The scope of the optimization framework is designed around the entire industrial park, containing of four sub-systems, i.e. [NGCC + MEA], [NGCC + PSA], [Reforming + FT] and [Reforming + MS]. To determine an optimal configuration, models of sub-systems are necessary. The current industrial practice involves the application of tailored simulators for specific systems (e.g. Dymola for dynamic process modelling, Aspen for reactors and separation units). We anticipated that it might be insightful to search a global decision space by simultaneously optimizing all sub-systems, ideally from a level of a higher interactive platform. To achieve this goal, as well as improve the computational efficiency of the complex optimization task, we digitalized the sub-systems using surrogates and proposed a three-level framework shown in Figure . This work mainly considers Artificial Neural Networks (ANNs) as surrogates, because ANNs are claimed to be universal approximators. Figure . Three-level approach for the optimization of complex processes, illustrated by the case-study of decarbonization of an integrated industrial park.