Special Session 1: Digital Twins and Predictive Control for Heavy Industrial Equipment
Introduction: This session
will focus on the use of digital twin models, predictive
control methods, and real-time monitoring in heavy
industrial equipment, with an emphasis on large mining
machines such as electric rope shovels. Over the past few
years, industrial automation has evolved quickly, and the
ability to combine physics-based models with data-driven
insights has created new opportunities for improving safety,
performance, and reliability.
The aim of this session is to bring together contributors
who are working on practical and research-driven solutions:
cycle detection and segmentation, vibration-based condition
monitoring, fault prediction, event-driven control, and
high-availability PLC/SCADA system design. These methods are
increasingly being adopted in areas where equipment operates
under extreme duty cycles and where downtime is costly.
The session will encourage papers with real engineering
results, experimental validation, or clear industrial
relevance. Work from mining, construction, manufacturing,
and other heavy-equipment environments is welcome.
Keywords:
• Predictive control of large machines
• Cycle segmentation and operational analytics
• Real-time monitoring and condition assessment
• Data-driven and event-driven control strategies
• PLC/SCADA integration and automation methods
• Vibration-based diagnostics for mining systems
• Fault-tolerant and safety-critical control
• Edge computing in industrial equipment
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Organizer
Avadh Nagaralawala
Wilmington University, USA
Bio: Avadh
Nagaralawala is a Control Systems Consultant and Project
Management Professional (PMP) with more than 12 years of
experience in industrial automation, advanced control
engineering, and digital transformation across large-scale
industrial environments. His work focuses on PLC–SCADA
integration, digital twin development, predictive monitoring
architectures, and modern reliability-centered control
strategies for complex machinery and critical operations.
He has contributed to several automation modernization
initiatives involving legacy-to-next-generation PLC
migration, real-time diagnostic frameworks, vibration-based
condition monitoring, and data-driven performance analysis.
Avadh has also developed model-based testing methods and
system-level simulation tools that strengthen operational
efficiency, equipment availability, and intelligent fault
detection.
He has delivered invited talks at SME, CIM, PMI Chapters,
and other technical forums, and his commentary on
automation, mineral-processing technologies, and emerging
industrial innovations has been cited in international
publications. Avadh actively participates in professional
societies including IEEE, ISA, and PMI, and contributes to
peer review and technical community activities.
His current interests include cyber-physical systems,
intelligent automation, robotics-enabled monitoring, digital
simulation frameworks, and advanced control solutions for
future industrial systems.