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Determination of optimum operating conditions for industrial dye wastewater treatment using adaptive heuristic criticism pH control
ARTICLE IN PRESS
Journal of Environmental Management 85 (2007) 404–414 www.elsevier.com/locate/jenvman
Determination of optimum operating conditions for industrial dye wastewater treatment using adaptive heuristic criticism pH control Z. Zeybeka,, S. Yu¨ce Cetinkayab, F. Aliogluc, M. Alpbaza a
Department of Chemical Engineering, Ankara University, 06100 Tandogan, Ankara, Turkey b Department of Chemical Engineering, Cumhuriyet University, Sivas, Turkey c Department of Statistics, Ankara University, 06100 Tandogan, Ankara, Turkey
Received 16 February 2005; received in revised form 8 October 2006; accepted 17 October 2006 Available online 4 December 2006
Abstract For a pilot-scale application, pH control in the treatment of highly contaminated dye industrial wastewater containing metallic compounds as the main pollutants has been investigated with a method using adaptive heuristic criticism control (AHCC). Subsequent experimentation on between 12 and 18 l of the wastewater was carried out using statistical experimental design methodology to evaluate the effects of three critical factors: slaked lime (calcium hydroxide, Ca(OH)2) concentration, iron chloride (FeCl3) concentration and wastewater volume. With these critical factors, the wastewater treatment process is modeled as an appropriate quadratic cost function of the turbidity of the clarified water. The model is optimized with Rosenbrock’s method. Response surface topology of the wastewater treatment is given in terms of optimal concentrations of lime water and FeCl3 and optimal wastewater volume at pH 11. r 2006 Elsevier Ltd. All rights reserved. Keywords: Dye wastewater; Pilot plant; AHCC algorithm; Experimental design; Turbidity; Chemical treatment
1. Introduction Dye manufacture is one of the world’s major industries. Synthetic organic dyes are essential for the coloration of a growing number of substances in terms of quality, variety, fastness and other technical requirements. However, these developments have brought about severe hazards and environmental problems, including wastewater pollution. The composition of wastewater varies greatly with its production category, but usually it has a strong color, a high content of salts, and a high chemical oxygen demand (COD). The design of treatment plants that meet the required quality objectives for water and wastewater under these conditions may need to include the development of new processes. In the dye industry, many impurities in wastewater are present as colloidal dispersions of particles with an approximate size range of 1–100 nm. For dye pollution, chemical treatment is an essential component in convenCorresponding author.
E-mail address: [email protected] (Z. Zeybek). 0301-4797/$ - see front matter r 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.jenvman.2006.10.013
tional water treatment practice in which coagulation, sedimentation, and filtration are utilized in series to remove particles and natural organic matter from raw wastewater. The size of the reaction vessel required depends on the reaction kinetics. In general, process design should aim to optimize the reaction kinetics so that the desired reaction is completed in the minimum time. This often requires that the reaction pH be maintained at a fixed value by the addition of acid or alkali. Precise control of the pH is then required, and for this the use of automated control systems is essential. The dyeing and finishing of textile yarns and fabrics are extremely important processes in terms of both quality and environmental concerns. Among the commercial textile dyes, dispersed particulate dyestuffs are of environmental interest because of their widespread use, their potential formation of toxic aromatic amines and their low removal rate during aerobic waste treatment and advanced chemical oxidation. Arslan (2001) addressed these issues by using ozonation with and without ferrous iron coagulation at varying pH (3–13). In that study, Fe(II)-ion doses ranged from 0.09 to 18 mM for the treatment of a simulated
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dispersed dye-bath (COD0 ¼ 3784 mg l1) that more closely resembled an actual dye house effluent than an aqueous dispersed dye solution. Coagulation with 5000 mg l1 FeSO4 7H2O (18 mM Fe2+) at pH 11 removed up to 97% of the color and reduced COD by 54%, whereas oxidation via ozonation alone (applied ozone dose ¼ 2300 mg l1) was only effective at pH 3, resulting in 77% color removal and 11% COD reduction. The removal of dyes from textile dye wastewater by recycled alum sludge (RAS) generated in the coagulation process itself has been studied and optimized. Chu (2001) used one hydrophobic and one hydrophilic dye as probes to examine the performance of this process. It was found that RAS effectively removes hydrophobic dye in wastewater, while simultaneously reducing the required dose of fresh alum by one-third. The back-diffusion of residual dye from the recycling sludge can be detected but is easily controlled as long as a small amount of fresh alum is added to the system. The use of RAS is not recommended for the removal of hydrophilic dyes, since the high solubility characteristics of such dyes can cause deterioration in the water quality during recycling. Another set of experiments was carried out on wastewater from H-acid manufacturing processes. This wastewater is rich in various substituted derivatives of naphthalene and is one of the most difficult to treat. As a pretreatment method, ferrous ion-peroxide oxidation combined with coagulation was used. The results showed that ferrous ion-peroxide oxidation can improve the efficiency of coagulation treatment (Yang and Wang, 1996). In the present study, ferrous iron coagulation of dye wastewater was performed at pH 11. With the use of a regression model, the dependence of coagulation on the relevant parameters was expressed in terms of the coefficients of first and second grade polynomials. The model was formed for the pilot plant with statistical analysis. A further purpose of this study was to improve and apply pH control in the treatment of dye wastewater by using adaptive heuristic criticism control (AHCC).
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2. Experimental setup In the present study, a semi-batch reactor was used in which the volume varied from 12 to 18 l according to the experimental design. The experimental system consisted of associated piping, control valves, flow sensors, measuring electrodes, A/D converters and a personal computer as shown in Fig. 1. Wastewater samples were obtained from a local dye manufacturing plant. The contaminant levels of the wastewater are given in Table 1. The experiment was performed as follows. For each run, 12,000–18,000 cm3 of wastewater was measured and placed in the reactor vessel, and mechanical stirring was started at 150 rpm. The initial pH of the wastewater was 6–7. To start the coagulation process, FeCl3 was added, with the FeCl3 flow rate being set according to the reactor volume. With the arrival of FeCl3 the pH of the wastewater dropped as a disturbance effect. In the case of FeCl3, the optimal pH for coagulation tends to lie between 10 and 12. In this study coagulation was therefore performed at pH 11 as adjusted by lime water, which was pumped into the reactor as the coagulant was added. The reaction proceeded for 20 min, during which time the pH was continuously adjusted to 11. The FeCl3 coagulant was added to the system via a gravity-fed pipe, and required no energy for pumping. For the treatment, the Fe(III)-ion dose was 0.242 M and the Ca(2+)-ion dose was 1.46 M (see Table 2). The chemical reaction in which these reagents form hydroxide precipitates is given in Table 3. At the end of the reaction Polyelectrolyte was added to flocculate the precipitant.
Table 1 Analysis of metallic compounds in industrial dye wastewater Industrial waste (mg l1) Pb2+ Cd2+ Zn2+
Fig. 1. Experimental set-up.
1.79 0.137 —
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In this system, once the desired steady state is reached, control is switched to the algorithm under study. The control valve on the lime water stream is the final control element while the FeCl3 is used to introduce the desired disturbance in the coagulant flow in the reactor. As shown in Fig. 1, the acidic stream (FeCl3) flows in as a step disturbance effect. At the end of the reaction, after the polymer was added, the reactor contents were then left to settle for another 30 min. After settling, the color intensity or turbidity of the supernatant water was measured. From the results, the turbidity values of the wastewater treated via AHCC were optimized according to the Rosenbrock algorithm (Rosenbrock, 1960), with the use of factorial experimental design. The design parameters of the present study are given in Table 4. In order to compare AHCC to PID control, eight experimental runs were performed with AHCC and four with PID. 3. Statistical design of the experiment Generally, in the statistical planning of experiments, mathematical models are described with linear and quadratic equations (Box, 1954). Experimental design involves what is known as a universe of predictions, because it deals with combinatorial relationships between independent variables. The functional relationships be-
tween parameters are then treated with regression methods. The dye wastewater volume (X1), the mass fraction of Ca(OH)2 in water (X2) and the FeCl3 flow rate (X3) are considered to be the main variables affecting the turbidity in dye wastewater treatment. Other important variables are fixed. The initial pH of the wastewater is 6–8, and the temperature of the system is 201 C. The experimental design adopted had three factors (iron chloride concentration, slaked lime concentration, and volume of reactor contents) and one level central composite. For each independent variable studied, the central value and interval between the levels were chosen according to preliminary studies. The coded values of the independent variables (1 ¼ lowest level, 0 ¼ medium level, 1 ¼ highest level) were calculated. Correspondences between these coded values and actual values are given in Table 4. The dependence of the turbidity on these parameters was determined with first and second-degree polynomials. Linear and non-linear models are given below. Linear model (Raymond, 1971): Y ¼ f ðxÞ ¼ b0 þ
3 X
bi X i þ
i¼1
3 X
bij X i X j .
(1)
i¼1 ioj
Non-linear model: 3 X
3 X
3 X
bii X 2i ,
Table 2 Operational condition of the system
Y ¼ f ðxÞ ¼ b0 þ
Ca(OH)2:14 g CaO/171 ml H2O FeCI3:2 ml FeCl3/25 ml H2O Polyelectrolyte:1 g/500 ml H2O Flow rate of Ca (OH)2 ¼ 0.1–80 ml/min
where b0, bi, bii, bij are constant and regression coefficients of the model, and Xi are the independent variables in coded values. In the experimental design method, model parameters are estimated by forming an optimal plan matrix. Generally, the coded values of the parameters are used in the plan matrix.
bi X i þ
i¼1
Table 3 Chemistry of iron salts and lime coagulation
Xi ¼
2FeCl3 þ 3CaðOHÞ2 ! 2FeðOHÞ3 ðsÞ þ 3CaCl2
bij X i X j þ
i¼1 ioj
(2)
i¼1
U i U io . DU io
Table 4 Statistical experimental design Experiment no.
X1
X2
X3
V(l)
Ca(OH)2,%
FfeC13 (ml/min)
Turbidity (%)
Control Type
1 2 3 4 5 6 7 8 9 10 11 12
1 1 1 1 1 1 1 1 1 0 0 0
1 1 1 1 1 1 1 1 0 1 1 0
1 1 1 1 1 1 1 1 1 1 1 1
18 18 12 12 18 18 12 12 12 15 15 15
10 5 10 5 10 5 10 5 7.5 10 5 7.5
24 24 24 24 2.4 2.4 2.4 24 24 24 24 24
89.5 95.9 92.1 96.5 74.1 78.2 94.5 97.7 80 42.6 67.4 92.5
PID
X1 ¼ V(l); X2 ¼ Ca(OH)2; X3 ¼ FeCl3 flow rate.
AHCC
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Here, Xi is the coded value of the variables, Ui the real values of the variables, Ui0 the average values of the variables, and DUi the step interval of the variables. The plan matrices are formed in the following sequential order. Firstly, the area to be searched and a central plan are selected. The initial coordinate is applied to the central plan. Next, the step interval of the change for each parameter is determined. The selection of the center of the plan and of the step interval is due to the definition of the process. In order to form the plan matrix for a linear model, 2n experiments must be carried out. The number of experiments for a nonlinear model is 2n+2n+1. Here, ‘2’ indicates two levels, the highest and the lowest, of the selected operating parameters Xi and n is the number of parameters.
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In this learning system, the primary reinforcement signal observed from a system environment is converted into a heuristic reinforcement scalar signal with a higher quality through a critic network. The knowledge base is upgraded by actions of a learning element. The function of the element is to select the actions randomly in terms of information received from both the system environment and the knowledge base. As a result, a mapping of input–output distribution can be established through learning. Reinforcements that are closer to ‘‘0’’ have a weak prediction of failure condition, and those near ‘‘–1’’ have a strong prediction of failure. This also means that states typically will have either a high or low value of manipulated variable just before failure. 4.1. Algorithm of ASE
4. AHCC algorithm This algorithm is a three-layer feed-forward artificial neural network (ANN) that uses supervised learning with reinforcement in a unique topology. It shows how a system consisting of two neuron-like adaptive elements can solve a difficult learning control problem, i.e. the learning system consists of a single associative search element (ASE) and a single adaptive critical element (ACE). AHCC uses a type of control system in which the output is either maximum or minimum values (Barto and Sutton, 1982; Zeybek et al., 2004; Zeybek, 2006, Zeybek et al., 2006). In AHCC, training of the algorithm is not needed. The neural network approach used here does not need to model the pH changes in the reactor; it is used here simply to classify the pH changes into steady state or overload conditions. The current application of a neural network could be described as process monitoring with remedial action, and uses the neural network classification to adjust the Ca(OH)2 inside the reactor, i.e. if the classification is overload then Ca(OH)2 solution is added at the minimum level. As the classification made by the neural network can take any value between -1 and 1, it is possible to vary continuously the rate of Ca(OH)2 addition. The control system is shown diagrammatically in Fig. 2 and as an algorithm in Fig. 3. Also, the task of the algorithm is explained below.
The ASE element has a reinforcement input pathway, n pathways for the non-reinforcement input, and a single output pathway. Let {pHi(t),1pipn}, denote the realvalued signal on the ith non-reinforcement input pathway and y(t) the output at time t. The element output can be determined as follows: " # X yðtÞ ¼ f wi ðtÞpH i ðtÞ þ noiseðtÞ , (3) i
where noise (t) is defined as a real random variable with a probability function, e.g., the zero mean Gaussian distribution with covariance, and f(x) is defined as either a sigmoid or a threshold function. The weights are upgraded in terms of r0 (t), internal reinforcement, and ei(t) eligibility of input pathway i, as follows: wi ðt þ 1Þ ¼ wi ðtÞ þ Zr0 ðtÞei ðtÞ,
(4)
where Z denotes the learning rate. The exponentially decaying eligibility traces ei can be formulated as follows: eiþ1 ðtÞ ¼ dei ðtÞ þ ð1 dÞyðtÞpH i ðtÞ,
(5)
where (0pdp1) determines the trace decay rate. 4.2. Algorithm of ACE The prediction of eventual reinforcement can be described as a linear function of the input vector pH: X pðtÞ ¼ vi ðtÞpH i ðtÞ (6) i
The prediction of p(t) can converge to an accurate prediction by upgrading the weights vi as follows: vi ðt þ 1Þ ¼ vi ðtÞ þ l½rðtÞ þ gpðtÞ pðt 1Þ¯pH,
Fig. 2. Block diagram of adaptive heuristic criticism for pH control.
(7)
where l is the learning rate, r(t) the reinforcement signal provided by system environment, p¯ HðtÞ the average of the input vector pHi(t), g, the positive constant, 0pgp1, that makes predications decay in the absence of an external reinforcement. In other words, the change in the value of p plus the value of the external reinforcement constitutes the
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Fig. 3. Flow chart of the AHC for pH control.
heuristic or internal reinforcement r0 where the future value of p is discounted more the further it is from the current state of the system. The improved or internal reinforcement signal, i.e., the output of ACE, can be calculated as follows: r0 ðtÞ ¼ rðtÞ þ gpðtÞ pðt 1Þ.
(8)
This is also known as ‘‘temporal difference’’ (TD) error (Bucak, and Zohdy, 2001). The on-line data acquisition system was set to sample at 1-s intervals to monitor changes in the pH and Ca(OH)2 signals. The output from the neural network was scaled so that the minimum value of the pH corresponded to the maximum output of the variable speed pump delivering Ca(OH)2 solution at a flow rate of 80 ml/min to the reactor. The on-
line use of the neural network allowed it to investigate the absolute values of Ca(OH)2 and the rate of change. The AHC controller set points were 5% and 80% of the flow rate of Ca(OH)2. A total of 12 different dye wastewater overloads were carried out in the agitated semi batch reactor to compare the system’s response to the different operating conditions according to the 23 factorial experimental design. The conditions are given in Table 4.
5. Results and discussion One important objective of large-scale experiments on industrial wastewater is to determine the minimum
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amounts of lime water and ferrous salt which have to be used while remaining within the required time frame for pollutant degradation. In this study we have therefore applied the experimental design methodology from Zeybek (1997) to investigate the lime water and ferrous salt quantities in the degradation rate of dye colloids. The main features of response surface methods lend themselves well to the study of multiple response situations. Diagrams showing the fitted surface in the form of contours of constant response often indicate more than one region where the predicted response is at a level which is considered to be satisfactory. The experimenter can then use this information, in addition to similar contours for a second response, to arrive at a setting (X1, X2, y, Xk) that represents approximately the ‘‘best’’ operating conditions. The turbidity of the treated wastewater was chosen as the dependent variable to be studied. In the experiments, three independent variables investigated were slaked lime concentration (U1), the volume of the dye wastewater (U2) and the FeCl3 flow rate (U3). The ranges of variation were 5% to 10%, 12 to 18 l and 2.4 to 24 ml/min, respectively. The coded factor levels are given in Table 5. The following equation shows coded variables. X1 ¼
%CaðOHÞ2 %CaðOHÞ2 V V¯ , ; X2 ¼ DV D%CaðOHÞ2
X3 ¼
F FeCl3 F FeCl3 . DF FeCl3
Initially, a simple 23 factorial experiment was planned in order that yield and cost could be studied and then four observations were taken at the origin of the design. The illustration of the first phase with 23 factorial experiments is shown in the first eight rows of Table 4. For the linear model, these eight experiments were performed. Four experiments were added for the quadratic model. Eqs. (9) and (10) represent the linear model and interactions of the variables, respectively. The first-order response is given by ylin1 ¼ 0:832 0:052X 1 0:048X 2 0:024X 3 .
(9)
The standard error of Eq. (9) is 0.034. y ¼ 0:844 0:065X 1 0:051X 2 0:031X 3 þ 0:011X 1 X 2 0:019X 2 X 3 þ 0:048X 1 X 3
ð10Þ
2
For Eq. (10), R is 0.972. The second phase of the study involved augmenting the factorial experiment illustrated by Table 4 with additional
Table 5 The coded factor levels
U1 U2 U3
1
0
1
12 0.05 2.4
15 0.075 13.2
18 0.10 24
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points in order to allow the fitting of second-order surfaces via the second-order polynomial models of Eqs. (11) and (12). One observation was added to each of the two already recorded for the factorial treatment combinations. The resulting configuration formed a central composite design, with a total of 12 observations. The value of the parameter a was chosen to be 1.0. The estimated response function found by using those data is given by y ¼ 0:615 0:052X 1 0:021X 2 þ 0:031X 3 þ 0:002X 1 X 2 þ 0:052X 1 X 3 þ 0:002X 2 X 3 þ 0213X 21 þ 0:068X 22 . ð11Þ Least-squares estimation in LU form: Standard deviation: 0.1060608 y ¼ 0:730 0:039X 1 0:045X 2 þ 0:016X 3 0:004X 1 X 2 þ 0:067X 1 X 3 0:020X 2 X 3 þ 0:260X 21 0:170X 22 . ð12Þ From Levenberg–Marquardt using SYSTAT VERSION 7.0: Standard Deviation: 0.035 Raw R2 (1Residual/Total) ¼ 0.988, where y is the turbidity of the refined wastewater from the model. P-values of regression, lack of fit, and corresponding coefficients were tested. Lack of fit was not significant, but regression was significant at a 3% a level only for the quadratic model, which indicated that this model fits the response surface. Therefore, the quadratic model was selected to describe the response surface of turbidity within this region. Two- and three-dimensional response surfaces of the quadratic model for turbidity of the treated wastewater and the corresponding estimated optimums are shown in Fig. 4. Figs. 4(a), (c), and (e) give three yield contours, corresponding to fixed levels of the variables X3, X2, and X1. Notice that the middle figure, although not displaying exactly the optimum yield, does indicate an estimated yield that is close to the maximum in the boundary region. The response surface of turbidity showed a clear peak, which indicated that the optimum condition was well inside the design boundary in Figs. 4(b), (d), and (f). It was learned from this response surface study that the operating conditions should be altered from the standard plant conditions. The original practice of adding both reagents in excess in order to improve yield is not optimal. Only the FeCl3 flow rate (variable X3) should be increased. In fact, lowering the slaked lime concentration (reagent X2) to the region of the maximum yield point will result in an estimated increase in yield of 5%. In this way the conditions that maximize the turbidity, defined as the optimal conditions earlier, were calculated by setting the partial derivatives of the function to zero with respect to the corresponding variables according to the Rosenbrock
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Fig. 4. The contour plots (a), (c) and (e) represent the turbidity of treated wastewater in terms of the relationships between the coded variables x1–x2; x1–x3; x2–x3 respectively, and (b), (d), and (f) show the corresponding three-dimensional representations.
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algorithm. At the optimal conditions, turbidity was 0.99. The stationary point was found to be an optimal result from the Rosenbrock algorithm.
The purpose of this algorithm is to find the minimum of a multivariable, unconstrained, nonlinear function. Minimize F ðx1 ; x2 ; :::; xN Þ. The procedure is based on the direct search method proposed by H.H. Rosenbrock. No derivatives are required. The procedure assumes a unimodal functions; therefore, several sets of starting values for the independent variables should be used if it is known that more than one minimum exists or if the shape of the surface is unknown. Coding values of parameters:
4.4 4.2 open loop with FeCl3
4.0 3.8 3.6 pH
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3.4 3.2
X 1 ¼ 1:41409; X 2 ¼ 0:2627; X 3 ¼ 0:124.
3.0
Real values of parameters: V ¼ 10.8 l. Ca(OH)2 ¼ 6.8% qFecl3 ¼ 14:54 ml=min The estimated yield at this point is given by y^ 1 ¼ 0:99. Thus the stationary point gives maximum yield. The treatment efficiency shows a complicated nonlinear function of the dosages of coagulant, lime water and reactor volume. However, influent water quality is constantly fluctuating, which makes it hard to accurately describe the
2.8 2.6 2.4 2.2 0
100
200
300 400 Time (second)
500
600
The ratio of pump opening%
Fig. 5. Open-loop response curve for a step disturbance of FeCl3 flow.
pH
15 10 5 0 0
500 1000 Time, second, s
100 80 60 40 20 0
1500
0
500 1000 Time, second
1500
The ratio of pump opening %
pH
Fig. 6. Performance in experiment no. 5 by AHC control.
12 10 8 6 4 2 0
100 80 60 40 20 0
0
300
600 900 Time, second
0
1200
500 1000 Time, second
1500
50
14 12 10 8 6 4 2 0
The ratio of pump opening %
pH
Fig. 7. Performance in experiment no. 6 by AHC control.
40 30 20 10 0
0
500 1000 Time, second
1500
0
200
400
600 800 Time, second
Fig. 8. Performance in experiment no. 7 by AHC control.
1000
1200
1400
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complex relationship between the removal rates and the chemical dosages. Since some deviations appear inevitably when applying the experimental results to real situations, continuous training of the neural network is necessary to keep it accurate. During the control process for the AHC controller, output includes pH value, which must be 11. This shows us the expected removal rates for turbidity. The controller output is the optimal control action which is the percentage of the Ca(OH)2 pump flow rate. Fig. 5 shows the experimentally dynamic behavior of the process to the unit step change in acid flow rate. From Figs. 6–9, it can be seen that both pH value and turbidity are able to follow the expected removal rates. Fig. 6’s removal rates are higher than those in Fig. 7, but in all the figures, under the given setting, the control action remains at the optimal pH of 11 after 400 s. Figs. 6–9 show the experimental results when the influent water quality varies dynamically. Thus, the control and the relevant data lead to satisfactory control effects. This study investigates the possibilities and methods of purifying and improving the quality of wastewater released
(13) Parameters obtained from the Yuwana–Seborg method: Kc 51.48
10 5 0 0
500 1000 Time, second
tI 20.875
tD 5.060
Finally, Fig. 11a shows the closed-loop response under AHCC and PID control to regulate the clarified wastewater before it is discharged into the river. It shows a very large process gain change from pH ¼ 11 to the
The ratio of pump opening %
15 pH
by the dye industry and also addresses the control of pH in dye wastewater. The performance results of (proportionalintegral-derivative) PID and adaptive heuristic criticism (AHC) control were compared in terms of effectiveness, as shown in Fig. 10. The PID control equation is given below. The related parameters in this equation are calculated according to the Yuwana–Seborg Method (Hapoglu, 1993). Z CðtÞ ¼ Kc ðtÞ þ Kc=tI ðtÞ dðtÞ þ Kc tD d ðtÞ=dðtÞ þ Cs.
100 80 60 40 20 0 0
1500
500 1000 Time,second
1500
Fig. 9. Performance in experiment no. 8 by AHC control.
c 12
a pH
12 pH
8
8
4 4 0
0 0
300
600 900 Time,second
b
300
600 900 Time,second
1200
d
100
The ratio of pump opening, %
The ratio of pump opening, %
0
1200
80 60 40 20 0 0
300
600 900 Time, second
1200
100
50
0 0
500 Time, second
1000
Fig. 10. For parallel experiments comparative results of the AHCC (Experiment no:8) and PID (Experiment no:4) respectively: (a) pH response, (b) manipulated variable movements of AHCC, (c) pH response and (d)manipulated variable movements of PID.
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The ratio of pump opening, %
b a 16
AHC
60 40 20 0 0
8
4
SET POINT PID AHC
0 0
1000 2000 Time, second
3000
The ratio of pump opening, %
pH
12
80
1000 2000 Time, Second
3000
80 PID
60 40 20 0 0
1000 2000 3000 Time, Second
4000
Fig. 11. (a) pH regulation by AHC and PID before discharge into the river. (b) Lime water flow.
neutralization point of pHs ¼ 7. The changes in the titrating stream are as in Fig. 11b. This can cause very large changes in pH with PID, as in Fig. 11a. Hence it is very difficult to control at the set point of pHs ¼ 7. Actually, many existing adaptive control systems fail to regulate this type of influent stream well. While the PID control strategy fails to control the process, the AHCC controller keeps the pH within a small deviation from pHs ¼ 7. Since specific nonlinear equations are not assumed, AHCC can cope with changes of nonlinearity due to component changes in the influent stream. 6. Conclusions This paper presents a control scheme for a waste coagulation process based on adaptive heuristic criticism control. This control enables the system to adapt to a large variety of operating conditions with an enhanced learning ability. This study also develops a model for the nonlinear relationships between the pollutant removal rates and the chemical additive dosages. The method can be adapted to a large-scale plant. The following other conclusions are based on data from optimization and control of the dye wastewater treatment processes. (1) Response surface methodology was successfully applied to determine the optimal physical conditions where the maximum treatment of dye wastewater occurred, in which the turbidity of the influent in the pilot plant was more than 90%. These were V ¼ 10.8 l, Ca(OH)2 ¼ 6.8%, qFecl3 ¼ 14:54 ml=min at pH 11.0 and 20.1 1C respectively. (2) The results reveal that for control, although the manipulated variable had only the minimum and maximum values in the plant operations, the control
quality is still comparable to PID control. The results also indicate that the capability of AHCC was excellent at the maximum rate of FeCl3 inflow, although the reactor contents increased in volume. However, at nonoptimal conditions, the performance of AHCC decreased. If the kinetic mechanism for the dye wastewater were known, the volume could be calculated for scale-up. The other parameters are important and independent from the volume. After scaling up the model, optimal conditions should be re-determined for the specific treatment system. In this way, experimental design methodology becomes important in determining the startup conditions when a process is to be scaled up to the industrial level.
Acknowledgements The authors are grateful for financial support provided by the Ankara University Research Foundation (Grant no. 2002.07.45.006).
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Zeybek, Z., Yu¨ce, S., Hapog˘lu, H., Alpbaz, M., 2004. Adaptive heuristic temperature control of a batch polymerisation reactor. Chemical Engineering and Processing 43 (7), 911–920. Zeybek, Z., 2006. Role of adaptive heuristic criticism in cascade temperature control of an industrial tubular furnace. Applied Thermal Engineering 26, 152–160. Zeybek, Z., Karpinar, T., Alpbaz, M., Hopog˘lu, H., 2006. Application of adaptive heuristic criticism control (AHCC) to dye wastewater, Journal of Environmental Management, doi:10.1016/j.jenvman. 2006.06.018 (in press). (www.elsevier.com/locate/jenvman)
Journal of Environmental Management 85 (2007) 404–414 www.elsevier.com/locate/jenvman
Determination of optimum operating conditions for industrial dye wastewater treatment using adaptive heuristic criticism pH control Z. Zeybeka,, S. Yu¨ce Cetinkayab, F. Aliogluc, M. Alpbaza a
Department of Chemical Engineering, Ankara University, 06100 Tandogan, Ankara, Turkey b Department of Chemical Engineering, Cumhuriyet University, Sivas, Turkey c Department of Statistics, Ankara University, 06100 Tandogan, Ankara, Turkey
Received 16 February 2005; received in revised form 8 October 2006; accepted 17 October 2006 Available online 4 December 2006
Abstract For a pilot-scale application, pH control in the treatment of highly contaminated dye industrial wastewater containing metallic compounds as the main pollutants has been investigated with a method using adaptive heuristic criticism control (AHCC). Subsequent experimentation on between 12 and 18 l of the wastewater was carried out using statistical experimental design methodology to evaluate the effects of three critical factors: slaked lime (calcium hydroxide, Ca(OH)2) concentration, iron chloride (FeCl3) concentration and wastewater volume. With these critical factors, the wastewater treatment process is modeled as an appropriate quadratic cost function of the turbidity of the clarified water. The model is optimized with Rosenbrock’s method. Response surface topology of the wastewater treatment is given in terms of optimal concentrations of lime water and FeCl3 and optimal wastewater volume at pH 11. r 2006 Elsevier Ltd. All rights reserved. Keywords: Dye wastewater; Pilot plant; AHCC algorithm; Experimental design; Turbidity; Chemical treatment
1. Introduction Dye manufacture is one of the world’s major industries. Synthetic organic dyes are essential for the coloration of a growing number of substances in terms of quality, variety, fastness and other technical requirements. However, these developments have brought about severe hazards and environmental problems, including wastewater pollution. The composition of wastewater varies greatly with its production category, but usually it has a strong color, a high content of salts, and a high chemical oxygen demand (COD). The design of treatment plants that meet the required quality objectives for water and wastewater under these conditions may need to include the development of new processes. In the dye industry, many impurities in wastewater are present as colloidal dispersions of particles with an approximate size range of 1–100 nm. For dye pollution, chemical treatment is an essential component in convenCorresponding author.
E-mail address: [email protected] (Z. Zeybek). 0301-4797/$ - see front matter r 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.jenvman.2006.10.013
tional water treatment practice in which coagulation, sedimentation, and filtration are utilized in series to remove particles and natural organic matter from raw wastewater. The size of the reaction vessel required depends on the reaction kinetics. In general, process design should aim to optimize the reaction kinetics so that the desired reaction is completed in the minimum time. This often requires that the reaction pH be maintained at a fixed value by the addition of acid or alkali. Precise control of the pH is then required, and for this the use of automated control systems is essential. The dyeing and finishing of textile yarns and fabrics are extremely important processes in terms of both quality and environmental concerns. Among the commercial textile dyes, dispersed particulate dyestuffs are of environmental interest because of their widespread use, their potential formation of toxic aromatic amines and their low removal rate during aerobic waste treatment and advanced chemical oxidation. Arslan (2001) addressed these issues by using ozonation with and without ferrous iron coagulation at varying pH (3–13). In that study, Fe(II)-ion doses ranged from 0.09 to 18 mM for the treatment of a simulated
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dispersed dye-bath (COD0 ¼ 3784 mg l1) that more closely resembled an actual dye house effluent than an aqueous dispersed dye solution. Coagulation with 5000 mg l1 FeSO4 7H2O (18 mM Fe2+) at pH 11 removed up to 97% of the color and reduced COD by 54%, whereas oxidation via ozonation alone (applied ozone dose ¼ 2300 mg l1) was only effective at pH 3, resulting in 77% color removal and 11% COD reduction. The removal of dyes from textile dye wastewater by recycled alum sludge (RAS) generated in the coagulation process itself has been studied and optimized. Chu (2001) used one hydrophobic and one hydrophilic dye as probes to examine the performance of this process. It was found that RAS effectively removes hydrophobic dye in wastewater, while simultaneously reducing the required dose of fresh alum by one-third. The back-diffusion of residual dye from the recycling sludge can be detected but is easily controlled as long as a small amount of fresh alum is added to the system. The use of RAS is not recommended for the removal of hydrophilic dyes, since the high solubility characteristics of such dyes can cause deterioration in the water quality during recycling. Another set of experiments was carried out on wastewater from H-acid manufacturing processes. This wastewater is rich in various substituted derivatives of naphthalene and is one of the most difficult to treat. As a pretreatment method, ferrous ion-peroxide oxidation combined with coagulation was used. The results showed that ferrous ion-peroxide oxidation can improve the efficiency of coagulation treatment (Yang and Wang, 1996). In the present study, ferrous iron coagulation of dye wastewater was performed at pH 11. With the use of a regression model, the dependence of coagulation on the relevant parameters was expressed in terms of the coefficients of first and second grade polynomials. The model was formed for the pilot plant with statistical analysis. A further purpose of this study was to improve and apply pH control in the treatment of dye wastewater by using adaptive heuristic criticism control (AHCC).
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2. Experimental setup In the present study, a semi-batch reactor was used in which the volume varied from 12 to 18 l according to the experimental design. The experimental system consisted of associated piping, control valves, flow sensors, measuring electrodes, A/D converters and a personal computer as shown in Fig. 1. Wastewater samples were obtained from a local dye manufacturing plant. The contaminant levels of the wastewater are given in Table 1. The experiment was performed as follows. For each run, 12,000–18,000 cm3 of wastewater was measured and placed in the reactor vessel, and mechanical stirring was started at 150 rpm. The initial pH of the wastewater was 6–7. To start the coagulation process, FeCl3 was added, with the FeCl3 flow rate being set according to the reactor volume. With the arrival of FeCl3 the pH of the wastewater dropped as a disturbance effect. In the case of FeCl3, the optimal pH for coagulation tends to lie between 10 and 12. In this study coagulation was therefore performed at pH 11 as adjusted by lime water, which was pumped into the reactor as the coagulant was added. The reaction proceeded for 20 min, during which time the pH was continuously adjusted to 11. The FeCl3 coagulant was added to the system via a gravity-fed pipe, and required no energy for pumping. For the treatment, the Fe(III)-ion dose was 0.242 M and the Ca(2+)-ion dose was 1.46 M (see Table 2). The chemical reaction in which these reagents form hydroxide precipitates is given in Table 3. At the end of the reaction Polyelectrolyte was added to flocculate the precipitant.
Table 1 Analysis of metallic compounds in industrial dye wastewater Industrial waste (mg l1) Pb2+ Cd2+ Zn2+
Fig. 1. Experimental set-up.
1.79 0.137 —
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In this system, once the desired steady state is reached, control is switched to the algorithm under study. The control valve on the lime water stream is the final control element while the FeCl3 is used to introduce the desired disturbance in the coagulant flow in the reactor. As shown in Fig. 1, the acidic stream (FeCl3) flows in as a step disturbance effect. At the end of the reaction, after the polymer was added, the reactor contents were then left to settle for another 30 min. After settling, the color intensity or turbidity of the supernatant water was measured. From the results, the turbidity values of the wastewater treated via AHCC were optimized according to the Rosenbrock algorithm (Rosenbrock, 1960), with the use of factorial experimental design. The design parameters of the present study are given in Table 4. In order to compare AHCC to PID control, eight experimental runs were performed with AHCC and four with PID. 3. Statistical design of the experiment Generally, in the statistical planning of experiments, mathematical models are described with linear and quadratic equations (Box, 1954). Experimental design involves what is known as a universe of predictions, because it deals with combinatorial relationships between independent variables. The functional relationships be-
tween parameters are then treated with regression methods. The dye wastewater volume (X1), the mass fraction of Ca(OH)2 in water (X2) and the FeCl3 flow rate (X3) are considered to be the main variables affecting the turbidity in dye wastewater treatment. Other important variables are fixed. The initial pH of the wastewater is 6–8, and the temperature of the system is 201 C. The experimental design adopted had three factors (iron chloride concentration, slaked lime concentration, and volume of reactor contents) and one level central composite. For each independent variable studied, the central value and interval between the levels were chosen according to preliminary studies. The coded values of the independent variables (1 ¼ lowest level, 0 ¼ medium level, 1 ¼ highest level) were calculated. Correspondences between these coded values and actual values are given in Table 4. The dependence of the turbidity on these parameters was determined with first and second-degree polynomials. Linear and non-linear models are given below. Linear model (Raymond, 1971): Y ¼ f ðxÞ ¼ b0 þ
3 X
bi X i þ
i¼1
3 X
bij X i X j .
(1)
i¼1 ioj
Non-linear model: 3 X
3 X
3 X
bii X 2i ,
Table 2 Operational condition of the system
Y ¼ f ðxÞ ¼ b0 þ
Ca(OH)2:14 g CaO/171 ml H2O FeCI3:2 ml FeCl3/25 ml H2O Polyelectrolyte:1 g/500 ml H2O Flow rate of Ca (OH)2 ¼ 0.1–80 ml/min
where b0, bi, bii, bij are constant and regression coefficients of the model, and Xi are the independent variables in coded values. In the experimental design method, model parameters are estimated by forming an optimal plan matrix. Generally, the coded values of the parameters are used in the plan matrix.
bi X i þ
i¼1
Table 3 Chemistry of iron salts and lime coagulation
Xi ¼
2FeCl3 þ 3CaðOHÞ2 ! 2FeðOHÞ3 ðsÞ þ 3CaCl2
bij X i X j þ
i¼1 ioj
(2)
i¼1
U i U io . DU io
Table 4 Statistical experimental design Experiment no.
X1
X2
X3
V(l)
Ca(OH)2,%
FfeC13 (ml/min)
Turbidity (%)
Control Type
1 2 3 4 5 6 7 8 9 10 11 12
1 1 1 1 1 1 1 1 1 0 0 0
1 1 1 1 1 1 1 1 0 1 1 0
1 1 1 1 1 1 1 1 1 1 1 1
18 18 12 12 18 18 12 12 12 15 15 15
10 5 10 5 10 5 10 5 7.5 10 5 7.5
24 24 24 24 2.4 2.4 2.4 24 24 24 24 24
89.5 95.9 92.1 96.5 74.1 78.2 94.5 97.7 80 42.6 67.4 92.5
PID
X1 ¼ V(l); X2 ¼ Ca(OH)2; X3 ¼ FeCl3 flow rate.
AHCC
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Here, Xi is the coded value of the variables, Ui the real values of the variables, Ui0 the average values of the variables, and DUi the step interval of the variables. The plan matrices are formed in the following sequential order. Firstly, the area to be searched and a central plan are selected. The initial coordinate is applied to the central plan. Next, the step interval of the change for each parameter is determined. The selection of the center of the plan and of the step interval is due to the definition of the process. In order to form the plan matrix for a linear model, 2n experiments must be carried out. The number of experiments for a nonlinear model is 2n+2n+1. Here, ‘2’ indicates two levels, the highest and the lowest, of the selected operating parameters Xi and n is the number of parameters.
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In this learning system, the primary reinforcement signal observed from a system environment is converted into a heuristic reinforcement scalar signal with a higher quality through a critic network. The knowledge base is upgraded by actions of a learning element. The function of the element is to select the actions randomly in terms of information received from both the system environment and the knowledge base. As a result, a mapping of input–output distribution can be established through learning. Reinforcements that are closer to ‘‘0’’ have a weak prediction of failure condition, and those near ‘‘–1’’ have a strong prediction of failure. This also means that states typically will have either a high or low value of manipulated variable just before failure. 4.1. Algorithm of ASE
4. AHCC algorithm This algorithm is a three-layer feed-forward artificial neural network (ANN) that uses supervised learning with reinforcement in a unique topology. It shows how a system consisting of two neuron-like adaptive elements can solve a difficult learning control problem, i.e. the learning system consists of a single associative search element (ASE) and a single adaptive critical element (ACE). AHCC uses a type of control system in which the output is either maximum or minimum values (Barto and Sutton, 1982; Zeybek et al., 2004; Zeybek, 2006, Zeybek et al., 2006). In AHCC, training of the algorithm is not needed. The neural network approach used here does not need to model the pH changes in the reactor; it is used here simply to classify the pH changes into steady state or overload conditions. The current application of a neural network could be described as process monitoring with remedial action, and uses the neural network classification to adjust the Ca(OH)2 inside the reactor, i.e. if the classification is overload then Ca(OH)2 solution is added at the minimum level. As the classification made by the neural network can take any value between -1 and 1, it is possible to vary continuously the rate of Ca(OH)2 addition. The control system is shown diagrammatically in Fig. 2 and as an algorithm in Fig. 3. Also, the task of the algorithm is explained below.
The ASE element has a reinforcement input pathway, n pathways for the non-reinforcement input, and a single output pathway. Let {pHi(t),1pipn}, denote the realvalued signal on the ith non-reinforcement input pathway and y(t) the output at time t. The element output can be determined as follows: " # X yðtÞ ¼ f wi ðtÞpH i ðtÞ þ noiseðtÞ , (3) i
where noise (t) is defined as a real random variable with a probability function, e.g., the zero mean Gaussian distribution with covariance, and f(x) is defined as either a sigmoid or a threshold function. The weights are upgraded in terms of r0 (t), internal reinforcement, and ei(t) eligibility of input pathway i, as follows: wi ðt þ 1Þ ¼ wi ðtÞ þ Zr0 ðtÞei ðtÞ,
(4)
where Z denotes the learning rate. The exponentially decaying eligibility traces ei can be formulated as follows: eiþ1 ðtÞ ¼ dei ðtÞ þ ð1 dÞyðtÞpH i ðtÞ,
(5)
where (0pdp1) determines the trace decay rate. 4.2. Algorithm of ACE The prediction of eventual reinforcement can be described as a linear function of the input vector pH: X pðtÞ ¼ vi ðtÞpH i ðtÞ (6) i
The prediction of p(t) can converge to an accurate prediction by upgrading the weights vi as follows: vi ðt þ 1Þ ¼ vi ðtÞ þ l½rðtÞ þ gpðtÞ pðt 1Þ¯pH,
Fig. 2. Block diagram of adaptive heuristic criticism for pH control.
(7)
where l is the learning rate, r(t) the reinforcement signal provided by system environment, p¯ HðtÞ the average of the input vector pHi(t), g, the positive constant, 0pgp1, that makes predications decay in the absence of an external reinforcement. In other words, the change in the value of p plus the value of the external reinforcement constitutes the
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Fig. 3. Flow chart of the AHC for pH control.
heuristic or internal reinforcement r0 where the future value of p is discounted more the further it is from the current state of the system. The improved or internal reinforcement signal, i.e., the output of ACE, can be calculated as follows: r0 ðtÞ ¼ rðtÞ þ gpðtÞ pðt 1Þ.
(8)
This is also known as ‘‘temporal difference’’ (TD) error (Bucak, and Zohdy, 2001). The on-line data acquisition system was set to sample at 1-s intervals to monitor changes in the pH and Ca(OH)2 signals. The output from the neural network was scaled so that the minimum value of the pH corresponded to the maximum output of the variable speed pump delivering Ca(OH)2 solution at a flow rate of 80 ml/min to the reactor. The on-
line use of the neural network allowed it to investigate the absolute values of Ca(OH)2 and the rate of change. The AHC controller set points were 5% and 80% of the flow rate of Ca(OH)2. A total of 12 different dye wastewater overloads were carried out in the agitated semi batch reactor to compare the system’s response to the different operating conditions according to the 23 factorial experimental design. The conditions are given in Table 4.
5. Results and discussion One important objective of large-scale experiments on industrial wastewater is to determine the minimum
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amounts of lime water and ferrous salt which have to be used while remaining within the required time frame for pollutant degradation. In this study we have therefore applied the experimental design methodology from Zeybek (1997) to investigate the lime water and ferrous salt quantities in the degradation rate of dye colloids. The main features of response surface methods lend themselves well to the study of multiple response situations. Diagrams showing the fitted surface in the form of contours of constant response often indicate more than one region where the predicted response is at a level which is considered to be satisfactory. The experimenter can then use this information, in addition to similar contours for a second response, to arrive at a setting (X1, X2, y, Xk) that represents approximately the ‘‘best’’ operating conditions. The turbidity of the treated wastewater was chosen as the dependent variable to be studied. In the experiments, three independent variables investigated were slaked lime concentration (U1), the volume of the dye wastewater (U2) and the FeCl3 flow rate (U3). The ranges of variation were 5% to 10%, 12 to 18 l and 2.4 to 24 ml/min, respectively. The coded factor levels are given in Table 5. The following equation shows coded variables. X1 ¼
%CaðOHÞ2 %CaðOHÞ2 V V¯ , ; X2 ¼ DV D%CaðOHÞ2
X3 ¼
F FeCl3 F FeCl3 . DF FeCl3
Initially, a simple 23 factorial experiment was planned in order that yield and cost could be studied and then four observations were taken at the origin of the design. The illustration of the first phase with 23 factorial experiments is shown in the first eight rows of Table 4. For the linear model, these eight experiments were performed. Four experiments were added for the quadratic model. Eqs. (9) and (10) represent the linear model and interactions of the variables, respectively. The first-order response is given by ylin1 ¼ 0:832 0:052X 1 0:048X 2 0:024X 3 .
(9)
The standard error of Eq. (9) is 0.034. y ¼ 0:844 0:065X 1 0:051X 2 0:031X 3 þ 0:011X 1 X 2 0:019X 2 X 3 þ 0:048X 1 X 3
ð10Þ
2
For Eq. (10), R is 0.972. The second phase of the study involved augmenting the factorial experiment illustrated by Table 4 with additional
Table 5 The coded factor levels
U1 U2 U3
1
0
1
12 0.05 2.4
15 0.075 13.2
18 0.10 24
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points in order to allow the fitting of second-order surfaces via the second-order polynomial models of Eqs. (11) and (12). One observation was added to each of the two already recorded for the factorial treatment combinations. The resulting configuration formed a central composite design, with a total of 12 observations. The value of the parameter a was chosen to be 1.0. The estimated response function found by using those data is given by y ¼ 0:615 0:052X 1 0:021X 2 þ 0:031X 3 þ 0:002X 1 X 2 þ 0:052X 1 X 3 þ 0:002X 2 X 3 þ 0213X 21 þ 0:068X 22 . ð11Þ Least-squares estimation in LU form: Standard deviation: 0.1060608 y ¼ 0:730 0:039X 1 0:045X 2 þ 0:016X 3 0:004X 1 X 2 þ 0:067X 1 X 3 0:020X 2 X 3 þ 0:260X 21 0:170X 22 . ð12Þ From Levenberg–Marquardt using SYSTAT VERSION 7.0: Standard Deviation: 0.035 Raw R2 (1Residual/Total) ¼ 0.988, where y is the turbidity of the refined wastewater from the model. P-values of regression, lack of fit, and corresponding coefficients were tested. Lack of fit was not significant, but regression was significant at a 3% a level only for the quadratic model, which indicated that this model fits the response surface. Therefore, the quadratic model was selected to describe the response surface of turbidity within this region. Two- and three-dimensional response surfaces of the quadratic model for turbidity of the treated wastewater and the corresponding estimated optimums are shown in Fig. 4. Figs. 4(a), (c), and (e) give three yield contours, corresponding to fixed levels of the variables X3, X2, and X1. Notice that the middle figure, although not displaying exactly the optimum yield, does indicate an estimated yield that is close to the maximum in the boundary region. The response surface of turbidity showed a clear peak, which indicated that the optimum condition was well inside the design boundary in Figs. 4(b), (d), and (f). It was learned from this response surface study that the operating conditions should be altered from the standard plant conditions. The original practice of adding both reagents in excess in order to improve yield is not optimal. Only the FeCl3 flow rate (variable X3) should be increased. In fact, lowering the slaked lime concentration (reagent X2) to the region of the maximum yield point will result in an estimated increase in yield of 5%. In this way the conditions that maximize the turbidity, defined as the optimal conditions earlier, were calculated by setting the partial derivatives of the function to zero with respect to the corresponding variables according to the Rosenbrock
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Fig. 4. The contour plots (a), (c) and (e) represent the turbidity of treated wastewater in terms of the relationships between the coded variables x1–x2; x1–x3; x2–x3 respectively, and (b), (d), and (f) show the corresponding three-dimensional representations.
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algorithm. At the optimal conditions, turbidity was 0.99. The stationary point was found to be an optimal result from the Rosenbrock algorithm.
The purpose of this algorithm is to find the minimum of a multivariable, unconstrained, nonlinear function. Minimize F ðx1 ; x2 ; :::; xN Þ. The procedure is based on the direct search method proposed by H.H. Rosenbrock. No derivatives are required. The procedure assumes a unimodal functions; therefore, several sets of starting values for the independent variables should be used if it is known that more than one minimum exists or if the shape of the surface is unknown. Coding values of parameters:
4.4 4.2 open loop with FeCl3
4.0 3.8 3.6 pH
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3.4 3.2
X 1 ¼ 1:41409; X 2 ¼ 0:2627; X 3 ¼ 0:124.
3.0
Real values of parameters: V ¼ 10.8 l. Ca(OH)2 ¼ 6.8% qFecl3 ¼ 14:54 ml=min The estimated yield at this point is given by y^ 1 ¼ 0:99. Thus the stationary point gives maximum yield. The treatment efficiency shows a complicated nonlinear function of the dosages of coagulant, lime water and reactor volume. However, influent water quality is constantly fluctuating, which makes it hard to accurately describe the
2.8 2.6 2.4 2.2 0
100
200
300 400 Time (second)
500
600
The ratio of pump opening%
Fig. 5. Open-loop response curve for a step disturbance of FeCl3 flow.
pH
15 10 5 0 0
500 1000 Time, second, s
100 80 60 40 20 0
1500
0
500 1000 Time, second
1500
The ratio of pump opening %
pH
Fig. 6. Performance in experiment no. 5 by AHC control.
12 10 8 6 4 2 0
100 80 60 40 20 0
0
300
600 900 Time, second
0
1200
500 1000 Time, second
1500
50
14 12 10 8 6 4 2 0
The ratio of pump opening %
pH
Fig. 7. Performance in experiment no. 6 by AHC control.
40 30 20 10 0
0
500 1000 Time, second
1500
0
200
400
600 800 Time, second
Fig. 8. Performance in experiment no. 7 by AHC control.
1000
1200
1400
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complex relationship between the removal rates and the chemical dosages. Since some deviations appear inevitably when applying the experimental results to real situations, continuous training of the neural network is necessary to keep it accurate. During the control process for the AHC controller, output includes pH value, which must be 11. This shows us the expected removal rates for turbidity. The controller output is the optimal control action which is the percentage of the Ca(OH)2 pump flow rate. Fig. 5 shows the experimentally dynamic behavior of the process to the unit step change in acid flow rate. From Figs. 6–9, it can be seen that both pH value and turbidity are able to follow the expected removal rates. Fig. 6’s removal rates are higher than those in Fig. 7, but in all the figures, under the given setting, the control action remains at the optimal pH of 11 after 400 s. Figs. 6–9 show the experimental results when the influent water quality varies dynamically. Thus, the control and the relevant data lead to satisfactory control effects. This study investigates the possibilities and methods of purifying and improving the quality of wastewater released
(13) Parameters obtained from the Yuwana–Seborg method: Kc 51.48
10 5 0 0
500 1000 Time, second
tI 20.875
tD 5.060
Finally, Fig. 11a shows the closed-loop response under AHCC and PID control to regulate the clarified wastewater before it is discharged into the river. It shows a very large process gain change from pH ¼ 11 to the
The ratio of pump opening %
15 pH
by the dye industry and also addresses the control of pH in dye wastewater. The performance results of (proportionalintegral-derivative) PID and adaptive heuristic criticism (AHC) control were compared in terms of effectiveness, as shown in Fig. 10. The PID control equation is given below. The related parameters in this equation are calculated according to the Yuwana–Seborg Method (Hapoglu, 1993). Z CðtÞ ¼ Kc ðtÞ þ Kc=tI ðtÞ dðtÞ þ Kc tD d ðtÞ=dðtÞ þ Cs.
100 80 60 40 20 0 0
1500
500 1000 Time,second
1500
Fig. 9. Performance in experiment no. 8 by AHC control.
c 12
a pH
12 pH
8
8
4 4 0
0 0
300
600 900 Time,second
b
300
600 900 Time,second
1200
d
100
The ratio of pump opening, %
The ratio of pump opening, %
0
1200
80 60 40 20 0 0
300
600 900 Time, second
1200
100
50
0 0
500 Time, second
1000
Fig. 10. For parallel experiments comparative results of the AHCC (Experiment no:8) and PID (Experiment no:4) respectively: (a) pH response, (b) manipulated variable movements of AHCC, (c) pH response and (d)manipulated variable movements of PID.
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The ratio of pump opening, %
b a 16
AHC
60 40 20 0 0
8
4
SET POINT PID AHC
0 0
1000 2000 Time, second
3000
The ratio of pump opening, %
pH
12
80
1000 2000 Time, Second
3000
80 PID
60 40 20 0 0
1000 2000 3000 Time, Second
4000
Fig. 11. (a) pH regulation by AHC and PID before discharge into the river. (b) Lime water flow.
neutralization point of pHs ¼ 7. The changes in the titrating stream are as in Fig. 11b. This can cause very large changes in pH with PID, as in Fig. 11a. Hence it is very difficult to control at the set point of pHs ¼ 7. Actually, many existing adaptive control systems fail to regulate this type of influent stream well. While the PID control strategy fails to control the process, the AHCC controller keeps the pH within a small deviation from pHs ¼ 7. Since specific nonlinear equations are not assumed, AHCC can cope with changes of nonlinearity due to component changes in the influent stream. 6. Conclusions This paper presents a control scheme for a waste coagulation process based on adaptive heuristic criticism control. This control enables the system to adapt to a large variety of operating conditions with an enhanced learning ability. This study also develops a model for the nonlinear relationships between the pollutant removal rates and the chemical additive dosages. The method can be adapted to a large-scale plant. The following other conclusions are based on data from optimization and control of the dye wastewater treatment processes. (1) Response surface methodology was successfully applied to determine the optimal physical conditions where the maximum treatment of dye wastewater occurred, in which the turbidity of the influent in the pilot plant was more than 90%. These were V ¼ 10.8 l, Ca(OH)2 ¼ 6.8%, qFecl3 ¼ 14:54 ml=min at pH 11.0 and 20.1 1C respectively. (2) The results reveal that for control, although the manipulated variable had only the minimum and maximum values in the plant operations, the control
quality is still comparable to PID control. The results also indicate that the capability of AHCC was excellent at the maximum rate of FeCl3 inflow, although the reactor contents increased in volume. However, at nonoptimal conditions, the performance of AHCC decreased. If the kinetic mechanism for the dye wastewater were known, the volume could be calculated for scale-up. The other parameters are important and independent from the volume. After scaling up the model, optimal conditions should be re-determined for the specific treatment system. In this way, experimental design methodology becomes important in determining the startup conditions when a process is to be scaled up to the industrial level.
Acknowledgements The authors are grateful for financial support provided by the Ankara University Research Foundation (Grant no. 2002.07.45.006).
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