Metals & Mining

CCG - Case Study Metal Manufacturer Better Predicts Steel Melting Results with Azure Machine Learning


Steel is the world's most popular construction material because of its unique combination of durability, workability, and cost. Methods for manufacturing steel have evolved significantly since industrial production began in the late 19th century. Today, steel production makes use of recycled materials. A top U.S. steel manufacturer, "Metals, Inc.," (name withheld) purchases scrap metal and melts it into steel billets either to sell or to cast into other finished goods for sale. Despite the high stakes, the quality measurements for each batch are not available until the last few minutes in the 90-minute melting process.

Six European cities tap AI to cut carbon emissions


Helsinki, Amsterdam, Copenhagen, Paris Region, Stavanger and Tallinn will challenge companies to develop energy and mobility solutions using artificial intelligence (AI) as well as 5G, Internet of Things (IoT) and other related technologies. The initiative is part of AI4Cities, a three-year EU-funded project bringing together European cities looking for AI solutions to reduce their greenhouse gas emissions and meet climate commitments. The cities and regions will go through a pre-commercial procurement (PCP) process, which allows them to steer the development of new solutions directly towards their needs. Once they have defined their requirements, the cities will challenge start-ups, SMEs and larger companies to design solutions applying the use of AI and other technologies. Total funding of €4.6 million will be divided among the selected suppliers throughout the whole PCP process.

Freeport to invest in data science, AI programs at North/South America mines - International Mining


After carrying out a successful pilot at its Bagdad copper operation, Freeport McMoRan says it is rolling out a program across its North America and South America mines involving the use of data science, machine learning and integrated functional teams. The program, aimed at addressing bottlenecks, providing cost benefits and driving improved overall performance, was announced in its December quarter results this week. It said: "During 2019, FCX (Freeport) advanced initiatives in its North America and South America mining operations to enhance productivity, expand margins and reduce the capital intensity of the business through the utilisation of new technology applications in combination with a more interactive operating structure." It said the Bagdad mine (Arizona, USA) pilot program, initiated in late 2018, was "highly successful" in utilising these innovative technologies and it would build on this for the implementation across its other mines in North and South America. According to a report in the Financial Times, the system at Bagdad found that the mine was producing seven distinct types of ore and that the processing method, which involves flotation, could be adjusted to recover more copper by adjusting the PH level.

Semi-Autoregressive Training Improves Mask-Predict Decoding Machine Learning

The recently proposed mask-predict decoding algorithm has narrowed the performance gap between semi-autoregressive machine translation models and the traditional left-to-right approach. We introduce a new training method for conditional masked language models, SMART, which mimics the semi-autoregressive behavior of mask-predict, producing training examples that contain model predictions as part of their inputs. Models trained with SMART produce higher-quality translations when using mask-predict decoding, effectively closing the remaining performance gap with fully autoregressive models.

6 Process Excellence Trends to watch out for in 2020


New technologies like artificial intelligence and machine learning are changing the way work gets done all over the world. We believe that 2020 is the year that companies will embrace these powerful technologies and apply them to revolutionize their business processes. Here's how process-minded leaders can capture the opportunity. In the past few years, Process Mining grew faster than any other technology in the BPM and process excellence space -- even faster than RPA, according to theInternational Data Corporation, IDC. In 2020, the rapid growth will continue.

Business Problems and Data Science Solutions Part 1


An important principle of data science is that data mining is a process. It includes the application of information technology, such as the automated discovery and evaluation of patterns from data. It also includes an analyst's creativity, business knowledge, and common sense. Understanding the whole process helps to structure data mining projects. Since the data mining process breaks up the overall task of finding patterns from data into a set of well-defined subtasks, it is also useful for structuring discussions about data science.

SCR-Apriori for Mining `Sets of Contrasting Rules' Machine Learning

--In this paper, we propose an efficient algorithm for mining novel'Set of Contrasting Rules'-pattern (SCR-pattern), which consists of several association rules. This pattern is of high interest due to the guaranteed quality of the rules forming it and its ability to discover useful knowledge. However, SCR-pattern has no efficient mining algorithm. We propose SCR-Apriori algorithm, which results in the same set of SCR-patterns as the state-of-the-art approache, but is less computationally expensive. We also show experimentally that by incorporating the knowledge about the pattern structure into Apriori algorithm, SCR-Apriori can significantly prune the search space of frequent itemsets to be analysed. I NTRODUCTION Association rules learning is a popular technique in data mining [1]. However, it is known that finding rules of high quality is not always an easy task [2]. This issue is even more significant in domains where the reliability of the obtained knowledge is required to be high (for example, in medicine). Also, association rules mining techniques usually generate a huge number of rules that have to be analysed by a human in order to choose meaningful and useful ones [3].

The environmental impact of a PlayStation 4


Just behind us, a giant industrial magnet powered up with warning signs dotted about its perimeter so we wouldn't scramble our phones. Before long, John Durrell, a specialist in superconductor engineering (who took apart more machines as a teenager than he can remember), arrived with a set of tools in his hands and a glint in his eye.

Modeling and Prediction of Iran's Steel Consumption Based on Economic Activity Using Support Vector Machines Machine Learning

The steel industry has great impacts on the economy and the environment of both developed and underdeveloped countries. The importance of this industry and these impacts have led many researchers to investigate the relationship between a country's steel consumption and its economic activity resulting in the so-called intensity of use model. This paper investigates the validity of the intensity of use model for the case of Iran's steel consumption and extends this hypothesis by using the indexes of economic activity to model the steel consumption. We use the proposed model to train support vector machines and predict the future values for Iran's steel consumption. The paper provides detailed correlation tests for the factors used in the model to check for their relationships with the steel consumption. The results indicate that Iran's steel consumption is strongly correlated with its economic activity following the same pattern as the economy has been in the last four decades.

Australia to host crowd sourced mineral exploration


The Marshall Liberal Government will be the first government globally to host a $250,000 crowd sourced open data competition to fast-track the discovery of mineral deposits in South Australia. ExploreSA: The Gawler Challenge partners with open innovation platform, Unearthed, in a world-wide call for geologists and data scientists to uncover new exploration targets in the state's Gawler Craton region. Using the Geological Survey of South Australia's historical records, primary data and research, the competition combines geological expertise with new mathematical, machine learning and artificial intelligence to increase the number of potential drill targets across central South Australia. "This state-of-the-art competition has the potential to unearth the next Olympic Dam or Carrapateena by encouraging global thinkers and innovators to interrogate our open-file data and generate new exploration models and ideas for targeting," said Minister van Holst Pellekaan. "The Marshall Liberal Government is thinking outside the square to drive investment and jobs in South Australia's vital resources sector. "Mining is one of the pillars of the South Australian economy and this competition should add to the pipeline of projects in the resources and minerals processing sector.