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Performance monitoring of non-gaussian chemical processes with modes-switching using globality-locality preserving projection

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Frontiers of chemical Science and Engineering 2017年第3期11卷 429-439页

作者： Xin Peng Yang Tang Wenli Du Feng Qian Key Laboratory of Advanced Control and Optimization for Chemical Process Ministry of Education East China University of Science and Technology Shanghai 200237 China

In this paper, we propose a novel performance monitoring and fault detection method, which is based on modified structure analysis and globality and locality preserving （MSAGL） projection, for non-Gaussian processes with multiple operation conditions. By using locality preserving projection to analyze the embedding geometrical manifold and extracting the non-Gaussian features by independent component analysis, MSAGL preserves both the global and local structures of the data simultaneously. Furthermore, the tradeoff parameter of MSAGL is tuned adaptively in order to find the projection direction optimal for revealing the hidden structural information. The validity and effectiveness of this approach are illustrated by applying the proposed technique to the Tennessee Eastman process simulation under multiple operation conditions. The results demonstrate the advantages of the proposed method over conventional eigendecomposition-based monitoring methotis.

关键词： non-Gaussian processes subspace projection independent component analysis locality preserving projection finite mixture model

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Plant-wide optimization for high value added products - A steam cracking case study 30

Plant-wide optimization for high value added products - A st...

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30th Ethylene Producers Conference 2018 - Topical Conference at the 2018 AIChE Spring Meeting and 14th Global Congress on process Safety

作者： Van Geem, Kevin M. Marin, Guy B. Muñoz, Andrés E.G. Zhang, Yu Du, Wenli Qian, Feng Laboratory for Chemical Technology Ghent University Belgium AVGI Syria Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology China

ISBN: (纸本)9781510864375

Recent progress in accurate modeling of steam cracking plants has enabled global optimization to be performed at a plant-wide level. This paper presents a real time optimization (RTO) system developed by East China University of Science and Technology (ECUST) to maximize the net profit of the ethylene plant of Sinopec Shanghai Petrochemical Company (SSPC). The system has a three-layer structure consisting of the global RTO, the local RTO and the advanced process control (APC) layer from top to bottom, among which the APC layer has already been applied to SSPC and is currently in service. COILSIM1D-RTO is used to simulate the steam cracking reactors. A simplified radiant box model and a convection section model are developed to account for the fuel consumption and superheated high pressure steam generation. The downstream compression and separation units are modeled in Aspen Plus to evaluate the maximum allowed process gas load and energy consumption. As the plant-wide RTO system is currently under implementation and no real time optimization results can be obtained, a case study is performed using historical operating data from year 2017 to illustrate the benefit that this plantwide RTO system can deliver. optimization results show that most of the crackers should be operated under higher cracking severity to improve yields of the high value added products. By optimizing the cracking severity and dilution steam ratio, the estimated increase of net profit of the ethylene plant in Case 1 and Case 2 is about 29.50 million and 30.75 million yuan per year, respectively (i.e. approx. 4.7 million USD and 4.9 million USD, at current exchange rates). Copyright © American Institute Of chemical Engineers. All rights reserved.

关键词： Global optimization

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A novel hybrid estimation of distribution algorithm for solving hybrid flowshop scheduling problem with unrelated parallel machine

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Journal of Central South University 2017年第8期24卷 1779-1788页

作者：孙泽文顾幸生 Key Laboratory of Advanced Control and Optimization for Chemical Process Ministry of Education(East China University of Science and Technology)Shanghai 200237China

The hybrid flow shop scheduling problem with unrelated parallel machine is a typical NP-hard combinatorial optimization problem, and it exists widely in chemical, manufacturing and pharmaceutical industry. In this work, a novel mathematic model for the hybrid flow shop scheduling problem with unrelated parallel machine(HFSPUPM) was proposed. Additionally, an effective hybrid estimation of distribution algorithm was proposed to solve the HFSPUPM, taking advantage of the features in the mathematic model. In the optimization algorithm, a new individual representation method was adopted. The(EDA) structure was used for global search while the teaching learning based optimization(TLBO) strategy was used for local search. Based on the structure of the HFSPUPM, this work presents a series of discrete operations. Simulation results show the effectiveness of the proposed hybrid algorithm compared with other algorithms.

关键词： hybrid estimation of distribution algorithm teaching learning based optimization strategy hybrid flow shop unrelated parallel machine scheduling

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An Adaptive Event-Triggered Synchronization Approach for Chaotic Lur’e Systems Subject to Aperiodic Sampled Data

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IEEE Transactions on Circuits and Systems II: Express Briefs 2019年第3期66卷 442-446页

作者： Yueying Wang Hamid Reza Karimi Huaicheng Yan School of Aeronautics and Astronautics Shanghai Jiao Tong University Shanghai China Department of Mechanical Engineering Politecnico di Milano Milan Italy Key Laboratory of Advanced Control and Optimization for Chemical Process of Ministry of Education East China University of Science and Technology Shanghai China

This brief deals with the problem of master-slave synchronization for chaotic Lur'e systems with aperiodic sampled data. Specifically, a novel aperiodic adaptive event-triggered communication mechanism is introduced to reduce the transmission load, which covers the previous ones as special cases. By partially resorting to the time-dependent Lyapunov function, a new synchronization criterion is derived, which depends on both the upper and lower bounds of variable sampling interval. Finally, Chua's circuit system is chosen as an illustrative example to show the virtue and effectiveness of the achieved synchronization strategies.

关键词： Synchronization Chaotic communication Master-slave Circuits and systems Extraterrestrial measurements Symmetric matrices Adaptive systems

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Specific index-related process monitoring using a two-step information extraction method 30

Specific index-related process monitoring using a two-step i...

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30th Chinese control and Decision Conference, CCDC 2018

作者： Zhao, Bo Song, Bing Tan, Shuai Shi, Hongbo Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology Shanghai200237 China

ISBN: (纸本)9781538612439

Specific index-related process monitoring covers a wide range of requirements from industrial production. At present, it is still a challenge to divide into the specific index-related information and the specific index-unrelated information more accurately. In this paper, a two-step information extraction method is presented to solve this problem. In the first step, the overall process variables are separated into a strongly related variable set and a weakly related variable set. In the second step, the two sets are further divided into a specific index-related subset and a specific index-unrelated subset, respectively. Two specific index-related subsets form a new subspace related to the specific index. Two specific index-unrelated subsets are combined into a specific index-unrelated subspace. Two T2 statistics are built in the specific index-related subspace and the specific index-unrelated subspace, respectively. Finally, the proposed method is applied for Tennessee Eastman (TE). The results indicate the effectiveness of the proposed method. © 2018 IEEE.

关键词： Indexes Information retrieval Monitoring Principal component analysis Correlation coefficient Correlation Fault detection

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Improved RCSP and AdaBoost-based classification for Motor-Imagery BCI

Improved RCSP and AdaBoost-based classification for Motor-Im...

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IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications

作者： Yangyang Miao Feiyu Yin Cili Zuo Xingyu Wang Jing Jin Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai P. R. China

ISBN: (数字)9781538683446

ISBN: (纸本)9781538683453

One of the popular feature extraction algorithms for motor imagery (MI)-based brain-computer interface (BCI) is common spatial pattern (CSP). However, CSP is also very susceptive to the selection of the filter bands, the time windows, and the channels. In this paper, we proposed a novel regularized CSP (RCSP) method to optimize feature extraction in MI-BCI. Then, a robust classifier based on AdaBoost algorithm was presented to perform the classification of MI tasks. Finally, the framework was verified on two public BCI datasets (dataset 1 from the BCI Competition IV and dataset IVa from BCI Competition III). The results suggest the proposed approach achieved superior performance compared with classical CSP and other competing methods. Overall, this method not only improved classification performance, but also reduced the data requirements of other subjects.

关键词： Feature extraction Covariance matrices Support vector machines Electroencephalography Classification algorithms Matrix decomposition optimization

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Multi-objective modeling and optimization for scheduling of cracking furnace systems

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Chinese Journal of chemical Engineering 2017年第8期25卷 992-999页

作者： Peng Jiang Wenli Du Key Laboratory of Advanced Control and Optimization for Chemical Process Ministry of Education School of Information Science and Engineering East China University of Science and Technology

Cracking furnace is the core device for ethylene production. In practice, multiple ethylene furnaces are usually run in parallel. The scheduling of the entire cracking furnace system has great significance when multiple feeds are simultaneously processed in multiple cracking furnaces with the changing of operating cost and yield of product. In this paper, given the requirements of both profit and energy saving in actual production process, a multi-objective optimization model contains two objectives, maximizing the average benefits and minimizing the average coking amount was proposed. The model can be abstracted as a multi-objective mixed integer non- linear programming problem. Considering the mixed integer decision variables of this multi-objective problem, an improved hybrid encoding non-dominated sorting genetic algorithm with mixed discrete variables （MDNSGA-II） is used to solve the Pareto optimal front of this model, the algorithm adopted crossover and muta- tion strategy with multi-operators, which overcomes the deficiency that normal genetic algorithm cannot handle the optimization problem with mixed variables. Finally, using an ethylene plant with multiple cracking furnaces as an example to illustrate the effectiveness of the scheduling results by comparing the optimization results of multi-objective and single objective model.

关键词： Cracking furnace systems Feed scheduling Multi-objective mixed integer nonlinear optimization Genetic algorithm

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Facilitating renewable natural gas production for a circular bioeconomy: AI-Driven process visualization and data augmentation on biochar-mediated anaerobic digestion

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chemical Engineering Journal 2025年 516卷

作者： Xiaoman He Jingyuan Guo Xihui Kang Xue Ning Huichao Chen Daolun Liang Chen Deng Zutan Li Dekui Shen Huiyan Zhang Richen Lin Jerry D. Murphy Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education School of Energy and Environment Southeast University Nanjing 211189 China Advanced Biofuels and Bioproducts Process Development Unit Biological Systems and Engineering Division Lawrence Berkeley National Laboratory Emeryville CA 94608 USA School of Engineering and Architecture University College Cork T23XE10 Cork Ireland Research Ireland MaREI Centre for Energy Climate and Marine Environmental Research Institute University College Cork T23XE10 Cork Ireland School of Chemistry and Chemical Engineering Southeast University Nanjing 211189 China School of Engineering Westlake University Hangzhou Zhejiang 310030 China Institute of Advanced Technology Westlake Institute for Advanced Study Hangzhou Zhejiang 310024 China

The conversion of biomass residues into biochar is a promising strategy for enhancing sustainability within the circular bioeconomy, particularly through its role in improving renewable natural gas production. However, engineering biochar with optimal properties remains a complex challenge, as the relationship between preparation conditions, biochar characteristics, and anaerobic digestion (AD) performance is not fully understood. This study presents an AI-derived full-process prediction approach that integrates machine learning and generative models to guide the rational design of biochar, and optimize its use for biomethane production. Three tree-based regression models were employed to predict AD performance, with the eXtreme Gradient Boosting Regression model demonstrating superior accuracy. Feature importance analysis identified key biochar properties, including electrical conductivity, oxygen content, and specific surface area, as critical factors influencing biomethane production. These properties can be fine-tuned by adjusting pyrolysis conditions and selecting suitable biomass sources. A generative adversarial network was further used to explore a broader data space, helping to identify the optimal combination of parameters for maximizing AD efficiency. This novel AI-driven framework facilitates biochar-mediated renewable natural gas production, offering a scalable and sustainable approach for advancing circular bioeconomy.

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Advancing aqueous zinc-ion batteries with carbon dots: A comprehensive review

EcoEnergy

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EcoEnergy 2024年

作者： Mingying Chen Junjie Ma Yanhong Feng Quanping Yuan Yinghong Wu Yifan Liu Guangzhi Hu Xijun Liu Guangxi Key Laboratory of Electrochemical Energy Materials School of Chemistry and Chemical Engineering Guangxi University Nanning China MOE Key Laboratory of New Processing Technology for Nonferrous Metals and Materials Guangxi Key Laboratory of Processing for Non-ferrous Metals and Featured Materials School of Resources Environment and Materials Guangxi University Nanning China National Engineering Research Center of Green Recycling for Strategic Metal Resources Institute of Process Engineering Chinese Academy of Sciences Beijing China Department of Health Sciences and Technology Biomedical and Mobile Health Technology (BMHT) Group ETH Zürich Zürich Switzerland Institute of Advanced Science Facilities Shenzhen Guangdong China Institute for Ecological Research and Pollution Control of Plateau Lakes School of Ecology and Environmental Science Yunnan University Kunming China

Recent years have witnessed a surge in research on aqueous zinc-ion batteries (AZIBs) due to their low cost, stability, and exceptional electrochemical performance, among other advantages. However, practical manufacturing and deployment of AZIBs have been hindered by challenges such as low energy density, significant precipitation-related side reactions, slow ion migration, and dendritic growth. Addressing these issues and enhancing the practical application of AZIBs necessitates the development of novel materials. Carbon dots (CDs), with their distinctive structure and superior electrochemical properties, represent an innovative class of carbon-based materials with broad potential applications for optimizing AZIBs' performance. This study offers a comprehensive review of how CDs can address the aforementioned challenges of AZIBs. It begins with an overview of AZIBs composition and mechanism before delving into the classification, preparation techniques, and functionalization strategies of CDs. The review also thoroughly summarizes the sophisticated roles of CDs as modifiers in electrolytes and electrodes, both positive and negative, and briefly discusses their potential application in membranes. Additionally, it provides a summary of current issues and difficulties encountered in utilizing CDs in AZIBs. This review aims to provide insights and guidance for designing and manufacturing the next generation of high-performance AZIBs.

关键词： aqueous zinc-ion batteries carbon dots electrode design electrolyte energy storage

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Nonlinear Behavior of Surface Charge Density and Zeta Potential in Microchannel Electrokinetic Flow

SSRN

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SSRN 2023年

作者： Chen, Daming Arancibia-Miranda, Nicolas Escudey, Mauricio Fu, Jiao Lu, Qin Amon, Cristina H. Galatro, Daniela Guzmán, Amador M. Facultad de Ingeniería Departamento de Ingeniería Mecánica Universidad de Santiago de Chile Av. B. O’Higgins 3363 Santiago9170020 Chile Center for the Development of Nanoscience and Nanotechnology CEDENNA Santiago9170124 Chile Facultad de Química y Biología Universidad de Santiago de Chile Av. B. O’Higgins Santiago3363 Chile Xi'an key laboratory of advanced control and intelligent process Xi'an University of Posts and Telecommunications Xi'An710121 China Solar and Thermal Energy Conversion and Storage Device and System Laboratory STECTEC Santiago Chile Escuela de Ingenieria Industrial Facultad de Ingenieria y Ciencias Universidad Diego Portales Ejercito 441 Santiago Chile Department of Mechanical and Industrial Engineering Faculty of Applied Science and Engineering University of Toronto 5 King’s College Road TorontoON Canada Department of Chemical Engineering and Applied Chemistry Faculty of Applied Science and Engineering University of Toronto 200 College Street TorontoON Canada

In this work, a combined numerical and experimental investigation of the surface charge density and zeta potential behavior is investigated for borosilicate immersed in KCl and NaCl electrolytes and for imogolite immersed in KCl, CaCl2, and MgCl2 electrolytes. Simulations and experiments of the electrokinetic flows with electrolyte solutions were performed to accurately determine the electric double layer (EDL), the zeta potential, and the surface charge density at various electrolyte concentrations and pH. The zeta potential was experimentally determined and numerically predicted by solving the coupled governing equations of mass, species, momentum, and electrical field iteratively. The zeta potential for borosilicate develops strong nonlinear behavior with the ion concentration following a power-law. Likewise, the surface charge density obeys a nonlinear behavior, decreasing as the concentration increases. Moreover, for imogolite, both the zeta potential and the surface charge density behave nonlinearly with the pH. The EDL for borosilicate and imogolite becomes thinner as the electrolyte concentration and pH increase;this behavior is caused by increased surface charge density, resulting in the higher attraction of the free charges. The reported nonlinear behavior describes more accurately the interaction of the nanoparticle surface charge with the electrolytes and its effect on the electrolyte transport properties. © 2023, The Authors. All rights reserved.

关键词： Zeta potential

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