Digital twins are becoming essential tools in the development and improvement of industrial process such as ammonia and methanol synthesis. By replicating the key physical and chemical behaviors of real systems, these virtual models make it possible to explore a wide range of design and operating conditions without the need for extensive experimental testing. This approach helps identify optimal configurations early on, reducing both uncertainty and cost during scale-up.
When paired with Decision Support Systems (DSS), digital twins provide a structured way to assess trade-offs between competing design criteria—such as conversion efficiency, pressure drop, and thermal control. Engineers can run parametric studies, evaluate different design options, and focus development efforts on the most promising scenarios, all while minimsing the number of physical prototypes required.
For high-investment processes like ammonia and methanol production, even small improvements in reactor design can have a significant economic impact. The ability to virtually test and refine concepts before committing to fabrication helps avoid costly redesigns and ensures that final systems are both technically sound and economically viable. In this context, digital twins and DSS offer not just a faster path to implementation, but a smarter one as well.