Chemical Reactor Research Articles

Optimisation of the operating parameters of a thermal plasma system for the conversion of waste containing polychlorinated biphenyls by thermodynamic modelling

This paper proposes a mathematical modelling approach to determine the thermodynamic equilibrium of chemical systems in order to optimise the operating parameters of the plasma chemical processes in a low-temperature thermal plasma pilot reactor with a direct current electric arc for the treatment of hazardous waste containing polychlorinated biphenyls. The model is primarily based on the principles of mass action and minimum Gibbs free energy. The obtained results can serve as a basis for multi-criteria optimisation based on ecological aspects, reduction of energy consumption and the possibility of using the produced synthetic gas for energy recovery in the process. The analysis carried out clearly showed the advantages of using water vapour as a plasma working medium and the appropriate mixing of the organic waste containing polychlorinated biphenyls with used engine oil for enriched syngas production. The presented analysis determines the optimal reactor temperature for an ecologically efficient treatment and shows possibilities for energy recovery in the process. The proposed model can also be used for other chemical reactors and for the treatment of other chemical substances.

Hybrid Controller Based on Numerical Methods for Chemical Processes with a Long Time Delay.

A hybrid control framework is proposed as an alternative for long time delays in chemical processes. The hybrid approach mixes the numerical methods in an internal mode control (IMC) structure, which uses the particle swarm optimization (PSO) algorithm to improve the adjustment of the controller parameters. Simulation tests are carried out on linear systems of high order and inverse response, both with dominant delay, and tests on a nonlinear process (chemical reactor). The performance of the proposed controller is stable and satisfactory despite nonlinearities in various operating conditions, set-point changes, process disturbances, and modeling errors. In addition, experimental tests were performed on a setup composed of two heaters and two temperature sensors mounted on an Arduino microcontroller-based board called the Temperature Control Laboratory (TCLab), with an additional software delay introduced. The merits and drawbacks of each scheme are analyzed using radar charts, comparing the control methods with different performance measures for set-point and disturbance changes. Furthermore, the new controller uses PSO to improve the tuning parameters.

An investigation on flame structure and NOx formation in a gas turbine model combustor using large eddy simulation

In this work, large eddy simulations (LES) of a Gas Turbine Model Combustor (GTMC) are done using a five-step global mechanism that includes separate thermal and non-thermal NOx formation parts. To verify the accuracy of the solution, time-averaged profiles of the flow variables and fluctuations are compared to the available experimental and numerical data. The LES results show that the vortical structures inside the chamber are highly connected to the temperature field and chemical reactions, and despite having a major role in fast premixing and consequent NOx reductions, they contribute to NOx generation by forming high temperature spots inclusive of chemical radicals. Also, the importance of the baroclinic torque in vorticity creation is demonstrated by comparing the corresponding values to vortex stretching in upstream parts of the chamber. It is shown that the baroclinic torque mostly takes action between high vorticity and high strain regions and can possibly intensify the strong vortices, while the vortex stretching is mostly active near the strong vortices. Furthermore, observation of detailed statistics shows that most of the heat release occurs in samples with mixture fractions near the global value, while NO generation is highly biased toward the strong vortices and the stoichiometric mixture fraction. To investigate the role of the radicals in more details, a chemical reactor network (CRN) is created by clustering the LES solution. Also, the integration of Partially Stirred Reactors (PaSRs) with Perfectly Stirred Reactor (PSR) networks is used to improve the accuracy of predicting the reactant jet penetration and ignition radicals.

Colloidal State Machines as Smart Tracers for Chemical Reactor Analysis

A widely utilized tool in reactor analysis is passive tracers that report the residence time distribution, allowing estimation of the conversion and other properties of the system. Recently, advances in microrobotics have introduced powered and functional entities with sizes comparable to some traditional tracers. This has motivated the concept of Smart Tracers that could record the local chemical concentrations, temperature, or other conditions as they progress through reactors. Herein, the design constraints and advantages of Smart Tracers by simulating their operation in a laminar flow reactor model conducting chemical reactions of various orders are analyzed. It is noted that far fewer particles are necessary to completely map even the most complex concentration gradients compared with their conventional counterparts. Design criteria explored herein include sampling frequency, memory storage capacity, and ensemble number necessary to achieve the required accuracy to inform a reactor model. Cases of severe particle diffusion and sensor noise appear to bind the functional upper limit of such probes and require consideration for future design. The results of the study provide a starting framework for applying the new technology of microrobotics to the broad and impactful set of problems classified as chemical reactor analysis.

Effects of Ohmic heating, induced magnetic field and Newtonian heating on magnetohydrodynamic generalized Couette flow of Jeffrey nanofluid between two parallel horizontal plates with convective cooling

In this article, the effects of Ohmic heating, induced magnetic field and Newtonian heating on MHD generalized Couette flow of Jeffrey nanofluid between two horizontal plates with convective cooling has been investigated. The mathematical model used for the magnetohydrodynamic generalized Couette flow of pure water as the fluid phase containing Copper (Cu) as nanoparticles takes into account the effects of viscous dissipation, Soret and Dufour. The numerical solution of the system of nonlinear partial differential equations controlling the flow was performed using the finite difference method. To acquire the flow variable profiles, such as velocity, induced magnetic field, temperature, and concentration profiles graphically, the resulting numerical schemes are simulated in MATLAB software. The findings reveal that an increase in the Schmidt number causes a decrease in the concentration profiles, rise in the Jeffrey parameter causes a decrease in the velocity profiles. The temperature profiles also rise as a result of an increase in the Ohmic heating parameter and the heat generation parameter. Induced magnetic field profiles decreases with an increase in magnetic Prandtl number. The results demonstrate that Cu - water shows significant impact over nanofluid on temperature and velocity in the generalized Couette channel. The results of this study are valuable because they can be applied to the development of many chemical technologies, such as the production of polymers, MHD pumps, food processing, chemical catalytic reactors, MHD flow meters, astronomy, and lubrication. The results are compared with standard literature and show good agreement.

Nongrowth regime in microwave chemical vapor deposition reactor due to formation of plasma nonhomogeneity

AbstractA new form of microwave discharge with a filament inside plasma volume was investigated in a microwave plasma‐assisted chemical vapor deposition (CVD) reactor. The threshold values of methane content in a hydrogen–methane gas mixture, gas pressure, and microwave power for discharge transition to the new form were found. The parameters' range of existence of the new form of discharge was investigated in three types of CVD reactors. Measurements of the electron density, gas temperature, and spatial distributions of the plasma optical emission lines intensity were carried out for both forms of the discharge. The reasons for the transition of the discharge to a new form and the possibility of using the new discharge form in the microwave plasma‐assisted (MPA) CVD reactor are discussed.

Thermal performance of MHD natural convection flow in a concentric semi-circle porous enclosure having corrugated radius

The amplitude and period of the corrugation can significantly affect heat transport in a concentric semi-circular enclosure with corrugated radius. By increasing the surface area of the walls and promoting convective heat transmission between the fluid and the walls, the corrugated walls can improve heat transfer. Such a type of system is utilized in energy storage systems, heat exchangers, chemical reactors, materials processing, and electronic cooling systems. The system's overall efficiency and dependability can be increased by using transverse MHD together with corrugated wall to control fluid flow and improve heat and mass transfer. Heat transfer enclosed in a semi-circular porous cavity with corrugated radius walls is addressed. Inner and out semi-circle are joint through heated corrugated walls and these walls are depending upon the amplitude and period of corrugation. The outer semi-circle is divided into three different portions with an angle of 60o each. The central portion is considered as adiabatic however the left and right part of outer semi-circle is cold. The governing equation are developed through law of conservation of mass, momentum, and energy. An inclined magnetic field is applied to the system that makes an angle αwith x−axis. The dimensionless system of nonlinear partial differential equations (PDE) is tackled through Finite Element Method (FEM) and parametric control analysis is developed. The impact of each parameter, for instance: inclination angle −60o≤α≤60o,Rayleigh number (104≤Ra≤108), the Permeability parameter (10−7≤Da≤10−2), Ha is the Hartmann number, amplitude (A) and period of corrugation (20≤f≤60) is reflected through isotherms and streamlines. The results depict that due to transverse angle of MHD, natural convection process within the cavity tilted either on right or left side based upon angle inclination angle. It is concluded that rate of heat transfer is increased by corrugation by increasing the radius's amplitude and higher Darcy numbers signify greater fluid flow resistance, and lower Darcy numbers signify less resistance. They acquire to the conclusion that the Raleigh number's impact on the isotherms profile can be further modified by the strength and direction of the magnetic field.

Favorable trade-off between heat transfer and pressure drop in 3D printed baffled logpile catalyst structures

Additive manufacturing has the potential to unlock a large degree of geometric freedom in the shaping of catalytic material, thereby providing new possibilities to optimize catalyst holdup, pressure drop and heat and mass transfer characteristics. In this modelling study, baffled logpile structures are proposed as a promising candidate to exploit this potential, by shaping the catalytic material as a static mixer, generating cross-flow. An OpenFOAM Computational Fluid Dynamics study was performed on various 2D structure designs to map the trade-off between heat transfer, pressure drop and residence time distribution as a function of the design, length and gap spacing of the baffle. It is observed that structures with the longest baffles provide optimal heat transfer performance, and that the baffle gap spacing can be used to tailor the trade-off between heat transfer and pressure drop. In comparison to a packed bed filled with spherical particles, the novel structures offer a heat transfer rate four to six times as high at the same pressure drop. Whilst full 3D simulations, validated by experiments, remain to complete the analysis, the current work illustrates the potential of this novel class of structured catalyst materials for intensified chemical reactors.

Synthesis, Characterization, and In Vitro Cytotoxicity Evaluation of Doxorubicin-Loaded Magnetite Nanoparticles on Triple-Negative Breast Cancer Cell Lines.

In this study, we investigated the cytotoxicity of doxorubicin (DOX)-loaded magnetic nanofluids on 4T1 mouse tumor epithelial cells and MDA-MB-468 human triple-negative breast cancer (TNBC) cells. Superparamagnetic iron oxide nanoparticles were synthesized using sonochemical coprecipitation by applying electrohydraulic discharge treatment (EHD) in an automated chemical reactor, modified with citric acid and loaded with DOX. The resulting magnetic nanofluids exhibited strong magnetic properties and maintained sedimentation stability in physiological pH conditions. The obtained samples were characterized using X-ray diffraction (XRD), transmission electron microscopy (TEM), Fourier-transform infrared spectroscopy, UV-spectrophotometry, dynamic light scattering (DLS), electrophoretic light scattering (ELS), vibrating sample magnetometry (VSM), and transmission electron microscopy (TEM). In vitro studies using the MTT method revealed a synergistic effect of the DOX-loaded citric-acid-modified magnetic nanoparticles on the inhibition of cancer cell growth and proliferation compared to treatment with pure DOX. The combination of the drug and magnetic nanosystem showed promising potential for targeted drug delivery, with the possibility of optimizing the dosage to reduce side-effects and enhance the cytotoxic effect on cancer cells. The nanoparticles' cytotoxic effects were attributed to the generation of reactive oxygen species and the enhancement of DOX-induced apoptosis. The findings suggest a novel approach for enhancing the therapeutic efficacy of anticancer drugs and reducing their associated side-effects. Overall, the results demonstrate the potential of DOX-loaded citric-acid-modified magnetic nanoparticles as a promising strategy in tumor therapy, and provide insights into their synergistic effects.

Decentralized adaptive PI fault tolerant tracking control for strong interconnected nonlinear systems subject to unmodeled dynamics and actuator faults

In this study, a novel decentralized adaptive proportional–integral (PI) fault tolerant tracking control strategy is proposed for a class of strong interconnection nonlinear systems with unmodeled dynamics and actuator faults. The adverse effects generated by unmodeled dynamics could be eliminated by introducing a dynamic signal and the multiplicative actuator faults could be dealt with by using adaptive control technique. In addition, broad learning system (BLS) scheme is used to approximate the unknown nonlinear functions and additive actuator faults, the minimal-learning-parameter (MLP) scheme is used in this study to reduce the number of adaptive update parameters, which resltus in less computation overhead. The decentralized adaptive PI fault tolerant tracking controller including a switching compensation mechanism is designed for the considered interconnected systems, such that all the signals in the closed-loop systems are uniformly ultimately bounded (UUB) and the tracking errors converge to a small residual set. In the end, a chemical reactor example is utilized to illustrate the feasibility and significance of the designed control scheme.

Biogas conversion to liquid fuels via chemical looping single reactor system with CO2 utilization

The chemical looping single reactor (CLSR) system utilizing calcium ferrite as oxygen carriers represents an innovative approach to biogas reforming, which has the potential to enhance the efficiency and sustainability of biogas production. In this process, an oxy-combustion burner supplies the necessary endothermic heat while CO2 is simultaneously utilized, resulting in an autothermal process that generates high-purity syngas. Nearly 20% higher CO2 utilization can be achieved compared to the tri-reforming, owing to the higher driving force for CO2 oxidation in the counter-current moving bed and steam knock-out before inletting the flue gas into the reactor bottom. Additionally, the CLSR system is robust to variations in biogas to oxygen carrier ratios and CO2 concentration of the inlet feedstock, maintaining the outlet syngas purity within 1% variation. The effects of temperature, pressure, heat integration, and additional H2O/CO2 flow rate on the system performance are discussed, and the optimized scenarios are used for liquid fuel generation. The proposed process achieves about 13% reduction in syngas requirement compared to the conventional tri-reforming for the same amount of liquid fuel production. The feasibility of the CLSR system is further experimentally verified under various conditions. The results reveal the occurrence of CO2 counter-oxidation reactions on the surface of calcium ferrite oxygen carriers and demonstrate that higher temperatures are beneficial for the CH4 reforming reaction.

Physics-informed recurrent neural network modeling for predictive control of nonlinear processes

In this work, we present a physics-informed recurrent neural network (PIRNN) modeling approach, and a PIRNN-based predictive control scheme for a general class of nonlinear dynamic systems. Specifically, we first develop a hybrid data-driven and physics-guided modeling framework that integrates measurement data and mechanistic mathematical models to construct high-fidelity RNN models. Then, we derive a generalization error bound of the PIRNN model based on a nominal system model via the Rademacher complexity technique from statistical machine learning theory. Subsequently, the PIRNN model is utilized in Lyapunov-based model predictive controllers and applied to a chemical reactor example with Gaussian measurement noise to demonstrate its improved noise rejection and generalization performance in comparison to the purely data-driven and the purely physics-guided RNN-based predictive control schemes.

Digitizing protocols into single reactors for the one-pot synthesis of nanomaterials

Digitizing protocols into single reactors for the one-pot synthesis of nanomaterials

Eulerian–Eulerian RSTM-PDF Modeling of Turbulent Particulate Flow

A novel 3D computational model was developed for the turbulent particulate two-phase flow simulation in the rectangular channel. The model is based on the Eulerian approach applied to 3D Reynolds-averaged Navier–Stokes modeling and statistical Probability Distribution Function method. The uniqueness of the method lies in the direct calculation of normal and transverse components of the Reynolds stresses for both gas and particles. Two cases were examined: a conventional channel flow and grid-generated turbulence flow. The obtained numerical results have been verified and validated by the experimental data, received from the turbulent particle dispersion test. The computed values of the particles’ turbulent dispersion and the maximum value of the particulate concentration distribution show good agreement with the experimental results. The examples are ranged from coal and other bulk material pneumatic transport, vertical fluidized beds, coal gasifiers, and chemical reactors.

Optimal structure of multiple resource supply systems with storages

Production systems with storages have recently attracted considerable attention from the reliability research community. The existing models assume a single type of resource consumed for product generation. This work extends the state of the art by considering a more general and practical model, where multiple resource supply subsystems (RSS) provide different kinds of necessary resources during the production cycle. Each RSS has a storage with limited capacity to save surplus resource, which may be supplied to the production unit when the RSS fails. The product unit may operate with different productivity levels, and RSS and storages can be chosen from multiple types. We formulate a new optimization problem, which chooses the productivity level of the production unit and the types of RSS and storage for each kind of resource required for the production to maximize the expected manufacturer's profit. A new numerical algorithm is proposed to evaluate the expected profit and the genetic algorithm is implemented to solve the proposed optimization problem. A detailed case study of a chemical reactor system with four RSSs supplying two reagents, catalyzer, and cooling water is provided to demonstrate the proposed model and influences of several model parameters on the optimization solutions.

Dynamics pattern of a radioactive rGO-magnetite-water flowed by a vibrated Riga plate sensor with ramped temperature and concentration

In recent times, the dynamics study of an electrically weak performing fluid stream regulated by Riga sensors has become an emerging research topic for scientists. Riga sensors’ utility for improving the effectiveness of heat and mass transport rates in industrial and engineering systems is diverse. This motivates us to inspect the stream pattern and heat-mass transmission mechanism of an electrically low-performing hybrid nanofluid (rGO-magnetite-water) near a vertically straightened Riga plate sensor embedding with absorbing materials under the guidance of thermal and concentration buoyancy and magnetization. The taken flow is being modelled by incorporating pertinent physical influences, namely radiation heat emission, chemical reaction, and ramped temperature and concentration at the boundary wall. The flow is presented mathematically in terms of unsteady partial differential equations. The compact-form expressions for model entities are founded by opting for the Laplace transform methodology. The Riga plate’s shear stress, heat and mass transfer rates are tabulated and graphed. The physical behaviours of substantial flow entities against model factors are conversed and judged graphically. The vital findings of this study demonstrate a swelling in the velocity distribution with an enhancement in modified Hartmann number and diminishing with an enlargement in the width of electrodes. The temperature and concentration are higher for constant plate temperature (CPT) and lower for ramped plate temperature (RPT). It is also motivating to note down that hybrid nanofluid containing reduced graphene nanomaterials will transmit extra heat in the flow regime. The heat flow across the Riga sensor elevates against the higher radiation parameter’s value. These novel findings will be extremely applicable in steam generators, chemical reactors, hybrid Riga plate electromagnetic devices, and phase transitions during material processing.

Structured nonlinear process modeling using neural networks and application to economic optimization

This paper presents a model identification approach to develop structured dynamic models that utilize both the available first principles knowledge and neural networks (NNs) estimated from data. The NNs in the dynamic model are used to approximate some complex unknown functions that may be challenging to model in applications using the available first principles knowledge. The parameters in the NNs are estimated by solving a multistep ahead prediction error minimization problem. The efficacy of the modeling approach is demonstrated via case studies with an illustrative chemical reactor and an industrially relevant styrene polymerization process. In the latter example, we use NNs to approximate unknown reaction kinetics and polymer moment functions in the structured model. The economic performances of the structured models are analyzed by solving a steady-state optimization problem. First, we elucidate that to obtain a good steady-state economic performance using the structured models, the training data collected from the plant should contain sufficient steady-state information. In the styrene polymerization example, we examine the economic performances of two structured models with different NN parameterization choices. We show that a structured model that utilizes the most possible physical information about the process provides a median loss of 6.4% compared to the optimal performance of the true plant across a range of steady-state problems. We emphasize in the case studies that physical insight about the process is critical to obtain good economic performance using the structured models.

Second law analysis of thermo-magneto-hydrodynamic couple-stress fluid flow in a cavity with heated fin

The objective of current study is to analyze the magnetohydrodynamic (MHD) natural convection flow of couple-stress fluid and entropy generation (second law analysis) inside the cavity with heated fin by using the Galerkin finite element method. The heated fin has been placed in middle of the heated bottom wall, the temperature T c is fixed on the vertical walls and an insulated situation is considered at the top wall. The irreversibility ratio and heat flux inside the enclosure are examined through entropy generation and Bejan heatlines, respectively. The obtained results show that the intensity of circulations of streamlines decrease from 3.9744 to 0.03148 and heatlines from 7 to 4.5 due to increasing couple-stress parameter ( K ) from 0 to 1.5 . It is noted that the heatlines bowls appear due to enhanced buoyancy-driven flow against high Rayleigh number. The reverse relation is noted between the entropy generation and the Hartmann number. The overall heat transfer rate along with the bottom and vertical side walls increase 111.65 % and 75.8297 % , respectively, with increasing the fin height from 0 to 0.25 . Obtained results could help to optimize heat transfer in engineering systems like heat exchangers, cooling processes of devices, chemical reactors, and solar collectors.

Fuzzy neural network for mapping CFD data of air-water flow in chemical reactors: Computational simulation

ANFIS (Adaptive Neuro-Fuzzy Inference Systems) artificial intelligence is used to enhance the computational fluid dynamic (CFD) modeling of an air-water bubble column reactor for the first time. Air temperature and velocity are 500 K and 0.006 m/s, respective in the process. The water temperature is considered at 295 K. This turbulent kinetic energy prediction is performed to measure the mixing flow within the reactor. The ANFIS projection is examined to see what impact the number of membership functions and input has. The findings showed that the most intelligent output is calculated by inputting two numbers and using a membership function with a value of 10. With respect to that, under this condition the ANFIS is capable of estimating the turbulent kinetic energy with more nodes on the domain while skipping mesh refinement in the CFD domain. The hybrid model was indicated to be robust in predicting the performance of liquid-phase reactor.

Thermodynamical study of chemically-reactive and thermal-radiative magnetized oscillatory Couette flow in a porous medium filled channel

The thermodynamical study of a mathematical model of unsteady natural convective MHD oscillatory flow through a porous medium-filled channel of infinite plates with chemical reaction and thermal radiation effect is taken into account in this respective research. The time-dependent flow governing equations (PDEs); comprises momentum, energy and concentration equations are derived for the concerned physical model and converted into dimensionless second-order ordinary differential equations (ODEs) for fluctuation of small amplitude. Further, the set of ODEs is solved by MATLAB's built-in dsolve function. The graphical analysis of fluid velocity, temperature and concentration profiles has been explicated for flow parameters. According to the results, the transient velocity and concentration profiles expand when the chemical reaction parameter and Schmidt number increase, whereas a strong magnetic field retards the transient velocity profile. In addition, the velocity, temperature, and concentration profiles decline whenever the frequency of oscillation is increased. The elucidated flow model and mathematical results are significant to be used in real-life applications, including packed bed reactors, chemical reactors, cooling the towers and rocket engines, designing of heat exchangers, sewage and wastewater treatment, chemical and magnetic filtration, and separation etc.