Advanced process control (APC) applications require accurate inputs for stream qualities, including product compositions and distillation curve points. While online analyzers are effective in supporting model predictive controllers, they are expensive and often unavailable for important process streams. There is a more cost-effective approach for building virtual analyzers or inferentials to accurately estimate stream qualities and it starts with digital twins.

The growing interest in hydrogen as the fuel of the future requires manufacturing plants to be operating efficiently using data-driven methods. With online data servers, engineers have a seemingly infinite amount of data at their fingertips. Reconciling this data for use in more advanced tools or calculations can be a tedious and labor-intensive process.

Surrogate models (or Reduced-Order Models) allow simulation users to explore and identify optimal process performance conditions faster than full, rigorous simulations. But there are times when users may find they are extrapolating beyond the data used to develop the surrogate model or when there is a desire to confirm the accuracy of the surrogate model. In these cases, the surrogate model can be used to enhance model performance, from both a robustness and performance perspective.

The steam reforming reaction to generate hydrogen from natural gas takes place at high temperatures. Conventional rigorous reactor modeling requires a temperature profile of the process fluid, which is difficult to estimate or measure. Using the latest First Principles Driven Hybrid Models, it was found that a relatively simple model can accurately represent a wide range of plant data. In this presentation, the methodology of first principles driven Aspen Hybrid Models, the importance of data conditioning, comparison with conventional methods and potential benefits are all discussed.  

This webinar offers use cases that explain how to leverage the power of AIoT to lower costs, accelerate performance and improve sustainability.

Nissan Chemical was looking to optimize its ammonia manufacturing process and reduce operating costs. To do so, they needed to find a model that would enable them to simulate the behavior of a real plant.

InformationWeek - 3 Ways CDOs Drive Successful Industrial Digital Transformation

CIO - To make industrial data actionable evolve your data historian with an AIoT strategy

Refiners and olefins producers continue to look for innovative ways to reduce carbon emissions. New innovations now enable companies to meet emission targets and still ensure profitable operations. In this on-demand webinar experts discuss how Production Optimization in Aspen Unified™ can be used to predict and optimize the tradeoff between emissions and profits.

Thought leadership article by Ron Beck