PROCESS CONTROL
Yokogawa ’ s OpreX AI solutions offer predictive and prescriptive maintenance that optimise operations while reducing downtime
The plant of the future
The fully autonomous mineral processing plant continues to become closer to reality thanks to advances in AI , machine learning , predictive & prescriptive maintenance , as well as better sensors and analysers . Paul Moore reports
For a deep dive viewpoint into everything process control related in mineral processing , we started by asking Japanheadquartered Yokogawa , the turnkey supplier of high performance , high quality process control and field instrumentation systems , solutions and services to what extent are today ’ s mineral processing plants making use of prescriptive and preventative maintenance and to what extent is the potential still misunderstood or not recognised .
Anu Mahesh , Mining Industry Marketing Manager at Yokogawa America told IM : “ Modern mineral processing plants are steadily advancing toward prescriptive and preventative maintenance strategies , leveraging AI , IoT , and sensor technologies to enhance operational efficiency . Real-time machine health monitoring and condition-based maintenance have become integral to increasing uptime , extending equipment lifespan , and reducing operational costs . However , these strategies are even more critical in mining , where the high costs of unplanned downtime make planned maintenance essential . Despite this , the industry continues to rely heavily on time-based maintenance , often underestimating the value of predictive approaches .”
In the current landscape , processing efficiency – throughput – is a primary focus for many producers . This emphasis is now paired with an increasing drive for energy efficiency , particularly for Tier 1 producers tasked with meeting ESG initiatives . Yokogawa says these priorities place additional pressure on maintenance strategies , especially in optimising mean time between failures ( MTBF ). As a result , asset health and optimisation are becoming central to discussions with end users , reflecting a shift toward more sophisticated maintenance planning .
But there are key challenges to broader adoption remaining . Mahesh : “ Many operators are unaware of the cost-benefit tradeoffs of predictive maintenance , leading to continued reliance on reactive and time-based methods . The significant upfront investment in advanced monitoring systems and AI-driven platforms often deters smaller operations , despite clear longterm benefits . And there are data integration challenges , as integrating modern AI-powered systems with legacy infrastructure remains a barrier for many plants .”
The company says the adoption of AI is emerging as a game changer , with significant potential to impact maintenance practices across mineral processing assets . Mahesh commented : “ Advanced predictive maintenance technologies enable plants to anticipate and mitigate equipment failures , reducing downtime and optimising maintenance schedules . However , the industry has yet to fully optimise the value of planned maintenance through the use of these advanced tools . For broader adoption , the focus must shift toward providing affordable and scalable solutions , better training for operators , and concrete evidence of ROI through case studies . By addressing these gaps , the industry can better align with ESG goals and operational
efficiency , ensuring preventative and predictive maintenance strategies deliver their full potential .”
Looking at specific Yokogawa technology solutions that meet mining ’ s needs for process optimisation , advanced analytics , and control system integration , it says its Centum VP and Exaquantum platforms provide a robust integration of real-time control and advanced analytics , enabling process modeling for efficient operations . Yokogawa ’ s OpreX AI solutions offer predictive and prescriptive maintenance that optimise operations while reducing downtime . And Yokogawa provides seamless edge-to-cloud integration through its Edge computing and IIoT solutions , enabling centralised control across distributed assets .
What role does predictive analytics play in making the autonomous processing plant a reality ? Mahesh states : “ Predictive analytics is fundamental to realising autonomous processing plants , playing a critical role in several key areas . By analysing equipment behaviour trends , predictive analytics can anticipate failures before they occur , minimising downtime and ensuring continuous operations . Advanced algorithms leverage historical and real-time data to suggest process adjustments , boosting recovery rates and throughput . Predictive models enable adaptive control systems to self-adjust in response to variables like fluctuating ore grades or operational conditions . AI-powered predictive systems optimise the use of energy , reagents , and labor , driving efficiency across the board .”
The shift toward autonomous operations relies on systems that not only act in real-time , but also anticipate future conditions , making predictive analytics indispensable in this transition . The company argues that increasing interest in autonomous operations stems from several drivers , including the growing acceptance of autonomous mobile equipment and the need to address human resources constraints . Mining operations that integrate predictive analytics gain advantages , such as reduced inventory needs , increased equipment uptime and availability , and improved energy optimisation .
“ At Yokogawa , predictive analytics is a cornerstone of our IA2IA ( Industrial Automation to Industrial Autonomy ) vision . Our approach extends analytics beyond individual assets to encompass the supply chain and production , providing a comprehensive foundation for autonomous operations . This holistic perspective ensures our solutions not only enhance plant efficiency , but also contribute significantly to the broader ecosystem of autonomous industrial processes .”
64 International Mining | JANUARY 2025