Leading manufacturers must strategically optimize the implementation of carbon capture, utilization, and storage (CCUS) technologies to reconcile ambitious sustainability goals with critical business objectives. Sound decisions are expected in the face of competing goals and varying approaches/options – from initial capital investment to economic viability and operational efficiency.

A Digital Twin Simulation Model will enable manufacturers to optimize design (sizing, stages), test drive configurations and settings (stage cut, feed pressure, etc.), so that sustainability targets can be achieved while taking business objectives into consideration.

In modern manufacturing, getting early and accurate indications of process health, providing insights to prevent off-spec production, and ensuring regulatory compliance with waste/emission prediction are becoming important competitive capabilities.

AI-driven Data Analytics can help find the real underlying sources of variation in production processes. Multivariate analysis converts the original set of input variables to a smaller set of latent variables that are easier to analyze and monitor, helping engineers understand and remedy the causes of recurring process upsets.

During operations, such techniques can identify key patterns of movement in process variables that trigger undesirable outcomes—such as quality degradation or emission deviation trends—within specific operating zones.