Publication detail

Hybrid Prediction-Driven High-Throughput Sustainability Screening for Advancing Waste-to-Dimethyl Ether Valorization

Fozer, Daniel Nimmegeers, Philippe Toth, Andras Jozsef Varbanov, Petar Sabev Klemes, Jiri Jaromir Mizsey, Peter Hauschild, Michael Zwicky Owsianiak, Mikolaj

Original Title

Hybrid Prediction-Driven High-Throughput Sustainability Screening for Advancing Waste-to-Dimethyl Ether Valorization

Type

journal article in Web of Science

Language

English

Original Abstract

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 is an element of(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.

Keywords

artificial neural network; explorative decarbonization; hybrid machine learning; hydrothermal gasification; optimization; process synthesis; sustainable-by-design; waste-to-chemicals

Authors

Fozer, Daniel; Nimmegeers, Philippe; Toth, Andras Jozsef; Varbanov, Petar Sabev; Klemes, Jiri Jaromir; Mizsey, Peter; Hauschild, Michael Zwicky; Owsianiak, Mikolaj

Released

29. 8. 2023

Publisher

AMER CHEMICAL SOC1155 16TH ST, NW, WASHINGTON, DC 20036

Location

AMER CHEMICAL SOC1155 16TH ST, NW, WASHINGTON, DC 20036

ISBN

0013-936X

Periodical

ENVIRONMENTAL SCIENCE & TECHNOLOGY

Year of study

36

Number

57

State

United States of America

Pages from

13449

Pages to

13462

Pages count

14

URL

https://pubs.acs.org/doi/10.1021/acs.est.3c01892

BibTex

@article{BUT187620,
  author="Fozer, Daniel and Nimmegeers, Philippe and Toth, Andras Jozsef and Varbanov, Petar Sabev and Klemes, Jiri Jaromir and Mizsey, Peter and Hauschild, Michael Zwicky and Owsianiak, Mikolaj",
  title="Hybrid Prediction-Driven High-Throughput Sustainability Screening for Advancing Waste-to-Dimethyl Ether Valorization",
  journal="ENVIRONMENTAL SCIENCE & TECHNOLOGY",
  year="2023",
  volume="36",
  number="57",
  pages="13449--13462",
  doi="10.1021/acs.est.3c01892",
  issn="0013-936X",
  url="https://pubs.acs.org/doi/10.1021/acs.est.3c01892"
}