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Real-Time Optimization of Vertical Roller Mills Using XGBoost Prediction and Q-Learning Control 认领 引用
1
作者 Anping Wan Yingchang Gao +2 位作者 Weikang Liu Rui Yin Khalil Al-Bukhaiti 《Computers, Materials & Continua》 SCIE EI 2026年第8期1515-1534,共20页
Vertical roller mills are essential for energy-intensive grinding in cement,minerals,and metallurgy industries,consuming up to 50%of plant electricity and frequently experiencing operational instabilities(including ex... Vertical roller mills are essential for energy-intensive grinding in cement,minerals,and metallurgy industries,consuming up to 50%of plant electricity and frequently experiencing operational instabilities(including excessive vibration and main motor current fluctuations)that drive unplanned downtime,increased wear,and reduced throughput.Despite their importance,real-time autonomous optimization remains challenging due to the nonlinear interactions among grinding pressure,feed rate,separator speed,and aerodynamic factors,which limit traditional control strategies under varying loads.This paper presents a real-time operational optimization system for large-scale vertical roller mills using big industrial data and artificial intelligence(AI).From a 5400 kW Loesche LM56.4 mill,2,764,800 samples were collected at 1 Hz over 32 days of continuous production.A systematic pipeline was developed:quartile-based outlier-robust cleaning;domain-informed feature engineering including Total Current;Random Forest(RF)permutation importance selection of the top 15 parameters;and Extreme Gradient Boosting(XGBoost)regression models with hyperparameters tuned by Tree-structured Parzen Estimator(TPE)Bayesian optimization.The resulting models achieved strong predictive performance,Mean Absolute Percentage Error(MAPE)of 1.3%(95%CI:1.1%–1.5%)for main motor current(R2=0.9997)and 5.8%(95%CI:5.3%–6.3%)for shell vibration(R2=0.9717),representing reductions of 89%and 59%,respectively,relative to the Long Short-Term Memory(LSTM)baseline.These surrogates were embedded into a tabular Q-learning Reinforcement Learning(RL)agent that autonomously adjusts feed rate,grinding pressure,separator speed,and exhaust damper position via a discrete action space and multi-objective reward function,communicating with the Distributed Control System(DCS)via Open Platform Communications Unified Architecture(OPC-UA).Closed-loop evaluation yielded simultaneous reductions of 6.0%in peak current(181.92→170.04 A)and 9.4%in peak vibration(5.51→4.99 mm/s)while maintaining throughput.A PyQt5-based graphical interface enabling real-time monitoring,predictive alerts,and automatic DCS write-back was deployed and operated stably for two weeks. 展开更多
关键词 Vertical roller mill operational optimization XGBoost Bayesian hyperparameter optimization Q-learning energy efficiency vibration reduction real-time control Industry 4.0
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Optimization Study on Operating Parameters of Rotary Gas-Gas Heat Exchanger in Low-and Medium-Temperature Denitrification System for Cement Kiln Flue Gas 认领 引用
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作者 Kaiwen Cheng Yi Sun +2 位作者 Dong Wang Chen Zhu Fuping Qian 《Frontiers in Heat and Mass Transfer》 EI CAS 2026年第3期141-157,共17页
Rotary gas-gas heat exchangers(GGHs)are pivotal for waste heat recovery in low-and mediumtemperature denitrification systems of cement kilns.This study examines the performance of GGHs within such systems by coupling ... Rotary gas-gas heat exchangers(GGHs)are pivotal for waste heat recovery in low-and mediumtemperature denitrification systems of cement kilns.This study examines the performance of GGHs within such systems by coupling computational fluid dynamics(CFD)with the response surface method(RSM),introducing overall system performance(OSP)as the principal optimization criterion.The investigation systematically elucidates the effects of treated flue gas inlet temperature,inlet velocity,and rotor speed on GGH efficiency.Findings reveal that OSP increases with rotor speed but reaches a plateau beyond 1 rpm;it decreases with higher inlet velocity and increases with higher inlet temperature.Response surface analysis identifies treated flue gas inlet temperature as the most influential parameter,highlighting a synergistic effect between rotor speed and inlet temperature,alongside an antagonistic interaction between inlet temperature and inlet velocity.To ensure safe system operation,engineering constraints were incorporated into the optimization framework using a Box-Behnken design.The optimal operational parameters were determined as a treated flue gas inlet temperature of 250℃,inlet velocity of 8 m/s,and rotor speed of 1 rpm,yielding a maximum OSP of 107.74.The integrated CFD-RSM methodology and constraint-aware optimization strategy presented in this study offer a practical reference for enhancing the operational efficiency of industrial waste heat recovery systems,particularly in cement kiln SCR applications. 展开更多
关键词 Waste heat recovery of cement kiln rotary gas-gas heat exchanger response surface method operating parameter optimization
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Review of the analysis and methods of natural gas pipeline network operation optimization 认领 引用
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作者 Deming Zhao Wuchang Wang Yuxing Li 《Natural Gas Industry B》 2026年第3期403-424,共22页
The expansion of natural gas consumption and pipeline construction makes integrating artificial intelligence into pipeline network operations increasingly essential.This review summarizes progress in operation optimiz... The expansion of natural gas consumption and pipeline construction makes integrating artificial intelligence into pipeline network operations increasingly essential.This review summarizes progress in operation optimization,gas transmission capacity evaluation,and solution algorithms.The review systematically summarizes objective functions,hydraulichermal and compressor constraints,and decision variables,all framed by operator objectives such as transmission capacity,economic benefits,and supply reliability.It highlights gas transmission capacity optimization and extended models,including those for hydrogen-blended and renewable energy-coupled scenarios.The review also analyzes applications of deterministic and stochastic intelligent algorithms,alongside deep learning and hyper-heuristic methods.Key findings indicate that:(1)Traditional models often lack safety,reliability,and low-carbon indicators;(2)Deterministic algorithms struggle with high dimensionality,while heuristic algorithms are prone to premature convergence;(3)Hydrogen blending and new energy integration necessitate revised constraints;and(4)Existing online dynamic optimization methods are insufficient.Finally,current shortcomings are identified,and future directions,such as advanced online dynamic optimization and cross-domain intelligence,are proposed.In conclusion,while artificial intelligence is crucial for natural gas pipeline network operations,significant limitations persist.Future research must prioritize addressing these gaps to advance the industry's intelligent,low-carbon,and reliable development. 展开更多
关键词 Natural gas pipeline network Operation optimization Optimization algorithm Gas transmission capacity evaluation
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Research on Energy Efficiency Improvement and Operation Optimization Strategies of Deep Peak Regulation Steam Turbines 认领 引用
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作者 ZHAO Chengqian 《外文科技期刊数据库（文摘版）工程技术》 2026年第1期153-157,共5页
In the construction of a new power system, with the large-scale grid integration of new energy sources such as wind power and photovoltaic power, the peak-valley difference of the power grid continues to expand, makin... In the construction of a new power system, with the large-scale grid integration of new energy sources such as wind power and photovoltaic power, the peak-valley difference of the power grid continues to expand, making the normalized deep peak regulation of coal-fired steam turbine generating units an inevitable trend. When steam turbines operate under low-load conditions of 30% rated load and below, the flow efficiency decreases, throttling losses increase, windage friction losses in the low-pressure cylinder intensify, and the matching of auxiliary machines is unbalanced. This leads to an increase in unit heat rate and a decline in energy efficiency, accompanied by potential safety hazards such as excessive vibration and abnormal exhaust temperature, which affect the economical and stable operation of the units. Taking supercritical coal-fired steam turbine units as the research object, this paper analyzes the energy efficiency attenuation mechanism of steam turbines under deep peak regulation conditions, explores the causes of energy efficiency loss from four dimensions: flow system, steam distribution regulation, auxiliary machine coordination, and operation control, and proposes equipment transformation and operation regulation optimization strategies combined with the operating characteristics of the units. Engineering practice verifies that the optimization strategies can reduce the unit heat rate and auxiliary power consumption rate, improve the flow efficiency and variable-load adaptability, and balance peak regulation flexibility, operational safety and power generation economy, providing a reference for energy efficiency improvement and operation management of similar units. 展开更多
关键词 deep peak regulation steam turbine energy efficiency improvement operation optimization heat rate variable-load control
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Optimization of Operating Parameters for Underground Gas Storage Based on Genetic Algorithm 认领 引用
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作者 Yuming Luo Wei Zhang +7 位作者 Anqi Zhao Ling Gou Li Chen Yaling Yang Xiaoping Wang Shichang Liu Huiqing Qi Shilai Hu 《Energy Engineering》 EI 2025年第8期3201-3221,共21页
This work proposes an optimization method for gas storage operation parameters under multi-factor coupled constraints to improve the peak-shaving capacity of gas storage reservoirs while ensuring operational safety.Pr... This work proposes an optimization method for gas storage operation parameters under multi-factor coupled constraints to improve the peak-shaving capacity of gas storage reservoirs while ensuring operational safety.Previous research primarily focused on integrating reservoir,wellbore,and surface facility constraints,often resulting in broad constraint ranges and slow model convergence.To solve this problem,the present study introduces additional constraints on maximum withdrawal rates by combining binomial deliverability equations with material balance equations for closed gas reservoirs,while considering extreme peak-shaving demands.This approach effectively narrows the constraint range.Subsequently,a collaborative optimization model with maximum gas production as the objective function is established,and the model employs a joint solution strategy combining genetic algorithms and numerical simulation techniques.Finally,this methodology was applied to optimize operational parameters for Gas Storage T.The results demonstrate:(1)The convergence of the model was achieved after 6 iterations,which significantly improved the convergence speed of the model;(2)The maximum working gas volume reached 11.605×108 m3,which increased by 13.78%compared with the traditional optimization method;(3)This method greatly improves the operation safety and the ultimate peak load balancing capability.The research provides important technical support for the intelligent decision of injection and production parameters of gas storage and improving peak load balancing ability. 展开更多
关键词 Underground gas storage operational parameter optimization extreme peak-shaving constraints genetic algorithm model
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Microgrid Scheduling with the Participation of Electric Vehicles under Extreme Weather Conditions 认领 引用
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作者 Zujun Ding Zhi Liu +7 位作者 Peng Huang Yuhan Qian Chengyi Li Zizhuo Yu Hui Huang Baolian Liu Wan Chen Jie Ji 《Energy Engineering》 EI 2026年第6期363-392,共30页
Under extreme weather conditions(such as hurricanes and heatwaves causing sudden drops in renewable energy output and surges in load),microgrid operations face severe challenges due to the uncertainty of renewable ene... Under extreme weather conditions(such as hurricanes and heatwaves causing sudden drops in renewable energy output and surges in load),microgrid operations face severe challenges due to the uncertainty of renewable energy and load fluctuations.Although existing research has focused on microgrid optimal scheduling or electric vehicle integration,there has not yet been a systematic approach to multi-timescale scheduling that combines electric vehicle fleets under extreme weather scenarios,and particularly,explicit modeling of weather events and their impact on component failure rates and transmission lines is lacking.This paper proposes,for the first time,a multi-timescale optimal scheduling strategy integrated with an electric vehicle fleet,filling this gap.By constructing a microgrid model containing diesel generators,micro gas turbines,renewable energy sources,energy storage,and demand response loads,and defining four typical extreme weather scenarios(high solar&high wind,high solar&low wind,low solar&high wind,low solar&low wind)to simulate the impact of extreme events,a day-ahead and intraday coordinated framework aiming to minimize total operating costs is established.In this framework,the day-ahead stage formulates a preliminary plan based on wind and solar forecasts,while the intraday stage employs the mobile energy storage characteristics of the electric vehicle fleet for rolling adjustments to cope with renewable fluctuations and sudden load changes.Simulations based on actual data from Huai’an City in 2024 show that this strategy can significantly reduce microgrid operating costs(by 5.6%–7.2%),increase renewable energy utilization(94%–96%),reduce carbon emissions(17.8%–22.6%),and enhance the system’s economic performance and resilience under extreme weather conditions. 展开更多
关键词 Microgrid electric vehicle cluster multi-time scale scheduling extreme weather renewable energy absorption optimal operation demand response
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Review of the comprehensive utilization of regenerative braking energy in alternating-current electrified railways 认领 引用
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作者 Youtong FANG Wenjing TIAN +3 位作者 Jien MA Yuanlin GUO Shifeng LIU Zhenzhi LIN 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS CSCD 2026年第2期87-108,共22页
In the context of global decarbonization initiatives and the rapid advancement of electrified railways,the efficient utilization of regenerative braking energy(RBE)has emerged as a critical energy policy in China.RBE ... In the context of global decarbonization initiatives and the rapid advancement of electrified railways,the efficient utilization of regenerative braking energy(RBE)has emerged as a critical energy policy in China.RBE not only significantly reduces railway energy consumption but also offers substantial potential for providing auxiliary services to the power grid,enhancing the coordination,economic efficiency,and stability of both railway and power systems.In this paper,we first analyze RBE utilization strategies,including the optimization of train operation scheduling,energy storage technologies,energy sharing mechanisms,and energy feedback configurations.Then,from a macro perspective,the hierarchical structure of the RBE control system is explored.The upper-level energy management system of regenerative braking exhibits development trends based mainly on thresholds,optimization,and learning.Meanwhile,the lower-level converter control system tends to adopt strategies that improve the voltage balance and circulating current performance of the modular multilevel converter-railway power conditioner(MMC-RPC)while reducing the computational burden.Finally,based on existing theoretical research and practical engineering applications,rational suggestions are proposed to enhance the utilization efficiency of RBE.These recommendations provide strong support for the efficient utilization of RBE in alternating-current electrified railways(ACERs),as well as for technological innovation and economic development. 展开更多
关键词 Regenerative braking energy(RBE) Train operation optimization Energy storage systems Energy sharing and feedback Alternating-current electrified railways(ACERs)
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Integration of Flexible Interconnection Device in the Reconstruction of Medium and Low Voltage Distribution Networks Using DRL 认领 引用
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作者 Ruosong Hou Jiakun An +2 位作者 Zihao Zhao Wei Guo Hua Shao 《Energy Engineering》 EI 2026年第8期158-174,共17页
This article evaluates the connectivity with energy sharing in low-voltage distribution areas.Indicators like wind-solar complementing effectiveness,source-load energy sharing possibility,or transformer capacity inter... This article evaluates the connectivity with energy sharing in low-voltage distribution areas.Indicators like wind-solar complementing effectiveness,source-load energy sharing possibility,or transformer capacity interconnection measurements are part of the assessment index framework for interconnection capacity that is established after an analysis of the features of linked scenarios.Radial and inflexible,conventional distribution systems can’t handle bidirectional power flow,fluctuating demand,or grid disruptions.Using real-world examples,we can see that the suggested strategy improves power supply efficiency across zones and increases the usage of distributed energy resources,proving the method’s validity.With the help of Flexible Interconnection Devices(FIDs),MV/LV networks may be reconfigured,power quality is improved,DERs are supported,and reliability is increased.With so many distributed PVs connected to distribution substations,managing low-and medium-voltage distribution networks is a real challenge.One novel kind of power gadget that permits adaptable connections between distribution substation segments is the soft open point(SOP).This article presents learning algorithm for low and medium voltage networks for power optimization,which takes into consideration the dynamic connectivity of different areas of substation.The next stage is to construct a multi-agent deep reinforcement learning(DRL)suitable for low and medium voltage distribution networks using Deep Q Network(DQN)model in DRL.The low and medium voltage distribution network employs flexible interconnection device for power loss reduction.Finally,the case studies show that the proposed approach has a good operating strategy for distribution networks with medium and low voltages,and it may lessen voltage fluctuations caused by high PV integration. 展开更多
关键词 Distribution substation area flexible interconnection device cooperative operation optimization distributed photovoltaic(PV) DQN DRL
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Optimization operation model of electricity market considering renewable energy accommodation and flexibility requirement 认领 引用 被引量：6
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作者 Jinye Yang Chunyang Liu +2 位作者 Yuanze Mi Hengxu Zhang Vladimir Terzija 《Global Energy Interconnection》 EI CSCD 2021年第3期227-238,共12页
The renewable portfolio standard has been promoted in parallel with the reform of the electricity market,and the flexibility requirement of the power system has rapidly increased.To promote renewable energy consumptio... The renewable portfolio standard has been promoted in parallel with the reform of the electricity market,and the flexibility requirement of the power system has rapidly increased.To promote renewable energy consumption and improve power system flexibility,a bi-level optimal operation model of the electricity market is proposed.A probabilistic model of the flexibility requirement is established,considering the correlation between wind power,photovoltaic power,and load.A bi-level optimization model is established for the multi-markets;the upper and lower models represent the intra-provincial market and inter-provincial market models,respectively.To efficiently solve the model,it is transformed into a mixed-integer linear programming model using the Karush–Kuhn–Tucker condition and Lagrangian duality theory.The economy and flexibility of the model are verified using a provincial power grid as an example. 展开更多
关键词 Renewable energy accommodation Renewable portfolio standards Flexibility requirement Optimization operation Mixed-integer linear programming
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Influence of Design Margin on Operation Optimization and Control Performance of Chemical Processes 认领 引用 被引量：9
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作者 许锋 蒋慧蓉 +1 位作者 王锐 罗雄麟 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2014年第1期51-58,共8页
Operation optimization is an effective method to explore potential economic benefits for existing plants. The m.aximum potential benefit from operationoptimization is determined by the distances between current operat... Operation optimization is an effective method to explore potential economic benefits for existing plants. The m.aximum potential benefit from operationoptimization is determined by the distances between current operating point and process constraints, which is related to the margins of design variables. Because of various ciisturbances in chemical processes, some distances must be reserved for fluctuations of process variables and the optimum operating point is not on some process constraints. Thus the benefit of steady-state optimization can not be fully achied（ed while that of dynamic optimization can be really achieved. In this study, the steady-state optimizationand dynamic optimization are used, and the potential benefit-is divided into achievable benefit for profit and unachievable benefit for control. The fluid catalytic cracking unit （FCCU） is used for case study. With the analysis on how the margins of design variables influence the economic benefit and control performance, the bottlenecks of process design are found and appropriate control structure can be selected. 展开更多
关键词 design margin operation optimization control performance bottleneck fluid catalytic cracking unit（FCCU）
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Operation optimization of the steel manufacturing process: A brief review 认领 引用 被引量：17
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作者 Zhao-jun Xu Zhong Zheng Xiao-qiang Gao 《International Journal of Minerals,Metallurgy and Materials》 SCIE EI CAS CSCD 2021年第8期1274-1287,共14页
Against the realistic background of excess production capacity, product structure imbalance, and high material and energy consumption in steel enterprises, the implementation of operation optimization for the steel ma... Against the realistic background of excess production capacity, product structure imbalance, and high material and energy consumption in steel enterprises, the implementation of operation optimization for the steel manufacturing process is essential to reduce the production cost, increase the production or energy efficiency, and improve production management. In this study, the operation optimization problem of the steel manufacturing process, which needed to go through a complex production organization from customers' orders to workshop production, was analyzed. The existing research on the operation optimization techniques, including process simulation, production planning, production scheduling, interface scheduling, and scheduling of auxiliary equipment, was reviewed. The literature review reveals that, although considerable research has been conducted to optimize the operation of steel production, these techniques are usually independent and unsystematic.Therefore, the future work related to operation optimization of the steel manufacturing process based on the integration of multi technologies and the intersection of multi disciplines were summarized. 展开更多
关键词 intelligent manufacturing operation optimization steel manufacturing process process simulation production planning production scheduling
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A Knowledge Base System for Operation Optimization: Design and Implementation Practice for the Polyethylene Process 认领 引用 被引量：3
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作者 Weimin Zhong Chaoyuan Li +3 位作者 Xin Peng Feng Wan Xufeng An Zhou Tian 《Engineering》 SCIE EI CAS 2019年第6期1041-1048,共8页
Setting up a knowledge base is a helpful way to optimize the operation of the polyethylene process by improving the performance and the ef ciency of reuse of information and knowledge two critical ele- ments in polyet... Setting up a knowledge base is a helpful way to optimize the operation of the polyethylene process by improving the performance and the ef ciency of reuse of information and knowledge two critical ele- ments in polyethylene smart manufacturing. In this paper, we propose an overall structure for a knowl- edge base based on practical customer demand and the mechanism of the polyethylene process. First, an ontology of the polyethylene process constructed using the seven-step method is introduced as a carrier for knowledge representation and sharing. Next, a prediction method is presented for the molecular weight distribution (MWD) based on a back propagation (BP) neural network model, by analyzing the relationships between the operating conditions and the parameters of the MWD. Based on this network, a differential evolution algorithm is introduced to optimize the operating conditions by tuning the MWD. Finally, utilizing a MySQL database and the Java programming language, a knowledge base system for the operation optimization of the polyethylene process based on a browser/server framework is realized. 展开更多
关键词 Ontology Operation optimization Knowledge base system Polyethylene process
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Interpretable machine learning optimization(InterOpt)for operational parameters:A case study of highly-efficient shale gas development 认领 引用 被引量：4
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作者 Yun-Tian Chen Dong-Xiao Zhang +1 位作者 Qun Zhao De-Xun Liu 《Petroleum Science》 SCIE EI CAS CSCD 2023年第3期1788-1805,共18页
An algorithm named InterOpt for optimizing operational parameters is proposed based on interpretable machine learning,and is demonstrated via optimization of shale gas development.InterOpt consists of three parts:a ne... An algorithm named InterOpt for optimizing operational parameters is proposed based on interpretable machine learning,and is demonstrated via optimization of shale gas development.InterOpt consists of three parts:a neural network is used to construct an emulator of the actual drilling and hydraulic fracturing process in the vector space(i.e.,virtual environment);:the Sharpley value method in inter-pretable machine learning is applied to analyzing the impact of geological and operational parameters in each well(i.e.,single well feature impact analysis):and ensemble randomized maximum likelihood(EnRML)is conducted to optimize the operational parameters to comprehensively improve the efficiency of shale gas development and reduce the average cost.In the experiment,InterOpt provides different drilling and fracturing plans for each well according to its specific geological conditions,and finally achieves an average cost reduction of 9.7%for a case study with 104 wells. 展开更多
关键词 Interpretable machine learning Operational parameters optimization Shapley value Shale gas development Neural network
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A novel 3-layer mixed cultural evolutionary optimization framework for optimal operation of syngas production in a Texaco coal-water slurry gasifier 认领 引用 被引量：5
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作者 曹萃文 张亚坤 +3 位作者 于腾 顾幸生 辛忠 李杰 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2015年第9期1484-1501,共18页
Optimizing operational parameters for syngas production of Texaco coal-water slurry gasifier studied in this paper is a complicated nonlinear constrained problem concerning 3 BP(Error Back Propagation) neural networks... Optimizing operational parameters for syngas production of Texaco coal-water slurry gasifier studied in this paper is a complicated nonlinear constrained problem concerning 3 BP(Error Back Propagation) neural networks. To solve this model, a new 3-layer cultural evolving algorithm framework which has a population space, a medium space and a belief space is firstly conceived. Standard differential evolution algorithm(DE), genetic algorithm(GA), and particle swarm optimization algorithm(PSO) are embedded in this framework to build 3-layer mixed cultural DE/GA/PSO(3LM-CDE, 3LM-CGA, and 3LM-CPSO) algorithms. The accuracy and efficiency of the proposed hybrid algorithms are firstly tested in 20 benchmark nonlinear constrained functions. Then, the operational optimization model for syngas production in a Texaco coal-water slurry gasifier of a real-world chemical plant is solved effectively. The simulation results are encouraging that the 3-layer cultural algorithm evolving framework suggests ways in which the performance of DE, GA, PSO and other population-based evolutionary algorithms(EAs) can be improved,and the optimal operational parameters based on 3LM-CDE algorithm of the syngas production in the Texaco coalwater slurry gasifier shows outstanding computing results than actual industry use and other algorithms. 展开更多
关键词 3-Layer mixed cultural evolutionary framework Optimal operation Syngas production Coal-water slurry gasifier
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Energy Management and Capacity Optimization of Photovoltaic, Energy Storage System, Flexible Building Power System Considering Combined Benefit 认领 引用 被引量：1
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作者 Chang Liu Bo Luo +5 位作者 Wei Wang Hongyuan Gao Zhixun Wang Hongfa Ding Mengqi Yu Yongquan Peng 《Energy Engineering》 EI 2023年第2期541-559,共19页
Building structures themselves are one of the key areas of urban energy consumption,therefore,are a major source of greenhouse gas emissions.With this understood,the carbon trading market is gradually expanding to the... Building structures themselves are one of the key areas of urban energy consumption,therefore,are a major source of greenhouse gas emissions.With this understood,the carbon trading market is gradually expanding to the building sector to control greenhouse gas emissions.Hence,to balance the interests of the environment and the building users,this paper proposes an optimal operation scheme for the photovoltaic,energy storage system,and flexible building power system(PEFB),considering the combined benefit of building.Based on the model of conventional photovoltaic(PV)and energy storage system(ESS),the mathematical optimization model of the system is proposed by taking the combined benefit of the building to the economy,society,and environment as the optimization objective,taking the near-zero energy consumption and carbon emission limitation of the building as the main constraints.The optimized operation strategy in this paper can give optimal results by making a trade-off between the users’costs and the combined benefits of the building.The efficiency and effectiveness of the proposed methods are verified by simulated experiments. 展开更多
关键词 Photovoltaic energy storage system energy management PEFB optimization operation
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Multi-Objective Optimization of Water-Sedimentation-Power in Reservoir Based on Pareto-Optimal Solution 认领 引用 被引量：3
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作者 李辉 练继建 《Transactions of Tianjin University》 EI CAS 2008年第4期282-288,共7页
A multi-objective optimal operation model of water-sedimentation-power in reservoir is established with power-generation, sedimentation and water storage taken into account. Moreover, the inertia weight self-adjusting... A multi-objective optimal operation model of water-sedimentation-power in reservoir is established with power-generation, sedimentation and water storage taken into account. Moreover, the inertia weight self-adjusting mechanism and Pareto-optimal archive are introduced into the particle swarm optimization and an improved multi-objective particle swarm optimization （IMOPSO） is proposed. The IMOPSO is employed to solve the optimal model and obtain the Pareto-optimal front. The multi-objective optimal operation of Wanjiazhai Reservoir during the spring breakup was investigated with three typical flood hydrographs. The results show that the former method is able to obtain the Pareto-optimal front with a uniform distribution property. Different regions （A, B, C） of the Pareto-optimal front correspond to the optimized schemes in terms of the objectives of sediment deposition, sediment deposition and power generation, and power generation, respectively. The level hydrographs and outflow hydrographs show the operation of the reservoir in details. Compared with the non-dominated sorting genetic algorithm-Ⅱ （NSGA-Ⅱ）, IMOPSO has close global optimization capability and is suitable for multi-objective optimization problems. 展开更多
关键词 multi-objective optimization of water-sedimentation-power optimal operation of reservoir Pareto-optimal solution particle swarm optimization
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Operation optimization mode for nozzle governing steam turbine unit 认领 引用 被引量：1
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作者 胥建群 马琳 +1 位作者 吕晓明 李玲 《Journal of Southeast University(English Edition)》 EI CAS 2014年第1期57-59,共3页
Based on tests and theoretical calculation an optimum steam admission mode is proposed which can effectively solve the steam-excited vibration.An operation mode jointly considering the valve point and operation load i... Based on tests and theoretical calculation an optimum steam admission mode is proposed which can effectively solve the steam-excited vibration.An operation mode jointly considering the valve point and operation load is proposed based on the analysis and study of a large number of unit operation optimization methods.According to the steam-excited vibration that occurs during the optimization process when the nozzle governing steam turbine switches from a single valve to multi-valves a steam admission optimization program is proposed.This comprehensive program considering the steam-excited vibration is applied to a 600 MW steam turbine unit to obtain the optimum sliding pressure curve and the optimum operation mode and the steam-excited vibration is solved successfully. 展开更多
关键词 nozzle governing steam-excited vibration operation optimization unit efficiency
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Intelligent decision support system of operation-optimization in copper smelting converter 认领 引用 被引量：1
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作者 姚俊峰 梅炽 +2 位作者 彭小奇 周安梁 吴冬华 《Journal of Central South University of Technology》 2002年第2期138-141,共4页
An artificial intelligence technique was applied to the optimization of flux adding systems and air blasting systems, the display of on line parameters, forecasting of mass and compositions of slag in the slagging per... An artificial intelligence technique was applied to the optimization of flux adding systems and air blasting systems, the display of on line parameters, forecasting of mass and compositions of slag in the slagging period, optimization of cold material adding systems and air blasting systems, the display of on line parameters, and the forecasting of copper mass in the copper blow period in copper smelting converters. They were integrated to build the Intelligent Decision Support System of the Operation Optimization of Copper Smelting Converter(IDSSOOCSC), which is self learning and self adaptating. Development steps, monoblock structure and basic functions of the IDSSOOCSC were introduced. After it was applied in a copper smelting converter, every production quota was clearly improved after IDSSOOCSC had been run for 4 months. Blister copper productivity is increased by 6%, processing load of cold input is increased by 8% and average converter life span is improved from 213 to 235 furnace times. 展开更多
关键词 intelligent decision support system neural network pattern identification chaos genetic algorithm operation optimization copper smelting converter
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Solving chemical dynamic optimization problems with ranking-based differential evolution algorithms 认领 引用 被引量：3
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作者 Xu Chen Wenli Du Feng Qian 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2016年第11期1600-1608,共9页
Dynamic optimization problems(DOPs) described by differential equations are often encountered in chemical engineering. Deterministic techniques based on mathematic programming become invalid when the models are non-di... Dynamic optimization problems(DOPs) described by differential equations are often encountered in chemical engineering. Deterministic techniques based on mathematic programming become invalid when the models are non-differentiable or explicit mathematical descriptions do not exist. Recently, evolutionary algorithms are gaining popularity for DOPs as they can be used as robust alternatives when the deterministic techniques are invalid. In this article, a technology named ranking-based mutation operator(RMO) is presented to enhance the previous differential evolution(DE) algorithms to solve DOPs using control vector parameterization. In the RMO, better individuals have higher probabilities to produce offspring, which is helpful for the performance enhancement of DE algorithms. Three DE-RMO algorithms are designed by incorporating the RMO. The three DE-RMO algorithms and their three original DE algorithms are applied to solve four constrained DOPs from the literature. Our simulation results indicate that DE-RMO algorithms exhibit better performance than previous non-ranking DE algorithms and other four evolutionary algorithms. 展开更多
关键词 Dynamic optimization Differential evolution Ranking-based mutation operator Control vector parameterization
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Synthesis and optimization of utility system using parameter adaptive differential evolution algorithm 认领 引用 被引量：1
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作者 李泽秋 杜文莉 +1 位作者 赵亮 钱锋 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2015年第8期1350-1356,共7页
Synthesis and optimization of utility system usually involve grassroots design, retrofitting and operation optimization, which should be considered in modeling process. This paper presents a general method for synthes... Synthesis and optimization of utility system usually involve grassroots design, retrofitting and operation optimization, which should be considered in modeling process. This paper presents a general method for synthesis and optimization of a utility system. In this method, superstructure based mathematical model is established, in which different modeling methods are chosen based on the application. A binary code based parameter adaptive differential evolution algorithm is used to obtain the optimal con figuration and operation conditions of the system. The evolution algorithm and models are interactively used in the calculation, which ensures the feasibility of con figuration and improves computational ef ficiency. The capability and effectiveness of the proposed approach are demonstrated by three typical case studies. 展开更多
关键词 Utility system synthesis MINLP Operation optimization
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