[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Matino et al., 2019 - Google Patents

Application of echo state neural networks to forecast blast furnace gas production: pave the way to off-gas optimized management

Matino et al., 2019

View PDF
Document ID
557823106457455013
Author
Matino I
Dettori S
Colla V
Weber V
Salame S
Publication year
Publication venue
Energy Procedia

External Links

Snippet

The efficient use of resources is a relevant research topic for integrated steelworks. Process off-gases, such as the ones produced during blast furnace operation, are valid substitutes of natural gas, as they are sources of a considerable amount of energy. Currently they are …
Continue reading at www.sciencedirect.com (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
    • G06Q10/063—Operations research or analysis
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
    • G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation, e.g. linear programming, "travelling salesman problem" or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators

Similar Documents

Publication Publication Date Title
Matino et al. Forecasting blast furnace gas production and demand through echo state neural network-based models: Pave the way to off-gas optimized management
Ofosu-Adarkwa et al. Forecasting CO2 emissions of China's cement industry using a hybrid Verhulst-GM (1, N) model and emissions' technical conversion
Matino et al. Two innovative modelling approaches in order to forecast consumption of blast furnace gas by hot blast stoves
Zhao et al. A MILP model concerning the optimisation of penalty factors for the short-term distribution of byproduct gases produced in the iron and steel making process
Hu et al. A multilevel prediction model of carbon efficiency based on the differential evolution algorithm for the iron ore sintering process
Matino et al. Application of echo state neural networks to forecast blast furnace gas production: pave the way to off-gas optimized management
Porzio et al. Comparison of multi-objective optimization techniques applied to off-gas management within an integrated steelwork
CN106096788B (en) Converter steelmaking process cost control method and system based on PSO _ ELM neural network
CN104181900B (en) Layered dynamic regulation method for multiple energy media
Liu et al. A stacked autoencoder with sparse Bayesian regression for end-point prediction problems in steelmaking process
CN107918368B (en) The dynamic prediction method and equipment of iron and steel enterprise's coal gas yield and consumption
Zhao et al. Granular prediction and dynamic scheduling based on adaptive dynamic programming for the blast furnace gas system
Yuan et al. Modeling and optimization of coal blending and coking costs using coal petrography
Zhao et al. A Bayesian networks structure learning and reasoning-based byproduct gas scheduling in steel industry
Li et al. Knee Point‐Guided Multiobjective Optimization Algorithm for Microgrid Dynamic Energy Management
Dettori et al. Neural network-based modeling methodologies for energy transformation equipment in integrated steelworks processes
Wang et al. A dynamic scheduling framework for byproduct gas system combining expert knowledge and production plan
Zhang et al. Dynamic forecasting and optimal scheduling of by-product gases in integrated iron and steel works
Wang et al. Optimization of the sustainable production pathways under multiple industries and objectives: A study of China's three energy-and emission-intensive industries
CN111952965A (en) CCHP system optimized operation method based on predictive control and interval planning
Jin et al. Causality diagram-based scheduling approach for blast furnace gas system
Li et al. Forecasting and optimal probabilistic scheduling of surplus gas systems in iron and steel industry
Matino et al. Machine learning-based models for supporting optimal exploitation of process off-gases in integrated steelworks
CN112541625A (en) Self-adaptive multi-working-condition steel secondary energy generation amount dynamic prediction method
Chen et al. Research on prediction methods of energy consumption data