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), usi...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.展开更多
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 nonlinea...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.展开更多
An on-line scheduling algorithm to maximize gross profit of penicillin fed-batch fermentation is proposed. According to the on-line classification method, fed-batch fermentation batches are classified into three categ...An on-line scheduling algorithm to maximize gross profit of penicillin fed-batch fermentation is proposed. According to the on-line classification method, fed-batch fermentation batches are classified into three categories. Using the scheduling strategy, the optimal termination sequence of batches is obtained. Pseudo on-line simulations for testing the proposed algorithm with the data from industrial scale penicillin fermentation are carried out.展开更多
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 batc...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.展开更多
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 laten...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.展开更多
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 n...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.展开更多
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 ...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.展开更多
针对间歇过程不同批次原材料属性不同,不同反应阶段过程变量设定值不同的问题,提出基于核矩阵的多方向多阶段全潜结构投影法(Kernel based Multi-way Multi-stage Total Projection to Latent Structure,KMMT-PLS)用于其质量相关故障的...针对间歇过程不同批次原材料属性不同,不同反应阶段过程变量设定值不同的问题,提出基于核矩阵的多方向多阶段全潜结构投影法(Kernel based Multi-way Multi-stage Total Projection to Latent Structure,KMMT-PLS)用于其质量相关故障的在线监测。该方法首先将间歇过程的三维立体数据集平面化处理;然后将原始空间中呈强非线性关系的历史数据映射至高维特征空间,实现非线性问题的线性化;最后在特征空间建立核矩阵与输出的线性T-PLS模型,实现间歇过程质量相关故障的在线监测。基于Pensim V 2.0的青霉素发酵过程仿真实验表明所提方法的有效性。展开更多
基金Supported by the National High-tech Program of China (No. 2001 AA413110).
文摘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.
基金Supported by the National Natural Science Foundation of China(61573052)
文摘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.
基金supported by the Open Project Program,State key Laboratory of Bioreactor Engineering/ECUSTthe Natural Science Foundation of China(No.60174024).
文摘An on-line scheduling algorithm to maximize gross profit of penicillin fed-batch fermentation is proposed. According to the on-line classification method, fed-batch fermentation batches are classified into three categories. Using the scheduling strategy, the optimal termination sequence of batches is obtained. Pseudo on-line simulations for testing the proposed algorithm with the data from industrial scale penicillin fermentation are carried out.
文摘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.
文摘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.
基金the National Natural Science Foundation of China (No.60504033).
文摘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.
基金Supported by the National Natural Science Foundation of China(21506075)the Natural Science Foundation of Jiangsu Province(BK20150131)the Fundamental Research Funds for the Central Universities(JUSRP51504)
文摘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.
文摘针对间歇过程不同批次原材料属性不同,不同反应阶段过程变量设定值不同的问题,提出基于核矩阵的多方向多阶段全潜结构投影法(Kernel based Multi-way Multi-stage Total Projection to Latent Structure,KMMT-PLS)用于其质量相关故障的在线监测。该方法首先将间歇过程的三维立体数据集平面化处理;然后将原始空间中呈强非线性关系的历史数据映射至高维特征空间,实现非线性问题的线性化;最后在特征空间建立核矩阵与输出的线性T-PLS模型,实现间歇过程质量相关故障的在线监测。基于Pensim V 2.0的青霉素发酵过程仿真实验表明所提方法的有效性。
