Edge computing is helping Newcrest Mining improve throughput and reduce downtime in Australia's largest underground block cave mine, the Cadia Valley gold mine in New South Wales. Newcrest Mining won the best Primary Industry Project in our 2019 IoT Awards for the project, which uses machine learning to optimise the level of crushed ore in bins, preventing downtime. Now Microsoft and its partner Insight Enterprises have released details about the solution and its benefits. The solution is improving productivity, reducing downtime and increasing throughput, Newcrest Mining CIO Gavin Wood stated in a press release. And the company has seen a return on investment within three months of starting to use the solution.

Environmentally benign methods for the industrial production of chemicals are urgently needed. LMU researchers recently described such a procedure for the synthesis of formaldehyde, and have now improved it with the aid of machine learning. Formaldehyde is one of the most important feedstocks employed in the chemical industry, and serves as the point of departure for the synthesis of many more complex chemical products. Industrial production of formaldehyde is currently based on a large-scale procedure which consumes fossil fuels and requires a high energy input. More efficient and more sustainable modes of synthesis are therefore urgently needed, which could make a significant contribution to the mitigation of climate.

We present a simulation framework for spunbond processes and use a design of experiments to investigate the cause-and-effect-relations of process and material parameters onto the fiber laydown on a conveyor belt. The virtual experiments are analyzed by a blocked neural network. This forms the basis for the prediction of the fiber laydown characteristics and enables a quick ranking of the significance of the influencing effects. We conclude our research by an analysis of the nonlinear cause-and-effect relations.

Opportunities for digital technologies implementation, including implementation of artificial intelligence, are being implemented in the mining sector. Technologies help to save money and to solve problems that humans can't solve. McKinseyestimates that by 2035, the use of data analysis and digital technologies will help coal, iron ore, and copper producers save between $290 billion and $390 billion annually. Digital technologies and artificial intelligence enable companies to extract minerals in hard-to-reach places and under extreme weather conditions. This article first appeared in Mining Review Africa Issue 10, 2019 Read the full digimag here or subscribe to receive a print copy here This means that in an environment when mineral resources are becoming increasingly scarce, it is possible to develop deposits that used to be inaccessible, to do it without endangering lives of employees and to minimize human errors that often lead to costly mistakes.

Here is an unavoidable truth. Resource extraction is hard physical work. And perhaps this is the very reason modern investors have wandered away from mining--whether or not it's to their benefit. New AI methods may change that. Just like society, many investors today are overlooking the connection between the products we use and the source of the materials to make them. And yet it's still true that "if you cannot grow it, you have to mine it."

Agricultural fields are no less than a battlefield. Irrespective of terrain, geography and type, crops have to compete against scores of different weeds, species of hungry insects, nematodes and a broad array of diseases. Weeds, or invasive plants, aggressively compete for soil nutrients, light and water, posing a serious threat to agricultural production and biodiversity. Weeds directly and indirectly result in tremendous losses to the farm sector, which convert to billions each year worldwide. To combat these challenges, the farm sector is looking at Artificial Intelligence (AI) based solutions.

In processing and manufacturing industries, there has been a large push to produce higher quality products and ensure maximum efficiency of processes. This requires approaches to effectively detect and resolve disturbances to ensure optimal operations. While the control system can compensate for many types of disturbances, there are changes to the process which it still cannot handle adequately. It is therefore important to further develop monitoring systems to effectively detect and identify those faults such that they can be quickly resolved by operators. In this paper, a novel probabilistic fault detection and identification method is proposed which adopts a newly developed deep learning approach using Bayesian recurrent neural networks (BRNNs) with variational dropout. The BRNN model is general and can model complex nonlinear dynamics. Moreover, compared to traditional statistic-based data-driven fault detection and identification methods, the proposed BRNN-based method yields uncertainty estimates which allow for simultaneous fault detection of chemical processes, direct fault identification, and fault propagation analysis. The outstanding performance of this method is demonstrated and contrasted to (dynamic) principal component analysis, which are widely applied in the industry, in the benchmark Tennessee Eastman process (TEP) and a real chemical manufacturing dataset.

Newcrest Mining has deployed Microsoft cloud, AI and IoT technologies at Australia's largest underground, block cave mine to monitor and manage crushed ore bin levels. The soft sensor delivered a return in investment within the first three months of operation. The technology, developed by Newcrest in association with Microsoft and its partner Insight Enterprises, has been rolled out at Newcrest's Cadia Valley gold mine in NSW. The challenge facing Newcrest at Cadia was managing the levels in the underground crushed ore bins. If the bins overfill, they have to be manually emptied introducing lengthy and expensive production delays.

This research spans academia, militaries (though it can be difficult to suss out the actual breakthroughs from government propaganda), and private enterprise. Perhaps the most well known privately-owned robotics developer is Boston Dynamics, makers of the Atlas. You may remember this bipedal robot from September when it showed off its uncanny parkour abilities, which the robot can pull off 80 percent of the time. The Atlas is able to move so fluidly thanks to a novel optimization algorithm that breaks down complex movements into smaller reference motions for its arms, torso, and legs. However, while Boston Dynamics' Big Dog was developed as a quadrupedal cargo carrier for military operations, the Atlas is strictly for use as an emergency first responder.

Standing onstage in an ornate conference room at the Delta Bessborough Hotel in downtown Saskatoon, former Saskatchewan premier Dr. Grant Devine pitched the agri-food industry on a new idea: a wheat tube. More specifically, a hypothetical hyperloop Devine says could fire shipments of wheat from Moose Jaw to Langley, B.C. at hundreds of kilometres an hour. He says students at the University of Saskatchewan, where he is a professor, had priced the idea at around $18 billion. "You'd load it like you would any other hopper car, load it in the capsule and -- zoom! -- it's out there in a matter of hours," Devine said. Dr. Grant Devine speaks at the AIC2019 conference in Saskatoon, SK on Wednesday, November 6, 2019.