• Represent real plant behaviour with models created from operational data and first-principles constraints • Create high fidelity models that can be used for rapid and accurate decisions in engineering and operations or to expand modelling scope • Better planning decisions to recapture benefits • Easily incorporate complex process units into the scope of closed-loop optimisation. A Damien Maintenant, Advanced Process Control Lead, Axens, damien.maintenant@axens.net By unifying planning/scheduling and APC and coordinating the controllers’ objectives, plantwide optimisation provides additional benefits beyond APC. AI/ML techniques can be used to simplify plantwide optimisation tools that are complex to operate and strenuous to maintain, but these algorithms need a large amount of data and have to be developed, guided, and monitored by engineers with rigor- ous knowledge of the process and the operation. In addition, in an increasingly connected global market context, refining and petrochemical schemes are more and more complex and integrated, complicating plantwide opti - misation. As a result, this is not only a matter of data science but also of process expertise, as it is of real importance to understand the interactions between the different process - ing units across the plant. Regarding tools and techniques, hybrid modelling, meaning a combination of historical oper- ating data and first-principles models, must be taken into consideration for developing such solutions. It is worth mentioning that some mandatory project phases must be respected. The first milestone is to express the objectives of the optimisation solution clearly; the second milestone is to accordingly define the strategy to achieve these objectives and make available all needed resources. Consider that agility is also key to redefining objectives or resizing resources as necessary. Agility is also a way to extend the scope of the solution over time by starting plantwide optimisation, for instance, of the utilities and hydrogen network, then including pools management, and so on. In conclusion, data availability and monitoring, profes- sional expertise (process, operation, control, data science), efficient project management, and resources (on the one hand, people and, on the other hand, the tools and tech - niques) are necessary for the successful implementation of plantwide optimisation using AI/ML. A Alvin Chen, Global Technology Application Manager, BASF Corporation, alvin.chen@basf.com, Mark Schmalfeld, Global Marketing Manager, BASF Corporation, mark.schmalfeld@basf.com Successful implementation of machines with artificial intel- ligence (ML/AI) in refinery or petrochemical complexes requires a deliberate and thoughtful targeted approach with clearly defined benefits, a robust and safe technology framework, and a clear economic benefit. These hydrocarbon processing facilities have some of the highest safety standards in the world, and the use of their technologies in ML/AI offers the potential for benefits, yet
they must achieve standards of safety and robustness that are often higher than many other industries. This can often set the speed of adoption in the industry, yet it cannot be missed as a tool because there are large potential benefits to operations that can be achieved with AI/ML. ML/AI offers a chance to continuously learn and improve, leading to better productivity, less downtime, and ulti- mately improved cost efficiency. Specific AI/ML strategies can provide additional benefits beyond automated process control (APC) by extending analysis to monitoring market conditions and adjusting crude acquisition plans. Additionally, it can allow catalyst formulation adjust- ments to accommodate market changes and uncertainty. ML/AI offers a chance to continuously learn and improve, leading to better productivity, less downtime, and ultimately improved cost efficiency A specific example is changing catalyst to move away from C₄= selectivity in favour of C₃/C₄ flexibility when there is uncertainty about which production will have the highest value and demand. An AI/ ML program can help in these types of decision- making recommendations while allowing a final human interface in the process. This is just one simple example of how these tools can be implemented, which is why today’s much more complex logistics, process control, and mainte - nance monitoring are being improved through AI/ ML. Q In the transition to digital worker and enablement solutions using predictive analytics and remote monitor- ing, how much input does the end-user have in creating the ‘dashboard’? A Ron Beck and Gerardo Munoz, AspenTech In this area of situational awareness, decision-support sys- tems (and we prefer to call them decision support rather than dashboards) are designed to support and assist end-users in making operating and strategic decisions. The best of these (for example, the situational awareness system from AspenTech’s OSI business) are designed for end-users to configure the visuals to their needs. They will need to be able to create displays ‘on-the-fly’ to help them quickly use the appropriate data and analytics to diagnose and solve operational problems faster and better. A Alvin Chen, BASF Corporation Without defining the specific ‘dashboard’ in question, in general, as systems get to be more complex and auto- mated, it becomes more important for staffers to monitor what the automation system is doing. This is especially important during the transition from a high level of human interaction to a lower level since the automation system will still be learning what it needs to do.
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PTQ Q3 2022
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