Machine Learning Leveraged Simulation Digital-twins Enabling Real-time Detailed Insights

Jenil Dedhia (

May 10, 2021

In the specialty materials industry (like in pharma or niche materials), where getting experimental data both at the lab or a large scale can be prohibitive, physics-based process models can provide the necessary initial guidance to the practitioner for decision making. Depending on the complexity of the physics involved, such process models may require advanced computations and simulations involving computational fluid dynamics (CFD) or Discrete Element Modeling (DEM). Even with increased computational power, such process models may not meet the needs of a practitioner to get visibility and predictability for real-time decision making. With example cases, this post focuses on leveraging machine learning approaches to address such practitioner needs in providing real time guidance grounded on process physics.

Processes with Rotor Stator Mixers: Leveraging Engineering Approaches To Bring Predictability and Take Informed Process Decisions

Priyanka Dhar (

May 12, 2021

Rotor-stator mixers are widely used in food, cosmetics, pharmaceuticals, and specialty inks. The distinguishing feature of rotor-stator is that they provide the required amount of energy, power, or shear which expediates the physical processes, for e.g., mixing, homogenization, dissolution, neutralization, mass transfer, and chemical reactions. This blog explains how chemical engineering principles based models and structured approaches can help in minimizing unnecessary experiments and taking informed decisions on on the process space.