Deterministic tools to estimate induction time for methane hydrate formation in the presence of Luvicap 55 W solutions

Zare, Marziyeh; Zendehboudi, Sohrab; Abdi, Majid Abedinzadegan

doi:10.1016/j.molliq.2021.118374

Cited by 8 publications

(5 citation statements)

References 104 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The statistical parameters such as the average absolute relative error percentage (AARE%), mean squared error (MSE), and coefficient of determination ( R 2 ) are employed to evaluate the accuracy and performance of the models developed in this study. The following equations represent the abovementioned statistical parameters − where x d i and x m i are the values of the experimental data and the prediction; x̅ is the average value of the experimental data; and n denotes the number of data.…”

Section: Methodsmentioning

confidence: 99%

“…The statistical parameters such as the average absolute relative error percentage (AARE%), mean squared error (MSE), and coefficient of determination ( R 2 ) are employed to evaluate the accuracy and performance of the models developed in this study. The following equations represent the abovementioned statistical parameters − normalA normalA normalR normalE % = 100 ∑ i = 1 n true| x normald i − x normalm i x normald i true| / n normalM normalS normalE = 1 n .25em ∑ i = 1 n ( x d i − x m i ) 2 R 2 = 1 − ∑ i = 1 n false( x normald i − x normalm i false) 2 ∑ i = 1 n ...…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Hybrid Smart Strategies to Predict Amine Thermal Degradation in Industrial CO₂ Capture Processes

Azarpour

Zendehboudi

2023

ACS Omega

Self Cite

View full text Add to dashboard Cite

CO2 emission reduction is an essential step to achieve the climate change targets. Solvent-based post-combustion CO2 capture (PCC) processes are efficient to be retrofitted to the existing industrial operations/installations. Solvent degradation (and/or loss) is one of the main concerns in the PCC processes. In this study, the thermal degradation of monoethanolamine (MEA) is investigated through the utilization of hybrid connectionist strategies, including an artificial neural network-particle swarm optimization (ANN-PSO), a coupled simulated annealing-least squares support vector machine (CSA-LSSVM), and an adaptive neuro-fuzzy inference system (ANFIS). Moreover, gene expression programming (GEP) is employed to generate a correlation that relates the solvent concentration to the operating variables involved in the adverse phenomenon of solvent thermal degradation. The input variables are the MEA initial concentration, CO2 loading, temperature, and time, and the output variable is the remaining/final MEA concentration after the degradation phenomenon. According to the training and testing phases, the most accurate model is ANFIS, and the reliability/performance of its optimal network is assessed by the coefficient of determination (R 2), mean squared error, and average absolute relative error percentage, which are 0.992, 0.066, and 2.745, respectively. This study reveals that the solvent initial concentration has the most significant impact, and temperature plays the second most influential effect on solvent degradation. The developed models can be used to predict the thermal degradation of any solvent in a solvent-based PCC process regardless of the complicated reactions involved in the degradation phenomenon. The models introduced in this study can be employed for the development of more accurate hybrid models to optimize the proposed systems in terms of cost, energy, and environmental prospects.

show abstract

Section: Methodsmentioning

confidence: 99%

“…The statistical parameters such as the average absolute relative error percentage (AARE%), mean squared error (MSE), and coefficient of determination ( R 2 ) are employed to evaluate the accuracy and performance of the models developed in this study. The following equations represent the abovementioned statistical parameters − normalA normalA normalR normalE % = 100 ∑ i = 1 n true| x normald i − x normalm i x normald i true| / n normalM normalS normalE = 1 n .25em ∑ i = 1 n ( x d i − x m i ) 2 R 2 = 1 − ∑ i = 1 n false( x normald i − x normalm i false) 2 ∑ i = 1 n ...…”

Section: Methodsmentioning

confidence: 99%

Hybrid Smart Strategies to Predict Amine Thermal Degradation in Industrial CO₂ Capture Processes

Azarpour

Zendehboudi

2023

ACS Omega

Self Cite

View full text Add to dashboard Cite

show abstract

“…Induction time must be increased or decreased depending on the application. The increase in induction time is beneficial in flow assurance applications for oil and gas recovery . Similarly, nucleation kinetics and hydrate formation enhancement are beneficial for gas storage and transportation applications .…”

Section: Introductionmentioning

confidence: 99%

“…The increase in induction time is beneficial in flow assurance applications for oil and gas recovery. 10 Similarly, nucleation kinetics and hydrate formation enhancement are beneficial for gas storage and transportation applications. 11 The reduction in the induction time can be achieved by either agitation or the addition of additives.…”

Section: ■ Introductionmentioning

confidence: 99%

Can Aluminum Cations Promote Nucleation for THF Hydrate Growth?

Ravesh,

Ansari,

Panigrahi

et al. 2024

Langmuir

View full text Add to dashboard Cite

Rapid nucleation of tetrahydrofuran (THF) hydrate is essential for developing a THF hydrate-based cold storage technology. Earlier works have hypothesized the role of aluminum complexes in initiating the nucleation of clathrate hydrates using aluminum metal electrodes and substrates. This study investigates if the nucleation promotional effect of hydrate can be achieved using the aluminum salt, AlCl3, due to the formation of aluminum aqua complexes in water. Metal chlorides NaCl and MgCl2 are also utilized to evaluate the effect of cation type in initiating nucleation, i.e., the effect of charge/radius ratio. The induction time is measured in a stirred reactor at various subcoolings and concentrations of 0.05, 0.1, 0.5, 1, and 2 wt %. The nucleation time is studied in two reactor configurations based on the nature of salt introduction in the THF solution, i.e., salt premixed in solution and salt injected inside the solution. The sudden rise in the reactor temperature due to hydrate formation is used as an indicator of hydrate formation. Results indicate that AlCl3 promotes hydrate nucleation as AlCl3 reduces induction time by 92.2% at 0.05 wt % concentration compared with water. Nearly instantaneous nucleation is also achieved by directly injecting AlCl3. MgCl2 and NaCl do not show a similar effect on induction time as AlCl3. The pH and Raman spectra measurements with and without salts are carried out to explain the effect of cations on the THF–water solution.

show abstract

“…It can be used to distinguish the given data based on their different patterns, extract useful information from the data and output predictions with new input variables [21]. Recently, a number of ML algorithms such as artificial neural networks, decision trees, k-nearest neighbor, gradient boosting regression, adaptive neuro Fuzzy interference system, gene expression programming, random forest (RF), support vector machines (SVM) have been used to predict gas hydrate formation and dissociation [19,20,[22][23][24][25][26][27][28][29]. These studies mainly focused on the hydrate phase transition in aqueous solutions with or without traditional hydrate inhibitor such as monoethylene glycol, Luvicap 55 W and ionic liquids which might have negative environmental impacts as aforementioned.…”

Section: Introductionmentioning

confidence: 99%

Machine learning models for fast selection of amino acids as green thermodynamic inhibitors for natural gas hydrate

Coulon

Feng

et al. 2023

Journal of Molecular Liquids

View full text Add to dashboard Cite

Deterministic tools to estimate induction time for methane hydrate formation in the presence of Luvicap 55 W solutions

Cited by 8 publications

References 104 publications

Hybrid Smart Strategies to Predict Amine Thermal Degradation in Industrial CO₂ Capture Processes

Hybrid Smart Strategies to Predict Amine Thermal Degradation in Industrial CO₂ Capture Processes

Can Aluminum Cations Promote Nucleation for THF Hydrate Growth?

Machine learning models for fast selection of amino acids as green thermodynamic inhibitors for natural gas hydrate

Contact Info

Product

Resources

About

Deterministic tools to estimate induction time for methane hydrate formation in the presence of Luvicap 55 W solutions

Cited by 8 publications

References 104 publications

Hybrid Smart Strategies to Predict Amine Thermal Degradation in Industrial CO2 Capture Processes

Hybrid Smart Strategies to Predict Amine Thermal Degradation in Industrial CO2 Capture Processes

Can Aluminum Cations Promote Nucleation for THF Hydrate Growth?

Machine learning models for fast selection of amino acids as green thermodynamic inhibitors for natural gas hydrate

Contact Info

Product

Resources

About

Hybrid Smart Strategies to Predict Amine Thermal Degradation in Industrial CO₂ Capture Processes

Hybrid Smart Strategies to Predict Amine Thermal Degradation in Industrial CO₂ Capture Processes