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In the modern iron and steel industry, the efficient distribution ofbyproduct gases faces significant challenges due to quantity- andquality-related uncertainties of gases. This study presents an optimal approachto gas distribution that addresses this issue by incorporating the energy flownetwork and the uncertain surplus gases from the manufacturing system. Theuncertain optimization problem is formulated as a two-stage robust optimization(TSRO) model, including "here-and-now" decisions aimed at minimizing thestart-stop cost of energy conversion units, as well as "wait-and-see" decisionsaimed at minimizing the operating cost of gasholders and the penaltiesresulting from energy excess or shortage. To facilitate practicalimplementation, we propose a "first quantify, then optimize" approach: (1)quantifying the uncertainty of surplus gases via a conditional quantileregression (CDQ)-based T-step time series model, and (2)finding the optimalsolution through a column-and-constraint generation algorithm. Furthermore, acase study is conducted on an industrial energy system to validate the proposedmethodology. Computational results, using evaluation indicators, such as MAPE,RMSE, PICP, and PINAW, confirm the effectiveness of the data-driven time seriesmodel in accurately quantifying uncertainties in each period. Sensitivityanalysis demonstrates that the proposed TSRO model achieves a favorable balancebetween robustness and flexibility by selecting the combination of "budget andquantile" and the parameters of storage and conversion units. Consequently,TSRO can efficiently find a robust gas distribution solution with the desiredlevel of conservativeness for integrated iron and steel plants.

Plant-wide byproduct gas distribution under uncertainty in iron and steel industry via quantile forecasting and robust optimization