In-situ and real-time aggregation size evolution of copper sulfide precipitates using focused beam reflectance measurement (FBRM)

Barros, Lorena; Gim-Krumm, Minghai; Seriche, Gabriel; Quilaqueo, Michelle; Castillo, Claudia; Ihle, Christian F.; Ruby‒Figueroa, René; Estay, Humberto

doi:10.1016/j.powtec.2020.11.038

Cited by 14 publications

(10 citation statements)

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“…In fact, the aggregation time should be considered alongside with the reaction time when designing the reactor. The recent study performed by Barros and colleagues [54], using the FBRM, showed that the aggregation time was higher than the reaction time required to reach a maximum and stable particle size. Therefore, the optimal solid-liquid separation process strongly depends on the residence time defined for the reaction stage.…”

Section: Reactor Type and Supersaturation Controlmentioning

confidence: 90%

“…The main conclusions are that the aggregation behavior strongly depends on the hydrophilicity/hydrophobicity capacity of metal sulfides, affecting the resulting PSD during the precipitation [49,91,92]. However, the PSD curves of hydrophobic and highly aggregated precipitates, such as copper sulfide, show a bi-modal behavior, even containing up to 30% of particles smaller than 10 µm [51,54]. These fine particles can remain disaggregated due to the supersaturation condition or the formation of oxidized species on the particles' surface, diminishing the aggregation capacity [54].…”

Section: Solid-liquid Separationmentioning

confidence: 99%

“…Our research group has published different studies related to the application of Equation ( 8) in cyanide solutions for recovering copper and zinc [49,50] or just copper [51][52][53][54]. The successful application of the SART process at an industrial scale is an excellent referent for the scaling-up of the metal sulfide precipitation process for other matrices or applications.…”

Section: Leachates Solutions From Ores and Tailingsmentioning

confidence: 99%

“…PSD were determined using laser diffraction (Mastersizer); however, this technique can be invasive to obtain the aggregates size in comparison to the results that can be obtained using non-invasive techniques to determine PSD. This can be done using an optical microscope with image analysis and processing [51], and through a focused beam reflectance measurement (FBRM) [54]. Both works studied the aggregate size distribution of copper sulfide generated from cyanide media, where a bi-modal behavior was found, which could not be observed with the conventional laser diffraction technique, probably due to the disaggregation effect promoted by the agitation.…”

Section: Characteristics Of Precipitatesmentioning

confidence: 99%

“…Additionally, the FBRM analysis showed that the aggregation capacity was lost at low copper concentration (200 mg/L). This result could be a consequence of: (i) the lower supersaturation achieved; or (ii) a possible sulfur layer formed by the oxidation of excess sulfide that avoided the particle aggregation [54]. Thus, the enhancement of the aggregation capacity of metal sulfide precipitates and the understanding of how small particles could be reduced will be a key aspect to improve the solid-liquid separation processes.…”

Section: Characteristics Of Precipitatesmentioning

confidence: 99%

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Metal Sulfide Precipitation: Recent Breakthroughs and Future Outlooks

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The interest in metal sulfide precipitation has recently increased given its capacity to efficiently recover several metals and metalloids from different aqueous sources, including wastewaters and hydrometallurgical solutions. This article reviews recent studies about metal sulfide precipitation, considering that the most relevant review article on the topic was published in 2010. Thus, our review emphasizes and focuses on the overall process and its main unit operations. This study follows the flow diagram definition, discussing the recent progress in the application of this process on different aqueous matrices to recover/remove diverse metals/metalloids from them, in addition to kinetic reaction and reactor types, different sulfide sources, precipitate behavior, improvements in solid–liquid separation, and future perspectives. The features included in this review are: operational conditions in terms of pH and Eh to perform a selective recovery of different metals contained in an aqueous source, the aggregation/colloidal behavior of precipitates, new materials for controlling sulfide release, and novel solid–liquid separation processes based on membrane filtration. It is therefore relevant that the direct production of nanoparticles (Nps) from this method could potentially become a future research approach with important implications on unit operations, which could possibly expand to several applications.

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Section: Reactor Type and Supersaturation Controlmentioning

confidence: 90%

Section: Solid-liquid Separationmentioning

confidence: 99%

Section: Leachates Solutions From Ores and Tailingsmentioning

confidence: 99%

Section: Characteristics Of Precipitatesmentioning

confidence: 99%

Section: Characteristics Of Precipitatesmentioning

confidence: 99%

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Metal Sulfide Precipitation: Recent Breakthroughs and Future Outlooks

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Online learning‐based predictive control of crystallization processes under batch‐to‐batch parametric drift

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This work considers a seeded fesoterodine fumarate (FF) cooling crystallization and presents the methodology and implementation of a real‐time machine learning modeling‐based predictive controller to handle batch‐to‐batch (B2B) parametric drift. Specifically, an autoencoder recurrent neural network‐based model predictive controller (AERNN‐MPC) is developed to optimize product yield, crystal size, and energy consumption while accounting for the physical constraints on cooling jacket temperature. Deviations in the kinetic parameters are considered in the closed‐loop simulations to account for the B2B parametric drift, and two error‐triggered online update mechanisms are proposed to address issues pertaining to the availability of real‐time crystal property measurements and are incorporated into the AERNN‐MPC to improve the model prediction accuracy. Closed‐loop simulation results demonstrate that the proposed AERNN‐MPC with online update, irrespective of the accessibility to real‐time crystal property data, achieves a desired closed‐loop performance in terms of maximizing product yield and minimizing energy consumption.

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