Optimizing sustainable multiple biomass-to-biofuel conversion network with integrated water resource management utilizing data-driven robust planning

• Improving efficiency of wastewater-energy SC using a multilateral approach. • Developing a novel uncertainty set using a data-driven intersected approach. • Employing a triple model analysis to assess the efficiency of the proposed approach. • Implementing a triple assessment scenario to evaluate system dynamics. • Conceptualizing a hybrid pre-processing phase to ensure continuous access. Until now, the biofuel supply chain has lacked an integrated approach to address its fundamental challenges in real-world implementation. The first challenge is maintaining a continuous supply flow in the biofuel network, which can significantly impact economic outcomes and performance measures. The second challenge involves managing water resources for biofuel production to preserve freshwater reserves. The third challenge is managing operational risks, such as uncertainty in biofuel demand, which leads to conservative planning and underscores the need for realistic uncertain programming approaches. To address these challenges, the proposed study designs a biofuel conversion network that utilizes third-generation feedstocks and non-edible seed sources, sourcing necessary water from wastewater resources. This practical solution addresses water-energy relationship concerns, reducing dependency on limited freshwater supplies. To achieve a continuous and cost-effective feed supply, this study incorporates a pre-processing phase using a hybrid Multi-Criteria Decision-Making model. This approach ensures the feed supply remains consistent, economical, and sustainable. To manage the uncertainty in biofuel demand, we developed a robust optimization model with an intersected uncertainty set using a data-driven approach. This approach aims to reduce conservatism in the obtained solutions, making the model more practical for real-world implementation. The numerical results reveal some practical implementation for policymakers. The proposed robust optimization using the configured uncertainty set offers a tool to manage the conservatism of renewable energy demand uncertainty and operational risks more effectively than traditional approaches (0.914%). This formulation can help policymakers to maintain greater robustness against fluctuations in confidence levels. Additionally, the simultaneous use of third-generation and non-edible feedstocks creates a multi-biomass-to-biofuel conversion network, offering practitioners a practical solution that significantly reduces reliance on freshwater resources. Furthermore, policymakers can leverage the proposed pre-processing phase to promote economic and continuous supply feed based on geographical, resource accessibility, and infrastructure criteria.