Effective decision-making is essential for minimizing the environmental footprint while strengthening the competitiveness of the chemical industry. This paper proposes a multistage stochastic programming (MSSP) framework to take into account the design and expansion planning simultaneously for a sustainable multinetwork that integrates the water treatment, renewable energy supply, and carbon capture, utilization and storage (CCUS) with a main chemical processing sector under endogenous uncertainty related to conversion rates arising from the possible processing steps. Both economic and environmental concerns are involved in the optimization model for the entire system. The resulting mixed-integer linear programming (MILP) model is then solved using the Lagrangean decomposition algorithm. The proposed framework is further implemented in xylitol process design problems. Four cases are established based on different combinations of uncertain parameter distributions and time horizons, with model complexity increasing sequentially. The effectiveness of the proposed framework is further validated under new randomized sampling scenarios. The results indicate that integrating multinetworks can significantly reduce carbon emissions, thereby mitigating environmental impacts while satisfying production demands. Specifically, carbon dioxide (CO₂) can be fully captured and optimally utilized, wastewater can be completely treated, and economic benefits can be effectively maximized. In comparison to the deterministic model, the MSSP counterpart offers a sustainable, robust, and reliable solution for integrated design and expansion planning over the specified time horizon. Additionally, it achieves the value of stochastic solution (VSS) of 2%, potentially saving millions of dollars in long-term capacity planning, thereby underscoring the advantages of incorporating endogenous uncertainty into the problem formulation.

中文翻译：

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集成可持续化工过程设计与扩展规划内生不确定性下的多阶段随机规划


有效的决策对于最大限度地减少环境足迹同时增强化工行业的竞争力至关重要。本文提出了一个多阶段随机规划 （MSSP） 框架，以同时考虑可持续多网络的设计和扩展规划，该网络将水处理、可再生能源供应和碳捕获、利用和封存 （CCUS） 与主要化学加工部门集成在与可能的加工步骤产生的转化率相关的内生不确定性下。整个系统的优化模型涉及经济和环境问题。然后使用拉格朗日分解算法求解得到的混合整数线性规划 （MILP） 模型。所提出的框架在木糖醇工艺设计问题中进一步实现。根据不确定参数分布和时间范围的不同组合建立了四个案例，模型复杂性依次增加。在新的随机抽样情景下，所提出的框架的有效性得到了进一步验证。结果表明，集成多网络可以显著减少碳排放，从而在满足生产需求的同时减轻环境影响。具体来说，二氧化碳 （CO₂） 可以被完全捕获和优化利用，废水可以得到彻底处理，并且可以有效地实现经济效益的最大化。与确定性模型相比，MSSP 对应模型为指定时间范围内的集成设计和扩展规划提供了可持续、稳健且可靠的解决方案。 此外，它实现了 2% 的随机解决方案 （VSS） 的价值，可能在长期容量规划中节省数百万美元，从而强调了将内生不确定性纳入问题表述的优势。