首页> 外文会议>European Symposium on Computer Aided Process Engineering >Deterministic and Stochastic Optimization of Acid Pretreatment for Lignocellulosic Ethanol Production
【24h】

Deterministic and Stochastic Optimization of Acid Pretreatment for Lignocellulosic Ethanol Production

机译:木质纤维素乙醇生产酸预处理的确定性和随机优化

获取原文

摘要

Pretreatment of lignocellulosic biomass is a critical and cost intensive step in ethanol production requiring optimization. Dilute acid pretreatment depolymerizes xylan into xylose and lignin into acid soluble lignin (ASL). Since ideal conditions for both depolymerization reactions are different, a multi-objective optimization problem (MOOP) has been formulated considering the batch mode of operation and reaction temperature as the decision variable. This study has solved two different bi-objective optimization problems, namely, the maximization of the weighted sum of xylose and ASL yields (problem-1), and minimization of the weighted variance of xylose and ASL yields around a fixed targeted value (problem-2). For both problems, the xylose and ASL yields at the end of the batch are considered. Initially, the deterministic MOOP for both problems was solved using the weighing method. The results showed a strong tradeoff between the xylose and ASL yield and the optimal temperatures varied between 144°C and 113°C depending on the weight. However, the acid pretreatment process is subject to several uncertainties including feedstock composition and kinetic parameters that govern the reactions. Therefore, expected values of both problems were optimized using stochastic MOOP. For MOOP with feedstock composition uncertainty, the results were similar to the deterministic MOOP due to the linear correlation of feedstock composition with yield. In contrast, the results of MOOP with kinetic parameter uncertainty were significantly different. The temperatures for Pareto optimal solutions for prbblem-1 varied relatively less (133-125°C) for different weights, indicating a conservative operation. Moreover, the temperatures for Pareto optimal solutions for problem-2 were more aggressive than the deterministic case due to highly sensitive xylose degradation kinetics. The use of problem-2 reduced the variability in the xylose yield by an average of 15% over problem-1, and the temperatures changed accordingly.
机译:木质纤维素生物量的预处理是需要优化的乙醇生产中的危重和成本密集的步骤。将稀酸预处理将木聚糖解聚,将木聚糖和木质素中的木质素和木质素中的酸溶性木质素(ASL)脱溶。由于两种解聚反应的理想条件不同,因此考虑到批量操作和反应温度作为决策变量,已经制定了多目标优化问题(MOOP)。该研究已经解决了两种不同的双目标优化问题,即木糖和ASL的加权和产生(问题-1)的最大化,以及最小化木糖和ASL的加权方差围绕固定的目标值(问题 - 2)。对于两种问题,考虑了批次末端的木糖和ASL产量。最初,使用称重方法解决了两个问题的确定性MOOP。结果表明,木糖和ASL产率之间的强效应,最佳温度在144℃和113℃之间取决于重量。然而,酸预处理过程受到若干不确定性,包括用于治理反应的原料组合物和动力学参数。因此,使用随机莫波普优化了两种问题的预期值。对于具有原料组合物不确定性的MOOP,结果与原料组合物与产率的线性相关性相似。相比之下,具有动力学参数不确定性的MOOP的结果显着不同。 PRABLO-1的Pareto最佳溶液的温度相对较少(133-125°C),不同重量,表明保守操作。此外,由于高敏感的木糖降解动力学,帕累托问题2的帕累托最佳溶液的温度比确定性壳更具侵略性。使用问题-2的使用将木糖产率的可变性降低了一个平均问题-1的15%,并且温度相应地变化。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号