The development of a physics-enhanced ML framework is driven by the goal of significantly enhancing computational efficiency, by several orders of magnitude, a key factor in the realisation of DTs. By incorporating physical principles via a physicsenhanced surrogate model, the complexities of the relationships between inputs and outputs can be accurately represented. The employment of advanced optimization techniques further accelerates the framework, enabling ultra-fast inverse modelling during the actual experiment evolution. The same acceleration step can be beneficial also in the case of sensitivity and uncertainty analysis, where identifying dominant mechanisms and sensitive parameters plays a crucial role. This insight enables the design of more effective and efficient experimental campaigns, ensuring that resources are focused on the most impactful areas of study. In physics-based simulations like LBM, the computational mesh resolution directly influences the time resolution of the simulation and the number of iterations needed for a convergent solution. Moreover, complex 3D geometries often require the use of millions or even tens of millions of computational grid points, making impossible the sensitivity analysis or optimization studies due to high computational demands of the needed large number of simulations. Future research will build on this foundation by extending the framework to incorporate chemical reactions and dissolution/precipitation within the same experimental setup. This will involve expanding the DT framework to handle reactive transport simulations, and the training of ANN for chemical reactions and speciation calculations similar to the approach taken by will be pursued. The chemical speciation calculations typically consume most of the computational time, especially when compared with the time required for mass transport calculations. As the complexity of simulations increases, particularly with processes such as crystal nucleation, dissolution, and precipitation, the dimension of the surrogate model and the number of training samples will also grow. However, the core methodology of the DT framework remains scalable and adaptable, providing a feasible approach to address these complex physico-chemical problems.