Search

Molybdenum is harmful and useful. The efficiency of molybdenum trade effluent treatment is low and it is difficult to extract and recycle. To solve this problem, a novel membrane chemical reactor with mixed organic-water solvent(MCR-OW) had been used for the investigation of impact factors on the migration characteristics of Mo(VI) in the simulated trade effluent. The novel MCR-OW contains three parts, such as feeding pool, reacting pool and renewal pool. Flat membrane of polyvinylidene fluoride(PVDF) membrane was used in the reacting pool, the mixed solutions of diesel and NaOH with N, N′-di(1-methyl-pentyl)-acetamide(N-503) as the carrier in the renewal pool and the simulated trade effluent with Mo(VI) as feeding solution. The influencing factors of pH and the ion strength in the feeding solutions, the volume ratio of diesel to NaOH solution and N-503 concentration in the renewal solutions were investigated for the testing of the migration efficiency of Mo(VI). It was found that the migration efficiency of Mo(VI) could reach 94.3% in 225 min, when the concentration of carrier(N-503) was 0.21 mol/L, the volume ratio of diesel to NaOH in the renewal pool was 4:3, pH in the feeding pool was 3.80 and the initial concentration of Mo(VI) was 2.50 × 10−4 mol/L. Moreover, the stability and feasibility of MCR-OW were discussed according to Mo(VI) retention on the membrane and the reuse of the membrane. [ABSTRACT FROM AUTHOR]

For the purpose of the intensification of an industrial-scale gas-liquid process, the implementation in an alternative reactor concept is investigated at Hamburg University of Technology (TUHH) in cooperation with Ehrfeld Mikrotechnik GmbH. Existing process operation data from a bubble column hint at a mass transfer limitation of the gas-liquid reaction. In the project, a jet loop reactor (JLR) is chosen to increase the specific interfacial area between gas and liquid, and thus increase mass transfer, while keeping the reactor system mechanically simple and low-maintenance. For the investigation, a laboratory scale reactor has been designed on the basis of an existing industrial scale process and scaled according to a pilot scale reactor available at TUHH. For scaling, geometric similarity is desired, while specific energy dissipation rate and volumetric gas input are kept constant for the chosen scale-up strategy. Between the two different scales, the reactors are successfully characterised in a water-air system with regards to the important mass transfer, among other parameters. A pressure- and chemical-resistant twin of the laboratory-scale reactor is provided to the project partner for trials under real process conditions with the original material system. The presented work shows that the JLR concept can be transferred sufficiently well between different scales when suitable parameters are chosen, and offers a wide operating window. The investigations aim to provide a basis for a future scale-up of the chemical process in the JLR system to the industrial scale. [ABSTRACT FROM AUTHOR]

Hydrogen-enriched natural gas (HENG) is a promising low-carbon fuel in the industry. The fuel flexibility of the existing natural gas-fueled industrial combustion equipment is a crucial issue for the HENG application. This study evaluates the thermal and emission performances of a large-scale industrial steam boiler fueled with HENG. Considering different operation strategies, three boiler operation scenarios were proposed, namely, the unchanged fuel/air valve opening (Scenario 1), the maintained boiler capacity with unchanged valve control logic (Scenario 2), and the maintained boiler capacity with unchanged excess air ratio (Scenario 3). A model considering the thermal equilibrium and heat transfer inside the boiler was built to study the thermal efficiency under the three scenarios, and a chemical reaction network model was established to investigate the NOx emission characteristics. The results showed that thermal efficiency and NOx emission presented a similar variation trend. Both of them decreased in Scenarios 1 and 2 while increased in Scenario 3 as hydrogen volumetric fraction in HENG increased from 0 to 0.9. The differences in decarbonization potentials among the three scenarios were limited. This study implies that the trade-off between thermal efficiency and NOx emission should be carefully concerned when applying HENG to industrial steam boilers. • Performance of an industrial steam boiler fueled with HENG was investigated. • Three boiler operation scenarios were proposed based on the real practice. • Thermal efficiency of the boiler fueled with HENG was evaluated. • NOx emissions were assessed using the chemical reactor network method. • Trade-off between the thermal efficiency and NOx emission should be concerned. [ABSTRACT FROM AUTHOR]

Discovering the governing laws underpinning physical and chemical phenomena entirely from data is a key step towards understanding and ultimately controlling systems in science and engineering. Noisy measurements and complex, highly nonlinear underlying dynamics hinder the identification of such governing laws. In this work, we introduce a machine learning framework rooted in moving horizon nonlinear optimization for identifying governing equations in the form of ordinary differential equations from noisy experimental data sets. Our approach evaluates sequential subsets of measurement data, and exploits statistical arguments to learn truly parsimonious governing equations from a large dictionary of basis functions. The proposed framework reduces gradient approximation errors by implicitly embedding an advanced numerical discretization scheme, which improves robustness to noise as well as to model stiffness. Canonical nonlinear dynamical system examples are used to demonstrate that our approach can accurately recover parsimonious governing laws under increasing levels of measurement noise, and outperform state of the art frameworks in the literature. Further, we consider a non-isothermal chemical reactor example to demonstrate that the proposed framework can cope with basis functions that have nonlinear (unknown) parameterizations. [ABSTRACT FROM AUTHOR]

Many systems with distributed dynamics are described by partial differential equations (PDEs). Coupled reaction-diffusion equations are a particular type of these systems. The measurement of the state over the entire spatial domain is usually required for their control. However, it is often impossible to obtain full state information with physical sensors only. For this problem, observers are developed to estimate the state based on boundary measurements. The method presented applies the so-called modulating function method, relying on an orthonormal function basis representation. Auxiliary systems are generated from the original system by applying modulating functions and formulating annihilation conditions. It is extended by a decoupling matrix step. The calculated kernels are utilized for modulating the input and output signals over a receding time window to obtain the coefficients for the basis expansion for the desired state estimation. The developed algorithm and its real-time functionality are verified via simulation of an example system related to the dynamics of chemical tubular reactors and compared to the conventional backstepping observer. The method achieves a successful state reconstruction of the system while mitigating white noise induced by the sensor. Ultimately, the modulating function approach represents a solution for the distributed state estimation problem without solving a PDE online. [ABSTRACT FROM AUTHOR]

Liquid phase oxidation (LPO) of hydrocarbon is an industrially important process to produce petrochemicals and pharmaceuticals. It follows a free radical path having initiation, propagation and termination. The initiation step is slow while the propagation and termination steps are fast. The main challenge of such process is to control product selectivity at an appreciable conversion level. With the advancement of microfluidic reactor technology, it is possible to control the free radical steps. The present contribution critically reviewed the reaction engineering aspects of LPO of hydrocarbon, the influence of microfluidic reactor design and operation on reaction mechanism, conversion and product selectivity. It also outlines the challenges associated with microfluidic reactor operation, and prospects to apply the understanding from microfluidic reactors in few sectors. The understanding from the free radical oxidation process can also be applied to any other free radical processes. [ABSTRACT FROM AUTHOR]

This paper deals with the tracking of a prespecified profile temperature for exothermic chemical tubular reactor whose dynamics is described by a set of nonlinear partial differential equations where the state variables are the reactor temperature and the reactant concentration. The coolant temperature, the inlet temperature and the inlet concentration are considered as control actions. For practical reasons, it is preferable to consider a non distributed control law to achieve the control objective. In contrast to our previous work that considers fully distributed control actions where the three control inputs are assumed to be distributed along the reactor, here the last two control inputs are applied at the reactor inlet and only the coolant temperature is distributed along the reactor. We show that the temperature of the reactor tends asymptotically to a ball of arbitrary prescribed radius λ > 0 , centred at the given temperature profile. [ABSTRACT FROM AUTHOR]

In this paper, a benchmark study between chemical and biochemical reactors is carried out using Computational Fluid Dynamics (CFD) approach. The main motivation is to develop an optimized design of a stirred sparged gas liquid reactor for preneutralisation of phosphoric acid. A CFD multiphase model is tailored under ANSYS Fluent 2020 R1, to simulate the industrial preneutralizer. A new gas sparger is designed based on fermentation bioreactors ring spargers for axial gas dispersion. The Euler-Euler multiphase reacting flow model is used within the preneutralizer to examinate the impact of the spargers type on the hydrodynamics, species transport phenomena, and mass transfer between the different phases. K-Epsilon turbulence model is adopted to integrate the turbulence due to the Pitch Blade Turbine (PBT) agitator, and Multiple Reference Frame (MRF) approach allows to handle efficiently the rotational movement of the agitator. The simulations show that ring spargers enhance mass transfer by ensuring a high level of gas phase holdup with the axial dispersion compared to the pipe spargers. The numerical results revealed that gas-liquid flow hydrodynamics is extremely sensitive to the sparger design. The proposed new preneutralizer design is promising to increase the preneutralisation yield and allows to avoid several monitoring challenges. [ABSTRACT FROM AUTHOR]

The conversion of preceramic polymers to ceramics normally requires multiple processing steps such as curing, pyrolysis, and crystallization, which can be time and energy‐consuming. Herein, one‐step rapid synthesis of cubic silicon carbide (β‐SiC) by laser‐assisted pyrolysis and crystallization of molten polycarbosilane (PCS) is shown. The resulting product is termed as "LISiC" (laser‐induced silicon carbide). It is demonstrated that repeated polymer infiltration and lasing can be used to make dense LISiC and SiC‐LISiC coatings, which are stable up to 1073 K in air. It was also demonstrated that LISiC can be patterned on various substrates such as copper, steel, aluminum, and silicon wafer with a rate of 3 cm2 min−1. Finally, it is found that the dense LISiC coatings rapidly heat up in response to radio frequency (RF) fields, which would enable out‐of‐oven manufacturing of SiC‐based composites, micro‐heaters, and efficient chemical synthesis in reactors employing SiC‐based catalyst supports. [ABSTRACT FROM AUTHOR]

Jet loop reactors are standard multiphase reactors used in chemical, biological and environmental processes. The strong liquid jet provided by a nozzle enforces both internal circulation of liquid and gas as well as entrainment and dispersion of the gas phase. We present a one-dimensional compartment model based on a momentum balance that describes the internal circulation of gas and liquid phase in the jet loop reactor. This model considers the influence of local variations of the gas volume fraction on the internal circulation. These local variations can be caused by coalescence of gas bubbles, additional gas-feeding points and gas consumption or production. In this work, we applied the model to study the influence of a gas-consuming reaction on the internal circulation. In a comprehensive sensitivity analysis, the interaction of different parameters such as rate of reaction, power input through the nozzle, gas holdup, reactor geometry, and circulation rate were investigated. The results show that gas consumption can have a significant impact on internal circulation. Industrially relevant operating conditions have even been found where the internal circulation comes to a complete standstill. [ABSTRACT FROM AUTHOR]

This paper considers the exponential stabilization problem for a class of strict-feedback nonlinear systems with multiple time-varying delays, whose nonlinear terms satisfy the linear growth condition. The state feedback controller that relies on a positive parameter to be determined is constructed to deal with nonlinear terms. By tactfully introducing the Lyapunov–Krasovskii functional with an exponential function and selecting the applicable parameter to be determined, the implementable state feedback controller can be obtained to guarantee that the closed-loop system is exponentially stable. The proposed state feedback control scheme is finally applied to the control design of two-stage chemical reactor system, which illustrates the effectiveness of the control method. [ABSTRACT FROM AUTHOR]

Mathcad is a simple‐to‐use and intuitive mathematical software that helps students to minimize the mathematical difficulties involved in solving engineering problems. The design of nonisothermal plug flow reactors (PFR) is a fundamental issue within the field of chemical reaction engineering; however, its teaching–learning process is hindered by students' mathematical difficulties in solving ordinary differential equations. In this paper, the software Mathcad was conveniently integrated into an educational experience through the resolution of two real case studies. In the first one, a simple liquid‐phase reaction is considered in a PFR working at different operating conditions, whereas the second case evaluates a PFR taking place multiple reactions (parallel reactions) with a heat exchanger attached. The assessment of this experience, which was held into two 5‐h Mathcad workshops, revealed that Mathcad made the design of non‐isothermal PFR more appealing, facilitated the understanding of the design process, and brought another dimension to the way the students perform complex calculations. [ABSTRACT FROM AUTHOR]

A photothermal nanoconfinement reactor (PNCR) system is proposed and demonstrated by using hollow carbon nanospheres (HCNs) to enhance the performance of the chemical reaction. Under light irradiation, the local temperature of the HCN inner void space was much higher than the bulk solution temperature because the confined space concentrates heat and inhibits heat loss. Using the temperature‐sensitive model reaction, peroxydisulfate (PDS) activation to oxidize micropollutant, it is shown that the degradation rate of sulfamethoxazole in the PNCR system is 7.1 times of that without nanoconfinement. It is further discovered that the high‐quality local heat inside the nanoconfined space shifted the model reaction from an otherwise non‐radical pathway to a radical‐based pathway. This work provides an interesting strategy to produce a locally high temperature, which has a wide range of applications to energy and environmental fields. [ABSTRACT FROM AUTHOR]

This study is devoted to constructing an approximate analytic solution of the fractional form of a strongly nonlinear boundary value problem with multi-fractional derivatives that comes in chemical reactor theory. We construct the solution algorithm based on the generalized differential transform technique in four simple steps. The fractional derivative is defined in the sense of Caputo. We also mathematically prove the convergence of the algorithm. The applicability and effectiveness of the given scheme are justified by simulating the equation for given parameter values presented in the system and compared with existing published results in the case of standard derivatives. In addition, residual error computation is used to check the algorithm's correctness. The results are presented in several tables and figures. The goal of this study is to justify the effects and importance of the proposed fractional derivative on the given nonlinear problem. The generalization of the adopted integer-order problem into a fractional-order sense which includes the memory in the system is the main novelty of this research. [ABSTRACT FROM AUTHOR]

In our previous work, we used the population balance method to develop a molybdenum disulphide kinetics model consisting of a set of differential equations and constants formulated to express the kinetics of complex chemical reactions leading to molybdenum disulphide precipitation. The purpose of the study is to improved the model to describe the occurring phenomena more thoroughly and have introduced computational fluid dynamics (CFD) modelling to conduct calculations for various reactor geometries. CFD simulations supplemented with our nucleation and growth kinetics model can predict the impact of mixing conditions on particle size with good accuracy. This introduces another engineering tool for designing efficient chemical reactors. [ABSTRACT FROM AUTHOR]

The production of highly polluted waste cooking oil (WCO) that contains both inorganic and organic compounds has been increased in Malaysia particularly in food industries. This study was aimed to utilize a sequencing batch reactor (SBR) in order to investigate the aerobic treatment of WCO. The system was operated at pH ranging from 4 to 5 and temperature ranged from 25°C to 31°C. The SBR reactor was fabricated using Perspex with a working volume of 2 L. Experiments were conducted daily at fill, react, settle, draw, and idle phase at 1, 1, 2, 1, and 1 h, respectively. The chemical oxygen demand (COD) and turbidity were assessed in determining SBR performance. Highest COD removal and turbidity values were at 67% and 0.94 NTU, respectively. A stable effluent quality was achieved after 13 d of operational investigation. In general, the SBR treatment was able to achieve acceptable discharge limit for the final treated effluent. [ABSTRACT FROM AUTHOR]

The application of neural networks can present some limitations for the control of strongly nonlinear systems. In this paper, a new control scheme based on a neural multicontroller (NMC) is proposed. Indeed, the developed strategy considers a set of local neural controllers which adapt their parameters thanks to an online adaptation algorithm. The instantaneous choice of the adequate local controller is based on a switching mechanism. The advantages of the proposed method are (1) to avoid the computational complexity issues due to the search for an optimal adapting rate when a single neural controller is used, and in presence of strongly nonlinear systems, and (2) to improve the control law by selecting the suitable controller which generates the most valid control law satisfying the desired closed-loop performances. Numerical examples are illustrated to evaluate the performance of the proposed control scheme compared to the classical one. An application of the new strategy on a chemical reactor model validates satisfactory results. [ABSTRACT FROM AUTHOR]

Pelletized thermochemical energy storage media has a potential for long-duration energy storage. Production of solid-state energy storage media can be done within a cavity chemical reactor that captures concentrated solar radiation from a solar thermal field. The temperature stability of a solar reactor is directly influenced by the solar flux intercepted. This paper presents a low-order physical model to simulate the dynamic response of temperature inside a tubular plug-flow reactor prototype. Solid granular particles are fed to the reactor from the top whereas a counter-current flowing gas enters the reactor from the bottom. An in-house code was developed to model transient heat transfer of the reactor wall, gas, and moving particles. The model was preliminarily validated with packed beds for different temperature ranges and two gas flowrates. Dynamic response of the reactor temperature is simulated for different input power and gas/particle flowrates. The results show that the system response can be controlled efficiently by utilizing input power (solar flux) as a control parameter. A conventional proportional integral (PI) controller is designed to control the temperature inside the reactor and to maintain it during the solar flux intermittency. The controller parameters are tuned using the Ziegler-Nichols method to ensure optimal system response. The results show that the feedback control model is successful in tracking different reference reactor temperatures within a reasonable settling time of 30 min and eliminated overshoot. This study can be extended to include a hybrid reactor with a multi-input, multi-output variable system. [ABSTRACT FROM AUTHOR]

Dispersion is the spreading of a solute while it is moved by a flowing medium. The study of dispersion in catalytic chemical reactors is fundamental to their design, since dispersion influences the reactant and product transport within the bed. In this paper, longitudinal and transverse dispersion of an inert tracer in slender packed beds of spheres and spherocylinders is studied using Computational Fluid Dynamics simulations. The focus is on the analysis of dispersion from full field data. The purpose is to develop a methodology that can later also be used to characterize dispersion from full field experimental data such as MRI measurements. Results obtained by means of particle-resolved CFD simulations are discussed. Spatial distributions and residence times are analyzed and the results are interpreted by comparison to results obtained from 1D and 2D convection-diffusion equations. [ABSTRACT FROM AUTHOR]

Research Octane Number (RON) is one of the primary indicators for the determination of the resistance of gasoline fuels to autoignition. This parameter is usually determined with a test procedure involving a standardized engine that requires expensive hardware and time-consuming tests. In this work, a set of different methods with which to determine the RON of gasoline fuel surrogates is presented, considering only computer simulations, which allows to reduce both cost and time for the evaluation. A palette of 11 chemical species has been chosen as the basis for the surrogates' database, which will be investigated in the work, allowing the representation of the complex chemical formulation of fuels in an easier way. A simplified zero-dimensional engine model of the standard variable compression ratio is used to provide pressure and temperature, then employed to calculate RON. This is done first by means of existing methods, and then by introducing new processes concerning a simplified chemical reactor built on kinetic schemes. Finally, these different methodologies are tested against a molar weighted sum of RONs of each chemical specie, allowing to have a criterion for comparison and evaluating their real prediction capabilities. [ABSTRACT FROM AUTHOR]

[Display omitted] • A comprehensive study of turbulent lean premixed NH 3 -CH 4 -air flame is conducted. • Heat loss effect on flame and nitric oxide (NO) emission is investigated in detail. • The DTF combustion model is applied successfully in LES of NH 3 -CH 4 -air combustion. • With reasonable parameters, CRN method can predict NH 3 -CH 4 combustion accurately. • Based on LES results, recirculation rates are calculated to be approximately 30%. 7 In this work, the combustion and emission characteristics of turbulent swirling flames with an equivalence ratio of 0.8 and NH 3 -CH 4 mixing volume ratio of 4:6, at different wall temperature/heat loss conditions, were investigated using large eddy simulation (LES) method with the dynamic thickened flame (DTF) combustion model. The results showed that when the wall temperature was 700 K and 1150 K, a significant amount of heat loss weakened the combustion intensity near the wall and even caused local flame extinction. The nitric oxide (NO) emission decreased with the decrease in wall temperature due to the decrease in the amount of HNO and other NO-forming sources. However, when the wall temperature was 700 K, a significant amount of N 2 O emission was observed at the combustor outlet. Comparison of the OH concentration, in the LES results of adiabatic turbulent flame with that in the one-dimensional adiabatic laminar flame simulation results, showed a significant increase in the LES results, but the NO production did not increase much. For LES with 1150 K, temperature value measured from the corresponding test, the calculated NO emission is the closest to the experimental result among the four simulations. This demonstrated that the influence of heat loss effect must be considered reasonably in calculating the NO emission of ammonia fuel combustion. Additionally, low-dimensional simulations with chemical reactor network (CRN) were conducted as well to check CRN's performance, and the results demonstrated that CRN can predict NH 3 -CH 4 /air accurately, suppose reasonable parameters are given. [ABSTRACT FROM AUTHOR]

In this work we introduce a manifold learning-based surrogate modeling framework for uncertainty quantification in high-dimensional stochastic systems. Our first goal is to perform data mining on the available simulation data to identify a set of low-dimensional (latent) descriptors that efficiently parameterize the response of the high-dimensional computational model. To this end, we employ Principal Geodesic Analysis on the Grassmann manifold of the response to identify a set of disjoint principal geodesic submanifolds, of possibly different dimension, that captures the variation in the data. Since operations on the Grassmann require the data to be concentrated, we propose an adaptive algorithm based on Riemannian K-means and the minimization of the sample Fréchet variance on the Grassmann manifold to identify "local" principal geodesic submanifolds that represent different system behavior across the parameter space. Polynomial chaos expansion is then used to construct a mapping between the random input parameters and the projection of the response on these local principal geodesic submanifolds. The method is demonstrated on four test cases, a toy-example that involves points on a hypersphere, a Lotka-Volterra dynamical system, a continuous-flow stirred-tank chemical reactor system, and a two-dimensional Rayleigh-Bénard convection problem. [ABSTRACT FROM AUTHOR]

This study examines the issue of annular finite-time H ∞ performance for interval type-2 fuzzy (IT2) fuzzy networked switched systems vulnerable to multiple attacks via non-fragile adaptive event-triggered control. An adaptive event-triggered scheme (AETS) is presented to minimize the impact of multiple attacks and it effectively reduces redundant data transmission while guaranteeing reliable system performance which differs from the traditional event-triggered scheme. We describe the impact of deception and reply attacks by introducing two random variables that satisfy the Bernoulli distribution. Furthermore, IT2 fuzzy networked switched systems were developed to demonstrate the effects of multiple attacks, and the IT2 fuzzy model describes the nonlinear characteristics. Also, a delay-dependent Lyapunov functional with a free-weighting matrix technique are used. There are limited study findings on the class of IT2 fuzzy networked switching systems using adaptive event-triggered scheme. This paper discusses the advantages of establishing an efficient adaptive event-triggered mechanism for IT2 fuzzy networked switching systems based on previous discussions and necessity. Furthermore, some sufficient conditions that are required to ensure that the system is annular finite-time stable with the stated H ∞ performance index is presented along with two examples to demonstrate the suggested control design methodology. [ABSTRACT FROM AUTHOR]

Gas-solid fluidized beds are widely applied as chemical reactors, and the fluidization quality in the bed is closely related to bubble behavior. Digital image processing is a commonly used non-invasive method for bubble behavior analysis, but it is usually constrained by experimental conditions such as lighting, making identification of bubble and emulsion phases still challenging. Herein, deep learning is applied in this study to optimize traditional digital image processing techniques. By evaluating different deep learning models (FCN, DeepLab V3, U-Net), rapid and accurate identification and segmentation of bubble images can be achieved, and the U-Net model performs best, achieving an identification accuracy of 99.05 %. Further application of U-Net to analyze bubble behavior demonstrates that deep learning methods enable efficient and accurate identification of bubbles and real-time analysis of bubble behavior, highlighting the significant potential application of deep learning in the field of complex hydrodynamics in fluidized beds. [Display omitted] • Semantic segmentation methods based on deep learning are explored. • Three deep learning models are used to identify and segment bubbles. • Bubble properties such as bubble shape, velocity, and size are determined. • Deep learning models show great potential for bubble dynamics evaluation. [ABSTRACT FROM AUTHOR]

This work develops an optimization-based multi-source transfer learning scheme for modeling of nonlinear chemical processes. Specifically, a transfer learning neural network model for a target process with limited data is developed using the pre-trained model obtained with multiple source processes. Since the performance of transfer learning models depends on the quality of the pre-trained models, we propose a novel Bayesian optimization problem to optimize the selection of multi-source data for the pre-trained models by first deriving a generalization error bound for multi-source domain adaptation using Y -discrepancy distance. Subsequently, the optimization problem is formulated using the theoretical error bound to select the optimal set of multiple sources, which can provide a good initial guess of the weight parameters for transfer learning model. Finally, a simulation study of a chemical reactor process in Aspen Plus Dynamics is conducted to illustrate the effectiveness of the optimization-based multi-source transfer learning scheme. • Transfer learning scheme for modeling of nonlinear systems using multiple sources. • Generalization error bound for multi-source domain adaptation. • Bayesian optimization-based method to select the optimal set of source datasets. • Application to an Aspen reactor to show the effectiveness of transfer learning. [ABSTRACT FROM AUTHOR]

• Study less explored electro-mechanical conversion route to chemicals for piston reactor technology. • Report the limitations and potential solutions to carry out endothermic reactions within the piston reactor. • Model-driven study covering wide range of conditions and economic studies to provide direction for future research. • Most promising regions for product value is different from traditional reactor product yields. Within the context of renewables and chemical energy storage, this work aims to explore chemical reaction conversion through the electro-mechanical route which is different from the commonly employed electro-thermal and electro-chemical conversion methods. Piston reactor is a novel equipment concept that can couple the rotational movement from an electric motor to a reciprocating piston within an enclosed reaction chamber resembling the operation of an internal combustion engine. Exploration of a reactor technology still in the laboratory stage requires an assessment of reaction intermediate mixtures to identify potential leads. This work uses a systematic methodology to assess these intermediate mixtures beyond typical reactor yield metrics to include economic values as part of the decision-making toward identifying lead candidates. Rather than focusing on a specific target reaction or product, this work is carried out as a model-driven study to cover a wider range of conditions and product scenarios. Propane as the main feedstock is selected because it is easier to trigger in the piston reactor, it is not a complex feed, a detailed kinetic mechanistic model is available to study it, and most importantly it can generate a wide range of industrially relevant products to serve the purpose of this study. The study revealed that the highly endothermic propane pyrolysis reaction requires intake temperatures larger than 950 K which is impractical. Diluting the feed with argon is one way to lower this intake temperature and achieve desirable conversion. This however is impractical because of additional separation steps and reduced production capacity. Thermal coupling of propane pyrolysis with exothermic side reactions by co-feeding oxygen, water, or carbon dioxide are investigated and showed promising results. A diverse range of products are produced such as hydrogen, carbon monoxide, ethylene, and propylene, achieving high conversions (>90 %), while simultaneously generating useful work and process heat. Unconventional triggers such as using ozone to generate radicals and trigger ignition are investigated to further lower the intake temperature requirements. The explored solution space is then evaluated considering a new reactor metric as the value of products mixture stream. Regions of high-value products do not match those obtained using the traditional yield metrics. This shows the importance of what metrics to use for assessing reactors with wide range of reaction mixture intermediates as the piston reactor. Full economic analysis and optimization are the right direction in the future to properly assess the potential of piston reactor technology. [Display omitted] [ABSTRACT FROM AUTHOR]

• Propose robust-optimization-guiding framework for reinforcement learning in scheduling problem. • Construct a mathematical programming for the chemical material production scheduling problem. • Compare to typical optimization models and reinforcement learning with respect to deterministic and stochastic cases. • Numerical study conducted to validate the proposed RLeRO framework. • Provide sensitivity analysis, solution quality analysis, computational time and Gantt similarity, compared with other benchmark methods. This study presents a novel guiding framework embedded with robust optimization (RO) for the training phase of reinforcement learning (RL), specifically tailored for dynamic scheduling of a single-stage multi-product chemical reactor in an uncertain environment. The proposed framework addresses the challenge of local optima in policy gradient methods by integrating optimization methods, enhancing both the objective value and learning stability of the RL. We further enhance the robustness of the proposed model against parameter distortions by incorporating RO as a guiding engine. A numerical study of chemical material production scheduling is conducted to validate the proposed model and the results demonstrate the effectiveness to address demand volatility with several metrics including sensitivity analysis, solution quality analysis, computational time, compared to a typical actor-critic RL. [ABSTRACT FROM AUTHOR]

The ironmaking blast furnace (BF) is a counter-current chemical reactor. The porosity distribution of burden layers in BF plays an important role for the gas distribution and gas–solid two-phase interaction. In this research paper, to analyze how porosity distributions affect gas velocity, gas temperatures and reduction, the different porosity distributions were investigated using three distinct processes by numerical models. The results showed that the change of porosity along the radius direction had a more obvious effect than along the height direction. When the porosity is exponentially distributed, the gas velocity was allowed a great development, promoting the gas temperature and reduction. Besides, in the lower part (height = 1/3H), with increasing porosity, the gas velocity increases by 30% and reduction by 25%. In the middle part (height = 2/3H), the gas velocity is increased by 30%, the gas temperature is increased by 200 °C, and the reduction is increased by 20%. [Display omitted] • Studied the influence of several different porosity distributions on the reactions of blast furnaces • The variation of porosity along the radius direction has a significant impact on the gas velocity • The impact of porosity is mainly due to its rate of change rather than its size [ABSTRACT FROM AUTHOR]

The rotary bed reactor, essentially a rotating drum involving chemical reactions, emerges as a promising option for multiphase reactions due to its effective gas-solid contacts and sustainable input/output of the granular phase. This study integrated the Euler-Euler multiphase model with experiments to examine velocity profiles of the bed layer under varying conditions, including rotating speed, filling level, and material density. Several optimization strategies were proposed for reducing the "dead zone" size under the rolling mode to improve overall heat and mass transfer efficiency. Results validated the model's accuracy and revealed flow behavior characteristics under low rotating speed modes (sliding and slumping). Additionally, increasing rotating speeds, decreasing filling levels, and using higher-density bed materials mitigated the "dead zone" size under the rolling mode. This work advances the understanding of granular hydrodynamic behavior in rotating drums and lays the foundation for the application of such reactors in the field of chemical engineering. [Display omitted] • Flow behaviors of the granular phase in a rotating drum were investigated. • Combination of Euler-Euler simulations with particle image velocimetry technique. • Characteristics of different regimes were revealed through velocity profiles. • Lower filling levels reduced the "dead zone" size under rolling mode. • High-density bed material potentially improved heat and mass transfer efficiency. [ABSTRACT FROM AUTHOR]

Designing chemical reactor equipment requires a thorough understanding of powder flow. Solid rheology modelling offers various models for this purpose. A comparative study of two different CFD models, the Kinetic Theory of Granular Flow (KTGF) and the dense granular flow (μ I law), is proposed. Both models were confronted with experimental results obtained on a rotating drum for different rotation speeds and powder flowabilities. Image processing was used to compare the experimental gas/solid interfaces with those obtained from CFD. The KTGF model did not represent the powder rheology at low rotation speeds, regardless of the powder, whereas it was closer to experiments at higher speeds. The dense granular flow model was more appropriate for this system as it described the powder shape inside a rotating drum relatively well for each experiment. The latter model is recommended for modelling dense granular flows, while the KTGF is better suited to gas-solid flows. [Display omitted] • Image processing is used to obtain the gas/solid interfaces in a rotating drum. • Two CFD models, KTGF and μ(I) law, were compared with the experimental interfaces. • The KTGF model is not suitable for low rotation speed and dense granular flow. • The μ I law accurately represents the powder rheology in a rotating drum. [ABSTRACT FROM AUTHOR]

Membrane reactor processes can be used to overcome the constraints of the chemical rate equilibrium of methanol (MeOH) synthesis products. In this thermodynamics-limited work, three different selective sulfonated poly(ether ketones) (SPEEK) membranes were applied in an engineered unit operation with a commercial Cu/ZnO/Al 2 O 3 /MgO surface catalyst for several CO 2 /CO-involving chemistries. A detailed mathematical model with micro-kinetics was developed, optimised and utilised to assess the vessel with barrier by using CERRES (Chemical Reaction and Reactor Engineering Simulations). Scaled separation tests were described by the integrated reference values of permeance. The permeability for all compound molecules (H 2 , H 2 O, CO, CO 2 , MeOH) was determined by adjusting parameters to account for the experimental gas composition on the permeate, interface and retention segment side after reduction. The specific kinetic characteristics of the mechanism of elementary step reactions were analysed in fixed bed design. A comparison of the estimated data prediction for the packed system with related definite numbers showed excellent statistical agreement. Similarly, a very good reliability was obtained between the results for 3 SPEEK membrane cases. Thus, the defined particular evaluations of derived theoretical expressions were benchmarked accurately. Although (validated) performance, i.e. the yield of MeOH, was overestimated, discrepancy was not so large so as to simulate behaviour verily. The (3-aminopropyl)triethoxysilane (polyamide) over a SPEEK layer performed best for intensification. Herein, the pressurised (>50 bar) CO 2 hydrogenation pathway was not only shifted by in situ removal as a proof of concept, but also modelled intrinsically, considering transport phenomena resistances, adsorption and desorption as well. The storage of hydrogen can benefit from MeOH production reengineering. [Display omitted] • Membrane reactors can overcome thermodynamic constraints in methanol production. • A model of membrane reactor with micro-kinetics for CO 2 conversion on CuZn catalyst. • Introducing CERRES (Chemical Reaction and Reactor Engineering Simulations). • Utilizing (3-aminopropyl)triethoxysilane (polyamide) over a SPEEK layer membranes. [ABSTRACT FROM AUTHOR]

The design and operation of embedded reactors involve chemical reaction efficiency issues, with the main consideration being heat and mass transfer characteristics. The contact area of the porous catalytic coating with methanol is affected by the reactor structure, to improve the hydrogen yield and reveal the mechanism of the channel structure on the hydrogen production performance, the hydrogen production performance of circle-triangle, circle-square, square-circle, square-square, and triangle-circle channels was investigated. The reasons for the differences in methanol conversion, hydrogen, and carbon monoxide selectivity were analyzed from the perspective of heat and mass transfer using a three-rate reaction mechanism. The results showed that the circle-triangle channel performed outstandingly in the hydrogen production from methanol steam reforming with 98.61 % methanol conversion, 88.74 % hydrogen selectivity, and 29.06 % CO selectivity. The shapes and sizes of the different channel structures affect the fluid flow pattern and velocity distribution, which in turn affect the mass transfer efficiency and lead to differences in hydrogen production performance. The circle-triangle channel exhibits superior heat transfer and material transport performance, which can improve the hydrogen production efficiency of methanol steam reforming and help provide an important reference for the embedded reactor design. [ABSTRACT FROM AUTHOR]

[Display omitted] • The raceway behaviours of a slow-moving packed bed solid fuel gasifier are studied mathematically. • The model considers multi-fluid flow, coupled with heat transfer and heterogenous reactions inside and around all the raceways of such a gasifier. • Probability theory and statistics are used to quantitatively analyse the raceway results. The moving-bed gasifier of solid fuels (e.g., coal or biomass) is a key chemical reactor to convert coal or biomass to clean fuel, where the profile of the raceway cavity and its flow-thermal-chemical behaviours can affect the performance of the gasifier, however, they are not well understood because of the process complexity and modelling challenge. In this study, an advanced 3D multi-fluid model, based on the Eulerian-Eulerian method, coupled with heat transfer and heterogenous reactions, is developed to describe the reacting flow behaviours inside and around the raceways. It is found that raceways inside the slow-moving packed bed gasifier are of balloon-like shapes; they are built in just a few seconds after issuing the jet from gas entry, and then both the average velocity for gas and particle are relatively stable. The gauge pressure conditions are not isobaric, and high temperatures, small gas densities and high reaction rates are observed near the raceway "shell". Besides, it is found the raceway behaviours on horizontal cross-section feature more symmetry compared to those on other directions. In addition, probability theory and statistics are used to analyse the quantitative raceway results, and possibilities related to the distributions of gas properties, momentum and species are discussed. This model provides an effective tool to systematically study and optimise raceway behaviours inside a slow-moving packed bed solid fuel gasifier. [ABSTRACT FROM AUTHOR]

• Avoiding unrealistic numerical solutions of detailed kinetic mechanism. • Reducing data bias of chemical species concentration. • Accuracy is same level with detailed kinetic mechanism. • The method shows several limitations at slightly 720 K/min heating rates. • Uniform distribution of concentration in logarithmic space is crucial. For the optimization of a chemical reactor or its control, computational fluid dynamics (CFD) that considers accurate reaction models is effective. Although detailed kinetic mechanisms can accurately predict gas-phase reactions, integrating them with CFD is too computationally expensive. In contrast, global reactions have lower computational costs but significantly reduced accuracy outside of tuned conditions. This study proposes a method for constructing a model using a neural network to predict the reaction rate of water gas shift reaction based on detailed kinetic mechanisms. It includes techniques to avoid unrealistic solutions inherent to detailed kinetic mechanisms and methods to minimize biases in training data. The neural network input was log-transformed concentrations and inverse temperatures, and the output was the logarithm of the absolute value of the reaction rate and direction, enabling the prediction of bidirectional reactions over a wide temperature range. The quality of the dataset significantly impacted the accuracy of the neural network. Using a dataset with less bias in chemical species concentrations, the model achieved accuracy similar to detailed models while facilitating an over 400 times faster computation. The study also discussed the impact of temperature change rate, showing that at about 72 K/min, the detailed kinetic mechanism's solution can be accurately replicated. Still, at 720 K/min, the radical concentration deviates from the quasi-steady state, leading to discrepancies. This study opens up new possibilities for computational methods in the prediction and optimization of chemical reactions. [ABSTRACT FROM AUTHOR]

Computationally inexpensive reduced order models such as Chemical Reactor Networks (CRN) are encouraging tools to obtain fast numerical solutions. However, the accuracy of such models is usually compromised compared to experiments or high-fidelity numerical simulations. Therefore, continued improvement of such models is necessary to ensure reasonable accuracy and fill the need for computationally inexpensive tools. To that end, inputs from computational fluid dynamics simulations have been used to build CRNs in the last 25 years. This can be further improved by the application of data-driven techniques. The present work uses a novel data-driven analysis based on high-fidelity simulation results where the high-dimensional data is first reduced to a low-dimensional manifold and is then clustered into chemically coherent regions. These results along with the high-fidelity simulation results are used to construct a CRN for wet combustion simulations. Wet combustion is a novel clean combustion technique, where an increase in the level of steam dilution distributes the flame, with a risk for flame extinction. This phenomenon, i.e., humid blowout (HBO), was modelled using the above-mentioned CRN. The HBO predicted by the CRN was also tested for thermal power, equivalence ratio, and fuel conditions different from its design point. The error in HBO prediction was quantified by comparing the steam-dilution level at HBO obtained using CRN and that obtained experimentally. The CRN predicted the HBO with an error magnitude less than 5% when the thermal power was unchanged. The maximum error in all tested conditions was 16.65%. Furthermore, a sensitivity analysis revealed that the inclusion of hot gas recirculation through the central recirculation zone, quantified by the mass flow split between the post-flame region and the CRZ, is important in the prediction of HBO, although its accuracy is inconsequential. • A novel chemical reactor network (CRN) construction methodology is proposed. • The aerodynamics is modelled using inputs from a high-fidelity large-eddy simulation. • A novel flame modelling methodology using data-driven techniques (t-SNE and k-Means). • CRN is tested for a range of thermal power, equivalence ratio and fuel conditions. • Peak error is 16.67% in all tested cases. Error <5% when thermal power is unchanged. [ABSTRACT FROM AUTHOR]

[Display omitted] • Review of CFD simulations that model dry reforming of methane reaction. • Various types of reactors can be optimized through CFD for efficient DRM reaction. • CFD can help identify catalytic properties needed to suppress carbon deposition. • Challenges and potential research directions on CFD simulation of DRM are presented. Computational Fluid Dynamics (CFD) is a powerful tool for modeling chemical reactors. While limited literature is available on CFD simulation of Methane Dry Reforming (DRM), this reaction presents several environmental and economic advantages. Modeling DRM using CFD can provide insights for optimizing chemical reactors, evaluating catalysts interactions with surrounding reactive flow, improving properties of catalysts that are not yet well developed and determining optimum operating conditions for high process efficiency. This review paper provides a comprehensive overview of the current state-of-the-art in CFD modeling of DRM. After a brief description of the reaction thermodynamics, the kinetic models applied in CFD simulation for DRM and their limitations are covered along with an analysis of the governing equations employed. The review also tackles various catalysts and types of reactors employed for DRM simulation. It then identifies remaining challenges and potential research directions for more efficient hydrogen and syngas production using DRM. [ABSTRACT FROM AUTHOR]

• A phenomenological design approach is constructed for the design of CLC-PPBR at/above pilot scale. • Macroscopic behaviors of the heat/reaction front are introduced to depict the dynamic operation of the reactors. • Bed size must conform to certain limitations to fulfill reasonable operational stability and cost-effectiveness. • Effects among design parameters are studied to optimize the preliminary design scheme for the 1.5 MW th reactor. The parallel packed bed reactor (PPBR) is a promising route for industrial application of chemical looping combustion. This paper introduces a phenomenological approach for the rational design of PPBR, serving as a manner to efficiently obtain comprehensive design results. Based on the macroscopic propagation of heat and reaction fronts, it elucidates the dynamic heat/mass transfer within the bed. Subsequently, it is applied to design a 1.5 MW th reactor. The risk of carbon deposition in the bed is firstly investigated from a thermodynamic perspective. Then, the study reveals that the height-to-diameter ratio of the reactor must meet certain constraints to ensure stable and cost-effective operation, and these constraints are extended to the scale-up process. Sensitivity analysis is utilized to investigate the effect of key parameters on the design schemes and performance. Effects of particle size on intra-particle gas diffusion is analyzed. Finally, detailed design schemes and performance of the reactor are determined. [ABSTRACT FROM AUTHOR]

An accurate and self-consistent methodology for mass transport of multi-component mixtures in multi phase media is a necessity for a proper description of complex physical and chemical processes in reactors such as catalytic packed beds. In this regard, a novel methodology has been developed to describe and couple underlying transport phenomena in fluid and porous media as well as at the solid-fluid interface. The methodology is symmetric as it treats all components in a mixture equally. The Maxwell-Stefan equations are symmetrically formulated, discretized conservatively and coupled with a compressible flow solver for the fluid part. The Dusty Gas Model is applied inside porous media by developing a self-consistent and robust numerical formulation. A ghost-cell Immersed Boundary Method is used to capture the physics at the solid-fluid interface with the implementation of a novel symmetric non-singular mass flux formulation. Several test cases are established to demonstrate the accuracy and robustness of the newly developed symmetric methodology in this paper. These test cases can be used as benchmark for the future development of symmetric methodologies for multicomponent systems in multi phase media. • A novel computational methodology has been developed to couple transport phenomena in fluid-solid multicomponent media. • The developed methodology is symmetric as all components in a mixture are treated equally. • A symmetric and unconditional stable formulation for the Dusty Gas Model has been developed. • A novel non-singular mass flux formulation has been developed and embedded in the ghost-cell Immersed Boundary Method. • Several test cases are designed for verification and validation of the developed methodology. [ABSTRACT FROM AUTHOR]

Chemical looping reforming (CLR) is emerging as a promising technique with potential to produce useful chemicals while capturing carbon dioxide emissions. This study proposes a novel multigeneration system consisting of three-reactor CLR integrated with CO 2 electroreduction cell to produce 13.3 kg/s commercial-grade of ammonia and 0.0113 kg formic acid/kg ammonia. In order to achieve the maximum use of waste heat and cold energy, the system also includes interconnected power generation cycles. The net power generation is 816.54 kJ/kg ammonia, along with 305 kg/s hot water at 80 °C. A technoeconomic analysis is conducted to economically assess the process and the results are compared with other studies. The results of technoeconomic analysis give a comparatively low levelized cost of ammonia equal to 0.48 $/kg. Furthermore, to understand the irreversibilities involved in the integrated process, a thermodynamic analysis is conducted in terms of conventional and advanced exergy analyses along with sensitivity analysis which help determine the practical limitations and opportunities for process improvement of the overall system. According to the results, the overall process exergy efficiency is 74.80% and the overall exergy destruction rate is 116338.53 kW. The results of advanced exergy analysis show strong interdependencies between the components of the system due to major endogenous exergy destruction rates. Additionally, the results are used to propose three distinct strategies for design optimization and process intensification. This work is the first to look at the potential of improving the sustainability index (SI) by using the results of an advanced exergy analysis. • A novel process with CO 2 capture is proposed with an exergy efficiency of 74.8%. • Chemical looping is coupled with CO 2 electrolyzer to produce NH 3 and formic acid. • Advanced exergy and sensitivity analyses are used to define practical feasibility. • Strategies to improve thermodynamic performance and sustainability index are given. • Technoeconomic analysis is conducted to get 0.483 $/kg levelized cost of ammonia. [ABSTRACT FROM AUTHOR]

Due to their predictive capabilities and computational efficiency, data-driven models are often employed in model predictive controller (MPC) design. These models offer precise predictions within their training domains, which aids in effective process control. However, real-world processes frequently experience operational changes, requiring control under new conditions that can lie beyond the training domains of existing data-driven models. Developing new models for these scenarios is challenging due to limited historical data. To address this limitation, we develop a novel data-driven control framework integrating an adaptive modeling approach called operable adaptive sparse identification of systems (OASIS) with the Luenberger observer. Firstly, we train the OASIS model and identify its domain of applicability (DA) using a support vector machine-based classifier. Subsequently, we formulate a Lyapunov-based MPC that relies on the OASIS model within the DA and the OASIS-based observer model beyond the DA. Additionally, we establish theoretical guarantees on the input-to-state stability of the observer, along with analyzing the stabilizability and recursive feasibility of the designed LMPC. The developed framework enhances the applicability of data-driven process control in diverse operating conditions. We highlighted its effectiveness using a chemical reactor example. • A Lyapunov-based model predictive controller for processes beyond training domains. • Controller is designed using an adaptive model (OASIS) and an observer. • Luenberger observer is employed to enhance OASIS predictions in new operating domains. • Derived conditions for observer's stability and controller's stabilizability. • Demonstrated using a non-isothermal continuous stirred tank reactor. [ABSTRACT FROM AUTHOR]

[Display omitted] • Ru/TiO 2 /Al 2 O 3 catalysts are developed for selective CO methanation reactions. • A higher TiO 2 content of the TiO 2 /Al 2 O 3 composite increases Ru metal dispersion. • The high metal dispersion and low electron density of Ru promote CO methanation. • The highest activity at a TOF of 8.60 × 10−3 s−1 is exhibited by Ru3/Ti3/Al 2 O 3. • Introducing excess TiO 2 creates oxygen vacancies, facilitating CO 2 methanation. Hydrogen-rich gas derived from hydrocarbons contains large amounts of CO 2 and CO, with the latter being poisonous for low-temperature polymer exchange membrane fuel cell anodes. Therefore, the efficient removal of CO from reformate gases is crucial while minimizing the methanation of CO 2 to reduce unnecessary hydrogen consumption. In this study, Ru-based catalysts, which are ready to be employed in a chemical reactor, are developed to selectively convert CO into CH 4 with minimal CO 2 methanation using a simulated gas mixture composed of 75 % H 2 , 24 % CO 2 , and 1 % CO. The CO adsorption capability of Ru catalysts was enhanced by controlling the surface pores with a TiO 2 coating layer. In this process, egg-shell type TiO 2 /Al 2 O 3 supports were synthesized by wet impregnation method, and then Ru was impregnated on the supports. This modification enabled the Ru/TiO 2 /Al 2 O 3 to preferentially adsorb CO over CO 2. Furthermore, by systematically varying the TiO 2 loading, the electronic structure of Ru is modified to induce CO adsorption, resulting in a catalyst with maximum activity for selective CO methanation. The catalyst demonstrates a turnover frequency value of 8.6 × 10−3 s−1 at 190°C, surpassing the performance of previously reported catalysts. [ABSTRACT FROM AUTHOR]

We propose computational fluid dynamics (CFD)-enveloped Bayesian optimization (EBO), a novel optimizer that integrates EBO with CFD to reduce the required CFD simulations by utilizing previous optimization data. The proposed optimizer was applied to determine the optimal catalyst packing ratio of the Fischer–Tropsch microchannel reactor that minimizes the maximum temperature and maximizes the productivity of long-chain hydrocarbons by utilizing the CFD model. The obtained results indicate that the number of iterations required to reach the optimal points is lower than that of BO, and the optimal result exhibits a 5% improvement from the initial condition. The optimizer was evaluated across various catalyst packing cases to assess its robustness. Nevertheless, the proposed optimizer was consistently able to reach optimal points that BO could not achieve. We anticipate that this optimizer can be widely applied to optimize the operating condition of a chemical reactor in the presence of previous optimization data. • Enveloped Bayesian optimization is integrated with CFD. • Catalyst packing ratios of Fischer–Tropsch microchannel reactor are optimized. • 4-channel reactor optimization is conducted utilizing single-channel reactor optimization data. • The optimization performances are evaluated in various cases of different catalyst packing ratio ranges for Fischer–Tropsch microchannel reactor. • The proposed method requires lower number of iterations when compared to Bayesian optimization. [ABSTRACT FROM AUTHOR]

The hydrodynamics and mass transfer in the Taylor-Couette mixing field are important activities for concurrent process intensification work for miniaturization of the chemical reactor volume and enhancing the energy input per unit volume. In the literature, many studies have been reported for mixing of conventional two-phase systems of industrial interest. However reported studies on mixing of PUREX aqueous organic pair in Taylor-Couette field are rare and few. Utilizing an indigenously developed Taylor-Couette column, hydrodynamic and mass transfer studies with PUREX aqueous-organic pair are reported in this study. Mass transfer and hydrodynamics runs were conducted with PUREX aqueous-organic pair. Aqueous dispersed conditions were employed for d→c mass transfer. Mass transfer performance was highly dependent on rotor speed till a critical value. Macro, meso and micromixing time values were evaluated for Taylor-Couette mixing. Prototyping of the system was completed with a special run with 0.1 M TODGA/ n -dodecane nitric acid pair for mutual separation of Cs(I) and Sr(II). [Display omitted] • Performance evaluation of an indigenously developed Taylor-Couette Column reported. • Mass transfer and hydrodynamics runs were conducted with PUREX aqueous-organic pair. • Aqueous dispersed conditions were employed for d.→c mass transfer. • Mass transfer performance was highly dependent on rotor speed till a critical value. • Macro, meso and micromixing time values were evaluated for Taylor-Couette mixing. • Rapid prototyping with Cs(I)/Sr(II) separation with 0.1 M TODGA was demonstrated. [ABSTRACT FROM AUTHOR]

The article titled "Computational fluid dynamic simulations to improve heat transfer in shell tube heat exchangers" has been retracted due to significant errors and concerns raised by a third-party. The retraction is based on missing boundary conditions and numerical validation, leading the journal to lose confidence in the reported data and results. The authors of the article are Rahmad Syah, Amir Bateni, Kamran Valizadeh, Marischa Elveny, Mehdi Shaeban Jahanian, Dadan Ramdan, and Afshin Davarpanah. [Extracted from the article]

Chemical looping combustion (CLC) is a promising CO 2 capture technology, which expected to achieve 100% capture efficiency with a low energy penalty. The principle of CLC is utilizing an oxygen carrier as a medium for transporting oxygen between two reactors, aiming to avoid nitrogen in combustion exhaust. This article discusses the advances in chemical looping with a focus on oxygen carriers, reactors, coupling systems, and other applications of CLC concept for solid fuels. The development and design of high performance oxygen carriers and suitable reactors are key factors in the development of CLC technology. The main challenge in scaling up the CLC process is the development of appropriate OCs with optimal properties, including high transport oxygen capacity, reactivity, and stability. Recently, a fair number of hours of continuous operation in pilot plants have instilled the confidence required in further development of this process towards commercialization. The use of solid fuels is gaining momentum, and the liquid fuels need further attention for its development. The integrated systems based on chemical looping technology for poly-generation applications simultaneously achieve the efficient conversion and cascade utilization of chemical energy and CO 2 separation. The article also describes the prospects of the future development in the chemical looping technology, and its viability for industrial deployment. [Display omitted] • CLC is a well-known technology for reducing the energy penalty of CO 2 capture. • Both oxygen carrier properties and reactor design are key to CLC development. • Various types of reactors used for chemical looping conversion have been summarized. • Different integrated systems based on chemical looping technology are discussed. • Different chemical looping technologies for combustion of solid fuels are considered. [ABSTRACT FROM AUTHOR]