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Forecasting of Basic Oxygen Furnace Gas Production Through Echo State Neural Networks
Abstract
Sustainability and competitiveness of integrated steelworks is correlated to the reuse of internal resources and efforts are spent to intensify such reuse. Off-gases coming from main production steps are valuable by-products due to their energy content. In particular, Basic Oxygen Furnace Gas is characterized by a significant net calorific value and can be used to replace natural gas for feeding internal processes that produce heat, steam or electricity. Optimized reuse of these gases allows achieving significant economic and environmental advantages. However, forecasting the amount and the features of these gases in the next future is a challenging task and an optimal management is a difficult goal to achieve. Currently, Basic Oxygen Furnace Gas is distributed to the network depending on the current demand of gas. In order to avoid flaring, due to the reaching of maximal capacity of the gasholder, a Basic Oxygen Furnace Gas production and energy demand forecast is necessary in order to optimize its use. The paper describes a hybrid data-driven model forecasting the amount and the features of Basic Oxygen Furnace Gas. The main aspects related to the involved processes have been considered and, consequently, a careful selection of input variables has been pursued. Echo State Neural Networks are exploited and the model has been trained and tested by exploiting real industrial data. The obtained accuracy is acceptable for optimization purposes.
... power plants) and do not dynamically forecast the off-gases (and related energy carriers) production and request. The new DSS is composed of different units: a model library, which is able to forecast the production and the demand of off-gases and related energy carriers by the different producers or users in the whole steelmaking plant, as for instance described in [13][14][15][16]; an optimization tool, containing different optimization techniques in order to offline and online optimize the distribution of off-gases in steelworks; a graphical user interface in order to easy visualize the results of the different units of the DSS also in the form of key performance indicators [17,18]. ...
The European steel industry is constantly promoting developments, which can increase efficiency and lower the environmental impact of the steel production processes. In particular, a strong focus refers to the minimization of the energy consumption. This paper presents part of the work of the research project entitled “Optimization of the management of the process gas network within the integrated steelworks” (GASNET), which aims at developing a decision support system supporting energy managers and other concerned technical personnel in the implementation of an optimized off-gases management and exploitation considering environmental and economic objectives. A mathematical model of the network as a capacitated digraph with costs on arcs is proposed and an optimization problem is formulated. The objective of the optimization consists in minimizing the wastes of process gases and maximizing the incomes. Several production constraints need to be accounted. In particular, different types of gases are mixing in the same network. The constraints that model the mixing make the problem computationally difficult: it is a non-convex quadratically constrained quadratic program (QCQP). Two formulations of the problem are presented: the first one is a minimum cost flow problem, which is a linear program and is thus computationally fast to solve, but suitable only for a single gas network. The second formulation is a quadratically constrained quadratic program, which is slower, but covers more general cases, such as the ones, which are characterized by the interaction among multiple gas networks. A user-friendly graphical interface has been developed and tests over existing plant networks are performed and analyzed.
... For this feature and for their fast training stage, they have been applied with success in different fields. In GASNET, ESNs are exploited for models that forecast the produced blast furnace gas (BFG) or the recoverable basic oxygen furnace gas (BOFG) in terms of gas volume flowrate and heating power that is computed after the prediction of the content of carbon monoxide and hydrogen by specialized ESN [50,51]. The simplified structure of these models is reported in Figure 3: here, inputs are highlighted in red, auxiliary outputs in green and final outputs in dark blue; the other blocks are the model "active" parts and the core is represented by the purple blocks of ESNs. ...
The European Steel industry is spending considerable efforts in order to improve the can increase efficiency and lower the environmental impact of the steel production processes. In particular, the European iron and steel sector is strongly committed toward the reduction of energy consumptions and CO 2 emissions. Process gases are a very valuable resource: possibilities exist to consider these gases as an intermediate by-product for the production of other valuable energy carriers or products with an associated environmental benefit. Therefore, the process gas networks, especially inside the integrated steelworks, have a fundamental function, as they allow meeting the demand of many processes and producing energy through dedicated facilities. They can also support the production processes by internal electric energy generation and often by supplying energy outside the plant boundaries. On the other hand, such networks are very complex systems interacting with many different production steps and the management of such complex systems is a very difficult task, where many often-counteracting factors need to be jointly taken into account. This paper presents the first outcomes of the research project entitled “Optimization of the management of the process gas network within the integrated steelworks (GASNET)”, which aims at developing a Decision Support System helping the energy managers and other concerned technical personnel to implement an optimized off-gases management and exploitation considering environmental and economic objectives. A series of Key Performance Indicators has been elaborated, in order to monitor the efficiency of the gas management and the objectives of the optimization have been defined. The overall structure of the project and the ongoing work will also be outlined in the paper.
... For this feature and for their fast training stage, they have been applied with success in different fields. In GASNET, ESNs are exploited for models that forecast the produced blast furnace gas (BFG) or the recoverable basic oxygen furnace gas (BOFG) in terms of gas volume flowrate and heating power that is computed after the prediction of the content of carbon monoxide and hydrogen by specialized ESN [50,51]. The simplified structure of these models is reported in Figure 3: here, inputs are highlighted in red, auxiliary outputs in green and final outputs in dark blue; the other blocks are the model "active" parts and the core is represented by the purple blocks of ESNs. ...
The European Steel industry is spending considerable efforts in order to improve the socio-economic and environmental sustainability of its processes by promoting any development, which can increase efficiency and lower the environmental impact of the steel production processes. In particular, the European iron and steel sector is strongly committed toward the reduction of energy consumptions and CO2 emissions.
Process gases are a very valuable resource: possibilities exist to consider these gases as an intermediate by-product for the production of other valuable energy carri-ers or products with an associated environmental benefit. Therefore, the process gas networks, especially inside the integrated steelworks, have a fundamental function, as they allow meeting the demand of many processes and producing energy through ded-icated facilities. They can also support the production processes by internal electric energy generation and often by supplying energy outside the plant boundaries. On the other hand, such networks are very complex systems interacting with many different production steps and the management of such complex systems is a very difficult task, where many often-counteracting factors need to be jointly taken into account.
This paper presents the first outcomes of the research project entitled "Optimiza-tion of the management of the process gas network within the integrated steelworks" (GASNET), which aims at developing a Decision Support System helping the energy managers and other concerned technical personnel to implement an optimized off-gases management and exploitation considering environmental and economic objec-tives. A series of Key Performance Indicators has been elaborated, in order to monitor the efficiency of the gas management and the objectives of the optimization have been defined. The overall structure of the project and the ongoing work will also be outlined in the paper.
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 recovered but sometimes part of such gas is flared due to non-optimal management of such resource. In order to exploit the off-gases produced in an integrated steelworks, the interactions between gas producers and users in the whole gas network need to be considered. The paper describes a model exploited by a Decision Support Tool that is under development within a European project. Such model forecasts the blast furnace gas amount and its heating power by obtaining an error between 3 and 7 % in a time horizon of 2 hours. The forecasted values of blast furnace gas allow a continuous optimal planning of the blast furnace gas usage according to its availability and to the needs in the steelworks, by avoiding losses of a valuable secondary resource and related emissions.
In the steel sector water management aims at improving the sustainability of the production cycle, resulting in resource efficiency benefits and in reduced water demand and costs. To be reused, water needs to be cooled and desalinized to avoid salt concentration in water circulation systems. The presented work includes two case studies carried out in an integrated steelmaking plant, respectively, to evaluate the possible implementation of ultrafiltration and reverse osmosis to reduce salt concentration in water streams and to investigate, through modelling and simulation, a process integration solution to improve water efficiency. Results showed that most salts are removed by reverse osmosis and that its coupling with ultrafiltration allows obtaining very high quality water; reuse of desalinated wastewater resulted in being more suitable and economically viable than its discharge. Moreover, modelling and simulation showed that the considered blowdown could be reused without significant changes in the receiving water network area. The industrial implementation of water recovery solutions can lead to a decrease of fresh water consumption, effluent discharge, and to improvement of product quality and equipment service life. The considered desalination technologies are transferable and easily implementable, and modelling and simulation are very useful in order to evaluate process modifications before real implementation.
The VALORCO project coordinated by ArcelorMittal and funded by ADEME aims at reducing and valorizing CO2 emissions from steel industry. This paper presents the main results of task 1.1A of the VALORCO project dedicated to CO2 capture on blast furnace gases by means of amine scrubbing technologies. Blast furnace gases are characterized by high CO2 and CO partial pressures and the absence of oxygen. Since few literature data are available on the effect of CO on solvent degradation and CO2 absorption, experimental work was needed. In this context, three IFP Energies nouvelles (IFPEN) processes initially developed for CO2 capture on coal power stations were evaluated for blast furnace applications: HicaptTM process (MEA 30wt.%), Hicapt+TM process (MEA 40wt.%) and DMXTM process (Demixing solvent).
While the terms Circular Economy and sustainability are increasingly gaining traction with academia, industry, and policymakers, the similarities and differences between both concepts remain ambiguous. The relationship between the concepts is not made explicit in literature, which is blurring their conceptual contours and constrains the efficacy of using the approaches in research and practice. This research addresses this gap and aims to provide conceptual clarity by distinguishing the terms and synthesising the different types of relationships between them. We conducted an extensive literature review, employing bibliometric analysis and snowballing techniques to investigate the state of the art in the field and synthesise the similarities, differences and relationships between both terms. We identified eight different relationship types in the literature and illustrated the most evident similarities and differences between both concepts.
An improvement of by-products reuse contributes to reduce the environmental impact of industries and to increase their competitiveness. Within the steel production cycle, several by-products are produced: slags, sludges, scales, dusts. Some of them are directly suitable to the internal reuse, other ones can be separated in fractions that can be used internally or sold for external use. This paper shows an analysis focused on the optimization of by-products reuse in an Italian Integrated Steelworks with a particular attention on the internal recycling in the pelletization process. Starting from previous studies on the selection of suitable by-products and treatments, the paper presents, firstly, the development of a superstructure model using reMIND® software in order to find optimal solutions, considering quality and environmental constraints, and to minimize operating costs. Furthermore, the evaluation and the optimization through laboratory or simulation tests of possible mixtures of by-products to be used in pelletization is also discussed. By-product mixtures were evaluated to the aim of achieving good pellets formation, according to the number, size of pellets and the compression test results. This study shows that a particular scenario is capable to jointly reduce costs and environmental impact as well as to obtain high quality pellets that can maximize the internal reuse in sinter plant of by-products in a particular” winning formula” (BOF slag 65 wt%, BOF sludge 27 wt%, dolime 1 wt%, cement 7 wt%). These preliminary results show that a good by-products management could provide important advantages to the companies by contributing to the “zero waste” target.
The assessment of the environmental impact of steelworks has been intensively discussed in the last decades, especially focusing on CO2 emissions and energy consumptions. Nowadays an increasing attention is paid to the evaluation of the resource efficiency and overall environmental impact; some work groups also focused on the development of various eco-efficiency indicators.
ISO Standards are available for the environmental management system (ISO14000) and for GHG emission quantification (ISO14064) together with the Eco-Management and Audit Scheme (EMAS). However, a practical and comprehensive metric to quantify and monitor the different aspects contributing to the overall resource efficiency especially of an electric steelmaking is not yet available.
The paper presents first results of the currently running research project entitled "Environmental Impact Evaluation and Effective Management of Resources in the EAF Steelmaking" (EIRES), which receives funding by the Research Fund for Coal and Steel (RFCS). Within this project several Key Performance Indicators were selected in order to monitor the environmental performance of the whole steelmaking process in terms of energy consumption, water and air emissions, by-products and waste management.
A tool was implemented in order to follow the temporal evolution of these KPIs on both real and simulated data, and suitable simulation models have been developed for scenario analyses.
Process Integration capabilities of promoting the internal recycling and reuse of material and energy streams allow reaching the goals of sustainable processing. Water is one of the main industrial resource which use has to be optimised to decrease freshwater intake and wastewater blowdown. Preliminary studies on the feasibility of different options are developed through Process Modelling and Simulation to predict process behaviour before plants conversion. The paper presents the approach to the problem of water reuse for an Italian integrated steelmaking plant. Application to a real case has been developed, related to reuse of continuous casting wastewater blowdown in basic oxygen steelmaking gas washing system. Process Modelling with main assumptions and results are described. The option is promising, as it involves a reduction of fresh water drawing up to 33 %, with changes in blowdown features of 2 % for pH value increase and 13 % for electrical conductivity reduction at constant global mass balance.
Blast Furnace Gas (BFG) system of an iron and steel works was considered. The relationship of gas amount and factors about BFG generation and consumption was analyzed by grey correlationand the BP neural network prediction model of blast furnace gaswas established based on artificial neural network for forecasting thesupply and demandof BFGinthe iron and steel-making processes.The scientific forecasting of BFG generation and consumption in each process was discussed undernormal production and accidental maintenance condition. The results show that established forecasting model is high precision, small errors, and can solve effectively actual production of BFG prediction problem and decreasing BFG flare, providing theoretical basis for establishing reasonable plans in the iron and steel works.
The paper presents an application of genetic algorithms to the problem of input variables selection for the design of neural systems. The basic idea of the proposed method lies in the use of genetic algorithms in order to select the set of variables to be fed to the neural networks. However, the main concept behind this approach is far more general and does not depend on the particular adopted model: it can be used for a wide category of systems, also non-neural, and with a variety of performance indicators. The proposed method has been tested on a simple case study, in order to demonstrate its effectiveness. The results obtained in the processing of experimental data are presented and discussed.
Reducing greenhouse gas emissions and limiting global warming to well below 2 °C are the major goals of the Paris Agreement. The technical realization of these goals continues to pose major challenges for many high‐emission industrial sectors. One promising approach is the synergetic combination of large‐scale industries in cross‐industrial networks. With Carbon2Chem®, the merger of the steel, chemical, and energy sectors has succeeded for the first time. The aim of the initiative is to use top gases from steel production as a raw material for chemical products. Carbon2Chem® is a cross‐industrial project with partners from basic research and industry. The aim of the project is the material use of greenhouse gases from the steel production including the CO2 contained therein as a raw material for the production of chemical products and fuels. The integration of renewable energies also contributes to the success of the energy transition.
The online optimization of the use of process off gases in integrated steelworks can greatly contribute to increase the sustainability of the steel production. A correct management of these gases could allow both the reduction of natural resources exploitation (e.g. natural gas) and of the facility’s environmental impact. However, in order to achieve an almost complete use of these gases, it is fundamental to forecast their production and consumption according to the plant production plan and to use such forecasting in order to optimize the distribution of the gases inside the network by considering possible interactions. According to these needs, this paper presents two models, which allow forecasting the consumption of blast furnace gas by hot blast stoves. Indeed, due to the almost regular operation of these plants, two kinds of models can be applied: an Echo State Network-based model, which is more complex and sensitive to the variations of the operating practices and a simpler switch model, which does not require training and is very easy to use. Both models provide good results and the user can interchangeably exploit them.
The European Steel industry is spending considerable efforts in order to improve the socio-economic and environmental sustainability of its processes by promoting any development, which can increase efficiency and lower the environmental impact of the steel production processes. In particular, the European iron and steel sector is strongly committed toward the reduction of energy consumptions and CO2 emissions.
Process gases are a very valuable resource: possibilities exist to consider these gases as an intermediate by-product for the production of other valuable energy carri-ers or products with an associated environmental benefit. Therefore, the process gas networks, especially inside the integrated steelworks, have a fundamental function, as they allow meeting the demand of many processes and producing energy through ded-icated facilities. They can also support the production processes by internal electric energy generation and often by supplying energy outside the plant boundaries. On the other hand, such networks are very complex systems interacting with many different production steps and the management of such complex systems is a very difficult task, where many often-counteracting factors need to be jointly taken into account.
This paper presents the first outcomes of the research project entitled "Optimiza-tion of the management of the process gas network within the integrated steelworks" (GASNET), which aims at developing a Decision Support System helping the energy managers and other concerned technical personnel to implement an optimized off-gases management and exploitation considering environmental and economic objec-tives. A series of Key Performance Indicators has been elaborated, in order to monitor the efficiency of the gas management and the objectives of the optimization have been defined. The overall structure of the project and the ongoing work will also be outlined in the paper.
The electric steel production is in line with the circular economy concept due to the reuse of scrap. However, being energy intensive industries with a significant environmental impact, electric steelworks can increase their competitiveness and environmental sustainability through an adequate management of resource and energy. The paper presents a work related to the quantification of electric energy consumption and environmental impact of unconventional electric steelmaking scenarios while simultaneously monitoring the steel composition. The exploitation of an ad-hoc developed Decision Support Tool highlights that scrap quality strongly affects the monitored energy and environmental parameters (quantified in terms of Key Performance Indicators and aggregated in a Global Index). Moreover, the developed simulations pointed out that the removal of Fe-alloy addition during EAF tapping allows reducing slag and improving the yield by preserving also the steel quality while slightly increasing the electric energy consumption: in countries where the price and the emissions related to the production of electricity are low, this can be a good compromise to improve the environmental sustainability of the sector. The study shows that also limited modifications of the well-known electric steelmaking process could help to increase the sustainability of this energy intensive industrial production route.
The European steel sector aims at improving its material efficiency by increasing the by-products recycling rate. This can lead to approach the ambitious “zero-waste” goal, by minimising the need for landfilling, saving raw materials, reducing emissions and contributing to improve the economic sustainability of the production cycle. The steelmaking process produce several by-products: slags are the main one and can be used to make different products, including cement, fertilisers and asphalt.
This paper presents a work aiming at obtaining reusable fractions from the Basic Oxygen Furnace slag. Two Aspen Plus®-based models were developed and exploited: a model of the slag treatments to make sensitivity analyses, providing information about different slag treatment configurations, particularly on different magnetic separation solutions, and a model computing the pellet composition according to the different inputs. Other models, developed through reMIND® software, are material flow superstructures that implemented results from the treatment model. They allowed assessing the best route for internal or external reuse of the slag fraction, taking into account process, environmental and economic impacts and making optimization assessment. They are able to identify the slag quality that is more suitable for the reuse. The developed models can provide significant improvements based on economic and environmental sustainability (e.g. increase of by-products recycling, reduction of slag recovering in the internal quarry, reduction of treatment costs, etc.) compared to the current use at ILVA Steelworks.
The steel industry is the main generator of CO2 among the different industrial sectors. That is why efforts are being made to reduce or avoid CO2 emissions by process optimisation or by Carbon Capture and Storage (CCS) processes. In the steel production by blast furnace technology, three main off-gases are generated, namely the Blast Furnace Gas (BFG), the Coke-Oven Gas (COG) and the Basic Oxygen Furnace Gas (BOFG). Many processes and technologies can be identified for their use, depending on the volume and composition of the steelwork off-gases. In the present work, a review and an analysis of several alternatives proposed during last years to use these off-gases are carried out, with a particular focus on thermochemical processes. Three main alternatives are considered: the thermal use of the gases, the recovery of valuable compounds for selling and the synthesis of a high-added value product. The possible implementation of these alternatives may conduct to improvements in energy efficiency of the steel making process with subsequent reduction of CO2 emissions.
The European Steel sector is committed to investigate solutions for reducing its environmental impact in terms of atmospheric and water emissions, energy consumption and solid waste production. To this aim, the project entitled "Environmental Impact Evaluation and Effective Management of Resources in the EAF Steelmaking" (EIRES), that is funded by the Research Fund for Coal and Steel, aims at providing electric steelworks with a tool for a systematic evaluation and investigation of solutions improving the process sustainability. As complex process modifications, before being practically implemented and tested on the field, need a thorough evaluation, a simulation model of the whole electric steelmaking route was developed as a part of EIRES tool. The paper presents such simulation model, which has been developed through Aspen Plus® V 8.6 in order to assess the impact of each operation unit, including both production processes and fumes and wastewater treatment plants. A case study is presented, which compares the environmental and energy impact related to the production of some of the most commonly produced steel grades. Correlations between the steel grade and the consumed electric energy or the slag amount and composition were highlighted, that can be a useful starting point for further assessment of potential margins of improvement of electric steelwork sustainability.
Several studies have been carried out in order to evaluate optimal process management practices and to assess specific aspects related to the environmental impacts (e.g. CO2 emission) of steelmaking facilities. On the other hand, limited efforts have been spent so far to jointly evaluate the enhancement of process performances, resource management and the overall environmental and energy impact.
This paper describes a simulation tool, which is a part of the integrated framework that is under development within the research project entitled "EIRES - Environmental Impact Evaluation and Effective Management of Resources in the EAF Steelmaking".
The modelling approach and validation for one steel grade is described to represent the electric steelmaking route through Aspen Plus®. Results show a global error on energy consumption of about 0.4%, negligible differences in steel composition and temperature. The model application for the evaluation of environmental impact of the process is depicted. The model computes energy consumptions and emissions that can be used to evaluate KPIs to allow evaluation of plant environmental impact during scenario analyses. Thus it can be a powerful instrument for plant manager in feasibility studies in order to enhance process sustainability.
Generated fluctuation of blast furnace gas has a great influence on gas scheduling and optimization. Generating blast furnace gas changes non-linearly, so the traditional prediction method is difficult to deal with it. Least squares support vector machine (LSSVM), which is a machine learning method based on statistical learning theory, can be good to solve non-linear problems. It will be established a LSSVM model to predict the number of blast furnace gas in this article. However, LSSVM forecasting precision for the model parameters are very sensitive. In order to achieve more ideal effect, this paper is introduced a kind of improved genetic algorithm to optimize the parameters of LSSVM model. Instruct a short-term forecasting model of blast furnace gas based on genetic algorithm and LSSVM so as to improve the prediction accuracy of forecasting model.
Cited By :26, Export Date: 4 January 2015
Reservoir computing has emerged in the last decade as an alternative to gradient descent methods for training recurrent neural networks. Echo State Network (ESN) is one of the key reservoir computing "flavors". While being practical, conceptually simple, and easy to implement, ESNs require some experience and insight to achieve the hailed good performance in many tasks. Here we present practical techniques and recommendations for successfully applying ESNs, as well as some more advanced application-specific modifications.
Process industries show an ever-increasing interest in reducing their environmental impact and energy consumption as well as maintaining an acceptable profit. This is particularly true for industries such as the steel one, which is among the highest energy consumers worldwide. Process modelling and optimization are techniques by which this problem can be effectively addressed, particularly if the overall system is optimised as a whole. In this article we describe a model for a discrete dynamic optimization of the process gas network in an integrated steel plant. The main sub-plants are modelled in order to calculate mass and energy balances in different scenarios of operation. The scenarios are then exploited within a multi-objective optimization problem, where cost and CO2 emissions are simultaneously minimised. The optimization is carried out by exploitation of evolutionary algorithms that enable a flexible problem formulation and to effectively generate a set of different trade-off solutions. Application of the model to an industrial case study results in an interesting potential for reduction of CO2 emissions and costs. The described optimisation model is embedded in a more general software tool to help the plant managers in their daily decision-making process.
The management of process industries is becoming in the recent years more and more challenging, given the stringent environmental policies as well as raising energy costs and the always-present drive for profit. A way to help plant decision makers in their daily choices is to refer to decision-support tools, which can give advice on the best practices on how to operate a plant in order to reduce the energy consumption and the CO2 emissions keeping at the same time the costs under control. Such an approach can be useful in a variety of industries, particularly the most energy-intensive ones such as iron and steel industries. In this paper, an approach to the realisation of a software system, which allows to generate internal reports on the plant performances, as well as to simulate the plant behaviour in different scenarios, is described. The main production processes (coke plant, blast furnace, steel shop, hot rolling mill) are described and simulated focusing on the prediction of products flow rates and composition, energy consumption and GHGs (Greenhouse Gases) emissions in different operating conditions. The importance of a correct management of the CO2 within the plant is underlined, particularly with regard to the new EU Emission Trading System, which will be based on European benchmarks. The software tool is illustrated and a case study is included, which focuses on the simultaneous minimisation of the CO2 emissions and maximisation of the profit through an optimised management of the by-product gases. The results from the case study show a good potential for process improvement, by a reduction in the cost and environmental impact.
The byproduct gas in steel industry is one of the most significant energy resources of an enterprise. Due to the large quantity of yield, fluctuation, and various categories of users encountered in a blast furnace gas (BFG) system, it is very difficult to accurately predict the amount of gas to be generated and forecast the users' consumption demand. In this paper, a two-stage online prediction method based on an improved echo state network (ESN) is proposed to realize forecasting in the BFG system. In this method, one completes the prediction realized at the levels of BFG generation and consumption using a class of ESN with input compensation and parameter optimization. At the second stage, to predict gas holder level of the BFG system, the energy flows being predicted at the first stage are denoised, and their correlation with the holder level are determined by using a concept of grey correlation with time delay. Then the effect factors exhibiting high correlation levels are extracted to construct the model of the gas holder. The prediction system designed in this manner is applied in the Energy Center of Baosteel Co., Ltd, China. The results demonstrate that the prediction system exhibits high accuracy and can provide an effective guidance for balancing and scheduling of the byproduct energy.
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