Multifeedstock and Multiproduct Process Design Using Neural Network Surrogate Flexibility Constraints
Multifeedstock and Multiproduct Process Design Using Neural Network Surrogate Flexibility Constraints
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DOI：
10.1021/acs.iecr.2c02968
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发表时间：
2023-01-16
影响因子：
4.2
通讯作者：
Ierapetritou, Marianthi
中科院分区：
文献类型：
--
作者：
Luo, Yuqing;Ierapetritou, Marianthi
生物精炼厂旨在利用各种技术的组合将生物质衍生的原材料转化为不同的增值产品。文献中强调了这一策略，可减少浪费、提高盈利能力并提高对不确定生物质原料的工艺弹性。在这项工作中，开发了一个两阶段随机规划（TSSP）模型，以在不同的不确定性来源下实现利润最大化和排放最小化。数据驱动的替代模型是为生物炼制厂的灵活性指数（FI）构建的，以量化和提高其运营灵活性。具有修正线性单元（ReLU）激活函数的神经网络被建立为适当的代理模型，因为它非常接近灵活性指数，同时保留了总体设计公式的混合整数线性特征。此外，随机规划使用经验价格/需求/供应不确定性信息定量地展示了每种情景中环境影响不确定性的大小，这是传统的基于谱系的生命周期评估（LCA）不确定性分析无法解决的。
Biorefineries are designed to utilize a combination of various technologies to transform biomass derived raw materials into different valueadded products. This strategy has been highlighted in the literature for reducing waste, increasing profitability, and improving the process resilience to uncertain biomass feedstocks. In this work, a two-stage stochastic programming (TSSP) model is developed to maximize profit and minimize emissions under different sources of uncertainties. Data-driven surrogate models are built for biorefinery's flexibility index (FI) to quantify and improve its operational flexibility. The neural network with rectified linear unit (ReLU) activation function is established as the appropriate surrogate model because it closely approximates the flexibility index while retaining the mixed-integer linear characteristics of the overall design formulation. Moreover, the stochastic programming demonstrates the magnitude of environmental impact uncertainty quantitatively in each scenario using empirical price/demand/supply uncertainty information, which cannot be addressed by the traditional Pedigree-based life cycle assessment (LCA) uncertainty analysis.