Novel prosperous computational estimations for greenhouse gas adsorptive control by zeolites using machine learning methods

To predict CO₂ adsorptive capture, as a vital environmental issue, using different zeolites including 5A, 13X, T-Type, SSZ-13, and SAPO-34, different models have been developed by implementing artificial intelligence algorithms. Hybrid adaptive neuro-fuzzy inference system (Hybrid-ANFIS), particle swarm optimization-adaptive neuro-fuzzy inference system (PSO-ANFIS) and the least-squares support vector machine (LSSVM) modeling optimized with the coupled simulated annealing (CSA) optimization have been employed for the models. The developed models, validated by utilizing various graphical and statistical methods exhibited that the Hybrid-ANFIS model estimations for the gas adsorption on 5A, T-Type, SSZ-13, and SAPO-34 zeolites with average absolute relative deviation (AARD) % of 8.21, 1.92, 4.99 and 2.26, and PSO ANFIS model estimations for the gas adsorption on zeolite 13X with an AARD of 4.85% were in good agreement with corresponding experimental data. It could be deduced that the proposed models were more prosperous and efficient in favor of the design and analysis of adsorption processes than previous ones.

Evaluation of CO2 Absorption by Amino Acid Salt Aqueous Solution Using Hybrid Soft Computing Methods

Amino acid salt (AAs) aqueous solutions have recently exhibited a great potential in CO2 absorption from various gas mixtures. In this work, four hybrid machine learning methods were developed to evaluate 626 CO2 and AAs equilibrium data for different aqueous solutions of AAs (potassium sarcosinate, potassium l-asparaginate, potassium l-glutaminate, sodium l-phenylalanine, sodium glycinate, and potassium lysinate) gathered from reliable references. The models are the hybrids of the least squares support vector machine and coupled simulated annealing optimization algorithm, radial basis function neural network (RBF-NN), particle swarm optimization-adaptive neuro-fuzzy inference system, and hybrid adaptive neuro-fuzzy inference system. The inputs of the models are the CO2 partial pressure, temperature, mass concentration in the aqueous solution, molecular weight of AAs, hydrogen bond donor count, hydrogen bond acceptor count, rotatable bond count, heavy atom count, and complexity, and the CO2 loading capacity of AAs aqueous solution is considered as the output of the models. The accuracies of the models' results were verified through graphical and statistical analyses. RBF-NN performance is promising and surpassed that of other models in estimating the CO2 loading capacities of AAs aqueous solutions.

Process Design of Ammonia Separation for Nitrification Control in Aeration Basins at an IKORC's Oily Wastewater Treatment Unit

In the current decades, water shortage is well understood as one of the main limiting factors for oil industry development all over the world. One of the available and reasonable solutions is reusing wastewater. The oily wastewater treatment unit of the IKORC oil refinery provides a portion of the makeup water for cooling towers, applying physical, biological, and chemical treatments. Ammonia shocks are the only crisis that disrupts the nitrification process. This condition eventuates in destroying the microorganisms of aeration basins and leads to a high ammonia containing effluent. In order to protect the aeration process, it is mandatory to apply a suitable system for removing excess ammonia. In this study, at first, ammonia removal history is reviewed. Then quantity and quality of the oily sewer are investigated. Because of high volatility of ammonia contamination and high TDS, a stripping tower with air is selected among diverse solutions. Taking into account the principles of project econometrics, operating parameters such as stripping factor, pressure drop, tower volumetric flow rate, and number of towers are determined. Then, the process is designed and its environmental survey is conducted. Finally, calculating indices proved that this project is economically profitable in addition to its environmental benefits.

The Improvement Effects of Different Treatment Methods of Soil Wastewater Washing on Environmental Pollution

This paper focus on how to treat the wastewater after soil washing since water pollution is a severe threat to the water security of China. Ca (OH)2 and two kinds of biochars (biochar and ZnCl2 modified biochar) were tested to treat the waste FeCl3 washing effluent. Two kinds of biochars (biochar and ZnCl2-modified biochar) were prepared from maize straws. Soil samples were collected near a smelter for adsorption experiments. ICP-OES was used to determine the concentration of metal ions in the samples, as well as calculating their adsorption capacity and removal rate. As to Ca(OH)2 treatment, the maximum removal rates of Cd, Pb, Cu, and Zn could exceed 99%, and the concentrations of Cd, Pb, Cu, and Zn in solution could reduce to 0.08, 0.018, 0.15, 0.44 mg/dm3, respectively. However, both of the two biochars had relatively low removal rates compared with Ca (OH)2 treatment. The wastewater shows significantly lower environmental implications after the two treatments, and the lime precipitation method has better effects than biochar adsorption. The activated carbon adsorption method discussed can significantly improve the environmental pollution caused by soil washing wastewater, which is suitable for environmental treatment projects.

Optimization of EPB Shield Performance with Adaptive Neuro-Fuzzy Inference System and Genetic Algorithm

The prediction of earth pressure balance (EPB) shield performance is an essential part of project scheduling and cost estimation of tunneling projects. This paper establishes an efficient multi-objective optimization model to predict the shield performance during the tunneling process. This model integrates the adaptive neuro-fuzzy inference system (ANFIS) with the genetic algorithm (GA). The hybrid model uses shield operational parameters as inputs and computes the advance rate as output. GA enhances the accuracy of ANFIS for runtime parameters tuning by multi-objective fitness function. Prior to modeling, datasets were established, and critical operating parameters were identified through principal component analysis. Then, the tunneling case for Guangzhou metro line number 9 was adopted to verify the applicability of the proposed model. Results were then compared with those of the ANFIS model. The comparison showed that the multi-objective ANFIS-GA model is more successful than the ANFIS model in predicting the advance rate with a high accuracy, which can be used to guide the tunnel performance in the field.

Application of neural networks in membrane separation

Artificial neural networks (ANNs) as a powerful technique for solving complicated problems in membrane separation processes have been employed in a wide range of chemical engineering applications. ANNs can be used in the modeling of different processes more easily than other modeling methods. Besides that, the computing time in the design of a membrane separation plant is shorter compared to many mass transfer models. The membrane separation field requires an alternative model that can work alone or in parallel with theoretical or numerical types, which can be quicker and, many a time, much more reliable. They are helpful in cases when scientists do not thoroughly know the physical and chemical rules that govern systems. In ANN modeling, there is no requirement for a deep knowledge of the processes and mathematical equations that govern them. Neural networks are commonly used for the estimation of membrane performance characteristics such as the permeate flux and rejection over the entire range of the process variables, such as pressure, solute concentration, temperature, superficial flow velocity, etc. This review investigates the important aspects of ANNs such as methods of development and training, and modeling strategies in correlation with different types of applications [microfiltration (MF), ultrafiltration (UF), nanofiltration (NF), reverse osmosis (RO), electrodialysis (ED), etc.]. It also deals with particular types of ANNs that have been confirmed to be effective in practical applications and points out the advantages and disadvantages of using them. The combination of ANN with accurate model predictions and a mechanistic model with less accurate predictions that render physical and chemical laws can provide a thorough understanding of a process.

Wastewaters treatment containing phenol and ammonium using aerobic submerged membrane bioreactor

Phenolic wastewater was treated using anaerobic submerged membrane bioreactor (ASMBR). Effect of different solids retention times on MBR performance was studied. Various ratios of carbon to nitrogen were used in the synthetic wastewaters. During the operation, phenol concentration of feed was changed from 100 to 1000 mg L^-1. Phenol concentration was increased stepwise over the first 30 days and kept constant at 1000 mg L^-1, thereafter. For the first 100 days, a chemical oxygen demand (COD) to N ratio of 100:5.0 was used and this resulted in phenol and COD removal more than 99 and 95%, respectively. However, the ammonium removal decreased from 95 to 40% by increasing the phenol concentration of feed, from 100 to 1000 mg L^-1. For the last 25 days, a COD to N ratio of 100:2.1 was used due to the ammonium accumulation in the ASMBR. This led to the complete ammonium removal and no ammonium was detected in the ASMBR permeate. These results suggest that in the ASMBR at high phenol loading of 1000 mg L^-1, COD to N ratio of the phenolic wastewater must be 100:2.1 for ammonium removal, while at low phenol loading, COD to N ratio of 100:5.0 can be used.

Organic solvent removal by pervaporation membrane technology: experimental and simulation

This work presents purification of cyclohexane using polydimethylsiloxane (PDMS) membranes in pervaporation (PV) process. The PDMS is a rubbery polymer and appropriate as membrane material for purification of cyclohexane. PV which is a low-energy separation process was chosen for purification of cyclohexane due to its superior advantages compared to other processes. Effect of feed concentration on separation factor was investigated in order to optimize the process. It was indicated that dehydration of 80 wt% cyclohexane mixture at a temperature of 300 K and a vacuum pressure of 10 mmHg could be effectively achieved and high separation factor of 2500 was obtained. Furthermore, a two-dimensional mechanistic model was proposed for predicting mass transfer of cyclohexane in the process. The mechanistic model accounts for mass transfer of cyclohexane across the membrane, and concentration distribution of cyclohexane was determined. It was revealed that the most mass transfer flux of cyclohexane occur at the region near the inlet of feed channel, while the flux at the upper side of the module reaches zero value due to the effect of velocity distribution on the convective mass transfer of cyclohexane.

Hormones removal from municipal wastewater using ultrasound

Estrogens are one of the micro-pollutants in the wastewater which have detrimental effects on water living organisms. The aim of this study was to evaluate the efficiency of ultrasound to reduce the estrogen (E1) and 17 beta-estradiol (E2) from municipal wastewater. Hence, a cylindrical batch reactor was designed. The effects of powers, frequency, exposure time and pH on reduction efficiency were investigated. The residual concentration of E1 and E2 hormones was measured in reactor effluent by electrochemiluminescence (ECL) method. The results showed that ultrasound removed 85-96% of both E1 and E2 hormones after 45 min while other parameters changes in the range of their operations. Also, the frequency and power of ultrasound had a significant effect on reduction efficiency of hormones while the exposure had no significant effect. Furthermore, the interaction of power and frequency reduced their efficacy to 64.3% (P_value = 0.005). The result also indicated that the ultrasound waves have high ability to reduce Steroid hormones from municipal wastewater. The proposed method can be considered as one of the significant strategies for reduction or destruction of hormones from wastewater due to the non-generation of dangerous by-products and the low energy consumption.

CFD simulation of copper(II) extraction with TFA in non-dispersive hollow fiber membrane contactors

This study presents computational fluid dynamics (CFD) simulation of dispersion-free liquid-liquid extraction of copper(II) with trifluoroacetylacetone (TFA) in hollow fiber membrane contactor (HFMC). Mass and momentum balance Navier-Stokes equations were coupled to address the transport of copper(II) solute across membrane contactor. Model equations were simulated using COMSOL Multiphysics™. The simulation was run to study the detailed concentration distribution of copper(II) and to investigate the effects of various parameters like membrane characteristics, partition coefficient, and flow configuration on extraction efficiency. Once-through extraction was found to be increased from 10 to 100% when partition coefficient was raised from 1 to 10. Similarly, the extraction efficiency was almost doubled when porosity to tortuosity ratio of membrane was increased from 0.05 to 0.81. Furthermore, the study revealed that CFD can be used as an effective optimization tool for the development of economical membrane-based dispersion-free extraction processes.

Synthesis and characterization of polyurethane/poly(vinylpyridine) composite membranes for desulfurization of gasoline

Polyurethane/poly(vinylpyridine) (PU/PVP) composite membranes for desulfurizing gasoline were prepared.