Adaptive optimization of a continuous culture with the use of a bilevel forgetting factor

An adaptive optimization algorithm using a dynamic identification scheme with a bilevel forgetting factor (BFF) has been developed. The simulation results show superiority of this method to other methods when applied to maximize the cellular productivity of a continuous culture of baker's yeast, Saccharomyces cerievisiae. Within the limited ranges of tuning parameters tested the BFF algorithm is found to be superior in terms of initial optimization speed and accuracy and reoptimization speed and accuracy when there is an external change and long term stability (removal of "blowing up" phenomena). Algorithms tested include those based on a constant forgetting factor, an adaptive variable forgetting factor (VFF) and moving window (MW) identification.     

Experimental and simulation studies of multivariable adaptive optimization of continuous bioreactors using bilevel forgetting factors

A multivariable on-line adaptive optimization algorithm using a bilevel forgetting factor method was developed and applied to a continuous baker's yeast culture in simulation and experimental studies to maximize the cellular productivity by manipulating the dilution rate and the temperature. The algorithm showed a good optimization speed and a good adaptability and reoptimization capability. The algorithm was able to stably maintain the process around the optimum point for an extended period of time. Two cases were investigated: an unconstrained and a constrained optimization. In the constrained optimization the ethanol concentration was used as an index for the baking quality of yeast cells. An equality constraint with a quadratic penalty was imposed on the ethanol concentration to keep its level close to a hypothetical "optimum" value. The developed algorithm was experimentally applied to a baker's yeast culture to demonstrate its validity. Only unconstrained optimization was carried out experimentally. A set of tuning parameter values was suggested after evaluating the results from several experimental runs. With those tuning parameter values the optimization took 50-90 h. At the attained steady state the dilution rate was 0.310 h(-1) the temperature 32.8 degrees C, and the cellular productivity 1.50 g/L/h.     

Adaptive optimization of fed-batch culture of yeast by using genetic algorithms

A simulation and experimental study has been carried out on the adaptive optimization of fed-batch culture of yeast. In the simulation study, three genetic algorithms based on different optimization strategies were developed. The performance of those three algorithms were compared with one another and with that of a variational calculus approach. The one that showed the best performance was selected to be used in the subsequent experimental study. To confer an adaptability, an online adaptation (or model update) algorithm was developed and incorporated into the selected optimization algorithm. The resulting adaptive algorithm was experimentally applied to fed-batch cultures of a recombinant yeast producing salmon calcitonin, to maximize the cell mass production. It followed the actual process quite well and gave a much higher value of performance index than the simple genetic algorithm with no adaptability.     

Fast inferential adaptive optimization of a continuous yeast culture based on carbon dioxide evolution rate

A fast inferential, multivariable adaptive optimization algorithm based on a fast responding off-gas data, the carbon dioxide evolution rate (CER), has been developed and applied to a continuous baker's yeast culture to maximize the cellular productivity in simulation and experimental studies. In the simulation study the process was optimized based on CER measurements using readily available steady-state data on the ratio between the cellular productivity and the CER. It was shown that the algorithm is two to three times faster than the algorithm based on cell mass concentration measurements. In the experimental study the CER was maximized without any information on the relationship between the cellular productivity and the CER. It took about 40 h for the process to converge, while about 80 h was required when the optimization was based on cell mass measurements. The attained steady state was found to be different but fairly close to that obtained with cell measurements. Briefly discussed is a switching to the cell-mass-based algorithm at the final stage of the optimization to overcome a potential inaccuracy.     

Experimental adaptive on-line optimization of cellular productivity of a continuous bakers' yeast culture

An adaptive on-line optimization method that utilizes dynamic model identification has been applied to maximize the cellular productivity of a continuous bakers' yeast culture. Experiments were conducted on a sophisticated computerized fermentation system. Experimental results show that the adaptive on-line optimization method requires very little a priori information, is easy to implement, converges quickly, adapts to changes in the process, and is stable even when operational difficulties are encountered.     

甘草细胞放大培养中搅拌式反应器操作策略优化

为获得甘草细胞在反应器中放大培养的最佳条件,在建立稳定的甘草细胞搅拌式生物反应器放大培养体系的基础上,分别以单因素和正交实验获得的数据为样本,以细胞净增长生物量为考察指标,运用BP神经网络耦合遗传算法对反应器操作策略进行优化。结果表明,接种量6.4%、摇床转速89r/min、通气速率0.1vvm是甘草细胞进行反应器培养的最优条件;与传统的正交实验方法相比,这种基于神经网络耦合遗传算法的优化方法使反应器中细胞生物量的积累提高了6.9%。     

Performance evaluation of TRIBES, an adaptive particle swarm optimization algorithm

This paper presents a study of the performance of TRIBES, an adaptive particle swarm optimization algorithm. Particle Swarm Optimization (PSO) is a biologically-inspired optimization method. Recently, researchers have used it effectively in solving various optimization problems. However, like most optimization heuristics, PSO suffers from the drawback of being greatly influenced by the selection of its parameter values. Thus, the common belief is that the performance of a PSO algorithm is directly related to the tuning of such parameters. Usually, such tuning is a lengthy, time consuming and delicate process. A new adaptive PSO algorithm called TRIBES avoids manual tuning by defining adaptation rules which aim at automatically changing the particles’ behaviors as well as the topology of the swarm. In TRIBES, the topology is changed according to the swarm behavior and the strategies of displacement are chosen according to the performances of the particles. A comparative study carried out on a large set of benchmark functions shows that the performance of TRIBES is quite competitive compared to most other similar PSO algorithms that need manual tuning of parameters. The performance evaluation of TRIBES follows the testing procedure introduced during the 2005 IEEE Conference on Evolutionary Computation. The main objective of the present paper is to perform a global study of the behavior of TRIBES under several conditions, in order to determine strengths and drawbacks of this adaptive algorithm.     

Preparation of fed-batch culture of Nocardioides sp

A screening for carbon sources revealed ethanol to be the best substrate for condensed Nocardioides sp. culture. A strategy achieving the maximum (21 g/l) yield of biomass was developed for the control over the condensed fed-batch culture production. This control based on the algorithm ExpoDense should be predetermined in the first phase and adaptive in the second phase of two-phase process of condensed culture production.     

Optimization of an industrial microalgae fermentation

Optimization of cellular productivity of an industrial microalgae fermentation was investigated. The fermentation was carried out at Coors Biotech Products Company, Fort Collins, Colorado. A mathematical model was developed based on the data collected from pilot plant test runs at different operating conditions. Pontryagin's maximum principle was used for determining the optimal feed policy. A feedback control algorithm was also studied for maximizing the cellular productivity. During continuous operation, the optimum dilution rate was determined by an adaptive optimization scheme based on the steepest descent technique and a recursive least squares estimation of model parameters. A direct search algorithm was also applied to determine the optimum feed rate. Comparison of the theoretical results of the different optimization schemes revealed that the direct search algorithm was preferable because of its simplicity. The experimental results of real time application of the feedback algorithm agreed fairly well with those of the theoretical analyses. (c) 1994 John Wiley & Sons, Inc.     

Obtaining of a High-Density Culture ofNocardioides sp.

Selection of carbon sources demonstrated ethanol to be the best substrate for a high-density Nocardioides sp. culture. A strategy for control over high-density fed-batch culture production was developed, which permitted maximizing the yield of biomass (21 g/l). The control, based on the ExpoDense algorithm, should be predetermined at the first phase and adaptive in the second phase of the two-phase process of high-density culture production.     

Culture conditions optimization of hyaluronic acid production by Streptococcus zooepidemicus based on radial basis function neural network and quantum-behaved particle swarm optimization algorithm

This study aimed to optimize the culture conditions (agitation speed, aeration rate and stirrer number) of hyaluronic acid production by Streptococcus zooepidemicus. Two optimization algorithms were used for comparison: response surface methodology (RSM) and radial basis function neural network coupling quantum-behaved particle swarm optimization algorithm (RBF-QPSO). In RBF-QPSO approach, RBF is employed to model the microbial HA production and QPSO algorithm is used to find the optimal culture conditions with the established RBF estimator as the objective function. The predicted maximum HA yield by RSM and RBF-QPSO was 5.27 and 5.62 g/l, respectively, while a maximum HA yield of 5.21 and 5.58 g/l was achieved in the validation experiments under the optimal culture conditions obtained by RSM and RBF-QPSO, respectively. It was indicated that both models provided similar quality predictions for the above three independent variables in terms of HA yield, but RBF model gives a slightly better fit to the measured data compared to RSM model. This work shows that the combination of RBF neural network with QPSO algorithm has good predictability and accuracy for bioprocess optimization and may be helpful to the other industrial bioprocesses optimization to improve productivity.     

用遗传算法优化流加培养的底物流加轨迹

遗传算法（Genetic Algorithm,GA)j是把生物进化论和遗传学原理应用于工程优化而创造出来的新的优化算法,在复杂问题的优化方面显示出了优良性能。近年来GA开始应用于发酵工程领域,本文介绍了应用GA优化流加培养流加轨迹的原理和方法。     

Comparison of five clustering algorithms to classify phytoplankton from flow cytometry data.

BACKGROUND: Artificial neural networks (ANNs) have been shown to be valuable in the analysis of analytical flow cytometric (AFC) data in aquatic ecology. Automated extraction of clusters is an important first stage in deriving ANN training data from field samples, but AFC data pose a number of challenges for many types of clustering algorithm. The fuzzy k-means algorithm recently has been extended to address nonspherical clusters with the use of scatter matrices. Four variants were proposed, each optimizing a different measure of clustering "goodness." METHODS: With AFC data obtained from marine phytoplankton species in culture, the four fuzzy k-means algorithm variants were compared with each other and with another multivariate clustering algorithm based on critical distances currently used in flow cytometry. RESULTS: One of the algorithm variants (adaptive distances, also known as the Gustafson--Kessel algorithm) was found to be robust and reliable, whereas the others showed various problems. CONCLUSIONS: The adaptive distances algorithm was superior in use to the clustering algorithms against which it was tested, but the problem of automatic determination of the number of clusters remains to be addressed.     

A new particle swarm optimization algorithm for noisy optimization problems

We propose a new particle swarm optimization algorithm for problems where objective functions are subject to zero-mean, independent, and identically distributed stochastic noise. While particle swarm optimization has been successfully applied to solve many complex deterministic nonlinear optimization problems, straightforward applications of particle swarm optimization to noisy optimization problems are subject to failure because the noise in objective function values can lead the algorithm to incorrectly identify positions as the global/personal best positions. Instead of having the entire swarm follow a global best position based on the sample average of objective function values, the proposed new algorithm works with a set of statistically global best positions that include one or more positions with objective function values that are statistically equivalent, which is achieved using a combination of statistical subset selection and clustering analysis. The new PSO algorithm can be seamlessly integrated with adaptive resampling procedures to enhance the capability of PSO to cope with noisy objective functions. Numerical experiments demonstrate that the new algorithm is able to consistently find better solutions than the canonical particle swarm optimization algorithm in the presence of stochastic noise in objective function values with different resampling procedures.     

遗传算法在转录因子结合位点识别中的应用

遗传算法是模拟生物进化过程的计算模型,是一种全局优化搜索算法。将遗传算法与转录因子结合位点识别问题相结合的新方法,以一致性序列模型作为保守motif的描述模型,通过对motif序列与待测序列的比对问题进行编码,将其转化成搜索空间中的优化问题,利用遗传算法来搜索最优解,预测转录因子的结合位点。实验结果表明,这种新的方法是有效的,它在占用少量内存的情况下能够准确地识别出待测转录因子结合位点。     

培养基除菌过滤在百日咳菌苗生产中的应用

经一系列试验证实百日咳菌大罐培养浓度的下降与培养基中不耐热营养因子不足有关,将培养基的高压灭菌方式改为除菌过滤,保留热不稳定营养因子,可以提高百日咳菌大罐培养浓度;优化酸沉淀条件后,提高了菌苗回收率,从而节省了大量培养基,缩短了生产时间,获得了良好的经济效益。     

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.

     

Evolutionary optimization in multimodal search space

Optimization by a simple evolution strategy based on a mutation and selection scheme without recombination was tested for its efficiency in multimodal search space. A modified Rastrigin function served as an objective function providing fitness landscapes with many local optima. It turned out that the evolutionary algorithm including adaptive stepsize control is wellsuited for optimization. The process is able to efficiently surmount local energy barriers and converge to the global optimum. The relation between the optimization time available and the optimal number of offspring was investigated and a simple rule proposed. Several numbers of offspring are nearly equally suited in a smooth search space, whereas in rough fitness landscapes an optimum is observed. In either case both very large and very small numbers of offspring turned out to be unfavourable for optimization.     

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.     

An adaptive control algorithm for fed-batch culture

The implementation of adaptive control for a fed-batch culture in order to maximize the output of product based on a self-adjusting model is discussed in the present work. Optimization methods were applied to the generalized mathematical model of a fed-batch fermentation process to determine control algorithms that could be used for on-line process control. The efficiency of the proposed adaptive algorithms was investigated by simulating a model system. The model of amylotytic enzyme fermentation that was proposed by the authors was taken from a real process. Dynamic modelling has shown that the main problem of realization is connected with the on-line identification of the adaptive model's parameters. To avoid this problem, we have introduced special limitations on the parameters' time variations that increased the convergence of the identification algorithm. The results of the investigation have shown the efficiency of the proposed adaptive algorithms, and the results of this work should be investigated for real process control.