Batch Approach Research Articles

A novel strontium-based MOF: synthesis, characterization, and promising application in removal of 152+154Eu from active waste.

Removal of hazardous radioactive materials such as 152+154Eu from active waste using the batch approach has attracted attention nowadays. In this work, a novel melamine–terephthalic strontium metal–organic framework (MTSr-MOF) was prepared via a hydrothermal method. The MTSr-MOF was characterized by various analytical techniques such as FT-IR, 1H/13C-NMR, mass spectroscopy, XPS, XRD, TGA, BET, FE-SEM/EDX, TEM, and UV. The obtained data revealed that MTSr-MOF exhibited brick-like building blocks that were bridged together by the linkers, and each block had a thickness of ∼120 nm. The BET surface area was 74.04 m2 g−1. MTSr-MOF was used for the removal of 152+154Eu radionuclides from active waste. Further functionalization using various modifiers, including oxalic acid, EDTA, sulfuric acid, and sodium hydroxide was carried out to improve the sorption efficiency of MTSr-MOF towards 152+154Eu radionuclides. Among them, MTSr-MOF modified with oxalic acid (MTSr-OX-MOF) demonstrated a superior removal efficiency toward 152+154Eu radionuclides when compared to MTSr-MOF or other published reports, with a removal efficiency of more than 96%. The higher sorption efficiency of the MTSr-OX-MOF indicates that it could be a promising candidate for the removal of 152+154Eu radionuclides from radioactive waste

Life cycle assessment of multistep benzoxazole synthesis: from batch to waste-minimised continuous flow systems

A comprehensive life cycle assessment of different 2-aryl benzoxazoles routes was performed to a flow chemistry approach, its batch equivalent, and other batch approaches to show how innovations in green chemistry and circularity result in real sustainability benefits.

Adaptive Batch Size Selection in Active Learning for Regression

Training supervised machine learning models requires labeled examples. A judicious choice of examples is helpful when there is a significant cost associated with assigning labels. This article improves upon a promising extant method – Batch-mode Expected Model Change Maximization (B-EMCM) method – for selecting examples to be labeled for regression problems. Specifically, it develops and evaluates alternate strategies for adaptively selecting batch size in B-EMCM.<br/> By determining the cumulative error that occurs from the estimation of the stochastic gradient descent, a stop criteria for each iteration of the batch can be specified to ensure that selected candidates are the most beneficial to model learning. This new methodology is compared to B-EMCM via mean absolute error and root mean square error over ten iterations benchmarked against machine learning data sets.<br/> Using multiple data sets and metrics across all methods, one variation of AB-EMCM, the max bound of the accumulated error (AB-EMCM Max), showed the best results for an adaptive batch approach. It achieved better root mean squared error (RMSE) and mean absolute error (MAE) than the other adaptive and nonadaptive batch methods while reaching the result in nearly the same number of iterations as the non-adaptive batch methods.

Coupling coagulation-flocculation to volcanic tuff-magnetite nanoparticles adsorption for olive mill wastewater treatment

A combined coagulation-flocculation/adsorption approach has been developed to remove organic and phenolic compounds from Olive mill wastewater (OMW). The effect of utilizing coagulation-flocculation on the removal of total suspended solids (TSS) prior to volcanic tuff (VT) /magnetite nanoparticles (MNPs) adsorption was investigated. On the other hand, the influence of VT activation either by calcination or by acid/base treatment followed by calcination was established. Moreover, the adsorption capabilities of activated and non-activated VT coupled with magnetite nanoparticles were evaluated for chemical oxygen demand (COD) and total phenolic content (TPC) removal via batch and column approaches. The coagulation-flocculation processes were successfully reduced the TSS, COD, and TPC by 99%, 48% and 44%, respectively. The removal efficiency of VT for COD was increased by acid activation followed by calcination. Further enhancement was achieved by coupling with 0.5% by weight of magnetite nanoparticles with maximum COD removal of 76% at pH 8. Instead, VT activated by calcination and coupled with 0.5% by weight magnetite nanoparticles at pH 10 provided good removal for both TPC and COD of 73% and 70%, respectively. Overall, the results of this research reveal that the combined coagulation-flocculation/adsorption processes can enhance the removal of organic contaminants from OMW. This study has the potential to be considered as an efficient treatment approach for the local and worldwide olive mill industry.

Graphene oxide-mediated the reduction of U(VI), Re(VII), Se(VI) and Se(IV) by Fe(II) in aqueous solutions investigated via combined batch, DFT calculation and spectroscopic approaches

Although thermodynamically feasible, reduction of U(VI), Re(VII), Se(VI) and Se(IV) in homogeneous Fe(II) solution could be hardly observed. Whereas, surface-mediated reduction by Fe(II) has been generally regarded as a major pathway for the immobilization of these radionuclides. In this paper, graphene oxide (GO) was firstly revealed to mediate the reduction of U(VI), Re(VII), Se(VI) and Se(IV) by aqueous Fe(II) via a combined batch, DFT calculation and spectroscopic investigation. The kinetics for all adsorption systems could be fitted by the pseudo-second-order model, which was indicative of a chemical interaction. The isotherms for all reaction systems could be described by the Freundlich model better than that by the Langmuir model. Spectroscopic studies indicated that the enhancement effects of GO were attributable to the facilitated electron transfer by the graphitic surfaces and particularly to the decreased Fe(III)-Fe(II) redox potential by surface adsorption of Fe(II) with O-bearing functional groups on GO. Additionally, the presence of coexisting dissolved fulvic acid (FA) could significantly promoted GO-mediated reduction, by enhancing the electron shuttle ability of GO. Therefore, owning to the combined strong mediation efficiency and excellent adsorption affinity, GO exhibited an important role in mediating the reductive immobilization of radionuclides in a wide range of redox-stratified environments.

Modified dry bean pod waste (Phaseolus vulgaris) as a biosorbent for fluorescein removal from aqueous media: Batch and fixed bed studies

This study presents the use of dry bean pods as a solid phase for fluorescein removal from water. The non-pretreated solid phase did not display any sorption properties for the chosen dye. However, interesting sorption properties were observed following a chemical derivative treatment with nitric acid. The study was carried out using both batch and column approaches. Regarding the batch study, all parameters that influence sorption capacity, such, as pH, adsorbent mass, ionic strength, temperature and contact time, were evaluated. A sorptive capacity of 36.80 mg g−1 was obtained in the optimized condition. In the fixed column bed study, the influence of particle size, flow rate and initial concentration of the dye were evaluated through breakthrough curves and a sorptive capacity of 4.35 mg g−1 was obtained. Thermodynamic studies revealed that the adsorption is exothermic and spontaneous. Four different models, Langmuir, Freundlich, Temkin and Redlich-Patterson, were employed. The Akaike information criterion (AIC) was employed to rank the best equilibrium model, which was determined as the Freundlich isotherm. The method was applied to a real sample and the same removal rate was obtained, thus indicating its suitability to wastewater treatment.

Synthesis of 2H-indazoles via the Cadogan reaction in batch and flow mode

A continuous flow protocol is reported for the synthesis of 2H-indazoles via the thermal Cadogan reaction. Triethyl phosphite serves as a readily available deoxygenation reagent providing a facile entry to a series of drug-like 2H-indazoles in high chemical yields. Compared to an analogous batch approach, the flow process was advantageous as it provides for a contained environment where boiling of the reagent is suppressed by using a back-pressure regulator (BPR) which in turn leads to faster kinetics. Straightforward scale-up provided multi-gram quantities of 2H-indazoles whose further functionalisation via light-driven and regiospecific acetylation was demonstrated in flow mode.

Nano-rod hydroxyapatite for the uptake of nickel ions: Effect of sintering behaviour on adsorption parameters

Sorption studies in batch approach were carried out to investigate the elimination of Ni(II) ions from aqueous solution by fish scale derived nano-rod hydroxyapatite (FSDNHA) obtained via thermal decomposition at 800 and 900 oC respectively. The effects of Ni2+ concentration, contact time, pH, FSDNHA dose and temperature on elimination process were examined. Adsorption studies showed that maximum uptake amount of Ni2+ was attained at initial Ni2+concentration of 240 mg L-1, sorption time of 120 min, FSDNHA dose of 40 mg and solution pH of 6.0. The highest value of R2 obtained for Langmuir, Temkin, and Freundlich isotherms models are (0.996, 0.994, and 0.990) for FSDNHA-800 and (0.998, 0.986, and 0.992) for FSDNHA-900 respectively. The values of the maximum monolayer adsorption capacities obtained from Langmuir isotherm for FSDNHA-800 and FSDNHA-900 are 114.151 and 183.321 mg g-1 respectively. Kinetic modeling of the adsorption data clearly revealed that the sorption of Ni2+ onto the active sites of the FSDNHA surface was governed by Pseudo-first-order kinetic model. Thermodynamics parameters of Gibbs free energy (ΔG°), enthalpy (ΔH°) and entropy change (ΔS°) revealed that the sorption of Ni2+on FSDNHA was spontaneous, feasible and exothermic in nature. Well resolved peaks of a characteristic pure hydroxyapatite (HA) containing hydroxyl, phosphate and carbonate groups were obtained as the major functional groups responsible for the uptake of Ni2+, while the morphological analysis by FE-SEM and TEM revealed a nano-rod shape. This current study showed that Ni2+ removal by FSDNHA compared favorable with other adsorbents and is feasible.

First online X‐ray fluorescence characterization of liquid‐liquid extraction in microfluidics

AbstractLiquid‐liquid extraction is a complex chemical purification process, which is associated with many thermodynamic and kinetic values. This makes its application in the recycling industry difficult, as it deals with waste streams that have highly variable compositions. In this regard, modelling an extraction process using microfluidics proves to be a useful approach to allow rapid adaptation to such composition changes, if development can be shown to be more accurate, faster, and safer than the classical batch approach with separate analysis. Here, the first automated microfluidic tool integrated with online X‐ray fluorescence (XRF) is reported to study liquid‐liquid extraction processes by enabling metal concentration quantification. The measurement is automated and performed for both aqueous and organic phases to improve accuracy. Overall, this fully automated approach shows that: (i) Thermodynamic and kinetic values associated with these processes can rapidly and efficiently be obtained simultaneously (in less than 13 hours with a resulting liquid use of less than 20 mL). (ii) Numerical simulations are consistent with the experimental data and provide rare insights regarding the respective contributions to the overall kinetic of the extraction system.

Incremental learning strategies for credit cards fraud detection

Every second, thousands of credit or debit card transactions are processed in financial institutions. This extensive amount of data and its sequential nature make the problem of fraud detection particularly challenging. Most analytical strategies used in production are still based on batch learning, which is inadequate for two reasons: Models quickly become outdated and require sensitive data storage. The evolving nature of bank fraud enshrines the importance of having up-to-date models, and sensitive data retention makes companies vulnerable to infringements of the European General Data Protection Regulation. For these reasons, evaluating incremental learning strategies is recommended. This paper designs and evaluates incremental learning solutions for real-world fraud detection systems. The aim is to demonstrate the competitiveness of incremental learning over conventional batch approaches and, consequently, improve its accuracy employing ensemble learning, diversity and transfer learning. An experimental analysis is conducted on a full-scale case study including five months of e-commerce transactions and made available by our industry partner, Worldline.

Master batch approach for developing PVDF/EVA/CNT nanocomposites with co‐continuous morphology and improved electrical conductivity

AbstractConducting polymer composites constituted by co‐continuous poly (vinylidene fluoride) (PVDF)/ ethylene‐ vinyl acetate copolymer (EVA) blends with multiwalled carbon nanotube (CNT) were prepared by melt mixing using different procedures. The effect of the master batch approach on the conductivity, morphology, mechanical, thermal and rheological properties of PVDF/EVA/CNT nanocomposites was compared with that based on one step mixing strategy. The selective extraction experiments revealed that CNT was preferentially localized in the EVA phase in all situations, even when PVDF@CNT master batch was employed. Nanocomposites prepared with EVA@CNT master batch displayed higher conductivity, whose value reached around 10−1 S m−1 with the addition of 0.56 vol% of CNT. The better electrical performance was attributed to the better distribution of the filler, as indicated by transmission electron microscopy and rheological behavior. The electrical and rheological behavior were also investigated as a function of the CNT content.

A case study of batch and incremental recommender systems in supermarket data under concept drifts and cold start

Recommender systems uncover relationships between users and items, thus allowing personalized recommendations. Nonetheless, users’ preferences may change over time, the so-called concept drifts; or new users and items may appear, making the recommender system unable to accurately map the relationship between users and items due to the cold start problem. Consequently, concept drift and cold start are challenges that downgrade the recommender system’s predictive performance. This paper assesses existing approaches for collaborative-filtering recommender systems over a real supermarket dataset that exhibits both of the issues mentioned above. For this purpose, our comparative analysis encompasses batch and streaming learning approaches. As a result, we can observe that streaming-based models achieve better recommendation rates since these are tailored to fit the concept drift. More specifically, the predictive performance of streaming-based recommendations increases by up to 21% over those provided by batch methods. The supermarket dataset used in experimentation is also made publicly available for future studies and recommender systems comparisons.

Enzymatic esterification of lauric acid to give monolaurin in a microreactor

Monolaurin is a naturally occurring compound widely utilized in food and cosmetics. In this paper, we present a new method for the synthesis of monolaurin by esterification between lauric acid and glycerol catalyzed by Novozym® 435 using a microreactor. The conversion of lauric acid is 87.04% in 20 min, compared with 70.54% via the batch approach in 5 h. Using an optimized solvent system consisting of t-BuOH/ tert-amyl alcohol (1:1, v/v), the selectivity using the microreactor method is enhanced to 90.63% and the space–time yield of the process is 380.91 g/h/L. This newly devised method has the potential for application to other multiphase and enzymatic reactions.

Online Learning of Any-to-Any Path Loss Maps

Learning any-to-any (A2A) path loss maps might be a key enabler for many applications that rely on a device-to-device (D2D) communication, such as vehicle-to-vehicle (V2V) communications. Current approaches for learning A2A maps have a number of important limitations, including i) a high complexity that increases rapidly with the number of samples, making the problems quickly intractable, and ii) the inability of coping with a time-varying environment, among others. In this letter, we propose a novel approach that reconstruct A2A path loss maps in an online fashion. To that end, we leverage on the framework of stochastic learning to deal with the sequential arrival of samples, and propose an online algorithm based on the forward-backward splitting method. Preliminary simulation results show a significant decrease in complexity, while its performance is comparable to that of a batch approach.

Empirical estimation of various data stream mining methods

Online learning is done in order to work on dynamic environments in which the concept tends to change with time and the accuracy of classifiers decreases. The current and previous research is done in static environments, but there is a need of a real time data streaming due to the potentially larger number of applications available in the scientific and business domains. There are several methods used in learning in the presence of dynamic environments like single classifier methods such as batch and incremental learning approaches, classification methods with explicit drift detection method, windowing techniques and ensemble approaches. This paper, investigates these approaches for determining the best suitable method among them. We utilised light emitting diode (LED) data generator for evaluating the performance of the methods.

Direct C−H Arylation of Indole‐3‐Acetic Acid Derivatives Enabled by an Autonomous Self‐Optimizing Flow Reactor

AbstractDescribed herein is a continuous‐flow strategy for the palladium‐catalyzed direct C−H arylation of indole‐3‐acetic acid derivatives with arenediazonium salts. A fully autonomous self‐optimizing flow platform was used to efficiently optimize the coupling reaction in a three‐dimensional space. The flow methodology developed is experimentally simple, mild, broad in scope, and safer than traditional batch approaches. Our continuous‐flow approach is particularly convenient to prepare precursors of pharmaceutically relevant compounds.magnified image

Sequential Estimation of Relative Transfer Function in Application of Acoustic Beamforming

In this paper, a sequential approach is proposed to estimate the relative transfer functions (RTF) used in developing a generalized sidelobe canceller (GSC). The latency in calibrating microphone arrays for GSC, often suffered by conventional approaches involving batch operations, is significantly reduced in the proposed sequential method. This is accomplished by an immediate generation of the RTF from initial input segments and subsequent updates of the RTF as the input stream continues. From the experimental results via the mean square error (MSE) criterion, it has been shown that the proposed method exhibits improved performance over the conventional batch approach as well as over recently introduced least mean squares approaches.

Telescoped Continuous Flow Synthesis of Optically Active γ-Nitrobutyric Acids as Key Intermediates of Baclofen, Phenibut, and Fluorophenibut.

The two-step flow asymmetric synthesis of chiral γ-nitrobutyric acids as key intermediates of the GABA analogues baclofen, phenibut, and fluorophenibut is reported on a multigram scale. The telescoped process comprises an enantioselective Michael-type addition facilitated by a polystyrene-supported heterogeneous organocatalyst under neat conditions followed by in situ-generated performic acid-mediated aldehyde oxidation. Simple access to valuable optically active substances is provided with key advances in terms of productivity and sustainability compared to those of previous batch approaches.

Removal of uranium by immobilized biomass of a tropical marine yeast Yarrowia lipolytica

A marine yeast, Yarrowia lipolytica isolated from an oil polluted sea water and shown earlier to sequester dissolved uranium (U) at pH 7.5, was utilized in the present study for developing an immobilized-cell process for U removal from aqueous solutions under batch and continuous flow through systems. In batch system, optimum biosorption conditions for U removal were assessed by investigating the effects of biomass dose, initial U concentration, contact time and pH of solution using Y. lipolytica cells immobilized in calcium alginate beads. Appreciable uranium-binding capabilities over a wide pH range (3–9) were observed with the alginate beads bearing yeast cells. Out of Langmuir and Freundlich models employed for describing the sorption equilibrium data under batch mode, uranyl adsorption followed Langmuir approach with satisfactory correlation coefficient higher than 0.9. Uranyl adsorption kinetics by Y. lipolytica entrapped in alginate beads was best described by the pseudo-second-order model. While the environmental scanning electron microscopy established the immobilization and the uniform distribution of Y. lipolytica cells in the alginate beads, the Energy Dispersive X-ray spectroscopy analysis confirmed the deposition of U in the beads following their exposure to uranyl solution. Fixed bed flow-through column comprising of Y. lipolytica biomass immobilized in polyacrylamide matrix displayed high efficacy for continuous removal of uranium at pH 7.5 up to five adsorption-desorption cycles. Adsorbed U by immobilized cells could be significantly desorbed using 0.1 N HCl. Overall, our results present the superior efficiency of immobilized Y. lipolytica biomass for U removal using batch and regenerative approaches.

Genetic algorithm based approaches to solve the order batching problem and a case study in a distribution center

The order batching problem is a combinatorial optimization problem that arises in the warehouse order picking process. In the order batching problem, the aim is to find groups of orders and picking routes of these groups to minimize distance travelled by the order picker. This problem is encountered especially in manual order picking systems where the capacity of picking vehicle is limited. Solving the order batching problem becomes more important when the size of the problem (e.g. number of storage locations, number of aisles, number of customer orders, etc.) is large. The content of the batch and picking route affect the retrieval-time of the orders. Therefore, an effective batching and routing approach is essential in reducing the time needed to collect ordered items. The main objective of this study is to develop fast and effective metaheuristic approaches to solve the order batching problem. For this purpose, two genetic algorithm based metaheuristic approaches are proposed. The numerical test of the proposed algorithms is performed with generated data sets. The proposed methods are thought to be useful to solve real-life problems in different warehouse configurations. Accordingly, a real case study is conducted in the distribution center of a well-known retailer in Turkey. The case study includes the storage assignment process of incoming products. The results demonstrate that developed algorithms are practical and useful in real-life problems.