Application of Adsorbate Solid Solution Theory To Design Novel Adsorbents for Arsenic Removal Using CAMD

Doshi, Rajat; Mukherjee, Rabindra Nath; Diwekar, Urmila M.

doi:10.1021/acssuschemeng.7b04094

Cited by 14 publications

(11 citation statements)

References 23 publications

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“…CAMD methods have been applied extensively in various areas such as extraction solvents [ 103 , 104 , 105 , 106 , 107 , 108 ], polymer designs [ 109 , 110 ], degreasing solvents [ 111 ], blanket wash solvents [ 112 , 113 ], absorption solvents [ 114 , 115 , 116 , 117 , 118 ], refrigerant design [ 119 , 120 ], distillation solvents [ 102 , 106 , 121 , 122 ], reaction solvents [ 123 , 124 ], catalysts [ 124 ], value-added products [ 125 ], crystallization solvents [ 126 ], and foaming agents [ 127 ]. CAMD has been used effectively to design novel clay-based adsorbents to adsorb radioactive elements from flowback/produced water [ 128 ] and remove arsenic from water [ 129 ]. Mukherjee et al [ 110 ] used CAMD to design a novel polymer resin for metal ion removal from water.…”

Section: Theoretical Methodsmentioning

confidence: 99%

Recent Advances in Theoretical Development of Thermal Atomic Layer Deposition: A Review

et al. 2022

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Atomic layer deposition (ALD) is a vapor-phase deposition technique that has attracted increasing attention from both experimentalists and theoreticians in the last few decades. ALD is well-known to produce conformal, uniform, and pinhole-free thin films across the surface of substrates. Due to these advantages, ALD has found many engineering and biomedical applications. However, drawbacks of ALD should be considered. For example, the reaction mechanisms cannot be thoroughly understood through experiments. Moreover, ALD conditions such as materials, pulse and purge durations, and temperature should be optimized for every experiment. It is practically impossible to perform many experiments to find materials and deposition conditions that achieve a thin film with desired applications. Additionally, only existing materials can be tested experimentally, which are often expensive and hazardous, and their use should be minimized. To overcome ALD limitations, theoretical methods are beneficial and essential complements to experimental data. Recently, theoretical approaches have been reported to model, predict, and optimize different ALD aspects, such as materials, mechanisms, and deposition characteristics. Those methods can be validated using a different theoretical approach or a few knowledge-based experiments. This review focuses on recent computational advances in thermal ALD and discusses how theoretical methods can make experiments more efficient.

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Section: Theoretical Methodsmentioning

confidence: 99%

Recent Advances in Theoretical Development of Thermal Atomic Layer Deposition: A Review

et al. 2022

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“…EACO can be used for highly noisy and nonlinear objective functions and are most likely to arrive at or near the globally optimal solution. EACO has been extensively used for solving chemical engineering problems which include the computer-aided molecular design , and solvent selection process . In the present work, we are using EACO for a surrogate model-based chemical process optimization.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective Optimization of the TEG Dehydration Process for BTEX Emission Mitigation Using Machine-Learning and Metaheuristic Algorithms

Mukherjee

Diwekar

2021

ACS Sustainable Chem. Eng.

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Natural gas processing involves the removal of acidic gases, followed by dehydration. The dehydration process is primarily carried out through absorption in triethylene glycol (TEG). During the dehydration process, volatile organic compounds (VOCs) including benzene, toluene, ethylbenzene, and isomers of xylene together known as BTEX present in natural gas are absorbed into the glycol solvent. During the thermal regeneration process of TEG, a substantial amount of BTEX and VOCs are emitted resulting in adverse environmental and health impacts, leading to the need for strict regulations. Effective mitigation of emission is essential for the natural gas industry. There are several process parameters associated with major equipment that may impact BTEX emission. Regulation of some of these parameters is found to have an adverse impact on the dehydration process as well. In this work, a multi-objective optimization is performed in order to find the optimal operating conditions related to the process parameters to mitigate BTEX emission. It is also essential to select the most important parameters from a larger set so that a reliable analysis can be performed using the reduced set of parameters. The analysis is performed using data-driven modeling using machine-learning algorithms, followed by optimization with a probabilistic technique. The least absolute shrinkage and selection operator (lasso) method is used for variable selection; support vector regression (SVR) is used for metamodeling; and a novel metaheuristic optimization algorithm, efficient ant colony optimization (EACO), is used for optimization. The surrogate or metamodel is generated with data obtained from process simulation carried out using ProMax. Using the lasso–SVR–EACO strategy, different optimized sets of operating conditions that minimize the BTEX emission for a stipulated dry gas water content, were obtained. The results show that BTEX released from the dehydration process can be mitigated by optimally choosing the TEG circulation rate, reboiler temperature, stripping gas flow rate, and absorber pressure.

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“…For instance, the ALD growth rate was modeled and related to the size of the reactant, chemisorption process, and process conditions such as temperature or precursor pulse time [3][4][5]. Adsorbate Solid Solution Theory (ASST) can be applied to incorporate the effects of functional groups through a group Contribution Method (GCM) and predict adsorption of a compound [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Quantification of Water Impurity in an Atomic Layer Deposition Reactor Using Group Contribution Method

Takoudis

2021

RDMS

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Atomic Layer Deposition (ALD) is a vapor phase process to deposit thin films using precursors and co-reactants on a variety of substrates. The ALD system, the data of which is used here, had aimed to deposit TiO2 thin films on polymethyl methacrylate (PMMA) and silicon reference substrates using tetrakis(dimethylamido)titanium and ozone. Absorbed water molecules in PMMA were released into the reactor during deposition, acted as a co-reactant, and affected the growth rate on the silicon reference substrate. The objective of this work is to theoretically calculate the amount of water impurities in the ALD reactor during several number of cycles. A group contribution method based on Adsorbate Solid Solution Theory (ASST) is employed to theoretically estimate the number of moles of water in the abovementioned ALD reactor.

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Application of Adsorbate Solid Solution Theory To Design Novel Adsorbents for Arsenic Removal Using CAMD

Cited by 14 publications

References 23 publications

Recent Advances in Theoretical Development of Thermal Atomic Layer Deposition: A Review

Recent Advances in Theoretical Development of Thermal Atomic Layer Deposition: A Review

Multi-objective Optimization of the TEG Dehydration Process for BTEX Emission Mitigation Using Machine-Learning and Metaheuristic Algorithms

Quantification of Water Impurity in an Atomic Layer Deposition Reactor Using Group Contribution Method

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