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An expert system for monitoring and diagnosis of anaerobic wastewater treatment plants
Water Research 36 (2002) 2656–2666
An expert system for monitoring and diagnosis of anaerobic wastewater treatment plants * E. Roca, J.M. Lema* A. Punal, Department of Chemical Engineering, Institute of Technology, University of Santiago de Compostela, Avda. Ciencias s/n 15706, Santiago de Compostela, Spain Received 14 May 2001; accepted 12 October 2001
Abstract In this paper, an expert system (ES) developed for the monitoring and diagnosis of anaerobic wastewater treatment plants (AWT), is presented. The system was evaluated in a hybrid pilot plant of 1.1 m3 located in an industrial environment for the treatment of wastewaters from a fibreboard production factory. The reactor is a hybrid USBF, combining an upflow anaerobic sludge blanket (UASB) in the lower part and an upflow anaerobic filter (UAF) at the top. r 2002 Elsevier Science Ltd. All rights reserved.
1. Introduction Anaerobic wastewater treatment (AWT) is a proven useful technology for the treatment of highly polluted wastewaters [1,2] and diluted wastewaters [3]. However, the control applied to most of the anaerobic processes has been restricted to environmental variables, such as pH, temperature, etc. [4], using conventional controllers such as on-off and proportional-integral-derivative (PID) controllers. Nevertheless, control strategies applied to AWT plants could be based on the monitoring of a number of process indicators which give complementary information [5,6]. The most reliable information to control the process is the result of combining the available on-line data, off-line measurements and detailed knowledge of the process [7]. Control of the process depends directly on the type of reactor, since optimal operation conditions are different depending on the technology used. Most of the anaerobic reactors (UAFFupflow anaerobic filter, UASBFupflow anaerobic sludge blanket, EGSBFexpanded granular sludge bed; FBFfluidised bed, etc.) are efficient technologies, but they are complex from the *Corresponding author. Tel.: +34-981-563100; fax: +34981-595012. E-mail address: [email protected] (J.M. Lema).
biological, operational and hydrodynamic points of view, their supervision being a task requiring expertise. The complexity of the process makes the supervision and control of AWT difficult and therefore, different procedures based on artificial intelligence (fuzzy logic, expert systems, neural networks, etc.) have been applied in order to control the operation. Expert systems (ES) are computer programs applied to problem solving areas (narrow domains) where expertise, especially of heuristic nature, is available (rules of thumb). A proper ES must have the capacity to represent time-dependent knowledge, the ability to process incomplete or inaccurate knowledge and the capacity to combine simple rules with model based rules. When applied to AWT management, the long operational time required in anaerobic processes (hours or days) represents an advantage to ensure real time processing. The major difficulty in creating an ES is the generation, management and hierarchisation of knowledge concerning the process During the last 10 years, attention has been paid to the study and development of monitoring, diagnosis and, more generally, control systems for wastewater treatment plants [8,9,10] based on the concept of ES and examples of anaerobic processes can be found in literature. The earliest works in this area are related to the aerobic treatment of municipal wastewaters with activated sludge processes. Serra et al. [11] designed a
0043-1354/02/$ - see front matter r 2002 Elsevier Science Ltd. All rights reserved. PII: S 0 0 4 3 - 1 3 5 4 ( 0 1 ) 0 0 4 8 4 - 5
A. Punal * et al. / Water Research 36 (2002) 2656–2666
Nomenclature AWT COD ES FF GF HRT IA IA/TA OLR PA
anaerobic wastewater treatment chemical oxygen demand (g/l) expert system feed flow rate (l/h) gas flow rate (l/h) hydraulic residence time (h) intermediate alkalinity (g CaCO3/l) alkalinity ratio, intermediate alkalinity/ total alkalinity () organic loading rate (kg COD/m3 d) partial alkalinity (g CaCO3/l)
real time ES for the supervision and control of activated sludge processes for municipal wastewater treatment. The system includes an interface, which permits on-line data acquisition, a predictive algorithm for determining dissolved oxygen concentration and a graphic interface with the user. The elements, including the rules for detection, diagnosis, prediction and operation, were integrated in a knowledge base system that identified the status of the process, using both quantitative and qualitative information. Ladiges and Kayser [12,13] also developed on-line and off-line ESs to operate municipal wastewater treatment plants. The on-line system recognised plant faults based on the signals obtained from the process, making diagnosis of the plant possible. The offline system was useful for diagnosis because it determines the appropriate set-points for the control elements based on the knowledge acquired. These two examples are very helpful, basically from the structural point of view, in order to create an ES able to manage an anaerobic process. Some works in the field of advanced control techniques applied to anaerobic processes can also be found in the literature. Most of these works concern the operation and/or control of continuous stirred tank reactors (CSTR) at lab and industrial scale. Chynoweth et al. [14] developed an on-line control system with feedback strategies for the optimisation of a lab scale CSTR treating synthetic wastewater. The algorithm was implemented in an ES, which used methane production and hydraulic residence time (HRT) for the automatic detection of possible operational faults (such as overloads, inhibition or operation below the capacity of the system) and to allow the recovery of normal operation. Pullammanappallil et al. [15] developed and implemented an on-line decision support system for a brewery AWT plant at industrial scale. In this system a predictive feed-forward control algorithm, implemented in the plant through a programmable logic controller (PLC), used the flow rate and the on-line inorganic carbon concentration of the influent for the
PC PLC RF SMA Tc Tr TA TSS UASB UAF VFA VSS
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computer programmable logic controller recycling flow rate (l/h) specific methanogenic activity (kg COD/ kg VSS d) control temperature (1C) reactor temperature (1C) total alkalinity (g CaCO3/l) total suspended solids (g/l) upflow anaerobic sludge blanket upflow anaerobic filter volatile fatty acids (g/l) volatile suspended solids (g/l)
identification of the optimal operational set-points. Pullammanappallil et al. [16] also used ES-type strategies to control an anaerobic lab scale process modifying the HRT dilution rate according to four different modes: a conventional control law for the set-point values; a control law based on maintaining constant process performances; batch operation; and operation with constant dilution coefficients. The three papers mentioned [14–16] implemented the classic tools used for control (predictive laws, feed-back and feed-forward algorithms) as ES strategies. The successive approaches were improved enlarging the possible situations, from lab scale treating synthetic wastewater up to industrial scale. The wider range of situations required the use of a higher number of techniques, as reflected in the latest work [16], although this approach was only applied at lab-scale. Moletta et al. [17] used a rule-based system, which considers three parameters (pH, hydrogen concentration in the gas phase and biogas flow rate) for the control of an anaerobic fluidised bed reactor treating vinasses, at both lab- and pilot scale. The system allowed organic loading rates (OLR) of 30 kg COD/m3 d to be achieved. In this work, an ES for the monitoring and diagnosis of the operation of an AWT plant treating highly polluted wastewater from a fibreboard factory is presented. The system was applied to a hybrid UASB– UAF pilot scale reactor (1.1 m3), working in a factory.
2. Materials and methods 2.1. Architecture of the system To facilitate the further implementation of the monitoring system in the wastewater treatment plant, several standard tools were used. The software selected was Visual Basic 5.0 (for communication and graphic interface), Access 7.0 (for database management) and
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The different modules relating to the monitoring and diagnosis system (Fig. 1) are integrated in the same user interface. The time needed to acquire, filter and diagnose the data is short enough (seconds to minutes) compared with process dynamics (hours to days). This permits the modules comprised in the ES to be considered concurrent, as well as to ensure the real time capabilities of the system.
2.2. Data acquisition and filtering modules The data processing module ( in Fig. 1) acquires the raw data from the PLC. A first filter of aberrant values (out of the possible measurement range for each sensor) is used to detect and reject obvious wrong information. When this occurs, new data are collected
Fig. 1. Architecture of the software developed.
Matlab 5.0 (for scientific calculation), for their powerful and flexible application. In Fig. 1, the architecture of the system is shown. Due to its robustness in industrial environments, a programmable logic controller (PLC) (Siemens 95-U) was selected for data acquisition and for the actuation on the final control elements. The PLC sends the information to the computer through a RS-232 series port, which makes the development of a standard interactive information exchange possible. Two different types of tasks can be distinguished with regard to the operation of the plant. The tasks not depending on or not directly affecting the performance of the digester (physico-chemical treatment, stirrer on/ off, purges, tank levels, and so on) are controlled by the PLC in a closed loop. The tasks concerning the AWT plant operation (feed, recycling and nutrients and alkaline solution pumps, temperature) are introduced by the operator, the set-points of the control elements as well as other minor consigns, as the sampling time, being fixed in the utilities and system configuration module ( in Fig. 1) in the PC. The possibility of tele-supervision of the plant in real time from a control centre, located far away from the plant, was also considered. For this purpose, all warnings and alarms about equipment failures are collected in the PC, as well as the state of pumps, engines and sensors, allowing all the available information on the plant to be sent to the control centre. In the event of PC or PLC-PC communication failure, an automatic control program located in the PLC and based on the last set-points sent by the PC, manages the operation of the plant.
Table 1 Labels of the variables used for diagnosis Gas flow (GF) (l/h)
High Critical Normal Low
GF>200 200XGFX150 150>GF>60 GFp60
Methane (CH4) (%)
Normal Low Very low Extremely low
CH4 >45 45XCH4 X35 35>CH4 >32 CH4p32
Carbon monoxide (CO) (ppm)
High Low Zero
CO>5 5XCO>0 CO=0
Feed flow FF (l/h)
High Medium Normal Zero
FF>30 30XFFX10 10>FF>0 FF=0
Recycling flow RF (l/h)
Normal Critical Low Zero
RF>150 150XRFX135 135>RF>0 RF=0.5
pH
High Normal Low Very low
pH>7.5 7.5XpHX6.5 6.5>pH>5 pHp5
Control temperature Tc (1C)
High Normal Low
Tc > 44 44XTc X41 Tc o41
Reactor temperature Tr (1C)
High Normal Low Very low
Tr > 38 38XTr X35 35>Tr >30 Tr p30
(TcTr) (1C)
High Normal Low
ðTc Tr Þ > 6 6XðTc Tr ÞX5 ðTc Tr Þp5
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The signal filtering is carried out using a module developed in Matlab . This module filters the variables to eliminate noise, processing the last 21-data collected
again and are stored in the files corresponding to the raw acquired data in the data base (DB) module (Fig. 1). The sampling time is 15 min.
Diagnosis System Flow and pH On-line data
Information Temperature control system
Set of alarms
iii)
Actions
i) ii)
State of the process
Advices
Recommendations
Future trend of the process
(a)
(b)
Then
If Process state CH4
FF
Extremely Low
Zero
Organic Overload (OO)
Extremely Low
Low
Organic Overload
Extremely Low Very Low
Recommendations
GF
Normal
Organic Overload
Zero
Low
Low
Sensor fault High
Test COD
Organic Overload
Very Low
Low
Low
Organic Overload
Very Low
Normal
Low
Organic Overload
Extremely Low
High
Very Low
High
FF: zero RF: increase BA: increase
Hydraulic Overload (HO) Low
Hydraulic Overload
Very Low
Zero
High/Critical/Normal
Medium Acidification by OO
Very Low
Low
High/Critical/Normal
Medium Acidification by OO
Very Low
Normal
High/Critical Normal
Low
Zero
Medium Acidification by OO
Low
Low
Low
Medium Acidification by OO
Low
Normal
Low
Medium Acidification by OO
Very Low
High
Low
High
Low
Zero
High/Critical/Normal
Low Acidification by OO
Low
Low
High/Critical/Normal
Low Acidification by OO
Low
Normal
High/Critical/Normal
Low Acidification by OO
Low
High
High/Critical/Normal
Low Acidification by HO
Normal/High
High
High/Critical/Normal
Low Acidification by HO
Normal/High
Zero
Low
Normal
Normal/High
Low
Low
Normal
High/Critical/Normal Low
Normal/High
Normal
Normal/High
Zero
High/Critical/Normal
Normal/High
Low
High/Critical/Normal
Normal/High
High
Low
Medium Acidification by OO
Low
Check sensors FF: zero RF: increase BA: increase
Sensor fault High
Test COD
Check sensors FF : decrease RF: increase BA: increase
FF : decrease RF: increase BA: increase
Medium Acidification by HO Low
Medium Acidification by HO
Sensor fault High
Test COD
Check sensors FF : decrease RF: increase BA: increase
FF : decrease RF: increase BA: increase FF : maintain RF: maintain BA: maintain
Normal
Sensor fault
Check sensors
Low
Variable label
Process state
Information
Recommended action
Fig. 2. Supervision and diagnosis system: (a) (i) module for diagnosis of faults in engines, pumps and sensors, (ii) module for identification of the present state and trends of the process, (iii) module delivering the actions recommended by the ES to operator; (b) knowledge base for the diagnosis of the biological state of the process. In this case three variables are taken into account: CH4 percentage in biogas, feeding flow rate (FF) and gas flow rate (GF). (BA: bicarbonate and nutrients flow rate; RF: recycling flow rate).
A. Punal * et al. / Water Research 36 (2002) 2656–2666
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for each variable, which are stored in DB. Several mathematical and logic techniques [18–20] were used to filter data such as: (i) removal of important variations, due to signal displacement; (ii) a first order limited in magnitude filter, in order to preserve the changes larger than an established value or; (iii) a moving window with a variable size Winsorizing filter, corresponding to a moving media filter. These techniques were selected depending on the nature of the noise associated to each signal; in most cases a combination of two of them was applied [21].
2.3. Diagnosis module The diagnosis module was built up following the structure of an ES, comprising a rule base with structure IF ‘‘conditions’’ THEN ‘‘conclusions (state+action)’’. The rule base constituting the knowledge base of the ES is written in Matlab, using the features of this software as an inference engine. In order to be used by the ES, every signal is converted into qualitative values with different labels (e.g. zero, low, high, critical, etc.) according to the operational meaning of values. The conversion is made using threshold values, each label representing a given interval for a given variable, as defined in Table 1.
The ES should predict the failures that might destabilise the operation of the AWT plant. The knowledge base structure of the ES is shown in Fig. 2a. In order to organise the information better, three different areas were considered: (i) concerning the state of the pumps (on, off, high set-points, no communication with the PLC, etc.), engines and sensors; (ii) concerning the biological state and future trend of the process; and (iii) concerning advice and comments to be given to the operator about how to manage the process in each possible situation. The first area (i), is especially important for future purposes of tele-supervision. The second (ii) and third (iii) areas of the ES (Fig. 2a) constitute the knowledge base, as they include the expertise about the process. Although both are directly related, they were built up separately. The state and trend of the process are presented through the user interface in the terms defined in Table 2 (paragraphs (c) and (d)), while advises and recommendations appear only as indications to the operator. Two separate modules (Fig. 2a) determine the state and future trend of the process. The state of the process is identified after analysing the on-line data, while the diagnosis of the future trend also considers the whole current state of the process. Since several variables are available, different groups of three of them were
Table 2 States described for the diagnosis of: (i) state of pumps, engines and sensors and (ii) present state and future trend of the process (i)
(ii)
(a) Flow and pH
Normal Feeding pump stop Feeding flow rate increase Recycling pump stop pH sensor problems concerning the recycling system Feeding and recycling pumps stop or communication failure
(b) Temperature control system
Normal Temperature decrease due to an incorrect set-point value Temperature decrease due to a system incapacity Tc sensor failure Recycling system failure, which affects temperature External agents or Tr sensor failure
(c) State of operation
Normal Low acidification due to a hydraulic overload Low acidification due to an organic overload Medium acidification due to a hydraulic overload Medium acidification due to an organic overload Hydraulic overload Organic overload
(d) Process trend
Normal The process is getting worse The process is recovering the normal state The process trend is destabilisation
A. Punal * et al. / Water Research 36 (2002) 2656–2666
considered, in a subsequent subset of rules, for making the ES feasible even in the case that some sensors were out of order or for application in industrial plants with a more limited number of sensors. The subsets of variables established were: CH4 percentage in biogas, feed flow rate and gas flow rate; CH4 percentage, feed flow rate and CO contents in biogas; CH4 percentage in biogas, feed flow rate and pH and gas flow rate, feed flow rate and pH. In Fig. 2b the knowledge base for the diagnosis of the biological state of the process based on CH4 percentage in biogas, gas flow rate and feed flow rate is shown, as an example. The diagnosis and operator interaction results module ( in Fig. 1), as well as the graphic user interface ( in Fig. 1) show the output of the ES, indicating the current situation of the process and advising on the actions to be taken by the plant operator (Fig. 2). The results are collected in a DB ( in Fig. 1).
2.4. Utilities and system configuration and graphic interface modules The utilities and system configuration module ( in Fig. 1) allows the graphic interface to be configured (the variables to be represented, the number of figures on each screen, the number, frequency and type of
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parameters to be acquired, etc.). This information is stored in the corresponding file in a DB . 2.5. Anaerobic wastewater treatment plant The wastewater is produced in a fibreboard factory, and their main characteristics are shown in Table 3. The raw wastewater was diluted at several ratios in order to
Table 3 Fibreboard processing wastewater characteristics Total COD (g/l)
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Soluble COD (g/l) TSS (g/l) VSS (g/l) Acetic acid (g/l) Propionic acid (g/l) Tannins (g/l as COD) Phenols (g/l) p-Cresol (g/l) N-NH+ 4 (ppm) P-PO3 4 (ppm) Alkalinity (g/l) Temperature (1C) pH
2174 0.970.66 0.970.61 1.270.4 0.0870.01 0.970.1 0.0570.01 0.2570.02 10.270.4 29.870.9 0.7670.02 5072 3.170.1
Fig. 3. Layout of the pilot plant: (1) feeding tank; (2) feeding pump N#1; (3) settling tank; (4) flocculating solution dosage pump; (5) flocculating solution tank; (6) liquid (feeding flow) flowmeter; (7) feeding pump N#2; (8) pH meter; (9) heat exchanger; 10a, 10bFtemperature meter (Pt-100); (11) nutrient and alkalinity solution tank; (12) nutrient and alkalinity solution pump; (13) hybrid UASB–UAF reactor; (14) liquid (recycling flow) flowmeter; (15) recycling pump; (16) effluent tank; (17) gas analyser (CO and CH4); (18) gas flowmeter; (19) stirrer; (20) level sensor.
A. Punal * et al. / Water Research 36 (2002) 2656–2666
(this sampling period was considered long enough as it accounts for 0.21–1.04% of the operational HRT). Three variable speed pumps for wastewater feeding, recycling and nutrients and alkalinity supply were used as final control elements. Their set-points were selected by the operator, following the recommendations from the ES, and implemented through the PC.
50
200
40
175
30
150
20
125
10
3.1. Hydraulic overload The first experiment presented in this study represents a typical situation that occurs when a higher flow of 200 175
100 --2
0
2
4
6
150 125 100 -2
(b)
0
2
4
6
45
7.5
60
30
40
7
50
25
35
6.5
40
20
30
6
30
15
25
5.5
20
5
20
10
15
4.5
10
5
10
0
4
-2
(c)
% CH4
Tr, Tc (ºC)
(a)
To evaluate the robustness and reliability of the monitoring and diagnosis system, several experiments were carried out. In this paper, the response of the process to alterations from normal operating conditions, concerning hydraulic residence time and organic load, are presented and analysed, since they represent the most usual causes of instability in real plants. A wide set of operational conditions were applied, working at different COD wastewater concentrations (from 5 to 25 g/l), different organic loading rates (OLR) (from 1.67 to 15 kg COD/m3 d) and different HRT (from 1 to 3 d).
0
2 t (d)
4
0
-2
6
(d)
CO (ppm)
225
3. Results and discussion
RF (l/h)
60
pH
FF (l/h)
obtain widely varying conditions. The results presented in this work correspond to 5, 15 and 25 g COD/l. The low content in nutrients and the low alkalinity of the wastewater make the addition of nitrogen and phosphorous salts and bicarbonate necessary, in order to maintain a proper C/N/P ratio as well as buffering capacity, respectively, in the digester. A flowchart of the pilot plant is presented in Fig. 3. Wastewater from the factory is collected in a feeding tank for homogenisation. It is important to note that most suspended solids are volatile suspended solids and colloids that have to be removed before entering the digester [1,21]. To do that, wastewater is pumped to a settler where 70% of solids are removed by addition of a flocculant. After this pre-treatment, an important fraction of the hardly biodegradable compounds (40% of the lignin and 80% of the phenols) is separated [1]. Wastewater was then treated in an anaerobic hybrid UASB–UAF of 1.1 m3 (13 in Fig. 3) equipped with a biogas flow-meter (Mass flow-meters Brooks Models E5860i Rosemount, The Netherlands); feed and recycling flow-meters (Electromagnetic flow-meters 7Me2531 Siemens, Germany); two thermometers Pt-100; a biogas (CH4 and CO) analyser (Ultramat22P Gas Analyser Siemens, Germany) and a standard pHmeter (Siemens, Germany). Data from these sensors were measured continuously and recorded every 15 min
GF (l/h)
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0
2 t (d)
4
6
Fig. 4. On-line data (filtered) from the experiment with hydraulic overload. (a) FF, feeding flow rate (– – –) and RF, recycling flow rate (–); (b) GF, gas flow rate (F); (c) Tc, control temperature (–); Tr, reactor temperature (F) and pH (– – –) and; (d) CH4 (F) and CO (– – –) composition in biogas.
A. Punal * et al. / Water Research 36 (2002) 2656–2666
wastewater has to be treated in the reactor. The plant had previously been working in steady state at an OLR of 10 kg COD/m3 d and at a HRT of 1.5 days during one month. On day 0 (see Fig. 4), the feeding flow rate (FF) was seriously modified from 30 l/h to 40 l/h (HRT from 1.5 to 1 d approx.) (Fig. 4a), thus increasing OLR by 50%. The COD influent concentration (15 g/l) was maintained constant during the whole experiment. The on-line filtered data are shown in Fig. 4. Once FF increased (Fig. 4a), a very rapid effect on gas flow rate (Fig. 4b) and methane content (Fig. 4d) was detected, as well as a dramatic variation in pH (Fig. 4c), indicating the magnitude of the overload. Such an important decrease in pH makes it necessary to modify a particular set point of the process (e.g. decreasing FF and/or increasing the recycling flow rate (RF) and/or adding an extra-amount of buffering solution), in order to recover stability. The ES continuously identifies the current state of the plant in the terms presented in Table 2 and Fig. 2. In Fig. 5 the diagnosis results from the ES are presented. Fig. 5a and b correspond to paragraphs (a) and (b) in Table 2, and reflect the state of pumps, engines and sensors after monitoring and fault isolation. Figs. 5c and d concern to the biological state and trend of the process directly, which are described in paragraphs (c) and (d) in Table 2. As observed in Fig. 5a, the diagnosis module informs the operator of the sudden increase of FF (paragraph (a) in Table 2). Since RF was maintained at the previous value, and considering that the reactor was heated by a heat exchanger in the recycling line, a decrease in the temperature of the reactor took place. The diagnosis module identified this phenomenon very well (Fig. 5b), indicating the incapacity of the system to maintain temperature (paragraph (b) in Table 2). In obtaining this conclusion, the ES has also taken into account that the difference between recycling temperature (Tc ) and temperature in the reactor (Tr ) remained approximately constant (Fig. 4c). On day 0.5, the state of the system was characterised by the ES as ‘‘low acidification caused by hydraulic overload’’ (Table 2 and Fig. 5c), after the overall consideration of the values of the main variables, as classified in Table 1: gas flow rate (critical); methane content (low); carbon monoxide (zero); feed flow rate (high); recycling flow rate (normal); pH (low); control temperature (low); reactor temperature (low) and the difference of temperatures (normal). From day 0.5 to day 2 the methane percentage decreased from 35% to 30%, CO then being detectable (2 ppm) in the biogas (Fig. 5d). In this period, pH (Fig. 4c) continued decreasing to values of 4.5, in spite of the addition of bicarbonate (data not shown) in an attempt to maintain the buffering capacity within the reactor. The biogas flow rate (Fig. 4b) increased slightly. The present status of the system was, then, identified by the ES as ‘‘medium acidification caused by hydraulic
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Stop RF and FF pH sensor problems Stop RF
Increase FF Stop FF
Normal
(a)
-2
-1
0
1
2
3
4
5
6
-2
-1
0
1
2
3
4
5
6
External agents Recycling failure Tc sensor failure
Heat system incapacity Wrong set-point
Normal
(b) Organic Overload (OO)
Hydraulic Overload (HO) Medium Acidification by OO
Medium Acidification by HO Low Acidification by OO
Low Acidification by HO Normal
(c)
-2
-1
0
1
2
3
4
5
6
Destabilisation
Recovering
Getting worse
Normal
-2
(d)
-1
0
1
2
3
4
5
6
t (d)
Fig. 5. Ouputs from the expert system. Diagnosis during the experiment with hydraulic overload of: (a) flow rate; (b) temperature control system; (c) acidification state and (d) trend foreseen. The states identified appear in bold.
overload’’ (paragraph (c) in Table 2 and Fig. 5c). During the last part of the experiment, from day 2 to day 3, pH reached its lowest value of 4, gas flow rate (Fig. 4b) did not vary significantly, while methane percentage continued decreasing to 23%, and CO in biogas increased to 10 ppm (Fig. 4d). This situation was diagnosed by the ES as a ‘‘hydraulic overload’’, (paragraph (c) in Table 2 and Fig. 5c). The evolution of the variables acquired, together with the results obtained from the diagnosis make it possible to evaluate the trend of the process and to predict the future behaviour of the reactor when maintaining identical operational conditions. From day 0.5 to day 2.2, the trend was diagnosed as ‘‘getting worse’’ (paragraph (d) in Table 2 and Fig. 5c). The system then
A. Punal * et al. / Water Research 36 (2002) 2656–2666
During the overload, variables corresponding to biogas flow and composition were the most sensitive ones. The gas flow rate increased extremely fast (Fig. 6b), reaching five times the previous value within 3 days. In Fig. 6d, the gradual decrease in methane percentage from 48% to 35% from day 0 to day 3 can be seen, while only 2 ppm of CO were detected in the biogas. The pH (Fig. 6c) was maintained at 7, except during the first day, by means of the addition of bicarbonate solution, which increased the buffering capacity of the reactor. No problems related to temperature control occurred (Fig. 6c and Fig. 7b). The diagnosis system described the state as ‘‘low acidification due to organic overload’’ (paragraph (c) in Table 2 and Fig. 7c). From day 3 to day 5, the methane content in the biogas remained constant, as did the gas flow rate (GF), with a gradual increase in CO concentration up to 14 ppm, leading to the diagnosis of ‘‘medium acidification due to organic overload’’ (paragraph (c) in Table 2 and Fig. 7c). ‘‘High acidification’’ (paragraph (c) in Table 2) was not diagnosed since pH and the methane and carbon monoxide contents of the biogas did not vary during the last two days of the perturbation. The analysis of the evolution of the variables indicates that the trend foreseen for the process is ‘‘getting worse’’ from day 0.5 to day 5.
evolved towards a critical state of ‘‘destabilisation’’ (paragraph (d) in Table 2 and Fig. 5d). Finally, once the operational conditions had been changed (feeding flow rate (Fig. 4a) decreased from 40 to 20 l/h), the process trend was classified as ‘‘recovering’’ (paragraph (d) in Table 2 and Fig. 5c). Finally, on day 3.7 the ES delivered a diagnosis of ‘‘normal’’ operation, according to on-line data, together with the current state of the process. 3.2. Organic overload
275
200
40
250
160
30
225
20
200
10
175
0
(a)
GF (l/h)
50
RF (l/h)
FF (l/h)
In practice, the sudden modification of wastewater characteristics in a factory, even in the case of a homogenisation tank being installed, is a quite frequent event. The most dangerous situations are produced when a toxic compound enters in the reactor or when a serious increase in the organic load of wastewater, due to spills or problems in the factory, occurs. In this experiment, HRT was kept at its previous value of 3 d but the organic load of influent was suddenly increased from 5 to 25 kg COD/m3 on day 0 (data not shown); this overload was maintained for 5 d. This modification dramatically increased the OLR from 1.67 to 8.33 kg COD/m3 d. The data obtained from this experiment are shown in Fig. 6 while the results obtained from the ES are given in Fig. 7.
0
2
4
6
8
0
10
45
(b) 8
42.5 37.5 35
7
32.5 30
% CH4
7.5 pH
Tr, Tc (ºC)
40
80 40
150 -2
120
6.5
-2
0
2
4
6
8
10
60
30
50
25
40
20
30
15
20
10
10
5
CO (ppm)
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(c)
0
6 0
2
4 6 t (d)
8
0 -2
10
(d)
0
2
4
6
8
10
t (d)
Fig. 6. On-line data (filtered) from the experiment with organic overload. (a) FF, feeding flow rate (– – –) and RF, recycling flow rate (–); (b) GF, gas flow rate (F); (c) Tc , control temperature (–); Tr , reactor temperature (F) and pH (– – –); and (d) CH4 (F) and CO (– – –) composition in the biogas.
A. Punal * et al. / Water Research 36 (2002) 2656–2666 Stop RF and FF p H sensor failure Stop RF Increase FF
Stop FF Normal
(a)
-2
0
2
4
6
-2
0
2
4
6
8
10
External agents Recycling failure Tc sensor failure Heat system incapacity Wrong set-point
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mained unconverted within the reactor. The ‘‘normal’’ value of 45% was recovered after day 8, when the system was operated at the initial influent COD concentration. Likewise, carbon monoxide concentration in biogas (Fig. 6d) decreased progressively, this being indicative of a re-equilibrium between the activity of the different microbial trophic groups, responsible for the total transformation of organic matter into methane and carbon dioxide. Once the recovery actions were performed, the ESs modified their previsions from ‘‘getting worse’’ to ‘‘recovering’’ (Fig. 7d) and, finally, on day 7, the process was identified as working in a ‘‘normal’’ state.
Normal
(b)
8
10
4. Conclusions
Organic Overload (OO) Hydraulic Overload (HO)
Medium acidification by OO Medium Acidification by HO
Low Acidification by OO Low Acidification by HO
Normal
(c)
-2
0
2
4
6
8
10
Destabilisation
Recovering
Getting worse
Normal
-2
(d)
0
2
4
6
8
10
t (d)
Fig. 7. Ouputs from the expert system. Diagnosis during the experiment with organic overload of: (a) flow rate; (b) temperature control system; (c) acidification state and (d) trend foreseen. The states identified appear in bold.
On day 5, the recommendations from the ES (Fig. 2) where considered by the operator who decided, after checking the value of COD: (a) to increase the nutrients and bicarbonate flow rate, in order to maintain a proper buffering capacity within the reactor; (b) to maintain or even increase recycling flow rate to improve the mixing conditions, in order to favour the dilution of potential inhibitors (e.g. propionate, butyrate and acetate) and (c) to decrease (or even stop) the feeding flow rate to recover the stable operation of the process (Figs. 6a and 7a). Following these indications the feeding was stopped for two days (days 5–7), as can be seen in Figs. 6a and 7a. During the FF stop, performed from day 5 to 7, methane percentage increased from 35% to 60%, this was caused from the methanisation of the organic matter accumulated during the overload, which re-
In this work, an expert system (ES) was applied to the monitoring and diagnosis of the operation of AWT plants. The flexibility, reliability and safety of the application make it suitable for industrial environments. The software, developed and implemented in a PC, manages data acquisition, filters the signals to avoid noise and artefacts in the measurements, and stores data for further retrieval in a very simple way. The diagnosis module verifies the state of the mechanical elements and sensors, determines the state of the process by the analysis of on-line data and predicts the trend of the operation, based on the evolution of the different variables (gas flow and composition, pH, T, etc.), together with the issues diagnosed (flow rate, control temperature system and current state). The results obtained show the capacity of the ES for determining the current state of the process, classifying it in one of the four labels previously defined (normal, getting worse, recovering or destabilisation), which correspond to the commonest situations appearing in the operation of AWT plants. The ES successfully identified two frequent anomalous situations, hydraulic and organic overloads. In both cases, the ES delivered valuable recommendations and, after following them, the AWT recovered its normal state in a short period of time. The rapid identification of the evolution of the variables enables the operator to take preventive or recovering measures in order to avoid the process being led towards a serious ‘‘destabilisation’’ state, very difficult to be recovered.
Acknowledgements This work was financed by the European Commission through the Project FAIR-CT 96-1198 (AMOCO) and the Spanish Commission of Science and Technology (CICYT) through the project No. 1FD97-2184.
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References [1] Fern!andez JM, M!endez R, Lema JM. Anaerobic treatment of eucalyptus fibreboard manufacturing wastewater by a hybrid USBF lab-scale reactor. Environ Technol 1995;16(7):677–84. [2] Switzenbaum MS. Anaerobic biotechnology for industrial wastewaters. USA: Archae Press, 1995. [3] Hulshoff Pol LW, Euler H, Eitner A, Grohganz D. GTZ sectorial project ‘‘Promotion of anaerobic technology for the treatment of municipal and industrial sewage and waste’’. Proceedings of the Eighth International Conference on Anaerobic Digestion, vol. 2. Sendai, Japan, 1997, p. 285–92. [4] Heinzle E, Dunn IJ, Ryhiner GB. Modelling and control of anaerobic wastewater treatment. Adv Biochem Eng Biotechnol 1993;48:79–114. [5] Switzenbaum MS, Giraldo-Gomez E, Hickey RF. Monitoring of the anaerobic methane fermentation process. Enzyme Microb Technol 1990;12(10):722–30. [6] Vanrolleghem PA. Sensors for anaerobic digestion: an overview. International Workshop on Monitoring and Control of Anaerobic Digestion. December, 6th–7th, Narbonne, France, 1995. [7] Hickey RF, Wu WM, Veiga MC, Jones R. Start-up, operation, monitoring and control of high-rate anaerobic systems. Water Sci Tech 1991;24(8):207–55. [8] Henze M. Trends in advanced wastewater treatment. Water Sci Tech 1997;35(10):1–4. [9] Steyer JP, Rolland D, Bouvier JC, Moletta R. Hybrid fuzzy neural network for diagnosisFapplication to the anaerobic treatment of wine distillery wastewater in a fluidized bed reactor. Water Sci Tech 1997;36(6/7):209–17. [10] Steyer JP, Buffi"ere P, Rolland D, Moletta R. Advanced control of anaerobic digestion processes through disturbances monitoring. Water Res 1999;33(9):2059–68. [11] Serra P, Lafuente J, Moreno R, de Prada C, Poch M. Development of a real-time expert system for wastewater
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treatment plants control. Control Eng Practice 1993;1(2): 329–35. Ladiges G, Kayser R. On-line and off-line expert systems for the operation of wastewater treatment plants. Wat Sci Tech 1993;28(11/12):315–23. Ladiges G, Kayser R. Applied off-line expert system for effluent, operational and technical problems of wastewater treatment plants. Water Sci Tech 1994;30(2):157–64. Chynoweth DP, Svoronos SA, Lyberatos G, Harman JL, Pullammanappallil PD, Owens JM, Peck MJ. Real-time expert system control of anaerobic digestion. Water Sci Tech 1994;30(12):21–9. Pullammanappallil PD, Ramsay IR, Newell RB, Keller J, Lee PL, Newland M. An automatic online decision support system for the operation of high-rate anaerobic wastewater treatment plants. In: Proceedings of International Workshop on Monitoring and Control of Anaerobic Digestion Process, 6–7 December, Narbonne, France, 1995, p. 85–9. Pullammanappallil PC, Svoronos SA, Chynoweth DP, Lyberatos G. Expert systems for control of anaerobic digesters. Biotechnol Bioeng 1998;58(1):13–22. Moletta R, Escoffier Y, Ehlinger F, Coudert JP, Leyris JP. On-line automatic control system for monitoring an anaerobic fluidized bed reactor: response to organic overload. Water Sci Tech 1994;30(12):11–20. Labarrere M, Krief JP, Gimonet B. Filtering and its applications. Cepadues editions. Paris, France, 1982 (in French). Astola J, Haavisto P, Neuvo Y. Vector median filters. Proceedings of the IEEE 1990;78(4):678–89. Ragot J, Darouach M, Maquin D, Bloch G. Data validation and diagnosis. Hermes editions. Paris, France, 1990 (in French). * A, Lorenzo A, Roca E, Hern!andez C, Lema JM. Punal Advanced monitoring of an anaerobic pilot plant treating high strength wastewaters. Water Sci Technol 1999;40(8): 237–44.
An expert system for monitoring and diagnosis of anaerobic wastewater treatment plants * E. Roca, J.M. Lema* A. Punal, Department of Chemical Engineering, Institute of Technology, University of Santiago de Compostela, Avda. Ciencias s/n 15706, Santiago de Compostela, Spain Received 14 May 2001; accepted 12 October 2001
Abstract In this paper, an expert system (ES) developed for the monitoring and diagnosis of anaerobic wastewater treatment plants (AWT), is presented. The system was evaluated in a hybrid pilot plant of 1.1 m3 located in an industrial environment for the treatment of wastewaters from a fibreboard production factory. The reactor is a hybrid USBF, combining an upflow anaerobic sludge blanket (UASB) in the lower part and an upflow anaerobic filter (UAF) at the top. r 2002 Elsevier Science Ltd. All rights reserved.
1. Introduction Anaerobic wastewater treatment (AWT) is a proven useful technology for the treatment of highly polluted wastewaters [1,2] and diluted wastewaters [3]. However, the control applied to most of the anaerobic processes has been restricted to environmental variables, such as pH, temperature, etc. [4], using conventional controllers such as on-off and proportional-integral-derivative (PID) controllers. Nevertheless, control strategies applied to AWT plants could be based on the monitoring of a number of process indicators which give complementary information [5,6]. The most reliable information to control the process is the result of combining the available on-line data, off-line measurements and detailed knowledge of the process [7]. Control of the process depends directly on the type of reactor, since optimal operation conditions are different depending on the technology used. Most of the anaerobic reactors (UAFFupflow anaerobic filter, UASBFupflow anaerobic sludge blanket, EGSBFexpanded granular sludge bed; FBFfluidised bed, etc.) are efficient technologies, but they are complex from the *Corresponding author. Tel.: +34-981-563100; fax: +34981-595012. E-mail address: [email protected] (J.M. Lema).
biological, operational and hydrodynamic points of view, their supervision being a task requiring expertise. The complexity of the process makes the supervision and control of AWT difficult and therefore, different procedures based on artificial intelligence (fuzzy logic, expert systems, neural networks, etc.) have been applied in order to control the operation. Expert systems (ES) are computer programs applied to problem solving areas (narrow domains) where expertise, especially of heuristic nature, is available (rules of thumb). A proper ES must have the capacity to represent time-dependent knowledge, the ability to process incomplete or inaccurate knowledge and the capacity to combine simple rules with model based rules. When applied to AWT management, the long operational time required in anaerobic processes (hours or days) represents an advantage to ensure real time processing. The major difficulty in creating an ES is the generation, management and hierarchisation of knowledge concerning the process During the last 10 years, attention has been paid to the study and development of monitoring, diagnosis and, more generally, control systems for wastewater treatment plants [8,9,10] based on the concept of ES and examples of anaerobic processes can be found in literature. The earliest works in this area are related to the aerobic treatment of municipal wastewaters with activated sludge processes. Serra et al. [11] designed a
0043-1354/02/$ - see front matter r 2002 Elsevier Science Ltd. All rights reserved. PII: S 0 0 4 3 - 1 3 5 4 ( 0 1 ) 0 0 4 8 4 - 5
A. Punal * et al. / Water Research 36 (2002) 2656–2666
Nomenclature AWT COD ES FF GF HRT IA IA/TA OLR PA
anaerobic wastewater treatment chemical oxygen demand (g/l) expert system feed flow rate (l/h) gas flow rate (l/h) hydraulic residence time (h) intermediate alkalinity (g CaCO3/l) alkalinity ratio, intermediate alkalinity/ total alkalinity () organic loading rate (kg COD/m3 d) partial alkalinity (g CaCO3/l)
real time ES for the supervision and control of activated sludge processes for municipal wastewater treatment. The system includes an interface, which permits on-line data acquisition, a predictive algorithm for determining dissolved oxygen concentration and a graphic interface with the user. The elements, including the rules for detection, diagnosis, prediction and operation, were integrated in a knowledge base system that identified the status of the process, using both quantitative and qualitative information. Ladiges and Kayser [12,13] also developed on-line and off-line ESs to operate municipal wastewater treatment plants. The on-line system recognised plant faults based on the signals obtained from the process, making diagnosis of the plant possible. The offline system was useful for diagnosis because it determines the appropriate set-points for the control elements based on the knowledge acquired. These two examples are very helpful, basically from the structural point of view, in order to create an ES able to manage an anaerobic process. Some works in the field of advanced control techniques applied to anaerobic processes can also be found in the literature. Most of these works concern the operation and/or control of continuous stirred tank reactors (CSTR) at lab and industrial scale. Chynoweth et al. [14] developed an on-line control system with feedback strategies for the optimisation of a lab scale CSTR treating synthetic wastewater. The algorithm was implemented in an ES, which used methane production and hydraulic residence time (HRT) for the automatic detection of possible operational faults (such as overloads, inhibition or operation below the capacity of the system) and to allow the recovery of normal operation. Pullammanappallil et al. [15] developed and implemented an on-line decision support system for a brewery AWT plant at industrial scale. In this system a predictive feed-forward control algorithm, implemented in the plant through a programmable logic controller (PLC), used the flow rate and the on-line inorganic carbon concentration of the influent for the
PC PLC RF SMA Tc Tr TA TSS UASB UAF VFA VSS
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computer programmable logic controller recycling flow rate (l/h) specific methanogenic activity (kg COD/ kg VSS d) control temperature (1C) reactor temperature (1C) total alkalinity (g CaCO3/l) total suspended solids (g/l) upflow anaerobic sludge blanket upflow anaerobic filter volatile fatty acids (g/l) volatile suspended solids (g/l)
identification of the optimal operational set-points. Pullammanappallil et al. [16] also used ES-type strategies to control an anaerobic lab scale process modifying the HRT dilution rate according to four different modes: a conventional control law for the set-point values; a control law based on maintaining constant process performances; batch operation; and operation with constant dilution coefficients. The three papers mentioned [14–16] implemented the classic tools used for control (predictive laws, feed-back and feed-forward algorithms) as ES strategies. The successive approaches were improved enlarging the possible situations, from lab scale treating synthetic wastewater up to industrial scale. The wider range of situations required the use of a higher number of techniques, as reflected in the latest work [16], although this approach was only applied at lab-scale. Moletta et al. [17] used a rule-based system, which considers three parameters (pH, hydrogen concentration in the gas phase and biogas flow rate) for the control of an anaerobic fluidised bed reactor treating vinasses, at both lab- and pilot scale. The system allowed organic loading rates (OLR) of 30 kg COD/m3 d to be achieved. In this work, an ES for the monitoring and diagnosis of the operation of an AWT plant treating highly polluted wastewater from a fibreboard factory is presented. The system was applied to a hybrid UASB– UAF pilot scale reactor (1.1 m3), working in a factory.
2. Materials and methods 2.1. Architecture of the system To facilitate the further implementation of the monitoring system in the wastewater treatment plant, several standard tools were used. The software selected was Visual Basic 5.0 (for communication and graphic interface), Access 7.0 (for database management) and
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The different modules relating to the monitoring and diagnosis system (Fig. 1) are integrated in the same user interface. The time needed to acquire, filter and diagnose the data is short enough (seconds to minutes) compared with process dynamics (hours to days). This permits the modules comprised in the ES to be considered concurrent, as well as to ensure the real time capabilities of the system.
2.2. Data acquisition and filtering modules The data processing module ( in Fig. 1) acquires the raw data from the PLC. A first filter of aberrant values (out of the possible measurement range for each sensor) is used to detect and reject obvious wrong information. When this occurs, new data are collected
Fig. 1. Architecture of the software developed.
Matlab 5.0 (for scientific calculation), for their powerful and flexible application. In Fig. 1, the architecture of the system is shown. Due to its robustness in industrial environments, a programmable logic controller (PLC) (Siemens 95-U) was selected for data acquisition and for the actuation on the final control elements. The PLC sends the information to the computer through a RS-232 series port, which makes the development of a standard interactive information exchange possible. Two different types of tasks can be distinguished with regard to the operation of the plant. The tasks not depending on or not directly affecting the performance of the digester (physico-chemical treatment, stirrer on/ off, purges, tank levels, and so on) are controlled by the PLC in a closed loop. The tasks concerning the AWT plant operation (feed, recycling and nutrients and alkaline solution pumps, temperature) are introduced by the operator, the set-points of the control elements as well as other minor consigns, as the sampling time, being fixed in the utilities and system configuration module ( in Fig. 1) in the PC. The possibility of tele-supervision of the plant in real time from a control centre, located far away from the plant, was also considered. For this purpose, all warnings and alarms about equipment failures are collected in the PC, as well as the state of pumps, engines and sensors, allowing all the available information on the plant to be sent to the control centre. In the event of PC or PLC-PC communication failure, an automatic control program located in the PLC and based on the last set-points sent by the PC, manages the operation of the plant.
Table 1 Labels of the variables used for diagnosis Gas flow (GF) (l/h)
High Critical Normal Low
GF>200 200XGFX150 150>GF>60 GFp60
Methane (CH4) (%)
Normal Low Very low Extremely low
CH4 >45 45XCH4 X35 35>CH4 >32 CH4p32
Carbon monoxide (CO) (ppm)
High Low Zero
CO>5 5XCO>0 CO=0
Feed flow FF (l/h)
High Medium Normal Zero
FF>30 30XFFX10 10>FF>0 FF=0
Recycling flow RF (l/h)
Normal Critical Low Zero
RF>150 150XRFX135 135>RF>0 RF=0.5
pH
High Normal Low Very low
pH>7.5 7.5XpHX6.5 6.5>pH>5 pHp5
Control temperature Tc (1C)
High Normal Low
Tc > 44 44XTc X41 Tc o41
Reactor temperature Tr (1C)
High Normal Low Very low
Tr > 38 38XTr X35 35>Tr >30 Tr p30
(TcTr) (1C)
High Normal Low
ðTc Tr Þ > 6 6XðTc Tr ÞX5 ðTc Tr Þp5
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The signal filtering is carried out using a module developed in Matlab . This module filters the variables to eliminate noise, processing the last 21-data collected
again and are stored in the files corresponding to the raw acquired data in the data base (DB) module (Fig. 1). The sampling time is 15 min.
Diagnosis System Flow and pH On-line data
Information Temperature control system
Set of alarms
iii)
Actions
i) ii)
State of the process
Advices
Recommendations
Future trend of the process
(a)
(b)
Then
If Process state CH4
FF
Extremely Low
Zero
Organic Overload (OO)
Extremely Low
Low
Organic Overload
Extremely Low Very Low
Recommendations
GF
Normal
Organic Overload
Zero
Low
Low
Sensor fault High
Test COD
Organic Overload
Very Low
Low
Low
Organic Overload
Very Low
Normal
Low
Organic Overload
Extremely Low
High
Very Low
High
FF: zero RF: increase BA: increase
Hydraulic Overload (HO) Low
Hydraulic Overload
Very Low
Zero
High/Critical/Normal
Medium Acidification by OO
Very Low
Low
High/Critical/Normal
Medium Acidification by OO
Very Low
Normal
High/Critical Normal
Low
Zero
Medium Acidification by OO
Low
Low
Low
Medium Acidification by OO
Low
Normal
Low
Medium Acidification by OO
Very Low
High
Low
High
Low
Zero
High/Critical/Normal
Low Acidification by OO
Low
Low
High/Critical/Normal
Low Acidification by OO
Low
Normal
High/Critical/Normal
Low Acidification by OO
Low
High
High/Critical/Normal
Low Acidification by HO
Normal/High
High
High/Critical/Normal
Low Acidification by HO
Normal/High
Zero
Low
Normal
Normal/High
Low
Low
Normal
High/Critical/Normal Low
Normal/High
Normal
Normal/High
Zero
High/Critical/Normal
Normal/High
Low
High/Critical/Normal
Normal/High
High
Low
Medium Acidification by OO
Low
Check sensors FF: zero RF: increase BA: increase
Sensor fault High
Test COD
Check sensors FF : decrease RF: increase BA: increase
FF : decrease RF: increase BA: increase
Medium Acidification by HO Low
Medium Acidification by HO
Sensor fault High
Test COD
Check sensors FF : decrease RF: increase BA: increase
FF : decrease RF: increase BA: increase FF : maintain RF: maintain BA: maintain
Normal
Sensor fault
Check sensors
Low
Variable label
Process state
Information
Recommended action
Fig. 2. Supervision and diagnosis system: (a) (i) module for diagnosis of faults in engines, pumps and sensors, (ii) module for identification of the present state and trends of the process, (iii) module delivering the actions recommended by the ES to operator; (b) knowledge base for the diagnosis of the biological state of the process. In this case three variables are taken into account: CH4 percentage in biogas, feeding flow rate (FF) and gas flow rate (GF). (BA: bicarbonate and nutrients flow rate; RF: recycling flow rate).
A. Punal * et al. / Water Research 36 (2002) 2656–2666
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for each variable, which are stored in DB. Several mathematical and logic techniques [18–20] were used to filter data such as: (i) removal of important variations, due to signal displacement; (ii) a first order limited in magnitude filter, in order to preserve the changes larger than an established value or; (iii) a moving window with a variable size Winsorizing filter, corresponding to a moving media filter. These techniques were selected depending on the nature of the noise associated to each signal; in most cases a combination of two of them was applied [21].
2.3. Diagnosis module The diagnosis module was built up following the structure of an ES, comprising a rule base with structure IF ‘‘conditions’’ THEN ‘‘conclusions (state+action)’’. The rule base constituting the knowledge base of the ES is written in Matlab, using the features of this software as an inference engine. In order to be used by the ES, every signal is converted into qualitative values with different labels (e.g. zero, low, high, critical, etc.) according to the operational meaning of values. The conversion is made using threshold values, each label representing a given interval for a given variable, as defined in Table 1.
The ES should predict the failures that might destabilise the operation of the AWT plant. The knowledge base structure of the ES is shown in Fig. 2a. In order to organise the information better, three different areas were considered: (i) concerning the state of the pumps (on, off, high set-points, no communication with the PLC, etc.), engines and sensors; (ii) concerning the biological state and future trend of the process; and (iii) concerning advice and comments to be given to the operator about how to manage the process in each possible situation. The first area (i), is especially important for future purposes of tele-supervision. The second (ii) and third (iii) areas of the ES (Fig. 2a) constitute the knowledge base, as they include the expertise about the process. Although both are directly related, they were built up separately. The state and trend of the process are presented through the user interface in the terms defined in Table 2 (paragraphs (c) and (d)), while advises and recommendations appear only as indications to the operator. Two separate modules (Fig. 2a) determine the state and future trend of the process. The state of the process is identified after analysing the on-line data, while the diagnosis of the future trend also considers the whole current state of the process. Since several variables are available, different groups of three of them were
Table 2 States described for the diagnosis of: (i) state of pumps, engines and sensors and (ii) present state and future trend of the process (i)
(ii)
(a) Flow and pH
Normal Feeding pump stop Feeding flow rate increase Recycling pump stop pH sensor problems concerning the recycling system Feeding and recycling pumps stop or communication failure
(b) Temperature control system
Normal Temperature decrease due to an incorrect set-point value Temperature decrease due to a system incapacity Tc sensor failure Recycling system failure, which affects temperature External agents or Tr sensor failure
(c) State of operation
Normal Low acidification due to a hydraulic overload Low acidification due to an organic overload Medium acidification due to a hydraulic overload Medium acidification due to an organic overload Hydraulic overload Organic overload
(d) Process trend
Normal The process is getting worse The process is recovering the normal state The process trend is destabilisation
A. Punal * et al. / Water Research 36 (2002) 2656–2666
considered, in a subsequent subset of rules, for making the ES feasible even in the case that some sensors were out of order or for application in industrial plants with a more limited number of sensors. The subsets of variables established were: CH4 percentage in biogas, feed flow rate and gas flow rate; CH4 percentage, feed flow rate and CO contents in biogas; CH4 percentage in biogas, feed flow rate and pH and gas flow rate, feed flow rate and pH. In Fig. 2b the knowledge base for the diagnosis of the biological state of the process based on CH4 percentage in biogas, gas flow rate and feed flow rate is shown, as an example. The diagnosis and operator interaction results module ( in Fig. 1), as well as the graphic user interface ( in Fig. 1) show the output of the ES, indicating the current situation of the process and advising on the actions to be taken by the plant operator (Fig. 2). The results are collected in a DB ( in Fig. 1).
2.4. Utilities and system configuration and graphic interface modules The utilities and system configuration module ( in Fig. 1) allows the graphic interface to be configured (the variables to be represented, the number of figures on each screen, the number, frequency and type of
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parameters to be acquired, etc.). This information is stored in the corresponding file in a DB . 2.5. Anaerobic wastewater treatment plant The wastewater is produced in a fibreboard factory, and their main characteristics are shown in Table 3. The raw wastewater was diluted at several ratios in order to
Table 3 Fibreboard processing wastewater characteristics Total COD (g/l)
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Soluble COD (g/l) TSS (g/l) VSS (g/l) Acetic acid (g/l) Propionic acid (g/l) Tannins (g/l as COD) Phenols (g/l) p-Cresol (g/l) N-NH+ 4 (ppm) P-PO3 4 (ppm) Alkalinity (g/l) Temperature (1C) pH
2174 0.970.66 0.970.61 1.270.4 0.0870.01 0.970.1 0.0570.01 0.2570.02 10.270.4 29.870.9 0.7670.02 5072 3.170.1
Fig. 3. Layout of the pilot plant: (1) feeding tank; (2) feeding pump N#1; (3) settling tank; (4) flocculating solution dosage pump; (5) flocculating solution tank; (6) liquid (feeding flow) flowmeter; (7) feeding pump N#2; (8) pH meter; (9) heat exchanger; 10a, 10bFtemperature meter (Pt-100); (11) nutrient and alkalinity solution tank; (12) nutrient and alkalinity solution pump; (13) hybrid UASB–UAF reactor; (14) liquid (recycling flow) flowmeter; (15) recycling pump; (16) effluent tank; (17) gas analyser (CO and CH4); (18) gas flowmeter; (19) stirrer; (20) level sensor.
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(this sampling period was considered long enough as it accounts for 0.21–1.04% of the operational HRT). Three variable speed pumps for wastewater feeding, recycling and nutrients and alkalinity supply were used as final control elements. Their set-points were selected by the operator, following the recommendations from the ES, and implemented through the PC.
50
200
40
175
30
150
20
125
10
3.1. Hydraulic overload The first experiment presented in this study represents a typical situation that occurs when a higher flow of 200 175
100 --2
0
2
4
6
150 125 100 -2
(b)
0
2
4
6
45
7.5
60
30
40
7
50
25
35
6.5
40
20
30
6
30
15
25
5.5
20
5
20
10
15
4.5
10
5
10
0
4
-2
(c)
% CH4
Tr, Tc (ºC)
(a)
To evaluate the robustness and reliability of the monitoring and diagnosis system, several experiments were carried out. In this paper, the response of the process to alterations from normal operating conditions, concerning hydraulic residence time and organic load, are presented and analysed, since they represent the most usual causes of instability in real plants. A wide set of operational conditions were applied, working at different COD wastewater concentrations (from 5 to 25 g/l), different organic loading rates (OLR) (from 1.67 to 15 kg COD/m3 d) and different HRT (from 1 to 3 d).
0
2 t (d)
4
0
-2
6
(d)
CO (ppm)
225
3. Results and discussion
RF (l/h)
60
pH
FF (l/h)
obtain widely varying conditions. The results presented in this work correspond to 5, 15 and 25 g COD/l. The low content in nutrients and the low alkalinity of the wastewater make the addition of nitrogen and phosphorous salts and bicarbonate necessary, in order to maintain a proper C/N/P ratio as well as buffering capacity, respectively, in the digester. A flowchart of the pilot plant is presented in Fig. 3. Wastewater from the factory is collected in a feeding tank for homogenisation. It is important to note that most suspended solids are volatile suspended solids and colloids that have to be removed before entering the digester [1,21]. To do that, wastewater is pumped to a settler where 70% of solids are removed by addition of a flocculant. After this pre-treatment, an important fraction of the hardly biodegradable compounds (40% of the lignin and 80% of the phenols) is separated [1]. Wastewater was then treated in an anaerobic hybrid UASB–UAF of 1.1 m3 (13 in Fig. 3) equipped with a biogas flow-meter (Mass flow-meters Brooks Models E5860i Rosemount, The Netherlands); feed and recycling flow-meters (Electromagnetic flow-meters 7Me2531 Siemens, Germany); two thermometers Pt-100; a biogas (CH4 and CO) analyser (Ultramat22P Gas Analyser Siemens, Germany) and a standard pHmeter (Siemens, Germany). Data from these sensors were measured continuously and recorded every 15 min
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Fig. 4. On-line data (filtered) from the experiment with hydraulic overload. (a) FF, feeding flow rate (– – –) and RF, recycling flow rate (–); (b) GF, gas flow rate (F); (c) Tc, control temperature (–); Tr, reactor temperature (F) and pH (– – –) and; (d) CH4 (F) and CO (– – –) composition in biogas.
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wastewater has to be treated in the reactor. The plant had previously been working in steady state at an OLR of 10 kg COD/m3 d and at a HRT of 1.5 days during one month. On day 0 (see Fig. 4), the feeding flow rate (FF) was seriously modified from 30 l/h to 40 l/h (HRT from 1.5 to 1 d approx.) (Fig. 4a), thus increasing OLR by 50%. The COD influent concentration (15 g/l) was maintained constant during the whole experiment. The on-line filtered data are shown in Fig. 4. Once FF increased (Fig. 4a), a very rapid effect on gas flow rate (Fig. 4b) and methane content (Fig. 4d) was detected, as well as a dramatic variation in pH (Fig. 4c), indicating the magnitude of the overload. Such an important decrease in pH makes it necessary to modify a particular set point of the process (e.g. decreasing FF and/or increasing the recycling flow rate (RF) and/or adding an extra-amount of buffering solution), in order to recover stability. The ES continuously identifies the current state of the plant in the terms presented in Table 2 and Fig. 2. In Fig. 5 the diagnosis results from the ES are presented. Fig. 5a and b correspond to paragraphs (a) and (b) in Table 2, and reflect the state of pumps, engines and sensors after monitoring and fault isolation. Figs. 5c and d concern to the biological state and trend of the process directly, which are described in paragraphs (c) and (d) in Table 2. As observed in Fig. 5a, the diagnosis module informs the operator of the sudden increase of FF (paragraph (a) in Table 2). Since RF was maintained at the previous value, and considering that the reactor was heated by a heat exchanger in the recycling line, a decrease in the temperature of the reactor took place. The diagnosis module identified this phenomenon very well (Fig. 5b), indicating the incapacity of the system to maintain temperature (paragraph (b) in Table 2). In obtaining this conclusion, the ES has also taken into account that the difference between recycling temperature (Tc ) and temperature in the reactor (Tr ) remained approximately constant (Fig. 4c). On day 0.5, the state of the system was characterised by the ES as ‘‘low acidification caused by hydraulic overload’’ (Table 2 and Fig. 5c), after the overall consideration of the values of the main variables, as classified in Table 1: gas flow rate (critical); methane content (low); carbon monoxide (zero); feed flow rate (high); recycling flow rate (normal); pH (low); control temperature (low); reactor temperature (low) and the difference of temperatures (normal). From day 0.5 to day 2 the methane percentage decreased from 35% to 30%, CO then being detectable (2 ppm) in the biogas (Fig. 5d). In this period, pH (Fig. 4c) continued decreasing to values of 4.5, in spite of the addition of bicarbonate (data not shown) in an attempt to maintain the buffering capacity within the reactor. The biogas flow rate (Fig. 4b) increased slightly. The present status of the system was, then, identified by the ES as ‘‘medium acidification caused by hydraulic
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overload’’ (paragraph (c) in Table 2 and Fig. 5c). During the last part of the experiment, from day 2 to day 3, pH reached its lowest value of 4, gas flow rate (Fig. 4b) did not vary significantly, while methane percentage continued decreasing to 23%, and CO in biogas increased to 10 ppm (Fig. 4d). This situation was diagnosed by the ES as a ‘‘hydraulic overload’’, (paragraph (c) in Table 2 and Fig. 5c). The evolution of the variables acquired, together with the results obtained from the diagnosis make it possible to evaluate the trend of the process and to predict the future behaviour of the reactor when maintaining identical operational conditions. From day 0.5 to day 2.2, the trend was diagnosed as ‘‘getting worse’’ (paragraph (d) in Table 2 and Fig. 5c). The system then
A. Punal * et al. / Water Research 36 (2002) 2656–2666
During the overload, variables corresponding to biogas flow and composition were the most sensitive ones. The gas flow rate increased extremely fast (Fig. 6b), reaching five times the previous value within 3 days. In Fig. 6d, the gradual decrease in methane percentage from 48% to 35% from day 0 to day 3 can be seen, while only 2 ppm of CO were detected in the biogas. The pH (Fig. 6c) was maintained at 7, except during the first day, by means of the addition of bicarbonate solution, which increased the buffering capacity of the reactor. No problems related to temperature control occurred (Fig. 6c and Fig. 7b). The diagnosis system described the state as ‘‘low acidification due to organic overload’’ (paragraph (c) in Table 2 and Fig. 7c). From day 3 to day 5, the methane content in the biogas remained constant, as did the gas flow rate (GF), with a gradual increase in CO concentration up to 14 ppm, leading to the diagnosis of ‘‘medium acidification due to organic overload’’ (paragraph (c) in Table 2 and Fig. 7c). ‘‘High acidification’’ (paragraph (c) in Table 2) was not diagnosed since pH and the methane and carbon monoxide contents of the biogas did not vary during the last two days of the perturbation. The analysis of the evolution of the variables indicates that the trend foreseen for the process is ‘‘getting worse’’ from day 0.5 to day 5.
evolved towards a critical state of ‘‘destabilisation’’ (paragraph (d) in Table 2 and Fig. 5d). Finally, once the operational conditions had been changed (feeding flow rate (Fig. 4a) decreased from 40 to 20 l/h), the process trend was classified as ‘‘recovering’’ (paragraph (d) in Table 2 and Fig. 5c). Finally, on day 3.7 the ES delivered a diagnosis of ‘‘normal’’ operation, according to on-line data, together with the current state of the process. 3.2. Organic overload
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In practice, the sudden modification of wastewater characteristics in a factory, even in the case of a homogenisation tank being installed, is a quite frequent event. The most dangerous situations are produced when a toxic compound enters in the reactor or when a serious increase in the organic load of wastewater, due to spills or problems in the factory, occurs. In this experiment, HRT was kept at its previous value of 3 d but the organic load of influent was suddenly increased from 5 to 25 kg COD/m3 on day 0 (data not shown); this overload was maintained for 5 d. This modification dramatically increased the OLR from 1.67 to 8.33 kg COD/m3 d. The data obtained from this experiment are shown in Fig. 6 while the results obtained from the ES are given in Fig. 7.
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mained unconverted within the reactor. The ‘‘normal’’ value of 45% was recovered after day 8, when the system was operated at the initial influent COD concentration. Likewise, carbon monoxide concentration in biogas (Fig. 6d) decreased progressively, this being indicative of a re-equilibrium between the activity of the different microbial trophic groups, responsible for the total transformation of organic matter into methane and carbon dioxide. Once the recovery actions were performed, the ESs modified their previsions from ‘‘getting worse’’ to ‘‘recovering’’ (Fig. 7d) and, finally, on day 7, the process was identified as working in a ‘‘normal’’ state.
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Fig. 7. Ouputs from the expert system. Diagnosis during the experiment with organic overload of: (a) flow rate; (b) temperature control system; (c) acidification state and (d) trend foreseen. The states identified appear in bold.
On day 5, the recommendations from the ES (Fig. 2) where considered by the operator who decided, after checking the value of COD: (a) to increase the nutrients and bicarbonate flow rate, in order to maintain a proper buffering capacity within the reactor; (b) to maintain or even increase recycling flow rate to improve the mixing conditions, in order to favour the dilution of potential inhibitors (e.g. propionate, butyrate and acetate) and (c) to decrease (or even stop) the feeding flow rate to recover the stable operation of the process (Figs. 6a and 7a). Following these indications the feeding was stopped for two days (days 5–7), as can be seen in Figs. 6a and 7a. During the FF stop, performed from day 5 to 7, methane percentage increased from 35% to 60%, this was caused from the methanisation of the organic matter accumulated during the overload, which re-
In this work, an expert system (ES) was applied to the monitoring and diagnosis of the operation of AWT plants. The flexibility, reliability and safety of the application make it suitable for industrial environments. The software, developed and implemented in a PC, manages data acquisition, filters the signals to avoid noise and artefacts in the measurements, and stores data for further retrieval in a very simple way. The diagnosis module verifies the state of the mechanical elements and sensors, determines the state of the process by the analysis of on-line data and predicts the trend of the operation, based on the evolution of the different variables (gas flow and composition, pH, T, etc.), together with the issues diagnosed (flow rate, control temperature system and current state). The results obtained show the capacity of the ES for determining the current state of the process, classifying it in one of the four labels previously defined (normal, getting worse, recovering or destabilisation), which correspond to the commonest situations appearing in the operation of AWT plants. The ES successfully identified two frequent anomalous situations, hydraulic and organic overloads. In both cases, the ES delivered valuable recommendations and, after following them, the AWT recovered its normal state in a short period of time. The rapid identification of the evolution of the variables enables the operator to take preventive or recovering measures in order to avoid the process being led towards a serious ‘‘destabilisation’’ state, very difficult to be recovered.
Acknowledgements This work was financed by the European Commission through the Project FAIR-CT 96-1198 (AMOCO) and the Spanish Commission of Science and Technology (CICYT) through the project No. 1FD97-2184.
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