fermentation and food industries have frequently been disposed of to sewer or occasionally to sea. At the current time there is increasing interest in cost-effective, reliable, on-site biotreatment processes, including anaerobic digestion, for appropriate effluents. When utilised on a bio-industry site, it is certain that these treatment processes will be subject to wide variations in load, and systems available for monitoring tend to be rudimentary, often dependent on off-line measurement with no automatic control. Thus, there are major concerns about the ability of biological treatment processes to operate efficiently and consistently in factory situations. Major obstacles to more widespread implementation of biotreatment processes at factory sites include: • the lack of robust on-line sensors to monitor continually key parameters related to microbial activity and treatment efficiency (Hickey et al., 1991); ° the lack of flexible mathematieal models able to predict the dynamic response of the carbon, *Author to whom all correspondence should be addressed [Fax: +44 1443 482231].

nitrogen and phosphorus removing treatment steps to fluctuating operating conditions; and based on the last two points, the lack of a control strategy to optimise treatment efficiency, both for the individual stages and for the treatment plant as a whole. Because of this lack of knowledge, biotreatment plants are almost always operated at less than their optimum cost-effective load. If control strategies could be implemented, new plants constructed on bio-industry sites would be smaller and less costly. For existing plants, control would allow discharge of effluent of a more consistently high quality, avoiding prosecutions and reducing disposal costs. Control strategies for anaerobic digestion reported in the literature include an adaptive control algorithm to maintain propionate concentration at a given low value in the digester (Renard et al., 1991), alkali addition in response to a p H monitoring and control system (Denac et al., 1990) and the use of a neural network together with on-line bicarbonate alkalinity (BA) monitoring (Wilcox et al., 1995). The last study demonstrated the potential of the neural network to classify BA data into steady-state and overload conditions, but this control strategy was not implemented experimentally (Wilcox et al., 1995). In the present study, this neural network algorithm for

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Table 1. Compositionof the simulated baker's yeast wastewater in 100-1 tap water 1200 g Yeastex (yeast extract) (CPC, U.K.) 310 g of molasses beet syrup (British Sugars, Cambridge, U.K.) 29 cm3 of 90% ethanol (Fisons, U.K.) 39 cm3 of 99.8% acetic acid (Fisons, U.K.) 0.5 cm3 of trace metal supplement" 2.5 cm3 of anti-foam (Dehysan 32111, Henkel, Dusseldorf, Germany)

Bicgas to Sensors

~Sa~Aplemos~ r

"NiC126H20 11.6 g 1-~, COC126H20 7.0 g 1-1, MnSO4H20 11.0g l-I, NazMoO42H_,O 8.0 g 1-~.

controlling BA, trained on data from an anaerobic filter operating on ice-cream wastewater (Wilcox et al., 1995), was transferred without re-training to a fluidised-bed reactor system operating on a simulated baker's yeast wastewater. The ability o f the neural network based controller and an on-off controller to maintain BA levels during organic overloads was examined. The microbiology o f anaerobic digestion is intrinsically more complex than that o f aerobic wastewater treatment processes as it depends on a food chain o f different microbial trophic groups. There are well-established models for the aerobic process that could be used as part o f a control strategy, but the anaerobic process lacks this firm basis. BA reflects metabolic imbalances in the trophic groups within the anaerobic digester as well as physico-chemical changes. During an organic overload to an anaerobic reactor, the decrease in levels o f BA is directly related to the production o f metabolic intermediates, volatile fatty acids (VFA) (Hawkes et al., 1994).

Fig. 1. Fluidised-bed anaerobic digester: (A) recycle pump, (B) effluent discharge, (C) chambers preventing Siran carry-over, (D) pH electrodes.

method described by APHA (1989), phosphorus concentrations were determined using a standard method described by Cleseri et al. (1989) and sulphate concentration by the standard gravimetric method (HMSO, 1972). The major difference in macronutrients was the level of sulphate which was kept low in the simulated wastewater to avoid excessive corrosion of equipment by the biogas. Anaerobic digestion of the industrial waste gave rise to a biogas with approximately 5% H2S by volume (Dr A. Aivasidis of KFA, Jiilich, Germany, pers. commun.). With the low sulphate simulated wastewater used here, H2S in the biogas reached 0.2% by volume. Anaerobic fluidised-bed reactor, sensors and support system

MATERIALS AND METHODS

Feed

The reactor was fed with a simulated baker's yeast wastewater (see Table 1) with a trace metal supplement. The wastewater was made up periodically (Guwy et al., in press) and fed from a 200-1 jacketed tank cooled to 13-15°C by a Grant Flow Cooler (type FC15/FC20). The composition of the simulated wastewater was based on an industrial wastewater from Deutsche Hefewerke, Hamburg, Germany. The chemical oxygen demand (COD) of the components of the industrial wastewater was typically 1600 mg 1-~ total sugars, 8200 mg 1-~ proteins, 400 mg 1 acetic acid and 500 mg 1-~ ethanol. The simulated wastewater gave similar values, giving a total COD of 10,700mgl -~. Table 2 shows the macronutrient composition of the industrial and simulated wastewaters. Ammonium levels were determined using the standard Table 2. Macronutrientcomponentsof the simulated and industrial waste

Simulated waste Industrial waste (mg 1-1) (rag l-~) Nitrogen Phosphate Sodium Sulphate Potassium

930~ 229 800b 350 575u

958 12-14 320 2500 3350

aEstimated from NH4-N in digester. bCalculated from information supplied with yeast extract and molasses syrup.

The anaerobic digester consisted of two connected 7-1 fluidised-bed perspex reactors which had previously been operated individually for approximately 12 months (Fig. 1). The reactors were connected together for this work to provide sufficient flow of effluent for the BA monitor. The reactor (141 total volume, 11 1 iiquid volume) used Siran ~ sintered glass carrier (Schott Glaswerke, Germany) as solid support media, as described by Aivasidis et al. (1989, 1990). Each 7-1 reactor was originally filled with 2-I of cultured Siran:", obtained from a pilot-scale plant operating on baker's yeast wastewater (KFA, Jtitich, Germany). Feed was pumped (503S/R peristaltic pump and 303D/A pumphead, Watson-Marlow, Poole, U.K.) into the recycle llne common to both reactors, and effluent was discharged through a tube 5 cm below the reactor liquid level, which was maintained by a manometer overflow tube also common to both reactor units. The reactors were arranged such that the effluent from the top of each was recycled to the base of the other, via two 1031 EHEIM recycle pumps (Monside Ltd, Letchworth, U.K.), each providing a maximum flowrate of 25 I rain -~ with zero head pressure and generating an up-flow velocity of approximately 0.55retain -~, keeping the Siran~ carrier fluidised. A chamber connected to each reactor at the exit of the recycle line prevented Siran material carry-over and, hence, erosion of the recycle pumps. Reactor temperature was maintained at 37~C using water-heating jackets supplied by a thermostatically controlled water pump (Grant Instruments, Cambridge, U.K.). The gas exit line was common to both reactors. The percentage of carbon dioxide (CO.,) and the hydrogen concentration in the biogas were monitored on-line using an

Control of anaerobic digestion ADC monitor type SBG100-002-15290 (ADC Ltd, Hoddersdon, U.K.) and a GMI Exhaled Hydrogen Monitor (Gas Measurements Ltd, Renfrew, U.K.), respectively. To avoid corrosion of these instruments H2S was removed from the hiogas by passing through a solution of copper sulphate. The biogas flowrate was measured with a low flow on-line gas meter (Guwy et al., 1995) and the pH was measured by a Kent EIL9142 meter using a Ingold Xerolyte electrode (type HA405-DXK-S8/120) inserted in the top of each reactor. This on-line data was recorded every 6 rain by a computerised data acquisition system. A BA monitor described previously (Guwy et al., 1994; Hawkes et al., 1994) requiring an input of 13 cm3rain -~ was used on-line in experiments employing the two different control strategies to monitor the effluent stream. Both the neural network based and on-off control strategies took data from the on-line BA monitor and adjusted digester buffering with a 503U control pump (Watson and Marlow Ltd, Falmouth, U.K.). The control pump delivered a solution of 0.8 M sodium bicarbonate into the anaerobic reactor recycle line at a maximum rate of 0.4 1h- ~.

Control strategies A feed forward neural network with eight input neurons, one hidden layer of eight neurons and one output neuron was trained to classify BA data from steady-state and overload conditions with a back propagation algorithm (Wilcox et al., 1995). Although training data was derived from an anaerobic filter operating on ice-cream wastewater (BA average value 1400 mg CaCO31 J), the algorithm was not re-trained to the different waste and reactor type used in this work but implemented directly. However, as the steady-state value for the anaerobic digestion of baker's yeast production wastewater was 2700 mg CaCO31-~, this necessitated lowering the normal value by 1300mg CaCO31 -I to the level observed in the training data. The neural network was configured to take consecutive values from a moving window containing the last eight BA data points. It is important to note that in the control of one variable within the anaerobic process by the neural network it was not necessary to develop a neural model based controller. The neural network approach used here does not model the BA changes in the reactor, it is used here simply to classify the BA changes into steady-state or overload conditions. The current application of a neural network could be described as process monitoring with remedial action, and uses the neural network classification to adjust the BA inside the reactor, i.e. if the classification is "overload" then bicarbonate solution is added. As the classification made by the neural network can take any value between zero and one, it is possible to vary continuously the rate of bicarbonate addition. The control system is shown diagramatically in Fig. 2. The on-line data acquisition system was set to sample at 2-mix intervals and so eight consecutive values represented 16 rain, a suitable time period over which to monitor for

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Table 3. Conditions of 4-h organic Overload I and 2 Overload I

Overload 2

Initial (withoutBA (with BA conditions on-off control) on-off control) Hydraulic retention

time (h)

10.2

lnfluent flow rate (1 h -~) Loading rate (kg COD m 3 d-~)

1.08 27.1

5.1

4.5

2.17

2.43

57.6

64.5

changes in the BA signal. The output from the neural network was scaled so that the maximum value (1) corresponded to the maximum output (5V) of the variable-speed pump delivering 0.8 M sodium bicarbonate solution to the digester. The training of the neural network allowed it to not only investigate the absohtte values of BA but also the rate of change in a similar manner to a PID controller. The on-off controller set point was 2700 + 100rag CaCO31- ~.

Overload experiments All overload experiments were carried out by increasing the feed flowrate by approximately 100% without changing the feed COD. Two 4-h organic overloads were carried out on the anaerobic ftuidised-bed reactor to compare the system response without (Overload 1) and with (Overload 2) on-off control of BA. These overloads had an interval of more than four hydraulic retention times between them. The conditions of the 4-h overloads are given in Table 3. The initial influent COD concentration was 12,200 mg COD 1-~. For the initial conditions shown in Table 3, the steady-state sludge loading rate was 1.3 kg COD kg -~ VS. To examine the system response when BA in the reactor was maintained using the neural network controller, an 8-h organic overload (Overload 3) was carried out on the ftuidised-bed anaerobic digester. The overload corresponded to a loading rate increase from 21.1 to 42.2 kg COD m 3 d -~. The influent COD concentration was kept at about 12,000 mg COD 1-L For the initial conditions the steadystate sludge loading rate was 1.0 kg COD kg ~ VS. Off-line digester monitoring Samples for off-line measurement were collected at 1-2 hourly intervals between the overload experiments and at 30-rain intervals during the organic overloads by an automatic sampling collection system (Guwy et al., 1994), consisting of a rotary file cassette, a 10-rpm 503S/R reversible flow, variable speed Watson Marlow peristaltic pump and an electronic relay unit. Soluble COD was determined using standard methods. Levels of methane and CO2 in the biogas and of individual VFAs were determined by gas chromatography (Peck et al., 1986), the biogas composition measurements being used to check the on-line CO2 analysis. For attached biomass measurements samples were taken from the top and bottom of the Siran~ bed, washed with deionised water and the average VS in the 1l-1 reactor determined using standard methods (APHA, 1985). RESULTS AND DISCUSSION

1 Fig. 2. Schematic of neural network controlling the bicarbonate dosing to the digester.

The fluidised-bed anaerobic digester system was operated for extended periods at steady-state with a C O D removal o f approximately 75% at a loading rate o f 27.1 kg soluble C O D m 3d-~ and H R T o f 10.2h. The loading rate achieved here compares favourably with that ( 1 0 k g C O D m - 3 d -~, 36-h H R T , 60% soluble C O D removal efficiency) obtained

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using a similar wastewater and UASB reactors by Vinas et al. (1994). The Siran ~' carrier was free of operational problems over the 12 months of operation. At the end of the experimental period biomass levels of 21g VS 1-~ of reactor were measured. Because of the high protein content of the waste, the ammonium ions generated by protein breakdown were balanced by a corresponding increase in bicarbonate ion concentration, giving a natural alkalinity level in the digester of around 2500 mg CaCO31 -l. When the reactor was operated at steady state it required little maintenance, so a fluidised-bed reactor could be considered suitable for biotreatment of fermentation industry wastewaters of this type. Overload with and without on-off control

During a steady-state period the two 4-h overload experiments described in Table 3 were carried out, the second using on-off control of BA. The results are illustrated in Figs 3-6 and are reported over an 84-h period. The start and the end points of the overloads are highlighted as arrows on the figures. As seen in Fig. 3 the BA was relatively steady at 2500-2600mg CaCO31 -~ before both overloads. However, once the influent flow rate was increased in Overload 1 without on-off control, there was an immediate response in the bicarbonate in the system with a relatively linear decrease to a level of 2100 mg CaCO31-' at the end of the 4-h overload. Once the

30

0 8

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64

72

80

Fig. 5. On-line gas production and % CO2 during Overload 1 and 2. overload without on-off control was terminated the BA recovered and after approximately 8 h had returned to the initial steady-state level, as seen in Fig. 3 at 20 h. The BA was allowed to stabilise without control until hour 44, at which point the BA on-off control pump was switched on. From hour 44 until the start of Overload 2, at 55.5 h, the BA control pump was not activated as the BA did not drop below the lower set point. During the BA-controlled Overload 2 the reactor BA dropped only slightly compared to the uncontrolled Overload 1, although the organic loading rate of Overload 2 was 12% greater (Fig. 3). The BA dropped below the lower set point approximately 40 min after the overload start and the control pump was activated. It can be seen that, after the end of Overload 2, BA was added in excess, giving levels above the upper set point, due to the characteristics of the on-off controller. Although the BA was maintained in the reactor by the on-off controller, other parameters which indicate instability still changed (pH, gas production and CO2, Figs 4 and 5). The response of biogas hydrogen levels and effluent VFA concentrations to Overload 1 and 2 has been described previously (Guwy et al., in press). In Overload 1, acetic acid predominated at 800mgl -~, a percentage increase of 177%, and propionic acid concentration increased from 250 to a maximum of 700 mg 1-], an increase of 280%. In

7 new feed

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Time (Hours)

Fig. 3. On-line BA during Overload 1 and 2; on-off controller operational after hour 44 with set points as shown.

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Fig. 6. Off-line COD of the effluent during Overload 1 and 2.

Control of anaerobic digestion Ovodoad

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Fig. 7, Neural network output as a response to Overload 3. Fig. 9. CO2 and biogas production during Overload 3 with neural network control. Overload 2 the maximum acetic acid measured was 600 ppm, with propionic acid at 900 ppm. The off-line measurement of COD of the effluent from the anaerobic digester showed some changes during the overload experiments as seen in Fig. 6, but it is not possible to demonstrate that control of BA improved effluent quality. The COD removal was on average approximately 74% during the 72-h period shown in Fig. 6.

Overload with neural network based control Another overload (Overload 3) was carried out on the anaerobic digester using the neural network trained to act as a bicarbonate buffering controller. The overload was initiated by increasing the loading rate from 21.1 to 42.2kg COD m3d -I and maintained for 8 h. The neural network output (a dimensionless number, which in this instance represents the amount that the neural network "thinks" the digester is out-of-control) is shown in Fig. 7. As can be seen, when compared with Fig. 8, the neural network responded effectively to the overload and was able to maintain stable levels of bicarbonate in the digester. Even though the BA values are higher than in the training data (Wilcox et al., 1995) (2300 mg 1-~ compared to approximately 1400mgl 1 CaCO3 because of ammonium ion generation from protein in the wastewater), the network recognises the overload. The maximum value of the neural network output in Fig. 7, around 0.1, is lower than that seen in the previous "open-loop" work (Wilcox et al., 1995), mainly due

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to the fact that the network is now actually controlling levels of BA in the digester. The previous work merely logged the network's response to data acquired in earlier experimental work, and this in several cases led to a neural network output of 1.0, representing a maximal rate of control action. In a similar manner to Overload 2 the changes in gaseous hydrogen, % CO2 and gas production corresponding to Overload 3 (Figs 8 and 9) all demonstrate that only the digester bicarbonate alkalinity is being controlled.

Evaluation of neural network and on-off control The BA concentration overshot after the end of the 4-h Overload 2 because of the manner in which the on-off controller functions, i.e. the pump is either fully on or off. In order to tune such a controller it is necessary to calculate suitable gain factors for the control system so as to not induce unnecessary oscillations. The degree of overshoot could have been reduced by optimising the strength and rate of delivery of the bicarbonate solution, but in practice this is difficult to do, especially for a system with as many "unknowns" as anaerobic digestion. However, the BA controller prevented the BA in the reactor from dropping below the lower set point. Oscillations in BA were not induced by the neural network control action, even though Overload 3 was of 8-h duration. This is due to the use of feedback in the neural network based control action, since the rate of addition of 0.8 M sodium bicarbonate solution responded appropriately to the variation in buffering required. It can be seen from Fig. 8 that there is not a total compensation in BA for the effect of the overload, which is due to the manner in which the neural network was trained, as instead of trying to maintain a particular BA set-point the neural network was trained to keep BA values in a "safe region" which it was able to do. In applying both PID and neural networks to the control of anaerobic digestion it was found to be easier to set up and transfer the neural network controller than determine suitable time constants for the traditional PID controller based on data from the same anaerobic filter operating on ice-cream waste-

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water. Even though a large effort was expended in an attempt to develop an operating PID controller, it was not successful (Wilcox et al., 1994). This was felt by the authors to be due to: • the manner in which the PID constants are determined by the use of linear control models which may be inappropriate to anaerobic digestion; • the time variant nature of the anaerobic process; and • the long time constants of the anaerobic process which will tend to drive the integrator in the PID algorithm. In contrast, the neural network was simple to develop and implement, despite being a more complex algorithm. On-line control variables

The way in which absolute values of biogas hydrogen vary in this reactor system, including the response during Overload 1 and 2 and a 4-h overload with neural network based control of BA, has been described previously (Guwy et al., in press). Biogas hydrogen content may easily be measured on-line and responds rapidly to metabolic changes (e.g. increases in organic load and changes in feed pre-acidification). However, the non-specific nature of its response and the apparent non-linear relationship between its absolute value and the degree of disturbance (Guwy et al., in press) make biogas hydrogen content unsuitable as a stand-alone control variable. Although pH probes often fail, in these experiments with this upftow velocity in the reactor the Ingold Xerolyte electrodes performed well. In Fig. 4, the pH change in the digester can be seen during Overload 1 and 2. In Overload 1 the pH decreased from 7.1 to 6.9 during the 4-h period. Once the overload was terminated, the pH returned to a value which was slightly higher than just prior to the overload. At 29 h, there was a drop in pH, which can be explained by the use of a new batch of feed which had not yet undergone acidogenesis in the feed tank, so giving greater acidogenic conversion in the anaerobic reactor (Guwy et al., in press). After this the pH remained steady until the next overload. In Overload 2 with on-off control of BA, the pH dropped initially to 7.05; however, after approximately 1 h the pH was seen to rise to a value of 7.15 at 58 h, 2 h after the start of the overload. The effect of the overload on pH was less severe and lasted for a shorter duration because of the addition of bicarbonate. The increased pH after Overload 2 corresponds to the overshoot in bicarbonate addition seen in Fig. 3. Even at this relatively high alkalinity, pH is shown to be a good indicator of instability. Nevertheless, it must be noted that bicarbonate alkalinity, unlike pH, can provide information on the imminence of future failure of the reactor due to acidification.

Figures 5 and 9 show how both the percentage CO2 and the biogas production rate responded to overloads. Gas production rate response was almost instantaneous, whereas % CO: had approximately a 30-min lag due to sampling, since both H2 and CO2 were measured after the biogas was scrubbed to remove H2S. For Overload 1 (Fig. 5) the biogas production rate increased from 80 to 138 cm 3 min -~, an increase of 73%, and CO2 increased from 33 to 38%, with Overload 2 giving similar results. Most of the increase in % CO2 was probably a result of the bicarbonate destruction by the increase in VFA concentration. Introduction of new feed to the tank at 29 h caused a slight increase in % CO: and gas production. This was probably due to the difference in characteristics of the new feed and old feed, where little hydrolysis had yet occurred to the feed in the storage tank and variations in the COD strength between the old and new batches of feed (Guwy et al., in press). Changes in CO2 levels are the result of both metabolic and physico-chemical processes with complex inter-relationships. The suitability of CO2 percentage as a parameter for control is questionable, as significant variations in pco2 are dependent on the CO2 "stored" in the liquid phase as bicarbonates. The biogas production rate was seen to change rapidly after increasing the organic loading rate (Figs 5 and 9) and, in the experiments where the effluent quality did not appreciably deteriorate, indicated the degree of change of B~. Biogas production rate would be a most useful control parameter if changes in effluent quality were also measurable and the influent composition standardised. In order to completely optimise the performance of the digester in the face of fluctuating loads, it will be necessary to monitor and take action based on parameters in addition to BA. It is suggested that biogas production rate, biogas composition and a measure of effluent quality are also incorporated into the neural network based control algorithm. CONCLUSIONS On-off control during organic overloads led to an overshoot in BA levels, requiring adjustment of the strength of bicarbonate solution dosed and its rate of delivery. A controller based on a neural network trained on data from a different waste and reactor type was used without modification for the control of BA in this system. This controller did not lead to an overshoot and automatically adjusted the rate of delivery of the bicarbonate buffering solution. Control of BA, and hence pH, was not sufficient to prevent changes in the other parameters monitored (H2 concentration, %CO2, gas production rate and VFA concentrations). A more sophisticated multiparameter controller, possibly based on a neural network, could incorporate information from such parameters and thereby perform high-level control actions.

Control of anaerobic digestion Acknowledgements--The authors are grateful for the assistance of Mr J. Langton and Mr Rene Hoogstraate in the experimental work, to Prof. C. Wandrey and Dr A. Aivasidis of KFA, Jiilich, Germany, for the supply of Siran -", initial culture and cyclone chambers, and to the European Commission for funds to conduct part of this work under project EV5V-CT92-0233. REFERENCES

Aivasidis A., Wandrey C. and Hilla E. (1989) Studies on reaction techniques concerning reactor design for the anaerobic degradation of complex substrates with the example of the methanation of effluents in the fermentation industry. Bioprocess Eng. 4, 63-74. Aivasidis A. and Wandrey C. (1990) Development and scale-up of a high rate biogas process for treatment of organically polluted effluents. Biochem. Eng. VI Annal. New York Acad. Sci. 589, 599-615. APHA (1985) Standard Methods for the Examination of Water and Wastewater, American Public Health Association, New York, USA. APHA (1989) Standard Methods for the Examination of Water and Wastewater, American Public Health Association, New York, USA. Cleseri L. S., Greenberg A. E. and Rhodes Trussell R. (1989) Standard Methods for the Examination of Water and Wastewater, American Public Health Association New York, USA. Denac M., Lee P. L., Newell R. B. and Greenfield P. F. (1990) Automatic control of effluent quality from a high-rate anaerobic treatment system. Wat. Res. 24, 583-586. Guwy A. J., Hawkes D. L., Hawkes F. R. and Rozzi A. G. (1994) Characterisation of a prototype industrial on-line . analyser for bicarbonate/carbonate monitoring. Biotechnol. Bioeng. 44, 1325 1330.

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Guwy A. J., Hawkes D. L., Hawkes F. R. (1995) On-line low flow high-precision gas metering systems. War. Res. 29, 977-979. Guwy A. J., Hawkes F. R., Hawkes D. L. and Rozzi A. G. Hydrogen production in a high rate fluidised bed anaerobic digester. Wat. Res. (in press). Hawkes F. R., Guwy A. J., Hawkes D. L. and Rozzi A. G. (1994) On-line monitoring of anaerobic digestion: application of a device for continuous measurement of bicarbonate alkalinity. Wat. Sci. Technol 30(12), 1-10. Hickey R. F., Wu W. M., Veiga M. C. and Jones R. (1991) Start-up, operation, monitoring and control of high-rate anaerobic treatment systems. Wat. Sci. Technol. 24(8), 207-255. HMSO (1972) Analysis of Raw Potable Waste Waters, HMSO, London. Peck M. W., Skilton J. M., Hawkes F. R. and Hawkes D. L. (1986) Effects of temperature shock treatments on the stability of anaerobic digesters operated on separated cattle slurry. Wat. Res. 20, 453-462. Renard P., Van Breusegem V., Nguyen M.-T., Naveau H. and Nyns E.-J. (1991) Implementation of an adaptive controller for the startup and steady-state running of biomethanation process operated in the CSTR mode. Biotechnol. Bioeng. 38, 805-812. Vinas M., Borzacconi L. and Martinez J. (1994) Anaerobic treatment of yeast manufacturing wastewater in UASB reactors. Environ. Technol. 15, 79-85. Wilcox S. J., Guwy A., Hawkes D. L. and Hawkes F. R. (1994) The control of anaerobic digestion for agro-industrial wastewater, pp. 46-58. In Biogas Technology as an Environmental Solution to Pollution, REUR Technical Series 33 (Edited by Marchaim U. and Ney G.). Wilcox S. J., Hawkes D. L., Hawkes F. R. and Guwy A. J. (1995) A neural network, based on bicarbonate monitoring, to control anaerobic digestion. War. Res. 29, 1465-1470.