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performance enhancement of cyclone deoiling for oily sludge based on BP neural network model and genetic algorithm

POWDER TECHNOLOGY 2025年 460卷

作者： Zhang, Shifan Wu, Jiwei Gong, Bin Yu, Shiqi Qiao, Zelin Liu, Qianyu Su, Zhihao Sun, Jianqiang Wang, Xuehai Sun, Haocheng China Univ Geosci Sch Future Technol Wuhan 430074 Hubei Peoples R China Sichuan Univ Coll Carbon Neutral Future Technol Chengdu 610065 Sichuan Peoples R China Northeast Petr Univ Sch Mech Sci & Engn Daqing 163318 Heilongjiang Peoples R China Southwest Petr Univ Sch Mech & Elect Engn Chengdu 610500 Sichuan Peoples R China Sichuan Univ Coll Architecture & Environm Chengdu 610065 Sichuan Peoples R China SINOPEC Dalian Res Inst Petr & Petrochemicals Co L Dalian 116045 Liaoning Peoples R China

performance enhancement in cyclone deoiling systems plays a critical role in improving oily sludge treatment efficiency and cutting energy consumption. This paper delved into the mechanism of cyclone deoiling, constructed Back Propagation (BP) neural networks to analyze the predictive performance of average shear rate and pressure drop, and achieved collaborative optimization of the cyclone structure by using the Nondominated Sorting Genetic Algorithm ii (NSGA-ii), aiming to balance deoiling efficiency and energy consumption. Through a combination of Computational Fluid Dynamics (CFD) simulations and experimental validation, the study systematically revealed the changes in particle dynamics and properties of oil-based mud (OBM) cuttings before and after optimization. The results demonstrated that the BP model outperformed the response surface model, Support Vector Machine (SVM), and Random Forest (RF) methods in predicting the average shear rate and pressure drop. The optimal cyclone structure corresponded to an average shear rate of 3111.23 s-1 (an increase of 24.62 %) and a pressure drop of 992.54 Pa (an increase of 5.64 %), with prediction errors reduced to 0.80 % and 0.56 %, respectively. CFD simulations showed that the radial coupling centrifugal separation factor increased to 3.49 times that before optimization, and the pressure drop increased by 5.05 %. In the experiment, at an inlet velocity of 19 m/s, the oil content dropped to 0.49 %, the deoiling efficiency increased to 95.07 %, and the pressure drop increased by only 66.67 Pa, which was highly consistent with the predicted and simulated results. This study's intelligent optimization method provides an efficient, low-energy solution for oily sludge treatment, supporting sustainable oilfield development.

关键词： Oily sludge BP neural network Response surface Genetic algorithm Multi-objective optimization Artificial intelligence

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A Robust Sliding Mode control for Electro-optical Stabilized Servo Tracking System Based on Extended State Observer and Adaptive Neural network 18

A Robust Sliding Mode Control for Electro-optical Stabilized...

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18th International conference on control Automation Robotics and Vision

作者： Feng, Bin Fan, Weihua Chen, Qingwei Nanjing Univ Sci & Technol Automat Sch Nanjing 210094 Jiangsu Peoples R China

ISBN: (纸本)9798331518509;9798331518493

The tracking performance and line-of-sight stabilization of electro-optical stabilized servo tracking systems are often affected by various disturbances and uncertainties. To address the problems, a robust motion control scheme is developed. The control strategy effectively amalgamates sliding mode control (SMC), extended state observer (ESO), and adaptive radial basis function (RBF) neural network. Specifically, an ESO is constructed to estimate unknown disturbances and uncertainties in the system. An RBF neural network is employed to correct the estimation accuracy of ESO for time-varying disturbances. Furthermore, the sliding model control strategy is combined with ESO and RBF neural network to mitigate the impact of multi-source disturbances and uncertainties on control precision. The asymptotic stability of the system is demonstrated through Lyapunov theory and LaSalle invariance principle. Finally, simulations are implemented to illustrate the superiority and effectiveness of the proposed control method compared to the traditional SMC-ESO method.

关键词： State estimation

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An Architecture for Industrial control Mimic systems Based on Component and Multi-level 2-Mode Judgment Algorithm 10

An Architecture for Industrial Control Mimic Systems Based o...

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10th IEEE International conference on High performance and Smart Computing (IEEE HPSC)

作者： Sun, Changhua Chen, Gang Sun, Xin Li, Qinyuan Lv, Pang Dai, Hua State Grid Zhejiang Elect Power Co Ltd Elect Power Res Inst Hangzhou Peoples R China State Grid Corp China Beijing Peoples R China

ISBN: (纸本)9798350389463;9798350389470

As industrial control systems become more prevalent, cybersecurity concerns have gained significant attention. Mimic defense technology, serving as an effective security measure, combats network threats by continuously switching between functionally identical entities. However, current mimic control systems still have various shortcomings in executor design and judgment techniques, such as fixed heterogeneous executors and inefficient arbitration processes. In response to these challenges, this study introduces a component-based architecture for mimic control systems, enabling dynamic executor generation through modularized communication while minimizing system invasiveness. Additionally, a multi-level 2-mode judgment algorithm is designed, consider both real-time performance and security requirements, dynamically balancing decision efficiency and result reliability. Simulation results demonstrate that the proposed approach not only enhances the decision efficiency of mimic industrial control systems but also improves the judgment efficiency of mimic systems.

关键词： Mimic Defense Mimic control System Component Construction 2-Mode Judgment Algorithm

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Comparison of neural network architectures for feedforward active control of nonlinear systems 31

Comparison of neural network architectures for feedforward a...

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31st International conference on Noise and Vibration Engineering, ISMA 2024 and 10th International conference on Uncertainty in Structural Dynamics, USD 2024

作者： Pike, X. Cheer, J. ISVR University of Southampton University Road Highfield SouthamptonSO17 1BJ United Kingdom

ISBN: (纸本)9789082893175

In recent decades, advances in digital technologies have allowed for the development of increasingly complex active control solutions for both noise and vibration, which have been utilised in a wide range of applications from automotive to maritime. Such control systems commonly use linear digital filters that cannot fully capture the dynamics of the nonlinear systems under control. To overcome such issues, it has previously been demonstrated that replacing the conventional linear controllers with Neural networks (NNs) can improve control performance in the presence of nonlinearities in both the system plant and primary path. However, a key design decision in the implementation of such controllers is the choice of an appropriate NN architecture for the specific control application. In this paper, a method of training NN controllers for Single-Input-Single-Output (SISO) acoustic noise control is presented. Multilayer Perceptron (MLP), Elman Recurrent Neural network (RNN) and Long Short-Term Memory (LSTM) architectures are implemented as controllers in the presence of nonlinearities in the primary path. An analysis is presented of the relative control performance of the different architectures, and the computational cost in operation and network training is discussed. © 2024 Proceedings of ISMA 2024 - International conference on Noise and Vibration Engineering and USD 2024 - International conference on Uncertainty in Structural Dynamics. All rights reserved.

关键词： Long short-term memory

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control Plane Scalability of Software Defined networking

Control Plane Scalability of Software Defined Networking

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2024 ASU International conference in Emerging Technologies for Sustainability and Intelligent systems, ICETSIS 2024

作者： Sati, Salem Omar Sati, Mohamed Emshiheet, Moad Misurata University Information Technology Faculty Libya

ISBN: (纸本)9798350372229

Software Defined networks (SDN) present a novel network architecture designed to facilitate scalability and management. The SDN network comprises a control plane core responsible for governing and monitoring all SDN devices, which operate using the OpenFlow signaling protocol. However, the scalability of SDN networks poses a challenge. This research paper investigates the impact of network topology and size on the performance of the SDN control plane. The study employs performance metrics to compare three Java-based SDN controllers: Open network Operating System (ONOS), OpenDaylight (ODL), and Floodlight controllers. The paper assesses metrics such as topology discovery time and control plane message overhead. The comparison involves fat-tree and spine and leaf topologies. All simulations are conducted within a centralized Out-of-band SDN environment. The results of the study demonstrate that ONOS controllers outperform ODL and Floodlight controllers in terms of discovery time, while the ODL controller exhibits the lowest overhead. © 2024 IEEE.

关键词： controller Scalability Flood-light controller ODL controller ONOS controller performance Metrics Software Defined network

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Self-Tuning PID controller for an Arm-Angle Adjustable Quadrotor Using an Actor-Critic-Based Neural network 19

Self-Tuning PID Controller for an Arm-Angle Adjustable Quadr...

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19th Iranian conference on Intelligent systems, ICIS 2024

作者： Rezaei, Arshia Talaeizadeh, Amin Alasty, Aria Department of Mechanical Engineering Tehran Iran

ISBN: (纸本)9798331507565

The increasing prevalence of multirotors necessitates the development of new configurations for specific applications. Innovative morphing of multirotors has enhanced their stability, maneuverability, agility, and overall performance. The utilization of Artificial Intelligence (AI) in robotics has enabled robots to exhibit autonomous behaviors in areas such as control, path planning, state estimation, system identification, and image processing. This paper introduces a novel approach to improving the control system of an adjustable arm-Angle quadrotor through the integration of a self-Tuning Proportional-Integral-Derivative (PID) controller, utilizing an Actor-Critic-based Neural network (NN). Arm-Angle adjustable quadrotors, with their complex and variable dynamics, present challenges in achieving optimal control performance across different arm angles. Traditional PID controllers often struggle to adapt efficiently to these dynamics. To address this issue, the Actor-Critic framework from reinforcement learning is employed to develop a self-Tuning mechanism for the PID controller. The Actor network learns the optimal control actions, while the Critic network evaluates the performance of the control system, enabling continuous improvement. Extensive Software-in-The-Loop (SIL) and Hardware-in-The-Loop (HIL) tests demonstrate the effectiveness and feasibility of the proposed approach in achieving robust and adaptive control for tilt-Arm quadrotors under diverse operating conditions. The results highlight the superiority of the Actor-Critic-based self-Tuning PID controller over conventional methods, showcasing its potential for real-world devlopment in autonomous aerial systems. © 2024 IEEE.

关键词： Three term control systems

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Scalable Real-Time control in Industrial Cyber-Physical systems 24

Scalable Real-Time Control in Industrial Cyber-Physical Syst...

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25th International conference on Distributed Computing and networking, ICDCN 2024

作者： Haque, Md Ashikul Ismail, Dali Saifullah, Abusayeed Wayne State University DetroitMI United States Binghamton University BinghamtonNY United States

ISBN: (纸本)9798400716737

Real-time communication and control performance are the precursor of industrial cyber-physical systems that employ Wireless networked control System (WNCS) in critical industrial applications including process control and smart manufacturing. control performance and real-time communication are interdependent in a WNCS. Hence, optimizing control performance under limited resource of network requires a cyber-physical codesign approach. A codesign approach needs to be online to take into account both the current network condition and the current control behavior. Leading industrial wireless standards such as WirelessHART and ISA100 adopt software-defined networking as a centralized routing mechanism. Hence, in current approach, transmission schedules of the entire network are created centrally at a network manager in advance and are then disseminated to the nodes. control performance optimization usually requires to update sampling rates and/or priorities of the control loops, thereby requiring to re-create the schedules. Thus, it becomes highly inefficient under the current fully centralized scheduling approach. In this paper, we propose to optimize control performance in an industrial WNCS through a scheduling-control codesign based on a local and online scheduling approach proposed in a recent work. We formulate the scheduling-control codesign problem to optimize the control performance based on model predictive control theory. Unlike existing offline solution, our codesign complies with online scheduling and entails a rolling optimization to take into account the current control performance to dynamically update the rates and priorities of the control loops. We propose a highly scalable solution of the codesign problem based on a local search approach used as an anytime algorithm. © 2024 ACM.

关键词： Cyber Physical System

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Using Quantum Fuzzy Inference Engines in Smart Cities

Using Quantum Fuzzy Inference Engines in Smart Cities

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IEEE International conference on Fuzzy systems (FUZZ-IEEE)

作者： Acampora, Giovanni Schiattarella, Roberto Vitiello, Autilia Univ Naples Federico II Dept Phys Ettore Pancini Naples Italy

ISBN: (纸本)9798350319552;9798350319545

This paper addresses the escalating complexity of smart city environments by proposing the use of Quantum Fuzzy Inference Engine (QFIE) for enhanced control. Smart cities play a pivotal role in optimizing resource utilization and improving overall urban living. However, their intricate and interconnected nature demands advanced control algorithms. QFIE emerges as a promising solution due to its computational power and capability to handle uncertainty. In this research, the suitability of QFIE for the control of smart city environments is assessed for the very first time by the design and test of three different QFIE-based fuzzy rule base systems aiming to solve the problem of computing the average localization error in wireless sensor networks, the prediction of heating demands in buildings, and the control of traffic lights in a junction. In these scenarios, the experimental evaluation of QFIE shows improved control capability compared with classical algorithms.

关键词： Smart City Quantum Computing Fuzzy systems

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GPU performance Optimization via Intergroup Cache Cooperation

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND systems 2024年第11期43卷 4142-4153页

作者： Wang, Guosheng Du, Yajuan Huang, Weiming Wuhan Univ Technol Sch Comp Sci & Technol Wuhan 430070 Peoples R China Wuhan Univ Technol Shenzhen Res Inst Shenzhen 518000 Peoples R China

Modern GPUs have integrated multilevel cache hierarchy to provide high bandwidth and mitigate the memory wall problem. However, the benefit of on-chip cache is far from achieving optimal performance. In this article, we investigate existing cache architecture and find that the cache utilization is imbalanced and there exists serious data duplication among L1 cache *** order to exploit the duplicate data, we propose an intergroup cache cooperation (ICC) method to establish the cooperation across L1 cache groups. According the cooperation scope, we design two schemes of the adjacent cache cooperation (ICC-AGC) and the multiple cache cooperation (ICC-MGC). In ICC-AGC, we design an adjacent cooperative directory table to realize the perception of duplicate data and integrate a lightweight network for communication. In ICC-MGC, a ring bi-directional network is designed to realize the connection among multiple groups. And we present a two-way sending mechanism and a dynamic sending mechanism to balance the overhead and efficiency involved in request probing and *** results show that the proposed two ICC methods can reduce the average traffic to L2 cache by 10% and 20%, respectively, and improve overall GPU performance by 19% and 49% on average, respectively, compared with the existing work.

关键词： Integrated circuits Design automation Graphics processing units Computer architecture Bidirectional control Bandwidth Benchmark testing System-on-chip Optimization Cache cooperation GPU hit ratio

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Classification of Motor Imagery EEG Signals Based on Channel Attention Mechanism 12

Classification of Motor Imagery EEG Signals Based on Channel...

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IEEE 12th Data Driven control and Learning systems conference (DDCLS)

作者： Yu, Yue Ji, Wenkai Zhao, Liming Sun, Zhongbo Liu, Keping Changchun Univ Technol Dept Control Engn Changchun 130012 Peoples R China Jilin Engn Normal Univ Sch Elect & Informat Engn Changchun 130012 Peoples R China

ISBN: (纸本)9798350321050

Brain-computer interface (BCI) technology establishes communication between the brain and external devices by decoding EEG signals. BCI technology based on motor imagery (MI) has great application potential. There are many different methods to extract motor intention from electroencephalogram (EEG) based on motor imagery (MI).These methods rely on extracting the unique features of EEG in the process of imaginary movement, which directly affect the performance of neural decoding algorithm of BCI. Convolutional neural network (CNN) shows outstanding advantages in automatic extraction of image features. In this paper, an image representation method based on the EEG is proposed as the input of the network. Then, a CNN and a CNN based on Channel Attention Mechanism (CAM) are built as the classifier, convolution layers and activation functions of different sizes are validated. The performance of the method is evaluated. A CNN framework based on CAM, which contained three convolution layers (3-L) is better than the other state-of-the-art approaches. The accuracy on dataset IV from BCI competition ii reaches 72.6%.

关键词： Brain-computer interface (BCI) Motor Imagery (MI) Channel Attention Mechanism (CAM) Convolutional Neural network (CNN)

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