How Modern Digital Design Approaches can Help Realise the Potential of Process Intensification

Process Intensification (PI), which aims to dramatically improve manufacturing processes through the application of novel process schemes and equipment, is not a new concept. PI goes beyond the incremental improvements achieved through optimising existing equipment and process schemes, by, for example, combining processing phenomena into fewer and more-integrated processing units in order to achieve step changes in energy efficiency, capital and operating costs and environmental impact. However, despite its obvious potential benefits, PI has yet to transform the process industries, partly because of the perceived risks of bringing new and unproven technologies to market in a conservative industry where mistakes can be costly.

A key challenge is that intensified processes are by definition novel and unproven, as opposed to less-efficient processes that have been well-understood for many years and therefore carry less risk. A traditional approach to process developments dictates that new processes require extensive construction of prototypes and pilots. However even exhaustive pilot testing still leaves open questions of operability and reliability, and a lack of systematic quantification of the effects of poor performance or failure, as well as the usual (e.g. scale-up) general technology risks associated with implementing new processes. There is also a perceived lack of design tools and data to develop intensified processes, and lack of a generalised workflows for dealing with the complexity of intensified, integrated modular systems.

All of this means that significant advantages can be realised from applying emerging digital design approaches that allow rapid and systematic exploration of the process decision space and rigorous quantification and management of technology risk. Digital design employs a model-based approach coupled closely with targeted experimentation. Experimentation is used to support the construction of a high-fidelity predictive model (or ‘digital twin’ in digital design terminology); once a model of sufficient accuracy is established, the digital twin, rather than the experimental data, is used to optimise the process design and operation.

This presentation describes, with brief illustrations, the established digital design techniques, technologies and workflows that can be applied across the intensified process development lifecycle to accelerate development and manage risk systematically. Specific topics include: capturing novel IP in high-fidelity models; validation of models against experimental and pilot data using integrated design and experimentation workflows that minimise experimentation time and cost; the application of global system analysis (GSA), a key technology for exploring the design and operational decision space, understanding sensitivity to key process parameters and quantifying and managing uncertainty and risk; and steady-state and dynamic optimisation for determining the optimal the process design taking into account operability issues or complex operating schedules.