For decades, first principle models have been used for process design and optimization in the chemical industry. Recent innovations employ machine learning techniques—enriched with first principle constraints—to create a hybrid model that closely represents real plant behavior. Fast, easy access to industrial AI is helping organizations make accurate decisions to select an optimum design quickly and improve operations, maximizing productivity and minimizing energy usage.

Leading chemical companies across the globe face more competitive pressures with volatile market conditions and a rapidly changing workforce pushing the demand for increased sustainability and profitability. Digital twin software offers new levels of operational excellence through enterprise-wide insights that drive improved business operations.

A breakthrough innovation, Aspen Hybrid Models™ help energy companies quickly develop comprehensive, accurate models to address the most complex operational challenges. With the release of aspenONE® V12, Aspen Hybrid Models leverage the power of AI without engineers requiring data science or machine learning expertise—truly democratizing the application of industrial AI.

Reactor operations continue to be critical for today’s refineries, but reactor simulation solutions have traditionally been difficult to use and maintain. The problem is magnified as experienced engineers and operators retire at a faster rate.

A breakthrough innovation, Aspen Hybrid Models™ help chemical companies quickly develop comprehensive, accurate models to address the most complex operational challenges. With the release of aspenONE® V12, Aspen Hybrid Models leverage the power of AI without engineers requiring data science or machine learning expertise—truly democratizing the application of industrial AI.

As sustainability remains an industry imperative, it’s essential you make progress with both your near- and long-term sustainability targets.

Las innovaciones en IA industrial ahora permiten la descarbonización de los principales procesos químicos y de refinación, mejoran la eficiencia energética y del agua, reducen las emisiones de gases de efecto invernadero e impulsan la captura y el almacenamiento de carbono. Descubra cómo estas tecnologías digitales y aplicaciones pueden apoyarlo a alcanzar más rápido sus objetivos de sustentabilidad a través de la simulación de procesos, la optimización de servicios públicos, el control avanzado de procesos, el mantenimiento predictivo, la optimización de la planificación de la producción y la gestión de la cadena de suministro.

In this session from OPTIMIZE™ 21, learn how how Polymer Digital Twin models built by Equinox Software and Services Pvt. Ltd. (EQNX) helped build capabilities at a Greenfield Polymer manufacturing facility. The Digital Twin Process models using Aspen Plus® were deployed prior to the plant commissioning, helping the Operations team to quickly build capabilities for Polymer manufacturing enabling agility, collaboration and profitability from the first day of operations of the integrated refinery, petrochemical and polymer complex.

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.