A Novel Integrated Approach to the Enhanced Production of

Jul 14, 2010 - Copyright © 2010 American Chemical Society ... of the response surface methodology (RSM) and a novel machine learning method, that is,...
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Ind. Eng. Chem. Res. 2010, 49, 7478–7483

A Novel Integrated Approach to the Enhanced Production of Polyhydrobutyrate with Mixed Culture in Activated Sludge Fang Fang,†,‡ Guo-Ping Sheng,† and Han-Qing Yu*,† Department of Chemistry, UniVersity of Science & Technology of China, Hefei, 230026 China, and College of EnVironmental Science and Engineering, Hohai UniVersity, Nanjing, 210098, China

Polyhydrobutyrate (PHB) production from fatty acids by activated sludge is mainly influenced by the ratio of initial substrate concentration (S0) to initial biomass concentration (X0), ratio of chemical oxygen demand to nitrogen (COD/N), and aeration strength. In this work the PHB production by activated sludge was evaluated and enhanced using an integration of the response surface methodology (RSM) and a novel machine learning method, that is, support vector regression (SVR) coupled with the accelerating genetic algorithm (AGA). The results demonstrated that the SVR-AGA model was superior over the RSM model. With the SVR-AGA approach, a maximum PHB yield of 185.48 mg PHB/g VSS under the optimum conditions of S0/X0 of 0.286, COD/N of 144, and aeration intensity 0.004 m3/(L · h) was estimated. The verification experiment confirmed that the measured optimum PHB yield was close to that estimated by using the SVR-AGA approach. This work indicates that an integration of the SVR and AGA methods was an appropriate and effective approach for evaluating and enhancing the PHB production process by mixed cultures like activated sludge. 1. Introduction Polyhydroxybutyrate (PHB), a representative compound of the family of polyhydroxyalkanoates (PHAs), has many potential applications in medicine, veterinary practice, and agriculture. PHB production by activated sludge under unbalanced growth conditions has recently received increasing attention. Compared with PHB production from well-defined nutrient-deficient media with pure cultures of particular microorganisms,1,2 utilization of activated sludge and organic wastes can substantially reduce the PHB-producing costs and increase their market potential.3-6 The excess of external carbon substrate is preserved mainly for PHB storage during the “feast” period of activated sludge. It is reported that activated sludge is able to accumulate PHAs over 50% of cell dry weight.5 A PHB content in activated sludge as high as 78.5% using a pulse substrate feed addition strategy has been observed.6 PHB production by activated sludge is influenced by many operating factors, especially the ratio of initial substrate concentration (S0) to initial biomass concentration (X0), COD (chemical oxygen demand)/N, and aeration strength.7,8 Generally, the PHB content in activated sludge increased with an increasing S0/X0 ratio.8 At a C/N ratio of 96, a high yield of 0.093 g PHA/g substrate consumed was achieved.9 At a lower DO supply rate, a higher proportion of the substrate was preserved as PHB.7 These factors have an individual influence on PHB production by activated sludge. However, their interactive effects on PHB production are unknown yet. Thus, proper operating conditions should be pursued to maximize the PHB yield, in addition to satisfying the activated sludge growth. Response surface methodology (RSM) is a statistical technique for designing experiment, building models, evaluating the effects of several factors, searching optimum conditions, and reducing number of experiments. This method has been widely used in bioprocess optimization.10 The experimental results designed with RSM can be used to evaluate the interactions of * To whom correspondence should be addressed. Fax: +86-5513601592. E-mail: [email protected]. † University of Science & Technology of China. ‡ Hohai University.

potential influencing parameters on PHB production by activated sludge. However, the central point of the experiments with RSM has to be obtained through a mount of preliminary experiments. Moreover, the model of RSM is usually expressed by a full second-order polynomial.11 Support vector machine (SVM), a new machine learning method, is a powerful tool for constructing a “black-box” model between the input variables and the corresponding output variables.12 With the introduction of ε-insensitive loss function, SVM can be extended for performing regression.12 The support vector regression (SVR) has been used to solve nonlinear regression estimation problems.13,14 The SVR leads to a unique and global solution because of employment of a structural risk minimization principle.15 On the other hand, the accelerating genetic algorithm (AGA) has been recently established as a heuristic and global searching algorithm for both linear and nonlinear formulations.16 This approach can be used to solve both differentiable and nondifferentiable optimization problems, and can reduce computational amount, improve the computational precision, and overcome premature convergence problem.17 Considering the limitations of RSM and the complexity of the PHB-producing process by activated sludge, this study aims to develop a novel integrated approach to quantitatively evaluate the effects of factors on the process and enhance the PHB production by activated sludge through integrating RSM and SVR with AGA. RSM is utilized to design experiments and select the key factors for improving the PHB yield in activated sludge, and then the overall PHB production performance is evaluated and enhanced with an integration of RSM, SVR, and AGA. 2. Materials and Methods 2.1. Sludge and Wastewater. A laboratory-scale sequencing batch reactor (SBR), with a working volume of 2 L, was utilized to enrich PHB-producing activated sludge under periodic conditions. The reactor was operated in successive cycles of 4 h each. One cycle consisted of 3 min of influent addition, 222 min of aeration, 10 min of settling, and 5 min of effluent

10.1021/ie100297y  2010 American Chemical Society Published on Web 07/14/2010

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Table 1. Central Composite Experimental Design Matrix Defining S0/X0, COD/N, and Aeration Intensity, as Well as Measured and Predicted PHB Yields (mg PHB/g VSS) for Constructing the SVR and RSM Models coded valuea

real value

run

x1

x2

x3

X1

X2

X3

measured yield

predicted yield by RSM

predicted yield by SVR

1 2 3 4 5 6 7 8 9 10 11 12 13 σ

–1 –1 1 1 0 0 0 0 –1 1 –1 1 0

–1 1 –1 1 –1 –1 1 1 0 0 0 0 0

0 0 0 0 –1 1 –1 1 –1 –1 1 1 0

0.15 0.15 0.45 0.45 0.30 0.30 0.30 0.30 0.15 0.45 0.15 0.45 0.30

48 144 48 144 48 48 144 144 96 96 96 96 96

0.0350 0.0350 0.0350 0.0350 0.0125 0.0575 0.0125 0.0575 0.0125 0.0125 0.0575 0.0575 0.0350

54.18 ( 0.30 65.97 ( 10.40 82.01 ( 6.30 51.72 ( 3.80 60.22 ( 8.19 99.58 ( 9.91 171.09 ( 0.82 75.39 ( 7.09 9.03 ( 0.06 68.22 ( 3.80 24.54 ( 2.93 68.47 ( 5.26 31.98 ( 2.25

29.84 67.92 80.06 76.06 64.35 121.75 148.93 71.25 29.22 66.04 26.72 48.26 31.98 15.90

54.28 66.07 81.91 51.82 60.32 99.48 170.99 75.49 9.13 68.12 24.44 68.37 32.08 0.10

a

X1 )S0/X0, X2 ) COD/N, and X3 ) aeration intensity (m3/(L · h)).

withdrawal. The hydraulic retention time was 8 h, and the temperature was maintained at 20 °C. Air was introduced through an air diffusion installed at the reactor bottom by an air pump. The composition of the synthetic wastewater used was as follows: acetate, 1000 mg/L; NH4Cl, 25 mg/L; KH2PO4, 5 mg/ L; and trace element solution (in mg/L), EDTA, 50; ZnSO4 · 7H2O, 22; CaCl2 · 2H2O, 8.2; MnCl2 · 4H2O, 5.1; FeSO4 · 7H2O, 5.0; (NH4)6Mo7O24 · 4H2O, 1.1; CuSO4 · 5H2O, 1.8; CoCl2 · 6H2O, 1.6. The influent pH value was adjusted to 7.0 through a dose of NaOH. 2.2. Experimental Design. In this work the variables investigated were S0/X0, COD/N, and aeration intensity, which are given in Table 1. In developing the regression equation, the variables are coded according to the following equation: Xi - X*i xi ) ∆Xi

k

∑Ax

i i

i)1

k

+

∑A x

k

2

ii i

i)1

f(w, b) ) w · φ(x) + b

+

k

∑ ∑A xx

ij i j

(2)

1 2 1 w +C 2 l

Lε(zi) )

{

In this work, the model construction and optimization were divided into two steps. In the first step, the relationship between the input variables and the corresponding output variable, that is, the PHB yield, was modeled by SVR. In the second step,

ε

i

i

(4)

i)1

|zi - (w · φ(x) + b)| - ε, |zi - (w · φ(x) + b)| g ε |zi - (w · φ(x) + b)| e ε 0,

ε is the parameter prescribed, whereas C is the regularization constant. With the introduction of slack variables of ξi and ξ*i , eq 4 can be transformed into l

min

3. Model Construction and Optimization

l

∑ L (z , f(x ))

where

i)1 j)1

where xi is the input variable of coded value, which influences the response variable Y, A0 is the offset term, Ai is the ith linear coefficient, Aii is the quadratic coefficient, and Aij is the ijth interaction coefficient. 2.3. Analysis. Measurement of mixed liquor suspended solids (MLSS) and mixed liquor volatile suspended solids (MLVSS) was conducted according to the Standard Methods.18 The PHB concentration in activated sludge was determined using a gas chromatography as described by Fang et al.17

(3)

where xi is input vector, zi is target output, and w and b are coefficients. The coefficients w and b are estimated by minimizing the following function:

(1)

where xi is the coded value of the ith test variable, Xi is an real value of the ith test variable, X*i is the value of Xi at the center point of the investigated area, and ∆Xi is the step size. The response variables were fitted using a predictive polynomial quadratic equation to correlate the response variables to the independent variables. The general form of the predictive polynomial quadratic equation is Y ) A0 +

the model was optimized by using AGA to maximize the PHB yield and find out the corresponding optimal operating conditions. The model to describe the relationship between the operating conditions and the PHB yield was constructed using SVR, which maps the data X into a higher-dimensional feature space F via a nonlinear mapping Φ to do regression in this space.12 Given a set of data points, G ) {(xi,zi)}il, the regression function is approximated by:12

w,b,ξ,ξ*

∑

l

∑

1 2 w +C ξi + C ξ*i 2 i)1 i)1

(5)

subject to (w · φ(xi) + b) - zi e ε + ξi (zi - w · φ(xi)) - b e ε + ξ ξi, ξ*i g 0, i ) 1, ..., l Thus, the approximate function is transformed into the equation below: l

f(x) )

∑ (R

i

- R*)K(x i i, xj) + b

(6)

i)1

where R and Ri are Lagrange multipliers, which can be obtained from the following:

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Ind. Eng. Chem. Res., Vol. 49, No. 16, 2010 l

min R,R*

l

∑

∑

1 (R - R*)(R (Ri + R*) i i - R*)K(x i i, xj) + ε i 2 i,j)1 i i)1 l

∑ z (R i

i

- R*) i (7)

i)1

subject to l

∑ (R

i

i)1

0 e Ri,

- R*) i ) 0 R*i e C i ) 1, ..., l

where K(xi,xj) ) φ(xi) · φ(xj) is the kernel function in the feature space. In this study, a “ε-SVR” in the LIBSVM software19 was used to establish the regression model. Sequential minimal optimization (SMO), a rapid and efficient method in the LIBSVM package, was used to solve the large quadratic programming problems and thereby to estimate the function parameters R, R*, and b.19 The SVR-based model was then optimized by using AGA to search for the maximum PHB yield and the corresponding optimal operating conditions. In the AGA optimization process, the search for an optimal solution vector began from a randomly initialized population of probable solutions. Later, the solutions, coded in the form of real forms (chromosomes), were tested to measure their fitness in fulfilling the optimization objective. Then, the following steps were adopted: selection of better parent chromosomes; production of an offspring solution population by crossing over the genetic material between pairs of the fitter parent chromosomes; mutation of the offspring strings; acceleration of the interval every two evolution generations. In this way, a new population of candidate solutions could be generated, which is usually able to achieve a better optimization objective than the previous population. Such a process was repeated until convergence was reached or the maximum accelerating times were met. As a result, the solution to the optimization problems was found.17 The maximum PHB yield and the corresponding optimal operation conditions could be obtained. The computation for SVR and AGA was performed using software MatLab 7.0 (Mathworks, Natick, MA). 4. Results and Discussion 4.1. Reactor Performance for PHB Production. As the influent ammonia-nitrogen was stepwise decreased from 50 to 10 mg/L, and the correspondingly COD/N ratio was increased from 20 to 100, the PHB content in sludge increased. This indicates that the PHB-accumulating microorganisms could be enriched through limiting the ammonia availability in the medium.20 The PHB production in a typical batch test is shown in Figure 1. The transition point, at which the oxygen utilization rate (OUR) decreased, was used to determine the “feast” and “famine” periods and the point that maximum PHB was produced (Figure 1A).8 With the utilization of the exogenous substance, the OUR decreased, and PHB was produced and its maximum content was reached at the end of the “feast” period. After the substrate depletion, PHB was slowly consumed as carbon and energy resource during the “famine” period (Figure 1B). 4.2. RSM Model. Table 1 shows the experimental design and corresponding PHB yields, which were subjected to regression analysis to evaluate the influences of S0/X0, COD/N, and aeration intensity on the PHB production. The following

Figure 1. Profiles of (A) OUR and (B) COD and PHB in a typical batch test.

quadratic equation was generated with the experimental data listed in Table 1: PHB yield ) 31.98 + 14.59x1 + 8.52x2 - 5.07x3 13.76x12 + 45.25x22 + 24.34x32 - 10.52x1x2 - 3.82x1x3 33.77x2x3 (8) where x1, x2, and x3 are the coded values of S0/X0, COD/N, and aeration intensity, respectively. The predicted values by the quadratic equation of RSM are shown in Table 1. The value of correlation coefficients (R2) of 0.841 suggests that the regression model was appropriate to model the experimental data. In addition, the optimum conditions for PHB yield were achieved after setting the partial derivatives of eq 8 to zero with respect to the corresponding variables, and the local optimum could be obtained by solving the resulting sets of algebraic equations. The optimal conditions were S0/X0 equal to 1.29 (code value), COD/N equal to -2.97 (code value), and aeration intensity equal to 1.72 (code value). The three-dimensional response surfaces and two-dimensional contour lines in Figure 2 are calculated from eq 8, when one variable was kept constant at its optimum level and the other two variables were varied within their experimental ranges. Though the optimal conditions were calculated, no peak was found in Figure 2. This might be attributed to the inappropriate selection of the central point. Generally, in RSM design, the central point should be identified through preliminary experiments. If the central point is chosen unsuitably, the optimal conditions cannot be found. To sort out this problem, these data were regressed using the SVR. 4.3. SVR Model. The SVR model for PHB yield was developed using the experimental data in Table 1 as training sets. In the SVR model S0/X0, COD/N, and aeration intensity were used as input variables, and the PHB yield as output variable. The generalization performance of the SVR model was evaluated using the additional four experimental data in Table 2 as validating sets, which were not used for training. To obtain an accurate SVR estimation, an appropriate kernel function should be chosen. The radial basis function kernel

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Figure 3. Comparison between the measured and simulated PHB yields: (A) training by the SVR; (B) validating by the SVR; (C) training by the RSM; and (D) validating by the RSM.

Figure 2. There-dimensional graphs and two-dimensional contour plots of the PHB yield within the RSM design: (A) fixed S0/X0 level at its optimum value of 1.29 (code value); (B) fixed aeration intensity level at its optimum value of 1.72 (code value). Table 2. Measured and Predicted PHB Yields (mg PHB/g VSS) for Testifying the SVR and RSM Models S0/X0 –1 –0.33 0.33 0 σ

aeration COD/N intensity –1 –1 –1 0.4

–1 1 1 0.33

measured yield 54.88 ( 3.48 85.63 ( 4.95 110.14 ( 6.53 66.87 ( 5.19

predicted yield predicted yield by RSM by SVR 21.66 113.23 127.28 39.15 30.89

48.11 88.70 101.53 61.46 5.17

function was selected for constructing this SVR model, because it resulted in the least root mean squared error values for the training and testing sets of the outputs among three widely used kernel functions. Appropriate parameters for the SVR model, as kernel parameter (γ), cost coefficient (C), and prescribed parameter (ε), were chosen according to a 5-fold cross validation method.21 The comparison between the measured PHB yields and the corresponding values from model training is shown in Figure 3A. To better evaluate the results, a diagonal line was drawn. The high value of the training R2 for PHB yield (0.999) demonstrates that the SVR model learned the relationship between the inputs and the outputs well. The validation R2 value (0.956) suggests that the trained SVR model showed no systematic over- or underprediction with regard to the output variable (Figure 3B).

Figure 4. (A) Three-dimensional graphs and (B) two-dimensional contour plots of PHB yield within the SVR model: fixed COD/N level at its optimum value of 144.

The three-dimensional response surfaces and two-dimensional contour lines in Figure 4 are drawn based on the SVR model, when one variable was kept constant at its optimum level and the other two variables were varied within their experimental ranges. A lower PHB yield was achieved at a high aeration

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Table 3. Optimized Operating Conditions by the SVR-AGA Approach optimized inputs

PHB yield

X1

X2

X3

SVR-AGA output

measured output

error (%)

0.286

144

0.004

185.48

180.5

2.8

intensity and a high S0/X0 (Figure 4). This is in consistent with those reported by Third et al.7 and Dionisi et al.22 Figure 4 also indicates that the maximum PHB yield was achieved and that the optimal conditions could be found by using the AGA approach. 4.4. AGA-Based Optimization of the SVR Model. The SVR model was combined with the AGA to identify the optimal set of input conditions in order to produce a maximum PHB yield. The following AGA-specific parameters were heuristically chosen: population size (Npop) ) 300, smart population size (Nsmart) ) 10, maximum generations (Nmax gen ) ) 2, and maximum accelerating times (Ntmax) ) 30. When the accelerating time Nt reached 5, the convergence rate was rapid and the optimum solution vector could be found. The optimal operating conditions estimated from the AGA-based optimization of the SVR model are listed in Table 3. Under the optimal conditions for S0/X0 of 0.286, COD/N of 144, and aeration intensity of 0.004, the maximum PHB yield of 185.48 mg PHB/g VSS was achieved. This was the global solution to the SVR model searched by the AGA (Figure 4). Microbial growth under nutrient-deficient conditions may enhance the PHB synthesis. For instance, nitrogen-deficiency is well-known to favor the PHB synthesis in microbial cells.7 At a COD/N of 144, a high PHB yield was achieved, but the microbial growth was suppressed. Dionisi et al.22 also found that a higher S0/X0 resulted in a higher biomass concentration and lower PHB production. Therefore, a proper S0/X0 and COD/N should be selected to balance the microbial growth and PHA yield of activated sludge. The microbial uptake of fatty acids and subsequent conversion to PHB are strictly oxygen-dependent. An oversupply of oxygen could adversely affect the PHB production, while at a low dissolved oxygen concentration all available ATP could be used for the fatty acids uptake, reducing the assimilation reaction.7 The simulation results above also show that a lower aeration intensity was favorable to a higher PHB yield (Figure 4). Thus, at an appropriate S0/X0 value, high COD/N ratio, and low O2 level, a maximum PHB storage could be achieved with minimum microbial growth. 4.5. Experimental Verification of the SVR-AGA Approach. To verify the validity of the coupled SVR-AGA approach and gain a better understanding of PHB production by activated sludge, the optimized operating conditions obtained by the SVR-AGA approach were subjected to experimental verification, and the results are listed in Table 3. The measured optimal PHB yield was close to that estimated from the SVR-AGA approach. The PHB yield had a relative error of 2.8% from the calculated value. From the experimental data used for building the SVR model, the maximum PHB yield was 180.48 mg PHB/g VSS. A comparison between this value and that from the verification experiments reveals that with such an AGA-based optimizing approach, the PHB yield was enhanced. This further confirms that the SVR-AGA method was a useful approach to optimize the PHB production from wastewater by activated sludge. 4.6. Comparison between the RSM and SVR-AGA Models. The RSM and SVR-AGA models were compared in terms of the predicted PHB yield. The R2 values for the PHB

yield in Figure 3 show that the SVR model was superior to the RSM model. The root-mean square errors (σ) between the measured and predicted outputs for constructing and testing the SVR model (Tables 1 and 2) demonstrates that the SVR model gave a better fit to the experimental data over the RSM model. The RSM approach could provide the individual and interactive effects of factors on the optimal object.10 However, the quadratic regression by the RSM suggests that the model was not significant because of the low value of Fmodel (mean square regression/mean square residual) (Fmodel ) 1.76 < F3,9,0.05 ) 3.86).10 Furthermore, the optimum conditions for PHB production obtained were not in the range of the experimental design matrix, which could be misleading with respect to the exploration of maximum PHB yield. This was probably attributed to the limitation of the RSM approach, which was an optimization method mostly used for local optimization. By contrast, the SVR is a data-based nonlinear modeling paradigm, and it could infinitely approximate the function established by the input and output variables.14 The trained SVR model has excellent generalization ability because it has accurate prediction ability when a new data set is input. The AGA optimum method, with global search ability and strong adaptation of objection function, assures that the results calculated from the SVR-AGA model could be more reliable than those of the RSM optimization results.16 The results in Table 3 and Figure 4 also show that the SVR-AGA approach had searched more sound global optimum solutions than the RSM model. Both RSM and SVR models are empirical ones.11 To achieve the optimal conditions, many preliminary experiments should be carried out to determine the central point and step size.10,23 On the other hand, for the SVR-AGA model, the initial conditions could be randomly chosen, and no central point and step size are needed to select through preliminary experiments. In addition, the SVR could solve small-sample learning problems better,12 and the AGA could optimize optional functions which are not smooth, differentiable, and continuous.17 Taking the principles of structural risk minimization with greater generalization ability into consideration, the SVR-AGA model is able to perform global search and the globally optimum solution is thus most likely to be reached. The SVR-AGA approach was found to be more appropriate than the RSM method, and the AGA could be effectively used to identify the optimal input values and calculate the maximum PHB yield. The verification test confirms that the measured optimum PHB yield was close to that estimated using the SVR-AGA approach. This work indicates such an approach was effective and accurate for optimizing complex bioprocesses and could be useful to improve the PHB production by mixed cultures. The approach established in this work can also be applied for other biotechnological processes. Since most biotechnological systems are very complex, and their performance is affected by many operating conditions and environmental factors, when this integrated RSM-SVR-AGA approach is used to evaluate their influential factors, the results should be useful and can be utilized for designing and enhancing these biosystems. 5. Conclusions PHB production from a synthetic wastewater by activated sludge was modeled and optimized using both RSM method and an integrated SVR-AGA approach. The SVR-AGA approach was found to be more appropriate than the RSM method. The AGA could be effectively used to identify the

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optimal input values and calculate the maximum PHB yield. A maximum PHB yield of 185.48 mg PHB/g VSS under the optimum conditions of S0/X0 of 0.286, COD/N of 144 and aeration intensity 0.004 m3/(L · h) was estimated. The verification experiment confirmed that the measured optimum PHB yield was close to that estimated using the SVR-AGA approach. This work indicates that the SVR-AGA approach was effective and accurate for optimizing complex bioprocesses and could be useful to improve the PHB production by mixed cultures. Acknowledgment The authors wish to thank the NSFC (Grants 50625825 and 50738006), and the Key Special Program on the S&T for the Pollution Control and Treatment of Water Bodies (Grants 2008ZX07316-002 and 2008ZX07010-003) and the Fundamental Research Funds for Central Universities (Grants 2009B03414) for the partial support of this study. Nomenclature AGA ) accelerating genetic algorithm COD ) chemical oxygen demand PHB ) polyhydrobutyrate RSM ) response surface methodology S0 ) initial substrate concentration SVM ) support vector machine SVR ) support vector regression VSS ) volatile suspended solids X0 ) initial biomass concentration

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ReceiVed for reView February 7, 2010 ReVised manuscript receiVed May 26, 2010 Accepted June 30, 2010 IE100297Y