Chemical Reactor Research Articles

Estimation of Junction Temperature in Single 228 nm‐Band AlGaN Far‐Ultraviolet‐C Light‐Emitting Diode on c‐Sapphire Having 1.8 mW Power and 0.32% External Quantum Efficiency

The increasing resistance of methicillin‐resistant Staphylococcusaureus to antibiotics is a major challenge faced by mankind in thehistory of medical science and according to United Nations, 700‐000 patients worldwide die every year from an infection with multidrug‐resistant organisms (MROs). Aluminum gallium nitride‐based 228 nm Far‐ultraviolet‐C (Far‐UVC) lightsources can be safely used as a germicidal application in both manned as wellas in unmanned environments against these MROs. Previously, the 228 nm Far‐UVC light‐emitting diode (LED) with emission power of 1 mW was reported by ourgroup, however, the value of external quantum efficiency (EQE) was not reportedusing conventional thick Ni (20 nm)/Au (100 nm) p‐electrode. Herein, animproved Far‐UVC LED on c‐Sapphire is attempted using a special technique in SR4000 type of metal‐organic chemical vapor deposition reactor to control the Al composition in n‐AlGaN buffer and across the 2 inch‐wafer. As a result, the light emission power of 1.8 mW and EQE of 0.32% in 228 nm Far‐UVC LED aresuccessfully achieved using very thin p‐electrode (Ni/Au). However, arelatively high junction temperature of ≈100°C around thejunction of Far‐UVC LED is observed. Finally, some simple heat‐sink modules forheat dissipation of Far‐UVC LED panel with light power of 30 mW are implemented.

Robust-optimization-guiding deep reinforcement learning for chemical material production scheduling

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.

Adapting to the Abyss: Passive Ventilation in the Deep-Sea Glass Sponge Euplectella aspergillum.

We analyze the flow physics inside the body cavity and downstream the deep-sea glass sponge Euplectella aspergillum. We provide evidence that the helical skeletal motifs of the sponge give rise to a rich fluid dynamic field, allowing the organism to scavenge flow from the bottom of the sea and promoting a spontaneous, organized vertical flow within its body cavity toward the osculum. Our analysis points at a functional adaptation of the organism, which can passively divert flow through the osculum in unfavorable, low ambient currents, with no need for active pumping, with potential repercussions in functional ecology, as well as the design of chemical reactors, air-treatment units, and civil and aeronaval structures.

An impact of Richardson number on the inclined MHD mixed convective flow with heat and mass transfer

AbstractThe two‐dimensional mixed convective MHD flow with heat and mass transfer is investigated for its behavior with Dufour and Soret mechanisms over the porous sheet. The copper–alumina (Cu–Al2O3) hybrid nanoparticles are used in the base fluid water. The governing system of partial differential equations is converted into a system of ordinary differential equations via similarity transformations, obtaining the solution for velocity, temperature, and concentration fields in exponential form. The problem is demonstrated in the Darcy–Brinkman model, the impact of included parameters such as Richardson number, magnetic field, and Dufour numbers are studied for the obtained solution with the help of graphs. Increasing the magnetic field decreases both transverse and axial velocity profiles. Increasing the magnetic field and Richardson's number decreases the solution (Al2O3–H2O). Increasing the values magnetic field and Richardson's number decreases both transverse and axial velocity profiles. Increasing the values of the Dufour effect increases the axial and transverse velocity boundary layer. The magnetohydrodynamic hybrid nanofluid flow over porous media works efficiently in liquid cooling and, therefore, has significant applications in industrial heating and cooling systems, solar energy, magnetohydrodynamic flow meters and pumps, manufacturing, regenerative heat exchange, thermal energy storage, solar power collectors, geothermal recovery, and chemical catalytic reactors.

Optimizing the chemical vapor deposition process of 4H–SiC epitaxial layer growth with machine-learning-assisted multiphysics simulations

This work addresses a novel technique for selecting the best process parameters for the 4H–SiC epitaxial layer in a horizontal hot-wall chemical vapor reactor using a transient multi-physical (thermal-fluid-chemical) simulation model and combined with a machine-learning model. An experiment was performed to validate the feasibility of the numerical model. Secondly, a single-factor analysis was conducted to investigate the effects of process parameters, including the deposition temperature, inlet-flow volume, rotational speed of the susceptor, and cavity pressure, on the quality of the 4H–SiC epitaxial layer. Finally, a machine learning algorithm, the ant colony optimization-back propagation neural network (ACO–BPNN), was employed to develop the input/output model and optimize process parameters for obtaining a high-quality epitaxial layer and reducing the optimization cycle and costs. Notably, the optimized process was validated by real experiments, where the error between calculation and experiment is 4.03 % for deposition rate and 0.49 % for coefficient of variation, respectively. The results highlight the model as reliable and lay the foundation for the CVD growth of the 4H–SiC epitaxial layer.

Mixed convection characteristics in a long horizontal lid‐driven channel with periodically distributed local flow modulators

AbstractThis study explores the effectiveness of periodically placed rotating blades in enhancing heat transfer in a channel. The channel consists of a cold top plate moving at a constant speed and a fixed hot plate at the bottom. Thin rotating blades are placed periodically along the channel's centerline, with the spacing between their axes equal to the channel's height. This paper analyzes a transient, two‐dimensional, laminar flow problem using energy, momentum, and continuity equations. To address the challenges posed by moving blades, the Galerkin finite element method is implemented within an arbitrary Lagrangian–Eulerian framework, employing a triangular mesh discretization scheme. This study comprehensively explores thermal and hydrodynamic characteristics, including overall heat transfer, thermal frequency, and power consumption of the rotating blade for heat transfer in mixed convection scenarios with Richardson numbers (Ri) ranging from 0.1 to 10 at varying rotational frequency of the blade. Outcomes demonstrate that the inclusion of a rotating blade increases heat transfer up to 50% at lower Ri, after which the impact of the rotating blade diminishes and heat transfer reduces up to 20% at higher Ri. In addition, heat transfer enhances with increasing blade frequency up to Ri = 6.5, beyond which the effect of the frequency overturns. Examining thermal and hydrodynamic characteristics reveals that the blade achieves optimal performance when operating at f = 1 and Ri = 3. The study's insights into mixed convection heat transfer offer versatile applications, benefiting industries and equipment such as electronic cooling, chemical reactors, food processing, material fabrication, solar collectors, and nuclear reactor systems. Moreover, the findings are instrumental in the thermal ventilation of buildings and the development of micro‐electromechanical systems.

Multi-objective optimisation of a 2D backward-sfacing step channel with porous baffles

Porous baffles can be used to enhance heat transfer in various engineering applications, including electronic cooling, gas turbine blades, and chemical reactors. Also, the backward-facing step is a widely used configuration in fluid dynamics studies due to its simplicity and relevance to real-world geometries. This study examines heat transfer and flow characteristics in a backward-facing step channel featuring a heated bottom wall and two porous baffles. A computational fluid dynamics model, validated against prior research, is used to investigate flow and temperature fields. The innovation of this work lies in the application of multi-objective optimisation to search for a set of solutions that establish a trade-off between the average Nusselt number and the pressure drop. The optimisation specifically considers various parameters of the porous baffles, including height, width, distance from the step, and Darcy number, to identify optimal design configurations. Results show that porous baffles significantly improve heat transfer compared to a backward-facing step channel without them, despite an increase in pressure drop due to their presence. This work offers valuable insights into the trade-off between heat transfer performance and pressure drop, crucial for designing efficient heat transfer systems. By exploring the Pareto-Frontier, which represents various optimal design solutions, the study provides practical guidance when seeking to optimise heat transfer in backward-facing step channels with porous baffles. The findings contribute to advancing the understanding of heat transfer enhancement, highlighting the potential of porous baffles as a viable solution for improving thermal management in engineering systems.

Application of smart overpressure protection devices to batch and semi-batch processes: The smart relief mode

Smart overpressure protection devices (SmOPs) combine complex mathematical models implemented to safety-related programmable logic controllers (sPLC) with adaptable mechanical safety devices. The devices dynamically adjust set points and relief areas based on real-time hazard potential assessments and are being used to protect chemical reactors against exothermal runaway reactions. Providing two modes of operation, the "zero-emission mode" prevents reactions from exceeding reactor limits, while the "smartrelief mode" initiates controlled fluid discharge to avoid exceeding the permissible plant limits. In this article, the “smart relief mode” of smart overpressure protection devices as an innovative safety solution for chemical reactors is introduced. It is applied to two different types of reaction and shows three different typical industrial use cases.

Experimental investigations of hydrogen iodide (HI) decomposition process for different catalysts and various temperature conditions

This article presents the results of experimental work carried out at the Institute of Power Engineering on the process of thermal decomposition of hydrogen iodide. This process is one of the three key steps taking place in sulfur-iodine (S-I) thermochemical hydrogen production technology. For this purpose, a laboratory test rig equipped with an electrically heated chemical reactor was constructed, enabling the study of hydrogen iodide decomposition under controlled conditions. The research was carried out using various catalytic substances, as well as different temperature conditions. The process of hydrogen iodide decomposition required keeping the temperature of the reactor above 300°C and continuous analysis of the concentration of hydrogen formed over time. The results of the study will make it possible to determine the kinetics of the hydrogen iodide decomposition reaction under selected conditions of the process, allowing for future optimization of the process with the use of numerical methods.

Improvement of solar concentrator structure

Green energy includes solar, wind, geothermal and other types of energy sources generation. The object of this research is solar concentrators. The problem to be solved is related to the development of the structure frame, especially for solar concentrators with flat triangular or square mirrors that approximate a parabolic shape surface. The essence of the investigation is developing and producing several prototypes of solar concentrators that have a low cost of materials but the devices were assembled by hand and therefore the manufacturing cost is sufficiently high. Therefore, it is important to reduce the cost through automation of the solar concentrator production process. To obtain better conditions for future automation, we need to reduce the number of metal structural elements of the solar concentrator. In this case, the automation problem is simpler for its realization. The results obtained are related to the improved solar concentrator structure that can be technologically simpler than the previous one and lighter in weight. The development of improved solar concentrator design and structure can help reduce the cost of assembly and accelerate the solar concentrator assembly process. In the case of mass production, they can be used in practice. The proposed solar concentrators can be used for different applications, for example, green buildings in rural areas, or chemical reactors to accelerate the chemical process of organic waste processing. Another application is to use solar concentrators in combination with agricultural fields. These solar concentrators can be used with small-scale thermal energy storage (TES). Using TES, it is possible to make power plants for green buildings. Small solar power plants can support all the energy demands of residential houses

Implementation and validation of boundary conditions between a chemical reactor and ambient air

This work is dedicated to the development and validation of boundary conditions for a hot object cooled in a colder environment due to natural convection, radiation and evaporation. The main background of this research is to present boundary conditions for a hot reactor in cold ambient air, where the reactor walls are not thermally insulated. Water cooling in a standard small paper cup (coffee cup) was selected as a validation test. The experimental data comprise time histories of the cup’s side wall temperature measured using three thermoelements. The initial water temperature was set to 94 °C. The ambient air temperature was fixed at 25 °C and the air humidity at 20%. The computational model is based on unsteady laminar flow Navier–Stokes equations coupled with the heat transfer equation. The water density and all water transport properties (i.e., thermal conductivity, molecular viscosity, and specific heat capacity) are temperature-dependent. Numerical simulations in two-dimensional (2D) axisymmetry aligned well with the results from experiments. Including evaporative cooling on the top water surface proved essential to accurately determine the temperature profile. The boundary conditions are implemented into the Ansys Fluent 2022R2 computational fluid dynamics (CFD) commercial solver using user-defined functions (UDFs). The model equations can be used as a boundary condition for fixed beds and adsorption columns to improve the prediction of thermal profiles in both lab-scale and industrial-scale units.

Research on the Influence of Carbon Sources and Buffer Layers on the Homogeneous Epitaxial Growth of 4H-SiC.

In this study, a 4H-SiC homoepitaxial layer was grown on a 150 mm 4° off-axis substrate using a horizontal hot wall chemical vapor deposition reactor. Comparing C3H8 and C2H4 as C sources, the sample grown with C2H4 exhibited a slower growth rate and lower doping concentration, but superior uniformity and surface roughness compared to the C3H8-grown sample. Hence, C2H4 is deemed more suitable for commercial epitaxial wafer growth. Increasing growth pressure led to decreased growth rate, worsened thickness uniformity, reduced doping concentration, deteriorated uniformity, and initially improved and then worsened surface roughness. Optimal growth quality was observed at a lower growth pressure of 40 Torr. Furthermore, the impact of buffer layer growth on epitaxial quality varied significantly based on different C/Si ratios, emphasizing the importance of selecting the appropriate conditions for subsequent device manufacturing.

Modeling of water gas shift reaction using neural network trained on detailed kinetic mechanisms

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.

Development and testing of four stage chemical reactor (FSCR) for decontamination of liquid radioactive waste

Development and testing of four stage chemical reactor (FSCR) for decontamination of liquid radioactive waste

Optimization-based multi-source transfer learning for modeling of nonlinear processes

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.

Explicit machine learning-based model predictive control of nonlinear processes via multi-parametric programming

Machine learning-based model predictive control (ML-MPC) has been developed to control nonlinear processes with unknown first-principles models. While ML models can capture nonlinear dynamics of complex systems, the complexity of ML models leads to increased computation time for real-time implementation of ML-MPC. To address this issue, in this work, we propose an explicit ML-MPC framework for nonlinear processes using multi-parametric programming. Specifically, a self-adaptive approximation algorithm is first developed to obtain a piecewise linear affine function that approximates the behaviors of ML models. Then, multi-parametric quadratic programming (mpQP) problems are formulated to generate the solution map for states in discretized state–space. Furthermore, to accelerate the implementation of explicit ML-MPC, a neighbor-first search algorithm is developed. Finally, an example of a chemical reactor is used to demonstrate the effectiveness of the explicit ML-MPC.

A novel data-driven reduced order modelling methodology for simulation of humid blowout in wet combustion applications

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.

Impact of chemical reaction on hybrid nanofluid (GO + MoS2) flow over an exponentially stretching sheet with Soret and Dufour effects

AbstractThe Soret and Dufour effects play a crucial role in various fields such as geosciences, groundwater pollutant migration, chemical reactor operations, binary alloy solidification, and isotope separation. This study focuses on examining the impact of mixed convective flow on hybrid nanofluid through an exponentially stretching sheet with Soret and Dufour effects. The flow is affected by factors like variable viscosity, radiation, viscous dissipation, and activation energy. Instead of the no‐slip condition at the boundary, velocity slip, thermal slip, and concentration slip are considered. The physical problem is modeled using boundary layer theory, and flow patterns are expressed using partial differential equations (PDEs). These governing fluid flow equations are transformed into non‐linearly coupled ordinary differential equations (ODEs) using exponential similarity transformations. These simplified ODEs are resolved using the MATLAB bvp4c package. The effects of physical parameters on velocity, temperature, and concentration are illustrated through figures. Additionally, the drag force coefficient and heat and mass transfer rates are calculated for various parameters and presented graphically and in tabular form. It is observed that compared to nanofluids, the drag force coefficient of hybrid nanofluids increases by up to 21.05% with various solute buoyancy parameters (δ). Also, the mass transfer rate of hybrid nanofluids can be increased by .96% by the chemical reaction rate (σm). A comparison of this work with previously published research has been reported.

Extended State-Dependent Differential Riccati Equation (ESDDRE) Controller Design for a Chemical Plug-Flow Reactor via Time-Delay Partial Differential Equation

Extended State-Dependent Differential Riccati Equation (ESDDRE) Controller Design for a Chemical Plug-Flow Reactor via Time-Delay Partial Differential Equation

Experimental Analysis of Inductive Heating of Catalytic Beds

AbstractTo reduce CO2 emissions in the chemical industry, already established endothermic reactors should be heated with electrical energy. One promising possibility is inductive heating, in which electrical energy can be transferred to the susceptor (material to be heated). The uniform heating of the susceptor material is necessary for stable operation in chemical reactors and is therefore investigated in this work. For this purpose, a non‐reactive test setup was built that enables the investigation of inductive heating of different susceptor materials and measurement of the temperature distribution under defined non‐reactive conditions. In the first tests, temperatures of up to 800 °C were reached and an uneven heat input in a bed of stainless‐steel rings was observed.