Assessing the prospective climate preservation potential of novel, innovative, but immature chemical production techniques is limited by the high number of process synthesis options and the lack of reliable, high-throughput quantitative sustainability pre-screening methods. This study presents the sequential use of data-driven hybrid prediction (ANN-RSM-DOM) to streamline waste-to-dimethyl ether (DME) upcycling using a set of sustainability criteria. Artificial neural networks (ANNs) are developed to generate in silico waste valorization experimental results and ex-ante model the operating space of biorefineries applying the organic fraction of municipal solid waste (OFMSW) and sewage sludge (SS). Aspen Plus process flowsheeting and ANN simulations are postprocessed using the response surface methodology (RSM) and desirability optimization method (DOM) to improve the in-depth mechanistic understanding of environmental systems and identify the most benign configurations. The hybrid prediction highlights the importance of targeted waste selection based on elemental composition and the need to design waste-specific DME synthesis to improve techno-economic and environmental performances. The developed framework reveals plant configurations with concurrent climate benefits (-1.241 and -2.128 kg CO2-eq (kg DME)-1) and low DME production costs (0.382 and 0.492 € (kg DME)-1) using OFMSW and SS feedstocks. Overall, the multi-scale explorative hybrid prediction facilitates early stage process synthesis, assists in the design of block units with nonlinear characteristics, resolves the simultaneous analysis of qualitative and quantitative variables, and enables the high-throughput sustainability screening of low technological readiness level processes.

Advanced Materials and Intelligent Technologies Higher Education and Industrial Cooperation Centre University of Miskolc H 3515 Miskolc Egyetemváros Hungary

Department of Environmental and Resource Engineering Quantitative Sustainability Assessment Technical University of Denmark Bygningstorvet Building 115 DK 2800 Kgs Lyngby Denmark

Environmental Economics Department of Engineering Management University of Antwerp Prinsstraat 13 2000 Antwerp Belgium

Faculty of Chemical Technology and Biotechnology Budapest University of Technology and Economics Műegyetem rkp 3 1111 Budapest Hungary

Intelligence in Process Advanced Catalysts and Solvents Faculty of Applied Engineering University of Antwerp Groenenborgerlaan 171 2020 Antwerp Belgium

Sustainable Process Integration Laboratory─SPIL NETME Centre FME Brno University of Technology Technická 2896 2 616 69 Brno Czech Republic

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