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。     

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。     

基于傅里叶变换近红外光谱实时分析1,3-丙二醇发酵过程生物量的在线监测方法

生物量是反映生物发酵过程进展的重要参数,对生物量进行实时监测可用于对发酵过程的调控优化。为克服目前主要采用的离线方法检测生物量时间滞后和人工测量误差较大等缺点,本研究针对1,3-丙二醇发酵过程设计了一个基于傅里叶变换近红外光谱实时分析技术的生物量在线监测实验平台,通过对实时采集光谱预处理以及敏感光谱段分析,应用偏最小二乘算法,建立了1,3-丙二醇发酵过程生物量变化的动态预测模型。以底物甘油浓度为60 g/L和40 g/L的发酵过程作为外部验证实验,分析得到模型的预测均方根误差分别为0.341 6和0.274 3,结果表明所建立的模型具有较好的实时预测能力,能够实现对1,3-丙二醇发酵过程中生物量的有效在线监测。     

生化反应过程的多变量自校正控制——以重复流加青霉素发酵为例

本文采用两阶离散MIMO(Multi lnput Multi Output)动态模型描述生化反应过程的动态行为,并以青霉素重复流加发酵为例,借助于计算机在线检测系统,在40,OOOL大罐上进行参数辨识与多变量自校正控制仿真研究。结果表明;该控制策略能够克服噪声和过程不确定性影响,使发酵沿最优轨迹进行,实现跟踪优化控制。     

青霉素半连续发酵过程状态与参数在线辨识

本文建立了带有噪声的适合于工业规模青霉素(丝状菌)半连续发酵数学模型,以排气CO₂释放速率(GER)作为在线观测量,应用推广Kalman滤波器(EKF)进行状态与参数在线辨识,为过程的自适应优化控制奠定了基础。试验表明：滤波得到的状态值与实测值相当吻合,证明滤波器是一种较好的发酵状态及动力学参数变化的在线观察器。     

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。     

裂殖壶菌发酵产DHA过程呼吸参数的在线检测分析

利用尾气分析仪对发酵过程的尾气中的O2、CO2含量进行实时检测,建立了裂殖弧菌发酵生产DHA过程中的呼吸参数在线检测方法,实现了裂殖壶菌补料分批发酵过程及双阶段供氧控制发酵过程中的呼吸参数在线检测分析。通过呼吸参数在线检测分析,从氧消耗机制方面解释了双阶段氧传递控制工艺能获得较高生物量、油脂和DHA含量的原因,从而为该工艺过程提供了理论指导。根据发酵过程中菌体生长不同时期的呼吸参数的变化情况,建立了基于呼吸商变化的在线补料控制方法,设计了一种基于RQ-Stat的补料工艺。RQ-Stat补料方式最终获得的油脂含量、DHA产量和产率比间歇式补料工艺分别提高了11.58%、12.19%和11.40%。     

超声波在生物发酵工程中的应用

一定强度的超声波作用于发酵过程中缩短发酵时间,改善生物反应条件,提高生物产品的质量和产量,微弱超声可用于在线检测某些发酵过程参数,本简要介绍了超声波作用于生物发酵过程的基本原理,并详细讨论了超声波在遗传育种 ,改善发酵工艺有发酵产物的提取与分离,发酵产物浓度的在线检测等方面的主要应用。     

基于遗传算法和支持向量机的乳杆菌发酵过程建模

由于生化反应过程的复杂性和高度非线性,多数简单的数学模型不能准确描述。该文基于Matlab软件,利用改进的支持向量机(υ-SVR)对植物乳酸杆菌发酵这一典型生化过程进行研究,应用遗传算法估计模型最优参数,建立植物乳杆菌的菌体密度预测模型。同时建立传统的logistic动力学模型以进行比较。结果表明,采用结合遗传算法的υ-SVR预测模型拟合误差小,皮尔森相关系数(R)更高,可以较好地预测乳酸杆菌的发酵过程,为其优化控制及放大提供依据。     

电子嗅在线反馈控制毕赤酵母糖化酶发酵过程中甲醇浓度新方法的应用

探索了电子嗅传感仪直接通过发酵尾气进行发酵液中甲醇浓度在线检测的方法,建立了毕赤酵母表达糖化酶过程中甲醇浓度的自动化反馈补料控制模型,可准确实现发酵过程中甲醇浓度的精确控制;研究表明,当利用电子嗅将培养液中甲醇浓度稳定控制在(890±35)ppm水平下,发酵诱导培养到128h时目的蛋白糖化酶酶活达到了8 153U/ml,与甲醇浓度控制在(350±26)ppm时的发酵水平相比提升了48.8%。该方法具有无需前处理、与发酵液非接触、快速和准确性的优点,为提升工程酵母在工业发酵培养过程工艺的优化控制具有重要的指导作用。     

Modelling of a microbial fuel cell process

Summary An electrochemical model for a microbial fuel cell process is proposed here. The model was set up on the basis of the experimental results and analysis of biochemical and electrochemical processes. Simulation of the process shows that the model describes the process reasonably well. The analysis of model simulation illustrates how the current output depends on the substrate concentration, mediator concentration and other main variables. The relationship between the current output and over-voltage is revealed from the modelling study.     

An efficient model development strategy for bioprocesses based on neural networks in macroscopic balances: Part II

There is a need for efficient modeling strategies which quickly lead to reliable mathematical models that can be applied for design and optimization of (bio)-chemical processes. The serial gray box modeling strategy is potentially very efficient because no detailed knowledge is needed to construct the white box part of the model and because covenient black box modeling techniques like neural networks can be used for the black box part of the model. This paper shows for a typical biochemical conversion how the serial gray box modeling strategy can be applied efficiently to obtain a model with good frequency extrapolation properties. Models with good frequency extrapolation properties can be applied under dynamic conditions that were not present during the identification experiments. For a given application domain of a model, this property can be used to considerably reduce the number of identification experiments. The serial gray box modeling strategy is demonstrated to be successful for the modeling of the enzymatic conversion of penicillin G In the concentration range of 10-100 mM and temperature range of 298-335 K. Frequency extrapolation is shown by using only constant temperatures in the (batch) identification experiments, while the model can be used reliable with varying temperatures during the (batch) validation experiments. No reliable frequency extrapolation properties could be obtained for a black box model, and for a more knowledge-driven white box model reliable frequency extrapolation properties could only be obtained by incorporating more knowledge in the model. Copyright 1999 John Wiley & Sons, Inc.     

Cooperation and competition in mismatch repair: very short-patch repair and methyl-directed mismatch repair in Escherichia coli

In Escherichia coli and related enteric bacteria, repair of base-base mismatches is performed by two overlapping biochemical processes, methyl-directed mismatch repair (MMR) and very short-patch (VSP) repair. While MMR repairs replication errors, VSP repair corrects to C*G mispairs created by 5-methylcytosine deamination to T. The efficiency of the two pathways changes during the bacterial life cycle; MMR is more efficient during exponential growth and VSP repair is more efficient during the stationary phase. VSP repair and MMR share two proteins, MutS and MutL, and although the two repair pathways are not equally dependent on these proteins, their dual use creates a competition within the cells between the repair processes. The structural and biochemical data on the endonuclease that initiates VSP repair, Vsr, suggest that this protein plays a role similar to MutH (also an endonuclease) in MMR. Biochemical and genetic studies of the two repair pathways have helped eliminate certain models for MMR and put restrictions on models that can be developed regarding either repair process. We review here recent information about the biochemistry of both repair processes and describe the balancing act performed by cells to optimize the competing processes during different phases of the bacterial life cycle.     

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.     

Fabrication of a highly sensitive penicillin sensor based on charge transfer techniques

A highly sensitive penicillin biosensor based on a charge-transfer technique (CTTPS) has been fabricated and demonstrated in this paper. CTTPS comprised a charge accumulation technique for penicilloic acid and H(+) ions perception system. With the proposed CTTPS, it is possible to amplify the sensing signals without external amplifier by using the charge accumulation cycles. The fabricated CTTPS exhibits excellent performance for penicillin detection and exhibit a high-sensitivity (47.852 mV/mM), high signal-to-noise ratio (SNR), large span (1445 mV), wide linear range (0-25 mM), fast response time (<3s), and very good reproducibility. A very lower detection limit of about 0.01 mM was observed from the proposed sensor. Under optimum conditions, the proposed CTTPS outstripped the performance of the widely used ISFET penicillin sensor and exhibited almost eight times greater sensitivity as compared to ISFET (6.56 mV/mM). The sensor system is implemented for the measurement of the penicillin concentration in penicillin fermentation broth.     

Reduced-order modelling of biochemical networks: application to the GTPase-cycle signalling module

Biochemical systems embed complex networks and hence development and analysis of their detailed models pose a challenge for computation. Coarse-grained biochemical models, called reduced-order models (ROMs), consisting of essential biochemical mechanisms are more useful for computational analysis and for studying important features of a biochemical network. The authors present a novel method to model-reduction by identifying potentially important parameters using multidimensional sensitivity analysis. A ROM is generated for the GTPase-cycle module of m1 muscarinic acetylcholine receptor, Gq, and regulator of G-protein signalling 4 (a GTPase-activating protein or GAP) starting from a detailed model of 48 reactions. The resulting ROM has only 17 reactions. The ROM suggested that complexes of G-protein coupled receptor (GPCR) and GAP--which were proposed in the detailed model as a hypothesis--are required to fit the experimental data. Models previously published in the literature are also simulated and compared with the ROM. Through this comparison, a minimal ROM, that also requires complexes of GPCR and GAP, with just 15 parameters is generated. The proposed reduced-order modelling methodology is scalable to larger networks and provides a general framework for the reduction of models of biochemical systems.     

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.     

Neither an enhancement of autolytic wall degradation nor an inhibition of the incorporation of cell wall material are pre-requisites for penicillin-induced bacteriolysis in staphylococci

In contrast to what has been postulated, penicillin G at its optimal lytic concentration of 0.1 g per ml did not lead to a detectable activation of autolytic wall processes in staphylococci in terms of the release of uniformly labelled wall fragments from cells pretreated with the drug for 1 h. Rather a considerable inhibition of this release was observed. A similarly profound inhibition of the release of peptidoglycan fragments occurred when staphylococci pretreated for 1 h with 0.1 g penicillin per ml acted as a source of crude autolysins on peptidoglycan isolated from labelled normal cells of the same strain. This clearly demonstrated that the overall inhibition of autolytic wall processes caused by penicillin was mainly due to a decreased total autolysin action rather than to an altered wall structure. Furthermore, no substantial penicillin-induced inhibition of the incorporation of ¹⁴C-N-acetylglucosamine into the staphylococcal wall could be observed before bacteriolysis started, i. e., approximately during the first 80 min of penicillin action. These results are not consistent with any of the models hitherto proposed for the action of penicillin.Dedicated to Prof. Dr. Gerhart Drews on the occasion of his 60th birthday     

Different modelling tools of aquatic ecosystems: A proposal for a unified approach

Over the last few decades, several modelling tools have been developed for the simulation of hydrodynamic and biogeochemical processes in aquatic ecosystems. Until late 70's, coupling hydrodynamic models to biogeochemical models was not common and today, problems linked to the different scales of interest remain. The time scale of hydrodynamic phenomena in coastal zone (minutes to hours) is much lower than that of biogeochemistry (few days). Over the last years, there has been an increasing tendency to couple hydrodynamic and biogeochemical models in a clear recognition of the importance of incorporating in one model the feedbacks between physical, chemical and biological processes. However, different modelling teams tend to adopt different modelling tools, with the result that benchmarking exercises are sometimes difficult to achieve in projects involving several institutions. Therefore, the objectives of this paper are to provide a quick overview of available modelling approaches for hydrodynamic and biogeochemical modelling, to help people choose among the diversity of available models, as a function of their particular needs, and to propose a unified approach to allow modellers to share software code, based on the object oriented programming potentiality. This approach is based on having object dynamic link libraries that may be linked to different model shells. Each object represents different processes and respective variables, e.g. hydrodynamic, phytoplankton and zooplankton objects. Some simple rules are proposed to link available objects to programs written in different source codes.     

Modelling biochemical networks with intrinsic time delays: a hybrid semi-parametric approach

Background  

This paper presents a method for modelling dynamical biochemical networks with intrinsic time delays. Since the fundamental mechanisms leading to such delays are many times unknown, non conventional modelling approaches become necessary. Herein, a hybrid semi-parametric identification methodology is proposed in which discrete time series are incorporated into fundamental material balance models. This integration results in hybrid delay differential equations which can be applied to identify unknown cellular dynamics.