Processing Q&A: AspenTech's Ron Beck discusses the concept of Self-Optimizing Plant

Sept. 29, 2020
AspenTech's Ron Beck explains the concept of Self-Optimizing Plant and how it will support industrial organizations as they adapt to new conditions, helping them achieve greater resilience and better business outcomes.

Due to the many challenges presented to businesses today, it is increasingly important for them to find a way to thrive in an unpredictable environment and gain a competitive advantage. Technology may be the key to achieving that.

Processing recently connected with Ron Beck, Energy Industry Director at AspenTech, who explains the concept of Self-Optimizing Plant (SOP) and how it will support industrial organizations as they adapt to new conditions, helping them achieve greater resilience and better business outcomes.

Q: During this time of increased uncertainty, many companies are in search for technology that will provide their operations with greater reliability and efficiency. What are companies' greatest concerns?  

Ron Beck: I have the opportunity to talk to many refining, chemical and energy companies globally. Their overriding issues relate to navigating economic turbulence and supply and demand disruptions in a way that minimizes employee risk, avoids process safety issues and protects asset integrity. With the need to run assets in completely new ways and to keep employees healthy, digitalization that enables agility, flexibility and remote working have increased in urgency for executives. While some companies were prepared for these shifts, the majority realized just how unprepared they were. This has turned executive attention on the need to accelerate their digitalization programs, and to focus them on areas they support the above mentioned challenges.

Q: Can you explain what a Self-Optimizing Plant (SOP) is and how this new technology can help deliver positive business outcomes?   

Ron Beck: The Self-Optimizing Plant is AspenTech’s strategy for taking customers on a journey to the future intelligent plant, asset or set of assets. Our vision for the Self-Optimizing Plant is a plant that is self-learning, self-adapting and self-sustaining. By self-learning, we mean that the plant, as it operates, learns from each action it takes, learns from the data streams reporting on the plant and learns from the digital twins providing operating insights. Therefore, it improves its ability to reach its potential and even set the potential higher based on its learnings. By self-adapting, we mean that the plant will continually adjust to changes in the condition of the asset itself, as well as to external factors, to change the objectives of the operation continually. By self-sustaining, we mean that the plant will intelligently monitor the health of its equipment, processes and systems, based on data streams and insights from those data streams. It will then take corrective actions to ensure the integrity of the asset, and the health of the equipment, to avoid or minimize degradation and to avoid missing customer targets.  

Notice that I have said self-sustaining, not autonomous. That choice of words is a conscious strategy decision on AspenTech’s part. Typically, oil and chemical assets are too complex to be able to run completely autonomously, at least within the next five to ten years. Instead, we are driving toward enabling a self-sustaining plant. Here the models, analytics and artificial intelligence (AI) will provide operations, improvement and monitoring solutions that will work in partnership with the knowledge workers to increase the overall opportunity to optimize operations. Additionally, it will increase the asset and the enterprise agility, resilience and insights to support the volatile and uncertain environment that companies will continue to encounter. The Self-Optimizing Plant is not only a vision and strategy. It is a blueprint and roadmap for AspenTech’s innovation efforts over the next several years. We are already delivering on the initial steps on the path to the Self-Optimizing Plant, and will continue to innovate in an orchestrated way to deliver real value to the industries we server incrementally.

Q: What is the new product AspenTech has developed in a first step to making the SOP a reality — can you elaborate on what it entails?  

Ron Beck: AspenTech has just announced commercial availability of the first solution that will make the Self-Optimizing Plant a reality. This solution is called Aspen Unified. We have introduced three products in this solution set on September 1. These are Aspen Unified PIMS (planning), Aspen Unified Scheduling and Aspen Unified GDOT (dynamic optimization) Builder. This provides three closely coordinated products that bring refinery (and olefin) planning, scheduling and dynamic optimization together. Users of all three products will find an extremely intuitive user experience, which is completely consistent between the three functional areas. An individual can work in all three areas effectively, with very little additional training, and with a high degree of productivity. Most importantly, it provides a tightly coordinated solution so that forward and backward feedback loops naturally exist between the three functions. It is this tight coordination between these three areas that makes a giant leap forward towards self-optimizing production. We know from the testing of this solution with several customers that the value opportunity is tens of millions of dollars in capturing elusive margins in refining that we and customers call "margin leakage."  This is a first for the industry in many ways. It is the first time that planning and scheduling have been brought together in a truly unified environment, and the efficiency and agility gained from being able to immediately create a schedule from an economic plan is huge. This also enable companies to move to “re-plan” the operation more frequently, which takes a refinery immediately closer to best-practices operations.

Q: What exactly is Industrial AI and how does this technology fit into the broader picture of the SOP? 

Ron Beck: Industrial AI is AspenTech’s approach to applying AI to the process industries. Industrial AI is the combination of data science and AI analytics with deep domain expertise, to develop a new class of solutions that are far more applicable and create far more value than general AI.

By embedding AI and data analytics in first principles models, solutions are created that first of all can be used by “normal” workers in a company, and second of all that overcome the weaknesses of both approaches by themselves. Domain expertise provides the crucial “guardrails” to place around AI to ensure that the results conform to the rules of chemistry and physics crucial to achieving correct insights. These guardrails are crucial also to give industry confidence in the safety of implementing these solutions. Machine learning and analytics provide the crucial power and speed to find patterns in data that accelerate understanding of assets, and therefore help solve extremely complex and time consuming problems that first principles approaches are challenged by.

For industry, and specifically for the chemical, refining and oil and gas industries, where everything done is governed by engineering principles based on chemical engineering and physics, industrial AI enables new insights within a framework of process safety, asset integrity and understanding of the underlying processes. AspenTech is currently testing an exciting way of delivering industrial AI through hybrid models, that combine both approaches, and we have recently posted a public white paper on our website.

Q: Maintaining safe conditions during operations or on the production floor is incredibly important, and this is especially true now.  How does the SOP drive new levels of plant safety?

Ron Beck: This is an excellent and crucial question. New levels of plant safety will be driven in several areas.

First, the prescriptive maintenance and machine learning solutions, represented by the already released Aspen Mtell software, will play an extremely important role in improving plant safety. Plant owners know that around 50% of plant incidents happen during shutdown and startup, and especially unplanned shutdowns. Prescriptive maintenance agents, which can identify future equipment and process degradation 30 to 60 days, or in some cases even 90 days in advance, can flag the need to take action, which can prevent the future shutdown, or at a minimum enable scheduling it in an organized way. This is already available today.

Second, the engineering models that are provided by AspenTech provide highly rigorous ways of designing and re-validating the presence of correct process safety systems. These are in areas such as blowdown analysis of material degradation and pressure relief systems. The Self-Optimizing Plant will integrate these effectively with plant operating plans and execution, and will enable higher confidence that safety risks are being avoided. Several leading companies are beginning to apply these already existing models in these new ways working with us.

Third, by providing better visual cues based on AI, the Self-Optimizing Plant will better synthesize all of the operating data streams to identify risk conditions, and adjust operations to avoid them. This is a future area which we are currently evaluating.

Q: Environmental sustainability is a goal that most organizations strive for —  how can the SOP minimize environmental impact, yet ensure significant reliability and efficiency? 

Ron Beck: Most companies in the chemical and energy fields are beginning to set ambitious sustainability targets, to ensure that they contribute to future sustainability and maintain access to capital. Achieving progress in areas such as carbon neutrality, a circular plastics economy and water conservation, are complex challenges. It is a complex optimization challenge that requires looking at a company’s assets, its value chain, and its optionality.

The Self-Optimizing Plant concepts will support companies in operating in a way that can achieve these targets while also meeting customer delivery and profitability goals. The way to think about it is relatively simple. With the capabilities that will be available in the Self-Optimizing Plant, also in the capabilities we are already delivering with Aspen Unified, an executive team can operate assets to drive sustainability targets in the same way that they drive margin, profit and safety targets. It is a matter of setting targets the right way, and employing advanced capabilities to fully exploit the opportunity to run assets and make value chain decisions in a way that achieve those targets.

Q: As we navigate the COVID-19 pandemic, it is becoming clear that remote working will continue to be a cornerstone of our workforce for the long term. How can this technology empower the next generation workforce, while embracing a remote working environment?

Ron Beck: The ability to work in an increasingly remote environment will be a fundamental outcome of all the components of the Self-Optimizing Plant. The Self-Optimizing Plant will hungrily consume all of the data streams available in the plant. The digital twins, which are an element of the Self-Optimizing Plant, will provide workers either locally at the asset or remotely, with visibility and insight into the operation of the plant and the choice to be made in ongoing operations. So this is an extremely powerful enabler of remote working. Companies have accelerated their projects with us to implement digital twins using today’s technology for this reason.

With respect to empowering the new generation of workers, the insights, transparency and visual KPIs that are provided by the Self-Optimizing Plant will provide a powerful educational and knowledge transfer engine for workers. At a simple level, you can think of the digital twins in the Self-Optimizing Plant as more complex versions of a flight simulator. In the same way that a flight simulator educates the pilot as to how to safely operate an airplane, the plant digital twins educate plant workers at a more sophisticatedly level as to the operations of a plant’s processes and equipment, so that the impact of each and every decision can be simulated; and the new worker can quickly understand the impacts of his proposed action and become educated and knowledgeable quickly.

Ron Beck is the Director of Energy Industry Marketing at AspenTech. He has been responsible for engineering product marketing, Aspen Economic Evaluation and Aspen Basic Engineering. He has more than 30 years of experience in providing software solutions to the process industries and 15 years of experience in chemical engineering technology commercialization. Mr. Beck has authored papers on key industry topics and has presented at several public industry events. He is a graduate of Princeton University in New Jersey.

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