Showing papers in "Chemical Engineering Research & Design in 2022"

Taguchi design-assisted co-immobilization of lipase A and B from Candida antarctica onto chitosan: Characterization, kinetic resolution application, and docking studies

Abstract: In the present communication, the simultaneous co-immobilization by covalent binding of lipase A from Candida antarctica (CALA) and lipase B from Candida antarctica (CALB) in glutaraldehyde (GLU) activated chitosan (CHI) was optimized using the Taguchi method. Under optimized conditions (pH 9, 5 mM, 6:1 (protein load/g of support and 1 h), it was possible to reach 80.00 ± 0.01% for the immobilization yield (IY) and 46.01 ± 0.35 U/g for the activity of the derivative (AtD); in this case, load protein and ionic strength were the only statistically significant parameters and, therefore, those that most influenced the immobilization process. Furthermore, at pH 7, CALA-CALB-CHI had a half-life 2–6 times longer than the mixture of CALA and CALB for a temperature range of 50−80 °C. CALA-CALB showed the highest activity at pH 7, whereas CALA-CALB-CHI, except at pH 7, was more active than the soluble lipase mixture in the pH range (5–9), especially at pH 9. CHI, CHI-GLU, and CALA-CALB-CHI were characterized by X-ray powder diffraction (XRPD), Fourier Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscope (SEM), Thermogravimetry (TGA), and Energy Dispersive Spectroscopy (EDS), proving the immobilization of CALA and CALB in chitosan. CALA-CALB-CHI derivative evaluated in the kinetic resolution of halohydrins acetates rac-2-bromo-1-(2-chlorophenyl) ethyl acetate (2a) and rac-2-chloro-1-(2,4-dichlorophenyl) ethyl acetate (2b), to produce the corresponding halohydrins 3a-b, which are intermediates in the synthesis of the drugs chlorprelanine (antiarrhythmic) and luliconazol (antifungal), respectively. (S)-bromohydrin 3a was obtained with 79% enantiomeric excess (ee), whereas (S)-chlorohydrin 3b produced with 98% ee, conversion of 46% and E > 200. Additionally, molecular docking was performed to elucidate the hydrolysis interaction reaction between β-halohydrin acetates and lipases CALA-CALB.

Hydrogen production via sodium borohydride hydrolysis catalyzed by cobalt ferrite anchored nitrogen-and sulfur co-doped graphene hybrid nanocatalyst: Artificial neural network modeling approach

Abstract: The sluggish kinetics of the Sodium borohydride (NaBH4) hydrolysis process particularly in alkaline conditions requires the design of high-performance low-cost catalysts. Herein, it was aimed to tailor cobalt ferrite anchored nitrogen-and sulfur-doped graphene architecture (CoFe2O4 @N,S-G) via a facile production pathway, to explore its potential application as a catalyst in alkaline NaBH4 hydrolysis reaction for hydrogen production, and to develop an optimal artificial neural network (ANN) architecture to predict hydrogen production rate. In this regard, the influence of several variables such as reaction temperature, NaBH4 concentration, and catalyst loading was explored to determine the optimal operational conditions for effective hydrogen generation. Furthermore, the performance metrics of ANN topologies were investigated to establish the best ANN model for predicting hydrogen generation rate under different operational conditions. The experimental results offered the outstanding catalytic activity of CoFe2O4 @N,S-G towards NaBH4 hydrolysis with the volumetric hydrogen production rate of 8.5 L.min−1.gcat−1 at 25 ℃, and catalyst loading of 0.02 g, and 1.0 M NaBH4 concentration. The CoFe2O4 @N,S-G nanocatalyst was found to retain 94.9% of its initial catalytic activity after 5 consecutive uses, according to the reusability tests. The optimum performance metrics that were determined by the mean squared error (MSE) of 0.00052 and the coefficient of determination (R2) of 0.9989 were achieved for the ANN model with the configuration of 3–10–5–1 trained by Levenberg-Marquardt backpropagation algorithm. The activation function of tansig and purelin functions at hidden and output layers, respectively. The findings revealed that the experimental data were in harmony with the ANN-predicted one, thereby inferring the optimized ANN model could be employed in the forecasting of hydrogen production rate at various operational conditions.

NaY zeolite-polyethersulfone-modified membranes for the removal of cesium-137 from liquid radioactive waste

Abstract: Isotope cesium-137 (137Cs) is a major fission product that results from nuclear processes. This radioactive material constitutes a hazardous source of contamination to the environment even at low concentrations. Removal of this harmful radioactive isotope is deemed as an intricate challenge to resolve. The present study aims to synthesize a novel NaYzeolite modified Polyethersulfone (PES) membranes for 137Cs removal from real nuclear liquid waste. The zeolite has been synthesized through hydrothermal method on seedless static aging. Various zeolite contents were then impregnated within the PES membrane matrix to modify the membrane characteristics and ion-exchange properties. Besides, the proposed interaction mechanism of the modified NaYzeolite and PES has been illustrated for the first time in this study. The characteristics of the NaY zeolite, neat PES, and modified membranes were characterized comprehensively via X-ray diffraction (XRD), energy-dispersive X-ray spectroscopy (EDX), scanning electron microscopy (SEM), fourier-transform infrared spectroscopy (FT-IR), dynamic light scattering (DLS), and contact angle (CA) techniques. Results disclosed that optimum removal rate (90.2%) was obtained by the membrane prepared using 0.15% NaY while the decontamination factor (DF) was 10.2 at pH 7.5. Therefore, a legend agent copper ferrocyanides (CuFC) has been added to the feed solution aiming to promote the removal efficiency of 137Cs and enhance the decontamination factor. As a result, about 99.2% Cesium retention and 121.2 decontamination factor were achieved.

The Influence of ZnO/SiO2 nanocomposite concentration on rheology, interfacial tension, and wettability for enhanced oil recovery

Abstract: Oil reservoirs around the world are facing issues with the extraction ability of the accessible natural resources in the oil fields. Recent investigations on oil recovery have revealed that nanoparticles (NPs) possess a great potential on some parameters like rheology, interfacial tension (IFT), and rock wettability which help in uplifting the trapped oil. In this study, the influence of ZnO/SiO2 NPs concentration on oil recovery parameters (rheology, IFT, and wettability) has been investigated. The ZnO/SiO2 nanocomposite was synthesized and characterized using state-of-the-art techniques, afterwards, NPs were dissolved in brine followed by the formation of nanofluids at various concentrations. The results have indicated that the ZnO/SiO2 NPs at high concentration (0.1 wt. %) produced a considerable change in the rheology, IFT, and wettability. The viscosity (cP) of ZnO/SiO2 composite fluids has increased from 0.95 ± 0.03 to 1.29 ± 0.14, while the IFT (mN/m) was reduced from 12.93 ± 1.55 to 1.02 ± 0.05, and the contact angle (°) from 141 ± 28 to 62 ± 11. Overall, the changes in the rheology, IFT, and wettability were found to improve with an increase in ZnO/SiO2 NPs concentrations.

Process structure-based recurrent neural network modeling for predictive control: A comparative study

Abstract: • Development of process structure-award RNN models. • RNN model training using plant data from an ASPEN simulator. • MPC design using various RNN models and implementation to ASPEN simulator. • Evaluation of MPC performance and computational time. Recurrent neural networks (RNN) have demonstrated their ability in providing a remarkably accurate modeling approximation to describe the dynamic evolution of complex, nonlinear chemical processes in several applications. Although conventional fully-connected RNN models have been successfully utilized in model predictive control (MPC) to regulate chemical processes with desired approximation accuracy, the development of RNN models in terms of model structure can be further improved by incorporating physical knowledge to achieve better accuracy and computational efficiency. This work investigates the performance of MPC based on two different RNN structures. Specifically, a fully-connected RNN model, and a partially-connected RNN model developed using a prior physical knowledge, are considered. This study uses an example of a large-scale complex chemical process simulated by Aspen Plus Dynamics to demonstrate improvements in the RNN model and an RNN-based MPC performance, when the prior knowledge of the process is taken into account.

Facile synthesis of bismuth(III) based metal-organic framework with difference ligands using microwave irradiation method

Abstract: In this study, we studied the effect of starting ligands such as 1,4-benzenetdicarboxylic (H2BDC), 1,3,5-benzenetricarboxylic (H3BTC), and 4,4′,4″-s-triazine-2,4,6-triyl-tribenzoic (H3TATB) acids on the photocatalytic activity of three bismuth-based MOFs (Bi-MOF) obtained via a microwave-assisted solvothermal process. Different shapes and sizes of ligands displayed different structure properties from the corresponding Bi-MOF. Specifically, Bi-MOF composed of Bi3+ and H3TATB (Bi-TATB) exhibits the largest specific surface area of 355 m2/g, highest surface-oxygen vacancy amount, a more vigorous light absorption intensity with a broader range of visible light absorption and red-shifted absorption edge than that of Bi-MOF composed of Bi3+ and H2BDC (Bi-BDC) and Bi-MOF composed of Bi3+ and H3BTC (Bi-BTC), suggesting the extension in the photocatalytic activity for Bi-TATB. The reason is attributed to the difference in their structural features. Compared with H2BDC and H3BTC ligands in Bi-BDC and Bi-BTC, H3TATB ligands in the structure of Bi-TATB contained more delocalized π electrons. This outcome may facilitate the ligand-to-metal charge transfer (LMCT) and decrease the electronic bandgap of the Bi-TATB, thus contributing to the enhanced photocatalytic rate. The enhanced photocatalytic activity of Bi-TATB was further confirmed by the photodegradation of rhodamine B (RhB) under LED light irradiation, which is 99.1% of RhB removal after 180 min of light irradiation. The as-synthesized Bi-TATB showed promising photocatalytic activity for the degradation of organic dye with an excellent recyclable catalytic efficiency. With the above understanding, Bi-MOF was finally used for the photocatalytic O2 evolution from water under LED light irradiation. The Bi-TATB had a maximum photocatalytic O2 evolution rate of 691 μmol h−1. To the best of the author’s knowledge, there has been no research on both the photocatalytic degradation of organic model dye pollutants and photocatalytic O2 evolution studies using Bi-MOFs with different organic linkers. The results should open an alternative approach of ligand selection that could increase the applicability of Bi-MOF in the field of catalysis.

Hydrogen Production Optimization from Sewage Sludge Supercritical Gasification Process using Machine Learning Methods Integrated with Genetic Algorithm

Abstract: Hydrogen production from the supercritical water gasification (SCWG) of sewage sludge (SS) is a sustainable and efficient process. However, the challenging and intricate task for the experimental technique is to find out the correlation between proximate, ultimate analysis and gasification conditions with H 2 production. This process is complicated, expensive and requires many experimental techniques. To accurately predict and analyze the effect of input parameters on SCWG of SS process economically, an efficient model must be developed. The novelty of this study includes the consideration of four different machine learning (ML) methods integrated with Genetic Algorithm for the prediction, analysis, and evaluation of Hydrogen yield from the supercritical water gasification of sewage sludge. The ML methods included Support Vector Machine, Ensembled Tree, Gaussian Process Regression, and Artificial Neural Network. The results suggests that GPR is favored for predicting Hydrogen yield (Coefficient of determination (R 2 ) = 0.997, Root Mean Square Error (RMSE) = 0.093, and is highly recommended for dealing with complex variable-target correlation. On the other hand, the performance of Support Vector Machine (SVM) was poor with R 2 = 0.761 and RMSE = 2.479. The R 2 and RMSE for Ensembled Tree (ET) and Artificial Neural Network (ANN) was 0.994, 0.560 and 0.943, 1.521 respectively. The partial dependence plot shows that temperature, moisture content and pressure are among the effective parameters of SCWG. Furthermore, optimization techniques such as genetic algorithms are incorporated to optimize H 2 production by tuning the ML hyperparameters. Additionally, a Graphical User Interface was developed by utilizing the optimized GPR model for ease in computing H 2 yield.The optimum ML method integrated with GA will be beneficial for researcher to predict the H 2 yield for the experimental work. Graphical Abstract • Machine learning methods integrated with Genetic Algorithm are analyzed for prediction, analysis & evaluation of H 2 yield. • Partial dependence plot shows that temperature, moisture content and pressure are among the effective parameters. • Graphical User Interface was developed by utilizing the optimized GPR model for ease in computing H 2 yield.

Boosting the electrocatalytic activity of ZrO2/MWCNT supported PdPt bi-metallic electrocatalyst towards ethanol oxidation reaction by electrochemical activation process and modeling by artificial neural network approach

Abstract: The cornerstone for improving the performance of direct-ethanol fuel cells (DEFCs) is to engineer highly efficient and stable electrocatalysts, however, there are still numerous hurdles to overcome. The PdPt bimetallic alloys have long gotten considerable interest as to be the most promising electrocatalysts for DEFCs, yet the ways to boost their electrocatalytic activity are still needed to be investigated comprehensively. Herein, it was aimed to boost the electrocatalytic activity and long-term stability of PdPt electrocatalyst towards ethanol oxidation reaction (EOR), besides forecasting the performance of the catalyst by means of the artificial neural network (ANN) approach. In this regard, zirconium dioxide/multi-walled carbon nanotube (ZrO2/MWCNT) nanohybrid composite was employed as catalyst support to fine-tune the electrical conductivity, enlarge the electrochemically active surface area, and besides oxidize the adsorbed toxic intermediate species on the catalyst surface for preventing poisoning. Moreover, an electrochemical activation approach was implemented to augment the electrocatalytic activity of PdPt-ZrO2MWCNT nanohybrid electrocatalyst. The physicochemical characterizations including X-ray diffraction, scanning electron microscopy with energy dispersive X-ray analysis, and transmission electron microspopy, it was confirmed that the PdPt-ZrO2/MWCNT nanohybrid was successfully fabricated via a facile hydrothermal approach. The electrochemical characterizations in alkaline media suggested that the activation process could significantly improve (ca.200-fold increment in cathodic peak current compared to non-activated one) the electrocatalytic activity of nanohybrid electrocatalyst towards EOR. Moreover, the findings revealed that thanks to its synergistic effect, the activated- PdPt-ZrO2/MWCNT nanohybrid was of great potential to be utilized as an electrocatalyst for hydrogen evolution reaction (HER). Moreover, artificial neural networks model indicated performance of PdPt-ZrO2/MWCNT nanohybrid as the most suitable catalyst for HER. This research potentially paves the way for the engineering of high-performance carbon composite supported bimetallic nanohybrid electrocatalysts to be exploited in large-scale energy applications.

Novel photocatalytic polyether sulphone ultrafiltration (UF) membrane reinforced with oxygen-deficient Tungsten Oxide (WO2.89) for Congo red dye removal

Abstract: Merging the photocatalytic activity of nanomaterials with membrane technology is an auspicious approach towards diminishing membrane fouling consequences. Herein, 0.1 to 2 wt.% of Tungsten Oxide (WO2.89) were harnessed to synthesis novel mixed matrix nanocomposite ultrafiltration membranes via the classical noninduced phase separation. A systematic comprehensive characterization was conducted to probe the additive influence on the ultimate characteristics of the UF nanocomposites. This comprised membranes morphology, chemical structure, mechanical properties, hydrophilicity, and their performance against Congo red (CR) dye. Results manifested that greater water flux along with higher hydrophilicity and water uptake capacity was achieved at 0.4 WO2.89 loading weight %. Under an hour of UV light irradiation, the flux recovery ratio of the 0.4 wt.% nanocomposite membranes was 83.3% compared to 71.5% for the control UF membrane. Meanwhile, pore blockage was realized at a higher additive ratio due to agglomeration occurrence, causing greater dye rejection, roughness, contact angle, and inducing lower permeate flux and flux recovery ratio. In the presence of WO2.89 nanoparticles, dye degradation has hit the 86% level after only 30 minutes of UV irradiation. Exposing the nanocomposite membranes to UV irradiation has induced a further enhancement in the FRR of the membranes. Decomposition of the organic molecules by WO2.89 nanoparticles photocatalytic activity on/near the membrane surface was the major factor to bring about this enhancement. Ultimately, the photocatalytic activity of the nanocomposites prepared in the current work could open new insights towards their application for wastewater treatment applications.

Choline Chloride-based Deep Eutectic Solvents for Ultrasonic-assisted Oxidative Desulfurization of Actual Heavy Crude Oil

Abstract: Deep eutectic solvents (DESs) are acquiring increasing interest as ionic liquid analogues because of their wide application, low-cost characteristics and environmentally friendly. In this study, choline chloride-based (ChCl) as a type of DESs were synthesized using polyethylene glycol (PEG), and applied to reduce sulfur content of actual heavy crude oil with sulfur content 37900 ppm (3.79 wt%). The synthesized DESs were characterized using Fourier-transform infrared spectroscopy (FTIR), as well as viscosity and density measurements. The DESs were evaluated for extractive ultrasound-assisted oxidative desulfurization (EUAODS), with 30 wt% H2O2 as the oxidant and formic acid as the catalyst. This study looked at the effects of oxidative desulfurization (ODS) and single ODS under ultrasonic treatment. Different systems for DESs were tested to find the desulfurization selectivity of the better reaction system; and it was found that extractive desulfurization (EDS) removed 24.57% of the sulfur, followed by extractive and ultrasonic-assisted desulfurization (EUADS) at 26.78%, then ODS at 37.28%, with ultrasonic-assisted oxidative desulfurization (EUAODS) providing the best result of 62%. According to the comparison trials, combining DESs with ultrasonic treatment improved processing. This study contributes to the body of information on the use of ultrasonic treatment in heavy crude oil desulfurization.

Hydrogen production and photocatalytic activities from NaBH4 using trimetallic biogenic PdPtCo nanoparticles: Development of machine learning model

Abstract: The production of economical and environmentally friendly monodisperse and stable nanocatalysts is of great interest in the field of catalysis. In this study, palladium-platinum-cobalt nanoparticles (PdPtCo NPs) were synthesized by the green synthesis method using apple (Malus domestica) peels. The resulting PdPtCo NPs were characterized by UV-Visible spectrometry (UV-Vis), Fourier-transformed infrared spectrum- near infrared spectrum (FTIR), and X-ray diffraction patterns (XRD). According to the UV-Vis spectrum, it was observed that Malus domestica peels gave a peak at 283 nm. According to XRD analysis, the crystal particle size of PdPtCo NPs was determined to be 2.53 nm in size. It was observed that PdPtCo NPs acted as catalysts and increased the rate of hydrogen production in the presence of NaBH4 substrate. The turnover frequency (TOF), activation energy (Ea), enthalpy (∆H), and entropy values (∆S) were found to be as 1109.85 h−1, 36.98 kJ/mol, 34.44 kJ/mol, −138.9 J/mol.K, respectively. Based on NaBH4 concentrations, catalyst concentration temperature, and reaction time, the rate of hydrogen production was modeled using Artificial Neural Networks (ANNs). Furthermore, the photodegradation of NPs against methylene blue (MB) dye was investigated using visible light irradiation, and their photodegradation percent was calculated as 84.7%, the results showing that the production of hydrogen and photodegradation against MB was successfully achieved. The study emphasizes the safety, sustainability, and high catalytic activity of biogenic PdPtCo NPs, which generate less toxic waste, proving its efficiency for sustainable energy production that is harmless to the environment and living health. The novelty of the study is sustained by the use of apple peals for NPs synthesis and by the application of ANNs to predict the rate of hydrogen production.

Process Intensification from conventional to advanced distillations: Past, present, and future

Abstract: This perspective paper features the process intensification (PI) application for advanced distillation-based processes. Starting with the historical background of generic PI, we subsequently narrow down the discussion to extractive distillation (ED), reactive distillation (RD), and hybrid reactive-extractive distillation (RED). We categorize the existing PI techniques onto internal and external intensification, where the former does not involve altering the distillation configuration while the latter does. Instead of deliberating the technical aspects, we explicitly highlight the contribution of PI applied to ED, RD, and RED towards societal impact covering energy, economic, environmental, control, and safety perspectives. The future perspectives of PI are discussed in the last section, covering the development of hybrid PI technologies, exploring the energy efficiency of different PI configurations, prioritizing PI beyond energy by considering some other sustainability aspects, and linking PI with the ever-increasing Industry 4.0 applications. • Historical application of PI of conventional distillation • Extension of PI to advanced distillation processes covering ED, RD, RED • Categorization of internal and external PI techniques • Contribution of PI to sustainability and societal impact • Future perspectives of PI applied to ED, RD, and RED

Architectures for Neural Networks as Surrogates for Dynamic Systems in Chemical Engineering

Abstract: Surrogate models for dynamic systems in chemical engineering are increasingly of interest. Neural networks have already been applied in research, but it remains unclear which types of neural network architectures are actually required for practical systems. The focus here lies on recurrent neural networks of type Jordan, Elman, and LSTM layers. These are investigated for different types of data sets as training basis: batch trajectories, data of a proper excitation of a continuous process, and a typical operation trajectory of a large chemical plant. To ensure a rigorous investigation, hyperparameter tuning by Bayesian and Bandit optimization is included. As a first, a dynamic surrogate model using LSTM layers for a batch distillation system is presented, which is valid from start-up until shutdown. The evaluation shows further need for adjustments in data preparation and objective / loss function compared to the state of the art.

Efficient absorption of low partial pressure SO2 by deep eutectic solvents based on pyridine derivatives

Abstract: Efficient and reversible absorption of SO2 by deep eutectic solvents (DESs) has drawn much attention in recent years. In this work, a new type of deep eutectic solvents (DESs) were synthesized via pyridine derivatives, nicotinamide, aminopyridines and hydroxypyridines, as the hydrogen bond donors (HBDs) with common quaternary ammonium salt ionic liquids (ILs) as hydrogen bond acceptors (HBAs). The studied DESs exhibited an attractive absorption behavior for SO2, especially for low concentration SO2. The 1-allyl-3-methylimidazolium chloride (AmimCl)/2-aminopyridine (2-NH2Py) (2:1) and 1-butyl-3-methylimidazolium chloride (BmimCl)/3-aminopyridine (3-NH2Py) (2:1) could capture 0.273 and 0.223 g SO2/g DES at 293 K and 1.0 kPa, respectively, surpassing that of most DESs and ILs in the present literature. The influence of DES composition, temperature and pressure on SO2 absorption performance was systematically investigated. Moreover, the absorption mechanism studied by nuclear magnetic resonance (NMR) and Fourier transform infrared (FT-IR) spectroscopies indicated that the efficient absorption of SO2 was ascribed to the chemical interaction between the pyridine nitrogen atom and SO2.

A Review of Recent Progress on Photocatalytic Carbon dioxide Reduction into Sustainable Energy Products using Carbon Nitride

Abstract: Worldwide air pollution caused a severe damage to ecosystem by the emission of greenhouse gases. The emission percentage of principal greenhouse gas- carbon dioxide, other gases methane, fluorinated gases and nitrous oxide still not decreased even after the Covid-19 pandemic lockdown. In this regard, many research works were reported on the techniques involved to convert the principal greenhouse gas- carbon dioxide into useful sustainable energy fuel. The advancement in the field of nanoscience and nanotechnology, nanomaterials are used as a photocatalyst to produce energy from greenhouse gas. Carbon nitride is a semiconductor with high stability to produce quantum confinement and reducing the recombination rate of charges, with the introduction of external nanoparticles. It was available in the reports hybrid carbon nitride nanocomposite was employed to reduce the carbon dioxide into valuable products. This review article emerged with an interest towards the carbon nitride’s activeness in the visible region as an effective photocatalyst in the photoreduction of carbon dioxide. Past six years research progress in developing carbon nitride's features to enhance its role in energy production by performing carbon dioxide reduction were given in detail.

Microscopic and data-driven modeling and operation of thermal atomic layer etching of aluminum oxide thin films

Abstract: With increasing demands for microchips and increasing needs in the nano-scale semiconductor manufacturing industry, atomic layer etching (ALE) has been developing into a critical etching process. Unlike its counterpart in the film deposition domain, atomic layer deposition (ALD), which has been extensively studied, ALE has not been fully studied yet from a modeling and operation point of view. Therefore, this work develops microscopic models to characterize the thermal ALE process of aluminum oxide thin films with two precursors (hydrogen fluoride and trimethylaluminum). First, the reaction mechanisms for the two half-cycles for the thermal ALE process are established. Electronically predicted geometries of the Al2O3 structure with two precursors are optimized. Along with the optimized geometries, possible reaction pathways are proposed and calculated by density functional theory (DFT)-based electronic structure calculations. The proposed reaction paths and their kinetic parameters are used in a kinetic Monte Carlo (kMC) algorithm, which is capable of capturing the features of the thermal ALE of aluminum oxide. The kMC simulation provides an etch time for the given steady-state operating conditions, which are validated via comparison with available experimental results. Finally, data sets collected from the kMC simulation are used to train a feed-forward artificial neural network (FNN) model. The trained FNN model accurately predicts an etch time and dramatically reduces the computation time compared to the kMC simulation, thereby making it possible to carry out real-time, model-based operational parameter calculations. In addition, the trained FNN model can be used to establish a feasible range of operating conditions without demanding experimental work.

A Novel Energy-efficient Process of Converting CO2 to Dimethyl Ether with Techno-economic and Environmental Evaluation

Abstract: This work aims at discovering the potential of CO2 reduction by implementing techniques of process intensification in the production processes of green alternative fuel, dimethyl ether (DME), from CO2 and renewable hydrogen (H2) with both one-step and two-step configurations. A novel intensified process using the two-step configuration (named as TSHI), which converts CO2 to methanol followed by the dehydration of methanol to DME, is proposed in this study. Developed based on the validated thermodynamic representation and the reaction kinetics expression, TSHI shows the greatest potential of CO2 reduction – 1.704 ton CO2/ton DME – among the five discussed process scenarios. TSHI’s energy consumption per unit weight of DME is compared with reported case in the literature, which also uses CO2 and renewable H2 as feedstock in a two-step configuration, and the result has shown an energy saving amount of 23%. Though TSHI exhibits high capability of reducing CO2 amount in the atmosphere, the result of techno-economic analysis showed that there are still rooms for further improvements to produce green alternative fuel cost-effectively.

Carbon Dioxide Removal from Biogas through Pressure Swing Adsorption – A Review

Abstract: Biogas is considered a future energy, alternative to natural gas due to its high methane content. Different substrate feedstock determines the composition of the produced raw biogases. Upgrading or purifying the raw biogas by removing carbon dioxide and thereby increasing its methane content to produce high purity biomethane is mandatory before delivering for natural gas substitute. At present, the pressure swing adsorption (PSA) plays a vital role in biogas upgrading along with water scrubbing, chemical absorption, and membrane permeation. In PSA, the core system lies in the adsorbent choice, which determine the effectiveness of separation by measuring the adsorbent’s adsorptive capacity and selectivity. In fact, the PSA system itself is bound for many configurations that require optimization for better tuning the system. As such, it is imperative to comprehend the parameters affecting the PSA performance, thus optimizing them to achieve the objective functions. The optimization through the design of experiment techniques provides realistic statistical information and is among the simplest PSA optimization strategies. Owing to the attractiveness of PSA in many gas separation applications from historical and practical evidences, it is expected that this technology will become more competitive for biogas upgrading • Biogas is a future sustainable energy source alternative to natural gas. • Biomethane is produced by removing CO 2 by biogas upgradation. • Pressure swing adsorption contributes to the third-highest market share for biogas upgrading. • Importance of comprehending and optimizing PSA system to make it more competitive. • Effect of important parameters affecting PSA performance for better optimizing the system.

A green and economical MgO/biochar composite for the removal of U(VI) from aqueous solutions

Abstract: A magnesium oxide/biochar (MgO/biochar) composite, which was green and economical compared with numerous materials, was synthesized by a facile method. And MgO/biochar performed excellent adsorption ability in the sequestration of U(VI) with the satisfied maximum adsorption capacity (514.72 mg g−1) at pH = 4.0, m/V = 0.2 g L−1, CU(VI)initial = 10 mg L−1, I = 0.01 M NaNO3, T = 298 K. The morphology, spectroscopic properties, chemical composition, etc. of MgO/biochar were characterized systemically by scanning electron microscopy (SEM), powder X-ray diffraction (XRD), Fourier transform infrared (FTIR). The batch experiments showed that the adsorption process was predominated by pseudo-second-order rate model and the adsorption isotherm conformed to the Langmuir model. The prepared MgO/biochar exhibited commendable U(VI) adsorption performance in a wide pH range. X-ray photoelectron spectroscopy (XPS) analysis suggested that the interactions between U(VI) and the hydroxyl groups on the surface of the MgO/biochar contribute greatly to the efficient adsorption process. This work underlines new opportunities in combining the advantages of MgO and biochar, and further broadens new horizons of separating pollutants from the aquatic environment by an environment-friendly materials.

Nanoporous polypropylene membrane contactors for CO2 and H2S capture using alkali absorbents

Abstract: The paper reports a comprehensive review of the performance of nanoporous gas-liquid polypropylene membrane contactor with NaOH absorbents for removal of H2S and CO2 components from the gas streams. The experiments at different operation conditions, including variation of acid gas content in a feed stream, alkali concentration in absorbent, gas and liquid absorbent volume flow rates, absolute and gas-liquid differential pressures are discussed as a function of the absorbent saturation levels. In-liquid diffusion of components and gas/membrane contact times were determined as main governing factors limiting contactor efficiency. Mass transfer rates of acid gas removal on the membrane contactor over 3 × 10−3 mol/(m2 × s) for CO2 and 7.5 × 10−3 mol/(m2 × s) for H2S were attained. Ultimate processed gas quality with H2S content below 5 ppm and CO2 content below 0.01% was achieved at the contactor performance over 7 m3/(m2 × h) and initial acidic gas content of 2%, while achieving a membrane packing density in the contactor over 3000 m2/m3. The paper also provides an experimentally-proven theoretical model for calculating removal efficiency and residual acid gas partial pressures depending on the membrane parameters and operation conditions. It is shown, the mass transfer coefficient and removal efficiency differ significantly for H2S and CO2 due to the difference in dissolution mechanism involving kinetically limited deprotonation reaction of solvated water in case of CO2(aq) while engaging direct deprotonation of H2S. This allows to attain residual partial pressure of H2S in retentate stream equal to the equilibrium pressure above the absorbent solution, while CO2 residual pressure exceeds an equilibrium value few orders of magnitude. The effect has been successfully utilized for the selective removal of H2S from both CO2 and H2S-containing mixtures with H2S/CO2 selectivity exceeding 1500.

Reactive distillation for sustainable synthesis of bio-ethyl lactate: Kinetics, pilot-scale experiments and process analysis

Abstract: Bio-ethyl lactate (EL), an alternative green solvent to the traditional petroleum-based solvent, has a distinct advantage in the chemical industry. However, to solve the problem of high energy consumption and low yield in the production of EL is the premise of large-scale production and application. This work proposes an economical and energy-efficient reactive distillation (RD) process through excess lactic acid (LA). As the primary data of RD process design, the kinetics of esterification reaction between LA and EL were investigated using the ion exchange resins KRD001 catalyst. Besides, the developed RD model has been validated utilizing five pilot-scale RD experimental data for process design. Furthermore, the proposed process is optimized with the objective of the minimum heat duty per kilogram product (HDP) of EL, taking into account energy consumption, reaction conversion, economic and environmental impacts. The proposed process has obvious economic and environmental advantages, which can cause a reduction of 52.25% for the total annual cost per kilogram product (TACK) of EL and 4.63% for the gas emissions compared with the excess ethanol (EtOH) process. This provides guidance and reference for the green technology development and production of EL.

Preparation of high performance mixed matrix membranes by one-pot synthesis of ZIF-8 nanoparticles into Pebax-2533 for CO2 separation

Abstract: Herein, an interesting strategy, viz. one-pot synthesis, is proposed to incorporate ZIF-8 nanoparticles into the Pebax-2533 for facile fabrication of MMMs with high permeability and selectivity. Based on characterization analyses, incorporating 8 wt% of ZIF-8 particles via this route increased the fractional free volume (FFV) of the membranes, which contributed to the improvement in the CO 2 permeability, avoided the aggregation of the nanoparticles in the polymer matrix, increased the CO 2 sorption property due to imidazole linkers, boosted the screening properties owing to intrinsic pore size, thereby improved CO 2 /CH 4 and CO 2 /N 2 selectivities, and in addition, resulted in membranes with desirable thermal properties. As a result, MMM with 8 wt% of ZIF-8 by providing an admirable performance, improved the CO 2 permeability and CO 2 /CH 4 and CO 2 /N 2 selectivities of the neat Pebax-2533 membrane from 62.20 Barrer, 11.35, and 24.98 to 158.43 Barrer (+155% enhancement), 27.73 (+144.32%) and 50.76 (+103.21%), respectively. This membrane at an operating pressure of 8 bar overcame Robeson’s upper bound (1991) in the case of CO 2 /CH 4 separation. • Pebax-2533 MMMs containing ZIF-8 nanoparticles were prepared by one-pot synthesis method. • Appropriate polymer/filler interfacial compatibility was achieved by suitable dispersion of ZIF-8 within polymer matrix. • One-pot synthesized MMMs had higher gas-pair selectivities than the conventional MMMs containing ZIF-8. • MMM containing 8 wt% of ZIF-8 showed the highest CO 2 /CH 4 and CO 2 /N 2 selectivities with appropriate CO 2 permeability.

Study on effect of gas distributor in fluidized bed reactors by hydrodynamics-reaction-coupled simulations

Abstract: Deep understanding of the complex relationship between the complex hydrodynamics and reactor performance in a reactive gas-fluidized bed is crucial to optimization of engineering design and industrial operation. In this work, multiphase particle-in-cell (MP-PIC) simulations coupled with reaction kinetics models are conducted to investigate the effect of gas distribution on reactor performance. A batch fluidized bed reactor with different distributors for regenerating spent FCC catalyst is simulated under different superficial gas velocities. In all simulation cases, the air flowrate and the spent catalyst inventory are kept constant. The results show an indiscernible effect of superficial gas velocity, but the induced-maldistribution conditions generally deteriorate the reactor performance. In addition to wall-slug formation, solids holdup, and slip velocities also decrease significantly in the plugged-gas distribution cases. Strong linear relationship between hydrodynamics and reactor performance are established. Present study sheds light on the importance of uniform gas distribution in industrial fluidized bed reactors.

CFD-PBE modelling of continuous Ni-Mn-Co hydroxide co-precipitation for Li-ion batteries

Abstract: A modelling framework is proposed to simulate the co-precipitation of Ni-Mn-Co hydroxide as precursor of cathode material for lithium-ion batteries. It integrates a population balance equation with computational fluid dynamics to describe the evolution of the particle size in (particularly continuous) co-precipitation processes. The population balance equation is solved by employing the quadrature method of moments. In addition, a multi-environment micromixing model is employed to consider the potential effect of molecular mixing on the fast co-precipitation reaction. The modelling framework is used to investigate the co-precipitation of Ni 0.8 Mn 0.1 Co 0.1 (OH) 2 in a multi-inlet vortex micromixer, as a suitable candidate for the study of fast co-precipitation processes in continuous mode. Finally, the simulation results are discussed, and the role of the different phenomena involved in the formation and evolution of particles is identified by inspecting the predicted trends.

Plasma-catalytic oxidation of volatile organic compounds with honeycomb catalyst for industrial application

Abstract: Efficiently processing volatile organic compounds (VOCs) with nonthermal plasma and honeycomb catalyst for practical industrial applications presents a sizable challenge. An attempt has been made to generate a large volume of uniform plasma at atmospheric pressure in a practical-scale honeycomb catalyst. H-ZSM-5, a type of zeolite, was washcoated on a commercial bare honeycomb monolith as the catalyst-supporting material, after which the monolith was impregnated with Pd. The plasma discharge power can be controlled by controlling the humidity in the feed gas, metal content, applied voltage, and total flow rate of the feed gas. The plasma was characterized by the voltage and current waveforms, and optical emission spectroscopy (OES). In this study, 85% of dilute toluene (15 ppm) was successfully removed from an airstream at a large flow rate of 60 L/min with an energy density of 84 J/L. Under this condition, the selectivity of CO2 was 76%. This investigation demonstrated the practical applicability of the plasma-honeycomb catalytic reactor to process a fast-flowing feed gas without resulting in a significant large pressure drop, which can hardly be achieved with typical packed-bed plasma reactors.