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Data-driven Wasserstein distributionally robust chance-constrained optimization for crude oil scheduling under uncertainty

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Chinese Journal of Chemical engineering 2024年第5期69卷 152-166页

作者： Xin Dai Liang Zhao Renchu He Wenli Du Weimin Zhong Zhi Li Feng Qian Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of EducationEast China University of Science and TechnologyShanghai 200237China Engineering Research Center of Process System Engineering Ministry of EducationEast China University of Science and TechnologyShanghai 200237China

Crude oil scheduling optimization is an effective method to enhance the economic benefits of oil *** uncertainties,including uncertain demands of crude distillation units(CDUs),might make the production plans made by the traditional deterministic optimization models infeasible.A data-driven Wasserstein distributionally robust chance-constrained(WDRCC)optimization approach is proposed in this paper to deal with demand uncertainty in crude oil ***,a new deterministic crude oil scheduling optimization model is developed as the basis of this *** Wasserstein distance is then used to build ambiguity sets from historical data to describe the possible realizations of probability distributions of uncertain demands.A cross-validation method is advanced to choose suitable radii for these ambiguity *** deterministic model is reformulated as a WDRCC optimization model for crude oil scheduling to guarantee the demand constraints hold with a desired high probability even in the worst situation in ambiguity *** proposed WDRCC model is transferred into an equivalent conditional value-at-risk representation and further derived as a mixed-integer nonlinear programming *** case studies from a real-world refinery are conducted to show the effectiveness of the proposed ***-of-sample tests demonstrate that the solution of the WDRCC model is more robust than those of the deterministic model and the chance-constrained model.

关键词： Distributions Model Optimization Crude oil scheduling Wasserstein distance Distributionally robust chance constraints

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Facile fabrication of NiCo-hydroxysulfide nanosheets array for electrocatalytic urea oxidation

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Science China(Technological Sciences) 2025年第1期68卷 218-228页

作者： Jing ZOU Chang LIU Kun XIANG Qiong LI Shihao DAI Jizhou JIANG School of Chemistry and Environmental Engineering School of Environmental Ecology and Biological EngineeringKey Laboratory of Green Chemical Engineering Process of Ministry of EducationEngineering Research Center of Phosphorus Resources Development and Utilization of Ministry of EducationNovel Catalytic Materials of Hubei Engineering Research CenterWuhan Institute of TechnologyWuhan 430205China

Urea oxidation reaction(UOR)instead of anodic oxygen evolution reaction(OER)is considered an effective way to reduce energy consumption in electrocatalytic water splitting for hydrogen ***,the slow rate of reaction of UOR,which entails a procedure of transferring six electrons,has hindered its widespread ***,it is crucial to design highly effective electrocatalysts for the implementation of ***,a novel NiCo-hydroxysulfide(NiCo-HOS)electrocatalyst has been reported for UOR,which is obtained by the exchange of sulfur ions in NiCo layered double hydroxide(NiCo-LDH)nanosheets at room *** from the sulfurization process,the composition,electronic structure,and surface properties of electrocatalysts have undergone significant changes and *** sulfurization treatment,the resulting NiCo-HOS showed enhanced chemical resistance to alkaline electrolytes and improved electrical *** a 40 h operation test,it maintained high stability and provided a stable current density of 100 mA cm^(-2)at a relatively low potential of 1.33 V(***)in a solution of 1 mol L^(-1)KOH+0.5 mol L^(-1)***,the anode electrolytic product is analyzed through gas chromatography(GC),and N_2 is detected as the product without the presence of CO,indicating that the urea has undergone complete oxidation.

关键词： urea oxidation reaction NiCo-hydroxysulfide electrocatalyst hydrogen

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Spatiotemporal Soil Moisture Prediction Using a Causal-Guided Deep Learning Model

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12166-12179页

作者： Wu, Tingtao Xu, Lei Pan, Ziwei Cai, Ruinan Dai, Jin Yang, Shuang Zhang, Xihao Zhang, Xi Chen, Nengcheng National Engineering Research Center for Geographic Information System Wuhan430074 China

The spatiotemporal prediction of RZSM refers to the process of estimating its future spatial distribution and temporal variations using predictive models. Accurate spatiotemporal predictions of soil moisture provide insights into future conditions, supporting decision-making in applications such as crop yield optimization, irrigation planning, and drought management. However, existing models face limitations in capturing complex spatiotemporal dependencies and dynamic causal interactions. This paper proposes a spatiotemporal prediction framework that integrates causal inference with deep learning, termed the Causal-Guided Spatiotemporal Swin Transformer (Causal ST-SwinT). The model introduces a dynamic causal weight adjustment mechanism to adaptively optimize the causal relationship intensity between variables and adopts a hierarchical multi-level feature extraction strategy to effectively capture complex spatiotemporal dependencies, thereby enhancing prediction accuracy and model interpretability. The proposed method is validated on the ERA5 and Soil Moisture Active Passive (SMAP) datasets over the Tibetan Plateau and compared with multiple models. Experimental results show that Causal ST-SwinT significantly outperforms the classical Convolutional Long Short-Term Memory (ConvLSTM) model, reducing mean absolute error (MAE) from 0.0146 to 0.0055 m³/m³ on the ERA5 dataset and from 0.0088 to 0.0046 m³/m³ on the SMAP dataset. Robustness analysis reveals that Causal ST-SwinT maintains high prediction accuracy under various environmental conditions. Ablation experiments further confirm the critical role of the causal attention module in improving model performance. These findings demonstrate that integrating causal knowledge with deep learning models effectively enhances the modeling capabilities of complex spatiotemporal systems, providing a novel solution for broader spatiotemporal prediction tasks. © 2008-2012 IEEE.

关键词： Deep learning

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Spiking Reinforcement Learning Enhanced by Bioinspired Event Source of Multi-dendrite Spiking Neuron and Dynamic Thresholds

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 618-629页

作者： Xingyue Liang Qiaoyun Wu Yun Zhou Chunyu Tan Hongfu Yin Changyin Sun the School of Artificial Intelligence Anhui University Engineering Research Center of Autonomous Unmanned System Technology Ministry of Education IEEE the Institute of Artificial Intelligence Hefei Comprehensive National Science Center the School of Automation Southeast University the Engineering Research Center of Autonomous Unmanned System Technology Ministry of Education

Deep reinforcement learning(DRL) achieves success through the representational capabilities of deep neural networks(DNNs). Compared to DNNs, spiking neural networks(SNNs),known for their binary spike information processing, exhibit more biological characteristics. However, the challenge of using SNNs to simulate more biologically characteristic neuronal dynamics to optimize decision-making tasks remains, directly related to the information integration and transmission in *** by the advanced computational power of dendrites in biological neurons, we propose a multi-dendrite spiking neuron(MDSN) model based on Multi-compartment spiking neurons(MCN), expanding dendrite types from two to multiple and deriving the analytical solution of somatic membrane *** apply the MDSN to deep distributional reinforcement learning to enhance its performance in executing complex decisionmaking tasks. The proposed model can effectively and adaptively integrate and transmit meaningful information from different sources. Our model uses a bioinspired event-enhanced dendrite structure to emphasize features. Meanwhile, by utilizing dynamic membrane potential thresholds, it adaptively maintains the homeostasis of MDSN. Extensive experiments on Atari games show that the proposed model outperforms some state-of-the-art spiking distributional RL models by a significant margin.

关键词： Deep reinforcement learning multi-compartment spiking neurons spiking neural network

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Closed-loop scheduling optimization strategy based on particle swarm optimization with niche technology and soft sensor method of attributes-applied to gasoline blending process

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Chinese Journal of Chemical engineering 2023年第9期61卷 43-57页

作者： Jian Long Kai Deng Renchu He Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of EducationEast China University of Science and TechnologyShanghaiChina Engineering Research Center of Process System Engineering Ministry of EducationEast China University of Science and TechnologyShanghai 200237China

Gasoline blending scheduling optimization can bring significant economic and efficient benefits to ***,the optimization model is complex and difficult to build,which is a typical mixed integer nonlinear programming(MINLP)*** the large scale of the MINLP model,in order to improve the efficiency of the solution,the mixed integer linear programming-nonlinear programming(MILP-NLP)strategy is used to solve the *** paper uses the linear blending rules plus the blending effect correction to build the gasoline blending model,and a relaxed MILP model is constructed on this *** particle swarm optimization algorithm with niche technology(NPSO)is proposed to optimize the solution,and the high-precision soft-sensor method is used to calculate the deviation of gasoline attributes,the blending effect is dynamically corrected to ensure the accuracy of the blending effect and optimization results,thus forming a prediction-verification-reprediction closed-loop scheduling optimization strategy suitable for engineering *** optimization result of the MILP model provides a good initial *** fixing the integer variables to the MILPoptimal value,the approximate MINLP optimal solution can be obtained through a NLP *** above solution strategy has been successfully applied to the actual gasoline production case of a refinery(3.5 million tons per year),and the results show that the strategy is effective and *** optimization results based on the closed-loop scheduling optimization strategy have higher *** with the standard particle swarm optimization algorithm,NPSO algorithm improves the optimization ability and efficiency to a certain extent,effectively reduces the blending cost while ensuring the convergence speed.

关键词： Blend Optimization algorithm Neural networks Particle swarm optimization Mixed integer programming

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Adaptive Sensor-Fault Tolerant Control of Unmanned Underwater Vehicles With Input Saturation

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 907-918页

作者： Xuerao Wang Qingling Wang Yanxu Su Yuncheng Ouyang Changyin Sun the School of Artificial Intelligence the Engineering Research Center of Autonomous Unmanned System Technologyand the Anhui Provincial Engineering Research Center for Unmanned System and Intelligent TechnologyMinistry of EducationAnhui UniversityHefei 230601China the School of Automation Southeast UniversityNanjing 210096China

This paper investigates the tracking control problem for unmanned underwater vehicles(UUVs)systems with sensor faults,input saturation,and external disturbance caused by waves and ocean *** active sensor fault-tolerant control scheme is ***,the developed method only requires the inertia matrix of the UUV,without other dynamic information,and can handle both additive and multiplicative sensor ***,an adaptive fault-tolerant controller is designed to achieve asymptotic tracking control of the UUV by employing robust integral of the sign of error feedback *** is shown that the effect of sensor faults is online estimated and compensated by an adaptive *** the proposed controller,the tracking error and estimation error can asymptotically converge to ***,simulation results are performed to demonstrate the effectiveness of the proposed method.

关键词： Asymptotic stability fault-tolerant control input saturation robust integral of the sign of error unmanned underwater vehicle

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Synthesis of NaY zeolite from a submolten depolymerized perlite:Alkalinity effect and crystallization kinetics

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Chinese Journal of Chemical engineering 2024年第6期70卷 130-138页

作者： Yanli Qu Peng Dong Li Yang Yuanyuan Yue Haoliang Wang Jingcai Cheng Chao Yang CAS Key Laboratory of Green Process and Engineering State Key Laboratory of Petroleum Molecular&Process EngineeringInstitute of Process EngineeringChinese Academy of SciencesBeijing 100190China School of Chemical Engineering University of Chinese Academy of SciencesBeijing 100190China Qingyuan Innovation Laboratory Quanzhou 362801China National Engineering Research Center of Chemical Fertilizer Catalyst College of Chemical EngineeringFuzhou UniversityFuzhou 350108China

NaY zeolites are synthesized using submolten salt depolymerized natural perlite mineral as the main silica and alumina sources in a 0.94 L stirred *** of alkalinity ranging from 0.38 to 0.55(n(Na_(2)O)/n(SiO_(2)))on the relative crystallinity,textural properties and crystallization kinetics were *** results show that alkalinity exerts a nonmonotonic influence on the relative crystallinity and textural properties,which exhibit a maximum at the alkalinity of *** nucleation kinetics are studied by fitting the experimental data of relative crystallinity with the Gualtieri *** is shown that the nucleation rate constant increases with increasing alkalinity,while the duration period of nucleation decreases with increasing *** n(Na_(2)O)/n(SiO_(2))ratios ranging from 0.38 to 0.55,the as-synthesized NaY zeolites exhibit narrower crystal size distributions with the increase in *** growth rates determined from the variations of average crystal size with time are 51.09,157.50,46.17 and 24.75 nm·h^(-1),*** is found that the larger average crystal sizes at the alkalinity of 0.38 and 0.43 are attributed to the dominant role of crystal growth over ***,the combined action of prominent crystal growth and the longer duration periods of nucleation at the alkalinity of 0.38 and 0.43 results in broader crystal size *** findings demonstrate that control of the properties of NaY zeolite and the crystallization kinetics can be achieved by conducting the crystallization process in an appropriate range of alkalinity of the reaction mixture.

关键词： NaY zeolite Submolten salt depolymerized perlite Alkalinity Crystallization kinetics

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CFD-PBM coupled modeling of the liquid-liquid dispersion characteristics and structure optimization for Kenics static mixer

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Chinese Journal of Chemical engineering 2024年第6期70卷 173-188页

作者： Junhai Deng Shilin Lan Juchang Wu Shenghua Du Weidong Liu Luchang Han Yefeng Zhou National&Local United Engineering Research Centre for Chemical Process Simulation and Intensification Chemical Process Simulation and Optimization Engineering Research Center of Ministry of EducationXiangtan UniversityXiangtan 411105China Hunan Haili Chemical Industry Company Limited Changsha 410000China

Kenics static mixers(KSM)are extensively used in industrial mixing-reaction processes by virtue of high mixing efficiency,low power homogenization and easy continuous *** liquid droplet size and its distribution and thus revealing the dispersion characteristics are of great significance for structural optimization and process intensification in the *** this work,a computational fluid dynamics-population balance model(CFD-PBM)coupled method is employed to systematically investigate the effects of operating conditions and structural parameters of KSM on droplet size and its distribution,to further reveal the liquid-liquid dispersion *** indicate that higher Reynolds numbers or higher dispersed phase volume fractions increase energy dissipation,reducing Sauter mean diameter(SMD)of dispersed phase droplets and with a shift in droplet size distribution(DSD)towards smaller *** aspect ratios,greater blade twist and assembly angles amplify shear rate,leading to smaller droplet size and a narrower DSD in the smaller *** degree of impact exerted by the aspect ratio is notably ***,mixing elements with different spin enhance shear and stretching *** to the same spin,SMD becomes 3.7-5.8 times smaller in the smaller size range with a significantly narrower *** into account the pressure drop and efficiency in a comprehensive manner,optimized structural parameters for the mixing element encompass an aspect ratio of 1-1.5,a blade twist angle of 180°,an assembly angle of 90°,and interlaced assembly of adjacent elements with different *** work provides vital theoretical underpinning and future reference for enhancing KSM performance.

关键词： CFD Population balance Liquid-liquid dispersion Kenics static mixer

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Penalty Function-Based Distributed Primal-Dual Algorithm for Nonconvex Optimization Problem

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IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 394-402页

作者： Xiasheng Shi Changyin Sun IEEE the Engineering Research Center of Autonomous Unmanned System Technology Ministry of Education Anhui University the School of Artificial Intelligence Anhui University Anhui Provincial Engineering Research Center for Unmanned System and Intelligent Technology Anhui University the Anhui Key Laboratory Industrial Energy-Saving and Safety Anhui University

This paper addresses the distributed nonconvex optimization problem, where both the global cost function and local inequality constraint function are nonconvex. To tackle this issue, the p-power transformation and penalty function techniques are introduced to reframe the nonconvex optimization problem. This ensures that the Hessian matrix of the augmented Lagrangian function becomes local positive definite by choosing appropriate control parameters. A multi-timescale primal-dual method is then devised based on the Karush-Kuhn-Tucker(KKT) point of the reformulated nonconvex problem to attain convergence. The Lyapunov theory guarantees the model's stability in the presence of an undirected and connected communication network. Finally, two nonconvex optimization problems are presented to demonstrate the efficacy of the previously developed method.

关键词： Constrained optimization Karush-Kuhn-Tucker (KKT) point nonconvex p-power transformation

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A Novel Method for Multi-Function Radar Work Mode Boundary Detection

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Progress in Electromagnetics research C 2025年 153卷 213-218页

作者： Fu, Yuxin Wang, Jiantao Huang, Jie Dang, Tongxin Li, Yiming The National Digital Switching System Engineering and Technological Research and Development Center Zhengzhou450000 China

Work mode boundary detection can provide basic information units for the recognition of consecutive work modes in the intercepted multi-function radar (MFR) pulse sequence. The existing boundary detection methods tend to detect false boundaries when the pulse parameters vary drastically within the work mode, such as when the pulse repetition interval (PRI) modulation type is stagger or agile. To address the issue of over-detection of the work mode transition boundary, a new work mode boundary detection method is proposed based on the arc crossings (AC). It utilizes AC to quantify and annotate the similarities within MFR work modes. Without relying on prior knowledge, it can accurately capture the structural characteristics of the boundary transition and effectively adapt to different pulse parameter modulation types. The experimental results show that it reduces the segmentation probabilistic error by 8.7% and false alarm rate by 19.85% compared to the baseline algorithm. © 2025, Electromagnetics Academy. All rights reserved.

关键词： Tracking radar

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