John KW Hou, Ph.D. World- Wide Director, Industry Solutions Member of IBM Industry Academy IBM Corporation. José R. Favilla Jr

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1 1 A Big Data Approach to Optimize Energy Efficiency in the Steel Industry John KW Hou, Ph.D. Manager, IBM Center of Competence, Metals IBM Corporation José R. Favilla Jr World- Wide Director, Industry Solutions Member of IBM Industry Academy IBM Corporation Abstract Energy use in industrial operations is responsible for about 30% of total global energy consumption and greenhouse gas emissions (GHG). In steel mills, energy expense is the second largest expense after raw materials. For example, one day of energy consumption in a ten million tons steel plant is equivalent to one year of energy consumption in a data center. Finding ways to improve energy efficiency is critical to reduce costs and improve sustainability of the operations. Increasing energy efficiency has been and will continue to be an area of focus in industrial energy intensive processes. Much has been done at equipment and process control levels, but the advances in analytics, optimization and big data can provide news insights that can contribute significantly to further increase energy efficiency of industry operations, and also to contribute to meet GHG emission and sustainability goals. In this paper, we provide a brief introduction about big data and we will discuss some initial ideas of how to apply big data to help steel makers to improve energy efficiency. We will provide cases studies of the implementation of some of the ideas proposed in this paper. 1 Introduction Based on the World Steel Association Steel and Energy Fact Sheet, in 2007, 1.3 billion tones of steel were produced. Production levels are expected to double by 2050 to meet the growing

2 2 demand for steel around the world. Energy conservation in steelmaking is crucial, to ensure the competitiveness of the industry and to minimize environmental impacts. Improvements in energy efficiency have already led to reductions of about 50% of energy required to produce a ton of crude steel since 1975 in most of the top steel producing countries, as shown in Figure 1. Figure 1: Energy improvement per ton of crude steel Significant portion of the cost of steel production is related to energy, from 20% to 40%. Thus improvements in energy efficiency can impact production costs and thereby improve competitiveness. Energy efficiency varies widely in steel making plants depending on technology used in the production facilities, the production processes (cold, hot and direct hot charge), the type of raw materials and steel products, operation control technology, etc. Typically about 95% of an integrated facility s energy input comes from solid fuel (coal) and about 3-4% from gaseous fuels and 1-2% from liquid fuels. Even though major progress has been made over the years, the steel industry steel faces a number of challenges: Forecasts on energy supply and demand are not accurate Continuous monitoring, metrics and performance measurement have only partial coverage There are significant inefficiencies in consumption of energy in the process units; it is estimated that over 20% of the energy is lost prior to using on those units Process units are not properly synchronized to maximize energy efficiency Limited visibility of future status of energy pipeline and equipment Lack of tools and practices fully adequate to deal with energy shortage and contingency of energy pipeline

3 3 Lack of models that consider important factors of energy balance, e.g. time of use price, energy cost, diffusion loss and environment restrictions Stakeholders operate in silos. There is a need for holistic approaches that can consider all the factors related to energy efficiency to provide better insights on how to run the steel plant in the most cost effective way, while achieving all the production target commitments. Advances in computing power, mathematical modeling and capability to capture and process large amount of data have enabled new approaches to industry problems that were not possible before. These new approaches are commonly referred as big data & analytics. The term big data is pervasive, and yet still the notion engenders confusion. Big data has been used to convey all sorts of concepts, including: huge quantities of data, social media, next generation data management capabilities, real- time data, and much more. Whatever the label, organizations are starting to understand and explore how to process and analyze a vast array of information in new ways [1]. The big data approach to optimize energy efficiency in the steel industry should address all the aspects related to energy efficiency, including the forecasting of demand and supply, the monitoring of energy generation and consumption, plan and schedule energy use, anomalies in equipment operations that lead to higher energy use, production schedule that are fully energy- aware, environmental impact, government and regulatory compliance, response to global regulations, energy shortage, energy prices, etc. aiming to provide better insights on how to run the steel plant in the most energy efficient way. 2 Existing Approaches As energy efficiency is a critical topic for any industrial plants with energy intensive processes, many vendors have been providing energy management solutions, as indicated in the Table Note: it is not the intent here to provide a detailed review of functionality, but rather to provide a brief overview of the main vendors in this space and their existing offerings. For more information, it is suggested to look at the respective websites and/or contact these vendors directly.

4 4 Vendor ABB Schneider Electric Siemens VAI Honeywell MOX Solution Name cpmplus Energy Manager PowerLogic ION SIMETAL Energy Management System Description Captures the real-time information from process monitoring and control and production planning systems to produce energy monitoring and reporting, energy load planning and energy optimization. Optimization includes the use of the supply resources to meet the predicted consumption at minimum total cost. Captures real-time data from energy meters, Power Measurement ION series to control loads and power sources, peak rate management, power factor correction Captures real-time data from process monitoring and energy meters to provide detailed information on forecast and actual energy consumption by each process step, cost center or production units aiming to minimize load peaks, high tariff rates and energy waste as flaring. Energy Portfolio of hardware, software and services to help improve energy efficiency and reduce greenhouse Management gas (GHG) emission. It measures actual performance against dynamic targets to identify deviations and to System ensure the energy and emission levels are achieved. MOX Energy Provides automatic control and monitoring of the water, gas, electricity, air and oil needs of production. Management It provides functions for energy system supervisory control and data acquisition, real-time dispatch Systems management, data communication between the centers, etc. Table 1: Existing Energy Management Solutions Most of these solutions provide important capabilities and functionality and have contributed to improve the level of energy efficiency in the steel industry over the years. The question now is how we can leverage on what has been done to bring energy efficiency to the next level. 3 Big Data Approach for Energy Optimization As stated in [2], big data is all about seeing and understanding the relations within and among pieces of information that, until very recently, we struggled to fully grasp. The challenge of processing large amount of data accurately has been with us for a while. For most of history we worked with only a little data because our tools to collect, organize, store and analyze it were limited. Today the technical environment has changed. Of course, there still is, and always will be, a constraint on how much data we can manage, but it is far less limiting than it used to be and

5 5 will become even less so as time goes on. Considering the capabilities of the technical environment, the challenge now is what we can do with the capabilities that are at our disposal. We need to start thinking out of the box with a new mindset to explore new opportunities to create business value. Applying big data and analytics to improve things is a journey. We can start this journey by tackling simple, but larger problems than before. The learnings from our initial steps can fuel new ideas and approaches to keeping us progressing in our journey. The big data approach for energy efficiency should start with a vision that captures the understanding of all the factors affecting energy efficiency in a steel plant. The figure 2 provides an attempt to capture such vision. Figure 2: Vision: Big Data Approach to Energy Efficiency Energy is an essential element of every aspect of steel making, from the reduction of iron ore to molten iron, through the various stages of processing and finishing. The main sources of energy in steel making are coal, electricity, natural gas and by- product gas from coke ovens and blast furnace. One of the key raw materials in steel making is coal. It is used to convert iron oxide into metallic iron, as a carbon source to produce heat, and in the blast furnace to support the burden (preventing the iron and fluxes from collapsing into the liquid iron, and providing a porous medium through which gases can pass). Coal gas can also used at power stations to generate electricity. Selling the extra electricity back to the grid during peak hours, if government regulation allows, could mean additional revenue growth for the steel plant. On the steel production plan side, the plant is composed by a number of production units that, in most cases, are energy intensive processes. Additionally, the complexity of scheduling the steel making plant, hot rolling and cold rolling and finishing processes is quite high. Large number of conflicting constraints exists. The field of applying Operations Research (OR) in materials designs and operations scheduling has evolved significantly during the last decade [3]. The external

6 6 factors such as government and regulatory compliance, greenhouse gas emission (GHG), time of use prices, etc. should also be an integral aspect of achieving energy efficiency. As we progress in the big data journey, higher business value should be realized by leveraging on what has been done before already and taking steps towards creating holistic analytical models that will bring together all aspects of energy and operations efficiency simultaneously. Figure 3: Big Data Journey Towards Higher Business Value Figure 3 proposes some initial ideas towards the adoption of analytical models to continuously optimize energy efficiency based on energy price, models that take into account all operations and energy constraints simultaneously to optimize plant throughput at the most energy efficient way. The following session provides an overview of some cases studies that demonstrate progress being made towards the big data journey.

7 7 4 Case Studies Increase energy efficiency with advanced optimization in a Japanese Steel Maker Melt shops and hot strip mills are energy intensive processes. In a typical steel production environment, the temperature of a slab is about 1800 C when it is fresh out of the caster. When casting and hot rolling processes are not synchronized, slabs are placed in the slab yard to cool down before it can be transported to the hot strip mill for rolling. Before the hot rolling starts, slabs enter the reheat furnace to raise the temperature to 1200 C where huge amount of energy is consumed. Hot charging rolling (HCR) and direct hot charging rolling (DHCR) are used to reduce energy use: slabs are sent within a time window or directly into the hot strip mill as soon as they exit the caster without having to cool down, and therefore there is no need for reheat. This is a highly sophisticated operation that requires casting and rolling mills working very closely in order to synchronize the output from the caster with the input to the rolling machines. Even though DHCR is not new in the steel industry though achieving high DHCR ratio required the implementation of sophisticated analytical models that enables the synchronization of the casting sequences with the rolling sequences so the need to reheat the slabs is reduced as much as possible (Figure 4). Figure 4: Direct Hot Charge Rolling to Maximize Energy Efficiency In terms of mathematical jargons, the DHCR problem is modeled as a Mixed Integer Program (MIP) where the goal is to minimize the wait time of each slab such that the constraints associated with the casters and the rolling mills are satisfied. Constraint Programming, heuristic

8 8 algorithms, and graph theory were used to find the optimal solution. Figure 5 provides a partial view of the model formulation. Figure 5: Model Formulation for Direct Hot Charge Rolling This solution has been implemented in a major steel plant in Japan. It generates significant business results: the average energy saving is 10% while the productivity of the hot strip mill increases by 22% (measured by the number of slabs and the lengths of the coils processed by the rollers). Optimizing Energy Dispatch in a Chinese Steel Maker An integrated steel plant uses a variety of energy sources. An example is shown in Figure 6 that illustrates Chinese steel maker utilizes eight types of energy. Figure 6: Energy Sources in a Chinese Steel Maker

9 9 Coal gas, a mixture of gases such as hydrogen, methane, and carbon monoxide, is created as a by- product in the blast furnaces, coke ovens, or basic oxygen furnace (BOF). Coal gas is fully recoverable and can be reused to supplement other energy to reduce the consumption cost. Coal gas can also used at power stations to generate electricity. Selling the extra electricity back to the grid during peak hours, if government regulation allows, could mean additional revenue growth for our client. Unfortunately, most steel companies do not capture all these opportunities. The emission rate of coal gases ranges from 4% - 12%. For a 18- million tons steel plant, the loss of energy at blast furnace alone is equivalent to the heating value of 300,000 tons of standard grade coal. The analysis identified that this inefficiency is caused by inadequate business processes, insufficient adoption of business analytics and lack of a common data platform to capture plant wide information (Figure 7). Figure 7: Root Cause of Energy Inefficiency and Recommended Actions The energy inefficiency problem should be tackled simultaneously from three perspectives: processes, modeling, and enabling IT. Business Processes: old habit of silo operation has to be broken. The consulting team works with the client to streamline the dispatch processes among departments. For example, when the coal gas shortage is on the horizon, energy dispatch center needs to work with the downstream users to accommodate the situation and also informs the power station with updated coal gas supply plan (with reduced amount of coal gas).

10 10 Modeling: It was identified that one of the biggest challenges for energy dispatch is to make quick decision based on the latest network status. However, making decision in such environment is not that easy; in addition to the complex constraints and rules that dominate the decision, there are conflicting goals for the stakeholders. For example, under a certain supply and demand profile, is it more economical (in terms of cost, revenue) to send the coal gas to production plants or to the power station? To support the decision making, two analytical models were developed. The first model is a forecast model that forecast energy supply and demand. The model was trained with 6- month worth of historical data and then used forecast the supply and demand for the next 8 hours with 15 minutes time granularity (Figure 8). Figure 8: Forecast model for energy supply and demand The second model is a Mixed Integer Programming (MIP) model that minimizes the overall energy cost by optimizing the mixing of coal gases such that energy heat requirement and other physical network constraints are satisfied.

11 11 Figure 9: MIP model to minimize energy cost Enabling IT: To establish the foundation (or a platform) for a big data program for energy efficiency, it is necessary to implement technologies to collect data from a variety of sources, store operational data using pre built data schemes for energy prediction and optimization as well as leverage on powerful and specialized tools statistical analysis, mathematical optimization, decision management and business analytics. The enabling platform should provide an ease to use graphical user interface that should be accessible from anywhere, including mobile devices. This architecture is presented in Figure 10

12 12 Figure 10: Enabling IT Platform for Energy Optimization The implementation of this solution enabled this steel maker to improve its energy efficiency reducing its energy cost in about $20million USD every year by managing its coal gas smartly. 5 Conclusions and Lessons Learned Even though we are still in the beginning of our journey to leverage on new approaches to apply big data & analytics to improve energy efficiency in the steel industries, the advances in computing power, mathematical modeling and capability to capture and process large amount of data have enabled new approaches to industry problems that were not possible before. The case studies provided in this paper have demonstrated that the utilization of advanced analytics tools can result in significant improvement in the usage of energy in the steel operations. Of course, much more can be done. In order to making additional progress in this journey, there is a need to establish tight collaboration between experts from different domains: experts in energy management in steel plants, regulatory compliance personnel, procurement, production operations scheduling as well as the technology experts to come up with novel ideas that could be materialized using the big data & analytics technology that are now at our disposal.

13 13 6 References [1] The real- world use of big data, IBM Institute of Business Value, [2] Big Data, Viktor Mayer Schönberger and Kenneth Cukier, 2014, ISBN [3] Driving Efficiency by Optimizing Core Process in Steel Production, IBM White Paper, 2008

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