Vision of the self-optimizing plant is fast becoming a reality

Heiko Claussen, co-chief technology officer at AspenTech, explains how assets at all levels, from equipment to entire facilities, leverage advanced technologies and methodologies to create autonomous and semi-autonomous processes, unlocking greater value from all investments.

Organizations are looking to increase their competitiveness by enabling tighter collaboration across functions, optimizing increasingly complex assets to achieve multiple objectives simultaneously, and empowering workers to make informed and strategic decisions. The self-optimizing plant will deliver these capabilities, allowing companies to achieve sustainable operational excellence. Pursing a Boundless Automation^SM vision provides the foundation for self-optimizing plants by helping teams integrate advanced technologies and methodologies to create autonomous and semi-autonomous processes. The key components of these processes include robust networks of sensors and responsive control elements, interconnected systems, advanced analytics, real-time monitoring, autonomous decision-making and scalability.

Leveraging ever-increasing amounts of structured and unstructured data, industrial artificial intelligence (AI) improves visibility into operations and delivers insight into the future, providing the basis for increased autonomy. Seamlessly connected intelligent field, edge and cloud technologies provide a richer data set and easier access to that data. This interconnectivity enables software solutions to be deployed and integrated throughout the plant and support the speed of analysis needed to provide timely insights. In addition, new usability paradigms improve access for decision-making and collaboration across the business.

Defining the self-optimizing plant
The concept of the self-optimizing plant is based upon a self-adapting, self-learning and self-sustaining set of software and process control technologies that work together to anticipate future conditions and act accordingly, adjusting operations within the context of the enterprise. The plant does this through pervasive real-time data access, combining engineering fundamentals and industrial AI. Also, it captures and uses knowledge to optimize across multiple levels, provide actionable recommendations, and automate actions securely in a closed feedback loop.

Achieving a self-optimizing plant
To begin moving towards the self-optimizing plant, many companies are seeking to enhance and better align their existing business processes, with an eye on reducing the gap between planned and actual performance. This implies that key functions such as planning and scheduling can become more closely integrated and aligned with closed-loop automation systems like advanced process control (APC) and dynamic optimization - a focus of the next generation of production optimization solutions. By incorporating insights from engineering, maintenance and supply chains, companies gain the holistic view needed to achieve even higher levels of performance.

What the self-optimizing plant can offer
The self-optimizing plant provides a pathway to increased profitability. Unlocking new levels of production efficiency enables companies to find new and previously untapped areas for margin optimization and achieve greater stability. It also enables them to become more agile and be better placed to swiftly meet shifting product demands, respond to supply chain disruptions, and adapt to varying business conditions. This agility helps to ensure operational stability and customer satisfaction. In addition, the self-optimizing plant enhances safety by significantly reducing hazardous conditions, and mitigates greenhouse gas emissions by preventing process upsets and unplanned shutdowns, contributing to sustainability goals. Furthermore, it supports the next-generation workforce by facilitating better decision-making and faster upskilling.

Each step on the path to a self-optimizing plant will create incremental value along the way, as companies target this technology to address specific business needs throughout the operation. As an example, companies that have implemented AI-powered predictive maintenance across hundreds of assets and multiple sites are already realizing gains, recouping their investment in just a few months.

Capabilities driving self-optimization
The building blocks and capabilities that companies can implement to create the self-optimizing plant include advanced innovations from Emerson and AspenTech:

• Closed-loop planning and scheduling, to tightly integrate advanced process control, process envelope optimization, planning and scheduling.
• Process performance monitoring with a network of online models to provide automated 24x7 insights to propose adjustments, building on the efficiency of industrial AI technology.
• Asset health monitoring leveraging prescriptive analytics, built on industrial AI, to track equipment performance and predict breakdowns, maximizing uptime and making operations safer.
• Workforce enablement technology, such as cognitive guidance for decision-making and interactive operator training.
• Digital twin technology using real-time data to provide an evolving digital profile of the historical, current and future behavior of a process or asset.
• Model alliance enabling the sharing of key master data and model components between different applications to maximize synergies throughout the organization.
• Advanced modeling solutions allowing engineers to quickly build models to optimize plants online and offline.
• Data visualization helping to power a next-generation collaborative, adaptable work environment.
• Control systems providing real-time monitoring and control, and supporting autonomous decision-making, helping to optimize a dynamic and adaptive environment.
• Final control elements to execute control actions quickly and precisely, helping the plant to achieve optimal performance.

Empowering workers
Importantly, people are not being removed from the equation in the self-optimizing plant; rather, they are becoming empowered to work more effectively on the highest-value tasks. The role of the planner and scheduler will increasingly evolve to comprise strategic review and decision-making, instead of manually creating plans and conducting analysis. Over time, the roles of automated decision-making and human decision-making will evolve, and the insights gathered from the plant and the actions taken by personnel will be leveraged to drive a new level of intelligence and automation.

To learn more about our vision for the self-optimizing plant, visit here.

Learn all about Boundless Automation^SM in "Innovations in Automation".