Simulating and Comparing CO2/CH4 Separation Performance of Membrane-Zeolite Contactors by Cascade Neural Networks

Separating carbon dioxide (CO₂) from gaseous streams released into the atmosphere is becoming critical due to its greenhouse effect. Membrane technology is one of the promising technologies for CO₂ capture. SAPO-34 filler was incorporated in polymeric media to synthesize mixed matrix membrane (MMM) and enhance the CO₂ separation performance of this process. Despite relatively extensive experimental studies, there are limited studies that cover the modeling aspects of CO₂ capture by MMMs. This research applies a special type of machine learning modeling scenario, namely, cascade neural networks (CNN), to simulate as well as compare the CO₂/CH₄ selectivity of a wide range of MMMs containing SAPO-34 zeolite. A combination of trial-and-error analysis and statistical accuracy monitoring has been applied to fine-tune the CNN topology. It was found that the CNN with a 4-11-1 topology has the highest accuracy for the modeling of the considered task. The designed CNN model is able to precisely predict the CO₂/CH₄ selectivity of seven different MMMs in a broad range of filler concentrations, pressures, and temperatures. The model predicts 118 actual measurements of CO₂/CH₄ selectivity with an outstanding accuracy (i.e., AARD = 2.92%, MSE = 1.55, R = 0.9964).

A novel dual feedwater circuit for a parabolic trough solar power plant

The validated dynamic model of a parabolic trough power plant (PTPP) is improved by the combination of a new feedwater circuit (feedwater/HTF circuit) and a reference feedwater circuit (feedwater/steam circuit) as well as the development of the steam turbine model. Such design represents the first effort of research to utilize a dual feedwater circuit inside the PTPP to increase the power output in the daylight from 50 to 68 MW_el and raise night operating hours at a lower cost. The purpose of increasing the operating night hours at a power (48 MW_el) as in the reference PTPP is to get rid of the fossil fuel backup system and rely only on the absorbed solar energy and the stored energy in the molten salt. During daylight hours, the feedwater circuit is operated using Feedwater/HTF. In the transient period, the feedwater/HTF circuit will gradually be closed due to a decrease in solar radiation. Furthermore, the rest of the nominal feedwater mass flow rate (49 kg/s) is gradually replenished from the feedwater/steam circuit. After sunset, the entirety of the feedwater is heated based on the steam extracted from the turbine. The purpose of this improvement is to raise the number of nightly operational hours by reducing the nominal load from 61.93 to 48 MW_el as a result of low energy demand during the evening hours. Therefore, a comparison study between the reference model and this optimization (optimization 2) is conducted for clear days (26th-27th/June and 13th-14th/July 2010) in order to understand the influence of dual feedwater circuit. The comparison indicates that the operational hours of the power block (PB) will be obviously increased. Moreover, this improvement reduces based on the fossil fuel system at night. As the last step, an economic analysis was performed on the costs of the referenced and the optimized PTPP as a function of the levelized energy cost (LEC). The results illustrate that the specific energy cost of a PTPP with 7.5 h of storage capacity is lowered by about 14.5% by increasing the output of the PTPP from 50 to 68 MW_el.

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. Accordingly, this study employed the Elman recurrent neural network (ENN) to anticipate the HHV of different biomass samples from both the ultimate and proximate compositional analyses as the model inputs. The number of hidden neurons and the training algorithm were determined in such a way that the ENN model showed the highest prediction and generalization accuracy. The single hidden layer ENN with only four nodes, trained by the Levenberg–Marquardt algorithm, was identified as the most accurate model. The proposed ENN exhibited reliable prediction and generalization performance for estimating 532 experimental HHVs with a low mean absolute error of 0.67 and a mean square error of 0.96. In addition, the proposed ENN model provides a ground to clearly understand the dependency of the HHV on the fixed carbon, volatile matter, ash, carbon, hydrogen, nitrogen, oxygen, and sulfur content of biomass feedstocks.

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. Among these contaminants, the presence of arsenic in the aquatic environment has always been a global concern. As (V) and As (III) are the two most common oxidation states of inorganic arsenic ions. This research concentrates on the kinetics, isotherms, and thermodynamics of metal-organic frameworks (MOFs), which have been applied for arsenic ions uptake from aqueous solutions. This review provides an overview of the current capabilities and properties of MOFs used for arsenic removal, focusing on its kinetics and isotherms of adsorption, as well as its thermodynamic behavior in water and wastewater.

Developing a Hybrid Neuro-Fuzzy Method to Predict Carbon Dioxide (CO2) 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 (CO₂) in mixed matrix membrane (MMM) containing the SAPO-34 zeolite. The hybrid neuro-fuzzy technique uses the MMM chemistry, pressure, and temperature to estimate CO₂ permeability. Indeed, grid partitioning (GP), fuzzy C-means (FCM), and subtractive clustering (SC) strategies are used to divide the input space of ANFIS. Statistical analyses compare the performance of these strategies, and the spider graph technique selects the best one. As a result of the prediction of more than 100 experimental samples, the ANFIS with the subtractive clustering method shows better accuracy than the other classes. The hybrid optimization algorithm and cluster radius = 0.55 are the best hyperparameters of this ANFIS model. This neuro-fuzzy model predicts the experimental database with an absolute average relative deviation (AARD) of less than 3% and a correlation of determination higher than 0.995. Such an intelligent model is not only straightforward but also helps to find the best MMM chemistry and operating conditions to maximize CO₂ separation.

Experimental Study of the Influence of Different Load Changes in Inlet Gas and Solvent Flow Rate on CO2 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% CO₂ and 70%. A 10% mass fraction of methyl diethanolamine (MDEA) aqueous solution was used as a solvent. Three ramp-up tests were performed to investigate the effect of different load changes in inlet gas and solvent flow rate on CO₂ absorption. The rate of change in gas flow rate was 0.1 Nm³/h/s, and the rate of change in MDEA aqueous solution was about 0.7 NL/h/s. It was found that different load changes in inlet gas and solvent flow rate significantly affect the CO₂ volume fraction at the outlet during the transient state. The CO₂ volume fraction reaches a peak value during the transient state. The effect of different load changes in inlet gas and solvent flow rate on the hydrodynamic properties of the sieve tray were also investigated. The authors studied the correlation between the performance of the absorber column for CO₂ capture during the transient state and the hydrodynamic properties of the sieve tray. In addition, this paper presents an experimental investigation of the bubble-liquid interaction as a contributor to entropy generation on a sieve tray in the absorption column used for CO₂ absorption during the transient state of different load changes.

Micro magnetofluidics: droplet manipulation of double emulsions based on paramagnetic ionic liquids

The ability to control and manipulate discrete fluid droplets by magnetic fields offers new opportunities in microfluidics. A surfactant-free and easy to realize technique for the continuous generation of double emulsion droplets, composed of an organic solvent and a paramagnetic ionic liquid, is applied. The inner phase of the emulsion droplet consists of imidazolium-based ionic liquids with either iron, manganese, nickel or dysprosium containing anions which provide paramagnetic behaviour. The double emulsion droplets are dispersed in a continuous phase of FC-40. All substances - the organic phase, the paramagnetic ionic liquid and the continuous phase -are immiscible. The magnetic properties of ionic liquids allow, through the influence of external magnetic fields, the manipulation of individual emulsion droplets such as capture and release, rotation and distortion. Arrays of magnets allow a coalescence of emulsion droplets and their subsequent mixing by flowing through an alternating permanent magnetic field. In addition, the double emulsion droplets can be split and reunified, or continuously separated into their original phases.