Showing papers in "Industrial & Engineering Chemistry Research in 2023"

Bacillus thuringiensis Based Ruthenium/Nickel Co-Doped Zinc as a Green Nanocatalyst: Enhanced Photocatalytic Activity, Mechanism, and Efficient H2 Production from Sodium Borohydride Methanolysis

Abstract: Today, the development of green nanocatalysts is among the popular topics due to the need for energy production and the cleaning of organic pollutants. In this approach, Bacillus thuringiensis, a bacterium, was used as a biosupport of ruthenium/nickel co-doped zinc nanoparticles (btRNZn NPs) to release hydrogen from the methanolysis of sodium borohydride (NaBH4). In addition, their photocatalytic activity was reported against Methyl Orange (MO) organic dye. This study focused on the preparation, characterization, and catalytic and photocatalytic activity of the btRNZn biocatalyst for the release of hydrogen from the methanolysis of NaBH4 and removal of MO dye. According to TEM analysis, the average size of btRNZn NPs was found to be 11.78 nm; in addition, btRNZn NPs showed a photodegradation effect of 68.2% against MO dye at 90 min, and its photocatalytic mechanism was discussed. The effects of the catalyst, substrate, and temperature in the methanolysis reaction of NaBH4 in the presence of the catalyst were investigated extensively. The reaction kinetics was calculated, and TOF, activation energy, and enthalpy energy were measured as 2497.14 h–1, 14.89 kJ/mol, and 12.35 kJ/mol, respectively. It was observed that the methanolysis process is a first-order reaction based on the amount of the catalyst and substrate. This study aimed to synthesize a nanobiocatalyst (btRNZn NPs) by a biological method, and it will be used as a great photocatalyst to prevent wastewater pollution; also, it can be an excellent catalyst to produce hydrogen from NaBH4 methanolysis. The application of btRNZn NPs in solar photocatalysis to prevent wastewater pollution and to research it for energy production through hydrogen creation are both made clear by these studies.

Simplified Approach to the Parameterization of the NRTL Model for Partially Miscible Binary Systems: Tττ LLE Methodology

Abstract: A new approach to parameterizing the NRTL model for binary liquid–liquid equilibria (LLE) is presented. It allows for the incorporation of a priori knowledge into the parameterization procedure rather than using the “black box” approach often employed. The method first converts the compositional Txx data to a set of unique binary interaction parameters, Tττ data. Thereafter, the second step improves the parameterization of frequently used temperature-dependent parameters (TDPs) by reducing the traditional nonlinear regression problem to simpler linear regression. This method is less susceptible to poor initial guesses, finding local minima, and significantly reduces computational requirements, with comparable/improved performance to published parameters. A total of 29 binary systems, including upper and/or lower critical solution temperatures, were evaluated using the approach to provide generalized recommendations and understanding of the TDP requirements for each system type. Inclusion of such information and use of the approach will significantly simplify multicomponent LLE model parameterization.

Semi-interpenetrating Polymer-Network Anion Exchange Membrane Based on Quaternized Polyepichlorohydrin and Polyvinyl Alcohol for Acid Recovery by Diffusion Dialysis

Abstract: In this paper, polyepichlorohydrin (PECH) quaternized with N-methylimidazole (ImPECH) was successfully synthesized. ImPECH, polyvinyl alcohol, and tetraethyl orthosilicate were used to prepare anion exchange membranes that possessed a semi-interpenetrating polymer-network structure. These membranes were designed for acid recovery by diffusion dialysis (DD). The successful synthesis of ImPECH was verified via Fourier transform infrared spectroscopy (FTIR) and 1H NMR spectroscopy. Meanwhile, the prepared anion exchange membranes were characterized by FTIR, scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), and thermogravimetric analysis (TGA). In addition, the ion exchange capacity (IEC), water uptake (WR), tensile strength (TS), linear expansion rate (LER), and elongation at break (Eb) of each membrane were characterized. In DD application tests, acid dialysis coefficients (UH+) of the membranes ranged from 8.56 × 10–3 m/h to 27.33 × 10–3 m/h and separation factor (S) values were all above 22.55. These results showed that anion exchange membranes had higher UH+ and S values than commercial membrane DF-120 (UH+ is 9.00 × 10–3 m/h and S is 18.00) and other better general properties including the LER, TGA, TS, and so on. The prepared membranes showed a wide application prospective for acid recovery via DD.

Direct Numerical Simulation of Fluid Flow and Heat Transfer of a Reactive Particle Layer with Stefan Flow in Supercritical Water

Abstract: Supercritical water gasification is an efficient and clean way of energy conversion. The research on different scales, such as the system, reactor, and particle, has different temporal and spatial significance. A study on particle–particle and particle–fluid–particle interaction on the particle scale has a fundamental guiding value for revealing gasification performance on the reactor scale. Reactive particles such as coal are pyrolyzed and gasified in a high-temperature and high-pressure reactor to form Stefan flow, which affects the mass, momentum, and energy transfer between particles and supercritical water. In this paper, a particle-resolved direct numerical simulation study of a reactive particle layer in supercritical water is carried out to investigate the effect of different particle layer solid holdups and Stefan flow intensities and distributions on the flow and heat transfer process between the particle layer and supercritical water. This work analyzes the pressure and friction drag coefficients to which the particles are subjected and specifies the flow, velocity, and temperature distribution inside and around the particle layer. The results show that the drag coefficient and Nusselt number of particles in the particle layer decrease gradually along the flow direction, and the presence of particle Stefan flow further reduces the drag force and Nusselt number of particles. With the increasing solid holdup of the particle layer, the particle–fluid–particle interaction becomes more intense, and the effect of Stefan flow cannot be negligible.

Physics-Informed Online Machine Learning and Predictive Control of Nonlinear Processes with Parameter Uncertainty

Abstract: In this work, we present a physics-informed recurrent neural network (PIRNN)-based modeling approach for nonlinear dynamic systems with parameter uncertainty. Physics-informed modeling approaches can improve the generalization performance of machine learning models by embedding the knowledge of physical laws in the learning process. Based on the standard PIRNN modeling approach for the nominal system without model uncertainties, we develop a novel PIRNN-enhanced modeling method that integrates online estimation of uncertain process parameters into the training process using the latest process data. The PIRNN-enhanced modeling approach is incorporated into the design of model predictive control (MPC), where an error-triggered mechanism is employed to trigger the quantification of modeling uncertainties and update of PIRNN models accordingly. Finally, a chemical process example is used to demonstrate the superiority of the proposed PIRNN-enhanced online learning mechanism in comparison to the conventional purely data-driven online update strategy for nonlinear systems subject to process parameter uncertainty.

Multifeedstock and Multiproduct Process Design Using Neural Network Surrogate Flexibility Constraints

Abstract: Biorefineries are designed to utilize a combination of various technologies to transform biomass derived raw materials into different value-added products. This strategy has been highlighted in the literature for reducing waste, increasing profitability, and improving the process resilience to uncertain biomass feedstocks. In this work, a two-stage stochastic programming (TSSP) model is developed to maximize profit and minimize emissions under different sources of uncertainties. Data-driven surrogate models are built for biorefinery’s flexibility index (FI) to quantify and improve its operational flexibility. The neural network with rectified linear unit (ReLU) activation function is established as the appropriate surrogate model because it closely approximates the flexibility index while retaining the mixed-integer linear characteristics of the overall design formulation. Moreover, the stochastic programming demonstrates the magnitude of environmental impact uncertainty quantitatively in each scenario using empirical price/demand/supply uncertainty information, which cannot be addressed by the traditional Pedigree-based life cycle assessment (LCA) uncertainty analysis.

Integrated Production Planning and Model Predictive Control of a Fluidized Bed Catalytic Cracking-Fractionator Unit

Abstract: The present study proposes a new production planning framework that considers the effect of closed-loop control and process disturbances/disruptions. The production planning layer is augmented to include process control degrees of freedom, process operating constraints, operating uncertain parameters (related to closed-loop feedback), and disturbance information. This results in a stochastic production planning problem, whose solution successfully reduces the economic gap between production planning predictions and realized production. The proposed approach is applied to a large-scale model of a refinery section comprising a fluid catalytic cracker (FCC) and a fractionator. Extensive simulations involving different disturbances, operating modes, models, and production planning formulations are performed. The results demonstrate the benefits of the proposed framework in terms of economic performance, computational tractability, and ease of application. Finally, the impact of the economic model and plant-model mismatches between production planning and model predictive control (MPC), which arise in practice, is investigated.

Design of [BmIm]3PW12O40 Ionic Liquid Encapsulated-ZIF-8 Nanocomposite for Cationic Dye Adsorptive Removal: Modeling by Response Surface Methodology

Abstract: In this study, the modification of metal–organic frameworks with polyoxometalate-based ionic liquids was assessed and their efficient role in the improvement of their adsorption performance was studied. Metal–organic framework ZIF-8 was encapsulated with [BmIm]3PW12O40, a polyoxometalate-based ionic liquid, through reaction with the ZIF-8 imidazolate groups. The novel ZIF-8-[BmIm]3PW12O40 nanocomposite was characterized using XRD, FTIR, SEM, EDX, and BET analyses, which confirmed the successful insertion of [BmIm]3PW12O40 on the surface of the ZIF-8 structure. It was used to study the adsorptive removal of a methylene blue (MB) cationic dye model and showed high adsorption performance between pH 2 and 11 with an optimum sorbent dosage of 4 g/L. The adsorption kinetics corresponded well to the pseudo-second-order kinetic model. Moreover, response surface methodology was used to optimize experimental conditions and determine the highest MB removal percentage. The correlation between variables (time, pH, and sorbent dosage) and responses (removal percentage) were well fitted to the second-order polynomial model (R2 = 0.949, Adj. R2 = 0.9032, and pred. R2 = 0.6129). The optimal conditions were found to be adsorbent amount = 0.04 g/L, pH = 11, and contact time = 45 min. Under these conditions, the highest removal percentage and amount of desirability were found to be 95.75% and 0.9, respectively.

Regulation of the B Site at La(Ni0.1)MnO3 Perovskite Decorated with N-Doped Carbon for a Bifunctional Electrocatalyst in Zn–Air Batteries

Abstract: The development of nanocomposites with unique structures by combining perovskites (ABO3) is of significant importance for improving oxygen reduction reaction (ORR) and oxygen evolution reaction (OER). The introduction of transition metals in active B sites is considered a useful pathway to regulate the chemical and electronic properties of perovskites. In this study, a bifunctional activity-enhanced La(Ni0.1)MnO3 perovskite decorated with N-doped carbon (NC) is developed by a B-site doping strategy. The resulting La(Ni0.1)MnO3@NC catalyst possesses numerous benefits including unique morphology, controllable synthesis, high conductivity, bifunctional activity, and durability. The enhancement was attributed to the synergistic effect of N-doped porous carbon and [MnO6] with the incorporation of [NiO6], resulting in the regulated charge redistribution and disorder degree. Remarkably, the rechargeable Zn–air battery assembled with La(Ni0.1)MnO3@NC in the air cathode also displays satisfactory performance due to the regulation of coordination units when compared with a commercial catalyst. This study shows that the catalytic performance of perovskite oxide-based electrocatalysts can be significantly improved by B-site regulation and allows for the construction of effective cathode catalysts for metal–air batteries.

A Reusable Nickel Oxide Reduced Graphene Oxide Modified Platinum Electrode for the Detection of Linezolid Drug

Abstract: Linezolid is most widely used antibiotic that inhibits different bacteria and micro-organisms. The overuse of linezolid causes several health complications, such as vomiting, tongue decolorization, and low blood glucose. An efficient and sensitive electrochemical sensor based on a NiO and reduced graphene oxide nanocomposite with a modified platinum electrode (NiO/rGO/PtE) was therefore developed for the monitoring of the linezolid drug. The confirmation of synthesis of graphene oxide and NiO/rGO was conducted through several physicochemical characterization techniques. The Fourier transform infrared spectroscopy results confirmed the Ni–O symmetric and antisymmetric stretching frequency at 741.4 and 732.1 cm–1, X-ray diffraction spectroscopy revealed exceptional crystallinity, while the size of the prepared materials, which was confirmed through atomic force microscopy, was determiend to be 1.675 nm. The incorporation of NiO into GO sheets is visualized through scanning electron microscopy images. Cyclic voltammetry and electrochemical impedance spectroscopy techniques were implemented to assess the electrochemical activity and the conductivity of NiO/rGO/PtE. Under the optimized conditions (phosphate buffer with a pH value of 6.0, scan rate of 70 mV/s, and linear dynamic range (LDR) value between 0.1 and 90 μM), the engineered sensor NiO/rGO/PtE manifested an excellent response for linezolid. The limit of detection (LOD) value of proposed technique for monitoring linezolid was computed to be 0.0031 μM, which was the lowest possible detection limit, compared to the literature. The anti-interference pattern and long-term stability further demonstrated the efficiency and reliability of NiO/rGO/PtE. The analytical application of NiO/rGO/PtE for linezolid was examined in urine samples as well as a commercial pharmaceutical sample. The sensitivity and reliability at repeated runs of modified sensor suggest that it could be used for the onsite detection of linezolid. Experiments have been conducted regarding the application of a fabricated NiO/rGO/PtE sensor as an effective and simple detection tool for antibiotics with a lower LOD than that of the reported electrochemical sensors for linezolid.

Bayesian Computational Technique for Modeling Caffeine Adsorption in a Fixed-Bed Column: Use of the Maximum Adsorption Capacity Deterministically and Experimental Design

Abstract: This work applied a Bayesian computational technique for parameter estimation of adsorption breakthrough curve models with experimental data of caffeine (CAF) adsorption onto granular activated carbon (GAC). Different operational conditions were evaluated (volumetric flow: Q, adsorbent mass: W, and initial CAF concentration: C0) by a two-level factorial experimental design (23) to determine the best operational conditions. The models (Thomas, Yoon–Nelson, Yan, Clark, Gompertz, and Log-Gompertz) were fitted to the experimental data, estimating and not estimating the maximum adsorption capacity (qS). For model selection, five statistical metrics were calculated. The results showed that the proposed Bayesian technique, not estimating qS, was effective and all analyzed operational conditions obtained 95% of CAF removal. In the best condition, when qS reached 7.317 mgCAF/gGAC, the model that best adjusted the experimental data was Log-Gompertz, being suitable for practical approaches, and for its mechanisms, the Clark model best predicted the evaluated fixed-bed column.

Synergistic Production of Pyruvic Acid and Propionic Acid over Defect-Rich MoS2

Abstract: This work reported the coupling reaction of lactic acid (LA) to produce pyruvic acid (PyA) and propionic acid (PA) simultaneously over defect-rich MoS2 nanosheets. The process intensification linked to dehydrogenation of LA and hydrodeoxygenation of LA brings about the advantage that is not supplied additionally with oxygen and hydrogen, achieving a green and safe route. The defected structure is characterized by high-resolution transmission electron microscopy (HRTEM) and positron annihilation lifetime spectroscopy (PALS), which promotes the catalytic performance for the coupling reaction of LA. To fully understand the importance of the defected structure, the catalytic performance of defect-free MoS2 is also evaluated. It is found that the PyA/PA ratio is less than 1 and acetaldehyde selectivity is relatively high, suggesting that the produced hydrogen from the dehydrogenation of LA is insufficient for hydrodeoxygenation of LA over defect-free MoS2, and the difference of hydrogen comes from the side reactions of decarbonylation and decarboxylation of LA. Unlike the defect-free MoS2, the dehydrogenation of LA over the defect-rich MoS2 can provide sufficient hydrogen for hydrodeoxygenation of LA, so that the side reactions are drastically weakened. The widened interplanar spacing can fully expose the defect and the LA reactant molecule is more accessible to defective sites, which can endow a better catalytic activity. Surface hydrophilicity can improve adsorption and transfer, and better activity is observed over the defect-rich MoS2 and widened defect-rich MoS2. At 300 °C and a LA LHSV of 2.6 h–1 under an inert atmosphere, the catalyst offers a satisfactory total selectivity to pyruvic acid and propionic acid of about 85%, and the molar ratio of pyruvic acid/propionic acid is 1.1.

Dispersion Enhancement of Boron Nitride Nanotubes in a Wide Range of Solvents Using Plant Polyphenol-Based Surface Modification

Abstract: Boron nitride nanotubes (BNNTs) have gained significant attention as a nanofiller additive to enhance the mechanical and thermal properties of polymer composites. However, despite the advancements in BNNT large-scale synthesis methods, the inherent hydrophobicity and the presence of van der Waals attraction hinder their potential application due to poor dispersion in polar and/or nonpolar solvents. In this communication, a facile deposition approach of plant-based polyphenols intercalated with an amine source was postulated. Given the economic advantage and ease of deposition, tannic acid (TA) was directly deposited onto the BNNT surface to enunciate hydrophilic attributes. Afterward, decylamine (DA) was introduced into the BNNT-TA (BNNT-TA–DA) to heighten the BNNT interaction with the polymeric system. The dried BNNT-TA and BNNT-TA–DA powders can be readily redispersed at various concentrations in polar and nonpolar solvents, enhancing the BNNT dispersion and interaction with the polymeric matrix to improve the composite performance. Moreover, to demonstrate the interfacial modification viability, BNNT-TA–DA was used as a filler in epoxy resin to form polymer composites. The fabricated composites displayed 26.8% tensile stress and 52.2% breakpoint of strain increases compared to those of the neat polymer at 1 wt % loading.

Dynamic Operability Analysis for Process Design and Control of Modular Natural Gas Utilization Systems

Abstract: Process modularization is an alternative process design and construction framework, in which modular units are independent and replaceable blocks of a process system. While modular plants have higher efficiency and are safer to construct than conventional stick-built plants (Roy, S. Chem. Eng. Prog. 2017, 113, 28–31), they are significantly more challenging to operate because of the loss in the control degrees of freedom that comes with process integration and intensification (Bishop, B. A.; Lima, F. V. Processes2021, 9, 2165). To address this challenge, in this work, operability analyses are performed to consider the design and operation of modular units. Initially, a steady-state operability analysis is employed to find a set of feasible modular designs that are able to operate considering different modular plant conditions. A dynamic operability analysis is then applied to the feasible designs to identify the operable designs that are capable of rejecting the operational disturbances. Lastly, a closed-loop control measure is introduced to compare the performances of the different operable designs. The proposed approach is implemented in a modular membrane reactor to find a set of operable designs considering different natural gas wells, and the respective closed-loop nonlinear model predictive control performance of these units is evaluated.

Design and Simulation of a Plastic Waste to Methanol Process: Yields and Economics

Abstract: Plastic waste treatment is a key sector for circular economic models as well as for the energy transition since polymer precursors are among the main energy and raw material consumers of the chemical sector. This work analyzes the state of the art of the sector and tries to fill the gap present for chemical valorization of low quality plastic waste with a high impurity content. The proposed path aims at converting plastic waste to methanol through gasification. The process is developed and simulated with a combination of thermodynamic characterization of the feedstock and kinetic modeling of syngas treatments. Different loads of polystyrene in a polypropylene/polyethylene mixture are used to evaluate the behavior of the process for a less hydrogen-rich feedstock. The performance of the process is evaluated for each feedstock and used as a basis for an assessment of the operative expenditures of the technology, as well as evaluating the net present value and internal rate of return at different plant capacities.

Mitigation of Ship Emissions: Overview of Recent Trends

Abstract: The urgency of reducing flue gas pollution in maritime transport makes it necessary to take a holistic view of its sustainability and environmental impacts. Among the approaches to reducing ship exhaust emissions, marinized gas scrubbers, given their ability to be retrofitted to existing ships, are a central element in the tradeoff against the use of expensive low-sulfur fuels. However, compounding this issue, the priority of reducing greenhouse gas (GHG) emissions in the coming decades poses new challenges to emissions compliance. The use of exhaust gas cleaning systems to remove SOX, NOX, and particulate matter (PM) emissions will be enhanced in the short to medium term by GHG reductions. In this study, the different types of scrubbers for seaborne operation and the potential risks associated with their secondary emissions will be critically reviewed. In addition, NOX reduction systems and recent efforts to reduce CO2 through on-board carbon capture systems or alternative fuel combustion will also be covered.

High Production of Cellulase and Xylanase in Solid-State Fermentation by Trichoderma reesei Using Spent Copra and Wheat Bran in Rotary Bioreactor

Abstract: The enzyme production for lignocellulose saccharification by solid-state fermentation (SSF) of a food manufacturing byproduct was successfully carried out in a 30 L rotary bioreactor. Defatted spent copra (SC) supplemented with wheat bran (WB) was used as a substrate for the SSF of Trichoderma reesei and aerated at various rates. Regression analysis showed that the carbohydrate/protein (C/P) ratio of the substrate and the supplied aeration rate were the important factors for producing the enzyme cocktail, including cellulases (FPase, CMCase, and cellobiase) and xylanase. The substrate containing SC:WB of 3:2 (or the C/P ratio of 5.4) and the aeration of 1.0 L kg–1substrate min–1 were found to enhance the production of the enzymes up to 5.68, 8.66, 29.2, and 34.44 U g–1 of dry substrate for FPase, CMCase, cellobiase, and xylanase activities, respectively. This discovery provided a promising environment for other substrates to produce multi-enzymes for lignocellulosic saccharification. Additionally, mathematical models were generated to predict the saccharifying degree of the produced enzyme for lignocellulose saccharification.

Synthesis and Characterization of Sulfonated Magnetic Graphene-Based Cation Exchangers for the Removal of Methylene Blue from Aqueous Solutions

Abstract: This article describes the synthesis, characterization, and performance of graphene-based magnetic cation exchangers as low-cost nanoadsorbers for the removal of methylene blue from aqueous solutions. The structure and surface morphology of the nanoadsorbers were characterized by X-ray diffraction, Fourier transform infrared, Raman, Brunauer–Emmett–Teller, X-ray photoelectron spectroscopy, field emission scanning electron microscopy, high-resolution transmission electron microscopy, vibrating sample magnetometry, and thermogravimetric analysis. The effect of pH, incubation time, saturated adsorption capacities, and adsorption isotherms and kinetics on the adsorption of the sulfonated magnetic graphene oxide (SMGO) cation exchanger has been systematically studied. A comparative analysis of the samples was conducted with graphene oxide, sulfonated graphene oxide, and magnetic graphene oxide. Kinetic studies showed that adsorption followed a pseudo-second-order model, while the estimated maximum adsorption capacity using the Langmuir nonlinear isotherm was 246.47 mg g–1. Multiple regeneration and reuse experiments indicated that the SMGO performance remained above 80% after seven cycles of removal of cationic pollutants from aqueous solutions that could be applied to wastewater treatment.

Process Technology and Sustainability Assessment of Wastewater Treatment

Abstract: Removal of heavy metals in wastewater treatment is crucial to protect the environment, wildlife, and human health. Various techniques have been developed focusing on removal of heavy metal ions, pharmaceuticals, and other contaminants from different wastewater sources. The main methods include adsorption, filtration, ion exchange, electrochemical, reverse osmosis, precipitation, flotation/coagulation/flocculation, and photocatalytic-based treatments. This paper comprehensively assesses the sustainability of those common technologies used for wastewater process treatment. The sustainability profile depends mostly on the exact approach followed for each technology, including its energy consumption, type of radiation (where appropriate), auxiliary materials used (e.g., catalysts, adsorbents), and further specific experimental process settings. Thus, while sustainability inevitably provides a multifaceted answer, the review finally aims for sustainability benchmarking of all technologies, by compressing the manifold outcomes toward a compact information set, such as a table and radar plot.

Scale Synthesis of Poly(butylene carbonate-co-terephthalate) and Its Depolymerization–Repolymerization Recycling Process

Abstract: Development of biodegradable aliphatic–aromatic copolyesters has been widely accepted by society as a promising strategy to solve plastic pollution. Here, a kilogram-scale aliphatic–aromatic copolyester, poly(butylene carbonate-co-terephthalate) (PBCT), has been successfully synthesized using a 5 L steel reactor. The physical–chemical properties, including composition, microstructure, thermal properties, crystal structure, rheology behavior, mechanical properties, and water barrier property, were systematically investigated. The results illustrated that PBCT could be used as an ideal barrier packaging film material. In addition, the closed-loop recycling property of PBCT was preliminarily explored. The aromatic units of PBCT copolyesters were able to be recycled using the esterification byproduct, which consisted of methanol and dimethyl carbonate. The evolution of molar weights during the alcoholysis process indicated that PBCT could be completely converted to dimethyl terephthalate (DMT). The repolymerized PBCT prepared with the recycled DMT showed no significant property loss compared with the initial PBCT. This work provides a novel insight and direction for treating waste biodegradable polyesters, which could overcome the post-consumer plastic waste accumulation in the environment.

Removal of Antidiabetic Pharmaceutical (Metformin) Using Graphene Oxide Microcrystalline Cellulose (GOMCC): Insights to Process Optimization, Equilibrium, Kinetics, And Machine Learning

Abstract: Cellulose is a basic material for the manufacture of filters and adsorbent materials, as well as for the removal of contaminants, especially in food, pharma and other industries. The goal of this research is to see how a novel arrangement of biomass resources (hybrid biomass) can be used to improve the characteristics and behavior of microcrystalline cellulose (MCC) produced by the traditional method of acid hydrolysis. Chemical oxidation cum exfoliation was used to synthesize graphene oxide (GO) nanoparticles, which were then composited with microcrystalline cellulose (GOMCC) and used for metformin (MFM) adsorption from an aqueous medium. The GOMCC characterized using particle size distribution, X-ray analysis (XRD), Raman spectra analysis, Fourier transform infrared spectroscopy (FTIR), field-emission scanning electron microscopy (FESEM) analysis, and transmission electron microscopy (TEM). The GOMCC that had been prepared was mesoporous and had a reasonable surface area (196 m2/g), with a pore volume of 0.134 cm3/g and pore width of 17.85 nm. Batch-mode adsorption was performed at various temperatures (288, 303, and 318 K), GOMCC amounts (50, 100, and 150 mg), MFM concentrations (30, 50, and 70 mg/L), and pH values (4.5, 6.5, and 8.5) and were investigated with the Box-Behnken statistical design. The machine learning, models─Support Vector Machine (SVM), Gaussian Process Regression (GPR), Regression Trees (TREE), and Ensemble of Regression Trees (Ensemble)─are used to predict the adsorptive removal of MFM onto GOMCC from the BBD design input. Different isotherm equations and various kinetic models were used to assess the sorption data and ideal values were determined using the sum of normalized errors methodology. MFM maximal sorption capacity was investigated to be 132.10 mg/g. The kinetics revealed that the pseudo-first-order model fit the data exactly. MFM sorption was found to be spontaneous (ΔG°) and exothermic (ΔH°) in a thermodynamic analysis. The chemisorption of MFM onto GOMCC was followed by subsequent pore diffusion, which was exothermic and spontaneous. The main mechanisms responsible for the removal of MFM were found to be π–π interactions, alone interactions, sulfur interactions, hydrophobic and hydrogen bonding.

Nitrate Removal Performance of Different Granular Adsorbents Using a Novel Fe-Exchanged Nanoporous Clinoptilolite

Abstract: The primary purpose of this investigation is to assess the nitrate removal efficiency of the two diverse synthesized beads from natural clinoptilolite modified with iron cations (Fe/Clin). Beads were prepared utilizing nanoporous Fe/Clin by employing alginate (A) and chitosan (Ch). X-ray diffraction, Fourier transform infrared, energy dispersive X-ray spectroscopy, field emission scanning electron microscopy, and Brunauer–Emmett–Teller methods were operated to scrutinize the clinoptilolite, Fe/Clin, Fe/Clin-A-bead, and Fe/Clin-Ch-bead. A comprehensive batch adsorption investigation was performed using both synthesized specimens to determine the effective parameters. The effects of pH, bead dosage, and time were assessed. The optimum removal rates of 86.13 and 88.79% were computed for Fe/Clin-A-bead and Fe/Clin-Ch-bead at the optimized dosage and pH. Moreover, the best nitrate removal performance was at 4 and 3 g/L for Fe/Clin-A-bead and Fe/Clin-Ch-bead, respectively. The removal rates of 2, 3, 4, and 5 g/L of Fe/Clin-A-beads were 78.80, 82.40, 86.13, and 87.45%, respectively. On the other hand, Fe/Clin-Ch-beads had better removal performance at the measured range. The removal rates of 2, 3, 4, and 5 g/L of Fe/Clin-Ch-beads were 83.35, 88.79, 89.50, 92.25, and 90.04%, respectively. The optimal adsorption time for Fe/Clin-A-bead and Fe/Clin-Ch-bead was 75 and 60 min, respectively. Both beads’ isotherms were investigated using Langmuir, Freundlich, and Dubinin–Radushkevich (D-R) isotherms. The maximum adsorption using Langmuir and D-R isotherms was 24.39 and 7.06 mg/g for the alginate bead and 36.63 mg/g and 10.93 mg/g for the chitosan bead, respectively. The kinetics of both beads were also studied utilizing Lagergren first-order, pseudo-second-order, and intraparticle diffusion. The kinetic model of both beads was best described by the pseudo-second-order.

Oxidative Dehydrogenation of Ethane with CO2 over the Fe-Co/Al2O3 Catalyst: Experimental Data Assisted AI Models for Prediction of Ethylene Yield

Abstract: In this work, Fe-Co-based mixed metal oxides supported on Al2O3 are proposed for ethylene production through oxidative dehydrogenation of ethane with CO2 (ODH-CO2). Thermodynamic feasibility analysis followed by a systematic experimental study is performed on catalyst synthesis and its composition optimization along with process condition optimization in a fixed bed reactor. The study revealed that 5% Fe loaded on 10% Co/Al2O3, 700 °C, and 1:1 are the optimal composition, temperature, and molar ratio of CO2 to ethane, respectively, achieving 29% ethane conversion and resulting in 16% ethylene yield. Further, the experimental data was used to develop different linear, nonlinear, and ensemble AI models for ethylene yield prediction through a systematic grid search and k-fold cross-validation procedure. Among all the models, the kernel ridge regression model is found to be the most accurate, exhibiting the highest R2 value of 0.966 and lowest root mean-squared error (RMSE) of 0.032 on test data, successfully capturing the underlying nonlinear dynamics of ODH-CO2.

MIL-160(Al) as a Candidate for Biogas Upgrading and CO2 Capture by Adsorption Processes

Abstract: The microporous bioderived Al dicarboxylate MIL-160(Al) MOF in its shaped form has been evaluated as a candidate for biogas upgrading (BU) and/or carbon capture and storage (CCS) by studying adsorption isotherms of CO2, CH4, and N2 at 313, 343, and 373 K until 8 bar. The isotherms disclosed the following loading capacities: 4.2 (CO2), 2.07 (CH4), and 0.69 (N2) mol/kg at 5.8 bar and 313 K, which fitted with the dual-site Langmuir model. The linear-driving-force coefficients (LDFs) for CO2 and CH4 calculated from uptake rate experiments are in the order of 0.021–0.096 and 0.041–0.165 s–1 at 313 K between 0.11 and 2.76 bar, respectively. The Response Surface Methodology (RSM) was also applied to maximize the selectivity for mixtures CO2/CH4 and CO2/N2 with interest for BU or CCS. Breakthrough curve experiments with mixtures CO2/CH4 and CO2/N2 at the optimum selectivity conditions were developed and simulated using ASPEN Adsorption. This work clearly demonstrates the potential of MIL-160(Al) to be used in BU- and/or CCS-related applications.

Elucidating the Sorption Mechanisms of Environmental Pollutants Using Molecular Simulation

Abstract: With the global expansion of industrial activities, the entry of various pollutants into the environment has remained a serious issue. One of the best ways to remove these pollutants is to use the adsorption method. Understanding adsorption mechanisms to improve and optimize adsorbents are pivotal for adsorbent development. In this study, the application of molecular simulation in developing various adsorbents has been reviewed. A variety of molecular simulation methods such as molecular dynamics (MD), density functional theory (DFT), hybrid quantum and classical molecular dynamics (QM/MM), ab initio molecular dynamics (AIMD), and coarse-grained molecular dynamics have been used to study these processes. Although hardware limitations prevented researchers from using this method for real systems, this problem has been solved thanks to the development of computing power units (CPUs) and graphic processing units (GPUs). Due to the increasing use of molecular simulations, an attempt has been made to review previous work in this field. Investigations were conducted on various capabilities of molecular simulations in studying the adsorption process and its limitations. In addition to lowering the cost and time of industrial research, this study advances molecular simulations in academic studies. These simulations can reveal the mechanisms underlying adsorption and the selection, development, and design of suitable adsorbents and adsorption processes. Although investigating the adsorption mechanisms for the selection and design of the process is a complicated problem, this work tends to shed light on almost all types of molecular simulations and their applications in studying the adsorption process of removing various environmental pollutants by various adsorbents.

Kinetic Modeling of a Poly(N-vinylcaprolactam-co-glycidyl methacrylate) Microgel Synthesis: A Hybrid In Silico and Experimental Approach

Abstract: Synthesizing microgels with new functionalities is vital for applications in various disciplines; yet microgel synthesis kinetics are mostly unknown. We present a bottom-up approach to model the synthesis of N-vinylcaprolactam-based microgels functionalized with glycidyl methacrylate. The existing synthesis model requires parameter values for unknown reaction rates, which we estimate by a hybrid approach based on quantum chemical calculations and experimental data. Using quantum mechanics, we compute propagation rate constants and enthalpies of the underlying polymerization reactions. We estimate 7 out of 21 reaction parameter values using the reaction calorimetry measurements and a mechanistic process model. Our hybrid approach averages a coefficient of determination of 0.97 for the enthalpy transfer rate over time during microgel synthesis. Our approach illustrates that quantum chemistry methods and physical experiments can be integrated into models toward better understanding and designing of pVCL/GMA microgel synthesis processes.

Machine-Learning-Based Detection of Pressure-Induced Faults in Continuous Glucose Monitors

Abstract: Continuous glucose monitors (CGMs) are prone to faults termed pressure-induced sensor attenuations (PISAs), particularly when the user rolls over on the sensor during sleep. PISAs result in false, low blood glucose readings, leading to undesirable pump shutoffs and an increased risk of hyperglycemia. Data from an outpatient trial with PISA glucose readings labeled for 1125 nights was used. Machine learning methods such as decision trees, support vector machines, neural networks, random forest, and gradient-boosted machine models are compared against each other and with a previously reported rules-based algorithm for PISA detection on the same data set. The best-performing gradient-boosted machine model is further improved using the developed start-PISA model. Model interpretation methods are used to confirm that PISA behavior is well-captured and provides insights into decision-making.

Investigations on Kinetics and Mechanisms of CaCO3 Calcination in Calcium Looping

Abstract: Calcium looping-based CO2 capture is generally considered more efficient than conventional postcombustion CO2 capture. Calcium carriers’ calcination in the regenerator is crucial because proper operating parameters can provide regenerated carriers suitable for the gasifier and prevent sintering. This work focuses on the calcination behavior of CaCO3 in calcium looping systems. In this study, the calcination kinetic parameters under various conditions were investigated using a model-free isoconversional method based on nonisothermal thermogravimetric analysis in a macroscope, in combination with X-ray diffraction tests of the samples in a microscope. Results in the macroscope showed that the apparent activation energy ranges from 121.8 to 163.0 kJ/mol under a conversion range of 0.3–0.9, and the CaCO3 calcination mechanism involves transient nucleation and two-dimensional growth. Results observed from the microscope demonstrated that CaCO3 tends to be calcined on a plane, consistent with the macroscope model with two-dimensional nucleation. Since the microscopic mechanism is reflected, the kinetic model can be widely adopted, and calcium-based carrier modification strategies can also be proposed to prevent sintering of the carrier perpendicular to the (1̅04) plane.