Traditional industries such as steel, mining, chemical, and other energy-intensive processes face ever increasing challenges with respect to operations and capital costs, energy and resource efficiency, and carbon emissions. Even moving past the 2020 pandemic, the outlook for the metals industry in particular suggests that sustainable competitiveness will depend on effectiveness rather than scale. And effectiveness finds in digitalization one of its most important levers.
Nevertheless, digitalizing the process industry requires a clear vision about business strategy, culture, and, of course, digital technologies.
The vision of the Learning [Steel] Plant
The Learning [Steel] Plant is SMS group’s vision of a holistically connected production facility, which uses intelligent pattern recognition algorithms to determine all scenarios in advance, draws the appropriate conclusions from real events, Learning [Steel] Plant and continually trains and monitors itself by means of artificial intelligence. The concept focuses on forward-looking production planning, product quality assurance, monitoring of the plant condition, and predictive energy management. To achieve optimal results along the entire production chain, it is necessary to adequately model the processes from material provision through production to distribution and to master the relevant machine, process, and product data.
The basis for a sound and intelligent plant management is the possibility of predicting the asset condition and scheduling production processes. An optimum plant health forms the basis for all subsequent processes. The predictability of equipment condition allows spontaneous reactions to process deviations. Energy-saving potentials are identified using predictive energy management tools like the Viridis Energy & Sustainability Platform. Predictive production planning, in turn, forms the basis for manufacturing products of the best possible quality. This approach responds to the new requirements in the industry: Today, it is no longer only sales that exert influence on the production process, but also production that is guiding sales. Open production slots are predicted, virtual orders generated, and applied efficiently. Corresponding production quantities and delivery dates on which sales are oriented are specified.
Main task of the Learning [Steel] Plant is to, fundamentally, turning data into information and information into value. It makes use of all the advantages of innovative technologies to increase its productivity and user-friendliness and supports sustainable green steel production.
Energy management made efficient
The Viridis Energy & Sustainability Platform – created by Vetta, an SMS group company – is an integrated solution for energy, resources, and sustainability management for the steel, mining, and other energy-intensive process industries. Viridis creates sustainable value according to three levers: energy and resources operational efficiency, energy and resources planning, and integrated energy and resources management.
Once deployed, Viridis establishes a single source of truth system for all information related to the plant’s entire energy matrix planning, consumption, and conversion, covering not only electricity, but also fuel gases, cryogenics, water, waste, cogeneration gases, and any critical resources. By combining perspectives between the shop-floor and top-floor, Viridis directly contributes to effectively implementing energy and resource operational efficiency and optimized planning, thus strengthening the continuous improvement cycle.
Viridis then adds an essential new pillar to the Learning [Steel] Plant by implementing predictive energy, resources, and sustainability management. That means establishing a comprehensive management perspective of a steel plant operation, from planning to execution, which not only seeks to optimize costs, throughput, and quality, but also energy-related costs, raw material usage, and even carbon, greenhouse gases, and waste emissions – an enhanced take on industrial competitiveness.
Converting Big Data into Smart Data
With Viridis, digitalization can directly accelerate and leverage the gains in energy efficiency and in the reduction of carbon emissions in industry. Smart sensors can collect energy consumption data and information about the operational context (e.g. production as well as process and environment variables). This enormous volume of data feeds artificial intelligence algorithms that analyze and recommend actions that optimize, in a decentralized way, the energy and environmental efficiency of industrial teams and processes. Supply and demand of each energy input can then be controlled dynamically, capturing efficiency gains in the entire value chain.
The data collected from the factory floor also feeds forecasting models that precisely estimate, starting from the evaluation of scenarios and production conditions, the volume of each energy resource required by the operation. Simulation tools allow the evaluation of different options for acquiring and executing contracts for buying and selling energy, aiming at reducing their unit costs and simultaneously prioritizing sources with lower levels of emissions. Robust plans can then be created, reducing exposure to operational and financial risks, and improving cost control.
In addition, digitalization allows the returns of necessary transformation efforts to be measured and verified with greater accuracy, strengthening a virtuous cycle of investment, reducing carbon emissions, capturing value, and reinvestment.
Optimization along the entire value chain
In fact, the way that energy and resources are used during the production process can have important effects over the complete value chain. Two aspects can be highlighted here: the sometimes “hidden” opportunities existing at the interfaces between different processes, and the direct contribution of energy and resources to improve product quality and asset health.
About the first aspect, Viridis can manage not only individual processes, but the entire plant, helping analyze the energy performance of specific equipment, as well as the interfaces between adjacent process lines. For instance, if the temperature with which a batch of direct reduced iron reaches the melt shop is below a specified target, the energy consumption at the melt shop may increase. At the other end, if steel slabs or billets can be fed into reheating furnaces as soon as they leave the caster strands, the consumption of fuel gases can be lowered. In some cases, Viridis was used to optimize the power dispatch of a thermoelectric cogeneration unit of an integrated steel plant by balancing the supply and demand of cogeneration gases, by taking into consideration the electricity and natural gas contract pricing rules, as well as their respective market spot prices and seeking minimal global operational costs. This can only be made possible by ensuring a deeply integrated perspective of energy and resources efficiency, planning, and management.
About the second aspect, Viridis has also been used to identify golden process batches, which correlate, using specialized machine learning algorithms, energy and resource consumption to key performance indicators of a production batch (e.g. heat), such as product quality and safe asset working conditions. Viridis can therefore, in combination with a deep knowledge about the process itself, support process engineers in determining performance benchmarks and the corresponding process variables, setpoints, and execution guidelines that would achieve them. Then, those guidelines could be delivered to operators for real-time orientation in process execution and, by doing that, avoiding quality issues or downtimes which would clearly incur in production reclassifications and inevitable losses in productivity.
Green steelmaking becomes reality
The demand for energy and resources is steadily increasing. Intelligent digital solutions are then essential to achieve a sustainable balance between economic growth and environmental responsibility. Effective energy, resources, and sustainability management is therefore of central importance for the metals and process industries and has a decisive effect on its cost efficiency and carbon footprint.
Process industries find themselves in the age of digitalization. High availability levels, consistently optimized asset conditions, safe working environments, and maximum product quality are key factors when it comes to maximizing a plant’s performance. The demand for more efficient and sustainable production processes is the norm, and the higher levels of digitalization call for modular, flexible digital solutions that ensure full plant connectivity.
With its production planning, asset health, product quality, and energy management pillars, the Learning [Steel] Plant vision is all about increasing efficiency and improving competitiveness. The benefits are direct and carry the potential to establish a virtuous cycle of investment, result and reinvestment: more competitiveness results in better financial results; with more cash in hand, more investments can be targeted at capacity expansion, productivity, operational efficiency, and energy efficiency; greater efficiency ensures lower levels of greenhouse gas emissions, reducing the environmental impact in addition to improving the quality of work.