Assessing the Techno-Economic Feasibility of Solvent-Based, Critical Material Recovery from Uncertain, End-of-Life Battery Feedstock

Iloeje, Chukwunwike O.; Khalid, Yusra; Cresko, Joe; Graziano, Diane J.

doi:10.1007/978-3-030-36830-2_26

Cited by 4 publications

(3 citation statements)

References 21 publications

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“…Figure 14 shows the corresponding kernel density estimate for sample agent generated design parameters for 20 randomly generated composition cases for configuration A, which is within the ballpark of values predicted by the optimizer. [ 16,25 ]…”

Section: Resultsmentioning

confidence: 99%

Solvent extraction process design using deep reinforcement learning

Plathottam

Richey

Curry

et al. 2021

J Adv Manuf & Process

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Many chemical manufacturing and separations processes like solvent extraction comprise hierarchically complex configurations of functional process units. With increasing complexity, strategies that rely on heuristics become less reliable for design optimization. In this study, we explore deep reinforcement learning for mapping the space of feasible designs to find an optimization strategy that can match or exceed the performance of conventional optimization. To this end, we implement a highly configurable learning environment for the solvent design process to which we can couple state‐of‐the‐art deep reinforcement learning agents. We evaluate the trained agents against the heuristic optimization for the solvent process design tasked to optimize recovery efficiency and product purity. Results demonstrated the agent successfully learned the strategy for predicting comparably optimal solvent extraction process designs for varying combinations of feed compositions.

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Section: Resultsmentioning

confidence: 99%

Solvent extraction process design using deep reinforcement learning

Plathottam

Richey

Curry

et al. 2021

J Adv Manuf & Process

Self Cite

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“…At present there is a significant lack of literature detailing full techno-economic analyses on recycling waste materials for the applications proposed here. One report by Iloeje et al 145 investigated how the techno-economic feasibility of recycling depends on uncertainties associated with the feed input to the recovery process and the impacts that has on decision making. In that work they examined a recycling facility for Ni metal hydride batteries using a solvent extraction process and an economic model for a separation facility.…”

Section: Cost Considerationsmentioning

confidence: 99%

Repurposing metal containing wastes and mass-produced materials as electrocatalysts for water electrolysis

Saleem

Khosravi

Maroufi

et al. 2022

Sustainable Energy Fuels

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“…Developing large-scale recycling for the U.S. needs detailed techno-economic analysis that extends beyond the boundaries of the processing facility to resolve uncertainty about associated costs. This study builds from the modeling and techno-economics of a hydrometallurgical process for recovering REE from NiMH batteries (Iloeje et al, 2019(Iloeje et al, , 2020.…”

Section: Introductionmentioning

confidence: 99%