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

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Abstract

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.

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

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Publication details

The article was received on 29 Apr 2019, accepted on 17 Jul 2019 and first published on 17 Jul 2019


Article type: Communication
DOI: 10.1039/C9RE00176J
React. Chem. Eng., 2019, Advance Article

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

    H. Bao, Z. Zhou, G. Kotsalis, G. Lan and Z. Tong, React. Chem. Eng., 2019, Advance Article , DOI: 10.1039/C9RE00176J

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