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Volume 116, AEE 2025
 
 
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Eng. Proc., 2025, ECP 2025

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8 pages, 1351 KB  
Proceeding Paper
Application of an Adaptive Neuro-Fuzzy Inference System for the Removal of Cadmium (II) from Acid Mine Drainage onto Modified Cellulose Nanocrystals
by Banza Jean Claude, Vhahangwele Masindi and Linda L. Sibali
Eng. Proc. 2025, 117(1), 1; https://doi.org/10.3390/engproc2025117001 - 18 Nov 2025
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Abstract
This research utilizes a modified cellulose nanocrystal composite as an adsorbent to remove cadmium (II) through a column study. A fixed-bed column was used to remove cadmium (II) at room temperature using varying process factors, such as pH (4–8), bed height (3–9 cm), [...] Read more.
This research utilizes a modified cellulose nanocrystal composite as an adsorbent to remove cadmium (II) through a column study. A fixed-bed column was used to remove cadmium (II) at room temperature using varying process factors, such as pH (4–8), bed height (3–9 cm), flow rate (3–7 mL/min), and concentration (10–20 mg/L). According to these findings, cadmium (II) breakthrough occurred more quickly at lower bed heights, higher flow rates, and higher cadmium (II) concentrations. The Thomas model is the most appropriate kinetic model. Deep learning models, such as the adaptive neuro-fuzzy inference model with two algorithms (backpropagation and least squares estimation), were effectively used to model the effectiveness of cadmium (II) removal in aqueous solutions via modified cellulose nanocrystals. To compare the model’s predicted results with experimental data, statistical approaches were employed, including calculating the coefficient of determination (R2) and mean square error (MSE). The ANFIS model used to predict cadmium (II) adsorption via modified cellulose nanocrystals had a strong correlation value of 0.997 for least squares estimation (LSE) and 0.999 for the gradient descent (backpropagation) method, indicating the effectiveness of the trained model in predicting the cadmium (II) adsorption process. Full article
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8 pages, 1416 KB  
Proceeding Paper
Enzyme-Assisted Extraction of Bioactive Compounds from Origanum dictamnus L.
by Zafeiria Lemoni, Roza Konstantina Leka, Theopisti Lymperopoulou and Diomi Mamma
Eng. Proc. 2025, 117(1), 2; https://doi.org/10.3390/engproc2025117002 - 19 Nov 2025
Abstract
Enzyme-assisted extraction (EAE) was applied to extract bioactive compounds from the leaves of Origanum dictamnus L. using the commercial enzyme preparation Cellic® CTec3 HS. A Taguchi experimental design was applied to determine the optimal EAE conditions. The variables were enzyme loading, solid-to-liquid [...] Read more.
Enzyme-assisted extraction (EAE) was applied to extract bioactive compounds from the leaves of Origanum dictamnus L. using the commercial enzyme preparation Cellic® CTec3 HS. A Taguchi experimental design was applied to determine the optimal EAE conditions. The variables were enzyme loading, solid-to-liquid ratio, extraction time and the responses of total phenolic content (TPC), and total flavonoid content (TFC). Under optimized conditions, EAE achieved TPC yield of 164.8 ± 5.2 mg GAE/g and TFC yield reached 92.5 ± 5.7 mg CAE/g. The results support the potential of EAE as an efficient method for extraction of bioactive compounds from Origanum dictamnus L. Full article
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