Preparation and characterization of flax, hemp and sisal fiber-derived mesoporous activated carbon adsorbents

The first aim of this study was to investigate mesoporous activated carbon adsorbents from sisal, hemp, and flax fibers by cost-effective methods. Fibers were impregnated with low concentration (20 wt.%) phosphoric acid. Carbonization temperatures were defined by thermal analysis. Bast fibers (hemp, flax) decompose at lower temperatures (419.36℃, 434.96℃) than leaf fibers (sisal, 512.92℃). The second aim was to compare bast and leaf fibers-derived activated carbon adsorbents by determining physical adsorption properties, chemical compositions, scanning electron microscope, and Fourier transform infrared spectroscopy. Results showed that natural fibers have good candidates to prepare mesoporous activated carbon adsorbents with high surface area (1186–1359 m2/g), high mesopore percentage (60–72%), and high C content (80–86%). Even though leaf-derived activated carbon developed more mesoporous structure (72%), bast-derived activated carbons provided higher surface areas (Shemp = 1359 m2/g; Sflax = 1257 m2/g) and C content. Fourier transform infrared spectra for bast fibers-derived activated carbon adsorbents were quite similar while leaf fiber-derived activated carbon adsorbent had a different spectrum

Preparation and characterization of flax, hemp and sisal fiber-derived mesoporous activated carbon adsorbents

The first aim of this study was to investigate mesoporous activated carbon adsorbents from sisal, hemp, and flax fibers by cost-effective methods. Fibers were impregnated with low concentration (20-‰wt.%) phosphoric acid. Carbonization temperatures were defined by thermal analysis. Bast fibers (hemp, flax) decompose at lower temperatures (419.36℃, 434.96℃) than leaf fibers (sisal, 512.92℃). The second aim was to compare bast and leaf fibers-derived activated carbon adsorbents by determining physical adsorption properties, chemical compositions, scanning electron microscope, and Fourier transform infrared spectroscopy. Results showed that natural fibers have good candidates to prepare mesoporous activated carbon adsorbents with high surface area (1186--1359-‰m2/g), high mesopore percentage (60--72%), and high C content (80--86%). Even though leaf-derived activated carbon developed more mesoporous structure (72%), bast-derived activated carbons provided higher surface areas (Shemp-‰=-‰1359-‰m2/g; Sflax-‰=-‰1257-‰m2/g) and C content. Fourier transform infrared spectra for bast fibers-derived activated carbon adsorbents were quite similar while leaf fiber-derived activated carbon adsorbent had a different spectrum

Cyber-Physical LPG Debutanizer Distillation Columns: Machine-Learning-Based Soft Sensors for Product Quality Monitoring

Refineries execute a series of interlinked processes, where the product of one unit serves as the input to another process. Potential failures within these processes affect the quality of the end products, operational efficiency, and revenue of the entire refinery. In this context, implementation of a real-time cognitive module, referring to predictive machine learning models, enables the provision of equipment state monitoring services and the generation of decision-making for equipment operations. In this paper, we propose two machine learning models: (1) to forecast the amount of pentane (C5) content in the final product mixture; (2) to identify if C5 content exceeds the specification thresholds for the final product quality. We validate our approach using a use case from a real-world refinery. In addition, we develop a visualization to assess which features are considered most important during feature selection, and later by the machine learning models. Finally, we provide insights on the sensor values in the dataset, which help to identify the operational conditions for using such machine learning models

Cyber-physical LPG debutanizer distillation columns: machine-learning-based soft sensors for product quality monitoring

Summarization: Refineries execute a series of interlinked processes, where the product of one unit serves as the input to another process. Potential failures within these processes affect the quality of the end products, operational efficiency, and revenue of the entire refinery. In this context, implementation of a real-time cognitive module, referring to predictive machine learning models, enables the provision of equipment state monitoring services and the generation of decision-making for equipment operations. In this paper, we propose two machine learning models: (1) to forecast the amount of pentane (C5) content in the final product mixture; (2) to identify if C5 content exceeds the specification thresholds for the final product quality. We validate our approach using a use case from a real-world refinery. In addition, we develop a visualization to assess which features are considered most important during feature selection, and later by the machine learning models. Finally, we provide insights on the sensor values in the dataset, which help to identify the operational conditions for using such machine learning models.Παρουσιάστηκε στο: Applied Science