Frequently
Asked
Questions

Artificial Intelligence (AI) is the broader concept of machines being able to carry out tasks in a way that we would consider "smart". It encompasses any technique that enables computers to mimic human intelligence.

Machine Learning (ML), a subset of AI, focuses on the idea that we can build machines to process data and learn on their own, without constant supervision. In machine learning, algorithms are used to discover patterns in data and make estimations or predictions based on those patterns. It's the technology behind many of the sophisticated services we use today, from search engines to recommendation systems and autonomous vehicles.

These technologies transform engineering by enhancing precision, efficiency, and innovation. They enable engineers to:

• Solve complex problems faster and more accurately by analysing vast amounts of data.

• Predict outcomes such as equipment failures, which can save time and reduce costs.

• Automate routine tasks, allowing engineers to focus on more strategic work.

• Innovate by designing solutions that would be impossible or impractical to achieve manually.

AI is particularly valuable in complex operational environments where it can automate repetitive tasks, enhance decision-making, and increase overall efficiency. It is crucial in industries that require high reliability and precision such as energy, mining, utilities, and motorsports.

Leveraging AI and advanced optimisation modelling techniques is transformative because it uses data-driven modelling to not only follow predefined rules but to actively learn and adapt from historical data. By leveraging machine learning, AI can dynamically optimise industrial systems by learning from past decisions. For example, it might adjust operational parameters in real-time within a power plant to enhance efficiency and reduce costs, based on what has been most effective in past operations. This ability to continually improve and adapt makes AI exceptionally powerful in managing the complexities of modern environments.

Industrial operations are inherently complex and dynamic. AI helps distill large amounts of information into what matters, empowering a workforce to make both fast and informed decisions. It transforms your most important objectives into models that are programmed to search for the best answers based on available data to guide optimal decision-making, specific to the task at hand.

In environments rich with data from numerous sensors and system components, AI provides clear, actionable guidance. As data grows in size, AI becomes a vital asset, enabling the development of smarter models that guide better decisions over time. These intelligent models evolve with your data, continuously improving their predictive accuracy and operational relevance.

This approach doesn't just streamline existing processes; it also anticipates and prepares for future challenges, positioning AI as an essential tool for delivering a competitive edge.

1. Enhancing Efficiency

Streamline operations with reduced manual oversight, decreasing repetition in operational tasks and increasing overall process efficiency.

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2. Maximising Asset Performance

Enhance operational performance through real-time insight and intelligent automations, which significantly reduce downtime and optimise performance.

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3. Reducing Cost & Emissions

Proactively monitor and maintain systems to prevent costly operational failures and reduce environmental impact.

While AI has broad applications, its benefits are particularly notable in sectors like energy, mining, utilities, manufacturing, motorsports and transportation, where complex operations and large volumes of data are common.

AI systems are designed to augment, not replace, engineering roles. AI-based systems cannot replace human creativity and innovation. They excel at automating manual and mundane tasks, such as the early stages of data processing and analysis, freeing humans to focus on more creative and complex problem-solving tasks.

AI is transforming engineering across various domains by enhancing capabilities in surveillance, control, failure prevention, forecasting, and strategic planning:

• Surveillance: Machine Learning (ML) enables continuous monitoring of engineering systems, helping to anticipate and address potential issues before they escalate. This application is crucial in environments where constant vigilance is required, such as in manufacturing facilities or infrastructure projects.
  

• Control: AI-driven systems dynamically adjust engineering processes in real-time, enhancing responsiveness and adaptability. This capability is especially beneficial in automated production lines or in energy management systems where conditions change rapidly.
  

• Failure Prevention: By analysing historical and real-time data, AI models can predict and prevent equipment failures. This proactive approach significantly reduces downtime and maintenance costs, which is vital for maintaining high operational efficiency in industrial settings.
  

• Forecasting: Machine Learning models excel at forecasting production needs and operational challenges, allowing organisations to make proactive adjustments. This foresight is critical in supply chain management and resource allocation, ensuring that operations run smoothly without interruptions.
  

• Strategy: Strategic planning benefits greatly from ML insights, as these systems provide data-driven guidance that aligns operational tactics with broader business objectives. This integration of ML in strategic planning is crucial for long-term sustainability and competitive advantage.

These applications showcase how AI technologies can not only support but actively enhance engineering functions by automating complex processes and providing insights that lead to more informed decisions and improved operational outcomes.

Dynamic optimisation is a method used in various fields such as economics, engineering, and management to make the best decisions over time. This approach involves continuously adjusting the variables in a system or process in response to changes in the environment or system state to achieve the best possible outcome.

Here are some key points about dynamic optimisation:

1. Time-Dependent Decisions: Unlike static optimisation, which seeks to find the best solution under a fixed set of conditions, dynamic optimisation considers that conditions may change over time. Therefore, the solutions must adapt dynamically to these changes.

2. Modeling and Algorithms: Dynamic optimisation typically involves mathematical modelling of the system, where differential equations describe how system states evolve over time. Algorithms such as dynamic programming, control theory, or other numerical methods are then used to find optimal solutions.

Applications include:

• Engineering: In process control, dynamic optimisation helps in adjusting operational parameters in real time to ensure the most efficient process operation, such as adjusting the temperature, pressure, or flow rates in chemical processes to maximise yield or minimise energy consumption.

• Finance: Used in portfolio management, where the allocation of assets is continuously adjusted in response to fluctuating market conditions to maximize returns or minimise risk.

• Supply Chain Management: Dynamic optimisation is used to adjust schedules and routes in real time in response to changes in demand or supply, traffic conditions, or other logistical challenges.

Benefits can include:

• Increased Efficiency: By continuously adapting to new data, dynamic optimisation ensures that systems operate as efficiently as possible under current conditions.

• Improved Performance: Systems are able to respond to changing environments proactively, which can improve overall performance.

• Cost Reduction: Optimising operations dynamically can lead to significant cost savings over time by avoiding inefficiencies and exploiting opportunities as they arise.

Dynamic optimisation is particularly valuable in systems where external conditions change rapidly or where it is crucial to respond quickly to new information. It ensures that operations remain optimal even as new data becomes available or as the system's goals and constraints evolve.

Artificial Intelligence (AI) and Its Subfields

• Artificial Intelligence (AI): Simulation of human intelligence in machines to perform tasks like reasoning, learning, and problem-solving.

• Machine Learning (ML): A branch of AI where algorithms learn from data to make predictions or decisions without being explicitly programmed.

• Deep Learning: An advanced ML technique using neural networks to analyse factors in decision-making, similar to the human brain.
  

Industry 4.0 and Related Technologies

• Industry 4.0: The fusion of advanced manufacturing/industrial operations techniques with the Internet of Things to create smart, automated factories.

• Smart Manufacturing: The application of AI and ML in manufacturing, enhancing efficiency and productivity.

• Internet of Things (IoT): Network of interconnected devices exchanging data, crucial in smart homes, cities, and industries.

• Industrial Internet of Things (IIoT): IoT applied in industrial settings, improving safety and efficiency.

• Edge Computing: Processing data near its source to reduce latency, often used in IoT applications.

• Cloud Computing: Delivery of computing services over the internet, including storage, processing, and analytics.

Control Systems and Models

• Control Systems: Mechanisms regulating other devices or systems, often employing feedback loops.

• PID Control: A widely used feedback control system in industries, balancing precision and stability (PID stands for proportional-integral-derivative).

• MPC Control (Model Predictive Control): Advanced control method predicting future outcomes for optimal operational adjustments.

Industrial Automation and Networking

• SCADA (Supervisory Control and Data Acquisition): High-level process supervisory management using computers and networked data communications.

• IT/OT Integration: Merging data-centric IT systems with operational machinery and sensors in OT for improved operational efficiency.

• Purdue Model: A framework for industrial control system architecture, categorizing various ICS elements.

Other Related Concepts

• Cyber-Physical Systems (CPS): Integrated computer and physical systems, prevalent in automation and smart grid technologies.

• Digital Twin: A virtual representation of a physical object or system, used for analysis and simulation.

• Predictive Maintenance: Techniques predicting equipment failures, allowing timely maintenance to prevent downtime.

• Human-Machine Interface (HMI): User interfaces connecting humans to machines, systems, or devices.

Key Terms in Modern Technology

• Automation: Making processes operate automatically, reducing human intervention.

• Autonomy: Systems performing tasks independently and adaptively.

• ‘Smart’ Technology: Devices or systems using advanced algorithms for tasks requiring human-like adaptability.

• Smart Assets: Assets integrated with data-driven technologies for improved control and management.

• Model: Simplified representations of systems, either knowledge-driven (based on expert understanding) or data-driven (learning from data).

Information and Operational Technology

• Information Technology (IT): Handling information using computers and other technologies, like networks and databases.

• Operational Technology (OT): Technology for running operations, including machinery, sensors, and control systems.

• Industrial Assets: Physical and non-physical assets used in production, such as machinery, buildings, software, or data. This cheat sheet is intended as a high-level guide. For a comprehensive understanding, further exploration of each topic is recommended. Stay tuned for future insights on ‘Building an Autonomous Operations Program’.