The optimization framework presented in this paper allows for optimization of complex problems with conflicting objectives, as illustrated with the case of CCU system (the proposed industrial park). To determine its best performance regarding environmental and economic aspects, we developed an optimization framework, where the industrial park is fully digitalized by ANN-based surrogates and simultaneously optimized in a cost-efficient manner. As such, the nonlinearity of sub-systems and the interaction between sub-systems are well considered during the optimization iterations. By scrutinizing the interactions between different unit operations proposed for carbon capture and utilization sections, optimization enables to determine a process configuration allowing for substantial reduction of CO2 emissions (-13%).