Lignin valorization process control under feedstock uncertainty through a dynamic stochastic programming approach

The randomness introduced by reactants is an issue when processing renewable bioresources. In this paper, we apply tools from dynamic stochastic programming theory to the biochemical process. Instead of introducing extra preprocessing units, we consider the inherent randomness of the process and opt...

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Bibliographic Details
Published in:Reaction chemistry & engineering 2019-09, Vol.4 (1), p.174-1747
Main Authors: Bao, Hanxi, Zhou, Zhiqiang, Kotsalis, Georgios, Lan, Guanghui, Tong, Zhaohui
Format: Article
Language:English
Subjects:
Dynamic programming
Lignin
Markov processes
Optimization
Randomness
Raw materials
Stochastic programming
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Summary:The randomness introduced by reactants is an issue when processing renewable bioresources. In this paper, we apply tools from dynamic stochastic programming theory to the biochemical process. Instead of introducing extra preprocessing units, we consider the inherent randomness of the process and optimize in expectation the performance of the system. In a general setting, this is a multistage stochastic optimization problem and we investigate its approximate solution via two approaches, namely stochastic dual dynamic programming (SDDP) and the finite state, finite action Markov decision process (MDP) framework. These two methods are implemented to a case study of lignin valorization that is crucial to a cost-effective biorefinery process using biomass as the feedstock. Herein we address the feedstock uncertainty for a robust lignin valorization process through a dynamic stochastic programming approach.
ISSN:2058-9883
2058-9883
DOI:10.1039/c9re00176j