Driving Autonomous Operations
In the fast-paced industrial landscape, autonomous operations are becoming a key transformative force, revolutionising numerous sectors by enhancing sustainability and efficiency. At the heart of this transformation is the advent of "smart assets" — machinery and infrastructure linked to advanced data-driven technologies for enhanced control and management of assets.
Smart assets are transforming traditional industrial operations into intelligent, adaptive, and efficient systems. They don't aim to replace human input but seek to augment human expertise with more intelligent technologies. Amid skills shortages, cost pressures, and industry uncertainties, these smarter systems offer a timely solution.
As we embark on this transformation, several intriguing questions come to the fore:
How can we leverage smarter technology to automate manual surveillance and optimisation tasks that we perform daily?
How can we embed our operational knowledge and experience into the technology to drive systematic repeatability in how we operate, to scale our workforce to more assets?
With increasingly flexible models, how can we appropriately specify the system's objectives to avoid introducing additional complexity or instability, and ensure we drive stability and simplicity?
What disciplines and expertise do we need to successfully pilot these technologies and implement them on a large scale?
The answers to these questions will be instrumental in shaping the future of autonomous operations and unlocking their full potential in diverse industrial sectors.
Why Now?
With the evolution of technology (Industry 4.0), the integration of assets into unified control and production management systems is now possible. The fusion of Information Technology (IT) and Operational Technology (OT) opens up opportunities for more sophisticated optimisation capabilities at the network level, minimising manual tasks, and fostering intelligent control.
In 2023, SIG ML has collaborated with Kelvin AI to unveil an intelligent control system, now proven at scale, orchestrating a network of hundreds of wellsites in Australia. With the feasibility of intelligent automation demonstrated, now is the perfect time to consider smart technology that can drive autonomous operations in your organisation.
For instance, smart assets can dynamically optimise a production system based on real-time data, dramatically reducing the time and resources required by traditional methods. Smart assets can coordinate the operation of a network, leading to net gains in production while simultaneously reducing power consumption - a feat that standalone control systems lacking network oversight might struggle to achieve.
In the oil and gas sector, configuring controllers has become 90% faster, and manual exception-based surveillance tasks reduced by 70%. There have also been significant improvements in production efficiency and power consumption through coordinated network operations.
These benefits underscore the immense potential and necessity for smart assets in the energy sector, for early- and mature-stage assets. As we progress, enabling smart assets will redefine how industries operate - more efficiently, sustainably, and intelligently.
Smarter Machines, Simpler Operations
Smart assets embody a progressive initiative to infuse intelligent technology into industrial operations. While specific use cases may differ across organisations, a prevailing industry trend is the effective utilisation of collected data to support informed, data-driven decision-making in real-time. After all, our goal is to learn from past experiences to avoid repeating the same mistakes and to formulate strategies that stand the test of time.
Smart assets are bolstered by intelligent applications, tailor-made software solutions designed to amplify and integrate with your existing infrastructure and technology. At SIG ML, we develop intelligent applications by embedding domain expertise and knowledge into applications that fuse machine learning, engineering principles and control theory. These applications are deployed in real-time environments to tackle specific operational challenges, providing timely, high signal-to-noise ratio actionable insights and in the case of applications deployed as supervisory network controllers, empowering front-line teams with autonomy.
In a world where software systems continually generate alerts and actions for front-line teams, the importance of high-quality insights is paramount. But, it's not just about quality. These insights need to be capable of connecting with automated workflows and automating setpoint adjustments to optimise complex systems.
In essence, intelligent applications facilitate the automation of mundane, repetitive tasks, enabling domain experts to concentrate on more complex, domain-specific challenges, armed with actionable insights and autonomy.
If ‘smart assets’ are the heart of autonomous operations, ‘intelligent control’ is the brains of the operation.
Intelligent Control: Driving Autonomous Operations
Central to the shift towards autonomous operations lies the concept of intelligent control. In this context, intelligent control refers to strategies that use machine learning models infused with domain-specific knowledge to optimise control performance. These models are capable of learning and adapting based on the specific context and data patterns, thereby improving decision-making and operational efficiency over time. This adaptive and iterative learning capability can make intelligent control more powerful and flexible than traditional control methodologies for optimisation purposes, despite being more complex and computationally demanding. It's crucial, therefore, to build these systems with domain embedding to ensure comprehensive understanding of domain-specific intricacies and operational requirements.
Intelligent control involves a tight integration of cutting-edge technology into your operations. This strategy not only maximises the use of existing systems and resources but also offers flexibility in matching the level of automation with available resources. In essence, it calibrates the complexity of the controller's decisions based on the demands of the situation.
The advent of new technologies has revolutionised asset management by efficiently processing immense volumes of data, decoding complex patterns, adaptively estimating states based on sensor quality and availability, and supervising multivariable, non-linear production systems. These technologies suggest operational setpoints across the coordinated network, signifying a significant shift from traditional methods such as Proportional-Integral-Derivate (PID) control and Model Predictive Control (MPC). The traditional methods were often hindered by limitations in computational capacity, availability of sensors, and a heavy dependence on pre-established mathematical models. These modern, transformative technologies can significantly enhance our ability to optimise industrial operations.
The fusion of domain knowledge, machine learning, and control theory holds the potential to revolutionise industrial operations, transforming conventional assets into smart assets.
The Rationale Behind Intelligent Control
A prevalent challenge in managing complex assets is the bottleneck created by the need for manual, repetitive tasks for exception-based surveillance and controller tuning/configuration. This encompasses tasks such as exception-based performance monitoring, raising work requests, updating, and reconfiguring control systems on an individual asset basis.
Intelligent control provides a solution to this bottleneck. It complements existing digital transformation projects by enabling discipline learnings from various projects and trials to be intelligently integrated into a system capable of network-level optimisation. In the past, the insights gained from different projects often ended up in a PowerPoint presentation but rarely contributed to updating or enhancing the controllers on site.
Rather than separately undertaking projects such as building a surface network model, upgrading an exception-based surveillance system, or performing an RTU upgrade, we can unify these scopes. These scopes can be linked to advance the network-level intelligence within the controllers of the assets.
Despite use cases varying across organisations, a consistent industry trend is the employment of domain-embedded machine learning for efficient processing of large datasets, especially in real-time environments. This results in advanced analytics underpinning supervisory control and insight systems for network assets.
For organisations aiming to 'do more with less', investing in these emerging, data-driven technologies could be a strategic opportunity. Advanced algorithms serve as a substantial force multiplier for your frontline teams, automating routine tasks and freeing up your team to focus on more complex issues. The result is a marked improvement in frontline operational efficiency.
The Future of Smart Assets
With the relentless advancements in machine learning and big data analytics, the future of smart assets shines brightly. Investing in technology to enable smart assets can fundamentally transform operations, driving efficiency and sustainability in an increasingly competitive global market.
Implementing Industry 4.0-enabled technology requires strategic planning, multi-skilled personnel and a culture embracing change and continuous learning. By collaborating with experienced vendors like SIG ML, businesses can mitigate challenges, de-risk investment in technology trials and maintain focus on core operations.
At SIG ML, we develop intelligent applications for network optimisation and control. Stay tuned for the next post in this series, “The Key to Smart Assets: Domain Embedded Software”.
Ready to learn more about specific applications to enable smart assets in your organisation?
Get in touch today. E: hello@sigmachinelearning.com.