BehrTech and MAJiK Systems have formed a strategic partnership to bring wireless connectivity to legacy Programmable Logic Controllers (PLCs). Based on BehrTech’s MYTHINGS wireless
connectivity platform and MAJiK’s PLC data monitoring and analytics software suite, the integrated PLC solution is claimed to provide robust, long-range and scalable connectivity for PLC data communications in remote and complex industrial environments.

BehrTech is a provider of next-gen wireless connectivity for Industrial IoT, headquartered in Toronto, Ontario, Canada. MAJik, also based in Ontario, provides a platform for collecting production data and equipment effectiveness in real time.

The MAJiK integration platform collects critical data points (i.e., PLC tags) from brownfield PLCs. Through a connection to the MAJiK platform, a MYTHINGS transceiver securely transfers this data to a remote base station. Data can then be relayed to an enterprise cloud platform or on-premise application system such as an Enterprise Resource Planning solution for storage, analytics and visualization. The solution does not require PLC reprogramming and supports multiple cross-vendor PLCs and physical interfaces.

“Most legacy industrial assets, machines and facilities are not designed to connect beyond campus networks. This creates huge data silos,” Jared Evans, COO at MAJiK Systems, said. “Our PLC integration solution enables companies to transform their brownfield plants into digital factories without absorbing the costs and complexities of building an entirely new greenfield plant or reprogramming legacy PLCs.”

Highlighting a recent case study involving a Nevada gold mine, the two companies explained that the heap-leach operation required trucks to load and transport lime from a remote silo to the plant. The lime silo was isolated and disconnected from the administration building, located on the far side of a 300-ft-high leach pile. Due to this physical obstruction, Ethernet cabling from the local PLC, which captures critical data about the silo level and lime dispense rate, was impossible. Traditional wireless solutions were also not feasible because of the weak penetration capability of the radio link. Implementing such solutions would also require complex PLC reprogramming.

The lack of connectivity left a visibility gap in the company’s heap-leaching process, with up-to-date lime consumption information unavailable unless someone inspected the silo several times daily. This manual approach was inefficient and failed to provide accurate information for scheduling refills.

The company turned to a joint solution involving BehrTech and MAJiK to bring IoT connectivity to the operation. At the core of the solution is MYTHINGS — a robust wireless connectivity platform, featuring MIOTY LPWAN that integrates into the existing PLC system in a non-invasive manner, leveraging MAJiK’s software. A pilot installation was successfully conducted to verify the technical viability of the solution at the mine site.

Following deployment of the system, an integration platform running MAJiK software now interfaces with the PLC at the lime silo, using Ethernet to derive vital data points. A MYTHINGS transceiver connected with the integration platform then transmits collected data every 5 minutes to a remote base station inside the administration building, where the data is relayed to both a central PLC that visualizes current mining processes and a cloud platform for predictive analytics.

Immediately upon installation, the mining company was able to extract and transmit PLC data with no packet errors, according to BehrTech. With the MYTHINGS platform in place, the gold producer can now monitor in real-time the lime silo level, the weight of each dispensed lime dose, and dose counts per hour. Based on the silo level, refills can be accurately planned and on-site inspection can be eliminated. This helps circumvent expensive production delays and over-ordering, while improving employee productivity.

The lime dispense rate can also be calculated and monitored for full transparency of lime usage. This improves the pH control of the cyanide to enhance production efficiency and worker safety. The company is also able to correlate ordered and usage amounts to detect any abnormalities and bottlenecks, such as silo leakages or inaccurate deliveries.

University of Nevada Program Focuses on Mining AI Issues

Mining companies from around the world have begun using artificial intelligence (AI) in their operations. From safety and maintenance, to exploration and autonomous vehicles and drills, AI is being used to navigate efficiencies and speed. With this new technology, however, comes an ever-growing need for a workforce who can navigate these new systems. The University of Nevada-Reno recently announced a $1.25 million grant from the National Institute for Occupational Safety and Health that will support an interdisciplinary academic team committed to graduating six doctoral and four master’s degree students who will address certain AI-related challenges pertaining to safety and health issues in mining operations.

“Future mine engineers need to understand emerging technology like AI, drones and big data,” Javad Sattarvand, University College of Science assistant professor of mining engineering and the project’s principal investigator, said. “We claim creating excellence in the workforce is the missing part of the chain, which would make mining engineers more aware of health and safety issues of the future.”

Sattarvand said this project will help elevate the safety of mines. “Failure is inevitable in any mine,” Sattarvand said. “The path to safer and healthier mining operations crosses only through development of an academic human resource capacity with a greater understanding of emerging technological infrastructures.”

These technological infrastructures include artificial intelligence, Internet of Things, big data, cloud computing, robotics, teleoperation, immersive technologies (Virtual/Augmented Reality), drones and mobile crowdsourcing.

Joining Sattarvand on this project are educators from the colleges of Business, Engineering and Science at the university. Co-investigators include Bahram Parvin, professor, College of Engineering; George Danko, professor, College of Science; Amir Talaei-Khoei, assistant professor, College of Business, and Bahrooz Abbasi, assistant professor, College of Science.
Support from the mining industry includes help from companies such as Freeport-McMoRan, Howden, Kinross Gold, Komatsu, Newmont Mining, Orica, Sandvik and Vale, among others. “Having these companies on board shows that they too are finding a missing piece in the workforce,” Sattarvand, said. “Each industrial partner is a key element to allowing for this project to work. In fact, the big data team of Freeport and the artificial intelligence team of Vale have added an invaluable partnership to this proposal.”

The six sub-projects proposed for the grant effort include:

  • Automated Rockfall Risk Alert System for Open-Pit Mines aims at automating and enhancing the highwall failure of open-pit mines and involves drones, AI and big data.
  • Tailing Instability Risk Alert System will create an automated monitoring system for tailing dams of mines and involves drones, AI and big data.
  • Explosive Energy Distribution Optimization System will generate customized charging for blastholes to accommodate drilling errors and involves drones, AI and big data.
  • Comprehensive Intelligent Exposure Monitoring System will eliminate the need for personal exposure monitoring units and involves mobile crowdsources, AI and Internet of Things.
  • Immersive Teleoperation of Mining Machines will enhance the productivity and safety of teleoperations of mining machines and involves virtual and augmented reality, robotics and teleoperation and AI.
  • Simulation-based Smart Evacuation of Underground Mines will improve the effectiveness of mine evacuation protocols using smartwatches and involves mobile crowdsourcing, AIO and big data.