An Improved Adaptive Multi-way Principal Component Analysis for Monitoring Streptomycin Fermentation Process

Multi-way principal component analysis (MPCA) had been successfully applied to monitoring the batch and semi-batch process in most chemical industry. An improved MPCA approach, step-by-step adaptive MPCA (SAMPCA), using the process variable trajectories to monitoring the batch process is presented in this paper. It does not need to estimate or fill in the unknown part of the process variable trajectory deviation from the current time until the end. The approach is based on a MPCA method that processes the data in a sequential and adaptive manner. The adaptive rate is easily controlled through a forgetting factor that controls the weight of past data in a summation. This algorithm is used to evaluate the industrial streptomycin fermentation process data and is compared with the traditional MPCA. The results show that the method is more advantageous than MPCA, especially when monitoring multi-stage batch process where the latent vector structure can change at several points during the batch.

An Approach to Continuous Approximation of Pareto Front Using Geometric Support Vector Regression for Multi-objective Optimization of Fermentation Process

The approaches to discrete approximation of Pareto front using multi-objective evolutionary algorithms have the problems of heavy computation burden, long running time and missing Pareto optimal points. In order to overcome these problems, an approach to continuous approximation of Pareto front using geometric support vector regression is presented. The regression model of the small size approximate discrete Pareto front is constructed by geometric support vector regression modeling and is described as the approximate continuous Pareto front. In the process of geometric support vector regression modeling, considering the distribution characteristic of Pareto optimal points, the separable augmented training sample sets are constructed by shifting original training sample points along multiple coordinated axes. Besides, an interactive decision-making(DM)procedure, in which the continuous approximation of Pareto front and decision-making is performed interactively, is designed for improving the accuracy of the preferred Pareto optimal point. The correctness of the continuous approximation of Pareto front is demonstrated with a typical multi-objective optimization problem. In addition,combined with the interactive decision-making procedure, the continuous approximation of Pareto front is applied in the multi-objective optimization for an industrial fed-batch yeast fermentation process. The experimental results show that the generated approximate continuous Pareto front has good accuracy and completeness. Compared with the multi-objective evolutionary algorithm with large size population, a more accurate preferred Pareto optimal point can be obtained from the approximate continuous Pareto front with less computation and shorter running time. The operation strategy corresponding to the final preferred Pareto optimal point generated by the interactive DM procedure can improve the production indexes of the fermentation process effectively.

Learning control of fermentation process with an improved DHP algorithm

Control of the fed-batch ethanol fermentation processes to produce maximum product ethanol is one of the key issues in the bioreactor system.However,ethanol fermentation processes exhibit complex behavior and nonlinear dynamics with respect to the cell mass,substrate,feed-rate,etc.An improved dual heuristic programming algorithm based on the least squares temporal difference with gradient correction(LSTDC) algorithm(LSTDC-DHP) is proposed to solve the learning control problem of a fed-batch ethanol fermentation process.As a new algorithm of adaptive critic designs,LSTDC-DHP is used to realize online learning control of chemical dynamical plants,where LSTDC is commonly employed to approximate the value functions.Application of the LSTDC-DHP algorithm to ethanol fermentation process can realize efficient online learning control in continuous spaces.Simulation results demonstrate the effectiveness of LSTDC-DHP,and show that LSTDC-DHP can obtain the near-optimal feed rate trajectory faster than other-based algorithms.

Principles and Case Studies of Fed Batch Fermentation and Continuous Fermentation

Modern fermentation processes include a variety of fermentation methods,such as fed batch fermentation and continuous fermentation.This paper will focus on the principles and case studies of these two methods.Fed batch fermentation originates from fractionation fermentation with closed culture and adjustment of the pH value of the carbon source,to which the process of feeding the carbon source to the cell culture in a controlled manner has been added.This type of fermentation is more commonly used compared to the other.Continuous fermentation is also a closed fermentation system,which can operate without restrictions by continuous or intermittent addition of fresh nutrient media to the fermenter;however,it is susceptible to contamination by stray bacteria and metabolic inconvenience.

Monitoring and Fault Diagnosis for Batch Process Based on Feature Extract in Fisher Subspace

Multivariate statistical process control methods have been widely used in biochemical industries. Batch process is usually monitored by the method of multi-way principal component analysis (MPCA). In this article, a new batch process monitoring and fault diagnosis method based on feature extract in Fisher subspace is proposed.The feature vector and the feature direction are extracted by projecting the high-dimension process data onto the low-dimension Fisher space. The similarity of feature vector between the current and the reference batch is calculated for on-line process monitoring and the contribution plot of weights in feature direction is calculated for fault diagnosis. The approach overcomes the need for estimating or tilling in the unknown portion of the process variables trajectories from the current time to the end of the batch. Simulation results on the benchmark model of penicillin fermentation process can demonstrate that in comparison to the MPCA method, the proposed method is more accurate and efficient for process monitoring and fault diagnosis.

A Robust Statistical Batch Process Monitoring Framework and Its Application

In order to reduce the variations of the product quality in batch processes, multivariate statistical process control methods according to multi-way principal component analysis (MPCA) or multi-way projection to latent structure (MPLS) were proposed for on-line batch process monitoring. However, they are based on the decomposition of relative covariance matrix and strongly affected by outlying observations. In this paper, in view of an efficient projection pursuit algorithm, a robust statistical batch process monitoring (RSBPM) framework,which is resistant to outliers, is proposed to reduce the high demand for modeling data. The construction of robust normal operating condition model and robust control limits are discussed in detail. It is evaluated on monitoring an industrial streptomycin fermentation process and compared with the conventional MPCA. The results show that the RSBPM framework is resistant to possible outliers and the robustness is confirmed.

Monitoring and Fault Diagnosis for Batch Process Based on Feature Extract in Fisher Subspace

Multivariate statistical process control methods have been widely used in biochemical industries. Batch process is usually monitored by the method of multi-way principal component analysis (MPCA). In this article, a new batch process monitoring and fault diagnosis method based on feature extract in Fisher subspace is proposed. The feature vector and the feature direction are extracted by projecting the high-dimension process data onto the low-dimension Fisher space. The similarity of feature vector between the current and the reference batch is calcu- lated for on-line process monitoring and the contribution plot of weights in feature direction is calculated for fault diagnosis. The approach overcomes the need for estimating or filling in the unknown portion of the process vari- ables trajectories from the current time to the end of the batch. Simulation results on the benchmark model of peni- cillin fermentation process can demonstrate that in comparison to the MPCA method, the proposed method is more accurate and efficient for process monitoring and fault diagnosis.

尾菜批式中温发酵和高温发酵过程对比试验研究

尾菜的快速无害化处理已成为高原夏菜产业发展的“瓶颈”。针对目前尾菜规模化处理厂普遍存在厌氧发酵过程不稳定,而国内外缺少尾菜恒温发酵过程研究的现状,在0.56 m^(3)的恒温发酵罐中开展了尾菜批式中温发酵和高温发酵过程的对比试验,对比了两个过程中沼气产气量、氨氮量、pH值和溶解性总固体的变化过程,并用修正的Gompertz方程分析了恒温厌氧发酵产甲烷过程,研究结果表明:在接种量30%、总固体含量(Total Solid,TS)2.2%的30天批式发酵过程中,中温发酵过程和高温发酵过程均可稳定进行,与修正的Gompertz方程拟合结果吻合度R^(2)均大于0.99。高温组累积产气量、产甲烷量分别为3939 L、2034.2 L,比中温组分别高出11.3%和15.5%,同时高温组甲烷体积分数达到50%的时间较中温组提前了3 d;中高温两试验组对应的最低pH值分别为6.66和6.52,后期均逐渐升高并稳定在7.30左右;两试验组氨氮浓度呈前期快速上升后持续上下浮动趋势,整体高温组大于中温组。试验研究为使用恒温厌氧发酵稳定快速无害化、规模化处理尾菜提供了借鉴和参考。

大肠杆菌高密度发酵制药废水处理工艺设计实例

大肠杆菌高密度发酵制药的废水包括发酵产生的工艺废水和清洗、冷却产生的低污染废水,前者水量小但污染物含量高,后者量大但污染物含量低。分析比较吹脱法、折点加氯法、生物法、离子交换法和化学沉淀法,确定对污染性强的工艺废水采用化学沉淀法、活性污泥法相结合的处理工艺,先MAP沉淀除氨脱磷,再用低污染废水10倍稀释后进行SBR生化处理,达标排放。MAP沉淀除氨脱磷药剂选用氯化镁和磷酸氢二钠,pH值9.5,投药比采用n(Mg^(2+))∶n(NH_(4)^(+))∶n(PO_(4)^(3-))=1.2∶1∶1,氨氮去除率达83%,COD Cr的去除率达19%,稀释10倍后氮磷含量符合SBR生化处理要求。该工艺污水综合处理成本约为51.8元/m^(3),但处理量小,污水处理总费用约为88元/d,综合费用低,且副产物MAP可用作肥料。

Development of a new cleaner production process for cassava ethanol

A new cleaner production process for cassava ethanol has been developed, in which the thin stillage by-product was treated initially by anaerobic digestion, and the digestate further processed by hydrogen-form cation exchange resin before being recycled as process water to make mash for the next ethanol fermentation batch.Thus wastewater was eliminated and freshwater and energy consumption was significantly reduced. To evaluate the new process, ten consecutive batches of ethanol fermentation and anaerobic digestion at lab scale were carried out. Average ethanol production in the recycling batches was 11.43%(v/v) which was similar to the first batch, where deionized(DI) water was used as process water. The chemical oxygen demand(COD) removal rate reached 98% and the methane yield was 322 ml per gram of COD removed, suggesting an efficient and stable operation of the anaerobic digestion. In conclusion, the application of the new process can contribute to sustainable development of the cassava ethanol industry.

汽爆秸秆酶解液补料赤霉素固态发酵工艺

赤霉素是一种重要的植物生长调节剂,广泛应用于农作物、苗圃、园艺等生产。赤霉素主要生产方式是微生物固态和液态发酵。与液态发酵相比,固态发酵有原料易得、生产成本低、环境污染小等优点。采用固态发酵工艺生产赤霉素,并优化工艺条件。通过在固态发酵过程中滴加适量汽爆秸秆酶解物来寻求赤霉素发酵替代原料。实验发现在第三天时滴加4 mL酶解液,赤霉素的产量最大。最佳固态发酵工艺参数为接种量为10%,温度为30℃,pH为8,固液比为1∶1.1,发酵周期为7天。

氯化胆碱对大肠杆菌发酵生产L-酪氨酸的影响

为提高大肠杆菌发酵生产L-酪氨酸的产量和糖酸转化率,降低副产物积累,试验对发酵培养基中氯化胆碱添加量进行了优化,并在此基础上探究出新的发酵工艺。试验结果表明,氯化胆碱的最佳添加量为0.4 g/L,最佳的发酵工艺为底物与流加培养基中氯化胆碱的添加比例为3∶7,流加培养基的流加速度随罐内溶氧值的波动而变化,以维持营养物质亚适量状态的全营养流加工艺。经过优化后,L-酪氨酸产量及糖酸转化率分别达到55.7 g/L、24.1%,副产物乙酸和谷氨酸产量分别为0.32,1.56 g/L,与原发酵策略相比,不仅实现了L-酪氨酸产量及糖酸转化率不同程度的提升,且副产物乙酸、谷氨酸的积累也大幅度降低,为今后工业化发酵生产L-酪氨酸提供了一定的参考依据。

利用啤酒废酵母间歇发酵合成谷胱甘肽的研究

研究利用啤酒废酵母生产谷胱甘肽的目的是降低谷胱甘肽生产成本、提高啤酒废酵母附加值.研究考察了在2升通气搅拌罐中通气量、搅拌速度、初始葡萄糖浓度以及废酵母加入量对谷胱甘肽合成的影响.实验表明,好氧活化可以显著提高酵母细胞内的谷胱甘肽含量.由于啤酒废酵母的添加量较大,使得反应液中菌体浓度较高,发酵过程中保持较大的通气量和搅拌速率有利于提高细胞活性和促进谷胱甘肽的积累.通过实验确定了2L发酵罐中较佳的操作参数为搅拌速度500r@min-1,通气量6 L@L-1@min-1.过高的初始葡萄糖浓度对谷胱甘肽的合成有一定的抑制作用,比较合适的初始葡萄糖用量为加入的废酵母湿重的10%.废酵母加入量增大会导致胞内谷胱甘肽含量下降,但由于细胞干重的大幅度增加,在实验范围内,最终单位反应器体积的谷胱甘肽产量则随废酵母加入量增大而增大.实验表明,废酵母加入量在湿酵母为100～300g@L-1之间是合适的.

步进MPCA及其在间歇过程监控中的应用

针对多向主元分析法(MPCA)在间歇过程监控过程中需要预测过程未来输出的困难,提出了一种新的步进多向主元分析方法。该方法通过建立一系列的PCA模型,避免了对预估过程变量未来输出的需要,通过引入遗忘因子能够自然地处理多阶段间歇过程的情况。对于多阶段链霉素发酵过程的监控表明,相对于普通MPCA,步进MPCA能够更精确地对过程故障行为进行描述。

基于ICA-MPCA的间歇过程监测方法

针对基于多向主元分析(multi-way principal component analysis,MPCA)的间歇过程监测方法需要事先假定变量呈高斯分布的缺陷,提出了一种新的ICA(independent component analysis,ICA)-MPCA多变量间歇过程统计监测方法。该方法先用ICA法从过程信息中抽取非正态分布的特征信号,并确定联合分布的统计置信限;然后对提取非正态特征信号后残余的服从多元正态分布的过程信息进行MPCA分析,从而避免了基于MPCA间歇过程监测方法需假设过程特征信号全部服从正态分布的前提。将该法应用于β-甘露聚糖酶间歇发酵过程监测,结果表明该法不仅能有效地监测出故障,尤其是对过程变量不满足正态分布的情况下,能够准确地对过程进行监测。

基于诺西肽分批发酵的过程建模

诺西肽是一种含硫多肽类抗生素,该抗生素能明显促进动物生长,而且在动物体内无残留,是一种优良的非吸收型饲料添加剂。以分批发酵工艺过程为对象,研究了基质浓度、抑制物及菌体自身浓度对菌体生长的影响,建立了诺西肽的菌体生长模型、基质消耗模型、产物生成模型,以及溶解氧与操作变量(搅拌转速和通气量)的关联模型。在所建模型基础上,对诺西肽的分批发酵过程进行了仿真研究,仿真结果与实验结果基本一致。

局部最小二乘支持向量机回归在线建模方法及其在间歇过程的应用

当间歇生产切换于不同的工艺条件时,由于新工况下的样本一般很少,且批次间存在着不确定性(由于原材料波动或过程动态特性波动等),基于全局学习的建模方法(如最小二乘支持向量机回归,LSSVR)建立的模型泛化性能不强。将局部学习融入LSSVR中,提出一种局部LSSVR(local LSSVR,LLSSVR)的间歇过程在线建模方法。结合前一批次离线优化后的LSSVR参数,针对待预测新样本在线选择与之相关的近邻样本集并基于此进行建模。以建立青霉素发酵过程的菌体浓度为例,验证了LLSSVR算法能够从过程的第2个生产批次开始在线建立较准确的预报模型,较LSSVR有着更好的推广能力、适应性和鲁棒性。

关键核网络及其在发酵过程在线建模中的应用

发酵过程通常采用流加补料操作,无稳态工作点、非线性强,且重要生物量往往无法在线测量。本文提出了一种适用于非线性多输入多输出的发酵过程在线建模方法:关键核网络(key kernelnetwork,KKN)。结合过程的先验知识给出控制模型关键节点加入的准则,使其能自适应调整模型的复杂度,以提高建模的精度和速度,并给出了关键节点增加时KKN模型的在线递推形式。将KKN应用于青霉素发酵过程的在线建模,研究表明,KKN能同时快速、准确地预报菌体和产物浓度,且随着批次的增加,过程信息不断得到积累,模型精度逐渐提高。

统计监控建模数据预处理离群点检测算法

针对基于多向主元分析(Multi-way Principal Component Analysis,MPCA)(包括主元分析(Principal Component Analysis,PCA)的统计监控模型易受建模数据中离群点影响的不足,通过对各种不同尺度的中心化和标准化方法及鲁棒离群点检测算法的对比研究,提出了一种基于改进尺度的中心最短距离/椭球多变量整理(Closest Distance to Center/ellipsoidal Multivariate Trimming,CDC/MVT)的建模数据离群点去除算法。该算法首先利用改进尺度得到离线建模正常数据的均值和标准差,并对数据进行中心化和标准化处理;然后利用CDC算法找出建模历史数据中最一致的一半正常点;最后用这最一致的一半正常点初始化MVT的马氏距离的均值和协方差,并通过迭代计算得到其他的正常点。将该算法应用于β-甘露聚糖酶发酵间歇过程离群点的去除,与其他鲁棒离群点检测算法相比,应用结果表明该算法能有效地去除建模数据中的离群点。

格尔德霉素发酵工艺的优化

目的研究格尔德霉素产生菌吸水链霉菌SIPI.A.2039的发酵工艺,以提高产生菌生物合成格尔德霉素的能力。方法以本实验室保藏的吸水链霉菌SIPI.A.2039为出发菌株,从摇床转速、摇瓶装量、碳源、50L发酵罐参数等方面进行发酵工艺的优化研究。结果采用经过优化获得的发酵培养基和培养条件,发酵周期为144h时,格尔德霉素的摇瓶发酵效价可由原来的230μg/mL提高至2500μg/mL。在50L发酵罐中采用优化的溶解氧和补料工艺能够使格尔德霉素的发酵效价达到3700μg/mL,并使发酵周期比摇瓶缩短了48h。结论本实验得到的结果比国内外报道的格尔德霉素的发酵效价提高了两倍以上,该发酵工艺已经达到了产业化的要求。