Showing papers in "Chemical Engineering Research & Design in 2021"

A statistical modeling-optimization approach for efficiency photocatalytic degradation of textile azo dye using cerium-doped mesoporous ZnO: A central composite design in response surface methodology

Abstract: This paper investigates a new modeling approach of central composite design (CCD) in Response Surface methodology (RSM) for the optimization of methyl orange (MO) photodegradation based to the prepared materials under UV–vis irradiation. Mesoporous zinc oxide (ZnO) have been synthesized at different cerium (Ce) contents (2, 5 and 7 wt%) through a single step sol gel method. Subsequently, cerium nanoparticles (CeNPs) have been impregnated on mesoporous ZnO. The physico-chemical characteristics of each catalyst are described through several approaches. The photocatalytic activity of the prepared catalysts shows that CeNPs enhance the photocatalytic performance of ZnO at 2 wt% Ce content. MO photodergadation processes of the optimal catalyst can be mathematically described as a function of pH-solution, catalyst dosage and MO concentration being modeled by a CCD-RSM. Based on a statistical modeling (quadratic regression) and an optimization process (ANOVA analysis), the optimum conditions were achieved for pH of solution of 5.58, MO concentration of 20.72 mg L−1and catalyst dosage of 0.63 g L−1 which allowed reaching 100% of photodegradation. Overall, the value of the adjusted coefficient of determination (R2 = 0.9901) indicates that the considered model was quite suitable and that the selected CCD-RSM was successful in optimizing the photodegradation conditions of MO.

Machine learning on sustainable energy: A review and outlook on renewable energy systems, catalysis, smart grid and energy storage

Abstract: This study presents a broad view of the current state of the art of ML applications in the manufacturing sectors that have a considerable impact on sustainability and the environment, namely renewable energies (solar, wind, hydropower, and biomass), smart grids, the industry of catalysis and power storage and distribution. Artificial neural networks are the most preferred techniques over other ML algorithms because of their generalization capabilities. Demands for ML techniques in the energy sectors will increase considerably in the coming years, since there is a growing demand of academic programmes related to artificial intelligence in science, math, and engineering. Data generation, management, and safety are expected to play a key role for the successful implementation of ML algorithms that can be shared by major stakeholders in the energy sector, thereby promoting the development of ambitious energy management projects. New algorithms for producing reliable data and the addition of other sources of information (e.g., novel sensors) will enhance flow of information between ML and systems. It is expected that unsupervised and reinforcement learning will take a central role in the energy sector, but this will depend on the expansion of other major fields in data science such as big data analytics. Massive implementations, specialized algorithms, and new technologies like 5G will promote the development of sustainable applications of ML in non-industrial applications for energy management.

NaOH-modified mesoporous biochar derived from tea residue for methylene Blue and Orange II removal

Abstract: To enhance the adsorption capacity via increasing the pore filling, the exchangeable ions content and electrostatic interaction, a late-model NaOH modified tea biochar for MB and OR-II removal was prepared. The effects of pyrolysis temperature, mass ratio of NaOH/tea residue powder (TRP) and other operation parameters, such as initial pH of the solution, contact time, initial concentration of dyes and adsorbent dosage on OR-II and MB removal in aqueous solution were studied by batch adsorption experiment. The results showed that with 10% w/w NaOH/TRP and pyrolysis at 700℃, the maximum adsorption capacities of BH700-10 of 105.27 and 91.68 mg/g for 250 mg/L MB and OR-II at pH10 or 2, respectively, were observed at 60 min. The adsorption isotherms of MB were in accordance with Langmuir and Sips models while the adsorption of OR-II was better described by the Freundlich model. The pseudo-second-order kinetic model could better describe the adsorption process. The possible interactions involved in the adsorption process was proposed. The results of this study indicated that BH700-10 is a promising low-cost adsorbent in dye wastewater treatment.

Nanostructured magnetic inverse spinel Ni–Zn ferrite as environmental friendly visible light driven photo-degradation of levofloxacin

Abstract: Spinel ferrites with a compatible electronic band structure are always excellent candidates for photo-catalytic environmental detoxification employing visible light and solar energy. However, the potential is not harnessed to its fullest owing to unnerving charge carrier recombination. In this work, we report the synthesis of Ni1-xZnxFe2O4 (x = 0, 0.1, 0.3, & 0.5) mixed spinel ferrites via combustion route. As prepared samples were characterized for phase identification using X-ray diffraction (XRD) and Reitveld refined pattern confirms the formation of single phased cubic structure with a nano-metric crystallite size. The homogeneous distribution of grains and particles is evidenced by shape and size morphological studies. Raman spectroscopy reveals the presence of motion of oxygen in tetrahedral and octahedral voids. The dc electrical resistivity measured using the two probe method is found to be in the range of 107 to 108 Ω-cm. The optical band gap measured for all photo-catalysts resides at 2.11–2.53 eV. The ferrite photocatalyst exhibits high visible absorption, superior charge transfer capacity, and highly suppressed recombination as suggested by electrochemical impedance spectroscopy and photoluminescence results. The change in band structure with variable Zn content was monitored by shifting of conduction and valence bands. The photo-catalyst Ni0.7Zn0.3Fe2O4 (N2) exhibited 96.8% levofloxacin (LEV) degradation in 90 min of visible light exposure. The effect of parameters such as pH, catalyst dosage, electrolytes and water matrix was analysed in detail. The photo-catalytic degradation rate was enhanced in the presence of persulfate and H2O2. Furthermore, the high magnetic character of the catalysts aids in their retrieval post utilization in catalysis. In terms of band structure analysis, role of dopants, metal redox, and scavenging studies, a suitable photo-catalytic process was proposed. Degradation intermediates discovered by liquid chromatography–mass spectrometry analysis were also recommended as a pathway of degradation. These findings open up exciting possibilities for developing novel solar active photo-catalytic systems based on spinel ferrites for efficient environmental cleanup.

Thin film nanocomposite RO membranes: Review on fabrication techniques and impacts of nanofiller characteristics on membrane properties

Abstract: Thin film composite (TFC) reverse osmosis (RO) membrane has been dominating commercial desalination process for several decades since its inception in the 1970s. Despite the ability of generating high quality water at promising water permeability, the commercial TFC RO membrane is still susceptible to fouling and chlorine attack which lead to performance instability as a function of operation time. Over the past decade, the prowess of nanomaterials integration in overcoming the drawbacks of conventional TFC membrane has been proven by many research studies and this has led to the significant progress of thin film nanocomposite (TFN) membrane development. However, it remains a great challenge in integrating the inorganic nanomaterials with the polyamide (PA) selective layer of TFN membrane due to the incompatibility between organic and inorganic materials and the uneven distribution of nanomaterials. In view of this, this article intends to critically review various fabrication techniques that have been developed or modified to incorporate nanofillers within or atop PA layer for the development of TFN membranes. In addition, the essential characteristics of nanofillers in order to achieve good integration with PA layer as well as their impacts in improving membrane performance with respect to water flux, rejection, antifouling, antibacterial and chlorine stability are also reviewed. We hope this review article could provide insights to researchers in fabricating defect-free TFN RO membranes for enhanced water desalination.

Recent advances on Nickel Nano-ferrite: A review on processing techniques, properties and diverse applications

Abstract: Ferrites belong to the wonder class of materials which are known for their wide application range. Among ferrites, spinel ferrites belong to the most promising soft magnetic materials with excellent properties like engineered band gap, high saturation magnetization, coercivity, and better thermal and electrical properties. Among spinel ferrites, Nickel ferrites: a soft, highly magnetic material that exhibit excellent electrical, magnetic, and optical characteristics. Nickel ferrites find their space in a variety of applications because of their unique properties when compared to other ferrite family members. These properties include high saturation magnetisation, less coercivity, high resistivity and permeability. In addition, nanometric Ni ferrites are unique in several properties with modified applications, such as high frequency applications, electronic devices with low loss, biomedical applications, and environmental remedial applications also. This review aim to explore possible synthesis routes, unique properties and diverse applications of Ni ferrite.

S-scheme heterojunction g-C3N4/TiO2 with enhanced photocatalytic activity for degradation of a binary mixture of cationic dyes using solar parabolic trough reactor

Abstract: Developing photocatalytic systems by larger design to achieve degradation of dye pollutants by using solar light is highly desirable. Present work is devoted to the synthesis of step-scheme (S-scheme) g-C3N4/TiO2 heterojunction which subsequently employed for simultaneous degradation of a binary mixture of Methylene blue (MB) and Rhodamine B (RhB) solution in parabolic trough collectors (PTC) as continuous flow loop photoreactor. The as-prepared g-C3N4/TiO2 was analyzed by various techniques such as FE-SEM, EDS, XRD, FT-IR, BET, elements mapping and DRS. The composite central design (CCD) was applied to express mathematical relationships among variables such as process time, catalyst mass and initial concentrations of MB and RhB in the degradation process. The photocatalytic activity of the as-prepared composite is higher than pure TiO2 and g-C3N4 that is attributed to the positive synergetic effect of S-scheme between g-C3N4 and TiO2 nanostructure. Under solar irradiation in PTC, g-C3N4/TiO2 was able to degrade about 94.92 and 93.07% of binary mixture MB and RhB, respectively.

Mechanistic aspects for the enhanced adsorption of bromophenol blue and atrazine over cyclodextrin modified polyacrylonitrile nanofiber membranes

Abstract: The present work describes a unique modification process for electrospun polyacrylonitrile (PAN) nanofiber membranes modified with β-cyclodextrin (β-CD) monomers and their application in the adsorption of bromophenol blue and atrazine from aqueous systems. PAN and β-CD were successfully crosslinked using citric acid followed by electrospinning of PAN-CD nanofiber membranes. XRD and FTIR spectroscopy demonstrated successful crosslinking of PAN, β-CD, and citric acid to form PAN-CD nanofiber membranes. The microstructure and surface morphology of nanofibers were investigated using SEM and AFM and our data show that the nanofibers are uniform, have an average diameter range of 497–557 nm and possess rougher surfaces. UV-Vis spectroscopic technique was used to determine the nanofiber membranes’ adsorption efficiency and to evaluate their adsorption capacity. Batch adsorption studies at optimized conditions revealed that the removal of 66% and 89% was achieved for bromophenol blue using PAN and PAN-CD nanofibers with adsorption capacity of 0.886 and 1.197 mg/g, respectively. PAN and PAN-CD nanofibers removed 67% and 91% atrazine with adsorption capacity of 0.603 and 0.817 mg/g, respectively. The adsorptive removal of these pollutants followed the pseudo-second order kinetics and best fitted the Freundlich isotherm model. The increased removal is therefore credited to the increased surface area per volume ratio obtained from reduced diameters, intermolecular interactions, and inclusion complexation after the incorporation of β-CD monomers. Thus, it is shown that the addition of CDs improved the adsorption capacity of the hybrid materials via distinctive adsorption mechanisms. Thus, the uniqueness of this work lies on the mechanistic adsorption of bromophenol blue and atrazine through modified PAN-CD nanofiber matrix induced by the grafting of citric acid crosslinked β-CD.

CO2/CH4 separation by mixed-matrix membranes holding functionalized NH2-MIL-101(Al) nanoparticles: Effect of amino-silane functionalization

Abstract: In this study, NH2-MIL-101(Al) metal-organic frameworks (MOFs) covered with 3-aminopropyltriethoxysilane (APTES) were incorporated into the polyethersulfone (PES) to produce mixed-matrix membranes (MMMs) for CO2 separation. The APTES functionalization was performed to improve the MOF dispersion in the PES matrix. Different analyses such as X-ray diffraction (XRD), Brunauer–Emmett–Teller (BET), Fourier transform infrared (FTIR) spectroscopy, thermogravimetric analysis (TGA), and field emission scanning electron microscopy (FESEM) revealed that the MOFs surface successfully functionalized with APTES. An improvement in CO2/CH4 separation efficiency was observed in MMMs, and the performance shifted towards Robeson upper bounds. Notably, the membrane containing 10 wt.% S-MIL-5 (NH2-MIL-101(Al) functionalized with 5.0 mL of APTES) showed an increment in CO2 permeability (50%), and ideal CO2/CH4 selectivity (80%) compared to the PES membrane. The FTIR, TGA, FESEM, and DSC analyses constituted an excellent dispersion of MOFs within the PES phase, which led to significant development in gas permeability and selectivity.

Adsorption of diclofenac on mesoporous activated carbons: Physical and chemical activation, modeling with genetic programming and molecular dynamic simulation

Abstract: This work aims at the preparation of AC from chemical activation (H3PO4, KOH, and HCl) and physical activation (thermal treatment under N2 atmosphere at 500 and 700 °C) of Astragalus Mongholicus (AM) (a low-cost bio-adsorbent and agro-industrial waste), used as carbon precursor. The obtained materials were further applied in the adsorption of diclofenac (DCF) from water/wastewater. The physicochemical properties of the as-prepared ACs and commercial activated carbons (CAC) were evaluated by SEM, XRD, FT-IR, and BET analyses, revealing the high surface area and mesoporous proportion of AC when compared to CAC . Adsorption results showed that the efficiency of AC-700 °C (774 m2 g−1) for DCF removal (92.29%) was greater than that of AC-500 °C (648 m2 g−1, 83.5%), AC-H3PO4 (596 m2 g−1, 80.8%), AC-KOH (450 m2 g−1, 59.3%), AC-HCl (156 m2 g−1, 29.8%) and CAC (455 m2 g−1, 67.8%). The optimization of effective parameters in adsorption was examined at a laboratory-scale using the selected AC-700 °C. The Langmuir isotherm and the pseudo-second-order model fitted well the experimental data. The regeneration efficiency was maintained at 96% (DI-water) and 97% (heating) after three cycles. Besides, genetic programming (GP) and molecular dynamics (MD) simulations were applied to predict the adsorption behavior of DCF from aqueous phase as well as in the ACs structure. It was found that the adsorption mechanisms involved were electrostatic interaction, cation–π interaction, and π–π electron interaction.

Experimental investigation on the possibility of defining the feasibility of CO2/CH4 exchange into a natural gas hydrate marine reservoir via fast analysis of sediment properties

Abstract: The present work deals with an experimental investigation on methane hydrate and carbon dioxide hydrate formation in presence of two different types of sand, which acted as seabed simulators The first typology of sand consists in pure quartz and is commonly used for laboratory experiments on gas hydrate The other type is named TS-2 and originated from the Tunisian seabed of the Mediterranean Sea It is silica-based (≈99%), however it also contains other elements and its grains have different shape, size and porosity Experiments were carried out in order to verify if the specific characteristic of the seabed, in which the hydrate reservoir is present, may intervene or not on the CO2/CH4 replacement process and if such contribution may improve or reduce the process efficiency Results proved that physical and chemical properties of materials which composed the seabed, may strongly intervene on the replacement process In particular, experiments revealed that sand TS-2 acted as kinetic and thermodynamic inhibitor for methane hydrate formation, while it promoted CO2 hydrate formation under the kinetic point of view In this sense, sand TS-2 represents a strong ally for improving the replacement efficiency, due to its capability to both improving the kinetic of the process and reducing the methane hydrate re-formation phenomena The present study revealed that, with a simple analysis of properties of sediments containing hydrate reservoirs, it would be possible to establish the convenience of intervening with a replacement strategy instead of a classical application for simple methane recovery

Cyber-security of centralized, decentralized, and distributed control-detector architectures for nonlinear processes

Abstract: Decentralized and distributed control systems provide an efficient solution to many challenges of controlling large-scale industrial processes. With the expansion in communication networks, vulnerability to cyber intrusions also increases. This work investigates the effect of different types of standard cyber-attacks on the operation of nonlinear processes under centralized, decentralized, and distributed model predictive control (MPC) systems. The robustness of the decentralized control architecture over distributed and centralized control architectures is analyzed. Moreover, a machine-learning-based detector is trained using sensor data to monitor the cyber security of the overall system. Specifically, detectors built using feed-forward neural networks are used to detect the presence of an attack or identify the subsystem being attacked. A nonlinear chemical process example is simulated to demonstrate the robustness of decentralized control architectures and the effectiveness of the neural-network detection scheme in maintaining the closed-loop stability of the system.

Treatment of pharmaceutical wastewater containing cefazolin by electrocoagulation (EC): Optimization of various parameters using response surface methodology (RSM), kinetics and isotherms study

Abstract: Pharmaceutical wastewaters presently remain as one of the primary roots of environmental pollution. The current study mainly concentrated on removing cefazolin (CEZ) from pharmaceutical wastewater employing the electrocoagulation (EC) process using iron electrodes. The EC experimental conditions were achieved by using response surface methodology (RSM) with an efficiency of 85.65% under optimal working conditions of pH = 8.0, current density (16 mA/cm2), initial CEZ concentration (25 mg/L), and inter-electrode distance (d = 1.0 cm) at an equilibrium electrolysis time of 40 min. The experimental results obtained were in good agreement with the predicted CEZ removal efficiency of 86.7%. Besides, Analysis of variance (ANOVA) revealed that the experimental model was best suited to a second-order polynomial equation, with an R2 value of 0.92. Moreover, the fisher's F-value of 13.67 and low probability value (p

Catalytic deoxygenation of palm oil and its residue in green diesel production: A current technological review

Abstract: Worldwide consumption of energy produced from fossil fuels is forecasted to grow. This trend leads to both environmental pollution and the depletion of fossil fuel resources. Green diesel is a suitable candidate to substitute petroleum based-diesel due to its plentiful raw materials, non-polluting production process, and cost-effectiveness. Green diesel production is seen as simple, efficient, and relatively clean process. Deoxygenation (DO) is crowned as the best available technology to produce green diesel from palm fatty acid distillate (a side product of palm oil production) and other oils using heterogeneous catalysts such as zeolites. The capability of catalysts can be improved by optimizing operating parameters, treating and modifying catalysts and with the use of nano-sized catalytic materials. These activities contribute to stronger and more active Bronsted-Lewis acid-base sites and enlarged crystallite sizes, which improve the DO efficiency, selectivity, and reusability, to produce high-grade green diesel with less oxygen content.

Machine-learning-based state estimation and predictive control of nonlinear processes

Abstract: Machine learning techniques have demonstrated their capability in capturing dynamic behavior of complex, nonlinear chemical processes from operational data. As full state measurements may be unavailable in chemical plants, this work proposes two machine-learning-based state estimation approaches. The first approach integrates recurrent neural networks (RNN) within the extended Luenberger observer framework to develop data-based state estimators. The second approach utilizes a hybrid model that integrates feed-forward neural networks with first-principles models to capture process dynamics in the state estimator. Then, an output feedback model predictive controller is designed based on the state estimates provided by the machine-learning-based estimators to stabilize the closed-loop system at the steady-state. A chemical process example is utilized to illustrate the effectiveness of the proposed machine-learning-based state estimation and control approaches.

Upcycling olive oil cake through wet torrefaction to produce hydrochar for water decontamination

Abstract: Oil extraction from drupes generate tons of olive oil cake (OOC) as a solid waste residue, raising alarming concern towards its safe and cost-effective disposal. Herein, an approach converting waste OOC to a value-added hydrochar (HC) for aqueous environment remediation, was proposed. Among synthesized HC, the one produced at 150 °C/6 h (OOCHC-150) displayed a most favorable yield (70.29%) and methylene blue (MB) uptake (24.24 mg/g), being selected for subsequent chemical modification. After testing different chemical agents, NaOH was chosen as modifier, since the resulting material (NaOH@OOCHC-150) displayed the best MB removal performance. Infra-red and X-ray photoelectron spectroscopic analyses revealed dominance of oxygen-containing functionalities over NaOH@OOCHC-150 surface, which played a critical role in MB adsorption through electrostatic and coordinate interactions. Three weight loss zones were observed during OOCHC-150 thermal degradation, which reduced to two zones for NaOH@OOCHC-150 due to the rupture of outer lignin layer, consequently exposing cellulose and hemicellulose. This was supported by NaOH@OOCHC-150 XRD pattern, which displayed multiple peaks between 14.8 and 23.9° (of OOCHC-150 pattern) and also for amorphous hemicellulose (at 15.8°) and crystalline cellulose (at 22.8, 26.4, and 34.5°). Presence of Na over NaOH@OOCHC-150 and N together with S traces on MB saturated NaOH@OOCHC-150 surfaces, as observed during elemental analysis, respectively confirmed successful chemical modification and MB adsorption. About 70–90% adsorption at varied initial concentration (Co) values was accomplished within 10 min. Adsorption isotherm and kinetic data were fitted to Temkin and pseudo-second-order (PSO) models. MB uptake over NaOH@OOCHC-150 was endothermic (depicted by positive ΔH°) and favorable (affirmed by negative ΔG°).

Review of melt crystallization in the pharmaceutical field, towards crystal engineering and continuous process development

Abstract: Crystallization is a significant unit operation in the pharmaceutical industry, and much attention has been paid to solution crystallization in the few last decades. Melt crystallization offers several attractive features including no need for a solvent, and ability to achieve specific drug properties and process merits, e.g., high purity (>99.9%) and high energy and process efficiency. It has great application potential in the pharmaceutical industry and continuous crystallization process development. In this paper, we review the advances of melt crystallization in the pharmaceutical field towards crystal engineering and continuous process development. We systematically summarize the recent developments of melt crystallization in polymorphs screening, additive effects on polymorphisms, spherical-shaped crystal, and co-crystal formation, crystal growth from the melt, and high purity drug intermediates production. We also review the challenges and strategies during the continuous melt crystallization development, including designing methods and types of continuous melt crystallizers. Finally, the critical concerns and opportunities for further research and perspectives are proposed.

Improvement of dye and protein filtration efficiency using modified PES membrane with 2-mercaptoethanol capped zinc sulfide quantum dots

Abstract: In this work, novel 2-mercaptoethanol capped zinc sulfide (ZnS) quantum dots (QDs) embedded polyethersulfone (PES) nanocomposite membranes were fabricated which revealed wh improved antifouling properties and dye separation performance A simple water-based precipitation approach was used to obtain 2-mercaptoethanol capped ZnS QDs at ambient temperature The resulting membranes were fully identified with SEM, AFM, ATR-FTIR analyses, and also underwent porosity and contact angle tests The developed nanostructure membranes exhibited a remarkable fouling reduction in bovine serum albumin (BSA) protein filtration, as featured by a declined average surface roughness The flux recovery ratio (FRR) was improved considerably from 526% for the neat PES to 879% for nanocomposite membrane by inserting 2 wt% ZnS QDs The contact angle of membrane decreased, while porosity size was enlarged by increase of the QDs loading The prepared nanocomposite membranes showed an increased Reactive Red 195 rejection (from 913% to 961%) and an enhanced water and dye solution flux (from 121 to 163 L m−2 h−1) with respect to the bare PES membrane Thus, the results proved the potential of the novel QDs/PES membrane for the dye separation applications

Process intensification of thumba methyl ester (Biodiesel) production using hydrodynamic cavitation

Abstract: The development of clean and sustainable biofuel generation from sustainable feedstock using an integrated process intensification approach like hydrodynamic cavitation (HC) is essential now. The current research is a 'first of its kind' where hydrodynamic cavitation is integrated with heterogeneous catalyst, i.e. TiO2, to prepare thumba methyl esters (TME). So far, no studies on biodiesel production using heterogeneous catalysts using HC are reported in the literature. Experiments were performed with an optimized orifice plate to investigate the effects of operating parameters viz., Thumba oil to methanol molar ratio (1:4–1:8), TiO2 concentration (1–1.4% by weight of oil), and operating temperature (50 °C to 70 °C). Maximum triglyceride conversion (71.8%) was obtained at thumba oil to methanol ratio of 1:6, TiO2 concentration of 1.2% weight percentage and operating temperature of 60 °C in hydrodynamic cavitation reactor within 1 h at 5 bar. The cavitational yield for HC was found to be 9.3 × 10−6 moles L/J, which was almost 27% higher than the value for the conventional approach (3.37 × 10−7 moles L/J). The experimental data fitted second-order reaction kinetics w.r.t limiting reactant, i.e., thumba oil and first-order w.r.t excess reactant, i.e., methanol. The pre-exponential factor (k0) and activation energy (E) of the alcoholysis reaction was found to be 82.26 L2 mol−2 min−1 and 15.44 kJ/mol, respectively. The thermodynamic analysis suggested that the alcoholysis of the thumba oil followed the endergonic reaction pathway. Thumba methyl ester (TME) synthesized via this intensified approach is a novel and energy-efficient method compared to the conventional method.

Recent advances of thin film nanocomposite membranes: Effects of shape/structure of nanomaterials and interfacial polymerization methods

Abstract: The emergence of nanomaterial arouses the development of thin film nanocomposite (TFN) in membrane separation technology. The incorporation of nanofillers into the TFN selective layer enhanced the membrane pure water permeability (PWP) with a slight improvement or even deterioration on the salt rejection. This trade-off is mainly due to the agglomeration of nanofillers, poor interactions between nanofiller and polymer matrix, and the formation of defects in the TFN selective layer. Hence, it is critical to explore and understand the fundamental knowledge of the effects of the shape/structure of nanofillers or interfacial polymerization methods on TFN membranes. This review summarizes the emerging development of TFN membranes incorporated with porous and non-porous nanofillers in the past 5 years, followed by the highlights of the effects of shape/structure of nanofillers on the TFN selective layer. Different shape/structure of nanofillers will influence the transport mechanisms of water molecules and solutes, resulting in a significant change in separation performance. Moreover, various modified interfacial polymerization methods to improve the dispersibility of nanofillers or fabrication of defect-free selective layers are discussed. The challenges and perspectives for TFN membranes are also highlighted.

Structure-heat transport analysis of periodic open-cell foams to be used as catalyst carriers

Abstract: Open-cell foams are promising catalyst supports as they provide a low pressure drop, radial mixing, and exceptional heat transport properties. Even though their large potential for the design of small-scale, dynamically operated reactors with strongly exothermic reactions is known, their application is not yet common. To design efficient and safe structured reactors in the future, the understanding of structure-heat transport relations is key. Fully resolved CFD simulations of non-isothermal structured reactors including chemical surface reactions require a high modeling effort and are computationally expensive. In a previous study we therefore implemented volumetrically distributed heat sources in the solid to mimic the heat production during an exothermal reaction, and evaluated the resulting heat flows and temperature distributions via CFD. The previous analysis, however, was limited to one specific open-cell foam geometry. In this study, we extend the conjugate heat transfer problem including heat production in the solid to five periodic open-cell foams (Kelvin cell-lattices) with defined but different structural parameters to establish structure-heat transport relations. We confirmed conduction being the dominant heat removal mechanism and found the strut diameter and the solid thermal conductivity being the key parameters to improve heat transport and reduce hot spots.

Optimal design and techno-economic analysis of a hybrid renewable energy system for off-grid power supply and hydrogen production: A case study of West China

Abstract: Reliable and sustainable energy supply is essential for the development of remote rural areas, especially in the context of stringent carbon emission reduction target. This study aims to demonstrate the techno-economic feasibility of a stand-alone hybrid renewable energy system (HRES) to satisfy the electric and hydrogen load for remote rural communities, where a case study of a village in West China is presented. By performing simulation and optimization, the most cost-competitive system configuration is identified, the net present cost (NPC), cost of energy (COE) and cost of hydrogen (COH) of which are $1.26 M, $0.162/kWh and $12.5/kg, respectively. Compared with grid extension, the proposed system is more economical with a breakeven grid extension distance (BGED) of 16.15 km, and less carbon footprint with CO2 emission avoidance of around 375.44 ton/year. In addition, sensitivity analysis on annual average hydrogen load growth and system capacity shortfall friction have been performed. The results indicated that the implementation of a hybrid power system can be a reliable and economic viable solution for remote rural electrification and decarbonizing local transport sector, whilst social and environmental benefits are also achieved.

Machine learning-based predictive control using noisy data: evaluating performance and robustness via a large-scale process simulator

Abstract: Machine learning modeling of chemical processes using noisy data is a practically challenging task due to the occurrence of overfitting during learning. In this work, we propose a dropout method and a co-teaching learning algorithm that develop long short-term memory (LSTM) neural networks to capture the ground truth (i.e., underlying process dynamics) from noisy data. To evaluate the performance and robustness of the proposed modeling approaches, we consider an industrial chemical reactor example and use a large-scale process simulator, Aspen Plus Dynamics that does not employ assumptions on reactor properties typically made in the derivation of first-principles models, to generate process operational data that are corrupted by sensor noise which is determined using industrial data. The dropout method is first utilized to reduce the overfitting of LSTM models to noisy data. Then, another approach termed co-teaching method is used to train LSTM models with additional noise-free data generated from simulations of the reactor first-principles model that employs several standard modeling assumptions not made in the Aspen model. Through open-loop and closed-loop simulations, we demonstrate the improvement of model prediction accuracy and of the open- and closed-loop performances under model predictive controllers using dropout and co-teaching LSTM neural network models compared to the LSTM model developed from the standard training process from the noisy data.

Recovery and characterization of useful benzene derivatives from spent engine oil through solvent extraction

Abstract: Engine oils are contaminated with hazardous species resulted from the oxidative degradation, which cause serious environmental issues. In this study, the extraction of useful hydrocarbons (HCs) from used engine oil through solvent extraction followed by separation in a column via adsorption is reported. n-Hexane, toluene, ethyl acetate, and their mixture (ethyl acetate/n-hexane) were used as solvent for extraction over powdered silica packed column, and the extracted samples were analyzed through thin layer chromatography, gas chromatography–mass spectrometry and Fourier transform infra-red spectroscopic techniques. Useful HCs like 1-fluoro heptane, n-hexane, methyl cyclopentane, toluene, p-xylene, o-xylene, toluene, benzaldehyde, 2,3-dimethyl pentane, and benzene-1,2-dicarboxylic acid were identified and subsequently distilled with respective percent recovery of 91, 71, 46, 45, 18, 08, 07, 06, 04 and 02%. The reported HC derivatives were obtained by interacting the sample oil with non-polar solvents (n-hexane and toluene) and slightly polar solvent (10% ethyl acetate/n-hexane mixture) in column which extracted the compounds having similar chemical nature in an appreciable amount via Londer dispersion forces and dipole-dipole interactions. The findings of this study concluded that the extraction of valuable benzene derivatives through highly cost-effective solvent extraction strategy is a promising alternative to the conventional burning and reclamation of spent engine oil. Witnessing the high efficiency and cost-effectiveness of the current process, it could be extended to the extraction of other useful compounds from used oils and concomitantly alleviating the related environmental pollution on larger scale.

Degradation of anti-inflammatory drug diclofenac using cobalt ferrite as photocatalyst

Abstract: Emerging pollutants, among them pharmaceutical compounds, are found in surface and ground waters, suggesting their ineffective removal by conventional wastewater treatment technologies. In this paper, the removal of sodium diclofenac, a widely used non-steroidal anti-inflammatory drug, was explored by a heterogeneous Photo-Fenton process using CoFe2O4, H2O2 and Uv germicide radiation (254 nm). Cobalt ferrites, were obtained by Pechini method and calcined at 600 °C and 800 °C. They were characterized by X-ray diffraction, Uv visible spectroscopy, Mossbauer and X-ray photoelectron spectroscopy, temperature-programmed reduction and specific surface area. The inverse spinel structure was obtained, and both materials presented good performance in the photo-Fenton reaction. A degradation mechanism based on the generation of OH groups is suggested. The complete sodium diclofenac degradation and 86% of mineralization of the total organic carbon were obtained using cobalt ferrite calcined at 800 °C. This catalyst was used in three cycles, without activity loss showing a negligible Fe leaching.

Halloysite nanotubes stabilized polyurethane foam carbon coupled with iron oxide for high-efficient and fast treatment of arsenic(III/V) wastewater

Abstract: Adsorbent with high efficiency, fast removal rate and good reusability is very important for arsenic remediation. Herein, a novel porous magnetic nanocomposite was prepared for efficient arsenic removal by simple one-pot coprecipitation of Fe3O4 NPs on porous halloysite nanotubes/C (HNTs/C) preformed using abandoned polyurethane foam as a template. HNTs were inserted into the pore of polyurethane foam and formed the skeleton of the nanocomposites after sticking by the carbonized phenolic resin. Fe3O4 NPs were well dispersed on the skeleton and finally formed the porous stable HNTs/C/Fe3O4 nanocomposites. The optimized HNTs/C/Fe3O4 showed ultrafast removal rate (>98% arsenic was removed within 10 min), high removal efficiency and good regeneration capacity. It is worth noting that the adsorption efficiency of As (V) by HNTs/C/Fe3O4 was still above 99% after 5 times reuse, indicating the sustainable application. In addition, the application of HNTs/C/Fe3O4 for arsenic adsorption is almost not limited by pH. The chemical adsorption process was confirmed by adsorption kinetics, XPS and isothermal adsorption. The qm of As(III) and As(V) is 34.54 mg/g and 1491.72 mg/g, respectively, which is the highest capacity of As (V) known at present. This study demonstrates an ultrafast, high-efficient and sustainable adsorbent for arsenic treatment in contaminated water, which might also give light on other heavy metals remediation.

Selection of surrogate modeling techniques for surface approximation and surrogate-based optimization

Abstract: Surrogate models are used to map input data to output data when the actual relationship between the two is unknown or computationally expensive to evaluate for several applications, including surface approximation and surrogate-based optimization. This work evaluates the performance of eight surrogate modeling techniques for those two applications over a set of generated datasets with known characteristics. With this work, we aim to provide general rules for selecting an appropriate surrogate model form based solely on the characteristics of the data being modeled. The computational experiments revealed that there is a dependence of the surrogate modeling performance on the data characteristics. However, in general, multivariate adaptive regression spline models and Gaussian process regression yielded the most accurate predictions for approximating a surface. Random forests, support vector machine regression, and Gaussian process regression models most reliably identified the optimum locations and values when used for surrogate-based optimization.

Current scenario and prospects in manufacture strategies for glass, quartz, polymers and metallic microreactors: A comprehensive review

Abstract: One of the most remarkable benefits of the microreactors is the achievement of more efficient processes by enhancing the heat and mass transfer phenomena, which is the key factor for processes intensification in chemical reactions, resulting in higher conversion, selectivity and yield towards desired products. Currently, the entire scenario of microreaction approach is an emergent technology and further advances are ongoing. Several strategies have been successfully applied for structuring processes that imply the fixation of the catalysts on the microreactors. However, there are features such as the physicochemical stability of the coatings under reaction conditions that must be improved, motivating the search for new protocols. This review provides a general overview of the most important methodologies applied for glass, quartz, polymers and metals microreactors manufacture and for their coating, analyzing the advantages and drawbacks of every procedure. Furthermore, an outline of the novel insights based on additive manufacturing techniques are described.

Current advances in liquid-liquid mixing in static mixers: A review

Abstract: This review article revisits the role of static mixers in the process industry nowadays and summarizes the most relevant developments and literature available on this type of mixers handling complex liquid-phase systems. In particular, this review seeks to discuss in depth the progress that has been made on the hydrodynamic understanding of immiscible liquid-liquid dispersions and emulsion formation using motionless types of mixers, both through experimental and computational approaches. Models and correlations on key process parameters, such as mean droplet size and pressure drop, proposed over the last couple of decades, are compiled and discussed. The latest progress on computational modelling through numerous frameworks is also thoroughly covered. In addition, this paper includes a brief review of the fundamental concepts in liquid static mixing and emulsion formation to further enrich the discussion on the innovations made on this field.

Ultrasonic aided sorption of oil from oil-in-water emulsion onto oleophilic natural organic-silver nanocomposite

Abstract: Herein, we reported for the first time the application of silver nanoparticle (AgNP) based sorbent for the sorption of oil from simulated oil-polluted water. An oleophilic natural organic-silver nanocomposite (AgAW) was synthesized in a one-pot system by impregnation of AgNPs on a natural organic sorbent (AW), which was applied for the ultrasonic sorption of oil. The characterizations proved successful synthesis of the nanocomposite containing 18.92 nm average size AgNPs. A shift from the microporous structure (1.184 nm) of AW to the mesoporous structure (2.231 nm) of AgAW was obtained. The impregnated AgNPs enhanced the hydrophobic characteristics of AgAW for the oil phase, with a maximum oil uptake of 2.66 g/g and 6.11 g/g obtained for AW and AgAW respectively. Besides AgAW exhibited higher oil uptake than the pristine AW at variations in pH (2.0–10.0), sonication time (5–60 min), temperature (395–325 K), and oil concentration (200–1000 g/L). The presence of competing Pb(II) and Cd(II) ions in solution did not significantly affect the oil uptake on the sorbents. In addition, successful regeneration of the sorbents using n-pentane and petroleum ether was achieved with over 85.3% oil desorption. In addition, the sorbents were efficiently reused for oil uptake with a slight decrease in oil sorption capacity. The mechanism of oil sorption on AgAW was found to be purely hydrophobic interactions brought about by the impregnated AgNPs. The efficacy of the synthesized AgAW composite for oil spill treatment based on the oleophilic character, high oil uptake as well as the efficient regeneration and reuse was established.