Simulating and Comparing CO/CH Separation Performance of Membrane-Zeolite Contactors by Cascade Neural Networks.

Separating carbon dioxide (CO) from gaseous streams released into the atmosphere is becoming critical due to its greenhouse effect. Membrane technology is one of the promising technologies for CO capture. SAPO-34 filler was incorporated in polymeric media to synthesize mixed matrix membrane (MMM) and enhance the CO separation performance of this process.

A Dynamic Recurrent Neural Network for Predicting Higher Heating Value of Biomass.

The higher heating value (HHV) is the main property showing the energy amount of biomass samples. Several linear correlations based on either the proximate or the ultimate analysis have already been proposed for predicting biomass HHV. Since the HHV relationship with the proximate and ultimate analyses is not linear, nonlinear models might be a better alternative.

A Brief Review of Recent Results in Arsenic Adsorption Process from Aquatic Environments by Metal-Organic Frameworks: Classification Based on Kinetics, Isotherms and Thermodynamics Behaviors.

In nature, arsenic, a metalloid found in soil, is one of the most dangerous elements that can be combined with heavy metals. Industrial wastewater containing heavy metals is considered one of the most dangerous environmental pollutants, especially for microorganisms and human health. An overabundance of heavy metals primarily leads to disturbances in the fundamental reactions and synthesis of essential macromolecules in living organisms.

Developing a Hybrid Neuro-Fuzzy Method to Predict Carbon Dioxide (CO) Permeability in Mixed Matrix Membranes Containing SAPO-34 Zeolite.

This study compares the predictive performance of different classes of adaptive neuro-fuzzy inference systems (ANFIS) in predicting the permeability of carbon dioxide (CO) in mixed matrix membrane (MMM) containing the SAPO-34 zeolite. The hybrid neuro-fuzzy technique uses the MMM chemistry, pressure, and temperature to estimate CO permeability. Indeed, grid partitioning (GP), fuzzy C-means (FCM), and subtractive clustering (SC) strategies are used to divide the input space of ANFIS.

Introducing a Linear Empirical Correlation for Predicting the Mass Heat Capacity of Biomaterials.

This study correlated biomass heat capacity (Cp) with the chemistry (sulfur and ash content), crystallinity index, and temperature of various samples. A five-parameter linear correlation predicted 576 biomass Cp samples from four different origins with the absolute average relative deviation (AARD%) of ~1.1%.

Experimental Study of the Influence of Different Load Changes in Inlet Gas and Solvent Flow Rate on CO Absorption in a Sieve Tray Column.

An experimental study was conducted in a sieve tray column. This study used a simulated flue gas consisting of 30% CO and 70%. A 10% mass fraction of methyl diethanolamine (MDEA) aqueous solution was used as a solvent.