The lighthouse organizations that have paved the way are showing continuous manufacturing is viable and scalable, leading to greater throughput & consistency in drug quality - decreasing time-to-market.

SABIC is a leading multinational manufacturing company, specializing in the manufacture of petrochemicals, chemicals, industrial polymers, fertilizers and metals. Energy efficiency is a key focus area for SABIC. To close the gap towards achieving 2025 goals, SABIC identified the need to quantify energy losses and execute actions to optimize operations while addressing daily plant challenges.

European refiners face a “dual challenge" – meeting the growing demand for resources and higher standards of living from a growing population while also addressing sustainability goals, reductions in carbon emissions and plastic waste in the environment. View this insightful conversation featuring industry leaders from Eni and MOL Group as they discuss both the challenges and opportunities in the transition to net zero by 2050.

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.

Over the past three years, the Alliance to End Plastic Waste has rallied over 90 member companies, allies and supporters who are committed to ending plastic waste in the environment and creating circularity. AspenTech is proud to be one of those members and to have the opportunity to work with some of the world’s leading companies to create and scale innovative solutions for this cause.

Las soluciones de AspenTech ayudan a ahorrar energía, a acelerar el desarrollo de nuevas tecnologías y a impulsar la innovación, pues piensan no solo en el presente, sino también en el futuro.

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.  

Drive more sustainable mining operations with digital technologies have the power to transform your mining business- from pit to port. Achieve higher levels of agility, efficiency, safety and profitability with the digital mine.