Forced periodic operation is a technique that periodically changes the manipulating variable of a chemical reaction system in order to exploit nonlinear dynamics to improve reactant conversion rate. However, the analy...
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Forced periodic operation is a technique that periodically changes the manipulating variable of a chemical reaction system in order to exploit nonlinear dynamics to improve reactant conversion rate. However, the analysis and design of a periodically operated chemical process is a significant challenge. To resolve this problem, recently, nonlinear frequency response (NFR) based methods have been proposed. However, because of the need to derive the NFR from a first principle model, existing NFR methods can only perform qualitative analysis to simple processes and are often difficult to be applied in engineering practice. This article proposes a novel data driven approach to the analysis and optimal design of forced periodic operation of chemical reactions. From the data generated numerically using the first principle model or experimentally from experimental tests, the approach produces a data-driven NFR model that can readily be used for both quantitative study and optimal design of forced periodic operation of any complexities. This can fundamentally address the challenges faced by the existing NFR methods, and provides an effective approach that can potentially be applied in engineering practice. Simulation studies and experimental works are carried out on the application of the new method to an isothermal continuous stirred tank reactor system and a laboratory-scale carbon dioxide absorption process, respectively. The results verify the effectiveness and advantage of the newly proposed data driven approach and demonstrate the potential of the new approach in engineering applications.
The paper is devoted to the process of constructing infographic models for visualizing the results of analysis in BI-systems. The subject of research is the regularities of the choice of data visualization tools for a...
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The rubber calendering process is critical in tire production, with the width of the rubber strip during calendering significantly impacting the final tire quality. This process is characterized by high noise, nonline...
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ISBN:
(纸本)9798331540845;9789887581598
The rubber calendering process is critical in tire production, with the width of the rubber strip during calendering significantly impacting the final tire quality. This process is characterized by high noise, nonlinearity, temporal dependency, and pronounced dynamic features, which complicate the development of accurate models using traditional mechanistic modeling methods. To address this issue, a predictive model based on data denoising and a CNN-BiLSTM framework is proposed. The method employs improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) to decompose rubber calendering width data into multiple intrinsic mode functions (IMFs). Wavelet threshold denoising is then applied to high-frequency noise-containing IMFs, and the denoised IMFs are reconstructed to form a clean data series. Subsequently, a deep learning model combining convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) networks is developed to forecast the denoised rubber calendering width sequence. Comparative experiments demonstrate that this method effectively handles complex time-series data, with the model showing favorable evaluation metrics, highlighting significant advantages in predicting rubber calendering width.
The development of data-driven soft sensors for modeling complex data, particularly in scenarios characterized by strong nonlinearity, high dimensionality, cross-correlation and autocorrelation, remains a significant ...
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Predicting the nonlinear torsional stiffness of harmonic drives is important not only for system control but also for the further development of the drive. However, it is difficult to predict the torsional stiffness b...
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Predicting the nonlinear torsional stiffness of harmonic drives is important not only for system control but also for the further development of the drive. However, it is difficult to predict the torsional stiffness because of the elastic deformation of the flexspline and the many contacts involved. Furthermore, nonlinear dynamic contact simulations between the elastic components of the harmonic drive are disadvantaged by high computational burden and convergence problems. To address these challenges, we establish a flexible multibody modelingprocess using augmented formulations of a floating reference frame to simulate the operation of the harmonic drive. The elastic deformation of the flexspline and the dynamic contacts between the main components are considered in the reproduction of the contact mechanism of the drive. Coordinate reduction techniques are used to properly replicate an elastic shaft, enabling the effective simulation of flexible multibody dynamics, including nonlinear elastic tooth contact analysis. The performance of the proposed modelingprocess is evaluated using catalog and experimental data on two types of standard harmonic drives (SHG-25-100-UH and SHG-40-100-UH). The hysteresis curve is predicted through flexible multibody dynamics simulation and the accuracy of parameters of torsional stiffness is approximately 91.7% on average. The proposed modeling method can be improved to predict other measures of performance and it remains a potential research topic.
The use of industrial big data high-level optimization to diagnose and analyze DCC devices has been applied in the petrochemical production process, promoting the revolutionary breakthrough of production process contr...
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ISBN:
(纸本)9798350310801
The use of industrial big data high-level optimization to diagnose and analyze DCC devices has been applied in the petrochemical production process, promoting the revolutionary breakthrough of production processcontrol. Combined with the company's idea of enhancing its core competitiveness through information technology, it then elaborates the existing datamodeling methods, builds a software model, and tries to optimize its process big data and models through neural network algorithms to improve the potential of processcontrol, so as to achieve the purpose of reducing costs and increasing efficiency. Finally, the characteristics of highlevel optimization of industrial big data and the problems and challenges that may be faced in the process of realizing this are discussed, in order to achieve a breakthrough in the company's overall business value.
Rumor propagation poses a significant threat to social stability and public order, and controlling its spread can effectively reduce unnecessary panic and misunderstanding. Rumor control is primarily achieved by simul...
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Rumor propagation poses a significant threat to social stability and public order, and controlling its spread can effectively reduce unnecessary panic and misunderstanding. Rumor control is primarily achieved by simulating rumors spread on social networks and disseminating the truth or restricting propagation pathways. However, current studies usually only apply the optimal control theory, which leads to difficulties in coping with complex and stochastic network propagation environments. To address these issues, this paper constructs a three-layer network rumor control model (SICR-3M3W) that considers the dual refutation mechanism and formulates an optimal control problem for this model. Based on the reinforcement learning framework, we design a Proximal Policy Optimization (PPO) algorithm to solve this problem intelligently. Finally, experiments based on a real-world data case are conducted, and the results demonstrate that our three-layer model can effectively simulate the rumor propagation process. Moreover, the designed PPO controller can achieve optimal control outcomes.
The super-sized pore-throat network model can reflect both microscopic pore characteristics and macroscopic heterogeneity and is excellent in describing cross-scale flow fields. At present, there is no algorithm that ...
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The super-sized pore-throat network model can reflect both microscopic pore characteristics and macroscopic heterogeneity and is excellent in describing cross-scale flow fields. At present, there is no algorithm that can generate a micro pore-throat network model at a macro reservoir scale. This study examines algorithms for super-sized pore-throat network reconstruction using actual core sample data. It conducts a random simulation of mineral growth and dissolution under the constraints of four microscopic pore structure parameters: porosity, coordination number, pore radius, and throat radius. This approach achieves high-precision, super-sized, and regional pore-throat network modeling. Comparative analysis shows that these four parameters effectively guide the random growth process of super-sized pore-throat networks. The overall similarity between the generated pore-throat network model and real core samples is 88.7% on average. In addition, the algorithm can partition and control the generation of pore-throat networks according to sedimentary facies. The 100-megapixel model with 85,000 pores was generated in 455.9 s. This method can generate cross-scale models and provides a basis for cross-scale modeling in physical simulation experiments and numerical simulations.
The effectiveness of external emergency response to ship fires is critical in minimizing the serious consequences of ship fires. However, the insufficient consideration of the dynamic evolution of the emergency respon...
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The effectiveness of external emergency response to ship fires is critical in minimizing the serious consequences of ship fires. However, the insufficient consideration of the dynamic evolution of the emergency response may result in an inaccurate assessment of the effectiveness of ship fire emergency response. Therefore, we employ the theories of hierarchical timed colored Petri net (HTCPN) and Markov chains to evaluate the emergency response process to ship fire, and guide how to further improve emergency response capabilities. Firstly, the ship fire emergency response process is analyzed with ship fire emergency plans and accident cases. Next, the theory of Hierarchical Timed Colored Petri Net is adopted to model the ship fire emergency response process. Then, we calculated the performance metrics of the HTCPN model for ship fires using Markov chains, to identify the key nodes of this emergency response process. Several simulation experiments based on a fire accident data of "LONG QING 1 '' are conducted to demonstrate the feasibility of this proposed method. The key nodes of this emergency response process are identified as "emergency resource transportation", "firefighting operations", "surface search and rescue", and "developing emergency disposal plans". Some targeted suggestions for optimizing the emergency response process were put forward.
A real-time identification scheme based on relay autotuning and state-space analysis for the estimation of dynamics of time-delayed plants is proposed in this article. A set of mathematical expressions for parameter e...
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A real-time identification scheme based on relay autotuning and state-space analysis for the estimation of dynamics of time-delayed plants is proposed in this article. A set of mathematical expressions for parameter estimation of the process dynamics in the form of first- and second-order plus time delay transfer functions is derived. A relay with a hysteresis band is utilized for the mitigation of undesired noise content present at the process output in the noisy environment. The proposed method is studied on well-known process dynamics in comparison with the models available in the literature. After that, the relay autotuning tests (with and without hysteresis band) are conducted for the identification of dynamics of a real-time level control unit on a distributed control system platform with industry-standard field instruments. Finally, the experimental results from the relay tests are appended to show the practical applicability of the proposed modeling scheme and verify the dynamics in comparison with the actual data.
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