An artificial neural network-based NRTL model for simulating liquid-liquid equilibria of systems present in biofuels production

Reynel-Ávila, Hilda Elizabeth; Bonilla-Petriciolet, Adrián; Tapia-Picazo, Juan Carlos

doi:10.1016/j.fluid.2018.11.009

Cited by 25 publications

(6 citation statements)

References 35 publications

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“…Artificial neural network (ANN) is an artificial intelligence method that adopts some mathematical principles to process information by simulating the human brain and has the ability to process complex data. ANN has been widely used in materials, energy, , and chemical ,− and has shown good analogue performance. Sheng et al and Wu et al predicted the mass-transfer coefficient using the ANN model, and the results showed that the experimental values and the predicted values were in good agreement.…”

Section: Modeling Study On η and K G Amentioning

confidence: 99%

Simultaneous Absorption of H₂S and CO₂ into the MDEA + PZ Aqueous Solution in a Rotating Packed Bed

Zhan

Wang

Zhang

et al. 2020

Ind. Eng. Chem. Res.

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This work presented experimental and modeling studies on the simultaneous absorption of H2S and CO2 into the N-methyldiethanolamine (MDEA) and piperazine (PZ) solution in a rotating packed bed (RPB). The effect of different operating conditions, including MDEA concentration (C MDEA), PZ concentration (C PZ), liquid volumetric flow rate (L), temperature (T), and high gravity factor (β) on the absorption efficiencies of H2S and CO2 (ηH2S and ηCO2 ) were investigated. The results showed that ηH2S and ηCO2 were significantly affected by C MDEA, C PZ, L, and β. ηH2S and ηCO2 could reach 99.98 and 96.51%, respectively. Furthermore, an artificial neural network (ANN) model was established to predict ηH2S, ηCO2 , and mass-transfer coefficient (K G a). Results showed that the predicted values were in good agreement with the experimental values (within deviations of ±10% for H2S and CO2). This work provides a potential technology of simultaneous absorption of H2S and CO2 for the biogas upgrade.

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Section: Modeling Study On η and K G Amentioning

confidence: 99%

Simultaneous Absorption of H₂S and CO₂ into the MDEA + PZ Aqueous Solution in a Rotating Packed Bed

Zhan

Wang

Zhang

et al. 2020

Ind. Eng. Chem. Res.

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“…We made three classifications of the methods, i.e., neural networks-based methods, single ML methods, and a separate group for deep learning, ensembles, and hybrid models. [59] To optimize the prediction of liquid-liquid equilibria which is employed in the simulation of the biofuel process by the use of a novel non-random two-liquid-ANN method.…”

Section: And DL Methods In Biofuels Researchmentioning

confidence: 99%

“…MLP -Enzyme function -machine learning [60] To develop the ANN method for the prediction of unmeasurable variables during hydrogen and methane production through the anaerobic digestion process. Reynel-Ávila et al [59] developed an innovative hybrid non-random two-liquid-ANN method in order to increase the estimation performance of the liquid-liquid equilibria, which is used to simulate the biofuel process. Non-random two-liquid method is considered as a thermodynamic method to be used in a multi-component system.…”

Section: Annmentioning

confidence: 99%

Systematic Review of Deep Learning and Machine Learning Models in Biofuels Research<strong> </strong>

Ardabili¹,

Mosavi

Várkonyi-Kóczy

2019

Preprint

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Biofuels construct an essential pillar of energy systems. Biofuels are considered as a popular resource for electricity production, heating, household, and industrial usage, liquid fuels, and mobility around the world. Thus, the need for handling, modeling, decision-making, demand, and forecasting for biofuels are of utmost importance. Recently, machine learning (ML) and deep learning (DL) techniques have been accessible in modeling, optimizing, and handling biofuels production, consumption, and environmental impacts. The main aim of this study is to review and evaluate ML and DL techniques and their applications in handling biofuels production, consumption, and environmental impacts, both for modeling and optimization purposes. Hybrid and ensemble ML methods, as well as DL methods, have found to provide higher performance and accuracy in modeling the biofuels.

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“…As the FAME content and yield are output variables of AI models for biomass conversion to biodiesel (Table 3), the trained AI models for cetane number could be integrated with those production models to improve/optimize biomass conversion to produce high‐quality biofuel. Besides, AI can also assist the simulation of biofuel phase equilibrium behaviors by predicting the binary interaction parameters of local composition models with given temperature and biofuel/solvent composition (Reynel‐Ávila et al, 2019).…”

Section: Applications Of Artificial Intelligence To Bioenergy Systemsmentioning

confidence: 99%

Applications of artificial intelligence‐based modeling for bioenergy systems: A review

Ming-yang

Yao

2021

GCB Bioenergy

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Bioenergy is widely considered a sustainable alternative to fossil fuels. However, large‐scale applications of biomass‐based energy products are limited due to challenges related to feedstock variability, conversion economics, and supply chain reliability. Artificial intelligence (AI), an emerging concept, has been applied to bioenergy systems in recent decades to address those challenges. This paper reviewed 164 articles published between 2005 and 2019 that applied different AI techniques to bioenergy systems. This review focuses on identifying the unique capabilities of various AI techniques in addressing bioenergy‐related research challenges and improving the performance of bioenergy systems. Specifically, we characterized AI studies by their input variables, output variables, AI techniques, dataset size, and performance. We examined AI applications throughout the life cycle of bioenergy systems. We identified four areas in which AI has been mostly applied, including (1) the prediction of biomass properties, (2) the prediction of process performance of biomass conversion, including different conversion pathways and technologies, (3) the prediction of biofuel properties and the performance of bioenergy end‐use systems, and (4) supply chain modeling and optimization. Based on the review, AI is particularly useful in generating data that are hard to be measured directly, improving traditional models of biomass conversion and biofuel end‐uses, and overcoming the challenges of traditional computing techniques for bioenergy supply chain design and optimization. For future research, efforts are needed to develop standardized and practical procedures for selecting AI techniques and determining training data samples, to enhance data collection, documentation, and sharing across bioenergy‐related areas, and to explore the potential of AI in supporting the sustainable development of bioenergy systems from holistic perspectives.

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An artificial neural network-based NRTL model for simulating liquid-liquid equilibria of systems present in biofuels production

Cited by 25 publications

References 35 publications

Simultaneous Absorption of H₂S and CO₂ into the MDEA + PZ Aqueous Solution in a Rotating Packed Bed

Simultaneous Absorption of H₂S and CO₂ into the MDEA + PZ Aqueous Solution in a Rotating Packed Bed

Systematic Review of Deep Learning and Machine Learning Models in Biofuels Research<strong> </strong>

Applications of artificial intelligence‐based modeling for bioenergy systems: A review

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An artificial neural network-based NRTL model for simulating liquid-liquid equilibria of systems present in biofuels production

Cited by 25 publications

References 35 publications

Simultaneous Absorption of H2S and CO2 into the MDEA + PZ Aqueous Solution in a Rotating Packed Bed

Simultaneous Absorption of H2S and CO2 into the MDEA + PZ Aqueous Solution in a Rotating Packed Bed

Systematic Review of Deep Learning and Machine Learning Models in Biofuels Research<strong> </strong>

Applications of artificial intelligence‐based modeling for bioenergy systems: A review

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Product

Resources

About

Simultaneous Absorption of H₂S and CO₂ into the MDEA + PZ Aqueous Solution in a Rotating Packed Bed

Simultaneous Absorption of H₂S and CO₂ into the MDEA + PZ Aqueous Solution in a Rotating Packed Bed