Reactor Temperature Research Articles

Performance evaluation on the chemical looping combustion of the chromium-containing leather waste and the fate of chromium using thermodynamic analysis

Leather waste (LW) consistently contains a high content of chromium (Cr), which significantly limits its utilization due to the toxicity of hexavalent chromium formed through oxidation. To prevent this oxidation, chemical looping combustion (CLC) is employed to dispose of the chromium-rich LW. First, the gaseous and solid pollutants generated during air combustion and CLC are compared when LW is used as fuel. The results indicate that CLC is an effective way to significantly decrease the oxidation of chromium (Cr(VI)/Crtotal) at typical operating temperatures. Subsequently, various operating parameters, namely the equivalence ratio of oxygen carrier to leather waste (αOC/LW), the equivalence ratio of steam to leather waste (αS/LW), and air reactor (AR) temperature, are changed to investigate their effects on the CLC combustion and pollutant behaviors. The findings reveal that no chromium is oxidized in the fuel reactor. The oxidation in AR is associated with the presence of Na in the AR, which can react with chromium to form Na2CrO4. Increasing αOC/LW from 0.8 to 1.2 reduces the oxidation of chromium in the AR from 11.68% to 1.76%, while increasing αS/LW from 0.2 to 1.6 has the opposite effect, improving the oxidation of chromium in the AR from 3.16% to 5.18%.

2-D modeling of torrefaction of a large biomass particle: effect of L/D ratio, internal convection and shrinkage

ABSTRACT Torrefaction of a large biomass particle was investigated using a transient 2-D model, including primary and secondary reactions kinetics and heat transfer, internal convection, and shrinkage. The model predictions matched with the experimental results within +2%. Spatial and temporal distributions of temperature, residual mass fraction, velocity, and pressure inside the particle and the effect of reactor temperature, residence time, particle size, and shrinkage on the torrefaction behaviour were investigated. Evolution of moisture, volatiles and gases due to drying and torrefaction increase the pressure inside the particle, setting up a velocity field and internal convection. Average velocity and pressure reached a peak during the initial drying period due to moisture evolution, and a subsequent second peak due to the formation of volatiles and gases by torrefaction at a reactor temperature > 493 K. Internal convection influenced torrefaction significantly but the particle shrinkage did not impact it appreciably. The degree of overshoot of the particle center temperature above the reactor temperature increased with the reactor temperature and the particle diameter. Simulations suggest that a reactor temperature of 550 K and residence of about 30 min would be suitable for torrefaction of a particle with L = D = 25.4 mm.

Methanogenesis—General Principles and Application in Wastewater Remediation

The first discovery of methanogens led to the formation of a new domain of life known as Archaea. The Archaea domain exhibits properties vastly different from previously known Bacteria and Eucarya domains. However, for a certain multi-step process, a syntrophic relationship between organisms from all domains is needed. This process is called methanogenesis and is defined as the biological production of methane. Different methanogenic pathways prevail depending on substrate availability and the employed order of methanogenic Archaea. Most methanogens reduce carbon dioxide to methane with hydrogen through a hydrogenotrophic pathway. For hydrogen activation, a group of enzymes called hydrogenases is required. Regardless of the methanogenic pathway, electrons are carried between microorganisms by hydrogen. Naturally occurring processes, such as methanogenesis, can be engineered for industrial use. With the growth and emergence of new industries, the amount of produced industrial waste is an ever-growing environmental problem. For successful wastewater remediation, a syntrophic correlation between various microorganisms is needed. The composition of microorganisms depends on wastewater type, organic loading rates, anaerobic reactor design, pH, and temperature. The last step of anaerobic wastewater treatment is production of biomethane by methanogenesis, which is thought to be a cost-effective means of energy production for this renewable biogas.

Application of microencapsulated phase change materials for controlling exothermic reactions

Abstract Thermal runaway is a frequent source of process safety issues, and the uncontrolled release of chemical energy puts reactors at risk. The design of the exothermic reactor faces challenges due to the selective sensitivity of the product to high temperatures and the need to increase the lifetime of the catalyst, optimize the product distribution, and improve the thermodynamic properties. Phase change material (PCM) encapsulation is recommended to reduce leakage, phase separation, and volume change problems. This work introduces encapsulated PCMs to improve reactor temperature control and minimize thermal runaway in exothermic processes. The warning temperature value setting effectively inhibits fugitive exothermic reactions and enhances heat transfer. When a sufficient quantity of encapsulated PCMs is input, the response speed will automatically accelerate. Spontaneous acceleration of the reaction rate due to thermal runaway of the reaction may be completely avoided by adding a sufficient amount of encapsulated PCM. Microencapsulation is used to control volume changes and inhibit thermal reactions. Preventive strategies include cooling, depressurization, safety release, emergency resources, and reaction containment. Encapsulated PCMs improve mechanical and thermal properties, surface-to-volume ratio, heat transfer surface, thermal capacity, and efficiency.

Development and performance evaluation of a novel solar mid-and-low temperature receiver/reactor with packed metal foam

Solar-driven hydrogen production from methanol decomposition (MD) is an important method of storing solar energy as clean fuels and improving the solar energy utilization efficiency. Catalyst preparation is one of the key steps, but conventional Cu-ZnO-Al2O3 particle catalysts lead to uneven reactor temperature, reducing the efficient conversion of solar energy into fuels. To alleviate this challenge, a monolithic catalyst utilizing packed foamy copper as a carrier is developed with exceptional thermal and mass transfer properties. The experimental results validate its excellent performance in hydrogen production using MD. The absolute conversion increases by an average of 12.11 % at 250 °C and further by 14.43 % at 270 °C compared to the particle catalyst. Based on these findings, a novel solar reactor is proposed, and a multi-field coupling model is built. The comparative results verify the performance advantage of the developed solar thermochemical receiver/reactor with packed metal foam-based catalyst. It maintains high conversion rates and solar-to-fuel energy efficiency, while alleviating overheating problems caused by traditional particle catalysts. Under the design conditions, the new reactor significantly improves the absolute conversion of methanol and the energy efficiency of solar-to-fuel conversion by 4.57 % and 3.00 %, respectively, resulting in 92.64 % and 67.56 %. This study provides a theoretical basis and technical solution for more stable and efficient use of mid-and-low temperature solar energy.

Optimizing neural network models for predicting nuclear reactor channel temperature: A study on hyperparameter tuning and performance analysis

This study emphasizes how important accurate prediction of channel temperatures in nuclear reactors is for safety and operational efficiency. While traditional methods require long and complex processes such as kernel modeling and mathematical simulations, artificial neural networks (ANN) provide more efficient predictions by accelerating this process. The superior ability of ANNs to process large data sets is intended to demonstrate that this study will provide a valuable alternative compared to conventional methods and increase the accuracy of reactor temperature predictions. In this study, the training performances of Artificial Neural Network (ANN) developed to determine the nuclear reactor channel temperature with different hyperparameter combinations were analysed. It was conducted several experimental studies to assess the influence of hyperparameters on our model for nuclear reactor parameter data prediction. The training and validation results indicates that learning rate, hidden layer sizes and number have critical effects for the more precisive prediction. It was observed that models with a learning rate of 0.05 and 0.5 achieved successful learning with less fluctuation in training and validation errors. When looking at hidden layer sizes, networks with 32 and 64 neurons performed better than networks with 16 neurons. For the test phase our model can successfully predict data with slight error margin. As a result, we demonstrated that neural networks are a powerful tool in nuclear reactor channel temperature prediction through our proposed model.

Potassium sulphate production from an aqueous sodium sulphate from lead‐acid battery recycling: Impact of feedstock impurities on products yields

AbstractThe increasing demand for renewable energy highlights the need for efficient energy storage solutions. Despite various available technologies, lead‐acid batteries remain preferred for many industrial applications due to their inherent advantages. However, their expanded use necessitates proper waste management and recycling practices. During lead‐acid battery recycling, Na₂SO₄ is generated as a waste product, which cannot be directly sold due to quality concerns and limited market demand. Consequently, advanced waste management techniques are required to comply with government regulations on industrial waste disposal. Despite these challenges, Na2SO4 serves as a vital precursor for producing K2SO4, a valuable fertilizer. Prior research on the glaserite process for converting Na2SO4 to K2SO4 has assumed Na2SO4 to be pure—without traces of impurities. However, Na2SO4 recovered from battery recycling contains various contaminants. To address this, HSC Chemistry software was used to model K2SO4 and NaCl production from impure Na2SO4 and KCl, considering feed impurities. Under ideal conditions—a 1 bar pressure, 25°C feed temperature, and 40°C reactor temperature—over 90% yield of K2SO4 and NaCl was achieved in the absence of impurities. However, the addition of impurities resulted in a reduction in yields. Notably, impurity levels ranging from 1% to 4% by weight still allowed for yields exceeding 90%. Furthermore, a review of reactor compositions revealed a significant depletion of potassium and chlorine ions which are crucial for K2SO4 and NaCl production as impurity levels varied from 0% to 10%. These findings emphasize the negative impact of impurities on K2SO4 and NaCl yields.

A new integrated system for carbon capture and clean hydrogen production for sustainable societal utilization

Both hydrogen production and carbon dioxide removal are considered in this study as two of the critical pieces to achieve the ultimate sustainability target. This study proposes and investigates a new variation of potassium hydroxide thermochemical cycle in order to combine hydrogen production and carbon dioxide removal synergistically. An alkali metal redox thermochemical cycle is developed to utilize the potassium hydroxide uniquely through a nonequilibrium reaction. Also, the multigeneration options are explored by employing two-stage steam Rankine cycle, multi-effect distillation desalination, and Li-Br absorption chiller, which is integrated with potassium hydroxide thermochemical cycle for hydrogen production, carbon capture, power generation, water desalination, and cooling purposes. A comparative assessment under different scenarios is carried out. The energy and exergy efficiencies of the hydrogen production thermochemical cycle are found to be 44.2 % and 67.66 % when the hydrogen generation reaction is carried out at 180 °C and the separation reactor temperature is set at 400 °C. Among the multigeneration scenarios considered, a trigeneration option for producing hydrogen, power and freshwater provides the highest energy efficiency as 66.02 %.

Engineering Optimization of Producing High-Purity Dichlorosilane in a Fixed-Bed Reactor by Trichlorosilane Decomposition.

High-purity dichlorosilane (DCS) is an important raw material for thin film deposition in the semiconductor industry, such as epitaxial silicon, which is mainly produced by trichlorosilane (TCS) catalytic decomposition in a fixed-bed reactor. The productivity of DCS is strongly dependent on the controlling of the TCS decomposition reaction process, associated with the cost in practical application. In this study, we have performed computational fluid dynamics (CFD) simulation on the TCS decomposition reaction kinetics in a cylindrical fixed-bed reactor, in which the effects of catalyst bed height, feed temperature, and feed flow rate are stressed to predict the conversion rate of TCS and the generation rate of DCS. This indicates that the increase of bed height helps the reaction to proceed adequately, but too large a bed height does not improve the DCS generation rate. Meanwhile, the feed temperature and reactor temperature have important effects on the DCS generation rate. However, it is found that changing the feed flow rate and L/D ratio cannot effectively improve the DCS generation rate while the bed volume remains constant. Furthermore, we have designed a fixed-bed reactor to verify the simulation results, which are in good agreement with each other. These results are of significance for the practical industrial production of high-purity DCS in a fixed-bed reactor.

FORECASTING AND OPTIMIZATION OF CATALYTIC CRACKING UNIT OPERATION UNDER CONDITIONS OF FUZZY INFORMATION

This paper discusses the application of nonlinear regression to forecast and optimize the operation of catalytic cracking units under conditions of fuzzy information. Catalytic cracking is a crucial process in oil refining that produces high-quality gasoline and other light hydrocarbon products. However, the complexity of the process and the uncertainty of initial data complicate the modeling and optimization of plant operations. To address this issue, a nonlinear regression method is proposed that accommodates the fuzziness of input and output parameters described by linguistic variables. The methodology includes the collection and formalization of expert knowledge, the construction of fuzzy models, and their integration into the process control system. Forecasting is performed by creating regression models that describe the relationships between operational parameters and product quality characteristics. The paper presents a procedure for developing and applying nonlinear regression models, describes algorithms for synthesizing linguistic models, and provides examples of their use to optimize the operation of catalytic cracking units. The modeling results demonstrate the high adequacy and accuracy of the proposed method, as well as its advantages over traditional approaches in conditions of uncertainty and data scarcity. The scientific novelty of the research lies in the development and testing of advanced nonlinear regression models adapted for analyzing and optimizing catalytic cracking processes based on fuzzy data. These methods take into account the specificity and uncertainty of process data, improving the accuracy and reliability of forecasts, which facilitates more effective management of production processes in the petrochemical industry. The main reason for conducting this study is the need to improve the control of oil refining processes, particularly catalytic cracking, which plays an important role in producing high-quality gasoline. The complexity of this process and the presence of fuzzy information caused by fuzzy initial data require the development of new modeling and optimization methods. Existing traditional models based on deterministic methods are often insufficient under uncertainty. This leads to a decrease in the accuracy of process control, which can negatively affect the quality of the final product and production efficiency. The use of nonlinear regression in combination with fuzzy logic is a more flexible and adaptive approach that allows you to take into account the fuzziness and uncertainty of data and use expert knowledge to build models that match the actual operating conditions of the units. Thus, this study aims to solve the key problems associated with data uncertainty and the complexity of the catalytic cracking process, which will improve the accuracy of forecasting and optimization of the units. The main contribution is creating a model that uses nonlinear regression methods in combination with fuzzy logic. This allows uncertainty in input data (such as reactor temperature or pressure) to be effectively considered and processed to improve gasoline and other product yield forecasts. It is shown that using nonlinear regression combined with fuzzy logic significantly improves the management of technological processes, increases the output and quality of products, and reduces production costs. The conclusion of the paper discusses the prospects for further development of the methodology and its application to solve similar tasks in other areas of chemical technology.

Lignocellulose Treatment Using a Flow-Through Variant of OrganoCat Process.

This study adapts the biphasic OrganoCat system into a flow-through (FT) reactor, using a heated tubular setup where a mixture of oxalic acid and 2-methyltetrahydrofuran (2-MTHF) is pumped through beech wood biomass. This method minimizes solvent-biomass contact time, facilitating rapid product removal and reducing the risk of secondary reactions. A comparative analysis with traditional batch processes reveals that the FT system, especially under severe conditions, significantly enhances extraction efficiency, yielding higher amounts of lignin and sugars with reduced solid residue. Notably, the FT system shows partial hydrolysis of the cellulose, which increases with temperature while not producing significant amounts of furfural or 5-HMF, indicating more efficient depolymerization of polysaccharides without substantial sugar degradation. A statistical design of experiments (DOE) using a Box-Behnken design elucidates the influence of process variables (time, solvent flow rate, temperature) on the yield. Key findings highlight reactor temperature as the dominant factor affecting yields, with process time showing a significant but less pronounced impact. This study demonstrates the potential of the FT OrganoCat system for efficient lignocellulosic biomass fractionation and represents an advancement towards continuous lignocellulose processing, contributing to our knowledge of process optimization for improved biorefinery applications.

Sustainable Biomethanol and Biomethane Production via Anaerobic Digestion, Oxy-Fuel Gas Turbine and Amine Scrubbing CO2 Capture

Energy policies around the world are increasingly highlighting the importance of hydrogen in the evolving energy landscape. In this regard, the use of hydrogen to produce biomethanol not only plays an essential role in the chemical industry but also holds great promise as an alternative fuel for global shipping. This study evaluates a system for generating biomethanol and biomethane based on anaerobic digestion, biogas upgrading, methanol synthesis unit, and high-temperature electrolysis. Thermal integration is implemented to enhance efficiency by linking the oxy-fuel gas turbine unit. The integrated system performance is evaluated through thermodynamic modeling, and Aspen Plus V12.1 is employed for the analysis. Our findings show that the primary power consumers are the Solid Oxide Electrolysis Cell (SOEC) and Methanol Synthesis Unit (MSU), with the SOEC system consuming 824 kW of power and the MSU consuming 129.5 kW of power, corresponding to a production scale of 23.2 kg/h of hydrogen and 269.54 kg/h of biomethanol, respectively. The overall energy efficiency is calculated at 58.09%, considering a production output of 188 kg/h of biomethane and 269 kg/h of biomethanol. The amount of carbon dioxide emitted per biofuel production is equal to 0.017, and the proposed system can be considered a low-carbon emission system. Key findings include significant enhancements in biomethanol capacity and energy efficiency with higher temperatures in the methanol reactor.

Enhancing predictive monitoring of ethylene oxychlorination reactor states through spatiotemporal coupling analysis

The production of polyvinyl chloride (PVC) encounters challenges stemming from the temporal and spatial coupling characteristics inherent in the fixed bed ethylene oxychlorination process. Consequently, the implementation of enhanced safety measures and risk reduction strategies becomes imperative. This study introduces a pioneering methodology leveraging a spectral temporal graph neural network. By leveraging reactor temperature data, spatial variable decoupling facilitated by the Fourier transform, and a self-attentive mechanism within graph neural networks, the proposed approach adeptly forecasts future reactor states. The model's seamless alignment with the physical knowledge of reaction processes, validated through the adjacency matrix and hotspot region identification, underscores its efficacy in ensuring process safety and mitigating operational risks in PVC production. Empirical findings further validate the effectiveness of the approach, with predictions demonstrating an error margin of less than 0.5°C in forecasting future reactor temperatures.

Equilibrium modelling of steam gasification of PKS system and CO2 sorption using CaO: A digitalized parametric and techno-economic analysis

The conversion of palm oil waste into energy can complement the energy mix with renewable energy and bring economic benefits to the palm oil industry. A process simulation model for palm kernel shell (PKS) steam gasification for H2-enriched syngas with the integration of CO2 capturing using CaO has been investigated using Aspen Plus V10®. Techno-economic and energy analyses have also been conducted to identify energy-saving opportunities for commercialization. The effect of process variables, including reactor temperature (600–800 °C), Steam/PKS ratio (0.5–2 wt/wt), and CaO/PKS ratio (0–1.5 wt/wt), have been determined, with the predicted results compared to the reported experimental data. H2 concentration has been increased with 76–78 vol% with the temperature elevation from 650 to 750 °C. Additionally, a substantial increase in H2 content from 68 to 72vol% was observed when the steam flow rate was increased from 0.5 to 1.5. Conversely, the CO2 concentration decreased from 25 to 8vol% as the adsorbent ratio was raised from 0.5 to 1.5. The techno-economic analysis showed that the capital investment is $4.11 million, and the operating cost is $3.89 million per year, which is also very high due to the high raw material costs. In the case of energy, saving that 3.03 and 1.513 Gcal/hr can be saved in terms of utilities and gas cooling that economized the process which shows that utilization of these in heat exchange networks can significantly reduce costs, with up to 78 % savings in heat exchanger capital and 35 % in total energy consumption. The energy potential could be harnessed through the digitalization of the process.

Steady-state and dynamic simulation of gas phase polyethylene process

Gas-phase polyethylene (PE) processes are among the most important methods for PE production. A deeper understanding of the process characteristics and dynamic behavior, such as properties of PE and reactor stability, holds substantial interest for both academic researchers and industries. In this study, both steady-state and dynamic models for a gas-phase polyethylene process are established as a simulation platform, which can be used to study a variety of operation tasks for commercial solution polyethylene processes, such as new product development, process control and real-time optimization. The copolymerization kinetic parameters are fitted by industrial data. Additionally, a multi-reactor series model is developed to characterize the temperature distribution within the fluidized bed reactor. The accuracy in predicting melt index and density of the polymer, and the dynamic behavior of the developed models are verified by real plant data. Moreover, the dynamic simulation platform is applied to compare four practical control schemes for reactor temperature by a series of simulation experiments, since temperature control is important in industrial production. The results reveal that all four schemes effectively track the setpoint temperature. However, only the demineralized water temperature cascade control demonstrates excellent performance in handling disturbances from both the recycle gas subsystem and the heat exchange subsystem.

Deactivation of iron particles during combustion and reduction

Iron is a promising carbon-free renewable energy carrier to replace fossil fuels. However, during a redox cycle, iron may experience deactivation due to structural changes, which can shorten the cycle lifetime. In this work, deactivation of iron particles in terms of changes in reactivity and morphology during the oxidation and reduction process was investigated, under both fixed-bed and suspension conditions. Simple thermal treatment in an inert atmosphere at 1200 °C had no influence on the reactivity of particles towards oxidation, even at long exposure resulting in mild agglomeration. In fixed-bed oxidation at 600 to 1100 °C, solid sintering was found to be rapid and severe at higher temperatures. However, the sintering showed no influence on the reactivity of particles towards H2 under slow-heating conditions in a TGA. In pulverized-fuel combustion in a drop tube reactor, the intensive combustion and heat release led to melting of particles and morphology change from irregular to spherical. As the reactor temperature was increased from 800 to 1200 °C, the particle reactivity in terms of reduction degree (fractional conversion from Fe2O3 to Fe) slightly decreased from 78 % to 72 %, possibly related to a minor loss of surface area. TGA experiments involving successive redox cycles showed a steady decline in reduction reactivity (750 °C, 2.5 % H2), slightly enhanced by agglomeration, while the oxidation degree (950 °C or 1200 °C, 21 % O2) remained constant. The present work indicates that both deactivation and loss of iron due to evaporation may serve to limit the energy release in iron combustion over extended cycles.

A comprehensive evaluation of enhanced temperature influence on gas and aerosol chemistry in the lamp-enclosed oxidation flow reactor (OFR) system

Abstract. Oxidation flow reactors (OFRs) have been extensively utilized to examine the formation of secondary organic aerosol (SOA). However, the UV lamps typically employed to initiate the photochemistry in OFRs can result in an elevated reactor temperature when their implications are not thoroughly evaluated. In this study, we conducted a comprehensive investigation into the temperature distribution within an Aerodyne potential aerosol mass OFR (PAM-OFR) and then examined the subsequent effects on flow and chemistry due to lamp heating. A lamp-induced temperature increase was observed, which was a function of lamp-driving voltage, number of lamps, lamp types, OFR residence time, and positions within the PAM-OFR. Under typical PAM-OFR operational conditions (e.g., < 5 d of equivalent atmospheric OH exposure under low-NOx conditions), the temperature increase typically ranged from 1–5 °C. Under extreme (but less frequently encountered) conditions, the heating could reach up to 15 °C. The influences of the increased temperature over ambient conditions on the flow distribution, gas, and condensed-phase chemistry within PAM-OFR were evaluated. Our findings indicate that the increase in temperature altered the flow field, resulting in a diminished tail on the residence time distribution and corresponding oxidant exposure due to faster recirculation. According to simulation results from a radical chemistry box model, the variation in absolute oxidant concentration within PAM-OFR due to temperature increase was minimal (< 5 %). The temperature influences on seed organic aerosol (OA) and newly formed secondary OA were also investigated, suggesting that an increase in temperature can impact the yield, size, and oxidation levels of representative biogenic and anthropogenic SOA types. Recommendations for temperature-dependent SOA yield corrections and PAM-OFR operating protocols that mitigate lamp-induced temperature enhancement and fluctuations are presented. We recommend blowing air around the reactor's exterior with fans during PAM-OFR experiments to minimize the temperature increase within PAM-OFR. Temperature increases are substantially lower for OFRs utilizing less powerful lamps compared to the Aerodyne version.

Modeling of a solution polymerization reactor operating in semi-batch mode for polymethyl methacrylate production

The severe consequences of thermal runaways, make process optimization of paramount importance for free radical polymerization reactors, in order to maximize in an integrated way their safety and productivity. Generally, this optimization is performed by simulation, for the sake of safety and cost. Today, the literature contains several models of free radical polymerization reactors in general; and methyl methacrylate (MMA) polymerization reactors, in particular. Although, MMA polymerization is commonly performed in semi-batch reactors at industrial level, most of the available models focus on batch reactors; while, the semi-batch reactor models are much scarcer. In this work, a model of a MMA solution polymerization batch reactor was modified in order to generalize it to semi-batch operation. The developed semi-batch model was used here for studying the effect of different operation conditions (feeding flow rate, initiator load, monomer initial load, and reactor temperature) on the quality characteristics of the produced polymer and on the safety and productivity of the polymerization reactor. The developed model can be used to optimize the operation conditions of a semi-batch reactor so that the final product quality meets the application requirements, while maintaining the reactor within its safe-operation envelope, and maximizing its productivity.

Thermodynamic and mass analysis of a novel two-phase liquid metal MHD enhanced energy conversion system for space nuclear power source

The higher-efficiency and more lightweight space nuclear power source is one of the most important prerequisites for future deep space exploration. Magnetohydrodynamic (MHD) power generation is the potential technical route to achieve significant improvement in energy conversion performance, but technology is not yet mature. By combining the characteristics of MHD and closed Brayton cycle (CBC) power generation systems, a novel quasi-Ericsson cycle with performance advantages is proposed in this study. The estimation method for the MHD power generator is refined and the thermodynamic and mass analysis models for the novel system are established. Comparative studies between the LMMHD-CBC cycle and the traditional CBC cycle are conducted under various boundary conditions and internal parameters. The results indicate that the LMMHD-CBC cycle increases the electricity generation per unit mass flow rate of working fluid under the same temperature conditions, thereby achieving a reduction in the specific mass of the power generation system. The LMMHD-CBC system is more suitable in lower reactor temperatures and higher radiator temperature conditions. Within the parameters range considered in this study, the relative reduction in specific mass compared to traditional CBC systems reaches up to 30.84 %.

Modeling and optimization for the continuous catalytic reforming process based on the hybrid surrogate optimization model

To address the modeling and optimization challenges of the complex reaction system in the continuous catalytic reforming process, a new integrated simulation and optimization framework is presented. First, a detailed mechanism model is established based on a reaction network involving 32 components and 50 reactions, coupled with mass transfer, heat transfer, pressure drop, and catalyst deactivation equations. Then, to solve the differential-algebraic equations in the mechanism model, a multi-objective hybrid optimization method with the adaptive infill strategy is introduced. GAMS and MATLAB are integrated to perform a joint iterative solution. Finally, two cases are conducted with the proposed algorithm. Results show that the mechanism model calculation deviations are below 4 % of reactor temperature, pressure, and composition distribution, and the Pareto front of various production plans is obtained. The accurate simulation and rapid trade-off optimization among the key goals can be achieved to provide scientific decision support for enterprise production.