Characteristics of carbide slag slurry flow in a bubble column carbonation reactor

The direct aqueous mineral carbonation of carbide slag was investigated. The flow characteristics of carbide slag-CO2-water reaction system in a bubble column were studied, which included the bubble Sauter mean diameter, gas holdup, bubble residence time, and the gas-liquid interfacial area. Bubble flow behaviors in the reactor were characterized by analyzing the bed pressure signals. The effects of the gas velocity (U g ) and liquid to solid ratio (L/S ratio) were discussed and analyzed. The results showed that the larger bubbles were easy to form at the larger L/S ratio, which indicated that the bubble coalescence was promoted. The gas holdup was larger when increasing U g or reducing the L/S ratio. The better gas-liquid interfacial areas were found in a wide range of L/S ratio at U g = 0.082 m/s. The optimum conditions were found at U g = 0.082 m/s and L/S ratio = 15–30 mL/g for the better gas-liquid interfacial area and the higher carbide slag conversion. The work provided the theoretical basis for the direct aqueous carbonation of the carbide slag and the operation condition optimization.

Constitutive modelling with a novel two-step optimization for an Al-Zn-Mg-Cu alloy and its application in FEA

The modified Johnson-Cook constitutive model was developed for describing the flow behavior of Al-7.8Zn-1.65Mg-2.0Cu (wt.%) alloy based on the flow curves in the temperature range of 300℃~450℃ and strain rate range of 0.01s-1~10s-1 which were obtained by isothermal compression tests conducted on a Gleeble-3500 isothermal simulator. A two-step optimization method was proposed to optimize the prediction precision according to the evaluation of average absolute relative error (AARE). By using a traversal procedure for calculating the model under different reference conditions, this evaluator was found varying in the range of 4.1837%~11.105%, revealing the great influence of reference condition on the precision, then the reference condition optimization (RCO) was conducted. Genetic algorithm (GA) was introduced as the second step of the two-step optimization (TSO) to optimize the material constants of the model, which furtherly improved the precision by reducing the AARE-value to 3.801%. The models before and after optimization were written into subroutines for the software DEFORM and the compression tests were investigated through finite element analysis (FEA). The simulated results (forming load and temperature rise) revealed that the model after TSO has the highest agreement with the experimental.

Multi-condition design optimization of groove flow control technique in an axial-flow pump

To address the limitations of conventional groove designs in groove flow control technique, this paper optimizes the groove flow control technique for an axial-flow pump combining the design of experiment (DOE), response surface methodology (RSM), and particle swarm optimization (PSO). The sample space is designed using a combination method (OD-LHS) of orthogonal design (OD) and Latin hypercube sampling (LHS). Performance prediction models for the axial-flow pump are established using RSM. Taking the multi-condition comprehensive evaluation function as the final optimization objective, PSO is used to find the optimum groove parameters. The results show that the proposed method is effective in solving multi-condition optimization problems for grooves in axial-flow pumps. The optimal groove length, depth, and distance from the center of the impeller are 0.8, 0.05, and 0.2 times the impeller diameter, respectively, and the number is three times the number of blades. In addition, the optimal grooves effectively improve the hydraulic performance of the axial-flow pump under stall conditions. This study sheds light on the design optimization of the groove flow control technique for axial-flow pumps and other types of hydraulic machinery.

Optimizing Natural Deep Eutectic Solvent Citric Acid-Glucose Based Microwave-Assisted Extraction of Total Polyphenols Content from Eleutherine bulbosa (Mill.) Bulb

Application of natural deep eutectic solvent (NADES) citric acid-glucose-based microwave-assisted extraction (MAE) method for total polyphenol content (TPC) enriches the use of Eleutherine bulbosa (Mill.) bulb more widely and commercially. Therefore, this study aims to optimize NADES citric acid-glucose-based MAE on the TPC from E. bulbosa bulbs using response surface methodology (RSM). In the present study, the dried sample of E. bulbosa bulb was extracted using the NADES based MAE. Determination of TPC was conducted using Folin-Ciocalteau reagent and standard gallic acid, then measured using a UV-Vis spectrophotometer at 761 nm. Extraction condition optimization of the NADES based MAE method was performed using RSM with Box Behnken Design (four-factors-three-levels and 29 runs) on Design Expert v12. Based on the results study, the optimum condition extraction on the TPC enrichment was obtained at the NADES ratio (citric acid: glucose) 1:1 g/g; solid-liquid ratio 1:8 g/mL; extraction time of 15 min; and 270 Watt microwave power. The confirmation test and scale-up (50 g samples) were obtained with a 61.63 ± 2.23 mg GA/g sample.

The Hopping Conditions Influence on the Beer Quality

Статья посвящена вопросам оптимизации условий охмеления за счет создания условий, позволяющих повысить концентрацию горьких альфа-кислот хмеля в сусле, существенным образом не влияющих на жизнедеятельность дрожжей. В статье установлена возможность применения карбонатных солей натрия, магния и их смеси до охмеления с целью регулирования уровня рН до значения 5,9, позволяющего увеличить содержание горьких кислот в охмеляемом сусле. Показано снижение содержания растворимого азота на 14%, аминного азота - на 20, бета-глюкана - на 10% в случае применения 1%-ного раствора MgCO3 вследствие связывания ионов Mg2+ c активными центрами белковых молекул, что приводит к выпадению осадков с белковыми соединениями и обеднению сусла азотистым питанием для дрожжей. Внесение солей Na2CO3 и смеси Na2CO3 и MgCO3 приводило к увеличению содержания изогумулона на 25-30% без заметного снижения азотистых соединений. Установлен эффект сокращения продолжительности брожения путем интенсификации транспорта питательных веществ сусла за счет ограниченного повышения концентрации ионов Na+, вносимых в составе Na2CO3 при охмелении в дрожжевую клетку, на 1 сут. Применение смеси карбонатов натрия и магния не приводило к ощутимому эффекту увеличения динамики брожения. Показано положительное влияние внесения карбоната натрия при получении охмеленного сусла на органолептические показатели готового пива, тогда как применение смеси солей привело к получению пива, не обладавшего полнотой вкуса и гармоничностью, с отсутствием пены и резким хмелевым тоном. The article is devoted to the hopping condition optimization by creating conditions that make it possible to increase the hop bitter alfa-acids concentration in the wort, which do not significantly affect the yeast vital activity. The article establishes the possibility of using sodium and magnesium carbonate salts and their mixtures before hopping in order to regulate the pH level to 5.9, which makes it possible to increase the bitter acids content in the hopped wort. A decrease in the content of soluble nitrogen by 14%, amine nitrogen - by 20%, beta-glucan - by 10% in the case of 1% MgCO3 solution using due to the binding of Mg2+ ions with protein molecule active centers, which leads to precipitation with protein compounds and depletion of the wort nitrogenous nutrition for yeast. The addition of Na2CO3 salts and a mixture of Na2CO3 and MgCO3 led to an increase in the isogumulone content by 25-30% without a noticeable nitrogenous compounds decrease. The effect of intensifying fermentation by intensifying the wort nutrient transport due to a limited increase in the Na+ ions concentration introduced into the Na2CO3 composition during hopping into a yeast cell for 1 day has been established. The sodium and magnesium carbonates mixture use did not lead to a noticeable effect of increasing the fermentation dynamics. The sodium carbonate addition positive effect in the hopped wort production on the finished beer organoleptic characteristics was shown, while the salts mixture use led to the production of beer that did not have a full flavor and harmony, with no foam and a sharp hop tone.

Rhodium Catalyzed Direct Allylation of Simple Arenes by Using Gem-Difluorinated Cyclopropanes as Allyl Surrogates

Gem-difluorinated cyclopropanes have become an important type of allyl surrogates through transition-metal catalyzed ring-opening process, which has been demonstrated recently by various important advances especially via palladium catalysis. The versatile fluorinated allyl species generated in this way from gem-difluorinated cyclopropanes exhibit unique advantages comparing with traditional allyl sources. By using gem-difluorinated cyclopropanes as allyl surrogates, we achieved direct allylation of simple arenes via rhodium catalysis under mild conditions. This transformation enables directing-group free allylation of simple arenes including electron-neutral, -rich and -deficient ones. Herein we will give a brief introduction of this area and discuss how we thought and designed in our recent work. 1 Introduction 2 Our Design 3 Condition Optimization and Substrate Scope 4 Synthetic Applications 5 Mechanistic Discussions 6 Conclusion and Outlook

NEXTorch: A Design and Bayesian Optimization Toolkit for Chemical Sciences and Engineering

<div> <div> <div> <p>Automation and optimization of chemical systems require well-inform decisions on what experiments to run to reduce time, materials, and/or computations. Data-driven active learning algorithms have emerged as valuable tools to solve such tasks. Bayesian optimization, a sequential global optimization approach, is a popular active-learning framework. Past studies have demonstrated its efficiency in solving chemistry and engineering problems. We introduce NEXTorch, a library in Python/PyTorch, to facilitate laboratory or computational design using Bayesian optimization. NEXTorch offers fast predictive modeling, flexible optimization loops, visualization capabilities, easy interfacing with legacy software, and multiple types of parameters and data type conversions. It provides GPU acceleration, parallelization, and state-of-the-art Bayesian Optimization algorithms and supports both automated and human-in-the-loop optimization. The comprehensive online documentation introduces Bayesian optimization theory and several examples from catalyst synthesis, reaction condition optimization, parameter estimation, and reactor geometry optimization. NEXTorch is open-source and available on GitHub. </p> </div> </div> </div>

NEXTorch: A Design and Bayesian Optimization Toolkit for Chemical Sciences and Engineering

<div> <div> <div> <p>Automation and optimization of chemical systems require well-inform decisions on what experiments to run to reduce time, materials, and/or computations. Data-driven active learning algorithms have emerged as valuable tools to solve such tasks. Bayesian optimization, a sequential global optimization approach, is a popular active-learning framework. Past studies have demonstrated its efficiency in solving chemistry and engineering problems. We introduce NEXTorch, a library in Python/PyTorch, to facilitate laboratory or computational design using Bayesian optimization. NEXTorch offers fast predictive modeling, flexible optimization loops, visualization capabilities, easy interfacing with legacy software, and multiple types of parameters and data type conversions. It provides GPU acceleration, parallelization, and state-of-the-art Bayesian Optimization algorithms and supports both automated and human-in-the-loop optimization. The comprehensive online documentation introduces Bayesian optimization theory and several examples from catalyst synthesis, reaction condition optimization, parameter estimation, and reactor geometry optimization. NEXTorch is open-source and available on GitHub. </p> </div> </div> </div>