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Dissolved oxygen regulation by logarithmic/ antilogarithmic control to improve a wastewater treatment process a
b
c
Victor R. Flores , Edgar N. Sanchez , Jean-François Béteau & Salvador Carlos Hernandez
d
a
Centro de Enseñanza Técnica Industrial Guadalajara, Calle Nueva Escocia 1885, 44620 Guadalajara, Mexico b
Cinvestav del IPN, Unidad Guadalajara, Apdo. Postal 31-438, Plaza La Luna, 45090 Guadalajara, Mexico c
Automatic Control Department, GIPSA Lab, Grenoble INP, BP 46, 38402 St Martin d'Hères, France d
Cinvestav del IPN, Unidad Saltillo, Av. Industria Metalurgica 1062 25900 Ramos Arizpe, Coahuila, Mexico Accepted author version posted online: 23 May 2013.Published online: 18 Jun 2013.
To cite this article: Victor R. Flores, Edgar N. Sanchez, Jean-François Béteau & Salvador Carlos Hernandez (2013) Dissolved oxygen regulation by logarithmic/antilogarithmic control to improve a wastewater treatment process, Environmental Technology, 34:23, 3103-3116, DOI: 10.1080/09593330.2013.803159 To link to this article: http://dx.doi.org/10.1080/09593330.2013.803159
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Environmental Technology, 2013 Vol. 34, No. 23, 3103–3116, http://dx.doi.org/10.1080/09593330.2013.803159
Dissolved oxygen regulation by logarithmic/antilogarithmic control to improve a wastewater treatment process Victor R. Floresa∗ , Edgar N. Sanchezb , Jean-François Béteauc and Salvador Carlos Hernandezd a Centro de Enseñanza Técnica Industrial Guadalajara, Calle Nueva Escocia 1885, 44620 Guadalajara, Mexico; b Cinvestav del IPN, Unidad Guadalajara, Apdo. Postal 31-438, Plaza La Luna, 45090 Guadalajara, Mexico; c Automatic Control Department, GIPSA Lab, Grenoble INP, BP 46, 38402, St Martin d’Hères, France; d Cinvestav del IPN, Unidad Saltillo, Av. Industria Metalurgica 1062 25900 Ramos Arizpe, Coahuila, Mexico
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(Received 15 October 2012; final version received 25 April 2013 ) This paper presents the automation of a real activated sludge wastewater treatment plant, which is located at San Antonio Ajijic in Jalisco, Mexico. The main objective is to create an on-line automatic supervision system, and to regulate the dissolved oxygen concentration in order to improve the performances of the process treating municipal wastewater. An approximate mathematical model is determined in order to evaluate via simulations different control strategies: proportional integral (PI), fuzzy PI and PI Logarithm/Antilogarithm (PI L/A). The controlled variable is dissolved oxygen and the control input is the injected oxygen. Based on this evaluation, the PI L/A controller is selected to be implemented in the real process. After that, the implementation, testing and fully operation of the plant automation are described. With this system, the considered wastewater treatment plant save energy and improves the effluent quality; also, the process monitoring is done online and it is easily operated by the plant users. Keywords: wastewater treatment; activated sludge; logarithmic/antilogarithmic control
1. Introduction 1.1. Problem statement Water is essential for all the human activities; from the daily life to the industrial production, large quantities of water are used every day all around the world producing also large quantities of wastewater. Nowadays, most of countries implement programmes to promote the reuse and recycle of treated wastewater. Then, the standards concerning the quality of treated water become more and more strict. For that reason, the improvement of treatment processes is an active research topic. In Mexico, there is a production around 240 m3 /s of municipal wastewater; approximately only 30% is treated with an efficiency of 30% concerning removal of biological oxygen demand (BOD).[1] Activated sludge is one of the main processes used to treat municipal wastewater; this kind of plants is able to treat very large volumes of wastewater, with a suitable quality. The most important difficulties detected in the operation of wastewater treatment plants in Mexico are: the influents are strongly affected by rain; the daily volume of influents is frequently higher than the plant capacity; designers of wastewater treatment plants include few variables to be manipulated; there are key variables hard to measure due to economical or technical restrictions. This situation implies important risks to the environment
and human health. On the other side, very few municipal wastewater treatment plants are designed considering an automatic control approach; usually, supervision, monitoring and control are done by the operators in manual mode. All these situations represent an immediate scientific and technological challenge all around the country. Automation is an interesting alternative to improve the performance and operation of wastewater treatment plants; the main idea is to design control laws for the automatic handling of key variables. In order to automate any process, it is highly advisable to develop at least an approximated mathematical model. For activated sludge wastewater treatment plants, the respective models are described by nonlinear differential equations, which represent the biological, biochemical and physical–chemical phenomena taking place inside the plant. Once a model is defined, different control techniques can be evaluated via simulation, in order to select the best one to be implemented in real time.
1.2. Bibliographic review Different researches have been focused in modelling and control of wastewater treatment processes by activated sludge since many years ago.[2–5] The Monod model was used to represent the kinetics of microorganisms and to
∗ Corresponding author. Email: vfl[email protected] This article was originally published with erroneous pagination. This version has been corrected. Please see Erratum (http://dx.doi.org/ 10.1080/09593330.2013.869395).
© 2013 Taylor & Francis
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ease the design of aeration tanks.[6] In,[7] it is reported the results of experiments performed to develop a simple kinetic model which incorporates the storage/metabolism mechanism and a growth/decay representation. Also, a dynamic model of carbonaceous and nitrogenous substrate removal for a real process was evaluated [8]; the work focuses in the use of a model for the interpretation of a field data collected from the real process for several days. In other work, a model which incorporates the interactions between the aeration tank and the final settler is proposed [9]; the model suggests that sludge settleability is improved with increasing sludge age. On the other side, the Activated Sludge Model No. 1 (ASM1) developed by the International Association on Water Quality is well accepted for dynamic simulation of the removal of carbonaceous and nitrous compounds from wastewater in activated sludge plants.[10] The ASM1 describes nitrogen and chemical oxygen demand (COD) within suspended-growth treatment processes, including mechanisms for nitrification and denitrification, and defines components, stoichiometry and kinetics of activated sludge processes.[10,11] Thirteen differential equations are formulated to represent eight processes: aerobic and anoxic growth of heterotrophic biomass, death of heterotrophic biomass, aerobic growth of autotrophic biomass, decay of autotrophic biomass, ammonification of soluble organic nitrogen and hydrolysis of both entrapped particulate organic matter and entrapped organic nitrogen.[10,11] Many research works have been developed based on this model,[12–15] and also, further works were performed in order to complement the ASM1.[16–20] Concerning control of wastewater treatment processes by activated sludge, several techniques have been already evaluated. An optimal control approach is applied to minimize effluent organics concentration and energy consumption [21]; the control problem is formulated with recycle and wastage rates as control variables and it is solved using an analytically based trajectory iteration control procedure; as results, the proposed optimal control technique can handle the process nonlinearity and high dimensionality. In,[22] an optimal control is used to minimize nitrogen discharges and energy consumption; both minimization problems are formulated in order to guarantee that the improvements can be maintained for long time; the results show a reduction of 37% for nitrogen discharges, a reduction of aeration time around 2 h and an energy saving of 27%. An interesting two-stage robust control scheme composed by a dynamic controller and a proportional-integral-derivative (PID) is developed and validated in simulation [23]; with this strategy three main benefits are obtained: an asymptotic following of the substrate concentration, asymptotic attenuation of the effects of disturbances on the influent over the output substrate and oxygen. A hierarchical controller for tracking a dissolved oxygen reference trajectory is proposed in [24] in order to remove nitrogen and phosphorous: the upper level generates trajectories of the desired airflows to be delivered to aerobic zones (a nonlinear predictive controller
is applied), the lower-level forces the aeration system to follow these trajectories; the hierarchical controller is validated by simulation based on real data. A feed forward– cascade controller based on a hierarchical structure is presented in [25]; the main objective was to reject changes in the influent load variations: a cascade controller selects the set-point of the low level controller which regulates the dissolved oxygen concentration in order to meet strict effluent quality standards at a minimum cost. Multivariable PID controllers to regulate dissolved oxygen (DO) and nitrate removing were proposed in [26]; an important contribution is the tuning method, which uses an optimization technique to meet standard quality effluent at a minimum cost. In other work,[27] a supervisory strategy is proposed in order to control aeration volume; from DO measures, it is determined what compartment must to be aerated depending on the input load; the strategy is validated by simulations and it is implemented in a pilot plant; on the other side, a model predictive control has been studied for DO control by manipulation of the oxygen mass transfer coefficient; the controller was validated by simulations considering input disturbances and set-point changes.[28] Other work reports an evaluation of six control strategies (PID control, model predictive control (MPC) with linear model, MPC with non-linear model, nonlinear autoregressive moving average L2 (NARMAL2) control, neural network based model predictive control (NN-MPC) and optimal control with statistical quality process (SQP) algorithm); dissolved oxygen was the controlled variable and the aeration rate was the control input. The authors conclude that NARMA-L2 controller and optimal control with SQP are better than the other alternatives.[29] In,[30] a multicriteria control strategy is proposed to decrease the global cost in wastewater treatment plants; the main idea is to select the manipulated and measured variables based on a sensitivity analysis, a Takagi–Sugeno supervisor is implemented in order to smooth the switching between control actions; the strategy provides good performances. In,[31] a fuzzy control is proposed and compared with classic control techniques to regulate dissolved oxygen in a sequential batch reactor at pilot plant scale; the fuzzy system considers two inputs (difference between set point and the measure for dissolved oxygen considering three fuzzy sets, and the step of the cycle expressed by six fuzzy sets) and one output (the air injected into the reactor represented by eight fuzzy sets); the obtained results show good performances of the process by using the fuzzy controller. Finally, more advanced studies are also developed considering distributed parameter for the biological reactors.[32] 1.3. Contribution of this work In this article, a real wastewater treatment plant is studied. The objective is to implement a control strategy to enhance the process performances by improving effluent quality and saving energy. First, an approximate mathematical model of the process is elaborated including a step for parameter
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Environmental Technology identification. Based on this model, different control strategies are designed and evaluated: proportional integral (PI), fuzzy PI (FPI) and PI Logarithm/Antilogarithm (PI L/A). Dissolved oxygen is considered as the controlled variable and the control input is the injected oxygen. The best controller is selected by a series of simulations and it is implemented in the real process. The model considered is based on the ASM1 and it is used as a tool to study the process behaviour and to design and test the controllers. A simulation stage is performed following the recommendations of the COST Simulation Benchmark.[33] The process parameters were obtained following the methodology described in the specialized literature.[34,35] The main contribution is the real-time implementation of a PI L/A which allows the performances of a wastewater treatment plant to be improved. This control technique considers the positivity restriction of the variables and is easy to tune and implement; the automation and supervision strategy is well understood and accepted by the plant operators. The automation system could be implemented in other municipal plants in order to improve the wastewater treatment and to ease the operation of the processes. It is important to remark that some preliminary results are described in the reference.[36] 2. Process description 2.1. Activated sludge preliminaries Activated sludge is a biological process widely used for municipal and industrial wastewater treatment. The organic components contained in the wastewater, known as substrate, are used as energy source for the growth of microorganisms, known as biomass or activated sludge which degrades the pollutants. Since these microorganisms are aerobic ones, a certain quantity of oxygen must be supplied to maintain adequate conditions for biomass growth. The obtained mix (wastewater and activated sludge) is purified in a clarification stage by settling of the microbial flocks in another tank known as settler. The thickened sludge is recycled in order to maintain enough biomass in the bioreactor, and to keep constant the amount of microorganisms, the exceeding biomass is regularly removed (wastage).[21] A general representation of this process is shown in Figure 1.
Figure 1. process.
Schematic representation of an activated sludge
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Wastewater treatment by activated sludge is a complex process due to the high variability of the influent flow rate, substrate load and physical characteristics; also, the process involves nonlinear dynamics, large uncertainty and multivariable structure.[24,37] Then, control strategies are required in order to prevent failures and to guarantee the adequate functioning of the processes. Dissolved oxygen is commonly used as a regulated variable since it is very important to provide the condition for biomass growth and then for an efficient wastewater pollutants degradation. 2.2. The case of study The wastewater treatment plant considered in this study is located at Ajijic, Jalisco, Mexico; it is operated by CEA (Jalisco State Water Board). This plant is intended to remove organic substrates, without removing nitrogen components since the original design does not consider an anoxic zone. This plant was designed to treat 32 L/s; at present time the influent is higher than this value according to measurements which indicates an influent around 40 L/s. A plant scheme is portrayed in Figure 2. As can be seen, the treatment is performed in four stages: (i) Pre-treatment. The big solids are separated for wastewater by means of a mesh; also, sand traps retain weight inorganic solids such as sand, small rocks and some metallic objects; finally, wastewater is filtered to separate organic material with a size bigger than 3 mm. (ii) Secondary treatment. This stage is performed by an activated sludge process as described in Section 2.1. The secondary treatment is composed by two biological reactors and two settlers, and it is the main concern of this study. (iii) Chlorination. In this stage, the treated wastewater receives a chloride solution with a concentration of 8 ppm, which is calculated considering a mean flow rate according to the process design. The objective is to eliminate pathogen microorganisms and it is done in order to reach local environmental regulations. (iv) Sludge treatment. The water contained in the resulting sludge is removed in order to decrease the sludge volume. Also, in this stage the remaining organic solids which could cause putrefaction are transformed into solid minerals or relatively stable organic solids. The wastewater sent to the Ajijic wastewater treatment plant is collected from two cities; then, the composition of the influent is mainly of domestic nature without industrial wastes. The basic characterization of influent is done in order to obtain the mean concentration of BOD5 , Total and Soluble CODt and CODs , total suspended solids (TSS) and
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Figure 2.
V.R. Flores et al.
Schematic distribution of Ajijic wastewater treatment plant. Table 1. Wastewater characterization (mean values in one month). Parameter (mg/L)
Value
BOD5 CODt CODs TSS VSS
239 728 271 287 1067
volatile suspended solids (VSS). It is important to mention that the respective analyses are elaborated in the wastewater treatment plant laboratory and they are provided for this study. Table 1 contains the mean values for each parameter; the data correspond to mean values in one month. 2.3. Process modelling The secondary treatment stage used in the Ajijic plant is represented by the scheme displayed in Figure 3. It corresponds to a common activated sludge process.[38] In Figure 3 Q0 and Z0 are the influent flow rate and concentrations; QL and ZL are the leachate flow rate and concentrations; Qf and Zf are the bioreactor output flow rate and concentrations; Qe and Ze are the effluent flow rate and concentrations; Qw and Zw are the wastage flow rate and concentrations and Qr is the recycled flow rate. The ASM1 model for this process considers 13 variables.[10] However, for this specific plant, it is supposed that the process contains the simplest biological wastewater treatment mechanisms; since it involves only the three basic components: biodegradable substrate, heterotrophic biomass and dissolved oxygen concentration.[39] In order to develop this model, mass balances are established for each bioreactor. The heterotrophic biomass mass balance
Figure 3.
Activated sludge process in the Ajijic plant.
can be expressed as dXBH,j 1 (Q0 XHB,0 + Qr Xr − Qf XBH,j ) + rH , = dt 2Vj
(1)
where XBH is the biomass concentration, Xr is the recycled biomass concentration, V is the volume, j is the bioreactor number and rH is the reaction rate for biomass growth. The influent and leachate microorganism concentration are neglected. The biodegradable substrates balance is defined as dSS,j 1 (Q0 SS,0 + QL SS,L + Qr SS,r − Qf SS,j ) + rS , = dt 2Vj (2) where SS is the biodegradable substrate concentration, SS,r is the recycled substrate concentration, SS,L is the leachate substrate concentration and rS is the substrate uptake rate. The oxygen mass balance is formulated as dSO,j 1 = kla(SO,sat − SO,j ) − Qf SO,j + rO , dt 2Vj
(3)
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where SO is the dissolved oxygen concentration, SO,sat is the saturated dissolved oxygen concentration, rO is the oxygen consumption rate and the kla term is the mass transfer from the aeration. The influent, recycled and leachate oxygen concentration are neglected. The reaction rates rH , rS and rO for each equation are represented as follows. The reaction rate for biomass growth can be expressed as SS,j SO,j rH = μˆ H XBH,j , (4) KS + SS,j KOH + SO,j where μˆ H is the maximum heterotrophic biomass growth rate, KS is the half-velocity coefficient for biodegradable substrate and KOH is the half-velocity coefficient for dissolved oxygen concentration. The substrate reaction rate is defined as SS,j SO,j 1 rS = − μˆ H XBH,j , (5) YH KS + SS,j KOH + SO,j where YH is the heterotrophic yield coefficient. The oxygen consumption rate is expressed as SS,j SO,j YH − 1 μˆ H XBH,j . (6) rO = YH KS + SS,j KOH + SO,j Finally, the heterotrophic biomass mass balance for settlers can be expressed as dXr,k 1 (Qf XHB,f + (Qr + Qw )Xr,k ), = dt 2Vk
(7)
where Qw is the waste flow rate and k is the settler number. It is assumed that effluent does not contain any biomass. 2.4. Parameters determination In order to validate the proposed model, it is necessary to determine the kinetic coefficients. This is done following recommendations in the specialized literature. The method to determine the aeration constant kla is based on that proposed by [33]: First step: the influent, leachate and recycle inlets are closed and the blower motor is turned on. The oxygen concentration is increased up to saturation (SO,eq ). The mixture of biomass and influent wastewater in the bioreactor is defined as mixed liquor. The mixed liquor oxygen concentration is measured and registered until it is impossible to increase the oxygen concentration. The mixed liquor oxygen concentration has a behaviour similar to that shown in Figure 4. Second step: when the mixed liquor is saturated, the blower motor is turned off. The oxygen concentration decreased since it is consumed by biomass; then, a value above the critical dissolved oxygen concentration is selected. It is important to recall that the dissolved oxygen concentration must not diminish below 0.5 mg/L.
Figure 4.
Dissolved oxygen time evolution.
Third step: when the oxygen concentration reaches the selected value (3.2 mg/L in this case), going back to the first step. It is advised to repeat this procedure at least twice. The mathematical representation of this method is described as follows. The concentration dynamics of the dissolved oxygen can be rewritten as dSO = kla(SO,sat − SO ) − XQO2 , dt
(8)
where XQO2 is the oxygen consumption rate by biomass. For the saturation concentration (first step), the dissolved oxygen fulfills the following relation: dSO = 0 ⇒ 0 = kla(SO,sat − SO,eq ) − XQO2 , dt
(9)
where SO,eq is the equilibrium dissolved oxygen concentration of mixed liquor. When blower motor is turned off (second step), the dissolved oxygen concentration evolution is described as dSO = −XQO2 . (10) dt Subtracting Equation (10) from Equation (9), the next expression is obtained dSO = kla(SO,sat − SO ). dt
(11)
Then, to determine kla, Equation (11) is integrated and the resulting expression is ln
(SO,eq − SO ) = −kla × t, (SO,eq − SO,0 )
(12)
where SO,0 is the oxygen concentration of the aeration restart (going from the second step to the first one). For the case studied here, an exponential curve is obtained as shown in Figure 5 (step 1). It is observed that the maximum oxygen concentration is 6.10 mg/L and SO,0 is equal to 3.2 mg/L.
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V.R. Flores et al. Table 2. Activated sludge kinetic coefficients for heterotrophic bacteria at 20◦ C. Coefficient
Range
μˆ H (g VSS/g VSS × d) KS (g bCOD/m3 ) YH (g VSS/g bCOD) KOH (mg/L) θ values μˆ H (Unitless) KS (Unitless) Figure 5.
3.0–13.2 5.0–40.0 0.30–0.50 0.1 1.03–1.08 1
Value 6 20 0.4 0.1 1.07 1
Dissolved oxygen measures for kla determination. Table 3. Activated sludge kinetic coefficients for heterotrophic bacteria at 26◦ C.
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Coefficient μˆ H KS YH KOH
Figure 6.
Unit
Value
g VSS/g VSS × d g bCOD/m3 g VSS/g bCOD mg/L
9.0 20.0 0.425 0.10
On the other hand, the kinetic coefficients for the removal of carbonaceous material are taken from values recommend for the ASM1.[10] Typical values are shown in Table 2. It is well known that temperature affects the metabolic activities of the microbial population as well as the gastransfer rates and the settling characteristics of the biological solids. The effect of temperature on the reaction rate of a biological treatment process is expressed as
Determination of kla coefficient.
kT = k20 θ (Tb −20) ,
Figure 7.
Dissolved oxygen concentration.
To calculate kla, the DO measurements are substituted in Equation (12) in order to trace the graph shown in Figure 6. Using lineal regression, the following equation is deduced: y = −0.04111x − 0.22165,
(13)
where kla is determined as the slope of Equation (13). The obtained experimental value is equal to 0.04111/min or 59.2/day. After that, the dissolved oxygen concentration is traced to obtain the graphic shown in Figure 7 (second step). Applying a lineal regression, the oxygen consumption slope is calculated as y = −0.06345x + 6.0245,
(14)
where XQO2 is determined as the slope of Equation (14). The obtained experimental value is equal to 0.06345 mg/L-min.
(15)
where kT is the kinetic coefficient at temperature Tb , k20 is the kinetic coefficient value at 20◦ C, θ is the temperature activity coefficient and Tb is the bioreactor temperature. The maximum specific growth rate (μˆ H ) and the half-velocity coefficient (KS ) are calculated using the values of Table 2 and Equation (15), then these parameters are included in Table 3. For the model validation, Equations (1)–(7) were implemented in MatlabTM [40,41]; also, experimental data obtained from dissolved oxygen measures and chemical analyses were considered. The initial conditions are: XBH = 2520 mg/L COD, SS = 0.64 mg/L COD and SO = 4.44 mg/L. In Figure 8, a comparison between simulations and measures is presented. It can be seen that DO concentration is very well reproduced; the difference at the beginning and the end of the simulation could be due to errors in the measurements. 3. Dissolved oxygen regulation Regulation of DO is commonly used as controlled variable since it is easily measurable and it is a good indicator of the biomass growth; then, it allows the operator to determine
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Environmental Technology
Figure 8.
Model validation with experimental data.
the performance of the process. In this paper, three controllers based on proportional-integral control are designed and tested: conventional PI (PI), minimal FPI and PI based on logarithmic/antilogarithmic control (PI L/A). The controlled variable is dissolved oxygen and the control input is the blower speed which is directly related to the injected oxygen. 3.1.
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PI controller
A conventional PI controller is designed as follows. First, the general expression in s-domain is considered Kc U c (s) = KPc + I E c (s), (16) s where KPc and KIc are the proportional and integral gains, respectively, and E(s) is the error signal. A standard bilinear transformation, s = 2/T (1 − z −1 /1 + z −1 ), is done in order to obtain an expression in z-domain (1 − z −1 )U (z) = (1 − z −1 )KP E(z) + KI E(z),
(17)
where KP = KPc − TK cI /2 and KI = TK cI /2 are the equivalent proportional and integral gains of a discrete PI controller, respectively. After that, an inverse z-transform is applied and rearranging the obtained result, the PI control law in discrete time is deduced: u(nT ) = u(nT − T ) + KP [e(nT ) − e(nT − T )] + KI e(nT ).
and the rate of change of error. As described and analyzed in,[42,43] two input variables, error and rate of change of error (named rate for short), and one output variable, are required. A schematic representation is shown in Figure 9. In Figure 9 y(nT), e(nT), r(nT) and u(nT) are the output, error, rate of change of error and input (output fuzzy controller) from the process; KE , KR and KU are scalars for error, rate and output from the fuzzy controller; e ∼ (n), r ∼ (nT) and u ∼ (nT) are the fuzzy sets corresponding to error, rate and output. L is selected according to the process dynamic; it represents the permissible amplitude of the error. Considering the respective fuzzy sets, four inference rules are formulated: If error = error positive AND rate = rate positive then output = output negative (r1) If error = error positive AND rate = rate negative then output = output zero (r2) If error = error negative AND rate = rate positive then output = output zero (r3) If error = error negative AND rate = rate negative then output = output positive (r4) According to the membership functions for the FPI controller, the value-range of e(nT ) and r(nT ) is divided into 20 input-combinations regions: IC1–IC20 (Figure 10). The AND operator is the minimum of the two values and the defuzzyfication is done by using the average centre method. The incremental control output is determined by u(nT ) = −[KId e(nT ) + KPd r(nT )],
(18) where
3.2. FPI controller A minimal FPI is considered as an alternative to regulate dissolved oxygen. This controller allows the proportional and integral gains to be adapted as a function of the error
0.5 ∗ L ∗ KU ∗ KE ⎫ ⎪ ⎪ 2L − KE ∗ |e(nT )| ⎬ 0.5 ∗ L ∗ KU ∗ KR ⎪ ⎪ ⎭ = 2L − KE ∗ |e(nT )|
KId = KPd
(19)
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Figure 9.
Fuzzy control scheme and variables fuzzyfication.
Figure 11.
L/A control structure.
L ∗ KU ∗ KR r(nT ) − L in IC13, IC14. 2L L ∗ KU u(nT ) = in IC17. 2L L ∗ KU u(nT ) = − in IC19. 2L u(nT ) = 0 in IC18, IC20. u(nT ) = −
Figure 10.
Regions of the fuzzy controller values.
if KR ∗ |r(nT )| ≤ KE ∗ |e(nT )| ≤ L, 0.5 ∗ L ∗ KU ∗ KE ⎫ ⎪ KId = ⎪ 2L − K ∗ |r(nT )| ⎬
(27) (28)
3.3.
(21)
The PI L/A is a control technique based in logarithmic and exponential transformations. These operations allow the positivity restrictions of concentrations to be considered.[44,45] A scheme of this technique is portrayed in Figure 11. The control structure is based on next transformations
0.5 ∗ L ∗ KU ∗ KR ⎪ ⎪ ⎭ 2L − KR ∗ |r(nT )|
if KE ∗ |e(nT )| ≤ KR ∗ |r(nT )| ≤ L,
(26)
(20)
R
KPd =
(25)
When the input signals are located in the regions IC9–IC20, then L ∗ KU ∗ KE e(nT ) u(nT ) = − − L in IC11, IC12. (22) 2L L ∗ KU ∗ KE e(nT ) u(nT ) = − − L in IC15, IC16. (23) 2L L ∗ KU ∗ KR r(nT ) u(nT ) = − + L in IC9, IC10. (24) 2L
PI L/A controller
Y (nT ) = ln(y(nT )), Ref (nT ) = ln(ref (nT )),
(29)
u(nT ) = exp(U (nT )), where y(nT ) is the output, ref(nT ) is the set point and u(nT ) is the control action. These transformations allow to select any conventional control law and to obtain a L/A
Environmental Technology Table 4.
equivalent. In,[46] a PI is proposed as follows: U (nT ) = U (nT − 1) + KP (Y (nT − T ) − Y (nT )) + KI (Ref (nT ) − Y (nT ))
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Parameters of the controllers. Tuning parameters
(30)
with KP and KI being the corresponding gains for the controller. The L/A equivalent of this control law is obtained replacing the mathematical operations as follows: addition by multiplication, subtraction by division and multiplication by exponential y(nT − 1) KP ref (nT ) KI u(nT ) = u(nT − 1) . (31) y(nT ) y(nT ) In addition to the positivity constraints which are overcome with the logarithmic and antilogarithmic transformations, the tuning method of this controller allows the actuator saturation and the minimum permissible error value to be considered in the whole control strategy. 3.4. Controller selection It is necessary to tune the controllers parameters; the followed procedure is described in next lines: (1) the process is linearized about the typical operation points of the plant
Figure 12.
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PI L/A PI FPI
Proportional gain (P)
Integral gain (I)
165 167.5 196.2
7 7 14
(influent substrates, influent flow rate and dissolved oxygen concentration); (2) the tuning parameters are found by the Ziegler–Nichols methods, which is a good approximation and (3) the proportional and integral gains are finely adjusted, it is done using the error index generated by integral absolute error (IAE). Table 4 is shown the tuning values for the controllers. A series of simulations considering different operating conditions was performed in order to evaluate each controller and to do a comparison. Figure 12 shows the obtained result for one of the experiments; in general, the process behaviour is similar for other scenarios. The graphics on left show the DO measurements obtained considering each controller. The graphs on right correspond to the control input, i.e. the percentage of operation for the blower motor which is directly related to
Controllers evaluation considering different operating conditions.
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Table 5. IAE performance index and energy consumption evaluation. IAE index
PI L/A PI FPI Open loop
Energy consumption (kWh/d)
Day 1
Day 2
Day 1
Day 2
0.0005014 0.002384 0.005648
0.0004454 0.002547 0.006094
350.8 351.3 351.8 450
392.3 393.2 393.6 450
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the aeration coefficient kla. The plant performance index is measured through aeration energy [33]; therefore, it is possible to evaluate the performance of the controllers, where kla is the control output or manipulated variable. The energy consumption during one day is calculated with next expression 24 EC = Te
1
{0.4032kla(t)2 + 7.8408kla(t)}dt,
Figure 13. Table 6.
Wastewater treatment plant automation scheme. Calibration of instruments.
(32)
0
where Te represents the evaluation time (1 day) and kla(t) represents the mass transfer coefficient h−1 at time t. As can be seen, the three controllers allow the process to reach the set point, which is 1.5 g/L for this case. The FPI controller induces an oscillatory behaviour on the control input; even if the dissolved oxygen remains around the set point, an oscillatory behaviour is remarked on the measurements; in the worst conditions, the amplitude of those oscillations is near to 10%. The classic PI induces a similar behaviour but the amplitude of the oscillations is less than the case of the FPI. Concerning the PI L/A, it is clear that this controller induces a smoother behaviour in the control action and, as direct effect of this situation, the dissolved oxygen reaches the set point and remains there without oscillations. In addition, the IAE and energy consumption are considered as evaluation elements using a set of data from measurements during two days. As shown in Table 5, the IAE index of the PI L/A controller is the lowest comparing the three controllers; also, the energy consumption using the PI L/A is better than that corresponding to the other controllers. Then, the PI L/A is taken for the real time implementation. It is worth to mention that there exist specific methods for the energy optimization in wastewater treatment plants as shown in,[47] then, it is possible to consider that kind of approaches in future works. 4. Real time implementation 4.1. Process instrumentation In order to implement the PI L/A controller to regulate dissolved oxygen, some basic instruments and sensors are required, which was not installed in the wastewater treatment. Then, sensors for the influent flow, the leachate flow, the bioreactor output flow, the effluent flow, the recycled
Controller output
Frequency converter
Blower motor
%
mA
Hz
Current (A)
Speed (rpm)
Air flow (ft3/min)
100 80 60 40 20 0
20 16.8 13.6 10.4 7.2 4
60 59 58 57 56 55
65.4 64.4 57.9 52.3 46.2 40.4
3600 3576 3504 3438 3366 3294
1110 990 850 720 590 460
flow, the dissolved oxygen concentration and the bioreactors turbidity were acquired, installed and integrated in a global system for supervision and control. All the signals provided by these sensors are concentrated on a programmable logic controller (PLC). Additionally, a personal computer is included for automation development and monitoring, the used software for this proposal is a supervisory control and data acquisition. Figure 13 presents a general scheme of such automation. The PI L/A controller for the dissolved oxygen is programmed in C++ and implemented inside the PLC, with a sample time T = 1 s. The relation between the PI L/A controller output, the frequency converter and the blower motor is presented in Table 6. The information of Table 6 is important since the blower motor must be operated in a specific range of current (38–69 A according to technical specifications). Then, the frequency converter should operate between 55 and 60 Hz; this interval ensures an adequate functioning of the blower motor. The PI L/A is implemented as shown in Figure 14 and it works as follows: the dissolved oxygen sensor measures the corresponding concentration in the biological reactor and send an electric signal; the transmitter receive the
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value (55–60 Hz); the motor blower interprets this value as a specific speed injecting a determined oxygen flow. In the bioreactor, the changes on the air flow produced by the PI L/A maintain the dissolved oxygen concentration in the set point.
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4.2.
Figure 14.
Control loop of dissolved oxygen.
sensor signal and transform it in a normalized signal (4– 20 mA), which corresponds to the operation range of the instrument (0–10 mg/L). The transmitter signal is sent to the PLC where (i) it is converted from analogical to digital format, (ii) normalized, (iii) processed by the PI L/A control algorithm generating the output signal which regulates the dissolved oxygen in the biological reactors, (iv) converted from digital to analogical format and (v) normalized in an interval of 4–20 mA. After that, the speed controller receives and converts the signal from the PLC to a frequency
Figure 15.
Open loop operation.
Figure 16.
Influent carbon sources for two different days.
Controller evaluation
The set point for the dissolved oxygen is 1.5 mg/L which is recommended for organic substrate removal.[48,49] Figure 15 shows the influent and the dissolved oxygen concentration before the PI L/A implementation (open loop operation with a single blower-motor turning on). Furthermore, carbonaceous loads are shown in Figure 16. It can be seen that in the day the dissolved oxygen is below 0.5 mg/L, but in the night there is a remarked increase due to the influent flow rate reduction and to the full operation of the blower motor. This represents unnecessary energy consumption. Figure 17 shows the controller PI L/A performance for typical days of the process operation. In fact, a second blower-motor is integrated to improve the process in the morning–afternoon. According to the dissolved oxygen concentrations in the bioreactors, an interlock is designed to turn on/off one blower-motor or both blowers-motor. The input flow rate is varying all along the day; from 8:00 h until 14:00 h the value corresponds to the design conditions, after that the input flow rate is more than 32 L/h. Before 11:00 h, the dissolved oxygen does not reach the set point which is 1 mg/L. Since the DO is more than the required for the bioreactor, the blower motor is saturated in the low level, namely it is turn off; when the dissolved oxygen is less than
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Figure 17.
Dissolved oxygen regulation.
the required level, the controller activates the blower motor in order to reach the set point. Between 11:00 and 14:00 h, the dissolved oxygen remains around 1 mg/L, the speed of the blower motor is adapted to the increase in the input flow rate according to the control law. After 14:00 h, the input flow rate cross over the design value; nevertheless, the dissolved oxygen remains very close to the set point, specially the second day (Figure 17(b)). It can be seen that the motor works according to the requirements; it reach its maximum power only in some intervals, otherwise, the speed is regulated by the PI L/A controller saving energy. Concerning this topic, a comparison between the open loop and the PI L/A application
is done as follows. The energy consumption during eight days is calculated with Equation (31) and where Te is equal to eight days. The open loop consumption is 3305 kWh/d; on the other side, the energy consumption using the PI L/A is 2749 kWh/d. The corresponding saving energy is 17%/d which can be directly transformed in economic savings for the plant operator. Finally, the treated wastewater is analyzed in the station laboratory; the results indicate that the process effluent meets the Mexican standard for treated wastewater, which requires a BOD5 less than 15 mg/L, TSS less than 20 mg/L and fats less than 15 mg/L. Table 7 shows the removal level
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Laboratory analysis of influent and effluent (units in mg/L). Influent
Month
Effluent
BOD5
CODt
TSS
Fats
BOD5
CODt
TSS
Fats
180 260 240 260 334 280 255 265
705 796 754 893 807 877 518 764
280 444 310 231 260 325 176 275
97.08 88.18 91.76 102.9 82.39 92.74 38.44 83.01
10 13 2.5 12 11.1 6.8 13 9.84
28 43 45 28 39 14 18 28.7
4.6 4.3 4.45 6.4 4.1 4.1 5.1 4.8
6.17 4.39 4.94 2.9 2.5 4.15 3.37 3.81
1 2 3 4 5 6 7 Average
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of those components from the influent; the results correspond to seven months (in 2012) of the analysis. It can be seen that the effluent could be reused even in public services where the water is in direct contact with people. 5.
Conclusions
A monitoring and control system for a real wastewater treatment plant is presented. A methodology for modeling (model development and model validation) is applied for the studied case. Also, three controller alternatives are designed and evaluated. The PI L/A controller represents a very good feedback scheme in order to keep the DO concentration at desired levels, even in presence of strong influent disturbances. Additionally, an energy saving of 17% is also obtained. It is important to remark that the implementation of the global system has improved the process monitoring and operation by the technical personal on the treatment plant. Also, the developed methodology could be applied in many other wastewater treatment processes all around the country; this is an important step in order to improve the Mexican infrastructure and technology concerning wastewater treatment. Research will be pursued to implement the recycled sludge feedback control loop and also to improve the tuning of the PI L/A in order to enhance process performances during the day period. Acknowledgements The authors thank the support of the Mexican State of Jalisco through COECYTJAL (Jalisco State Council for Scientific and Technological Research) and CEAS (Jalisco State Water Board). They also thank the support of CNRS, France and CONACYT, Mexico through the French Mexican research Initiative on Automatic Control (LAFMAA).
References [1] Comisión Nacional del Agua. Estadísticas del agua en México, edición 2011. Mexico: Secretaría de Medio Ambiente y Recursos Naturales; 2011. [2] Goodman BL, Englande AJ. A unified model of the activated sludge process. J War Pollut Control Fed. 1974;46: 312–332.
[3] Gaudy AF, Jr, Kincannon DF. Comparing design models for activated sludge. Water Sew Works. 1977;123:66–70. [4] Weijers S. Modelling, identification and control of activated sludge plants for nitrogen removal. Veldhoven, The Netherlands: Technische Universiteit Eindhoven; 2000. [5] Gernaey KV, van Loosdrecht MCM, Henze M, Lind M, Jørgensen SB. Activated sludge wastewater treatment plant modelling and simulation: state of the art. Environ Model Software. 2004;19:763–783. [6] Leslie Grady CP, Daigger GT, Lim HC. Biological wastewater treatment. New York: Marcel Dekker; 1999. [7] Padukone N, Andrews GF. A simple, conceptual mathematical model for the activated sludge process and its variants. Water Res. 1989;23 (12):1535–1543. [8] Lessard P, Beck MB. Dynamic modelling of the activated sludge process: a case study. Water Res. 1993;27 (6): 963–978. [9] Akca L, Kinaci C, Karpuzcu M. A model for optimum design of activated sludge plants. Water Res. 1993;27 (9): 1461–1468. [10] Henze M, Grady CPL, Jr, Gujer W, Marais GvR, Matsuo T. Activated Sludge Model No. 1. IAWQ Scientific and Technical Report No. 1, IAWQ, London, Great Britain; 1987. [11] Dold PL, Marais GvR. Evaluation of the general activated sludge model proposed by the IAWPRC task group. Water Sci Technol. 1986;18 (6):63–89. [12] Zhao H, Isaacs SH, Soeberg H, Kummel M. An analysis of nitrogen removal and control strategies in an alternating activated sludge process. Water Res. 1995;29 (2): 535–544. [13] Nelson MI, Sidhu HS. Analysis of the activated sludge model (number 1). Appl Math Lett. 2009;22:629–635. [14] Jeppsson U. Modelling aspects of wastewater treatment processes. [PhD Thesis]. Lund Institute of Technology, Sweden; 1996. Available from http://www.iea.lth.se/publications [15] Roeleveld PJ, van Loosdrecht MCM. Experience with guidelines for wastewater characterisation in The Netherlands. Water Sci Technol. 2002;45 (6):77–87. [16] Henze M, Gujer W, Mino T, Matsuo T, Wentzel MCM, Marais GVR. Activated Sludge Model No. 2. IWA Scientific and Technical Report No. 3, London, UK; 1995. [17] Henze M, Gujer W, Mino T, Matsuo T, Wentzel MC, Marais GVR, van Loosdrecht MCM. Activated sludge model No. 2d, ASM2D. Water Sci Technol. 1999;39 (1): 165–182. [18] Gujer W, Henze M, Mino T, van Loosdrecht MCM. Activated sludge model no. 3. Water Sci Technol. 1999;39 (1): 183–193. [19] Ky RC, Comeau Y, Perrier M, Takacs I. Modelling biological phosphorus removal from a cheese factory effluent by an SBR. Water Sci Technol. 2001;43 (3):257–264.
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[20] Rieger L, Koch G, Kühni M, Gujer W, Siegrist H. The EAWAG bio-P module for activated sludge model No. 3. Water Res. 2001;35:3887–3903. [21] Kabouris JC, Georgakako A. Optimal control of the activated sludge process. Water Res. 1990;24 (10):1197–1208. [22] Chachuat B, Roche N, Latifi MA. Optimal aeration control of industrial alternating activated sludge plants. Biochem Eng J. 2005;23:277–289. [23] Koumboulis FN, Kouvakas ND, King RE, Stathaki A. Two-stage robust control of substrate concentration for an activated sludge process. ISA Trans. 2008;47:267–278. [24] Piotrowski R, Brdys MA, Konarczak K, Duzinkiewicz K, Chotkowski W. Hierarchical dissolved oxygen control for activated sludge processes. Control Eng Pract. 2008;16:114– 131. [25] Zhang P, Yuan M, Wang H. Improvement of nitrogen removal and reduction of operating costs in an activated sludge process with feed forward–cascade control strategy. Biochem Eng J. 2008;41:53–58. [26] Wahab NA, Katebi R, Balderud J. Multivariable PID control design for activated sludge process with nitrification and denitrification. Biochem Eng J. 2009;45:239–248. [27] Ekman M, Bjorlenius B, Andersson M. Control of the aeration volume in an activated sludge process using supervisory control strategies. Water Res. 2006;40:1558–1676. [28] Holenda B, Domokos E, Redey A, Fazakas J. Dissolved oxygen control of the activated sludge wastewater treatment process using model predictive control. Comput Chem Eng. 2008;32:1270–1278. [29] Akyurek E, Yuceer M, Atasoy I, Berber R. Comparison of Control Strategies for Dissolved Oxygen Control in Activated Sludge Wastewater Treatment Process. Proceedings of 19th European Symposium on Computer Aided Process Engineering – ESCAPE19; 2009 June 17–22; Krakow, Poland; p. 1197–1201. [30] Cadet C, Beteau JF, Carlos Hernandez S. Multicriteria control strategy for cost/quality compromise in wastewater treatment plants. Control Eng Pract. 2004;12:335–347. [31] Traore A, Grieu S, Puig S, Corominas L, Thiery F, Polit M, Colprim J. Fuzzy control of dissolved oxygen in a sequencing batch reactor pilot plant. Chem Eng J. 2005;111:13–19. [32] Lee TT, Wang FY, Newell RB. Advances in distributed parameter approach to the dynamics and control of activated sludge processes for wastewater treatment. Water Res. 2006;40:853–869. [33] Copp JB. The COST simulation benchmark: description and simulator manual. Office for Official Publications of the European Community, Luxembourg; 2002. p. 154.
[34] Taguchi H, Humphrey E. Dynamic measurement of volumetric oxygen transfer coefficient in fermentation system. J Fermentation Technol. 1966;44:881–889. [35] Petersen B. Calibration, identifiability and optimal experimental design of activated sludge models [PhD Thesis]. Department of Applied Mathematics, Biometrics and Process Control: Universiteit Gent; 2000. [36] Flores V, Sanchez EN, Carlos-Hernandez S. Real-time PI L/A control for a wastewater treatment plant. Proceedings of World Automation Congress; 2012 June 24–28; Puerto Vallarta, Mexico. [37] Barnett MW, Takacs I, Stephenson J, Gall B, Perdeus M. Dynamic modeling. Water Environ Technol. 1995;12: 41–51. [38] Orhon D, Arton N. Modelling of activated sludge system. Lancaster, PA: Technomic Publishing Co., Inc; 1994. [39] Olsson G, Newell B. Wastewater treatment systems. Modelling, diagnosis and control. London: IWA Publishing; 1999. [40] Cavallo A, Setola R, Vasca F. Using Matlab, Simulink and control system tool box: a practical approach. Michigan: Prentice Hall PTR; 1996. [41] Mathworks. Technical support information. Available from: http://www.mathworks.com. Last access: December 2012. [42] Ying H, Siler W, Buckley JJ. Fuzzy of control theory: a nonlinear case. Automatica. 1990;26 (3):513–520. [43] Chen G, Ying H. On the stability of fuzzy PI control systems. International Conference on Industrial Fuzzy Control & Intelligent Systems; 1993 December 1–3; Houston, TX. [44] Lakrori M. Control of a continuous bioprocess by simple algorithms of P and L/A types. Proceedings of International IFAC Symposium on Nonlinear Control System Design; 1989; Capri, Italy. [45] Dantigny P, Ninow JL, Lakrori M. A new control strategy for yeast production based on the L/A[*] approach. Appl Microbiol Biotechnol. 1989;36 (3):352–357. [46] Beteau JF, Soehartanto T, Chaume F. Model based selection of an appropriate control strategy application to an anaerobic digester. Math Comput Model Dyn Syst. 1999;5 (4): 372–379. [47] Balku S, Yuceer M, Berber R. Control vector parameterization approach in optimization of alternating aerobic–anoxic systems. Optim Control Appl Methods. 2009;30 (6):573– 584. [48] Metcalf and Eddy. Wastewater engineering treatment and reuse. New York: McGraw-Hill; 2003. [49] Ramalho RS. Tratamiento de Aguas Residuales. Sevilla, España: Editorial Reverté; 2003.
Environmental Technology Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/tent20
Dissolved oxygen regulation by logarithmic/ antilogarithmic control to improve a wastewater treatment process a
b
c
Victor R. Flores , Edgar N. Sanchez , Jean-François Béteau & Salvador Carlos Hernandez
d
a
Centro de Enseñanza Técnica Industrial Guadalajara, Calle Nueva Escocia 1885, 44620 Guadalajara, Mexico b
Cinvestav del IPN, Unidad Guadalajara, Apdo. Postal 31-438, Plaza La Luna, 45090 Guadalajara, Mexico c
Automatic Control Department, GIPSA Lab, Grenoble INP, BP 46, 38402 St Martin d'Hères, France d
Cinvestav del IPN, Unidad Saltillo, Av. Industria Metalurgica 1062 25900 Ramos Arizpe, Coahuila, Mexico Accepted author version posted online: 23 May 2013.Published online: 18 Jun 2013.
To cite this article: Victor R. Flores, Edgar N. Sanchez, Jean-François Béteau & Salvador Carlos Hernandez (2013) Dissolved oxygen regulation by logarithmic/antilogarithmic control to improve a wastewater treatment process, Environmental Technology, 34:23, 3103-3116, DOI: 10.1080/09593330.2013.803159 To link to this article: http://dx.doi.org/10.1080/09593330.2013.803159
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Environmental Technology, 2013 Vol. 34, No. 23, 3103–3116, http://dx.doi.org/10.1080/09593330.2013.803159
Dissolved oxygen regulation by logarithmic/antilogarithmic control to improve a wastewater treatment process Victor R. Floresa∗ , Edgar N. Sanchezb , Jean-François Béteauc and Salvador Carlos Hernandezd a Centro de Enseñanza Técnica Industrial Guadalajara, Calle Nueva Escocia 1885, 44620 Guadalajara, Mexico; b Cinvestav del IPN, Unidad Guadalajara, Apdo. Postal 31-438, Plaza La Luna, 45090 Guadalajara, Mexico; c Automatic Control Department, GIPSA Lab, Grenoble INP, BP 46, 38402, St Martin d’Hères, France; d Cinvestav del IPN, Unidad Saltillo, Av. Industria Metalurgica 1062 25900 Ramos Arizpe, Coahuila, Mexico
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(Received 15 October 2012; final version received 25 April 2013 ) This paper presents the automation of a real activated sludge wastewater treatment plant, which is located at San Antonio Ajijic in Jalisco, Mexico. The main objective is to create an on-line automatic supervision system, and to regulate the dissolved oxygen concentration in order to improve the performances of the process treating municipal wastewater. An approximate mathematical model is determined in order to evaluate via simulations different control strategies: proportional integral (PI), fuzzy PI and PI Logarithm/Antilogarithm (PI L/A). The controlled variable is dissolved oxygen and the control input is the injected oxygen. Based on this evaluation, the PI L/A controller is selected to be implemented in the real process. After that, the implementation, testing and fully operation of the plant automation are described. With this system, the considered wastewater treatment plant save energy and improves the effluent quality; also, the process monitoring is done online and it is easily operated by the plant users. Keywords: wastewater treatment; activated sludge; logarithmic/antilogarithmic control
1. Introduction 1.1. Problem statement Water is essential for all the human activities; from the daily life to the industrial production, large quantities of water are used every day all around the world producing also large quantities of wastewater. Nowadays, most of countries implement programmes to promote the reuse and recycle of treated wastewater. Then, the standards concerning the quality of treated water become more and more strict. For that reason, the improvement of treatment processes is an active research topic. In Mexico, there is a production around 240 m3 /s of municipal wastewater; approximately only 30% is treated with an efficiency of 30% concerning removal of biological oxygen demand (BOD).[1] Activated sludge is one of the main processes used to treat municipal wastewater; this kind of plants is able to treat very large volumes of wastewater, with a suitable quality. The most important difficulties detected in the operation of wastewater treatment plants in Mexico are: the influents are strongly affected by rain; the daily volume of influents is frequently higher than the plant capacity; designers of wastewater treatment plants include few variables to be manipulated; there are key variables hard to measure due to economical or technical restrictions. This situation implies important risks to the environment
and human health. On the other side, very few municipal wastewater treatment plants are designed considering an automatic control approach; usually, supervision, monitoring and control are done by the operators in manual mode. All these situations represent an immediate scientific and technological challenge all around the country. Automation is an interesting alternative to improve the performance and operation of wastewater treatment plants; the main idea is to design control laws for the automatic handling of key variables. In order to automate any process, it is highly advisable to develop at least an approximated mathematical model. For activated sludge wastewater treatment plants, the respective models are described by nonlinear differential equations, which represent the biological, biochemical and physical–chemical phenomena taking place inside the plant. Once a model is defined, different control techniques can be evaluated via simulation, in order to select the best one to be implemented in real time.
1.2. Bibliographic review Different researches have been focused in modelling and control of wastewater treatment processes by activated sludge since many years ago.[2–5] The Monod model was used to represent the kinetics of microorganisms and to
∗ Corresponding author. Email: vfl[email protected] This article was originally published with erroneous pagination. This version has been corrected. Please see Erratum (http://dx.doi.org/ 10.1080/09593330.2013.869395).
© 2013 Taylor & Francis
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ease the design of aeration tanks.[6] In,[7] it is reported the results of experiments performed to develop a simple kinetic model which incorporates the storage/metabolism mechanism and a growth/decay representation. Also, a dynamic model of carbonaceous and nitrogenous substrate removal for a real process was evaluated [8]; the work focuses in the use of a model for the interpretation of a field data collected from the real process for several days. In other work, a model which incorporates the interactions between the aeration tank and the final settler is proposed [9]; the model suggests that sludge settleability is improved with increasing sludge age. On the other side, the Activated Sludge Model No. 1 (ASM1) developed by the International Association on Water Quality is well accepted for dynamic simulation of the removal of carbonaceous and nitrous compounds from wastewater in activated sludge plants.[10] The ASM1 describes nitrogen and chemical oxygen demand (COD) within suspended-growth treatment processes, including mechanisms for nitrification and denitrification, and defines components, stoichiometry and kinetics of activated sludge processes.[10,11] Thirteen differential equations are formulated to represent eight processes: aerobic and anoxic growth of heterotrophic biomass, death of heterotrophic biomass, aerobic growth of autotrophic biomass, decay of autotrophic biomass, ammonification of soluble organic nitrogen and hydrolysis of both entrapped particulate organic matter and entrapped organic nitrogen.[10,11] Many research works have been developed based on this model,[12–15] and also, further works were performed in order to complement the ASM1.[16–20] Concerning control of wastewater treatment processes by activated sludge, several techniques have been already evaluated. An optimal control approach is applied to minimize effluent organics concentration and energy consumption [21]; the control problem is formulated with recycle and wastage rates as control variables and it is solved using an analytically based trajectory iteration control procedure; as results, the proposed optimal control technique can handle the process nonlinearity and high dimensionality. In,[22] an optimal control is used to minimize nitrogen discharges and energy consumption; both minimization problems are formulated in order to guarantee that the improvements can be maintained for long time; the results show a reduction of 37% for nitrogen discharges, a reduction of aeration time around 2 h and an energy saving of 27%. An interesting two-stage robust control scheme composed by a dynamic controller and a proportional-integral-derivative (PID) is developed and validated in simulation [23]; with this strategy three main benefits are obtained: an asymptotic following of the substrate concentration, asymptotic attenuation of the effects of disturbances on the influent over the output substrate and oxygen. A hierarchical controller for tracking a dissolved oxygen reference trajectory is proposed in [24] in order to remove nitrogen and phosphorous: the upper level generates trajectories of the desired airflows to be delivered to aerobic zones (a nonlinear predictive controller
is applied), the lower-level forces the aeration system to follow these trajectories; the hierarchical controller is validated by simulation based on real data. A feed forward– cascade controller based on a hierarchical structure is presented in [25]; the main objective was to reject changes in the influent load variations: a cascade controller selects the set-point of the low level controller which regulates the dissolved oxygen concentration in order to meet strict effluent quality standards at a minimum cost. Multivariable PID controllers to regulate dissolved oxygen (DO) and nitrate removing were proposed in [26]; an important contribution is the tuning method, which uses an optimization technique to meet standard quality effluent at a minimum cost. In other work,[27] a supervisory strategy is proposed in order to control aeration volume; from DO measures, it is determined what compartment must to be aerated depending on the input load; the strategy is validated by simulations and it is implemented in a pilot plant; on the other side, a model predictive control has been studied for DO control by manipulation of the oxygen mass transfer coefficient; the controller was validated by simulations considering input disturbances and set-point changes.[28] Other work reports an evaluation of six control strategies (PID control, model predictive control (MPC) with linear model, MPC with non-linear model, nonlinear autoregressive moving average L2 (NARMAL2) control, neural network based model predictive control (NN-MPC) and optimal control with statistical quality process (SQP) algorithm); dissolved oxygen was the controlled variable and the aeration rate was the control input. The authors conclude that NARMA-L2 controller and optimal control with SQP are better than the other alternatives.[29] In,[30] a multicriteria control strategy is proposed to decrease the global cost in wastewater treatment plants; the main idea is to select the manipulated and measured variables based on a sensitivity analysis, a Takagi–Sugeno supervisor is implemented in order to smooth the switching between control actions; the strategy provides good performances. In,[31] a fuzzy control is proposed and compared with classic control techniques to regulate dissolved oxygen in a sequential batch reactor at pilot plant scale; the fuzzy system considers two inputs (difference between set point and the measure for dissolved oxygen considering three fuzzy sets, and the step of the cycle expressed by six fuzzy sets) and one output (the air injected into the reactor represented by eight fuzzy sets); the obtained results show good performances of the process by using the fuzzy controller. Finally, more advanced studies are also developed considering distributed parameter for the biological reactors.[32] 1.3. Contribution of this work In this article, a real wastewater treatment plant is studied. The objective is to implement a control strategy to enhance the process performances by improving effluent quality and saving energy. First, an approximate mathematical model of the process is elaborated including a step for parameter
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Environmental Technology identification. Based on this model, different control strategies are designed and evaluated: proportional integral (PI), fuzzy PI (FPI) and PI Logarithm/Antilogarithm (PI L/A). Dissolved oxygen is considered as the controlled variable and the control input is the injected oxygen. The best controller is selected by a series of simulations and it is implemented in the real process. The model considered is based on the ASM1 and it is used as a tool to study the process behaviour and to design and test the controllers. A simulation stage is performed following the recommendations of the COST Simulation Benchmark.[33] The process parameters were obtained following the methodology described in the specialized literature.[34,35] The main contribution is the real-time implementation of a PI L/A which allows the performances of a wastewater treatment plant to be improved. This control technique considers the positivity restriction of the variables and is easy to tune and implement; the automation and supervision strategy is well understood and accepted by the plant operators. The automation system could be implemented in other municipal plants in order to improve the wastewater treatment and to ease the operation of the processes. It is important to remark that some preliminary results are described in the reference.[36] 2. Process description 2.1. Activated sludge preliminaries Activated sludge is a biological process widely used for municipal and industrial wastewater treatment. The organic components contained in the wastewater, known as substrate, are used as energy source for the growth of microorganisms, known as biomass or activated sludge which degrades the pollutants. Since these microorganisms are aerobic ones, a certain quantity of oxygen must be supplied to maintain adequate conditions for biomass growth. The obtained mix (wastewater and activated sludge) is purified in a clarification stage by settling of the microbial flocks in another tank known as settler. The thickened sludge is recycled in order to maintain enough biomass in the bioreactor, and to keep constant the amount of microorganisms, the exceeding biomass is regularly removed (wastage).[21] A general representation of this process is shown in Figure 1.
Figure 1. process.
Schematic representation of an activated sludge
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Wastewater treatment by activated sludge is a complex process due to the high variability of the influent flow rate, substrate load and physical characteristics; also, the process involves nonlinear dynamics, large uncertainty and multivariable structure.[24,37] Then, control strategies are required in order to prevent failures and to guarantee the adequate functioning of the processes. Dissolved oxygen is commonly used as a regulated variable since it is very important to provide the condition for biomass growth and then for an efficient wastewater pollutants degradation. 2.2. The case of study The wastewater treatment plant considered in this study is located at Ajijic, Jalisco, Mexico; it is operated by CEA (Jalisco State Water Board). This plant is intended to remove organic substrates, without removing nitrogen components since the original design does not consider an anoxic zone. This plant was designed to treat 32 L/s; at present time the influent is higher than this value according to measurements which indicates an influent around 40 L/s. A plant scheme is portrayed in Figure 2. As can be seen, the treatment is performed in four stages: (i) Pre-treatment. The big solids are separated for wastewater by means of a mesh; also, sand traps retain weight inorganic solids such as sand, small rocks and some metallic objects; finally, wastewater is filtered to separate organic material with a size bigger than 3 mm. (ii) Secondary treatment. This stage is performed by an activated sludge process as described in Section 2.1. The secondary treatment is composed by two biological reactors and two settlers, and it is the main concern of this study. (iii) Chlorination. In this stage, the treated wastewater receives a chloride solution with a concentration of 8 ppm, which is calculated considering a mean flow rate according to the process design. The objective is to eliminate pathogen microorganisms and it is done in order to reach local environmental regulations. (iv) Sludge treatment. The water contained in the resulting sludge is removed in order to decrease the sludge volume. Also, in this stage the remaining organic solids which could cause putrefaction are transformed into solid minerals or relatively stable organic solids. The wastewater sent to the Ajijic wastewater treatment plant is collected from two cities; then, the composition of the influent is mainly of domestic nature without industrial wastes. The basic characterization of influent is done in order to obtain the mean concentration of BOD5 , Total and Soluble CODt and CODs , total suspended solids (TSS) and
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Figure 2.
V.R. Flores et al.
Schematic distribution of Ajijic wastewater treatment plant. Table 1. Wastewater characterization (mean values in one month). Parameter (mg/L)
Value
BOD5 CODt CODs TSS VSS
239 728 271 287 1067
volatile suspended solids (VSS). It is important to mention that the respective analyses are elaborated in the wastewater treatment plant laboratory and they are provided for this study. Table 1 contains the mean values for each parameter; the data correspond to mean values in one month. 2.3. Process modelling The secondary treatment stage used in the Ajijic plant is represented by the scheme displayed in Figure 3. It corresponds to a common activated sludge process.[38] In Figure 3 Q0 and Z0 are the influent flow rate and concentrations; QL and ZL are the leachate flow rate and concentrations; Qf and Zf are the bioreactor output flow rate and concentrations; Qe and Ze are the effluent flow rate and concentrations; Qw and Zw are the wastage flow rate and concentrations and Qr is the recycled flow rate. The ASM1 model for this process considers 13 variables.[10] However, for this specific plant, it is supposed that the process contains the simplest biological wastewater treatment mechanisms; since it involves only the three basic components: biodegradable substrate, heterotrophic biomass and dissolved oxygen concentration.[39] In order to develop this model, mass balances are established for each bioreactor. The heterotrophic biomass mass balance
Figure 3.
Activated sludge process in the Ajijic plant.
can be expressed as dXBH,j 1 (Q0 XHB,0 + Qr Xr − Qf XBH,j ) + rH , = dt 2Vj
(1)
where XBH is the biomass concentration, Xr is the recycled biomass concentration, V is the volume, j is the bioreactor number and rH is the reaction rate for biomass growth. The influent and leachate microorganism concentration are neglected. The biodegradable substrates balance is defined as dSS,j 1 (Q0 SS,0 + QL SS,L + Qr SS,r − Qf SS,j ) + rS , = dt 2Vj (2) where SS is the biodegradable substrate concentration, SS,r is the recycled substrate concentration, SS,L is the leachate substrate concentration and rS is the substrate uptake rate. The oxygen mass balance is formulated as dSO,j 1 = kla(SO,sat − SO,j ) − Qf SO,j + rO , dt 2Vj
(3)
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where SO is the dissolved oxygen concentration, SO,sat is the saturated dissolved oxygen concentration, rO is the oxygen consumption rate and the kla term is the mass transfer from the aeration. The influent, recycled and leachate oxygen concentration are neglected. The reaction rates rH , rS and rO for each equation are represented as follows. The reaction rate for biomass growth can be expressed as SS,j SO,j rH = μˆ H XBH,j , (4) KS + SS,j KOH + SO,j where μˆ H is the maximum heterotrophic biomass growth rate, KS is the half-velocity coefficient for biodegradable substrate and KOH is the half-velocity coefficient for dissolved oxygen concentration. The substrate reaction rate is defined as SS,j SO,j 1 rS = − μˆ H XBH,j , (5) YH KS + SS,j KOH + SO,j where YH is the heterotrophic yield coefficient. The oxygen consumption rate is expressed as SS,j SO,j YH − 1 μˆ H XBH,j . (6) rO = YH KS + SS,j KOH + SO,j Finally, the heterotrophic biomass mass balance for settlers can be expressed as dXr,k 1 (Qf XHB,f + (Qr + Qw )Xr,k ), = dt 2Vk
(7)
where Qw is the waste flow rate and k is the settler number. It is assumed that effluent does not contain any biomass. 2.4. Parameters determination In order to validate the proposed model, it is necessary to determine the kinetic coefficients. This is done following recommendations in the specialized literature. The method to determine the aeration constant kla is based on that proposed by [33]: First step: the influent, leachate and recycle inlets are closed and the blower motor is turned on. The oxygen concentration is increased up to saturation (SO,eq ). The mixture of biomass and influent wastewater in the bioreactor is defined as mixed liquor. The mixed liquor oxygen concentration is measured and registered until it is impossible to increase the oxygen concentration. The mixed liquor oxygen concentration has a behaviour similar to that shown in Figure 4. Second step: when the mixed liquor is saturated, the blower motor is turned off. The oxygen concentration decreased since it is consumed by biomass; then, a value above the critical dissolved oxygen concentration is selected. It is important to recall that the dissolved oxygen concentration must not diminish below 0.5 mg/L.
Figure 4.
Dissolved oxygen time evolution.
Third step: when the oxygen concentration reaches the selected value (3.2 mg/L in this case), going back to the first step. It is advised to repeat this procedure at least twice. The mathematical representation of this method is described as follows. The concentration dynamics of the dissolved oxygen can be rewritten as dSO = kla(SO,sat − SO ) − XQO2 , dt
(8)
where XQO2 is the oxygen consumption rate by biomass. For the saturation concentration (first step), the dissolved oxygen fulfills the following relation: dSO = 0 ⇒ 0 = kla(SO,sat − SO,eq ) − XQO2 , dt
(9)
where SO,eq is the equilibrium dissolved oxygen concentration of mixed liquor. When blower motor is turned off (second step), the dissolved oxygen concentration evolution is described as dSO = −XQO2 . (10) dt Subtracting Equation (10) from Equation (9), the next expression is obtained dSO = kla(SO,sat − SO ). dt
(11)
Then, to determine kla, Equation (11) is integrated and the resulting expression is ln
(SO,eq − SO ) = −kla × t, (SO,eq − SO,0 )
(12)
where SO,0 is the oxygen concentration of the aeration restart (going from the second step to the first one). For the case studied here, an exponential curve is obtained as shown in Figure 5 (step 1). It is observed that the maximum oxygen concentration is 6.10 mg/L and SO,0 is equal to 3.2 mg/L.
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V.R. Flores et al. Table 2. Activated sludge kinetic coefficients for heterotrophic bacteria at 20◦ C. Coefficient
Range
μˆ H (g VSS/g VSS × d) KS (g bCOD/m3 ) YH (g VSS/g bCOD) KOH (mg/L) θ values μˆ H (Unitless) KS (Unitless) Figure 5.
3.0–13.2 5.0–40.0 0.30–0.50 0.1 1.03–1.08 1
Value 6 20 0.4 0.1 1.07 1
Dissolved oxygen measures for kla determination. Table 3. Activated sludge kinetic coefficients for heterotrophic bacteria at 26◦ C.
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Coefficient μˆ H KS YH KOH
Figure 6.
Unit
Value
g VSS/g VSS × d g bCOD/m3 g VSS/g bCOD mg/L
9.0 20.0 0.425 0.10
On the other hand, the kinetic coefficients for the removal of carbonaceous material are taken from values recommend for the ASM1.[10] Typical values are shown in Table 2. It is well known that temperature affects the metabolic activities of the microbial population as well as the gastransfer rates and the settling characteristics of the biological solids. The effect of temperature on the reaction rate of a biological treatment process is expressed as
Determination of kla coefficient.
kT = k20 θ (Tb −20) ,
Figure 7.
Dissolved oxygen concentration.
To calculate kla, the DO measurements are substituted in Equation (12) in order to trace the graph shown in Figure 6. Using lineal regression, the following equation is deduced: y = −0.04111x − 0.22165,
(13)
where kla is determined as the slope of Equation (13). The obtained experimental value is equal to 0.04111/min or 59.2/day. After that, the dissolved oxygen concentration is traced to obtain the graphic shown in Figure 7 (second step). Applying a lineal regression, the oxygen consumption slope is calculated as y = −0.06345x + 6.0245,
(14)
where XQO2 is determined as the slope of Equation (14). The obtained experimental value is equal to 0.06345 mg/L-min.
(15)
where kT is the kinetic coefficient at temperature Tb , k20 is the kinetic coefficient value at 20◦ C, θ is the temperature activity coefficient and Tb is the bioreactor temperature. The maximum specific growth rate (μˆ H ) and the half-velocity coefficient (KS ) are calculated using the values of Table 2 and Equation (15), then these parameters are included in Table 3. For the model validation, Equations (1)–(7) were implemented in MatlabTM [40,41]; also, experimental data obtained from dissolved oxygen measures and chemical analyses were considered. The initial conditions are: XBH = 2520 mg/L COD, SS = 0.64 mg/L COD and SO = 4.44 mg/L. In Figure 8, a comparison between simulations and measures is presented. It can be seen that DO concentration is very well reproduced; the difference at the beginning and the end of the simulation could be due to errors in the measurements. 3. Dissolved oxygen regulation Regulation of DO is commonly used as controlled variable since it is easily measurable and it is a good indicator of the biomass growth; then, it allows the operator to determine
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Environmental Technology
Figure 8.
Model validation with experimental data.
the performance of the process. In this paper, three controllers based on proportional-integral control are designed and tested: conventional PI (PI), minimal FPI and PI based on logarithmic/antilogarithmic control (PI L/A). The controlled variable is dissolved oxygen and the control input is the blower speed which is directly related to the injected oxygen. 3.1.
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PI controller
A conventional PI controller is designed as follows. First, the general expression in s-domain is considered Kc U c (s) = KPc + I E c (s), (16) s where KPc and KIc are the proportional and integral gains, respectively, and E(s) is the error signal. A standard bilinear transformation, s = 2/T (1 − z −1 /1 + z −1 ), is done in order to obtain an expression in z-domain (1 − z −1 )U (z) = (1 − z −1 )KP E(z) + KI E(z),
(17)
where KP = KPc − TK cI /2 and KI = TK cI /2 are the equivalent proportional and integral gains of a discrete PI controller, respectively. After that, an inverse z-transform is applied and rearranging the obtained result, the PI control law in discrete time is deduced: u(nT ) = u(nT − T ) + KP [e(nT ) − e(nT − T )] + KI e(nT ).
and the rate of change of error. As described and analyzed in,[42,43] two input variables, error and rate of change of error (named rate for short), and one output variable, are required. A schematic representation is shown in Figure 9. In Figure 9 y(nT), e(nT), r(nT) and u(nT) are the output, error, rate of change of error and input (output fuzzy controller) from the process; KE , KR and KU are scalars for error, rate and output from the fuzzy controller; e ∼ (n), r ∼ (nT) and u ∼ (nT) are the fuzzy sets corresponding to error, rate and output. L is selected according to the process dynamic; it represents the permissible amplitude of the error. Considering the respective fuzzy sets, four inference rules are formulated: If error = error positive AND rate = rate positive then output = output negative (r1) If error = error positive AND rate = rate negative then output = output zero (r2) If error = error negative AND rate = rate positive then output = output zero (r3) If error = error negative AND rate = rate negative then output = output positive (r4) According to the membership functions for the FPI controller, the value-range of e(nT ) and r(nT ) is divided into 20 input-combinations regions: IC1–IC20 (Figure 10). The AND operator is the minimum of the two values and the defuzzyfication is done by using the average centre method. The incremental control output is determined by u(nT ) = −[KId e(nT ) + KPd r(nT )],
(18) where
3.2. FPI controller A minimal FPI is considered as an alternative to regulate dissolved oxygen. This controller allows the proportional and integral gains to be adapted as a function of the error
0.5 ∗ L ∗ KU ∗ KE ⎫ ⎪ ⎪ 2L − KE ∗ |e(nT )| ⎬ 0.5 ∗ L ∗ KU ∗ KR ⎪ ⎪ ⎭ = 2L − KE ∗ |e(nT )|
KId = KPd
(19)
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Figure 9.
Fuzzy control scheme and variables fuzzyfication.
Figure 11.
L/A control structure.
L ∗ KU ∗ KR r(nT ) − L in IC13, IC14. 2L L ∗ KU u(nT ) = in IC17. 2L L ∗ KU u(nT ) = − in IC19. 2L u(nT ) = 0 in IC18, IC20. u(nT ) = −
Figure 10.
Regions of the fuzzy controller values.
if KR ∗ |r(nT )| ≤ KE ∗ |e(nT )| ≤ L, 0.5 ∗ L ∗ KU ∗ KE ⎫ ⎪ KId = ⎪ 2L − K ∗ |r(nT )| ⎬
(27) (28)
3.3.
(21)
The PI L/A is a control technique based in logarithmic and exponential transformations. These operations allow the positivity restrictions of concentrations to be considered.[44,45] A scheme of this technique is portrayed in Figure 11. The control structure is based on next transformations
0.5 ∗ L ∗ KU ∗ KR ⎪ ⎪ ⎭ 2L − KR ∗ |r(nT )|
if KE ∗ |e(nT )| ≤ KR ∗ |r(nT )| ≤ L,
(26)
(20)
R
KPd =
(25)
When the input signals are located in the regions IC9–IC20, then L ∗ KU ∗ KE e(nT ) u(nT ) = − − L in IC11, IC12. (22) 2L L ∗ KU ∗ KE e(nT ) u(nT ) = − − L in IC15, IC16. (23) 2L L ∗ KU ∗ KR r(nT ) u(nT ) = − + L in IC9, IC10. (24) 2L
PI L/A controller
Y (nT ) = ln(y(nT )), Ref (nT ) = ln(ref (nT )),
(29)
u(nT ) = exp(U (nT )), where y(nT ) is the output, ref(nT ) is the set point and u(nT ) is the control action. These transformations allow to select any conventional control law and to obtain a L/A
Environmental Technology Table 4.
equivalent. In,[46] a PI is proposed as follows: U (nT ) = U (nT − 1) + KP (Y (nT − T ) − Y (nT )) + KI (Ref (nT ) − Y (nT ))
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Parameters of the controllers. Tuning parameters
(30)
with KP and KI being the corresponding gains for the controller. The L/A equivalent of this control law is obtained replacing the mathematical operations as follows: addition by multiplication, subtraction by division and multiplication by exponential y(nT − 1) KP ref (nT ) KI u(nT ) = u(nT − 1) . (31) y(nT ) y(nT ) In addition to the positivity constraints which are overcome with the logarithmic and antilogarithmic transformations, the tuning method of this controller allows the actuator saturation and the minimum permissible error value to be considered in the whole control strategy. 3.4. Controller selection It is necessary to tune the controllers parameters; the followed procedure is described in next lines: (1) the process is linearized about the typical operation points of the plant
Figure 12.
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PI L/A PI FPI
Proportional gain (P)
Integral gain (I)
165 167.5 196.2
7 7 14
(influent substrates, influent flow rate and dissolved oxygen concentration); (2) the tuning parameters are found by the Ziegler–Nichols methods, which is a good approximation and (3) the proportional and integral gains are finely adjusted, it is done using the error index generated by integral absolute error (IAE). Table 4 is shown the tuning values for the controllers. A series of simulations considering different operating conditions was performed in order to evaluate each controller and to do a comparison. Figure 12 shows the obtained result for one of the experiments; in general, the process behaviour is similar for other scenarios. The graphics on left show the DO measurements obtained considering each controller. The graphs on right correspond to the control input, i.e. the percentage of operation for the blower motor which is directly related to
Controllers evaluation considering different operating conditions.
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Table 5. IAE performance index and energy consumption evaluation. IAE index
PI L/A PI FPI Open loop
Energy consumption (kWh/d)
Day 1
Day 2
Day 1
Day 2
0.0005014 0.002384 0.005648
0.0004454 0.002547 0.006094
350.8 351.3 351.8 450
392.3 393.2 393.6 450
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the aeration coefficient kla. The plant performance index is measured through aeration energy [33]; therefore, it is possible to evaluate the performance of the controllers, where kla is the control output or manipulated variable. The energy consumption during one day is calculated with next expression 24 EC = Te
1
{0.4032kla(t)2 + 7.8408kla(t)}dt,
Figure 13. Table 6.
Wastewater treatment plant automation scheme. Calibration of instruments.
(32)
0
where Te represents the evaluation time (1 day) and kla(t) represents the mass transfer coefficient h−1 at time t. As can be seen, the three controllers allow the process to reach the set point, which is 1.5 g/L for this case. The FPI controller induces an oscillatory behaviour on the control input; even if the dissolved oxygen remains around the set point, an oscillatory behaviour is remarked on the measurements; in the worst conditions, the amplitude of those oscillations is near to 10%. The classic PI induces a similar behaviour but the amplitude of the oscillations is less than the case of the FPI. Concerning the PI L/A, it is clear that this controller induces a smoother behaviour in the control action and, as direct effect of this situation, the dissolved oxygen reaches the set point and remains there without oscillations. In addition, the IAE and energy consumption are considered as evaluation elements using a set of data from measurements during two days. As shown in Table 5, the IAE index of the PI L/A controller is the lowest comparing the three controllers; also, the energy consumption using the PI L/A is better than that corresponding to the other controllers. Then, the PI L/A is taken for the real time implementation. It is worth to mention that there exist specific methods for the energy optimization in wastewater treatment plants as shown in,[47] then, it is possible to consider that kind of approaches in future works. 4. Real time implementation 4.1. Process instrumentation In order to implement the PI L/A controller to regulate dissolved oxygen, some basic instruments and sensors are required, which was not installed in the wastewater treatment. Then, sensors for the influent flow, the leachate flow, the bioreactor output flow, the effluent flow, the recycled
Controller output
Frequency converter
Blower motor
%
mA
Hz
Current (A)
Speed (rpm)
Air flow (ft3/min)
100 80 60 40 20 0
20 16.8 13.6 10.4 7.2 4
60 59 58 57 56 55
65.4 64.4 57.9 52.3 46.2 40.4
3600 3576 3504 3438 3366 3294
1110 990 850 720 590 460
flow, the dissolved oxygen concentration and the bioreactors turbidity were acquired, installed and integrated in a global system for supervision and control. All the signals provided by these sensors are concentrated on a programmable logic controller (PLC). Additionally, a personal computer is included for automation development and monitoring, the used software for this proposal is a supervisory control and data acquisition. Figure 13 presents a general scheme of such automation. The PI L/A controller for the dissolved oxygen is programmed in C++ and implemented inside the PLC, with a sample time T = 1 s. The relation between the PI L/A controller output, the frequency converter and the blower motor is presented in Table 6. The information of Table 6 is important since the blower motor must be operated in a specific range of current (38–69 A according to technical specifications). Then, the frequency converter should operate between 55 and 60 Hz; this interval ensures an adequate functioning of the blower motor. The PI L/A is implemented as shown in Figure 14 and it works as follows: the dissolved oxygen sensor measures the corresponding concentration in the biological reactor and send an electric signal; the transmitter receive the
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value (55–60 Hz); the motor blower interprets this value as a specific speed injecting a determined oxygen flow. In the bioreactor, the changes on the air flow produced by the PI L/A maintain the dissolved oxygen concentration in the set point.
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4.2.
Figure 14.
Control loop of dissolved oxygen.
sensor signal and transform it in a normalized signal (4– 20 mA), which corresponds to the operation range of the instrument (0–10 mg/L). The transmitter signal is sent to the PLC where (i) it is converted from analogical to digital format, (ii) normalized, (iii) processed by the PI L/A control algorithm generating the output signal which regulates the dissolved oxygen in the biological reactors, (iv) converted from digital to analogical format and (v) normalized in an interval of 4–20 mA. After that, the speed controller receives and converts the signal from the PLC to a frequency
Figure 15.
Open loop operation.
Figure 16.
Influent carbon sources for two different days.
Controller evaluation
The set point for the dissolved oxygen is 1.5 mg/L which is recommended for organic substrate removal.[48,49] Figure 15 shows the influent and the dissolved oxygen concentration before the PI L/A implementation (open loop operation with a single blower-motor turning on). Furthermore, carbonaceous loads are shown in Figure 16. It can be seen that in the day the dissolved oxygen is below 0.5 mg/L, but in the night there is a remarked increase due to the influent flow rate reduction and to the full operation of the blower motor. This represents unnecessary energy consumption. Figure 17 shows the controller PI L/A performance for typical days of the process operation. In fact, a second blower-motor is integrated to improve the process in the morning–afternoon. According to the dissolved oxygen concentrations in the bioreactors, an interlock is designed to turn on/off one blower-motor or both blowers-motor. The input flow rate is varying all along the day; from 8:00 h until 14:00 h the value corresponds to the design conditions, after that the input flow rate is more than 32 L/h. Before 11:00 h, the dissolved oxygen does not reach the set point which is 1 mg/L. Since the DO is more than the required for the bioreactor, the blower motor is saturated in the low level, namely it is turn off; when the dissolved oxygen is less than
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Figure 17.
Dissolved oxygen regulation.
the required level, the controller activates the blower motor in order to reach the set point. Between 11:00 and 14:00 h, the dissolved oxygen remains around 1 mg/L, the speed of the blower motor is adapted to the increase in the input flow rate according to the control law. After 14:00 h, the input flow rate cross over the design value; nevertheless, the dissolved oxygen remains very close to the set point, specially the second day (Figure 17(b)). It can be seen that the motor works according to the requirements; it reach its maximum power only in some intervals, otherwise, the speed is regulated by the PI L/A controller saving energy. Concerning this topic, a comparison between the open loop and the PI L/A application
is done as follows. The energy consumption during eight days is calculated with Equation (31) and where Te is equal to eight days. The open loop consumption is 3305 kWh/d; on the other side, the energy consumption using the PI L/A is 2749 kWh/d. The corresponding saving energy is 17%/d which can be directly transformed in economic savings for the plant operator. Finally, the treated wastewater is analyzed in the station laboratory; the results indicate that the process effluent meets the Mexican standard for treated wastewater, which requires a BOD5 less than 15 mg/L, TSS less than 20 mg/L and fats less than 15 mg/L. Table 7 shows the removal level
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Laboratory analysis of influent and effluent (units in mg/L). Influent
Month
Effluent
BOD5
CODt
TSS
Fats
BOD5
CODt
TSS
Fats
180 260 240 260 334 280 255 265
705 796 754 893 807 877 518 764
280 444 310 231 260 325 176 275
97.08 88.18 91.76 102.9 82.39 92.74 38.44 83.01
10 13 2.5 12 11.1 6.8 13 9.84
28 43 45 28 39 14 18 28.7
4.6 4.3 4.45 6.4 4.1 4.1 5.1 4.8
6.17 4.39 4.94 2.9 2.5 4.15 3.37 3.81
1 2 3 4 5 6 7 Average
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of those components from the influent; the results correspond to seven months (in 2012) of the analysis. It can be seen that the effluent could be reused even in public services where the water is in direct contact with people. 5.
Conclusions
A monitoring and control system for a real wastewater treatment plant is presented. A methodology for modeling (model development and model validation) is applied for the studied case. Also, three controller alternatives are designed and evaluated. The PI L/A controller represents a very good feedback scheme in order to keep the DO concentration at desired levels, even in presence of strong influent disturbances. Additionally, an energy saving of 17% is also obtained. It is important to remark that the implementation of the global system has improved the process monitoring and operation by the technical personal on the treatment plant. Also, the developed methodology could be applied in many other wastewater treatment processes all around the country; this is an important step in order to improve the Mexican infrastructure and technology concerning wastewater treatment. Research will be pursued to implement the recycled sludge feedback control loop and also to improve the tuning of the PI L/A in order to enhance process performances during the day period. Acknowledgements The authors thank the support of the Mexican State of Jalisco through COECYTJAL (Jalisco State Council for Scientific and Technological Research) and CEAS (Jalisco State Water Board). They also thank the support of CNRS, France and CONACYT, Mexico through the French Mexican research Initiative on Automatic Control (LAFMAA).
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