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chemical process dynamic optimization based on hybrid differential evolution algorithm integrated with Alopex

Journal of Central South University 2013年第4期20卷 950-959页

作者：范勤勤吕照民颜学峰郭美锦 Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education (East China University of Science and Technology)

To solve dynamic optimization problem of chemical process (CPDOP), a hybrid differential evolution algorithm, which is integrated with Alopex and named as Alopex-DE, was proposed. In Alopex-DE, each original individual has its own symbiotic individual, which consists of control parameters. Differential evolution operator is applied for the original individuals to search the global optimization solution. Alopex algorithm is used to co-evolve the symbiotic individuals during the original individual evolution and enhance the fitness of the original individuals. Thus, control parameters are self-adaptively adjusted by Alopex to obtain the real-time optimum values for the original population. To illustrate the whole performance of Alopex-DE, several varietal DEs were applied to optimize 13 benchmark functions. The results show that the whole performance of Alopex-DE is the best. Further, Alopex-DE was applied to solve 4 typical CPDOPs, and the effect of the discrete time degree on the optimization solution was analyzed. The satisfactory result is obtained.

关键词： evolutionary computation dynamic optimization differential evolution algorithm Alopex algorithm self-adaptivity

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Multimodal process monitoring based on transition-constrained Gaussian mixture model

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Chinese Journal of chemical Engineering 2020年第12期28卷 3070-3078页

作者： Shutian Chen Qingchao Jiang Xuefeng Yan Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and TechnologyShanghai 200237China

Reliable process monitoring is important for ensuring process safety and product quality.A production process is generally characterized bymultiple operation modes,and monitoring thesemultimodal processes is *** multimodal monitoring methods rely on the assumption that the modes are independent of each other,which may not be appropriate for practical *** study proposes a transition-constrained Gaussian mixture model method for efficient multimodal process *** technique can reduce falsely and frequently occurring mode transitions by considering the time series information in the mode identification of historical and online *** process enables the identified modes to reflect the stability of actual working conditions,improve mode identification accuracy,and enhance monitoring reliability in cases of mode *** studies on a numerical simulation example and simulation of the penicillin fermentation process are provided to verify the effectiveness of the proposed approach inmultimodal process monitoring with mode overlap.

关键词： Multimodal process monitoring Gaussian mixture model State transition matrix Process control Process systems Systems engineering

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A novel chemical composition estimation model for cement raw material blending process

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Chinese Journal of chemical Engineering 2019年第11期27卷 2734-2741页

作者： Yaoyao Bao Yuanming Zhu Weimin Zhong Feng Qian Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and TechnologyShanghai 200237China

Raw material blending process is an essential part of the cement production process. The main purpose of the process is to guarantee a certain oxide composition for the raw meal at the outlet of the mill by regulating the four raw materials. But the chemical compositions of raw materials vary from time to time, resulting in difficulties to control the oxide compositions to a predefined value. Therefore, a novel algorithm to estimate the chemical compositions of the raw materials is developed. The paper mainly consists of two parts. In model construction part, a novel constrained least square model is proposed to overcome the deviation introduced by long-term drift of the material components, and the model parameters are estimated with an online strategy. And in validation part, the approach is implemented to two examples including datasets from simulation model and the actual industrial process. The final results show the effectiveness of the proposed method.

关键词： Raw material blending process chemical component estimation Modulus prediction System identification

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Balanced multiple weighted linear discriminant analysis and its application to visual process monitoring

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Chinese Journal of chemical Engineering 2021年第8期34卷 128-137页

作者： Weipeng Lu Xuefeng Yan Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and TechnologyShanghai 200237China

Visual process monitoring is important in complex chemical *** address the high state separation of industrial data,we propose a new criterion for feature extraction called balanced multiple weighted linear discriminant analysis(BMWLDA).Then,we combine BMWLDA with self-organizing map(SOM)for visual monitoring of industrial operation *** can extract the discriminative feature vectors from the original industrial data and maximally separate industrial operation states in the space spanned by these discriminative feature *** the discriminative feature vectors are used as the input to SOM,the training result of SOM can differentiate industrial operation states *** function improves the performance of visual *** stirred tank reactor is used to verify that the class separation performance of BMWLDA is more effective than that of traditional linear discriminant analysis,approximate pairwise accuracy criterion,max–min distance analysis,maximum margin criterion,and local Fisher discriminant *** addition,the method that combines BMWLDA with SOM can effectively perform visual process monitoring in real time.

关键词： Linear discriminant analysis Process monitoring Self-organizing map Feature extraction Continuous stirred tank reactor process

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Multi-mode process monitoring based on a novel weighted local standardization strategy and support vector data description

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Journal of Central South University 2016年第11期23卷 2896-2905页

作者：赵付洲宋冰侍洪波 Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education(East China University of Science and Technology) Shanghai 200237China

There are multiple operating modes in the real industrial process, and the collected data follow the complex multimodal distribution, so most traditional process monitoring methods are no longer applicable because their presumptions are that sampled-data should obey the single Gaussian distribution or non-Gaussian distribution. In order to solve these problems, a novel weighted local standardization(WLS) strategy is proposed to standardize the multimodal data, which can eliminate the multi-mode characteristics of the collected data, and normalize them into unimodal data distribution. After detailed analysis of the raised data preprocessing strategy, a new algorithm using WLS strategy with support vector data description(SVDD) is put forward to apply for multi-mode monitoring process. Unlike the strategy of building multiple local models, the developed method only contains a model without the prior knowledge of multi-mode process. To demonstrate the proposed method's validity, it is applied to a numerical example and a Tennessee Eastman(TE) process. Finally, the simulation results show that the WLS strategy is very effective to standardize multimodal data, and the WLS-SVDD monitoring method has great advantages over the traditional SVDD and PCA combined with a local standardization strategy(LNS-PCA) in multi-mode process monitoring.

关键词： multiple operating modes weighted local standardization support vector data description multi-mode monitoring

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A subspace ensemble regression model based slow feature for soft sensing application

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Chinese Journal of chemical Engineering 2020年第12期28卷 3061-3069页

作者： Qiong Jia Jun Cai Xinyi Jiang Shaojun Li Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and TechnologyMinistry of EducationShanghai 200237China

A novel adaptive subspace ensemble slow feature regression model was developed for soft sensing *** to traditional single models and random subspace models,the proposed method is improved in three ***,sub-datasets are constructed through slow feature directions and variables in each subdatasets are selected according to the output related importance ***,an adaptive slow feature regression is presented for ***,a Bayesian inference strategy based on a slow feature analysis process that monitors statistics is developed for probabilistic *** industrial examples were used to evaluate the proposed method.

关键词： Soft sensing Slow feature regression Subspace modeling Ensemble learning

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A novel deep quality-supervised regularized autoencoder model for quality-relevant fault detection

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Science China(Information Sciences) 2022年第5期65卷 280-282页

作者： Zhichao LI Li TIAN Xuefeng YAN Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology

Dear editor,In the industrial processes, timely detection of key quality variables is very important for tracking the product quality, monitoring the process status, and achieving stable and reliable control. However,... 详细信息

关键词： supervised learning deep learning regularized autoencoder process monitoring quality-relevant fault detection

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Exploring Si/Mg Composite Supported Ziegler-Natta Ti-based Catalysts for Propylene Polymerization

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Chinese Journal of Polymer Science 2017年第12期35卷 1474-1487页

作者： Wei Zhu 田洲 Rui-hua Cheng Xue-lian He Zhen Liu Ning Zhao 刘柏平 State Key Laboratory of Chemical Engineering East China University of Science and Technology Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology

A series of（SiO2/MgO/ID/MgCl2）·TiClx Ziegler-Natta catalysts for propylene polymerization has been prepared with a new method. These catalysts were synthesized using soluble Mg-compounds as the Mg-source and the preparation progress was relatively simple. The catalyst could copy the spherical shape of the carrier very well. The propylene polymerization results showed that the catalyst revealed the best activity with 9,9-di（methoxymethyl）fluorene（BMMF） as internal donor at 50 °C with the optimal molar ratio Al/Ti = 5, which was much lower than what the industrial polypropylene catalyst used（at least molar ratio Al/Ti = 100）, resulting in great cost saving. Additionally, the polymerization kinetics of the catalyst exhibited very stable property after achieving a relatively high value. These catalysts possessed rather high activity and good hydrogen response. The isotactic index（Ⅱ.） value of the PP products could be higher than 98% in the presence of both internal and external electron donors. Moreover, temperature rising elution fractionation method was used to understand the influence of donors and H2 on the properties of the PP products.

关键词： Polypropylene Ziegler-Natta catalyst External donor Intemal donor Kinetic curves

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Dynamic Multi-objective optimization of chemical processes Using Modified BareBones MOPSO Algorithm

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Journal of Donghua University(English Edition) 2014年第2期31卷 184-189页

作者：杜文莉王珊珊陈旭钱锋 Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of EducationEast China University of Science and Technology

Dynamic multi-objective optimization is a complex and difficult research topic of process systems engineering. In this paper,a modified multi-objective bare-bones particle swarm optimization( MOBBPSO) algorithm is proposed that takes advantage of a few parameters of bare-bones algorithm. To avoid premature convergence,Gaussian mutation is introduced; and an adaptive sampling distribution strategy is also used to improve the exploratory capability. Moreover, a circular crowded sorting approach is adopted to improve the uniformity of the population ***, by combining the algorithm with control vector parameterization,an approach is proposed to solve the dynamic optimization problems of chemical processes. It is proved that the new algorithm performs better compared with other classic multiobjective optimization algorithms through the results of solving three dynamic optimization problems.

关键词： dynamic multi-objective optimization bare-bones particle swarm optimization(PSO) algorithm chemical process

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On-Line Batch Process Monitoring Using Multiway Kernel Partial Least Squares

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Journal of Donghua University(English Edition) 2011年第6期28卷 585-590页

作者：胡益马贺贺侍洪波 Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of EducationEast China University of Science and Technology

An approach for batch processes monitoring and fault detection based on multiway kernel partial least squares(MKPLS) was *** is known that conventional batch process monitoring methods,such as multiway partial least squares(MPLS),are not suitable due to their intrinsic linearity when the variations are *** address this issue,kernel partial least squares(KPLS) was used to capture the nonlinear relationship between the latent structures and predictive *** addition,KPLS requires only linear algebra and does not involve any nonlinear *** this paper,the application of KPLS was extended to on-line monitoring of batch *** proposed batch monitoring method was applied to a simulation benchmark of fed-batch penicillin fermentation *** the results demonstrate the superior monitoring performance of MKPLS in comparison to MPLS monitoring.

关键词： process monitoring fault detection kernel partial least squares(KPLS) nonlinear process multiway kernel partial least squares(MKPLS)

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