Mining is a traditionally analogue business. After all, the industry's symbol worldwide is a hammer and pick. Yet, despite the sector's antiquated reputation, some major mining companies are taking a progressive stance and proving digitisation and automation can achieve much better operational outcomes. Known as Mine 4.0, the industry is seeing digital transformation creep into everything from trucks, drills and trains to back-office processes, such as procurement and supply chain logistics. Miners have very little control over the revenue side of their business, as the global commodities crash of 2014 to 2015, when prices plunged by more than 30 per cent, and indeed the coronavirus epidemic demonstrate.

Check out their product page … link Get the Chemometrics and Spectroscopy News in real time on Twitter @ CalibModel and follow us. Near-Infrared Spectroscopy (NIRS) "Integrated soluble solid and nitrate content assessment of spinach plants using portable NIRS sensors along the supply chain" LINK "Evaluation of Near Infrared Spectroscopy (NIRS) and Remote Sensing (RS) for Estimating Pasture Quality in Mediterranean Montado Ecosystem" LINK "Evaluation of Homogeneity in Drug Seizures Using Near-Infrared (NIR) Hyperspectral Imaging and Principal Component Analysis (PCA)"LINK "FT-NIRS Coupled with PLS Regression as a Complement to HPLC Routine Analysis of Caffeine in Tea Samples" Foods LINK Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR) "Model based optimization of transflection near infrared spectroscopy as a process analytical tool in a continuous flash pasteurizer" LINK "EXPRESS: Monitoring Polyurethane Foaming Reactions Using Near-Infrared Hyperspectral Imaging" LINK ...

We present a holistic data-driven approach to the problem of productivity increase on the example of a metallurgical pickling line. The proposed approach combines mathematical modeling as a base algorithm and a cooperative Multi-Agent Reinforcement Learning (MARL) system implemented such as to enhance the performance by multiple criteria while also meeting safety and reliability requirements and taking into account the unexpected volatility of certain technological processes. We demonstrate how Deep Q-Learning can be applied to a real-life task in a heavy industry, resulting in significant improvement of previously existing automation systems.The problem of input data scarcity is solved by a two-step combination of LSTM and CGAN, which helps to embrace both the tabular representation of the data and its sequential properties. Offline RL training, a necessity in this setting, has become possible through the sophisticated probabilistic kinematic environment.

The deployment of artificial intelligence (AI) in mining is enabling companies to improve their efficiency and productivity, which is crucial to their profitability. The mining industry is pivotal to the world's economy. The mining industry's top companies had a total revenue of approximately 683 billion U.S. dollars in 2018. Implementation of AI in mining activities can help push the industry even further forward by reducing the operating costs and simplifying the mining processes. A majority of mining companies still depend on traditional mining practices.

Many evaluation methods exist, each for a particular prediction task, and there are a number of prediction tasks commonly performed including classification and regression. In binarised regression, binary decisions are generated from a learned regression model (or real-valued dependent variable), which is useful when the division between instances that should be predicted positive or negative depends on the utility. For example, in mining, the boundary between a valuable rock and a waste rock depends on the market price of various metals, which varies with time. This paper proposes impact curves to evaluate binarised regression with instance-varying costs, where some instances are much worse to be classified as positive (or negative) than other instances; e.g., it is much worse to throw away a high-grade gold rock than a medium-grade copper-ore rock, even if the mine wishes to keep both because both are profitable. We show how to construct an impact curve for a variety of domains, including examples from healthcare, mining, and entertainment. Impact curves optimize binary decisions across all utilities of the chosen utility function, identify the conditions where one model may be favoured over another, and quantitatively assess improvement between competing models.

The 70-foot-tall colossus, called a "Field Scanalyzer," is the world's biggest agricultural robot, the project's researchers say. Resembling an oversize scaffold with a box perched in its middle, it lumbers daily over 2 acres of crops including sorghum, lettuce and wheat, its cluster of electronic eyes assessing their temperature, shape and hue, the angle of each leaf. The Scanalyzer beams this data--up to 10 terabytes a day, roughly equivalent to about 2.6 million copies of Tolstoy's "War and Peace"--to computers in Illinois and Missouri. Analyzing the range and depth of data generated is possible only with machine-learning algorithms, according to data scientists at George Washington University and St. Louis University, where researchers are teaching the computers to identify connections between specific genes and plant traits the Scanalyzer observes. Deep learning, a form of AI that uses conclusions from data to further refine a system, can also help pinpoint how some varieties of a plant may subtly differ from one another in ways that plant scientists may not anticipate, researchers say.

Membrane separations have long been recognized as energy-efficient processes with a rapidly growing market. In particular, organic solvent nanofiltration (OSN) technology has shown considerable potential when applied to various industries, such as petrochemicals, pharmaceuticals and natural products. The energy consumed by these industries accounts for 10 to 15 percent of the world's entire energy consumption. Nevertheless, difficulties in predicting the separation performance of OSN membranes have hindered smooth transition from lab discovery to industry implementation. Predicting the performance of membranes is a challenging task because of the complex nature of solvent, solute and membrane interactions.

Models are often given in terms of differential equations to represent physical systems. In the presence of uncertainty, accurate prediction of the behavior of these systems using the models requires understanding the effect of uncertainty in the response. In uncertainty quantification, statistics such as mean and variance of the response of these physical systems are sought. To estimate these statistics sampling-based methods like Monte Carlo often require many evaluations of the models' governing equations for multiple realizations of the uncertainty. However, for large complex engineering systems, these methods become computationally burdensome. In structural engineering, often an otherwise linear structure contains spatially local nonlinearities with uncertainty present in them. A standard nonlinear solver for them with sampling-based methods for uncertainty quantification incurs significant computational cost for estimating the statistics of the response. To ease this computational burden of uncertainty quantification of large-scale locally nonlinear dynamical systems, a method is proposed herein, which decomposes the response into two parts -- response of a nominal linear system and a corrective term. This corrective term is the response from a pseudoforce that contains the nonlinearity and uncertainty information. In this paper, neural network, a recently popular tool for universal function approximation in the scientific machine learning community due to the advancement of computational capability as well as the availability of open-sourced packages like PyTorch and TensorFlow is used to estimate the pseudoforce. Since only the nonlinear and uncertain pseudoforce is modeled using the neural networks the same network can be used to predict a different response of the system and hence no new network is required to train if the statistic of a different response is sought.

In the near future, autonomous vehicles and artificial intelligence (AI) will play a larger role in how your food is grown. Farming is as old as civilization itself, but with industrialization, modern agriculture grew in scale and sophistication to degrees never seen before in history, especially during the Green Revolution of the 1950s-60s. The sector may be poised to go through another comparable evolutionary step with machines doing important jobs in the fields. The United Nations projects the global population will increase to 9.73 billion people by 2050. While 60 percent of the global population lived in rural areas 35 years ago, about 54 percent now live in urban ones.

Venture Catalysts, India's first, largest and pioneering integrated incubator and accelerator platform, has invested an undisclosed amount in CUSMAT – a startup that builds high immersion training systems for enterprises moving metrics across productivity, safety and customer satisfaction. The seed funding round was led by Venture Catalysts investor – Raveen Sastry of Multiply Ventures. Co-investors Vaibhav Domkundwar, Better Capital, Rakesh Verma Chairman, MapMyIndia, Pratap Atwal, Director, CIPL (coronation Mining & Infra) also participated in the fund raise. Founded by three NIT Warangal, 2016 graduates Abhinav Ayan (CEO), Anirban Jyoti Chakravorty (CTO) and Soumyaranjan Harichandan (Head of Product), CUSMAT leverages AR/VR/MR and AI-based technologies to skill, upskill, train and assess people in enterprises. The company currently offers 5 training products, catering to more than 15 industries including Logistics, Electronics, Manufacturing, Mining, Steel, Cement, Pharmaceutical and Healthcare, among others.