A P-graph approach for the synthesis of national-wide bio-hydrogen network from palm oil mill effluent. (10th July 2020)

• Record Type: Journal Article
• Authors: Lee, Chee Hoong; Chong, Darren Yu Lun; Hemmati, Sadaf; Elnegihi, M. Mostafa; Foo, Dominic C.Y.; How, Bing Shen; Yoo, ChangKyoo
• Is Part Of: International journal of hydrogen energy. Volume 45:Number 35(2020)
• Page Start: 17200
• Page End: 17219
• Publication Date: 2020-07-10
• Subjects: Optimisation -- Renewable energy -- Hydrogen production -- Process synthesis; Hydrogen as fuel -- Periodicals; Hydrogène (Combustible) -- Périodiques; Hydrogen as fuel; Periodicals; 665.81
• DOI: 10.1016/j.ijhydene.2020.04.179
• Languages: English
• ISSNs: 0360-3199

Abstract

Abstract: The shift from conventional hydrogen production that utilised fossil-based energy towards a more sustainable practice is essential. Recently, the interest in utilising renewable hydrogen sources associated with bio-gas has risen. This work aims to develop a P-graph methodology for bio-hydrogen network synthesis, where oil refineries and ammonia plants act as bio-hydrogen sinks, while palm oil mills serve as the bio-hydrogen sources. The latter produces huge amount of palm oil mill effluent (POME) which may be converted to bio-hydrogen. The model is optimised with the aim to minimise the total network cost, whilst fulfilling the demand of all hydrogen sinks. Two case studies in Malaysia are used to illustrate the effectiveness of the proposed model. Results show that the overall network cost can be reduced by 45.6% (first case study) and 85.8% (second case study) when bio-hydrogen is supplied from the abundant POME in the study areas. Besides, the capability of the proposed P-graph framework in conducting uncertainty analysis and technology benchmarking study, are also demonstrated. Graphical abstract: Image 1 Highlights: A P-graph approach to synthesise the bio-hydrogen network is developed. Bio-hydrogen network can be more economic feasible compared to the existing one. Two case studies in Malaysia are used to illustrate the proposed method. The framework can be used to analyse supply chain uncertainty. The framework can be used to benchmark the feasibility of new technology.