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An integrated data-driven modeling and gas emission constraints for large-scale refinery production planning framework

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PROCESS SAFETY AND ENVIRONMENTAL PROTECTION 2024年 182卷 109-126页

作者： Li, Jing Ye, Haotian Lu, Rongrong Zou, Xiong Dong, Hongguang Dalian Univ Technol Sch Chem Engn Dalian 116024 Peoples R China

The accuracy of secondary unit models including fluidized catalytic cracking, catalytic reforming and delayed coking is essential for the refinery production planning. Through the reaction kinetics, it is clear well that feedstock composition directly determines the product yield of units. Therefore, a hybrid modeling framework was developed in this work, where the feed true boiling point temperature, hydrocarbon group composition, and critical properties were introduced to characterize the lumped kinetic composition of the three secondary units. These indicators were further employed to predict unit product yield, which were embedded in the hybrid models developed using a data-driven approach. This study proposed a production planning framework that coupled data-driven approach and gas emission constraints to improve profitability and reduce emission. Three refinery production planning examples were taken for illustration. The optimal scheme obtained with the proposed hybrid model was compared with that from method based on the fixed yield production planning models. The results demonstrated that the production planning based on the hybrid yield model could achieve an improvement of the refinery profitability by 21.75% and 22.88%. The production planning coupled gas emission constraints optimization scheme achieved reduction in CO2, SO2 and NOx emissions by 16.59%, 16.67% and 16.67% respectively.

关键词： Refinery Planning Gas emission reduction data-driven modeling

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data-driven modeling of process, structure and property in additive manufacturing: A review and future directions

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JOURNAL OF MANUFACTURING PROCESSES 2022年 77卷 13-31页

作者： Wang, Zhuo Yang, Wenhua Liu, Qingyang Zhao, Yingjie Liu, Pengwei Wu, Dazhong Banu, Mihaela Chen, Lei Univ Michigan Dearborn Dept Mech Engn Dearborn MI 48128 USA Mississippi State Univ Dept Mech Engn Starkville MS 39762 USA Univ Cent Florida Dept Mech & Aerosp Engn Orlando FL 32816 USA Yanshan Univ Sch Mech Engn Qinhuangdao 066004 Hebei Peoples R China Univ Michigan Dept Mech Engn Ann Arbor MI 48109 USA

A thorough understanding of complex process-structure-property (P-S-P) relationships in additive manufacturing (AM) has long been pursued due to its paramount importance in achieving AM process optimization and quality control. Physics-based modeling and experimental approaches are usually time-consuming and/or costly. With the increasing availability of digital AM data and rapid development of data-driven modeling techniques, especially machine learning (ML), data-driven AM modeling is emerging as an effective approach towards this end. It allows for automatic discovery of patterns and trends in the AM data, construction of quantitative models of P-S-P relationships over the parameter space and prediction at unseen points without having to perform new physical modeling or experiments. A proliferation of researches on data-driven modeling of process, structure and property in AM have been witnessed in recent years. In this context, this paper aims to provide a systematic review of existing data-driven AM modeling with respect to different quantities of interest (QoI) along the process-structure-property chain. Specifically, this paper provides a summary of important information (i.e., input features, QoI-related output, data source and data-driven models) on existing data-driven AM modeling, as well as an in-depth analysis on relevant success achieved so far. Based on the comprehensive review, this paper also critically discusses the major limitations faced today and identifies some research directions that are promising for significantly advancing data-driven AM modeling in the future.

关键词： data-driven modeling Machine learning Additive manufacturing Process-structure-property

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Observation and transition point detection in transient two-body abrasive wear process using machine vision system and data-driven modeling

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2025年第10期136卷 4511-4523页

作者： Fujisawa, Kei Yokohama Natl Univ Fac Engn Div Syst Res 79-5 TokiwadaiHodogaya Ku Yokohama Kanagawa 2408501 Japan

The evolution of transient two-body abrasive wear was investigated using a machine vision system. In the early stages, abrasives with sharp tips produced abrasive wear patterns characterized by isolated elongated grooves. As the abrasive wear progressed, these grooves disintegrated into shorter grooves owing to the overlap of existing and newly formed abrasive wear patterns. This increased the optical image features that correlated with both roughness and mass loss. The relationship between optical image features and mass loss was analyzed from the perspective of abrasive wear theory, and the transition points in the relationship was detected by Kalman filtering. Furthermore, a data-driven framework was developed to predict spatiotemporal variations in surface conditions and the associated optical image features.

关键词： Digital image processing Machine vision Regression analysis Gray-level intensity Abrasive wear Grinding data-driven modeling Kalman filter Deep learning

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Novel dynamic data-driven modeling based on feature enhancement with derivative memory LSTM for complex industrial process

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NEUROCOMPUTING 2025年 626卷

作者： Zhu, Xiuli Xu, Jiajun Fu, Zixuan Damarla, Seshu Kumar Wang, Peng Hao, Kuangrong Univ Shanghai Sci & Technol Dept Opt Elect & Comp Engn Shanghai 200093 Peoples R China Minist Educ Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China Univ Alberta Dept Chem & Mat Engn Edmonton AB T6G2V4 Canada Shanghai Jiao Tong Univ Dept Automat Shanghai 200241 Peoples R China Donghua Univ Coll Informat Sci & Technol Shanghai 201620 Peoples R China

Effective feature extraction is important for accurate data-driven soft sensor modeling in large-scale dynamic industrial processes. However, due to the temporal, nonlinear, and high-dimensional nature of the data collected from large-scale industrial processes, traditional data-driven modeling methods often suffer from imperfect feature extraction. To overcome this challenge, this paper proposes a novel feature enhancement framework based on derivative memory long short-term memory (FEDM-LSTM) algorithm for soft sensor modeling. First, inspired by the proportional-integral-derivative control theory, the LSTM is equipped with a derivative gate that dynamically predicts future information. Combined with inherent gates that resemble the proportional and integral parts in traditional LSTM, the derivative memory LSTM (DM-LSTM) captures the dynamic information of the past, the present, and the future. Then, to adapt to multiple phases in complex industrial systems, a feature enhancement framework is designed for DM-LSTM in which features representing important dynamic information from previous phases are fed to the DM-LSTM as additional input in the current phase. Finally, the effectiveness of the proposed method is evaluated through two real industrial datasets, showcasing its ability to achieve high prediction accuracy.

关键词： data-driven modeling Soft sensor Long short-term memory network Feature extraction Industrial process

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data-driven modeling and analysis based on complex network for multimode recognition of industrial processes

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JOURNAL OF MANUFACTURING SYSTEMS 2022年 62卷 915-924页

作者： Sun, Yan-Ning Zhuang, Zi-Long Xu, Hong-Wei Qin, Wei Feng, Meng-Jiao Shanghai Jiao Tong Univ Sch Mech Engn Shanghai 200240 Peoples R China Shanghai Aerosp Equipments Manufacturer Co Ltd Shanghai 200245 Peoples R China

An industrial process usually has multiple operating conditions or periods due to various factors such as the fluctuation of raw material quality, differences of worker levels, and changes of product specifications. Industrial data from different operating conditions or periods have different statistical characteristics, namely multimode behavior. In fact, multimode recognition of industrial processes is the first problem to develop effective intelligent manufacturing strategies for engineers. The clustering-based methods can help them characterize multi mode behavior in historical databases and group them together with little detailed knowledge of the process. However, these methods are facing the challenges of massive high-dimensional industrial big data and unknown mode number. This study proposes a construction method of complex network to describe the potential relationship in industrial big data. The topological structure of complex network can be acquired by taking the correlation matrix of multi-variables as nodes and the similarity between correlation matrices as edges. On this basis, community detection technology is employed to reveal the aggregation behavior of nodes in network. It takes the maximum modularity as the optimization objective, and partitions each node into one community through greedy search algorithm, where a community denotes a mode of industrial process. Finally, case studies have demonstrated the proposed method has good performance and application prospects.

关键词： data mining data-driven modeling Multimode recognition Complex network Industrial process

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On the Solution Uniqueness of data-driven modeling of Flexible Loads

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IEEE TRANSACTIONS ON SMART GRID 2025年第2期16卷 1993-1996页

作者： Lu, Shuai Ding, Jiayi Chen, Mingji Gu, Wei Zhu, Junpeng Xu, Yijun Dong, Zhaoyang Sun, Zezheng Southeast Univ Sch Elect Engn Nanjing 210096 Peoples R China Hohai Univ Sch Elect & Power Engn Nanjing 211100 Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China Univ New South Wales UNSW Business Sch Sydney NSW 2033 Australia

This letter first explores the solution uniqueness of the data-driven modeling of price-responsive flexible loads (PFL). The PFL on the demand side is critical in modern power systems. An accurate PFL model is fundamental for system operations. However, whether the PFL model can be uniquely and correctly identified from operational data remains unclear. To address this, we analyze the structural and practical identifiability of the PFL model, deriving the dataset condition that guarantees the solution uniqueness. Besides, we point out the practical implications of the results. Numerical tests validate this work.

关键词： Load modeling Optimization Numerical models data-driven modeling Upper bound Indexes Electricity Safety Reliability Batteries Flexible loads data-driven modeling identifiability inverse optimization solution uniqueness

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Physics-informed and data-driven modeling of an industrial wastewater treatment plant with actual validation

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COMPUTERS & CHEMICAL ENGINEERING 2024年 189卷

作者： Koksal, Ece Serenat Asrav, Tuse Esenboga, Elif Ecem Cosgun, Ahmet Kusoglu, Gizem Aydin, Erdal Koc Univ Dept Chem & Biol Engn TR-34450 Istanbul Turkiye Koc Univ Koc Univ TUPRAS Energy Ctr KUTEM TR-34450 Istanbul Turkiye Turkish Petr Refineries Corp TR-41790 Korfez Kocaeli Turkiye

data-driven modeling is essential in chemical engineering, especially in complex systems like wastewater treatment plants. Recurrent neural networks are effective for modeling parameters in wastewater treatment process such as dissolved oxygen concentration and chemical oxygen demand due to their nonlinear adaptability. However, traditional models face challenges such as the requirement for larger datasets and more frequent sampling, noisy measurements, and overfitting. To address this, physics-informed neural networks integrate physical knowledge for improved performance. In our study, we apply both approaches to a wastewater treatment plant, enhancing prediction performance. Our results demonstrate that physics-informed models perform successfully in offline and online validation, especially when standard methods fail. They maintain effectiveness without frequent updates. Yet, integrating physics-informed knowledge can introduce noise when standard methods suffice. This result points out the need for careful consideration of model choice in different scenarios.

关键词： Physics-informed neural networks Wastewater treatment Dissolved oxygen concentration Chemical oxygen demand data-driven modeling

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Adaptive Regulated Sparsity Promoting Approach for data-driven modeling and Control of Grid-Connected Solar Photovoltaic Generation

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IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2025年第1期16卷 560-572页

作者： Zhang, Zhongtian Khazaei, Javad Blum, Rick S. Lehigh Univ Dept Elect & Comp Engn Bethlehem PA 18015 USA

This paper introduces a new statistical learning technique based on sparsity promotion for data-driven modeling and control of solar photovoltaic (PV) systems. Compared with conventional sparse regression techniques that might introduce computational complexities when the number of candidate functions increases, an innovative algorithm, named adaptive regulated sparse regression (ARSR) is proposed. The ARSR adaptively regulates the hyperparameter weights of candidate functions to best represent the dynamics of PV systems. This method allows for the application of different sparsity-promoting hyperparameters for each state variable, whereas the conventional approach uses the same hyperparameter for all state variables, which may result in not excluding all the unrelated terms from the dynamics. Consequently, the proposed method can identify more complex dynamics with greater accuracy. Utilizing this algorithm, open-loop and closed-loop models of single-stage and two-stage PV systems are obtained from measurements and are utilized for control design purposes. Moreover, it is demonstrated that the proposed data-driven approach can be successfully employed for fault analysis studies, which distinguishes its capabilities from other data-driven techniques. Finally, the proposed approach is validated through real-time simulations.

关键词： Power system dynamics Adaptation models Nonlinear dynamical systems Voltage control Mathematical models Analytical models Heuristic algorithms data-driven modeling Photovoltaic systems Adaptive systems Adaptive regulated sparse regression (ARSR) closed-loop data-driven modeling photovoltaic (PV) systems single-stage PV two-stage PV

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data-driven modeling of heterogeneous viscoelastic biofilms

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BIOTECHNOLOGY AND BIOENGINEERING 2022年第5期119卷 1301-1313页

作者： Li, Mengfei Matous, Karel Nerenberg, Robert Univ Notre Dame Dept Civil & Environm Engn & Earth Sci 156 Fitzpatrick Hall Notre Dame IN 46556 USA Univ Notre Dame Dept Aerosp & Mech Engn Notre Dame IN 46556 USA

Biofilms are typically heterogeneous in morphology, structure, and composition, resulting in nonuniform mechanical properties. The distribution of mechanical properties, in turn, determines the biofilm behavior, such as deformation and detachment. Most biofilm models neglect biofilm heterogeneity, especially at the microscale. In this study, an image-based modeling approach was developed to transform two-dimensional optical coherence tomography (OCT) biofilm images to a pixel-scale non-Newtonian viscosity map of the biofilm. The map was calibrated using the bulk viscosity data from rheometer tests, based on assumed maximum and minimum viscosities and a relationship between OCT image intensity signals and non-Newtonian viscosity. While not quantitatively measuring biofilm viscosity for each pixel, it allows a rational spatial allocation of viscosities within the biofilm: areas with lower cell density, for example, voids, are assigned lower viscosities, and areas with high cell densities are assigned higher viscosities. The spatial distribution of non-Newtonian viscosity was applied in an established Oldroyd-B constitutive model and implemented using the phase-field continuum approach for the deformation and stress analysis. The heterogeneous model was able to predict deformations more accurately than a homogenous one. Stress distribution in the heterogeneous biofilm displayed better characteristics than that in the homogeneous one, because it is highly dependent on the viscosity distribution. This study, using a pixel-scale, image-based approach to map the mechanical heterogeneity of biofilms for computational deformation and stress analysis, provides a novel modeling approach that allows the consideration of biofilm structural and mechanical heterogeneity. Future research should better characterize the relationship between OCT signal and viscosity, and consider other constitutive models for biofilm mechanical behavior.

关键词： data-driven modeling heterogeneous biofilm optical coherence tomography phase-field model viscoelastic Oldroyd-B model

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data-driven modeling and Design of Multivariable Dynamic Sliding Mode Control for the Underground Coal Gasification Project Thar

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IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY 2022年第1期30卷 153-165页

作者： Javed, Syed B. Utkin, Vadim, I Uppal, Ali A. Samar, Raza Bhatti, Aamer Iqbal COMSATS Univ Islamabad Dept Elect & Comp Engn Islamabad 44000 Pakistan Capital Univ Sci & Technol Elect Engn Dept Islamabad 44000 Pakistan Ohio State Univ Dept Elect & Comp Engn Columbus OH 43210 USA

The energy output per unit time is an important performance metric to determine the potential of an underground coal gasification (UCG) site for electricity production. The energy output per unit time is a function of heating value and flow rate of syngas, and therefore, it is essential to devise a multivariable closed-loop system to enhance the efficiency of the UCG process. In this work, a model-based, multivariable dynamic sliding mode control (DSMC) has been designed for the cavity simulation model (CAVSIM), parameterized with the operating parameters and coal properties of the UCG Project Thar (UPT) field. The model-based control of CAVSIM is not possible due to its complex and multidimensional dynamics, and thus, a simple linear multivariable model is identified by employing a subspace (N4SID) system identification technique. The regular form of the linear model is formulated to design the model-based DSMC. Moreover, the stability of zero dynamics is shown on the approximate model of CAVSIM. Consequently, the designed controller is implemented on CAVSIM, and simulation results are compared with conventional sliding mode control (SMC). It has been observed that both the controllers have achieved the tracking objectives in the presence of input disturbance and modeling uncertainties. However, DSMC utilizes lesser control energy to achieve the desired objectives. Furthermore, the continuous control inputs in DSMC significantly reduce chattering.

关键词： Mathematical model Coal Syngas Heating systems Control design Process control Predictive models data-driven modeling energy conversion systems and underground coal gasification (UCG) variable structure sliding mode control (SMC)

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