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Biomass and waste plastics chemical looping co-gasification for hydrogen-electricity-DME conservation and recycling based on machine learning

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  • Tian, Wende
  • Zhang, Shuming
  • Wang, Shaochen
  • Liu, Bin
  • Cui, Zhe

Abstract

The reasonable utilization of organic solid waste can solve the problem of fossil energy shortage, but there are also challenges of high processing cost and difficulty. In this paper, a novel polygeneration system integrating biomass and waste plastic chemical looping co-gasification (BPCLG) for hydrogen production, power generation, and dimethyl ether (DME) synthesis is established and optimized by coupling process simulation and machine learning (ML) to achieve efficient utilization of organic solid waste. The artificial neural network (ANN) is employed to develop surrogate models for predicting the molar fractions of critical components in syngas and the system energy consumption. Then the non-dominated sorting genetic algorithm-II (NSGA-II) is utilized to optimize two objectives including the highest molar fraction of hydrogen in syngas and the lowest energy consumption. The optimized polygeneration system increased hydrogen production by 4.36 % and reduced energy consumption by 3.59 %. This work advances the resourceful utilization of organic solid waste while alleviating the energy crisis.

Suggested Citation

  • Tian, Wende & Zhang, Shuming & Wang, Shaochen & Liu, Bin & Cui, Zhe, 2025. "Biomass and waste plastics chemical looping co-gasification for hydrogen-electricity-DME conservation and recycling based on machine learning," Energy, Elsevier, vol. 318(C).
    • Handle: RePEc:eee:energy:v:318:y:2025:i:c:s0360544225005109
    DOI: 10.1016/j.energy.2025.134868
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