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Modeling and control of a chemical waste water treatment plant
Computers chem. Engng, Vol. 21, Suppl., pp. $947-$952, 1997 © 1997 Elsevier Science Ltd All rights reserved Printed in Great Britain
Pergamon PII:S0098-1354(97)00171-3
0098-1354/97 $17.00+0.00
Modeling and Control of a Chemical Waste Water Treatment Plant R.M. Miller*, K. Itoyama*, A. Uda t, H. Takada* and N. Bhat * I Mitsubishi Chemical Corporation, Development and Engineering Research Center, Mizushima Plant, 3-10 Usiodori, Kurashiki 712, Japan. Email: [email protected] * Pavilion Technologies, Inc., 11100 Metric Blvd., Suite 700, Austin, TX, USA.
Abstract A simplified hybrid neural net approach was applied for the modeling and subsequent analysis of a chemical waste water treatment plant in Mizushima, Japan. The objective of this study is to reduce the occurrences of overflow in the clarifier caused by lilamentous bulking and thereby increase waste water treatment capacity. Sensitivity analysis showed that temperature, activated sludge age and ammonia concentration were the most sensitive variables to the clarifier interface level. Sludge age control and a nutrient control strategy that utilises an ammonia software sensor are proposed. In addition, on-line diagnostics and optimisation are discussed. or a base depending on the input waste water composition. This neutralised stream is then fed into an aerated tank where it becomes the substrate for an activated sludge process. In this process dissolved organic and inorganic matter are converted into a floc forming biomass consisting of thousands of different micro-organism species along with their waste products. The resulting slurry flows into a settling tank or clarifier which separates clear treated water from the flocculate provided that the settling time is less than the residence time of the clarifier. A final filtering operation is performed before the treated water effluent is pumped into the sea. Concentrated sludge from the bottom of the settling tank is split into two streams: one is recycled to the activated sludge tank and one is incinerated. The recycle is necessary to sustain the micro-organism population in the first and subsequent chambers of the activated sludge tank. Under favourable conditions, a healthy population consisting of thousands of different microorganisms is supported that produce a dense flocculate which settles quickly. However, under unfavourable conditions, either an unhealthy population exists or
INTRODUCTION The waste water treatment facility at Mitsubishi Chemical Corporation's Mizushima plant in Japan was constructed along with the original chemical plant in 1964. This facility served the waste water treatment requirements of the chemical processes in Mizushima plant for over three decades without significant modification to the process or operating practices. However, in recent years, construction of new processes, chemical production increases and tightening environmental regulations have pushed the waste water treatment facility to and sometimes past its capacity. As a result, the existing facility and operating methods were seen as a limitation to future chemical process expansion. Physical expansion of the current waste water treatment facilities or construction of a new facility would require extensive land and capital resources. Such land resources are not available in the immediate vicinity of the existing treatment plant. In addition, any major retrofit of the existing equipment that necessitates a shutdown is highly undesirable. Production from all of the upstream processes must be stopped not to mention the fact that approvals, planning, construction and startup would take two to three years. Therefore, for these reasons, automation and control rather than expansion of the waste water facilities were sought as a fast and cost effective method of expanding the current waste treatment capacity.
- ~ Neutralization
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PROCESS AND PROBLEM DESCRIPTION The existing waste water treatment facility in Mizushima plant is a conventional activated sludge process as depicted in Figure I. Waste water from over 25 process units containing a wide range of organic and inorganic components are first pH neutralised with either an acid
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I Bio-Treatment:
~ _ .
I
Waste
Figure 1: Simplified flowsheet. $947
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Sandr
$948
PSE '97-ESCAPE-7 Joint Conference
several species of long branching or filamentous microorganisms flourish. "Conditions" in this context refer to the environment (i.e. temperature, pH, 02 conc. Etc.) and the inlet waste water composition. In the case of an unhealthy bio-population, organic consumption goes down resulting in an incomplete decomposition of the waste water and a high concentration of dissolved organic or toxic compounds in the treated effluent. In the presence of an overabundance of filamentous organisms, a loosely packed floc is formed that does not settle into a dense sludge and thus carries over into the treated effluent. This later situation is commonly referred to as bulking which is described in detail by Wanner (1994). Microscopic examination of samples of activated sludge taken during upset conditions confirm the presence of filamentous organisms in excess and that bulking is the primary upset condition. A three year historical trend of the suspended solids (SS) concentration measured at point A of Figure 1 and the clarifier level is shown in Figure 2 and Figure 3, respectively. Although the average SS concentration at point A is well within our target of 50 pprn, frequent SS spikes can be seen which is the result of overflow in the clarifier caused by bulking. During an overflow condition, the sand filters depicted in Figure 1 are significantlytaxed which rapidly shortens the life of the filter media. Consequently, two of the main health indicators of the waste water treatment plant are the SS concentration at point A and the clarifier interface level. Increasing waste water treatment demands, as shown in Figure 4, are primarily as a result of production increases through debottlenecking and plant expansion. This trend is expected to continue as several more construction projects are in progress. Existing operating practices were predominantly manual or heuristic performed by several highly skilled operators and process engineers. Relative to other processes, the waste water treatment plant was manpower intensive particularly in senior staff. The only degrees of freedom were considered to be the recycle flow rate, dissolved oxygen concentration in the third chamber of the activated sludge tank and the manual addition of a solid nutrient. Inlet waste water pH was controlled manually while the inlet waste water was only cooled in an uncontrolled fashion in the summer months. In response to an occurrence of high SS concentration, an investigation into the upstream plants would take place. In a few cases, the source of the problem that led to high SS concentration was identified, however, in most cases the SS concentration returned to its normal level without finding the cause of the problem. One major constraint of this work is the available historical information. Only basic continuous measurements in the waste water plant such as flow rates, pH, temperature, aerated tank dissolved oxygen concentration and settling tank interface level are available. The database contains one hour averaged data for a period of one year and daily averaged values for a period of three years. Off-line measurement of chemical oxygen demand (COD), total organic carbon (TOC), biological oxygen demand (BOD) and concentration of several compounds are conducted for some streams but at rather infrequent intervals (one to four weeks).
Thousands of variables from some 25 upstream plants are also available. In total there are over two hundred variables believed to be relevant to the waste water plant. OBJECTIVES OF STUDY Reduce the occurrences of SS overflow. Increase waste water treatment capacity without physical expansion. Reduce the need for expert and non expert manpower. Complete this study as quick as possible.
1. 2. 3. 4.
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Figure 2: Three year trend of SS at point A of Fig. 1. 4 E3
=,2 1 I
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Figure 3: Three year trend of the interface level.
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Figure 4: Three year trend of inlet WW flow rate.
MODELING The primary motive behind modeling the activated sludge plant is to identify the major causes of upset conditions in terms of the current measured variables. Of particular interest, is the relationship between the waste water plant condition and the degrees of freedom in the waste water plant and upstream plants. An operating strategy is then proposed based on this knowledge. In the previous section, the SS concentration and the clarifier interface level were identified as the two key "health" indicators. At first, a brief attempt was made to model the interface level and SS by linear multivariate time series models using the available continuous data and a spline interpolation of the laboratory data. However, almost no correlation could be found. Subsequently, a similar direct data approach with Process Insights" (1995), a commercial neural net modeling software, produced an improved result but it could not be validated. As will be further discussed, dominant time constants in the activated sludge process range from a few hours to several weeks. Given that our database contains a maximum of three years of process data and
$949
PSE '97-ESCAPE-7 Joint Conference that the maximum time constant is several weeks, the effective number of samples is less than the number of input variables. Such a small sample size combined with the fact that the activated sludge process is known to be highly non-linear (Orhon and Artan, 1994) clearly shows that this problem is grossly underspecified. It is therefore no surprise that these direct data approaches were not successful. On the other hand, physical or semiempirical models such as the Activated Sludge Model No. 1 (Henze et al., 1986) utilise much of the current knowledge on the activated sludge process. However, these models require numerous real time measurements and estimation of parameters which restrict their use to well instrumented waste water plants or pilot scale processes. The Mizushima waste water facility is not equipped to support modeling of such rigor. The approach taken in this work was to combine a neural net model, principal component analysis (PCA) and simple physical or semi empirical relationships supported by the available data within the Process Insights (1995) environment. By utilising physical relationships, structure is forced into the neural net and the number of input variables used in the net is reduced. A successful application utilising the combination of a neural network and the Activated Sludge Model No. 1 (Henze et al., 1986) is reported by Zhao et al. (1996). Through application of PCA to selective blocks of input data, the dimensionality and the redundancy in the input variable space are significantly reduced leading to a more meaningful and parsimonious model structure. In Process Insights (1995) PCA and simple physical relationships were implemented through the transform calculator. Flow rates of most inlet waste water streams and the total inlet flow are measured continuously. In addition, component concentrations in the waste water streams such as ammonia, copper and cyanide are measured off-line but infrequently. In the cases where the infrequently sampled concentration data showed strong auto-correlation, the component flow rate (by simply multiplying the inlet flow with the interpolated component concentration) instead of the total flow rate and the component concentration was used as an input to the neural net model. High frequency variations in flow rate data were attenuated by filtering. Infrequent sampled off-line data that showed a weak auto-correlation was not used at all. Unfortunately, much of our lab data showed weak auto-correlation and thus linear interpolation could not be used. Estimation of the intersample behaviour based on a PCA missing data algorithm was attempted but lead to poor results. Additionally, many of the lab data were modelled using the available upstream data also with limited success. Combining continuous data and infrequently sampled data proved to be one of the most difficult issues in this project. Oxygen consumption (OC) is important according to Orhon and Artan (1994) and was estimated by material balance of the aerated tank. TM
TM
- [mo2
- mo2
Sludge age O,t,age as reported by Wanner (1994) reflects the mean biomass residence time which plays an important role in substrate removal. Simply stated it is the ratio of the total solids in the system to the mass of solids that leave the system. Available measurements in our database permit the following steady state approximation VOI AT X AT + Mclar~er Osludge = X recycleqincin + ( qinlet - qincin ) X treated
Average biomass concentration in the aerated tank, X a r , is assumed to be the average of inlet and exit mixed liquor suspended solids (MLSS).
qrecye SAT
where XAT
k qinlet + qrecycle X recycle + X AT and
Xrecycle are the MISS measured at the
outlet of the aerated tank and the outlet of the clarifier, respectively. Biomass in the clarifier is approximated as M Clarifier = X recycle (Level - k ) Area
where k is a constant to account for the cone of the clarifier. The above relations show that there are two degrees of freedom in sludge age namely the recycle flow rate and the incinerator flow rate. Control of sludge age is thus possible. Selection of the biomass population is the main motivation for control of sludge age as is simply illustrated in Figure 5. At an age or retention time of 50, the dominant species is C while at 150 or 250 the slow growing A and B species are dominant. According to Wanner (1994), many filamentous species have slow growth rates relative to floc forming species suggesting that selection of floc forming micro-organisms can be achieved through sludge age control. Under this pretence, optimisation of an activated sludge model to determine sludge age is certainly attractive.
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Figure 5: Effect of sludge age on growth rate. Off-line ammonia concentration analysis from upstream process, "Plant A", was shown to have a very strong correlation with interface level in earlier neural net models. On further investigation, over 90% of the ammonia in the total waster water feed originates from this plant. Consequently, a step test in Plant A was conducted from which the ammonia concentration in Plant A' s waste water was modelled as a function of the process variables. Total inlet ammonia flow rate to the waste water plant calculated from the ammonia concentration software sensor prediction multiplied by the Plant A waste water flow rate was used as an input to subsequent models.
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PSE '97-ESCAPE-7 Joint Conference
As mentioned previously, there are thousands of measurements in upstream processes available such as temperature, pressure, etc. Based on discussions with process engineers in each process, a group of 3 to 10 variables was chosen for each upstream process considered to be relevant to its waste water composition. Each group of variables was scaled according to its nonshutdown range from which PCA was conducted. For each group of upstream process variables, the first principle component captured over 50 % of the original variance. A new upstream process index was then created by multiplying the upstream process principle component with its respective waste effluent flow rate. The process index is proportional to the effluent quality during normal operation and shutdown periods. In discrete time modeling, the sample interval should be chosen to be about 10 times faster than the dominant time constant of the process. The sludge age in our activated sludge process ranged from about 20 to 200 days. However, the effect of a rapid temperature change or a rapid change in nutrient level can be seen in only a few hours. Consequently, the dominant time constant ranges from a few hours to half a year which is indeed a stiff problem. Choosing a single sample interval would skew the model to represent the phenomena at that time scale. Therefore, a four hour interval was chosen for the one hour data set and a one day interval was used for the one day average dataset. Training the neural net models with several different test and training patterns was conducted to choose the number of training epochs that gave a good model with minimal overtraining. Hybrid models of SS concentration from four hour and one day data were built. As depicted in Figure 6, the base line trend is represented by the four hour SS model but the spikes in SS are just barely detected. Positive spikes in SS are due to overflow in the clarifier which represents an extremely non-linear but physical behaviour. However, it is this transition from normal to overflow that is most important in terms of understanding the cause of bulking. Our historical records show that frequent positive SS spikes commonly occur for periods of several months followed by periods of no spikes. It seems reasonable to assume that during each region of frequent SS spikes the conditions were favourable for filamentous bulking. Based on this assumption, a hybrid model of the ten day moving standard deviation of SS was built. Modelling results, shown in Figure 7, demonstrate that the clear distinction in standard deviation between regions of overflow and normal operation is captured by the hybrid model. A partial list of the sensitivity analysis of the moving t~ model is shown in table 1. As expected, the clarifier interface level is the most important variabJe. In addition, oil and cyanide act as inhibitors or poisons while an increase in ammonia has a positive effect in reducing the SS a. Emphasis of this investigation thus shifted to identifying clarifler interface level models from four hour and one day sampled data which are shown, respectively, in Figure 8 and Figure 9. As can be seen, both interface level models fit the general trend of the interface data remarkably well. Sensitivity analysis results of the four
hour and one day based interface level models are shown in Table 2 and Table 3, respectively. Of particular interest, temperature anm~nia flow from Plant A, acid flow and sludge age show a strong influence to the interface level in both models. These results are consistent with a recent survey of Japanese waste treatment plants which found that temperature was the most significant factor (Suzuki et al., ,1992). Based on the sign of the average sensitivity, the model suggests that we should increase temperature and ammonia flow and decrease acid flow and sludge age. Also noteworthy, sludge age ranks higher in the one day model compared to the four hour model indicating that the long time constant of sludge age is more distinct with slower sampling. Table I: Sensitivity results of the S ~ model. Variable Average sensitiyity Interface level 0.52 Sludge age 0.48 Inlet oil conc. 0.46 Inlet cyanide conc. 0.44 Aerated tank temp. -0.42 Inlet ammonia flow -0.33 Treated effluent pH -0.33 100 50
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Figure 8: Clarifier level 4 hour data (-) and model (- -).
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Figure 9: Clarifier level 1 day data (-) and model (- -).
PSE '97-ESCAPE-7 Joint Conference Table 2: Sensitivity of the 4 hour interface level model Variable Avera[;e sensitivity WW Temperature -0.27 Ammonia flow (Plant A) -0.24 Plant B index 0.23 Total input flow 0.23 Recycle MLSS -0.21 Plant C index -0.21 Acid flow 0.18 Slud[e ale 0.14 Table 3: Sensitivity of the 1 day interface level model Variable Avera[e sensitivity Ammonia flow (Plant A) -0.42 Sludge age 0.40 WW Temperature -0.38 Acid flow 0.35 Plant D index -0.33 Plant E index 0.32
CONTROL STRATEGY AND RESULTS INVESTIGATION OF UPSTREAM PLANTS Sensitivity analysis of the interface level model showed significant influence from several upstream plants. In particular, ammonia supplied from Plant A has a substantial effect on the interface level. This prompted a study into the relationship between the flow rate of anm~nia and plant A's behaviour. Analysis of the ammonia concentration model showed that the source of ammonia variability was iocalised to the performance of one unit in the plant. Initiatives, now underway, to stabilise the ammonia in the effluent will have a positive impact to both Plant A and the waste water plant. Similar investigations are also being conducted into several of the other upstream plants, identified by the sensitivity analysis of the clarifier model, that contribute significantly to the waste water plant variability. NUTRIENT CONTROL Maintaining sufficient and consistent nutrient levels are essential to sustain a healthy floc forming activated sludge. Industrial waste water is notorious for low nutrient levels according to Wanner (1994). Ammonia and orthophosphate are respectively cited as the best source of nitrogen and phosphorous required for biodecomposition by Wanner (1994). Ammonia concentration in the activated sludge tank is now calculated on-line using the Plant A ammonia software sensor, Plant A's, flow rate and the total waste water flow rate. Using an additional ammonia feed into the inlet waste water plant and the on-line Plant A soflware sensor value, the ammonia concentration is regulated. Optimum concentration of ammonia was determined by off-line optimisation of the hybrid model i.e. to minimise interface level. The effect of this action was to take a disturbance variable and convert it to a controlled variable. Concentration of phosphorous in the inlet waste water was not previously analysed so conclusions regarding its effect and its control cannot yet be made.
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TEMPERATURE CONTROL Temperature was identified to have the strongest influence to the interface level out of all the operating conditions. Previously, waste water temperature was uncontrolled and was therefore subject to seasonal variation. Direct steam injection to the inlet waste water stream controlled by PID was implemented and the temperature of the activated sludge tank is controlled in a cascade configuration. The temperature setpoint was determined by off-line optirnisation. SLUDGE AGE CONTROL Sludge age, identified as an important variable by the sensitivity results of Table 2 and Table 3, can be effectively controlled by the recycle flow rate and the incinerator flow rate. The hybrid model showed a much stronger influence from recycle flow rate compared to incinerator flow. In addition, there is a significant increase in cost involved with increasing the incinerator flow. A sludge age control strategy with recycle as the primary manipulated variable is proposed. Off-line optimisation of the hybrid model suggests that the sludge age should be significantly reduced from its previous value. Results of sludge age control are not available at the time of writing. DISSOLVED OXYGEN CONTROL Prior to this study, dissolved oxygen (DO) in the final chamber of the aerated tank was controlled manually by manipulating the purge valve shown in Figure 10. PID control of pressure in the aerated tank was implemented by manipulating the oxygen feed. Oscillations in dissolved oxygen between 16 and 20 ppm were typical as shown in the left side of the top plot in Figure 11. This operation was one of the labour intensive tasks for the waste water plant operators. Controllability analysis of an empirical time series model showed that the correct control loop pairing was DO to oxygen feed and pressure to purge. In addition, a significant time delay from the oxygen feed to the DO concentration was identified. To deal with this long time delay, a unique formulation of generalised predictive control with a PID structure was implemented in our control computer (Miller et al., 1996). To maintain the pressure within the high and low safety constraints of the aerated tank, a high/low select structure was implemented as shown in Figure 10. Performance of the new control scheme is substantially better than the existing method as can be seen in Figure 1 I. f c
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Pressure safety . . . . . . . . . . . . ccOntrOllers r II /au.ulu.,:uu °°c ~/ Predictive
urge
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Figure 10: Simplified P&ID of DO control.
$952
PSE '97-ESCAPE-7 Joint Conference
20
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14
will enforce cost minimisation when the interface level is already low. A safety mechanism is required to ensure that the optimised value is realistic. Operator influence in this step is expected to be necessary. Although at the time of writing it is too early to measure the net effect of all our initiatives, a positive impact on the interface level can be clearly seen.
1.
Figure 11: Dissolved oxygen control performance.
2.
Sensitivity analysis of the interface model showed that temperature, ammonia from one upstream plant, sludge age and acid flow have the strongest influence. Strategies for nutrient control and sludge age control are proposed.
3.
Results of control initiatives for dissolved oxygen, pH and temperature were discussed.
4.
On-line diagnostics and optimisation of the degrees of freedom based on the hybrid interface model is proposed.
pH CONTROL Although pH of the inlet waste water was not shown to have a significant effect on the interface level, the acid flow rate did have a strong positive influence. According to Wanner (1994), the range of pH suitable for supporting activated sludge is about 6.5 to 8.5. Therefore, small flow rates of the current acid are desirable which means operating at the maximum allowable pH. Control of pH has been successfully applied.
ON-LINE DIAGNOSTICS AND OPTIMISATION To fully utilise the hybrid neural net model, on-line optimisation of the operating conditions and on-line diagnostics are proposed. A plant wide step test based on an experimental design was conducted to strengthen the relationship between degrees of freedom in the plant and the interface level. Even after the step test, it is unlikely that our current dataset contains all of the possible combinations of waste water composition so it will be necessary to retrain the model periodically to widen its prediction range. In the first phase of this implementation we propose to use the hybrid model to predict the clarifier interface level under different scenarios within the range that the model was trained. For example, given the current operating point, what is the effect of changing the recycle flow rate or dissolved oxygen setpoint. Another example is to determine the effect of a simultaneous shutdown of Plant B and Plant D. In addition, we can assess the appropriate countermeasure for a particular disturbance in seconds rather than days or weeks as in the actual plant. Historically, expert operators were vital to the operation of the waste water plant. A smooth transition from manual control to an automated operation will therefore require operator acceptance of our control strategies. Having a validated diagnostic tool available to the operators will allow their own evaluation of our model. Later this can serve as a platform for operator training. The next phase of our plan is to adjust the degrees of freedom on-line by optimisation of the interface level hybrid model subject to constraints on the operating conditions. The objective of optimisation is obviously to minimise the interface level. A penalty function that considers cost of the manipulated variables
CONCLUSIONS A simplified hybrid neural net model of the clarifier interface level, a key health indicator of the activated sludge process, was shown to have excellent agreement with plant data.
REFERENCES Henze, M., C.P.L. Grady Jr., W. Gujer, R. Marais, and T. Matsuo, Activated Sludge Model No. 1, IAWPRC Scientific and Technical Report No. 1, London, IWPRC, 1986. Miller, R.M., A. Uda, S.L. Shah and R.K. Wood, "A long range predictive PID controller with application to an industrial process," Proc. SICE, pp. 11531156, Tottori, Japan, 1996. Orhon, D., and Artan, N., Modelling of Activated Sludge Systems, Tecnomic Pub. Co., PA, 1994. Pavilion Technologies Inc., Process Insights User's Guide Version 3.1, June 1995. Suzuki, IC, Y. Tooya and M. Kakumoto, "Statistical analysis of activated sludge process using fuzzy modelling," Kagaku KoGaku Ronbunshu (in Japanese), Vol. 18, No. 4, pp. 478-486, 1992. Wanner, J., Activated Sludge Bulking and Foaming Control, Tecnomic Pub. Co., PA, 1994. Zhao, H., O.J. Hao, T.J. McAvoy, and C-H. Chang, "Modeling nutrient dynamics in a sequencing batch reactor using a hybrid kinetic and artificial neural network," J. Environmental Engineering, Accepted, 1996.
Pergamon PII:S0098-1354(97)00171-3
0098-1354/97 $17.00+0.00
Modeling and Control of a Chemical Waste Water Treatment Plant R.M. Miller*, K. Itoyama*, A. Uda t, H. Takada* and N. Bhat * I Mitsubishi Chemical Corporation, Development and Engineering Research Center, Mizushima Plant, 3-10 Usiodori, Kurashiki 712, Japan. Email: [email protected] * Pavilion Technologies, Inc., 11100 Metric Blvd., Suite 700, Austin, TX, USA.
Abstract A simplified hybrid neural net approach was applied for the modeling and subsequent analysis of a chemical waste water treatment plant in Mizushima, Japan. The objective of this study is to reduce the occurrences of overflow in the clarifier caused by lilamentous bulking and thereby increase waste water treatment capacity. Sensitivity analysis showed that temperature, activated sludge age and ammonia concentration were the most sensitive variables to the clarifier interface level. Sludge age control and a nutrient control strategy that utilises an ammonia software sensor are proposed. In addition, on-line diagnostics and optimisation are discussed. or a base depending on the input waste water composition. This neutralised stream is then fed into an aerated tank where it becomes the substrate for an activated sludge process. In this process dissolved organic and inorganic matter are converted into a floc forming biomass consisting of thousands of different micro-organism species along with their waste products. The resulting slurry flows into a settling tank or clarifier which separates clear treated water from the flocculate provided that the settling time is less than the residence time of the clarifier. A final filtering operation is performed before the treated water effluent is pumped into the sea. Concentrated sludge from the bottom of the settling tank is split into two streams: one is recycled to the activated sludge tank and one is incinerated. The recycle is necessary to sustain the micro-organism population in the first and subsequent chambers of the activated sludge tank. Under favourable conditions, a healthy population consisting of thousands of different microorganisms is supported that produce a dense flocculate which settles quickly. However, under unfavourable conditions, either an unhealthy population exists or
INTRODUCTION The waste water treatment facility at Mitsubishi Chemical Corporation's Mizushima plant in Japan was constructed along with the original chemical plant in 1964. This facility served the waste water treatment requirements of the chemical processes in Mizushima plant for over three decades without significant modification to the process or operating practices. However, in recent years, construction of new processes, chemical production increases and tightening environmental regulations have pushed the waste water treatment facility to and sometimes past its capacity. As a result, the existing facility and operating methods were seen as a limitation to future chemical process expansion. Physical expansion of the current waste water treatment facilities or construction of a new facility would require extensive land and capital resources. Such land resources are not available in the immediate vicinity of the existing treatment plant. In addition, any major retrofit of the existing equipment that necessitates a shutdown is highly undesirable. Production from all of the upstream processes must be stopped not to mention the fact that approvals, planning, construction and startup would take two to three years. Therefore, for these reasons, automation and control rather than expansion of the waste water facilities were sought as a fast and cost effective method of expanding the current waste treatment capacity.
- ~ Neutralization
Inpull
PROCESS AND PROBLEM DESCRIPTION The existing waste water treatment facility in Mizushima plant is a conventional activated sludge process as depicted in Figure I. Waste water from over 25 process units containing a wide range of organic and inorganic components are first pH neutralised with either an acid
~
I Bio-Treatment:
~ _ .
I
Waste
Figure 1: Simplified flowsheet. $947
Fiel
Sandr
$948
PSE '97-ESCAPE-7 Joint Conference
several species of long branching or filamentous microorganisms flourish. "Conditions" in this context refer to the environment (i.e. temperature, pH, 02 conc. Etc.) and the inlet waste water composition. In the case of an unhealthy bio-population, organic consumption goes down resulting in an incomplete decomposition of the waste water and a high concentration of dissolved organic or toxic compounds in the treated effluent. In the presence of an overabundance of filamentous organisms, a loosely packed floc is formed that does not settle into a dense sludge and thus carries over into the treated effluent. This later situation is commonly referred to as bulking which is described in detail by Wanner (1994). Microscopic examination of samples of activated sludge taken during upset conditions confirm the presence of filamentous organisms in excess and that bulking is the primary upset condition. A three year historical trend of the suspended solids (SS) concentration measured at point A of Figure 1 and the clarifier level is shown in Figure 2 and Figure 3, respectively. Although the average SS concentration at point A is well within our target of 50 pprn, frequent SS spikes can be seen which is the result of overflow in the clarifier caused by bulking. During an overflow condition, the sand filters depicted in Figure 1 are significantlytaxed which rapidly shortens the life of the filter media. Consequently, two of the main health indicators of the waste water treatment plant are the SS concentration at point A and the clarifier interface level. Increasing waste water treatment demands, as shown in Figure 4, are primarily as a result of production increases through debottlenecking and plant expansion. This trend is expected to continue as several more construction projects are in progress. Existing operating practices were predominantly manual or heuristic performed by several highly skilled operators and process engineers. Relative to other processes, the waste water treatment plant was manpower intensive particularly in senior staff. The only degrees of freedom were considered to be the recycle flow rate, dissolved oxygen concentration in the third chamber of the activated sludge tank and the manual addition of a solid nutrient. Inlet waste water pH was controlled manually while the inlet waste water was only cooled in an uncontrolled fashion in the summer months. In response to an occurrence of high SS concentration, an investigation into the upstream plants would take place. In a few cases, the source of the problem that led to high SS concentration was identified, however, in most cases the SS concentration returned to its normal level without finding the cause of the problem. One major constraint of this work is the available historical information. Only basic continuous measurements in the waste water plant such as flow rates, pH, temperature, aerated tank dissolved oxygen concentration and settling tank interface level are available. The database contains one hour averaged data for a period of one year and daily averaged values for a period of three years. Off-line measurement of chemical oxygen demand (COD), total organic carbon (TOC), biological oxygen demand (BOD) and concentration of several compounds are conducted for some streams but at rather infrequent intervals (one to four weeks).
Thousands of variables from some 25 upstream plants are also available. In total there are over two hundred variables believed to be relevant to the waste water plant. OBJECTIVES OF STUDY Reduce the occurrences of SS overflow. Increase waste water treatment capacity without physical expansion. Reduce the need for expert and non expert manpower. Complete this study as quick as possible.
1. 2. 3. 4.
100.
~. 50
o
Figure 2: Three year trend of SS at point A of Fig. 1. 4 E3
=,2 1 I
I
I
Figure 3: Three year trend of the interface level.
200
A,lJ
Figure 4: Three year trend of inlet WW flow rate.
MODELING The primary motive behind modeling the activated sludge plant is to identify the major causes of upset conditions in terms of the current measured variables. Of particular interest, is the relationship between the waste water plant condition and the degrees of freedom in the waste water plant and upstream plants. An operating strategy is then proposed based on this knowledge. In the previous section, the SS concentration and the clarifier interface level were identified as the two key "health" indicators. At first, a brief attempt was made to model the interface level and SS by linear multivariate time series models using the available continuous data and a spline interpolation of the laboratory data. However, almost no correlation could be found. Subsequently, a similar direct data approach with Process Insights" (1995), a commercial neural net modeling software, produced an improved result but it could not be validated. As will be further discussed, dominant time constants in the activated sludge process range from a few hours to several weeks. Given that our database contains a maximum of three years of process data and
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PSE '97-ESCAPE-7 Joint Conference that the maximum time constant is several weeks, the effective number of samples is less than the number of input variables. Such a small sample size combined with the fact that the activated sludge process is known to be highly non-linear (Orhon and Artan, 1994) clearly shows that this problem is grossly underspecified. It is therefore no surprise that these direct data approaches were not successful. On the other hand, physical or semiempirical models such as the Activated Sludge Model No. 1 (Henze et al., 1986) utilise much of the current knowledge on the activated sludge process. However, these models require numerous real time measurements and estimation of parameters which restrict their use to well instrumented waste water plants or pilot scale processes. The Mizushima waste water facility is not equipped to support modeling of such rigor. The approach taken in this work was to combine a neural net model, principal component analysis (PCA) and simple physical or semi empirical relationships supported by the available data within the Process Insights (1995) environment. By utilising physical relationships, structure is forced into the neural net and the number of input variables used in the net is reduced. A successful application utilising the combination of a neural network and the Activated Sludge Model No. 1 (Henze et al., 1986) is reported by Zhao et al. (1996). Through application of PCA to selective blocks of input data, the dimensionality and the redundancy in the input variable space are significantly reduced leading to a more meaningful and parsimonious model structure. In Process Insights (1995) PCA and simple physical relationships were implemented through the transform calculator. Flow rates of most inlet waste water streams and the total inlet flow are measured continuously. In addition, component concentrations in the waste water streams such as ammonia, copper and cyanide are measured off-line but infrequently. In the cases where the infrequently sampled concentration data showed strong auto-correlation, the component flow rate (by simply multiplying the inlet flow with the interpolated component concentration) instead of the total flow rate and the component concentration was used as an input to the neural net model. High frequency variations in flow rate data were attenuated by filtering. Infrequent sampled off-line data that showed a weak auto-correlation was not used at all. Unfortunately, much of our lab data showed weak auto-correlation and thus linear interpolation could not be used. Estimation of the intersample behaviour based on a PCA missing data algorithm was attempted but lead to poor results. Additionally, many of the lab data were modelled using the available upstream data also with limited success. Combining continuous data and infrequently sampled data proved to be one of the most difficult issues in this project. Oxygen consumption (OC) is important according to Orhon and Artan (1994) and was estimated by material balance of the aerated tank. TM
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Sludge age O,t,age as reported by Wanner (1994) reflects the mean biomass residence time which plays an important role in substrate removal. Simply stated it is the ratio of the total solids in the system to the mass of solids that leave the system. Available measurements in our database permit the following steady state approximation VOI AT X AT + Mclar~er Osludge = X recycleqincin + ( qinlet - qincin ) X treated
Average biomass concentration in the aerated tank, X a r , is assumed to be the average of inlet and exit mixed liquor suspended solids (MLSS).
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outlet of the aerated tank and the outlet of the clarifier, respectively. Biomass in the clarifier is approximated as M Clarifier = X recycle (Level - k ) Area
where k is a constant to account for the cone of the clarifier. The above relations show that there are two degrees of freedom in sludge age namely the recycle flow rate and the incinerator flow rate. Control of sludge age is thus possible. Selection of the biomass population is the main motivation for control of sludge age as is simply illustrated in Figure 5. At an age or retention time of 50, the dominant species is C while at 150 or 250 the slow growing A and B species are dominant. According to Wanner (1994), many filamentous species have slow growth rates relative to floc forming species suggesting that selection of floc forming micro-organisms can be achieved through sludge age control. Under this pretence, optimisation of an activated sludge model to determine sludge age is certainly attractive.
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Figure 5: Effect of sludge age on growth rate. Off-line ammonia concentration analysis from upstream process, "Plant A", was shown to have a very strong correlation with interface level in earlier neural net models. On further investigation, over 90% of the ammonia in the total waster water feed originates from this plant. Consequently, a step test in Plant A was conducted from which the ammonia concentration in Plant A' s waste water was modelled as a function of the process variables. Total inlet ammonia flow rate to the waste water plant calculated from the ammonia concentration software sensor prediction multiplied by the Plant A waste water flow rate was used as an input to subsequent models.
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PSE '97-ESCAPE-7 Joint Conference
As mentioned previously, there are thousands of measurements in upstream processes available such as temperature, pressure, etc. Based on discussions with process engineers in each process, a group of 3 to 10 variables was chosen for each upstream process considered to be relevant to its waste water composition. Each group of variables was scaled according to its nonshutdown range from which PCA was conducted. For each group of upstream process variables, the first principle component captured over 50 % of the original variance. A new upstream process index was then created by multiplying the upstream process principle component with its respective waste effluent flow rate. The process index is proportional to the effluent quality during normal operation and shutdown periods. In discrete time modeling, the sample interval should be chosen to be about 10 times faster than the dominant time constant of the process. The sludge age in our activated sludge process ranged from about 20 to 200 days. However, the effect of a rapid temperature change or a rapid change in nutrient level can be seen in only a few hours. Consequently, the dominant time constant ranges from a few hours to half a year which is indeed a stiff problem. Choosing a single sample interval would skew the model to represent the phenomena at that time scale. Therefore, a four hour interval was chosen for the one hour data set and a one day interval was used for the one day average dataset. Training the neural net models with several different test and training patterns was conducted to choose the number of training epochs that gave a good model with minimal overtraining. Hybrid models of SS concentration from four hour and one day data were built. As depicted in Figure 6, the base line trend is represented by the four hour SS model but the spikes in SS are just barely detected. Positive spikes in SS are due to overflow in the clarifier which represents an extremely non-linear but physical behaviour. However, it is this transition from normal to overflow that is most important in terms of understanding the cause of bulking. Our historical records show that frequent positive SS spikes commonly occur for periods of several months followed by periods of no spikes. It seems reasonable to assume that during each region of frequent SS spikes the conditions were favourable for filamentous bulking. Based on this assumption, a hybrid model of the ten day moving standard deviation of SS was built. Modelling results, shown in Figure 7, demonstrate that the clear distinction in standard deviation between regions of overflow and normal operation is captured by the hybrid model. A partial list of the sensitivity analysis of the moving t~ model is shown in table 1. As expected, the clarifier interface level is the most important variabJe. In addition, oil and cyanide act as inhibitors or poisons while an increase in ammonia has a positive effect in reducing the SS a. Emphasis of this investigation thus shifted to identifying clarifler interface level models from four hour and one day sampled data which are shown, respectively, in Figure 8 and Figure 9. As can be seen, both interface level models fit the general trend of the interface data remarkably well. Sensitivity analysis results of the four
hour and one day based interface level models are shown in Table 2 and Table 3, respectively. Of particular interest, temperature anm~nia flow from Plant A, acid flow and sludge age show a strong influence to the interface level in both models. These results are consistent with a recent survey of Japanese waste treatment plants which found that temperature was the most significant factor (Suzuki et al., ,1992). Based on the sign of the average sensitivity, the model suggests that we should increase temperature and ammonia flow and decrease acid flow and sludge age. Also noteworthy, sludge age ranks higher in the one day model compared to the four hour model indicating that the long time constant of sludge age is more distinct with slower sampling. Table I: Sensitivity results of the S ~ model. Variable Average sensitiyity Interface level 0.52 Sludge age 0.48 Inlet oil conc. 0.46 Inlet cyanide conc. 0.44 Aerated tank temp. -0.42 Inlet ammonia flow -0.33 Treated effluent pH -0.33 100 50
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PSE '97-ESCAPE-7 Joint Conference Table 2: Sensitivity of the 4 hour interface level model Variable Avera[;e sensitivity WW Temperature -0.27 Ammonia flow (Plant A) -0.24 Plant B index 0.23 Total input flow 0.23 Recycle MLSS -0.21 Plant C index -0.21 Acid flow 0.18 Slud[e ale 0.14 Table 3: Sensitivity of the 1 day interface level model Variable Avera[e sensitivity Ammonia flow (Plant A) -0.42 Sludge age 0.40 WW Temperature -0.38 Acid flow 0.35 Plant D index -0.33 Plant E index 0.32
CONTROL STRATEGY AND RESULTS INVESTIGATION OF UPSTREAM PLANTS Sensitivity analysis of the interface level model showed significant influence from several upstream plants. In particular, ammonia supplied from Plant A has a substantial effect on the interface level. This prompted a study into the relationship between the flow rate of anm~nia and plant A's behaviour. Analysis of the ammonia concentration model showed that the source of ammonia variability was iocalised to the performance of one unit in the plant. Initiatives, now underway, to stabilise the ammonia in the effluent will have a positive impact to both Plant A and the waste water plant. Similar investigations are also being conducted into several of the other upstream plants, identified by the sensitivity analysis of the clarifier model, that contribute significantly to the waste water plant variability. NUTRIENT CONTROL Maintaining sufficient and consistent nutrient levels are essential to sustain a healthy floc forming activated sludge. Industrial waste water is notorious for low nutrient levels according to Wanner (1994). Ammonia and orthophosphate are respectively cited as the best source of nitrogen and phosphorous required for biodecomposition by Wanner (1994). Ammonia concentration in the activated sludge tank is now calculated on-line using the Plant A ammonia software sensor, Plant A's, flow rate and the total waste water flow rate. Using an additional ammonia feed into the inlet waste water plant and the on-line Plant A soflware sensor value, the ammonia concentration is regulated. Optimum concentration of ammonia was determined by off-line optimisation of the hybrid model i.e. to minimise interface level. The effect of this action was to take a disturbance variable and convert it to a controlled variable. Concentration of phosphorous in the inlet waste water was not previously analysed so conclusions regarding its effect and its control cannot yet be made.
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TEMPERATURE CONTROL Temperature was identified to have the strongest influence to the interface level out of all the operating conditions. Previously, waste water temperature was uncontrolled and was therefore subject to seasonal variation. Direct steam injection to the inlet waste water stream controlled by PID was implemented and the temperature of the activated sludge tank is controlled in a cascade configuration. The temperature setpoint was determined by off-line optirnisation. SLUDGE AGE CONTROL Sludge age, identified as an important variable by the sensitivity results of Table 2 and Table 3, can be effectively controlled by the recycle flow rate and the incinerator flow rate. The hybrid model showed a much stronger influence from recycle flow rate compared to incinerator flow. In addition, there is a significant increase in cost involved with increasing the incinerator flow. A sludge age control strategy with recycle as the primary manipulated variable is proposed. Off-line optimisation of the hybrid model suggests that the sludge age should be significantly reduced from its previous value. Results of sludge age control are not available at the time of writing. DISSOLVED OXYGEN CONTROL Prior to this study, dissolved oxygen (DO) in the final chamber of the aerated tank was controlled manually by manipulating the purge valve shown in Figure 10. PID control of pressure in the aerated tank was implemented by manipulating the oxygen feed. Oscillations in dissolved oxygen between 16 and 20 ppm were typical as shown in the left side of the top plot in Figure 11. This operation was one of the labour intensive tasks for the waste water plant operators. Controllability analysis of an empirical time series model showed that the correct control loop pairing was DO to oxygen feed and pressure to purge. In addition, a significant time delay from the oxygen feed to the DO concentration was identified. To deal with this long time delay, a unique formulation of generalised predictive control with a PID structure was implemented in our control computer (Miller et al., 1996). To maintain the pressure within the high and low safety constraints of the aerated tank, a high/low select structure was implemented as shown in Figure 10. Performance of the new control scheme is substantially better than the existing method as can be seen in Figure 1 I. f c
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will enforce cost minimisation when the interface level is already low. A safety mechanism is required to ensure that the optimised value is realistic. Operator influence in this step is expected to be necessary. Although at the time of writing it is too early to measure the net effect of all our initiatives, a positive impact on the interface level can be clearly seen.
1.
Figure 11: Dissolved oxygen control performance.
2.
Sensitivity analysis of the interface model showed that temperature, ammonia from one upstream plant, sludge age and acid flow have the strongest influence. Strategies for nutrient control and sludge age control are proposed.
3.
Results of control initiatives for dissolved oxygen, pH and temperature were discussed.
4.
On-line diagnostics and optimisation of the degrees of freedom based on the hybrid interface model is proposed.
pH CONTROL Although pH of the inlet waste water was not shown to have a significant effect on the interface level, the acid flow rate did have a strong positive influence. According to Wanner (1994), the range of pH suitable for supporting activated sludge is about 6.5 to 8.5. Therefore, small flow rates of the current acid are desirable which means operating at the maximum allowable pH. Control of pH has been successfully applied.
ON-LINE DIAGNOSTICS AND OPTIMISATION To fully utilise the hybrid neural net model, on-line optimisation of the operating conditions and on-line diagnostics are proposed. A plant wide step test based on an experimental design was conducted to strengthen the relationship between degrees of freedom in the plant and the interface level. Even after the step test, it is unlikely that our current dataset contains all of the possible combinations of waste water composition so it will be necessary to retrain the model periodically to widen its prediction range. In the first phase of this implementation we propose to use the hybrid model to predict the clarifier interface level under different scenarios within the range that the model was trained. For example, given the current operating point, what is the effect of changing the recycle flow rate or dissolved oxygen setpoint. Another example is to determine the effect of a simultaneous shutdown of Plant B and Plant D. In addition, we can assess the appropriate countermeasure for a particular disturbance in seconds rather than days or weeks as in the actual plant. Historically, expert operators were vital to the operation of the waste water plant. A smooth transition from manual control to an automated operation will therefore require operator acceptance of our control strategies. Having a validated diagnostic tool available to the operators will allow their own evaluation of our model. Later this can serve as a platform for operator training. The next phase of our plan is to adjust the degrees of freedom on-line by optimisation of the interface level hybrid model subject to constraints on the operating conditions. The objective of optimisation is obviously to minimise the interface level. A penalty function that considers cost of the manipulated variables
CONCLUSIONS A simplified hybrid neural net model of the clarifier interface level, a key health indicator of the activated sludge process, was shown to have excellent agreement with plant data.
REFERENCES Henze, M., C.P.L. Grady Jr., W. Gujer, R. Marais, and T. Matsuo, Activated Sludge Model No. 1, IAWPRC Scientific and Technical Report No. 1, London, IWPRC, 1986. Miller, R.M., A. Uda, S.L. Shah and R.K. Wood, "A long range predictive PID controller with application to an industrial process," Proc. SICE, pp. 11531156, Tottori, Japan, 1996. Orhon, D., and Artan, N., Modelling of Activated Sludge Systems, Tecnomic Pub. Co., PA, 1994. Pavilion Technologies Inc., Process Insights User's Guide Version 3.1, June 1995. Suzuki, IC, Y. Tooya and M. Kakumoto, "Statistical analysis of activated sludge process using fuzzy modelling," Kagaku KoGaku Ronbunshu (in Japanese), Vol. 18, No. 4, pp. 478-486, 1992. Wanner, J., Activated Sludge Bulking and Foaming Control, Tecnomic Pub. Co., PA, 1994. Zhao, H., O.J. Hao, T.J. McAvoy, and C-H. Chang, "Modeling nutrient dynamics in a sequencing batch reactor using a hybrid kinetic and artificial neural network," J. Environmental Engineering, Accepted, 1996.