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4 Reasons why Modern Operations need Industrial AI (check out #3!)

Industrial Artificial Intelligence (AI) is transforming operations in sectors such as energy, mining, utilities and motorsports through the introduction of AI techniques to enhance efficiency and optimise performance.

However, to see this technology succeed, it needs to be engineered to perform in real-world use cases. Here are four compelling reasons why integrating Industrial AI into your operations isn't just an option - it's a necessity for staying competitive.

1. Overcome the Limitations of Outdated Technology

Outdated technology refers to systems and tools that are no longer efficient because they cannot handle current demands. These older systems may struggle to process the large amounts of data generated by today's industrial operations, lack the ability to integrate new data sources smoothly, and/or offer limited analytics capabilities. This results in increased costs, slower responses to operational changes, and a greater likelihood of breakdowns.

For instance, if a mining operation uses manual methods for data entry and maintenance scheduling, it can lead to slow detection of equipment problems. This inefficiency often causes unexpected machine failures, directly impacting productivity and profits.

mining site

In contrast, Industrial AI solutions use physics-informed, data-driven models to build comprehensive profiles of equipment performance based on historical data and performance metrics. Built using machine learning algorithms, these solutions can continuously learn and adapt with new data, allowing operational processes to dynamically adapt to changing conditions without manual intervention. This capability enhances overall operational efficiency and decision-making.

2. Address Challenges, at Scale

Industrial operations across industries share key challenges:

managing complex systems, processing vast data volumes, and needing timely maintenance and optimisation.

These are magnified at scale, where systems must operate efficiently and adaptively in real-time to maintain productivity and safety. Industrial AI offers a robust solution by enabling organisations to implement systems that learn from real-world outcomes to enhance future performance.

For instance, in the oil and gas industry, real-time monitoring of wellsite flow rates, pressures, and compositions is essential. Industrial AI systems analyse this data instantaneously, offering recommendations to fine-tune operations, ensuring optimal flow and safety, while also meeting environmental and performance standards and preventing costly downtimes and carbon emissions.

monitoring oil & gas facilities

The Purdue Model (below) of industrial control is a useful framework for understanding how Industrial AI can be integrated across different levels of operation. It organises industrial processes into hierarchical levels from field-level control systems to enterprise decision-making.

Purdue Model

This structure not only clarifies the flow of data but also assists in developing a strategy for the adoption of new technologies at various levels, emphasising the challenge of scaling these solutions.

Supervisory Control Layer: Here, control system operators use systems like SCADA, interfaced with RTUs or PLCs, which are crucial for the real-time application of data. At scale, the challenge lies in continuously monitoring and adjusting these settings based on live data to maintain optimal system performance. This often requires manual tuning, configuration, and updates, which can become cumbersome and error-prone as the scale of operations grows.

Operations Layer: At this level, engineering teams perform strategic tasks such as exception-based surveillance (EBS). They collect and analyse data to make informed decisions about necessary adjustments. At scale, the frequency and complexity of decisions increase, necessitating more sophisticated, data-driven approaches to ensure timely and accurate responses.

Together, these layers must work in concert to efficiently handle real-time data application and strategic long-term improvements. At scale, the coordination between these layers becomes critical. The need to identify, reconfigure, or adjust controllers and other operational parameters swiftly and accurately, based on real-world data, highlights a key opportunity for AI-based systems. Industrial AI not only enhances operational efficiency but also scales up the capability of industrial systems to adapt and evolve in response to changing conditions, ultimately maximising asset reliability and performance.

Purdue model and asset workflows

3. 95% reduction in manual tasks

One of the most advanced and opportunistic applications of Industrial AI is utilising smart control software to enable industrial systems to operate more autonomously. Smart control software, an advanced application of Industrial AI, bridges the gap between existing technologies used for control and the surveillance of assets. These systems are designed to continuously learn and adapt, combining AI with Advanced Process Control (APC) to analyse vast amounts of data in real-time and deliver proactive insights that drive operational improvements.

Traditional control systems, like proportional-integral-derivative (PID) control, typically monitor single process variables. This limitation can lead to a significant number of operational events being undetected or managed manually at the operations level, resulting in delays in responding to issues. Such delays are critical drawbacks in traditional systems that need addressing, especially in dynamic or complex systems where response time to operational changes is key.

Smart control software is revolutionary as it helps organisations automate manual and repetitive tasks associated with routine optimisation, and autonomously recommends setpoint adjustments. This not only streamlines processes but also drives substantial improvements across other critical performance metrics, such as enhancing overall system performance and reducing operational costs. In a recent project, our customers saw a 95% reduction in time spent on manual tasks, and a 70% time saving for engineers, allowing them to focus on higher value activities.

As illustrated below, smart control software can be seamlessly integrate into existing operations. It can be connected with existing technologies in use to enable a simplified, smart, and fast feedback loop in routine optimisation. This advanced feedback mechanism drastically cuts down on the need for manual oversight, significantly curtails the risk of overlooking critical operational tweaks, and improves the connectivity and efficiency in surveillance and control of industrial systems.

smart control in existing operations

4. Shift the Paradigm with Proven, Tangible Benefits

The transition to modern, Industrial AI software is not only feasible but has been effectively demonstrated in real-world applications. Incorporating Industrial AI transforms how operations are managed, making them more efficient and significantly more responsive to changing conditions, which in turn enhances the overall value and sustainability of industrial operations.

In one of our recent implementations in the oil and gas industry, benefits included an increase in throughput, a reduction in the power consumption and emissions, while also increasing equipment runlife. (For more information, get the case study here).

The implementation of this technology fundamentally transforms how operations are managed. It shifts the paradigm from manual optimisation and control tasks to an autonomous system that self-optimises in real-time, guided by industry professionals.

Looking To Enhance Your Operations with Industrial AI?

Integrating Industrial AI is not just beneficial - it's essential. It paves the way for improved efficiency, lower costs, and a competitive advantage in today’s fast-paced industrial settings.

Are you ready to experience the transformative power of Industrial AI? Contact us at SIG ML to discuss Industrial AI solutions for your specific needs and propel your operations into a new era of efficiency and effectiveness.

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