Adaptive steady-state optimization of biomass productivity in continuous fermentors

An adaptive steady-state optimization algorithm is presented and applied to the problem of optimizing the production of biomass in continuous fermentation processes. The algorithm requires no modeling information but is based on an on-line identified linear model, locates the optimum dilution rate, and maintains the chemostat at its optimum operating condition at all times. The behavior of the algorithm is tested against a dynamic model of a chemostat that incorporates metabolic time delay, and it is shown that large disturbances in the subtrate feed concentration and the specific growth rate, causing a shift in the optimum, are handled well. The developed algorithm is also used to drive a methylotroph single-cell production process to its optimum.     

Efficient nonlinear predictive control of a biochemical reactor using neural models

This paper describes the application of artificial neural networks to modelling and control of a continuous fermentor. A computationally efficient nonlinear model predictive control (MPC) algorithm with nonlinear prediction and linearisation (MPC-NPL) which needs solving on-line a quadratic programming problem is developed. It is demonstrated that the algorithm results in closed-loop control performance similar to that obtained in nonlinear MPC, which hinges on full on-line non-convex optimisation. The computational complexity of the MPC-NPL algorithm is shown, control accuracy and robustness are also demonstrated in the case of noisy measurements and disturbances affecting the process.     

Importance of bacteriophages in fermentation processes

Any bacterial strain can be infected by virulent phages or harbour one or more prophages. Therefore, bacteria-phage interactions are to be regarded as fundamental properties of bacteria. In current industrial fermentation processes phages can be advantageously employed for the identification of bacterial production strains (phage typing). In some cases phages are involved in the production of enzymes and special substances. The fundamental importance of phages in any technical fermentation process, however, is based on the peculiarities of their obligately parasitic life cycle. The propagation of phages in fermentation processes can cause complete (or at least partial) lysis of the production strains and, consequently, serious disturbances in the production process and considerable economic losses. The phage problem in the fermentation industry has not yet been completely solved. For the protection of technical processes against virulent phages five measures are discussed: phage-protected sterile fermentation, employment of alternative cultures, employment of phage-resistant mutants, employment of phage inhibitors, and employment of immobilized bacterial cells. The problem of the protection of bacterial production strains from prophage induction is more difficult and practically unsolved. Two possibilities to minimize the process risk due to temperate phages, the elimination of inducing factors during the fermentation process, and the selection of production strains which are difficult to induce, are discussed.     

Bioprocess control from a multivariate process trajectory

A multivariate bioprocess control approach, capable of tracking a pre-set process trajectory correlated to the biomass or product concentration in the bioprocess is described. The trajectory was either a latent variable derived from multivariate statistical process monitoring (MSPC) based on partial least squares (PLS) modeling, or the absolute value of the process variable. In the control algorithm the substrate feed pump rate was calculated from on-line analyzer data. The only parameters needed were the substrate feed concentration and the substrate yield of the growth-limiting substrate. On-line near-infrared spectroscopy data were used to demonstrate the performance of the control algorithm on an Escherichia coli fed-batch cultivation for tryptophan production. The controller showed good ability to track a defined biomass trajectory during varying process dynamics. The robustness of the control was high, despite significant external disturbances on the cultivation and control parameters.     

Computational modeling of evolutionary learning processes in the brain

The evolutionary selection circuits model of learning has been specified algorithmically. The basic structural components of the selection circuits model are enzymatic neurons, that is, neurons whose firing behavior is controlled by membrane-bound macromolecules called excitases. Learning involves changes in the excitase contents of neurons through a process of variation and selection. In this paper we report on the behavior of a basic version of the learning algorithm which has been developed through extensive interactive experiments with the model. This algorithm is effective in that it enables single neurons or networks of neurons to learn simple pattern classification tasks in a number of time steps which appears experimentally to be a linear function of problem size, as measured by the number of patterns of presynaptic input. The experimental behavior of the algorithm establishes that evolutionary mechanisms of learning are competent to serve as major mechanisms of neuronal adaptation. As an example, we show how the evolutionary learning algorithm can contribute to adaptive motor control processes in which the learning system develops the ability to reach a target in the presence of randomly imposed disturbances.     

An Online Observer for Minimization of Pulsating Torque in SMPM Motors

A persistent problem of surface mounted permanent magnet (SMPM) motors is the non-uniformity of the developed torque. Either the motor design or the motor control needs to be improved in order to minimize the periodic disturbances. This paper proposes a new control technique for reducing periodic disturbances in permanent magnet (PM) electro-mechanical actuators, by advancing a new observer/estimator paradigm. A recursive estimation algorithm is implemented for online control. The compensating signal is identified and added as feedback to the control signal of the servo motor. Compensation is evaluated for different values of the input signal, to show robustness of the proposed method.     

Robust synchronization analysis in nonlinear stochastic cellular networks with time-varying delays, intracellular perturbations and intercellular noise

Naturally, a cellular network consisted of a large amount of interacting cells is complex. These cells have to be synchronized in order to emerge their phenomena for some biological purposes. However, the inherently stochastic intra and intercellular interactions are noisy and delayed from biochemical processes. In this study, a robust synchronization scheme is proposed for a nonlinear stochastic time-delay coupled cellular network (TdCCN) in spite of the time-varying process delay and intracellular parameter perturbations. Furthermore, a nonlinear stochastic noise filtering ability is also investigated for this synchronized TdCCN against stochastic intercellular and environmental disturbances. Since it is very difficult to solve a robust synchronization problem with the Hamilton-Jacobi inequality (HJI) matrix, a linear matrix inequality (LMI) is employed to solve this problem via the help of a global linearization method. Through this robust synchronization analysis, we can gain a more systemic insight into not only the robust synchronizability but also the noise filtering ability of TdCCN under time-varying process delays, intracellular perturbations and intercellular disturbances. The measures of robustness and noise filtering ability of a synchronized TdCCN have potential application to the designs of neuron transmitters, on-time mass production of biochemical molecules, and synthetic biology. Finally, a benchmark of robust synchronization design in Escherichia coli repressilators is given to confirm the effectiveness of the proposed methods.     

Biotechnological lycopene production by mated fermentation of <Emphasis Type="Italic">Blakeslea trispora</Emphasis>

A semi-industrial process (800-l fermentor) for lycopene production by mated fermentation of Blakeslea trispora plus (+) and minus (–) strains has been developed. The culture medium was designed at the flask scale, using a program based on a genetic algorithm; and a fermentation process by means of this medium was developed. Fermentation involves separate vegetative phases for (+) and (–) strains and inoculation of the production medium with a mix of both together. Feeding with imidazole or pyridine, molecules known to inhibit lycopene cyclase enzymatic activity, enhanced lycopene accumulation. Different raw materials and physical parameters, including dissolved oxygen, stirring speed, air flow rate, temperature, and pH, were checked in the fermentor to get maximum lycopene production. Typical data for the fermentation process are presented and discussed. This technology can be easily scaled-up to an industrial application for the production of this carotenoid nowadays widely in demand.     

An adaptive and predictive control strategy for an activated sludge process

This paper focuses on the development of a simple adaptive and predictive control algorithm, used to regulate the effluent quality of an activated sludge treatment process. This control algorithm is based on the development of a linear incremental second order model which takes distinctively into account the main disturbances on the process. The model is employed to predict the effluent pollution over a finite horizon. Then, the control inputs are computed from the predictions and the desired output set point. The simulations conducted with a non linear process model showed that such a control strategy could improve the process performances by minimizing the effluent pollution and the energetic cost of the system.     

Optimal feeding strategies by adaptive mesh selection for fed-batch bioprocesses

The objective of this contribution is the design of optimal feeding strategies for fed-batch bioprocesses, where complex dynamic models with input and state constraints are present. For the solution of this dynamic optimization problem a transformation to a finite dimensional optimization problem is made using piecewise linear control profiles. The optimization of these profiles is performed by a sequential approach, that includes an ODE solver for the solution of the model ODE's. Further an adaptive mesh selection algorithm was investigated for an appropriate discretization of the control profiles. The implementation of the resulting optimal feeding profiles is shown for a process example, namely the production of nikkomycin by Streptomyces tendae. This implementation uses a hierarchical process control framework, that consists of components for process monitoring, state estimation, and trajectory control.     

Dynamic data replacement and adaptive scheduling policies in spark

Improper data replacement and inappropriate selection of job scheduling policy are important reasons for the degradation of Spark system operation speed, which directly causes the performance degradation of Spark parallel computing. In this paper, we analyze the existing caching mechanism of Spark and find that there is still more room for optimization of the existing caching policy. For the task structure analysis, the key information of Spark tasks is taken out to obtain the data and memory usage during the task runtime, and based on this, an RDD weight calculation method is proposed, which integrates various factors affecting the RDD usage and establishes an RDD weight model. Based on this model, a minimum weight replacement algorithm based on RDD structure analyzing is proposed. The algorithm ensure that the relatively more valuable data in the data replacement process can be cached into memory. In addition, the default job scheduling algorithm of the Spark framework considers a single factor, which cannot form effective scheduling for jobs and causes a waste of cluster resources. In this paper, an adaptive job scheduling policy based on job classification is proposed to solve the above problem. The policy can classify job types and schedule resources more effectively for different types of jobs. The experimental results show that the proposed dynamic data replacement algorithm effectively improves Spark's memory utilization. The proposed job classification-based adaptive job scheduling algorithm effectively improves the system resource utilization and shortens the job completion time.

     

Rechnergestützte Führung von Fermentationsprozessen,Teil 2

In the first part of the publication [1] an algorithm for the adaptation of the static optimum was represented. This part demonstrates the application of this algorithim to a specific problem. The problems, which are connected with the application of the choice of the object function, of making the process model available and of the realizable economic effects are elaborated. The example of the tested on-line control of a strirred tank reactor shows the advantages and disadvantages of the used algorithm.     

Effektivität von Modellierung und Optimierung mikrobieller Prozesse

The aim of the technological treatment of microbial processes is the optimal working of the production plant. The treatment itself is to be seen as a process whose realization requires a certain amount of expenditure. The problem of optimization of this process can with certain restictions be approximately solved in a series of steps. After a general formulation of the problem explanations are given for two important and typical steps – the determination of the extend of modelling and the choice of production cultures.     

Optimal size of storage for recovery after unpredictable disturbances

Terrestrial plants often live in environments in which above-ground photosynthetic organs (production parts) are suddenly removed by unpredictable disturbances, such as fire, frost, desiccation, pathogen attack, breakage by wind and trampling, or herbivory by insects and mammals. We study the optimal growth schedule for a plant having a below-ground storage organ that is used for recovery (or regrowth) of photosynthetic organs after disturbances. We assume the following: (1) the daily production rate increases with the production part size, but saturates for large size due to shading and local resource depletion, (2) disturbances occur randomly and remove all the aerial parts, (3) plants are finally killed by fatal disturbances that also occur randomly and (4) the plant chooses the pattern of growth, reproduction, storage and recovery after disturbances by reallocation of stored material to maximize the total lifetime reproductive success. The model is analysed by stochastic dynamic programming. The results are as follows: (1) the ratio of storage size to production part size (S/F ratio) is large if the longevity is large and if the disturbance rate is large but a little smaller than the productivity coefficient, (2) the S/F ratio is larger for mature plants than for small immature plants, (3) after disturbances, the above-ground production part recovers relatively quickly, but reproductive activity is depressed until storage size recovers and (4) the variations over time and between habitats differing in disturbance frequency are larger for storage size and for reproductive activity than for production part size. These tendencies are more pronounced for a linear production function (with initial linear increase followed by a sudden stop), but less so for a hyperbolic production function (with a gradually decreasing slope). We also discuss the growth and regrowth behaviour of plants adapted to a disturbance frequency growing under one different disturbance frequency.     

Applications of PAT‐Process Analytical Technology in Recombinant Protein Processes with Escherichia coli

Monitoring of bioprocesses and thus observation and identification of such processes is one of the main aims of bioprocess engineering. It is of vital importance in bioprocess development to improve the overall productivity by avoiding unintentional limitations to ensure not only optimal process conditions but also the observation of established production processes. Furthermore, reproducibility needs to be improved and final product quality and quantity be guaranteed. Therefore, an advanced monitoring and control system has been developed, which is based on different in‐line, on‐line and at‐line measurements for substrates and products. Observation of cell viability applying in‐line radio frequency impedance measurement and on‐line determination of intracellular recombinant target protein using the reporter protein T‐Sapphire GFP based on in‐line fluorescence measurement show the ability for the detection of critical process states. In this way, the possibility for the on‐line recognition of optimal harvest times arises and disturbances in the scheduled process route can be perceived.     

Production of biosurfactant by <Emphasis Type="Italic">Bacillus subtilis</Emphasis> LB5a on a pilot scale using cassava wastewater as substrate

The main characteristic of biosurfactants is their property of reducing the superficial and interfacial tension between two immiscible liquids of different polarities. The main obstacle to the application of biosurfactants is the high production costs, the use of alternative substrates being indicated to solve this problem. This work report the production of biosurfactant by Bacillus subtilis LB5a on a pilot scale using cassava wastewater as the substrate, and the study of the parameters related to its production. The cassava wastewater was heated, centrifuged and poured into a 40-liter batch pilot bioreactor adapted for simultaneous foam collection during the fermentative process. The temperature was maintained at 35 degrees C, agitation at 150 rpm and aeration 0.38 vvm during the first 12 h, and 0.63 vvm for the rest of the process. Samples of liquid fermentate were collected at regular intervals for the analysis of total carbohydrates, reducing sugars, pH, CFU/mL count and superficial tension. The foam was centrifuged and the biosurfactant purified. The kinetic data of the process showed that both the microbial population, which reached a maximum after about 24 h, and the foam production of 10.6 L, peaked between 24 and 36 h, coinciding with the greatest production of biosurfactant. The yield of semi-purified surfactant in the foam was 2.4 g/L. The superficial tension of the medium was reduced from 51 to 27 mN/m and the critical micellar concentration was 11 mg/L, which, in principle, characterizes it as a good tensoactive agent. As a function of its composition and productivity, cassava wastewater was identified as a good substrate for the production of the biosurfactant.     

Radioaktivität – Wirkung auf die Zelle

Radioactive radiaton As long as the earth exists all living organisms are exposed to a natural radioactive radiation. Man succeeded by knowledge of the structure of matter, to use the natural process of radioactive decay for peaceful (medicine, energy) and military means (nuclear bombs). Hence, the artificial radiation created by humans and the amount of radioactive “waste” has considerably increased in the last 100 years. After the incidents of Chernobyl (Russia), and Fukushima (Japan) the use of nuclear energy appears to be not as controllable as expected. An unsolved problem is still the “disposal” and storage of radioactive materials. With the production and use of radioactive substances in large quantities, we leave a currently unsolved problem to future generations for thousands of years.     

Robust filtering for stochastic genetic regulatory networks with time-varying delay

This paper addresses the robust filtering problem for a class of linear genetic regulatory networks (GRNs) with stochastic disturbances, parameter uncertainties and time delays. The parameter uncertainties are assumed to reside in a polytopic region, the stochastic disturbance is state-dependent described by a scalar Brownian motion, and the time-varying delays enter into both the translation process and the feedback regulation process. We aim to estimate the true concentrations of mRNA and protein by designing a linear filter such that, for all admissible time delays, stochastic disturbances as well as polytopic uncertainties, the augmented state estimation dynamics is exponentially mean square stable with an expected decay rate. A delay-dependent linear matrix inequality (LMI) approach is first developed to derive sufficient conditions that guarantee the exponential stability of the augmented dynamics, and then the filter gains are parameterized in terms of the solution to a set of LMIs. Note that LMIs can be easily solved by using standard software packages. A simulation example is exploited in order to illustrate the effectiveness of the proposed design procedures.     

Classification of plant somatic embryos by computer vision

This article deals with the automation of the process of somatic embryogenesis for the propagation of plants. An important problem is the monitoring of the embryo production process in order to decide the time to start harvesting embryos for further processing. The classification algorithm development for somatic embryos of birch (Betula pendula Roth) showed that automated recognition of embryos at different developmental stages is possible. No globular stage embryos were classified to be heart or torpedo stage and no heart or torpedo stage embryos were classified to be at globular stage. Heart and torpedo stage embryos were classified into three developmental classes by a new index that describes the relation of embryo breadth to the length of the root. The probability of classifying a nonembryo as an embryo was less than 1%, and 14% of the object classified as embryos by a human expert were discarded by the algorithm. A computer vision system suitable for automated monitoring of samples from the bioreactor was constructed. (c) 1993 John Wiley & Sons, Inc.     

Effects of outdoor cultures on the growth and lipid production of <Emphasis Type="Italic">Phaeodactylum tricornutum</Emphasis> using closed photobioreactors

One of the principal challenges for large scale production of microalgae is the high costs of biomass production. Aiming for minimize this problem, microalgal biodiesel production should focus on outdoors cultures, using available solar light and allowing lower energy cost process. Testing species that proved to be common and easy to culture may be a good approach in this process. The present work reports indoor-outdoor cultures of Phaeodactylum tricornutum using different bioreactors types, using cell growth, biochemical composition, and the profiles of the fatty acids produced as the parameters to test the optimization processes. The results show that the use of outdoor cultures is a good choice to obtain P. tricornutum biomass with a good potential for biodiesel production. The microalgae produced reached better growth efficiency, major lipid content and showed an increment in the percentage of saturated fatty acids (required on the biodiesel production) respect indoor cultures. These results are important to show the relevance of using outdoor cultures as a way to improve the efficiency and the energetic balance of the biodiesel production with P. tricornutum algae.