Enhancement of PHB Biosynthesis by Ralstonia Eutropha in Fed-batch Cultures by Neural Filtering and Control1

0960–3085/06/$30.00+0.00 # 2006 Institution of Chemical Engineers Trans IChemE, Part C, June 2006 Food and Bioproducts Processing, 84(C2): 150– 156

www.icheme.org/fbp doi: 10.1205/fbp.05008

ENHANCEMENT OF PHB BIOSYNTHESIS BY RALSTONIA EUTROPHA IN FED-BATCH CULTURES BY NEURAL FILTERING AND CONTROL1 P. R. PATNAIK
Institute of Microbial Technology, Chandigarh, India

P

oly-b-hydroxybutyrate (PHB) is a biodegradable polymer with properties that make it suitable for many applications for which petroleum-based synthetic polymers are currently used. However, low productivities have hindered the commercial success of microbial PHB. In this study, two neural networks have been employed to enhance PHB production by Ralstonia eutropha in fed-batch fermentations under simulated industrial conditions. One network filtered the noise in the two feed streams and the other controlled their flow rates at the optimum dispersion. This arrangement more than doubled the volumetric PHB concentration relative to an ideal fermentation. Significantly, this improvement was achieved with finite dispersion and optimally filtered noise rather than no noise. These observations show that neural filtering and control offer a viable method to enhance PHB formation in industrial situations.

Keywords: poly-b-hydroxybutyrate (PHB); fed-batch fermentation; nonideal bioreactor; neural networks.

INTRODUCTION

carrier matrices for insecticides and medicines, and absorbable surgical dressings and sutures (Lee and Chang, 1995; Steibuchel, 1996; Braunegg et al., 1998). Despite these advantages, the commercial potential of PHB has not yet been fully realized. Raw materials are not a contributing factor since fermentations for PHB can use cheap and renewable substrates such as wheat straw and molasses. PHB fermentations are also energetically superior to petrochemical processes owing to the milder operating conditions of the former. The low productivity of fermentation processes is a main reason for the high product costs that have restrained the market demand for PHB (Braunegg et al., 1998; Khanna and Srivastava, 2005a). While many studies have focussed on the mechanisms of PHB synthesis by bacteria (Steinbuchel, 1996; Braunegg et al., 1998) and on laboratory scale cultivation (Wang and Lee, 1997; Khanna and Srivastava, 2005b), these results do not translate readily to commensurate values under more realistic conditions characteristic of production scale bioreactors. At least two features differentiate large bioreactors from their laboratory-scale counterparts: (1) incomplete mixing or dispersion in the broth and (2) disturbances from the environment. Neither can be eliminated fully, and their presence can have both negative and positive effects, depending on fermentation kinetics and the operating policy (Liden, 2001; Patnaik, 2002). Since improvement of fermentation efficiency under these conditions will help to make microbial production of PHB competitive with synthetic polymers, it is important

Under conditions adverse to cell growth, many bacteria synthesize polyhydroxyalkanoates (PHAs) to function as reservoirs of energy. Poly-b-hydroxybutyrate (PHB) is the most prominent member of the family of PHAs, and it has many potential uses either as a homopolymer or as a copolymer, with polyhydroxyvalerate being a common component. Although the possibility of utilizing microbes to synthesize PHB has been known for more than 40 years (Macrae and Wilkinson, 1958), significant commercial interest has developed recently, driven largely by growing environmental concerns and the rising cost and uncertainty of petroleum-based chemicals. In fact, polymers derived from petrochemical feed stocks have been the major competitor to PHB and similar bioploymers. Petroleum-based polymers such as polyethylene (PE) and polypropylene (PP) have many properties similar to those of PHB (Doi, 1990; Steinbuchel, 1996). However, unlike PE and PP, PHB can be synthesized under mild conditions from renewable resources and it is readily biodegradable and compatible with biological tissue. These advantages make PHB and its copolymers suitable for a variety of products such as food packaging films, disposable cosmetics, 1

IMTECH communication number 006/2005.




Correspondence to: Dr P. R. Patnaik, Institute of Microbial Technology, Sector 39-A, Chandigarh-160 036, India. E-mail: [email protected]

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ENHANCEMENT OF PHB BIOSYNTHESIS BY RALSTONIA EUTROPHA to understand how these nonideal features affect the performance of a bioreactor. This issue is the subject of the present investigation.

BACKGROUND TO PHB SYNTHESIS Bacteria such as Alcaligenes latus, Azotobacter vivelandii and Ralstonia eutropha (formerly Alcaligenes eutrophus) may be induced to synthesize PHB by depriving them of an essential nutrient such as nitrogen or phosphorus or sulphur. R. eutropha is the most widely used organism because of its well understood physiology, its ease of cultivation and its ability to accumulate high concentrations of PHB inside the cells. Deprivation of nitrogen is commonly practised (Lee and Chang, 1995; Braunegg et al., 1998; Khanna and Srivastava, 2005a), although recent work (Shang et al., 2003) points to the possibility of limiting the supply of phosphate as a viable method. Although starving the cells of nitrogen triggers PHB synthesis, excessive shortage retards cell growth (Khanna and Srivastava, 2005c) and promotes depolymerization of PHB (Jendrossek, 2001). To generate sufficient biomass, there should be an adequate supply of carbon (usually as glucose or fructose). However, similar to nitrogen starvation, an overwhelming excess of carbon inhibits cell growth (Liden, 2001; Khanna and Srivastava, 2005c) These observations suggest that control of both the absolute and the relative rates of supply of the carbon and nitrogen substrates is critical to the efficiency of PHB production. Unfortunately, in view of the complexity of the metabolism (Steinbuchel, 1996; Braunegg et al., 1998), these rates change nonlinearly as the fermentation progresses and differ from one strain to another. Therefore different investigators have employed different methods to determine the optimal feed rates (Lee et al., 1997; Katoh et al., 1999; Riascos and Pinto, 2004; Khanna and Srivastava, 2005b). They show that fed-batch operation is preferable to batch and continuous fermentations, and they illustrate both the benefits and the difficulties of optimizing the feed rates. The difficulties become more pronounced for large bioreactors, which are difficult to model and are subject to mixing problems and the ingress of noise. Since the benefits of optimizing large (production scale) bioreactors are also greater than those of laboratory scale reactors, the present study attempts such an optimization. It is based, as described next, on a laboratory scale mathematical model to which noise was added in the two feed streams to simulate industrial conditions.

FERMENTATION DESCRIPTION AND DATA GENERATION Economic, practical and proprietary considerations often place restrictions on the availability and disclosure of industrial fermentation data. Under these circumstances, a common practice is to generate simulated data by solving a model validated with laboratory data but ‘corrupted’ by adding incomplete (finite) dispersion in the broth and noise in the feed stream(s) (Simutis and Lubbert, 1997; Chen and Rollins, 2000). Apart from circumventing the difficulties explained above, this approach enables exploration

151

of bioreactor dynamics and control policies without disturbing plant operations. Following this rationale, a model proposed by Lee et al. (1997) was used as a basis to generate data for this study. They conducted fed-batch experiments with R. eutropha NCIMB 11,599 in a Bioflo III (New Bruncwick Scientific, USA) fermenter with a working volume of 2.5 l. Glucose was the main carbon source and ammonium chloride provide the nitrogen requirement. Each experiment ran typically for 40– 50 h. Lee et al. (1997) monitored the concentrations of four state variables: PHB, residual biomass, glucose and ammonium chloride. For these variables the mass balances for a fed-batch fermentation are presented below. dX1 dt dX2 dt dX3 dt dX4 dt

¼ mX1

(1)

¼ X2f F1  s1 X1

(2)

¼ X3f F2  s2 X1

(3)

¼ pX1

(4)

In addition, the volume of material in the vessel increases at the rate at which the two substrates are supplied. Therefore, dX5 ¼ F1 þ F2 dt

(5)

The starting values of all five variables are known (Table 1); so, in general, equations (1) –(5) may be solved with the initial conditions t ¼ 0: X1 ¼ X10 , X2 ¼ X20 , X3 ¼ X30 , X4 ¼ X40 , X5 ¼ X50

(6)

While ideally the initial value of X4 is zero since there should be no PHB when a fermentation run begins, in industrial practice the same vessel is used for repeated

Table 1. Parameters in the model of Lee et al. (1997). Parameter

Units

Value

Variable

Units

Initial value

KG KGI KN KNI KP KPG KPGI KPN KPNI KP me

g L21 g L21 g L21 g L21 g L21 g L21 g L21 g L21 g L21 g L21 1 h21

X1 X2 X3 X4 X5

g L21 g L21 g L21 g L21 L

0.04 19.0 0.7 0 10.0

(X4/X)m YP/C YR/C YR/N hm pm

— g g21 g g21 g g21 1 h21 1 h21

5.81 14.5 0.69 0.15 0.05 2.09 80.0 0.05 0.9 0.05 0.01 (0 when s1 ¼ 0) 0.85 0.47 0.45 2.11 0.875 0.402

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batches. Therefore some residual initial PHB may be expected in successive runs. The residual biomass [called active biomass by Lee et al. (1997)] is the difference between the total biomass and its PHB content, a concept borrowed from Mulchandani et al. (1989). Since PHB is retained inside the cells, it inhibits growth at high concentrations (Asenjo and Suk, 1985; Khanna and Srivastava, 2005c). This inhibition and the fact that viable cells can produce some PHB without ammonium resulted in the following specific rates for cell growth, m, and polymer synthesis, p.


m ¼ mm

X2 KG þ X2 þ X22 =KGI




X3 KN þ X3 þ X32 =KNI



 X4 =X X2 p ¼ pm 1  (X4 =X)m KPG þ X2 þ X22 =KPGI
 X3 þ KP  KPN þ X3 þ X32 =KPNI

 (7)

(8)

The specific rate of consumption of glucose is the sum of its rates for biomass synthesis, PHB formation and cellular maintenance.

s1 ¼

m YR=G

þ

m YP=G

þ me

(9)

Since curtailment of ammonium supply promotes PHB formation, this is utilized mainly for biomass synthesis. Hence,

s2 ¼

m YR=N

(10)

Since Lee et al.’s (1997) studies were carried out in a small well-mixed bioreactor, their results were unaffected by dispersion in the broth and noise in the feed streams. However, these factors are significant in large bioreactors (Rohner and Meyer, 1995; Chen and Rollins, 2000). Previous studies (Patnaik, 1997, 1999, 2001a) have shown that the flow rates of the feed streams are the major sources of noise from the environment and this noise may be characterized by a set of Gaussian distributions with a mean equal to the current deterministic value of the concentration of interest and different variances. So, the inflow rates of glucose and ammonium chloride were subjected to this kind of noise. To account for finite dispersion, we note that the degree of dispersion may be expressed by the Peclet number: Pe ¼ uL=De

(11)

For complete dispersion, as in small bioreactors, De ! 1 and hence Pe ! 0. The other limit, De ! 0 and Pe ! 1, corresponds to no dispersion (or plug flow). Real bioreactors have finite values of De, 0 , De , 1, implying that 1 . Pe . 0. The concentration profiles for any finite dispersion may be obtained from those for

infinite dispersion by using the distribution function (Moser, 1988): sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
ffi Pe Pe exp (1  t)2 f (t) ¼ tmix 4pt 4t 1

(12)

Correlations for the mixing time tmix are available from Mayr et al. (1992). To apply equation (12) to a noise-affected fermentation, equations (1) – (10) were solved in their present form, which applies to a noise-free fully dispersed bioreactor, using the parameter values and initial conditions in Table 1. Using these concentration plots as the basis, the equations were solved again with Gaussian noise in the flow rates of the two feed streams. The noise had mean values equal to the instantaneous deterministic concentrations, determined earlier, and different variances. The second set of profiles represented a noise-affected but fully dispersed bioreactor (Pe ! 0). Let q(t) denote any such concentration profile. The corresponding profile for any Pe . 0 is then determined as: ðt q^ (t) ¼

q(t)f (t)dt

(13)

0

Once the concentration profiles at different values of Pe are obtained, the model becomes redundant and the new profiles are sampled to obtain data mimicking a large nonideal bioreactor.

NEURAL FILTERING AND CONTROL Previous studies (Patnaik, 1997, 1999, 2001a) of noiseaffected nonideal bioreactors have discussed two requirements for optimum operation. They are (1) filtering of the noise in the inlet streams and (2) control of the feed rates at optimum dispersion. Although algorithmic devices such as the Kalman and cusum filters have been employed to reduce the noise inflow to fermentation systems, their static nature restricts their flexibility in adapting to variable Gaussian noise. Artificial neural networks are more adept at this, and their ability to learn and update their performances with repeated use improves their effectiveness for dynamic processes. Now, a noise filter essentially processes a set of inputs and generates as outputs the same variables with reduced noise. This function suggests an autoassociative neural network (ANN) as a generically suitable configuration (Patnaik, 2001a, b). It has also been shown recently (Patnaik, 2005a) that an ANN filter is better than many other commonly employed neural networks for noise-affected PHB fermentations. Since there are two inflow streams, those of ammonium chloride and glucose, the filter has two neurons each in the input and output layers. If the hidden layer has too few neurons, the network may not be able to portray all the relevant features of the process adequately. With too many neurons, a network learns spurious features in addition to those of interest (Patnaik, 1999, 2001a), thus again impairing its performance. So the number of neurons is varied until a performance index stabilizes at a maximum or minimum value. In the

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Figure 2. Flow sheet of the neural filtering and control strategy.

Figure 1. Variations in the performance indexes of the neural filter and the neural controller with the number of hidden neurons.

present study, the index was the percentage change in the output variance with each addition of a hidden neuron. The use of a percentage change provides an equivalent of a normalization process, whereby the optimum number of hidden neurons does not depend on the value set for the variance. Figure 1 shows the optimum number to be three, thus creating a 2-3-2 architecture for the neural filter, i.e., two input neurons, three hidden neurons and two output neurons. Many studies (Shi and Shimizu, 1992; Patnaik, 1997, 2001a) have demonstrated the superiority of a feed-forward neural controller over an adaptive PID controller. According to Lee et al.’s (1997) model, the neural controller should have four neurons in the input layer. Two of these are for the concentrations of residual biomass and PHB. For the other two, either the concentrations or the flow rates of the carbon and nitrogen substrates may be chosen. The flow rates were preferred for two reasons. First, the noise is present in the flow rates and not in the concentrations. Secondly, manipulation of the flow rates is preferred for many fed-batch fermentations because regulating the feed concentrations results in suboptimal singular control (Modak, 1993; Dochain and Perrier, 1997).

There were two output variables, for PHB and residual biomass. The optimum number of hidden neurons was determined as for the filter (Figure 1), the performance index here being the percentage change in the peak concentration of PHB. This resulted in a feed-forward controller of the architecture 4-4-2. Figure 2 depicts the signal flow diagram of the overall filtering-and-control strategy. The two feed streams are first filtered to reduce their noise content. The neural controller acts on the filtered signals and those fed back from the bioreactor to manipulate the feed rates of glucose and ammonium chloride across successive slices of time. The nonlinear optimizer updates the weights of the two neural networks such that the feed rates and the noise in them are always at discrete optimal levels. Briefly, discrete optimization means the fermentation is optimized, i.e., the PHB output is maximized, stepwise in time from the start until a peak value is reached. For each interval of time, the initial conditions are the outputs from the previous time step. This Markovian strategy with an open end point provides more stable fermentations with higher product concentrations than one-step optimization with a fixed final time (Shi and Shimizu, 1992; Modak, 1993; Ye et al., 1994). PERFORMANCE ANALYSIS The performance of the fermentation was determined for inflow noise with variances from 0% to 14%. This range amply covers the variances reported for most large scale fermentations (Montague and Morris, 1994; Rohner and Meyer, 1995). For each case the mean of the Gaussian

Figure 3. Performance of the fermentation as a function of the variance of the filtered noise.

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Figure 4. Variation of the residual biomass concentration with time under optimal filtering (6% variance) of the noise.

Figure 6. Variation of the PHB concentration per unit residual biomass with time under optimal filtering (6% variance) of the noise.

noise was set, as explained before, at the current deterministic values of the relevant concentration. The detailed deterministic results have been published elsewhere (Patnaik, 2005b). However, these concentrations are implicitly contained in the results presented in Figures 1– 7 since they pertain to the case of 0% variance in the noise. The results focus on two concentrations, those of the biomass and PHB, which are of primary interest in a production scale fermentation. Their peak values at each variance have been plotted in Figure 3. For each concentration, the lower (continuous) plots are for a fully dispersed broth (Pe ¼ 0), while the upper (discontinuous) lines correspond to Pe ¼ 20. The latter value of Pe has been shown recently (Patnaik, 2005b) to maximize PHB formation in the absence of noise. The most striking feature of all the plots in Figure 3 is that they pass through maxima at a variance of about 6% in the filtered streams. Moreover, both the actual

concentrations and their rates of improvement (until 6% variance) are higher for optimum dispersion (Pe ¼ 20) than for complete dispersion (Pe ¼ 0). The latter observation suggests that, contrary to classical bioreactor theory, instead of trying to maximize the dispersion in large bioreactors to mimic laboratory scale reactors, it might be more beneficial to control the dispersion at a prescribed finite level. The existence of an optimum dispersion is also practically useful since dispersion in large bioreactors is inherently incomplete. The degree of dispersion may be controlled by several methods—stirrer design and speed, sparging with inert gas or air, and recycling of the broth. The last method employs the classic concept that a plug flow reactor approaches a back-mixed reactor as the recycle rate approaches infinity. The existence of an optimum variance is an intriguing, but not unique, observation. It is intriguing because it seems paradoxical that noise can be helpful to a microbial

Figure 5. Variation of the volumetric PHB concentration with time under optimal filtering (6% variance) of the noise.

Figure 7. Dependence of the time of attainment of peak performance on the variance of the filtered noise.

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Table 2. Percentage improvement in the peak performance relative to noise-free fermentation with Pe ¼ 0. Pe ¼ 0

No noise Unfiltered noise Filtered noise

Pe ¼ 20

X1 (g L21)

PHB (g L21)

PHB (g g21)

X1 (g L21)

PHB (g L21)

PHB (g g21)

0 224.6 31.9

0 229.9 46.0

0 27.0 10.7

19.2 219.5 60.9

30.1 225.8 106.8

9.1 27.8 28.5

process. It is not unique because optimum variances have also been reported for streptokinase from a Streptococcus sp. (Patnaik, 2001a) and for b-galactosidase production by a recombinant Escherichia coli strain (Patnaik, 2001b). In those studies it has been proposed that at the optimum variance the fundamental frequency of the noise resonates with the biological system, thereby amplifying the cellular processes. This is analogous to the vibrations induced in a tall vase when a tuning fork of a particular frequency is brought close by. A second reason for the improvement in metabolic efficiency through controlled noise is the possibility of energy transfer as in the cases of photochemical and sonochemical reactions. However, excessive noise, like excessive light or sound, can trigger run-away reactions that eventually extinguish the desired process. Given the complexity of the PHB synthesis network (Lee and Chang, 1995; Steinbuchel, 1996; Braunegg et al., 1998) and the variations introduced by dispersion, it may be expected that both controlled dispersion and controlled filtering will improve the fermentation at different rates as time progresses and, at any point in time, at unequal rates for the biomass and PHB. These expectations are portrayed in the time-domain of the concentrations at Pe ¼ 0 and Pe ¼ 20, each without noise and with optimum noise (Figures 4– 6). The extent of enhancement of both cell growth (Figure 4) and PHB synthesis (Figure 5) increase with time, both without and with noise, i.e., the separation of each pair of plots increases progressively. However, an important difference is that up to about 8 h the cell mass increases perceptibly but PHB synthesis is not yet significant. Thereafter, the rate of PHB formation overtakes that of biomass growth. This difference results in the PHB concentration per unit residual biomass (i.e., total biomassPHB) decreasing up to 8 h and then increasing (Figure 6). Optimally filtered noise enhances as well accelerates PHB formation. This is evident from the plots in Figure 7, where the time required to reach the peak concentration of PHB is maximum at the optimum variance of 6%. The reduction in time is consistent with the possibilities of resonance and energy transfer suggested before. The improvements obtained through optimal neural filtering are quantitatively expressed in Table 2. All values are relative to their corresponding values for a fully dispersed noise-free (ideal) fermentation. Unfiltered noise reduces cell growth by 20– 25% but PHB production is affected even more (up to 30%). Thus, noise causes loss of cell viability in addition to impeding cell growth. However, properly filtered noise is helpful and enhances both cell growth and productivity of PHB beyond those of a noise-free ideal fermentation. Since product synthesis was suppressed more than biomass growth, ‘intelligent’ filtering has enabled larger increases in PHB concentration

(46 –107%) than in growth (32 – 61%). The beneficial effect of filtered noise has been observed in other fermentations also (Patnaik, 1999, 2001a, b, 2002), where it has been suggested that optimal filtering (corresponding to 6– 8% variance) allows only those components of the noise that resonate with the natural frequency of the biological system. Since the noise itself may vary with time, a neural filter can learn from and adapt to the changing situation in a more facile manner than an algorithmic filter. CONCLUDING OBSERVATIONS Productivity enhancement of PHB was investigated by applying two artificial neural networks to a bioreactor with finite dispersion and noise in the feed streams. One network filtered the noise and the other controlled the filtered feed rates of the carbon and nitrogen sources. Apart from greater flexibility and more stringent control than with algorithmic filters coupled with PID controllers, neural networks do not require a process model and they improve with use. The alleviation of a model is advantageous since real fermentations are difficult to describe by simple and accurate mathematical models. Simulations were done for a fully dispersed bioreactor and one with optimum dispersion, each with no noise, raw unfiltered noise and neurally filtered noise. While unfiltered noise reduced both biomass growth (20 –25%) and PHB formation (25 –30%), the use of a neural filter reversed this trend. At optimum dispersion, the peak concentration of PHB was more than twice that in an ideal bioreactor. Moreover, this maximum was attained sooner than for a noise-free well-dispersed fermentation. Together with similar results for other microbial systems, they show that neural filtering and control is a viable technique to enhance PHB production in nonideal bioreactors. NOMENCLATURE De F1 F2 KG KGI KN KNI KP KPG KPGI KPN KPNI L

effective dispersion coefficient, cm2 h21 feed rate of glucose, L h21 feed rate of ammonium chloride, L h21 Monod constant for growth on carbon source, g L21 inhibition constant for growth on carbon source, g L21 Monod constant for growth on nitrogen source, g L21 inhibition constant for growth on nitrogen source, g L21 rate constant for production of PHB without nitrogen source, g L21 Monod constant for production of PHB on carbon source, g L21 inhibition constant for production of PHB on carbon source, g L21 Monod constant for production of PHB on nitrogen source, g L21 inhibition constant for production of PHB on nitrogen source, g L21 characteristic dimension of bioreactor, cm

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156 me Pe t X X1 X2 X2f X3 X3f X4 X5 Xj0 u YR/G YP/G YR/N

PATNAIK specific maintenance energy, L h21 Peclet number ¼ uL/De real time, h total biomass concentration, g L21 concentration of active biomass, g L21 concentration of glucose, g L21 glucose concentration in feed stream, g L21 concentration of ammonium chloride, g L21 ammonium chloride concentration in feed stream, g L21 concentration of PHB, g L21 volume of the broth in the bioreactor, L initial value of Xj, g L21 or L average fluid velocity in bioreactor, cm h21 yield coefficient for active biomass on glucose, g g21 yield coefficient for PHB on glucose, g g21 yield coefficient for active biomass on ammonium chloride, g g21

Greek symbols m mm p pm s1 s2

specific growth rate of residual biomass, 1 h21 maximum value of m, 1 h21 specific rate of formation of PHB, 1 h21 maximum value of p, 1 h21 specific consumption rate of glucose, 1 h21 specific consumption rate of ammonium chloride, 1 h21

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