• Energy Research

This paper analyzes the impact of reservoir computing, and, in particular, of Deep Echo State Networks, to the modeling of highly non-linear dynamical systems that can be commonly found in the industry. Several applications are presented focusing on forecasting models related to energy content of steelwork byproduct gasses. Deep Echo State Network models are trained, validated and tested by exploiting datasets coming from a real industrial context, with good results in terms of accuracy of the predictions.

This paper aims at providing an overview of the ways for residue valorization in the iron and steelmaking industry. The important role of recycling in iron and steelmaking as a cornerstone for achieving a cleaner and resource-efficient potential is described. Several research results concerning metals and metal oxides (scrap, scale), slags, dusts, process gases, and water recycling from the iron and steelmaking process are reviewed here, aiming to detect those research gaps that still need implementation and suggest potential approaches toward potential solutions. Through a comprehensive evaluation, several possibilities are provided to incorporate effectively in metallurgical processes the bio-based or bio-derived carbon materials, namely biomass, biochar, biocoke, and polymers from waste plastics to reduce the dependence on fuel and reducing agents from fossil sources, and therefore mitigating the related environmental impact of the steel industry. Eventually, this review highlights the importance of embracing circular economy (CE) principles in iron and steelmaking, along with considering opportunities for industrial symbiosis (IS) and exploring the role of digitalization and digital solutions in recycling practices.

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.

Abstract 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 production plan and to use such forecasting to optimize the gases distribution 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 some major consumers: the hot blast stoves. 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.

Within integrated steelmaking industries significant research efforts are devoted to the efficient use of resources and the reduction of CO2 emissions. Integrated steelworks consume a considerable quantity of raw materials and produce a high amount of by-products, such as off-gases, currently used for the internal production of heat, steam or electricity. These off-gases can be further valorized as feedstock for methane and methanol syntheses, but their hydrogen content is often inadequate to reach high conversions in synthesis processes. The addition of hydrogen is fundamental and a suitable hydrogen production process must be selected to obtain advantages in process economy and sustainability. This paper presents a comparative analysis of different hydrogen production processes from renewable energy, namely polymer electrolyte membrane electrolysis, solid oxide electrolyze cell electrolysis, and biomass gasification. Aspen Plus® V11-based models were developed, and simulations were conducted for sensitivity analyses to acquire useful information related to the process behavior. Advantages and disadvantages for each considered process were highlighted. In addition, the integration of the analyzed hydrogen production methods with methane and methanol syntheses is analyzed through further Aspen Plus®-based simulations. The pros and cons of the different hydrogen production options coupled with methane and methanol syntheses included in steelmaking industries are analyzed.

Abstract 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.

This work investigates the cost-efficient integration of renewable hydrogen into steelworks for the production of methane and methanol as an efficient way to decarbonize the steel industry. Three case studies that utilize a mixture of steelworks off-gases (blast furnace gas, coke oven gas, and basic oxygen furnace gas), which differ on the amount of used off-gases as well as on the end product (methane and/or methanol), are analyzed and evaluated in terms of their economic performance. The most influential cost factors are identified and sensitivity analyses are conducted for different operating and economic parameters. Renewable hydrogen produced by PEM electrolysis is the most expensive component in this scheme and responsible for over 80% of the total costs. Progress in the hydrogen economy (lower electrolyzer capital costs, improved electrolyzer efficiency, and lower electricity prices) is necessary to establish this technology in the future.