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In developing countries like Indonesia, adding a new unit for hydroprocessing facilities is more efficient than modifying the existing equipment. When the addition occurs, refiners should analyze if the existing hydrogen network can supply enough hydrogen to the new unit before constructing a costly hydrogen plant. Current studies on the hydrogen network have yet to consider pressure drop adequately. Therefore, this paper integrates pressure drop estimation and density prediction into the multi-objective MINLP-based hydrogen network. The multi-objective problem is solved sequentially by adding a minor unit to obtain each configuration's maximum flowrate and total annual cost. The optimal configuration based on the combined objective function is to add 1 PSA and compressor for a hydrogen purity of 0.84 and specified pressure requirements. The pressure drop integration shows an insignificant impact averaging 0.004 m3/s of maximum flowrate difference compared to optimization without pressure drop. • The addition of a new hydroprocessing unit is more efficient than existing refiners. • Multi-objective MINLP-based hydrogen network is used to analyze retrofit options. • The pressure drop integration shows a small impact on the overall. • Network. • The optimal configuration is to add one PSA and one compressor. [ABSTRACT FROM AUTHOR]