Monitoring of a sequencing batch reactor using adaptive multiblock principal component analysis

Multiway principal component analysis (MPCA) for the analysis and monitoring of batch processes has recently been proposed. Although MPCA has found wide applications in batch process monitoring, it assumes that future batches behave in the same way as those used for model identification. In this study, a new monitoring algorithm, adaptive multiblock MPCA, is developed. The method overcomes the problem of changing process conditions by updating the covariance structure recursively. A historical set of operational data of a multiphase batch process was divided into local blocks in such a way that the variables from one phase of a batch run could be blocked in the corresponding blocks. This approach has significant benefits because the latent variable structure can change for each phase during the batch operation. The adaptive multiblock model also allows for easier fault detection and isolation by looking at the relationship between blocks and at smaller meaningful block models, and it therefore helps in the diagnosis of the disturbance. The proposed adaptive multiblock monitoring method is successfully applied to a sequencing batch reactor for biological wastewater treatment.     

Nonlinear biological batch process monitoring and fault identification based on kernel fisher discriminant analysis

A novel nonlinear biological batch process monitoring and fault identification approach based on kernel Fisher discriminant analysis (kernel FDA) is proposed. This method has a powerful ability to deal with nonlinear data and does not need to predict the future observations of variables. So it is more sensitive to fault detection. In order to improve the monitoring performance, variable trajectories of the batch processes are separated into several blocks. Then data in the original space is mapped into high-dimensional feature space via nonlinear kernel function and the optimal kernel Fisher feature vector and discriminant vector are extracted to perform process monitoring and fault identification. The key to the proposed approach is to calculate the distance of block data which are projected to the optimal kernel Fisher discriminant vector between new batch and reference batch. Through comparing distance with the predefined threshold, it can be considered whether the batch is normal or abnormal. Similar degree between the present discriminant vector and the optimal discriminant vector of fault in historical data set is used to perform fault diagnosis. The proposed method is applied to the process of fed-batch penicillin fermentation simulator benchmark and shows that it can effectively capture nonlinear relationships among process variables and is more efficient than MPCA approach.     

动态MPCA在发酵过程监测与故障诊断中的应用

针对发酵过程非线性和时变特点,提出了一种具有实时性的动态MPCA方法,采用多模型非线性结构代替传统MPCA单模型线性化结构,克服了后者不能处理非线性过程和实时性的问题,并避免了MPCA在线应用时预报未来测量值带来的误差,提高了发酵过程性能监测和故障诊断的准确性。对头孢菌素C发酵过程的拟在线仿真研究,验证了基于动态MPCA的统计过程监测的有效性。     

Adaptive multiscale principal component analysis for on-line monitoring of a sequencing batch reactor

In recent years, multiscale monitoring approaches, which combine principal component analysis (PCA) and multi-resolution analysis (MRA), have received considerable attention. These approaches are potentially very efficient for detecting and analyzing diverse ranges of faults and disturbances in chemical and biochemical processes. In this work, multiscale PCA is proposed for fault detection and diagnosis of batch processes. Using MRA, measurement data are decomposed into approximation and details at different scales. Adaptive multiway PCA (MPCA) models are developed to update the covariance structure at each scale to deal with changing process conditions. Process monitoring by a unifying adaptive multiscale MPCA involves combining only those scales where significant disturbances are detected. This multiscale approach facilitates diagnosis of the detected fault as it hints to the time-scale under which the fault affects the process. The proposed adaptive multiscale method is successfully applied to a pilot-scale sequencing batch reactor for biological wastewater treatment.     

Calculation errors of time-varying flux control coefficients obtained from elasticity coefficients by means of summation and connectivity theorems in metabolic control analysis

This paper investigates the accuracy of a matrix method proposed by other researchers to calculate time-varying flux control coefficients (dynamic FCCs) from elasticity coefficients by means of summation and connectivity theorems in the framework of metabolic control analysis. A mathematical model for the fed-batch penicillin V fermentation process is used as a case example for discussion. Calculated results reveal that this method produces significant calculation errors because the theorems are essentially valid only in steady state, although it may provide rough time-transient behaviors of FCCs. Strictly, therefore, dynamic FCCs should be directly calculated from the differential equations for metabolite concentrations and sensitivities.     

双碳源单细胞蛋白高密度培养

单细胞蛋白(SCP)培养具有增殖速度快、原料来源广、蛋白质含量高等优点,正在成为重要的蛋白质来源之一。生产SCP的原料包括烷烃、低碳醇类及碳水化合物。由碳水化合物,尤其是由各种农副产品加工的废料、造纸工业废水及木质纤维素水解产物等,生产SCP属于废弃资源的综合利用,具有重要的经济和社会意义。这类原料中常含有多种碳源,例如,在木质纤维水解液中,有以木糖为主的五碳糖,也有以葡萄糖为主的六碳糖,木糖与葡萄糖之比约为1:2。这样,木糖的利用就成了一个关键问题。葡萄糖对木糖代谢的抑制作用随葡萄糖比例的降低而减轻;Slinger和Bothast研究了用混合糖培养Candida shehetane的过程,当木糖与葡萄糖的比例为3:1及1:1时,葡萄糖的利用速度分别比木糖高1.6倍及3.4倍;在低还原势时,酵母产酒精速率降低而细胞增长速率加快,可以达到较高细胞浓度。     

Multi-model statistical process monitoring and diagnosis of a sequencing batch reactor

Biological processes exhibit different behavior depending on the influent loads, temperature, microorganism activity, and so on. It has been shown that a combination of several models can provide a suitable approach to model such processes. In the present study, we developed a multiple statistical model approach for the monitoring of biological batch processes. The proposed method consists of four main components: (1) multiway principal component analysis (MPCA) to reduce the dimensionality of data and to remove collinearity; (2) multiple models with a posterior probability for modeling different operating regions; (3) local batch monitoring by the T(2)- and Q-statistics of the specific local model; and (4) a new discrimination measure (DM) to identify when the system has shifted to a new operating condition. Under this approach, local monitoring by multiple models divides the entire historical data set into separate regions, which are then modeled separately. Then, these local regions can be supervised separately, leading to more effective batch monitoring. The proposed method is applied to a pilot-scale 80-L sequencing batch reactor (SBR) for biological wastewater treatment. This SBR is characterized by nonstationary, batchwise, and multiple operation modes. The results obtained for the pilot-scale SBR indicate that the proposed method has the ability to model multiple operating conditions, to identify various operating regions, and also to determine whether the biosystem has shifted to a new operating condition. Our findings show that the local monitoring approach can give more reliable and higher resolution monitoring results than the global model.     

High-throughput screening of Hansenula polymorpha clones in the batch compared with the controlled-release fed-batch mode on a small scale

Most large-scale production processes in biotechnology are performed in fed-batch operational mode. In contrast, the screenings for microbial production strains are run in batch mode, which results in the microorganisms being subjected to different physiological conditions. This significantly affects strain selection. To demonstrate differences in ranking during strain selection depending on the operational mode, screenings were performed in batch and fed-batch modes. Two model populations of the methylotrophic yeast Hansenula polymorpha RB11 with vector pC10-FMD (P_FMD-GFP) (220 clones) and vector pC10-MOX (P_MOX-GFP) (224 clones) were applied. For fed-batch cultivations in deep-well microtiter plates, a controlled-release system made of silicone elastomer discs containing glucose was used. Three experimental set-ups were investigated: batch cultivation with (1) glucose as a substrate, which catabolite represses product formation, and (2) glycerol as a carbon source, which is partially repressing, respectively, and (3) fed-batch cultivation with glucose as a limiting substrate using the controlled-release system. These three experimental set-ups showed significant variations in green fluorescent protein (GFP) yield. Interestingly, screenings in fed-batch mode with glucose as a substrate resulted in the selection of yeast strains different from those cultivated in batch mode with glycerol or glucose. Ultimately, fed-batch screening is considerably better than screening in batch mode for fed-batch production processes with glucose as a carbon source.     

Fed-batch versus batch fermentation

This article concerns the comparison between batch and fed-batch fermentation based on productivity and including the analysis of the implications on product manufacturing cost. The calculation method of productivity is based on the assumption that batch production rate data are applicable to fed-batch fermentation and that, within a certain range of concentrations, the percent rate of change of batch production rate with respect to time is proportional to the difference of product concentration at maximum production rate minus instant concentration. General correlations are developed and plotted in diagrams for easy practical application of the proposed method of calculation. Correlations so established may find application to design and to planning and control of operating conditions of fermentation plants. Example calculations refer to penicillin G production.     

模拟青霉素发酵过程中菌体生长动态的细胞自动机模型

在青霉素发酵生产机理及其动力学微分方程模型的基础上,建立了模拟青霉素分批发酵过程中菌体生长动态的细胞自动机模型(CABGM)。CABGM采用三维细胞自动机作为菌体生长空间,采用Moore型邻域作为细胞邻域,其演化规则根据青霉素分批发酵过程中菌体生长机理和动力学微分方程模型设计。CABGM中的每一个细胞既可代表单个的青霉素产生菌,又可代表特定数量的青霉素产生菌,它具有不同的状态。对CABGM进行了统计特性的理论分析和仿真实验,理论分析和仿真实验结果均证明了CABGM能一致地复现动力学微分方程模型所描述的青霉素分批发酵菌体生长过程。最后,对所建模型在实际生产过程中的应用问题进行了分析,指出了需要进一步研究的问题。     

Use of dynamic step response for control of fed-batch conversion of lignocellulosic hydrolyzates to ethanol

Optimization of fed-batch conversion of lignocellulosic hydrolyzates by the yeast Saccharomyces cerevisiae was studied. The feed rate was controlled using a step response strategy, in which the carbon dioxide evolution rate was used as input variable. The performance of the control strategy was examined using both an untreated and a detoxified dilute acid hydrolyzate, and the performance was compared to that obtained with a synthetic medium. In batch cultivation of the untreated hydrolyzate, only 23% of the hexose sugars were assimilated. However, by using the feed-back controlled fed-batch technique, it was possible to obtain complete conversion of the hexose sugars. Furthermore, the maximal specific ethanol productivity (q(E,max)) increased more than 10-fold, from 0.06 to 0.70 g g(-1) h(-1). In addition, the viability of the yeast cells decreased by more than 99% in batch cultivation, whereas a viability of more than 40% could be maintained during fed-batch cultivation. In contrast to untreated hydrolyzate, it was possible to convert the sugars in the detoxified hydrolyzate also in batch cultivation. However, a 50% higher specific ethanol productivity was obtained using fed-batch cultivation. During batch cultivation of both untreated and detoxified hydrolyzate a gradual decrease in specific ethanol productivity was observed. This decrease could largely be avoided in fed-batch cultivations.     

模拟青霉素分批补料发酵过程的细胞自动机模型

根据青霉素产生菌的生长机理和青霉素分批补料发酵过程的动力学特性,在Paull等建立的形态学结构动力学模型的基础上,建立了模拟青霉素分批补料发酵过程的细胞自动机模型。模型采用三维细胞自动机作为菌体生长空间,采用Moore型邻域作为细胞邻域,其演化规则根据青霉素分批补料发酵过程中菌体生长机理和简化动力学结构模型设计。模型中的每一个细胞既可代表单个产黄青霉菌体细胞,又可代表特定数量的这种菌体细胞,它具有不同的状态。对模型进行的仿真实验结果表明：模型不但能一致地复现形态学结构动力学模型所描述的青霉素分批补料发酵过程的演化特性,而且较形态学结构动力学模型更加直观地刻画了青霉素分批补料发酵过程的演化行为。最后,对所建模型在实际生产过程中的应用问题进行了分析,指出了需要进一步研究的问题。     

A cellular automata model for simulating fed-batch penicillin fermentation process

A cellular automata model to simulate penicillin fed-batch fermentation process(CAPFM)was established in this study,based on a morphologically structured dynamic penicillin production model,that is in turn based on the growth mechanism of penicillin producing microorganisms and the characteristics of penicillin fed-batch fermentation.CAPFM uses the three-dimensional cellular automata as a growth space,and a Moore-type neighborhood as the cellular neighborhood.The transition roles of CAPFM are designed based on mechanical and structural kinetic models of penicillin batch-fed fermentation processes.Every cell of CAPFM represents a single or specific number of penicillin producing microorganisms,and has various state.The simulation experimental results show that CAPFM replicates the evolutionary behavior of penicillin batch-fed fermentation processes described by the structured penicillin production kinetic model accordingly.     

Unstructured model for L-lysine fermentation under controlled dissolved oxygen

An unstructured model was developed for batch cultivation of Corynebacterium lactofermentum (ATCC 21799) under controlled dissolved oxygen. The model is capable of predicting batch experiments performed at various initial substrate concentrations. By extending the batch culture model to a fed-batch model and using a heuristic approach to optimize the fed-batch cultivation, it is shown that fed-batch cultivation is superior to batch operation due to increased productivity at high substrate concentrations.     

A morphologically structured model for penicillin production.

A morphologically structured model is proposed to describe penicillin production in fed-batch cultivations. The model accounts for the effects of dissolved oxygen on cell growth and penicillin production and variations in volume fractions of abiotic and biotic phases due to biomass formation. Penicillin production is considered to occur in the subapical hyphal cell compartment and to be affected by availability of glucose and oxygen. As it stands, the model provides a wide range of applicability in terms of operating conditions. The model has been tested for various conditions and has given satisfactory results. A series of glucose feeding profiles have been considered to demonstrate the capabilities of the proposed model. It is concluded that the model may be valuable for the interpretation of experimental data collected specifically for metabolic flux analysis during fed-batch cultivation because the elements of measured specific production rates are determined from measurements of the concentrations of the components and their mass balances. The proposed model may be further used for developing control strategies and model order reduction algorithms.     

A new strategy for fed-batch process control of HEK293 cell cultures based on alkali buffer addition monitoring: comparison with O.U.R. dynamic method

The increasing demand for biopharmaceuticals produced in mammalian cells has driven the industry to enhance productivity of bioprocesses through different strategies. This is why fed-batch and perfusion cultures are considered more attractive choices than batch processes. In this context, the availability of reliable online measuring systems for cell density and metabolic activity estimation will help the application of these processes. The present work focuses on the comparison of two different monitoring tools for indirect estimation of biomass concentration in a HEK293 cell cultures producing IFN-γ: on one side, the oxygen uptake rate (O.U.R.) determination, by means of application of the dynamic method measurement which is already a widely used tool and, on the other side, a new robust online monitoring tool based on the alkali buffer addition used to maintain the pH set point. Both strategies allow a proper monitoring of cell growth and metabolic activity, with precise identification of the balanced cell growth and the most important action in the process, as is the media feeding. The application of these monitoring systems in fed-batch processes allows extending the growth of HEK293 cells, which in turn results in higher final cell concentrations compared with Batch strategy (7 · 10⁶ cells mL⁻¹), achieving 14 · 10⁶ cells mL⁻¹ for the fed-batch based on O.U.R. and 19 · 10⁶ cells mL⁻¹ for the fed-batch based on the alkali addition. Product titter is also increased in respect of the batch strategy (3.70 mg L⁻¹), resulting in 8.27 mg L⁻¹ when fed-batch was based on O.U.R. and 11.49 mg L⁻¹ when it was based on the alkali buffer strategy. Results prove that fed-batch strategy based on the alkali buffer addition is a robust online monitoring method that has shown its great potential to optimize the feeding strategy in HEK293 fed-batch cultures.

     

On-line monitoring of recombinant bacterial cultures using multi-wavelength fluorescence spectroscopy

Multi-wavelength fluorescence spectroscopy was evaluated as a tool for on-line monitoring of recombinant Escherichia coli cultivations expressing human basic fibroblast growth factor (hFGF-2). The data sets for the various combinations of the excitation and emission spectra from batch cultivations were analyzed using principal component analysis. Chemometric models (the partial least squares method) were developed for correlating the fluorescence data and the experimentally measured variables such as the biomass and glucose concentrations as well as the carbon dioxide production rate. Excellent correlations were obtained for these variables for the calibration cultivations. The predictability of these models was further tested in batch and fed-batch cultivations. The batch cultivations were well predicted by the PLS models for biomass, glucose concentrations and carbon dioxide production rate (RMSEPs were respectively 5%, 7%, 9%). However, when tested for biomass concentrations in fed-batch cultivations (with final biomass three times higher than the highest calibration data) the models had good predictability at high growth rates (RMSEPs were 3% and 4%, respectively for uninduced and induced fed-batch cultivations), which was as good as for the batch cultivations used for developing the models (RMSEPs were 3% and 5%, respectively for uninduced and induced batch cultivations). The fed-batch cultivations performed at low growth rates exhibited much higher fluorescence for fluorophores such as flavin and NAD(P)H as compared to fed-batch cultivations at high growth rate. Therefore, the PLS models tended to over-predict the biomass concentrations at low growth rates. Obviously the cells changed their concentration of biogenic fluorophores depending on the growth rate. Although multi-wavelength fluorescence spectroscopy is a valuable tool for on-line monitoring of bioprocess, care must be taken to re-calibrate the PLS models at different growth rates to improve the accuracy of predictions.     

Variable-volume continuous cultivation

Equations are developed which describe variable-volume cultivations, including fed-batch systems. An analogy is drawn between the quasi-steady state in variable-volume cultivation and a dynamic steady state in variable-flow, constant-volume chemostat bioreactors. Switching procedures are developed to give a steady-state transition from batch to fed-batch and to continuous operation. In this respect, considerations in the literature have been extended. Computer solutions of the governing differential equations verify the theory and provide insight into the behavior of variable-volume stirred tank reactors. Application of variable-volume cultivation as a tool in investigating growth rates at low substrate levels is suggested. Variable-volume bioreactor systems could be also to obtain controlled dynamic conditions for research or production purposes.     

Fault diagnosis of a benchmark fermentation process: a comparative study of feature extraction and classification techniques

This paper investigates fault diagnosis in batch processes and presents a comparative study of feature extraction and classification techniques applied to a specific biotechnological case study: the fermentation process model by Birol et al. (Comput Chem Eng 26:1553–1565, 2002), which is a benchmark for advanced batch processes monitoring, diagnosis and control. Fault diagnosis is achieved using four approaches on four different process scenarios based on the different levels of noise so as to evaluate their effects on the performance. Each approach combines a feature extraction method, either multi-way principal component analysis (MPCA) or multi-way independent component analysis (MICA), with a classification method, either artificial neural network (ANN) or support vector machines (SVM). The performance obtained by the different approaches is assessed and discussed for a set of simulated faults under different scenarios. One of the faults (a loss in mixing power) could not be detected due to the minimal effect of mixing on the simulated data. The remaining faults could be easily diagnosed and the subsequent discussion provides practical insight into the selection and use of the available techniques to specific applications. Irrespective of the classification algorithm, MPCA renders better results than MICA, hence the diagnosis performance proves to be more sensitive to the selection of the feature extraction technique.     

Batch, fed-batch and repeated fed-batch fermentation processes of the marine thraustochytrid Schizochytrium sp. for producing docosahexaenoic acid

Different fermentation processes, including batch, fed-batch and repeated fed-batch processes by Schizochytrium sp., were studied and compared for the effective DHA-rich microbial lipids production. The comparison between different fermentation processes showed that fed-batch process was a more efficient cultivation strategy than the batch process. Among the four different feeding strategies, the glucose concentration feed-back feeding strategy had achieved the highest fermentation results of final cell dry weight, total lipids content, DHA content and DHA productivity of 72.37, 48.86, 18.38 g l^?1 and 138.8 mg l^?1 h^?1, respectively. The repeated fed-batch process had the advantages of reducing the time and cost for seed culture and inoculation between each fermentation cycles. The results of fermentation characteristics and lipid characterization of the repeated fed-batch process indicated that this repeated fed-batch process had promising industrialization prospect for the production of DHA-rich microbial lipids.