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carbon efficiency modeling and optimization of solar-powered cellular networks

Science China(Information Sciences) 2024年第5期67卷 228-243页

作者： Yuxi ZHAO Xiaohu GE Wen YAN Tao HAN Yi ZHONG School of Electronic Information and Communications Huazhong University of Science and Technology Huawei Technology Co. Ltd.

As wireless communication traffic experiences rapid growth, the carbon emissions caused by the communication industry are also on the rise. To achieve “carbon neutrality”, researchers are considering the use of renewable energy sources to power cellular networks, thereby reducing carbon emissions. However, a challenge arises when using renewable energy, specifically owing to the unpredictable nature of both the energy consumption of the cellular network and the power generation from renewable sources. This inconsistency results in low renewable energy utilization and reduced carbon efficiency. Herein, we construct a carbon efficiency model of solar-powered cellular networks using practical data from solar radiation. We propose a mechanism that alternately optimizes the performance of the renewable energy network and the cellular network. This approach is based on convex optimization theory and the Dinkelback algorithm, and it leads to the design of a carbon efficiency optimization algorithm. This algorithm aims to improve the carbon efficiency of cellular networks and reduce their carbon emissions. Simulation results demonstrate that our optimization scheme yields a maximum improvement of 2.56 × 108bps/g in the carbon efficiency of the cellular network as compared to conventional power allocation schemes such as the traditional water filling method and heuristic energy sharing and charge/discharge algorithms.

关键词： cellular networks renewable energy carbon efficiency convex optimization theory dinkelbach

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A carbon efficiency upgrading method for mechanical machining based on scheduling optimization strategy

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Frontiers of Mechanical Engineering 2020年第2期15卷 338-350页

作者： Shuo ZHU Hua ZHANG Zhigang JIANG Bernard HON Key Laboratory of Metallurgical Equipment and Control Technology(Ministry of Education) Wuhan University of Science and TechnologyWuhan 430081China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering(Ministry of Education) Wuhan University of Science and TechnologyWuhan 430081China Engineering Research Center for Metallurgical Automation and Detecting Technology(Ministry of Education) Wuhan University of Science and TechnologyWuhan 430081China School of Engineering University of LiverpoolLiverpool L693BXUK

Low-carbon manufacturing(LCM)is increasingly being regarded as a new sustainable manufacturing model of carbon emission reduction in the manufacturing *** this paper,a two-stage low-carbon scheduling optimization method of job shop is presented as part of the efforts to implement LCM,which also aims to reduce the processing cost and improve the efficiency of a mechanical machining *** the first stage,a task assignment optimization model is proposed to optimize carbon emissions without jeopardizing the processing efficiency and the profit of a machining ***-dominated sorting genetic algorithm II and technique for order preference by similarity to an ideal solution are then adopted to assign the most suitable batch task of different parts to each *** the second stage,a processing route optimization model is established to plan the processing sequence of different parts for each ***,niche genetic algorithm is utilized to minimize the makespan.A case study on the fabrication of four typical parts of a machine tool is demonstrated to validate the proposed method.

关键词： Low-carbon manufacturing carbon efficiency multi-objective optimization two-stage scheduling job shop

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Design of an Optimization and Control System for carbon efficiency in the Green Manufacturing of Sinter Ore 36

Design of an Optimization and Control System for Carbon Effi...

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第36届中国控制会议

作者： Sheng Du Min Wu Xin Chen Weihua Cao Jinhua She School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Engineering Tokyo University of Technology

ISBN: (纸本)9781538629185

Sinter ore is the raw material of the iron and steel production.A sintering production process is with high energy consumption and large CO *** is important to achieve the green manufacturing of the sinter *** this paper,an optimization and control system for the carbon efficiency(OCSCE) in the green manufacturing of the sinter ore is designed from the point of view of the maximum utilization of the carbon and the best protection of environment in the sintering *** OCSCE has three parts:the optimization for the carbon efficiency,the coordinated optimization and control for the production phases and the comprehensive performance evaluation for the carbon *** optimization for the carbon efficiency is used to optimize the state *** coordinated optimization and control for the production phases is applied to optimize and control the operation *** comprehensive performance evaluation for the carbon efficiency assesses whether the carbon efficiency of the whole sintering process is consistent with green ***,an implementation scheme of the OCSCE is put forward for the industrial *** results of preliminary tests show that the OCSCE is in line with the needs of the industrial site,and it will have a significant performance after the OCSCE is put into the industrial site.

关键词： carbon efficiency Sintering Process Optimization Evaluation Hierarchical Structure

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An Intelligent Control Scheme for Burn-Through Point to carbon efficiency Optimization in Iron Ore Sintering Process

An Intelligent Control Scheme for Burn-Through Point to Carb...

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第37届中国控制会议

作者： Sheng Du Min Wu Jinhua She School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Engineering Tokyo University of Technology

The sintering process is an important step of preparing raw material for iron and steel smelting. It is a series of complex physico-chemical changes with high energy consumption, high pollution and large CO emissions. How to reduce carbon consumption while ensuring the quality and yield of sinter ore are the problems to be solved urgently. In this paper,An intelligent control scheme for burn-through point(BTP) to carbon efficiency optimization in iron ore sintering process is presented. The comprehensive coke ratio is employed as a measure of carbon efficiency;and the BTP, a measure of the stability of the sintering process. First, a model is established to predict the comprehensive coke ratio using a back propagation neural network, and the carbon efficiency is optimized using the particle swarm optimization algorithm. This yields an optimal strand velocity. Then, the control of the BTP is taken into consideration using an expert-fuzzy control strategy. This yields another strand velocity. Finally, these two kinds of strand velocity are integrated using the fuzzy satisfaction method to produce a control input for the strand velocity. It not only improves the carbon efficiency, but also ensures the stable running of the sintering process. Part of the strategy was tested in an actual plant, and the results show the effectiveness of the scheme.

关键词： Sintering process carbon efficiency Optimization Burn-through point Intelligent control

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Optimization Method for carbon efficiency in the Green Manufacturing of Sinter Ore and Its Application

Optimization Method for Carbon Efficiency in the Green Manuf...

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第37届中国控制会议

作者： Kailong Zhou Xin Chen Weihua Cao School of Automation China University of Geoscience Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

Sintering process is the second most energy-consuming process in steel making and the main energy consumption of the process is the combustion of carbon. Under the background of the transformation of the world’s major economies to the low carbon economy, to improve the carbon efficiency for saving energy and reducing undesired emissions is of great *** this paper, the comprehensive coke ratio（CCR） is taken as the index of the carbon efficiency. Mechanism analysis was carried out to analyze the influences of the CCR and a predictive model of the CCR based on back-propagation neural network（BPNN）is built that contains state parameters predictive model and the CCR predictive model. Then the optimization method for the CCR is formulated, which aims to minimize the CCR by optimizing the operating parameters. Finally, the method was implemented in an intelligent optimization and control system for carbon efficiency（IOCSCE） in an iron and steel plant. The running results show that the method can reduce the CCR by an average of 1.98 kg/t and effectively reduce the energy consumption of the sintering process. Thus, it can provide guidance for the operators of the sintering process and improve the carbon efficiency.

关键词： Sintering process carbon efficiency Back-propagation neural network Optimization method Intelligent optimization and control system

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An Intelligent Optimization Strategy Based on Prediction Model for carbon efficiency in Sintering Process

An Intelligent Optimization Strategy Based on Prediction Mod...

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第37届中国控制会议

作者： Jie Hu Min Wu Jinhua She School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Engineering Tokyo University of Technology

In order to improve the utilization of carbon in a sintering process, it is necessary to predict and optimize carbon efficiency. In the paper, first, comprehensive coke ratio（CCR） is taken to be a metric of the carbon efficiency. By analyzing the sintering mechanism, sintering parameters affecting the CCR are determined. Next, the fuzzy C-means clustering algorithm is used to identify different operating conditions. Then, least-squares support vector machine（LS-SVM） sub-models are established for the different operating conditions, and a CCR prediction model is established by incorporating the sub-models using the TS fuzzy intelligent fusion method. Finally, based on the CCR prediction model, a differential evolution algorithm is used to optimize the CCR by adjusting the operating parameters. Simulations using actual run data show that the prediction accuracy of the CCR prediction model is higher than that of a back-propagation neural network model and a single LS-SVM model, and the carbon efficiency optimization strategy reduced the CCR by 1.97 kg/t on average. Thus, the method provides us a guidance for an actual sintering process.

关键词： Sintering process carbon efficiency Fuzzy C-means clustering Differential evolution algorithm Least square-support vector machine model

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Hierarchical Prediction Model Based on BP Neural Network for Predicting CO/CO2 in Iron Ore Sintering Process

Hierarchical Prediction Model Based on BP Neural Network for...

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第37届中国控制会议

作者： Ben Xu Xin Chen Yang Zhou School of Automation China University of Geosciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

The sintering process is one of the most energy-consuming processes in steelmaking, its carbon fuel consumption accounts for 8% to 10% in the steel production *** find ways of reducing the energy consumption, it is necessary to predict the carbon efficiency. The value of CO/CO in the carbon emission can reflect the utilization of carbon combustion in sintering process. In this study, the CO/CO is taken to be a measure of carbon efficiency and a hierarchical model is built to predict it. Firstly, the physical and chemical reactions and the carbon flow mechanism in the sintering process are analyzed, and the process parameters that affect the CO/CO are determined. Then, the gray relational analysis method is used to analyze the influence factors to determine the relationship between the parameters, and a hierarchical predictive model for CO/CO is established based on the relationship between the parameters. The hierarchical predictive model is divided into two parts: the predictive models for the thermal state parameters and the predictive model for CO/CO. The inputs of the predictive models for the thermal state parameters are the raw material parameters and the operating parameters, and the inputs of the predictive model for CO/CO are the predicted values of the predictive models for the thermal state parameters. Finally, the simulation results verify the effectiveness of the proposed modeling method. This method can provide a theoretical basis for the optimization and control of carbon efficiency in the sintering process.

关键词： carbon efficiency sintering process back-propagation neural network(BPNN)

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