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检索条件"任意字段=2023 6th International Conference on Advanced Algorithms and Control Engineering, ICAACE 2023"

共 292 条记录，以下是281-290 订阅

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Optimal Portfolio of Wind-solar-hydro Hybrid Renewable Generation based on Variable Speed Pumped Storage

Optimal Portfolio of Wind-solar-hydro Hybrid Renewable Gener...

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China international Electrical and Energy conference (CIEEC)

作者： Shizhe Xiang Da Xu Menglu Gao School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China DC Company of State Grid Hubei Electric Power Co. Ltd. Yichang China

Compared with separate solar or wind generation, the hybrid wind-solar-hydro renewable portfolio can achieve better generation characteristics due to their temporal-spatial complementarities. this paper proposes optimal portfolio of wind-solar-hydro hybrid renewable generation based on variable speed pumped storage (VSPS). In this portfolio, the complementarities of hydro-solar-wind hybrid renewable energy sources (RESs) are fully exploited via various conversion and storage devices for zero carbon 100% renewable supplies. Compared to traditional fixed speed pumped storage (FSPS), the VSPS has obvious advantages in power regulation and renewable accommodation, which is comprehensively modeled to capture its dynamic operational dispatchability and flexibility. the portfolio production, conversion, storage, and consumption are formulated via a two-stage framework, which takes the minimum system investment cost as the objective function. Case studies on southwest China are performed to show the effectiveness and superiority of the proposed methodology on cost-effective accommodation.

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Effective throughput Optimization of SAG Milling Process Based on BPNN and Genetic Algorithm

Effective Throughput Optimization of SAG Milling Process Bas...

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Industrial Cyber-Physical Systems (ICPS)

作者： Zhenhong Liao Ce Xu Wen Chen Qifu Chen Feng Wang Jinhua She School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Department of Mineral Resources Development and Utilization Changsha Research Institute of Mining and Metallurgy CO. LTD. Chagnsha China Hunan Valin Lianyuan Iron And Steel Co. Ltd. Loudi China School of Engineering Tokyo University of Technology Hachioji Tokyo Japan

Grinding is an energy-consuming process in mineral processing industry. Improving grinding processing capacity per unit power consumption is an effective means to reduce grinding production cost. In this paper, a new index for evaluating the effective processing throughput of SAG milling is proposed. the production process model is established by BP neural network (BPNN). through combining the process mechanism and production constraints, the genetic algorithm is adopted to optimize the operating parameters of the SAG milling process to maximize the effective throughput, thus improving the grinding efficiency. the experimental results showed that through optimization of effective throughput function proposed in this paper, the SAG mill processing capacity has been increased by 4% and the operating power drawn reduced by 1.12%. It has important guiding significance for the actual production process.

关键词： Industries Power demand Neural networks Milling throughput Minerals Materials processing

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Prediction and scheduling for blast furnace gas generation based on time series feature extraction

Prediction and scheduling for blast furnace gas generation b...

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Industrial Cyber-Physical Systems (ICPS)

作者： Huihang Li Jie Hu Qingfeng Yang Luefeng Chen Min Wu School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

Due to the significant time lag and under-regulation, predicting the blast furnace gas generation and formulating its scheduling strategy is complex. this paper proposes a blast furnace gas generation prediction method based on time series feature extraction and designs a blast furnace gas scheduling strategy based on the prediction results. Firstly, Pearson correlation analysis is used to identify the parameters that have a significant correlation with the blast furnace gas generation, and the selected parameters are decomposed into several intrinsic mode components with different frequency characteristics using the complete ensemble empirical mode decomposition; then, the principal component analysis method is used to extract the principal components of several intrinsic modal components, and these principal components are employed as the inputs of long short-term memory neural network to predict the blast furnace gas generation; Finally, according to the prediction results designs the scheduling strategy of blast furnace gas. the experiment and contrast experiments are carried out with the industrial field data, and experimental results illustrate that the proposed method is correct and effective.

关键词： Job shop scheduling Correlation Time series analysis Neural networks Predictive models Blast furnaces Feature extraction

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An Infrared Temperature Measurement System for Power Equipment based on Simple Multi-point Spatial Calibration Method

An Infrared Temperature Measurement System for Power Equipme...

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Electronics and Electrical engineering Technology (EEET), international conference on

作者： Zhongma Wang Wenwu Lu Rui Zuo Chaoyi Shi Tao Xu Xingxing Shao Xianhe Gao Sheng Zhang Chuangye Yin Xiaojiao Chen School of Advanced Engineering and Manufacturing Hefei University Hefei China Power and Control Engineering Division Chinese Academy of Sciences Institute of Plasma Hefei China

ISBN: (数字)9798350395594

ISBN: (纸本)9798350395600

In order to meet the monitoring requirements of different over-heat spots of power equipment in narrow and complex spaces, an Infrared Temperature Measurement System (ITMS) based on multi-point spatial calibration is proposed in this paper. the system is consisted of a three-axis motion platform, a pan-tilt and an infrared thermometer. the temperature acquisition at any position is realized through a five-degree-of-freedom motion controller, and also the real-time online cycle monitoring of multiple over-heat spots is realized. Several temperature measurement experiments were finished of single and multiple heat source. the accuracy and repeatability of the temperature measurement system are proved by these experiments, and the function of multi-degree-of-freedom cyclic temperature measurement is realized. this technological innovation is expected to improve the reliability and safety of power equipment.

关键词： Temperature measurement Technological innovation Accuracy Measurement uncertainty Extraterrestrial measurements Real-time systems Calibration

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Semantic Interoperability of Digital Twins: Ontology-based Capability Checking in AAS Modeling Framework

Semantic Interoperability of Digital Twins: Ontology-based C...

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Industrial Cyber-Physical Systems (ICPS)

作者： Yining Huang Saadia Dhouib Luis Palacios Medinacelli Jacques Malenfant Université Paris-Saclay CEA List Palaiseau France Laboratoire d'Informatique de Paris 6 (LIP6) CNRS Sorbonne Université Paris France

Industry 4.0 currently prepares a major shift towards extreme flexibility into production lines management. Digital Twins are one of the key enabling technologies for Industry 4.0. However, the interoperability gap among digital representation of Industry 4.0 assets is still one of the obstacles to the development and adoption of digital twins. If the Asset Administration Shell (AAS), the standard proposed to represent the I4.0 components, caters for syntactic interoperability, a more semantic kind of interoperability is deeply needed to develop flexible and adaptable production lines. In our work, we overcome the limitation of current syntactic-only resource matching algorithms by implementing semantic interoperability based on ontologies i.e., by transforming AAS-based plant models into MaRCO (Manufacturing Resource Capability Ontology) instances and then query the expanded ontology to find the needed resources. this article presents this ontology-based approach as the first step towards the design and implementation of an automated I4.0 flexible plant supervision and control system based on model-driven engineering (MDE) within the “Papyrus for Manufacturing” toolset. We show how an MDE approach can aggregate around digital twin modeling tools from the Papyrus platform both I4.0 technologies and AI (Knowledge Representation and Reasoning) tools. Our platform aligns modeling and ontological elements to get the best of both worlds. this method has two main advantages: (1) to provide semantic descriptions for digital twin models, (2) to complement model-driven engineering tools with automated reasoning. this paper showcases this approach through a robotic cell use case.

关键词： Adaptation models Unified modeling language Semantics Production Ontologies Cognition Digital twins

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Improved Power Quality of Distribution Grid by integrating DVR with Super Twisting Sliding Mode control

Improved Power Quality of Distribution Grid by integrating D...

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international conference on Energy Conservation and Efficiency (ICECE)

作者： Ijaz Ahmad Amjad Ullah Khattak Mian Farhan Ullah Electrical Engineering Department UET Peshawar Pakistan Elementary & Secondary Education Department Khyber Pakhtunkhwa Pakistan

the increase of sustainable energy sources and integration with the power distribution grid produced power quality (PQ) problems such as voltage sag and voltage swell. these problems are produced in the power distribution systems due to multiple reasons that affect sensitive loads. Voltage sag and voltage swell mostly occurring Power quality complications in the power system. these power quality issues in the power distribution system can be mitigated by using custom power devices, Dynamic Voltage Restorer (DVR) is the newly introduced device. Different techniques and algorithms have been used to mitigate these problems. In this research work, an ultra-capacitor-based DVR and a super-twisting sliding mode controller are used for the said problem. the addition of energy storage, such as an ultra-capacitor, to a dynamic voltage restorer, creates a voltage that can alleviate voltage issues such as sag and swell. For both the voltage sags and voltage swells, a high voltage is necessary for a short spell. A dynamic voltage restorer is an apparatus that is linked between the voltage source and the load side using an injection transformer to input voltage into the power distribution side to remedy a problem that has a significant impact on the load voltage. With the inclusion of a dynamic voltage restorer and sliding mode controller, the system will be able to fix PQ issues such as voltage sag and swell on its own, rather than relying on the grid as in past years. MATLAB/Simulink R2018a has been used to conduct simulation studies. Simulation studies have demonstrated that the proposed scheme is more effective, simple, robust, and more flexible and has a better mitigation time of 2.5 milliseconds and a total harmonic distortion of less than 5% as per IEEE standards.

关键词： Total harmonic distortion Voltage fluctuations Power quality Power system dynamics Power distribution Transformers Time factors

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Deep Learning for Traffic Data Mining

Deep Learning for Traffic Data Mining

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international conference on Electrical Information and Communication Technology (EICT)

作者： Abdullah Al Ahad Khan Siraj Us Salekin Mushfika Sharmin Rahman Dewan Md. Farid Department of Computer Science and Engineering United International University Dhaka Bangladesh Department of Computer Science Baylor University Waco Texas

Traditionally, traffic management relies on several methods, including but not limited to signage, traffic signaling, and manual control. Traffic engineers analyze traffic patterns collected through various sensors and use their expertise to manage traffic flow. While traditional traffic management methods have been adequate to some extent, they often need to be improved in handling traffic’s complexity and dynamic nature in modern urban environments. the usage of advanced technologies in autonomous traffic volume is adapted to address the limitation. Traffic data are complex, and mathematical models relying upon simplified assumptions often must be revised to represent and analyze the complexity. With the revival of neural networks and large-scale datasets, deep learning-based solutions can be adapted to handle real-world traffic scenarios. Since neural networks can capture complex, non-linear relationships in traffic data, they can be utilized for intricate traffic patterns and dependencies that may be challenging for simple mathematical or regression models. We present a Deep Learning approach for accurately predicting traffic volume in Porto, Portugal. the proposed model leverages an extensive dataset encompassing historical traffic volume data from diverse sensors and monitoring systems. By harnessing the power of Recurrent Neural Networks (RNNs), the proposed model effectively captures temporal dependencies and generates precise forecasts for future traffic volume. the proposed approach surpasses previous methodologies in the same dataset, outperforming traditional time series models in different evaluation metrics. these findings demonstrate the practical potential of deep learning-based models in complex urban environments and offer a way to utilize sensor data. the proposed approach can adapt to changing traffic conditions and evolving patterns with infrequent training, making them suitable for dynamic environments.

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Design of an Automated Flotation Cell for Laboratory Operations Using Model-Based System engineering

Design of an Automated Flotation Cell for Laboratory Operati...

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Eurocon

作者： Amine Ennawaoui Younes Chhiti Mohammed Bensitel Ilyass Mousaid Chouaib Ennawaoui Ahmed Chebak Benguerir Laboratoire des Matériaux Avancés et de Génie des Precédés Green Tech Institute UM6P École Nationale Superieure de Chimie Ibn Tofail University Kenitra Morocco Chemistry Department Laboratory of Catalysis and Corrosion of Materials (LCCM) Chouaib Doukkali UniversÏty El Jadida Morocco Technology Transfer Office (TTO) Mohammed VI Polytechnic University Benguerir Morocco Laboratory of Engineering Sciences for Energy (LabSIPE) National School of Applied Sciences Chouaib Doukkali University El jadida Morocco Green Tech Institute Mohammed VI Polytechnic University Benguerir Morocco

Automation in flotation cells has become an increasingly popular topic in the mineral processing industry due to its ability to increase efficiency and reduce operational costs. this paper discusses the design and implementation of an automated flotation cell system for a mineral processing plant. the system utilizes advanced control algorithms and sensors to optimize the flotation process, resulting in improved product quality and reduced energy consumption. this manuscript outlines the first stage in the creation and development of an automated flotation cell for use in educational and research settings, using model-based systems engineering (MBSE) and systems modeling language (SysML) via the CESAM modeling and architecting framework. Our case study employs this method to construct and demonstrate an operational perspective layout of the flotation cell's initial design phase. Moreover, the potential for the automation of flotation cells is highlighted as a promising avenue for improving sustainability and revolutionizing the mineral processing industry.

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Environmentally Friendly Optimization of Coupled Power-Transportation Network considering Mixed Traffic Flow

Environmentally Friendly Optimization of Coupled Power-Trans...

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Electronics and Electrical engineering Technology (EEET), international conference on

作者： Zhiao Cao Yinghua Han Qiang Zhao Shuyi Zhao Wenzhuo Zhao School of Control Engineering Northeastern University at Qinhuangdao Qinhuangdao China School of Computer and Communication Engineering Northeastern University at Qinhuangdao Qinhuangdao China School of Accounting and Finance Taylor’s University Lakeside Campus Subang Jaya Malaysia Sydney Smart Technology College Northeastern University Qinhuangdao China

ISBN: (数字)9798350395594

ISBN: (纸本)9798350395600

the explosive growth of electric vehicles makes the deep coupling between the power distribution network and the urban transportation network, in which the mixed flow consisting of electric vehicles and conventional gasoline vehicles exposes the coupled network to operational problems of congestion and carbon emission overruns. Considering this, this paper constructs an environmentally friendly oriented optimization model for coupled power and transportation networks. Specifically, the mixed flow in a coupled network is considered, the charging price is optimized to guide the charging behavior of electric vehicle users, and the carbon emission cost is adopted to schedule the routes of gasoline vehicles. To solve the optimization model of the coupled network, an accelerated Gauss-Seidel algorithm is designed to obtain the equilibrium state of the entire system, the optimization problems of both networks are solved in an iterative manner and eventually converge to the equilibrium state. A coupled power and transportation network consisting of a modified IEEE-33 bus distribution network and a 20 link transportation network is conducted as a case study, and the impact of charging prices and carbon emissions on the operational strategies of coupled networks is analyzed. the effectiveness of the proposed environmentally friendly oriented optimization model in promoting the cost reduction and flow equilibrium of the coupled network is verified.

关键词： Costs Uncertainty Optimization models Heuristic algorithms Transportation Carbon dioxide Electric vehicles

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Blast Furnace Gas Utilization Rate Prediction Model based on Northern Goshawk Optimization and Long Short-Term Memory in Massive Data Set

Blast Furnace Gas Utilization Rate Prediction Model based on...

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Industrial Cyber-Physical Systems (ICPS)

作者： Yue Zhou Weihua Cao Zhuofu Zhang Yan Yuan School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

Affected by the global New Crown Pneumonia epidemic, energy prices in the global market continue to rise. the high quality production and energy saving of steel companies have become an important task. the blast furnace is the front-end core of the steel manufacturing process. the blast furnace gas utilization rate can effectively characterize the internal airflow distribution, blast furnace operation and energy consumption level of the blast furnace. the blast furnace Gas Utilization Rate (GUR) can effectively characterize the internal airflow distribution, blast furnace operation and energy consumption level. Most of the existing studies are based on data-driven models. the shallow neural network model is selected. But blast furnace iron making has complex uncertainty. Massive data samples under industrial information technology need to be processed. the robustness and generalization ability of the prediction model are not satisfactory. To address the above problems, this paper proposes a prediction model based on NGO-LSTM regression. the model parameter searchs can be intelligently. It achieves high-precision prediction results for massive data samples. Firstly, the selection of feature parameters is completed by maximal information coefficient. Secondly, the strong coupling of blast furnace ground needs to fully predict the relationship between the characteristic parameters. the Long Short-Term Memory (LSTM) neural network with certain memory capability is selected. And multiple parameters of this neural network model are optimized by the Northern Goshawk Optimization (NGO) algorithm. An NGO-LSTM regression prediction model is established. In this paper, experiments are carried out using actual production data. the experimental results show that the proposed method can accurately predict the blast furnace gas utilization rate. this can provide a reference for improving blast furnace product quality, reducing costs and increasing efficiency.

关键词： Energy consumption Neural networks Production Predictive models Blast furnaces Prediction algorithms Robustness

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