Machine learning assisted prediction of solar to liquid fuel production: a case study

Shahzad, Muhammad Wakil; Nguyen, Viet Hung; Xu, Ben Bin; Tariq, Rasikh; Imran, Muhammad; Ashraf, Waqar Muhammad; Ng, Kim Choon; Jamil, Muhammad Ahmad; Ijaz, Amna; Sheikh, Nadeem Ahmed

doi:10.1016/j.psep.2024.02.060

Cited by 16 publications

(1 citation statement)

References 40 publications

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“…Machine learning (ML) techniques have become pivotal in enhancing the predictability [58] and understanding of adsorption processes across various domains, including environmental and materials science. This review synthesizes recent advancements in the integration of ML with adsorption modeling, highlighting its application in predicting the behavior of diverse adsorbents and adsorbates, from heavy metals to organic pollutants and gases.…”

Section: Machine Learning-assisted Optimization For Improving the Ads...mentioning

confidence: 99%

Sorption Behavior of Azo Dye Congo Red onto Activated Biochar from Haematoxylum campechianum Waste: Gradient Boosting Machine Learning-Assisted Bayesian Optimization for Improved Adsorption Process

Gamboa,

Abatal,

Lima

et al. 2024

IJMS

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This work aimed to describe the adsorption behavior of Congo red (CR) onto activated biochar material prepared from Haematoxylum campechianum waste (ABHC). The carbon precursor was soaked with phosphoric acid, followed by pyrolysis to convert the precursor into activated biochar. The surface morphology of the adsorbent (before and after dye adsorption) was characterized by scanning electron microscopy (SEM/EDS), BET method, X-ray powder diffraction (XRD), and Fourier-transform infrared spectroscopy (FTIR) and, lastly, pHpzc was also determined. Batch studies were carried out in the following intervals of pH = 4–10, temperature = 300.15–330.15 K, the dose of adsorbent = 1–10 g/L, and isotherms evaluated the adsorption process to determine the maximum adsorption capacity (Qmax, mg/g). Kinetic studies were performed starting from two different initial concentrations (25 and 50 mg/L) and at a maximum contact time of 48 h. The reusability potential of activated biochar was evaluated by adsorption–desorption cycles. The maximum adsorption capacity obtained with the Langmuir adsorption isotherm model was 114.8 mg/g at 300.15 K, pH = 5.4, and a dose of activated biochar of 1.0 g/L. This study also highlights the application of advanced machine learning techniques to optimize a chemical removal process. Leveraging a comprehensive dataset, a Gradient Boosting regression model was developed and fine-tuned using Bayesian optimization within a Python programming environment. The optimization algorithm efficiently navigated the input space to maximize the removal percentage, resulting in a predicted efficiency of approximately 90.47% under optimal conditions. These findings offer promising insights for enhancing efficiency in similar removal processes, showcasing the potential of machine learning in process optimization and environmental remediation.

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Section: Machine Learning-assisted Optimization For Improving the Ads...mentioning

confidence: 99%

Sorption Behavior of Azo Dye Congo Red onto Activated Biochar from Haematoxylum campechianum Waste: Gradient Boosting Machine Learning-Assisted Bayesian Optimization for Improved Adsorption Process

Gamboa,

Abatal,

Lima

et al. 2024

IJMS

Self Cite

View full text Add to dashboard Cite

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Artificial intelligence driven evolution of perovskite-based solar thermochemical systems for hydrogen production: a narrative review

Boretti

2024

emergent mater.

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Enhancing solar irradiance forecasting for hydrogen production: The MEMD-ALO-BiLSTM hybrid machine learning model

Zhu,

Wang,

Guo

et al. 2024

Computers and Electrical Engineering

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Machine learning assisted prediction of solar to liquid fuel production: a case study

Cited by 16 publications

References 40 publications

Sorption Behavior of Azo Dye Congo Red onto Activated Biochar from Haematoxylum campechianum Waste: Gradient Boosting Machine Learning-Assisted Bayesian Optimization for Improved Adsorption Process

Sorption Behavior of Azo Dye Congo Red onto Activated Biochar from Haematoxylum campechianum Waste: Gradient Boosting Machine Learning-Assisted Bayesian Optimization for Improved Adsorption Process

Artificial intelligence driven evolution of perovskite-based solar thermochemical systems for hydrogen production: a narrative review

Enhancing solar irradiance forecasting for hydrogen production: The MEMD-ALO-BiLSTM hybrid machine learning model

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