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Within integrated steelworks, several sub-processes produce off-gases, which are suitable for reuse as energy sources for other internal processes as well as for the production of energy. An adequate and optimal distribution of these gases among their users allows valorizing at best their energy content by minimizing the need to both burn them through torches due to storage issues and to acquire natural gas to satisfy the internal energetic demand. To this purpose, the volume and energetic value of produced gases as well as the demands from internal users must be known in advance, in order to implement model-predictive control strategies aimed at satisfying the demands on the short-medium term based on the production scheduling. Such forecasting knowledge also enhances the capability to react to the variability of the process scheduling as well as to other unforeseen events.

The paper presents a machine learning-based system aimed at improving the homogeneity of tensile properties of steel strips for automotive applications over their strip length in the annealing and hot dip galvanizing lines. A novel modular approach is proposed exploiting process and product data and combining smart data pre-processing and cleansing algorithms, an ensemble of neural networks targeted to specific product classes and an ad-hoc developed iterative procedure for identifying the variability ranges of the most relevant process variables. A decision support concept is implemented through a software tool, which facilitates exploitation by plant managers and operators. The system has been tested on site. The results show its effectiveness in improving the control of the thermal evolution of the strip with respect to the standard operating practice.

Reaching the maximum yield of produced stainless flat products is an important aim to be successful in the competitive market. This paper describes a solution to optimise the allocation of orders to coils taking into account the actual product quality as well as the quality expected by the customer. In opposite to already available solutions also the appearance of over-quality products is taken into account. Especially for high quality stainless flat products the re-allocation of over-quality products to orders with higher demands can increase the added value distinctly. Another topic is the processing of Manufacturing Specifications (MS). A concept for the digitization of usually freely formulated MS is presented as well as a systematic monitoring, if the MS are really considered by the plant operators. The shown use cases were implemented at the German sites of Outokumpu Oyj, the Finnish producer of stainless flat products sited in Espoo.

The paper describes an application of optimization techniques for the minimization of the specific electrical energy consumption related to the production of steel for a steelworks situated in Italy. The major electrical consumption derives from two internal plants: the Electric Arc Furnace and the Ladle Furnace. This work addresses the problem of understanding the best settings (based on predefined models) to produce a specific steel, which is mainly characterized by its steelgrade and quality, with the minimum energy consumption.

Over the past few decades the issues related to the energy consumption and the climate change have been increased and they have achieved a significant position on the sustainability agenda of the steel industry. Steel production is among the largest energy-intensive industrial processes in the world, as well as one of the most important CO2 emission sources. However, the major role of steel utilisation in the modern society is undeniable. The challenges of industrial energy systems aim at achieving CO2 minimization, without neglecting energy efficiency as well as the development of effective models and strategies for process optimization. The application of Process Integration (PI) methods to the integrated steelmaking route, aims at achieving a reduction in the CO2 emission by optimizing material and energy systems. The work presented in this paper is devoted to the development of a model for optimal exploitation of energy resources and by-products in integrated steelworks through application of multi-objective optimisation techniques. Cases of exploitation of the system within the management of the process gases are presented in a retrofit scenario and compared to the case of nominal operation.

The paper presents a novel method for steel cutting optimization, which is performed by means of a two dimensional bin packing algorithm, with bins of different sizes. Our model extends previous mixed integer programs for bin packing, accounting for many of the issues arising in the steel manufacturing industry. In particular the proposed method can satisfy customer requirements on product quality. The developed algorithm can also deal with parameters variability occurring after the rolling processes. The final problem is formulated as a mixed integer non linear program (MINLP). This model directly applies to steel cutting, but it can be easily adapted in all of its parts, or just some of them, to other (industrial) rectangular cutting problems on different surfaces.

The paper describes a vision system equipping a CNC machine tool for cutting marble and similar stones, which allows a semi-automatic detection of surface defects and then computes an optimal distribution of the workpieces to cut by avoiding the defective parts of the slab. In order to address the workpiece allocation, a nesting algorithm is exploted which solves the so-called irregular packing problem with a limited computational time by largely reducing the time required to prepare the cutting program. The system has been fully implemented and tested on a real CNC machine tool of a marble manufacturer: the experimental setup and some exemplar results of the workpiece allocation procedure are described and discussed.

Steel production is among the most energy-intensive industrial processes and is a very relevant source of CO2 emissions. Steelworks, like most process and manufacturing industries, are facing the complex challenge of minimizing CO2 emissions while improving energy efficiency and developing effective strategies for process optimization. To this end, Process Integration methods can be successfully applied to the integrated steelmaking route with the purpose of achieving a reduction in the CO2 emissions, while optimizing material and energy systems. The work presented in this paper is finalized at the development of a model for optimal exploitation of energy resources in integrated steelworks, through the application of multi-objective optimization techniques, which can be beneficial both in new design and as a retrofit. Using these techniques, a series of solutions can be found representing optimal trade-offs among different objective functions and the designer can select the best one or specify further criteria. Combining these methods, a retrofit was devised which optimized off-gas exploitation. This retrofit was then compared to the current operation.