• Dynamic operation of fixed bed reactors is becoming increasingly common • Unsteady 1D models do not capture heat transfer limitations in packed beds • Dynamic instrumental analytical techniques are needed but complex and expensive Recent research in chemical plant operation shows increasing interest in dynamic process operation as part of designed operating strategy for reasons such as increased dependency on renewable energy, and process intensification. Conventional analyses of fixed bed reactors are developed for steady state optimization and may not be adequate for dynamic operation. In fact, the important metrics and targets in dynamic process design are not entirely clear. The first objective of this article is to provide a state-of-the-art survey categorize types of dynamic operation, and rank the available common modelling and analytical tools suitable for quantification of dynamic process variables. The article then examines a case study of 1D and 2D model differences in a methanol steam reforming reactor. The case study shows model prediction differences of up to 15% for conversion, and up to 50% for CO concentration at the outlet during extreme load changes. The study concludes that the complexity of analytical and numerical techniques for dynamic processes is notably higher compared to steady state analyses, but appropriate tools and procedures are currently lacking.

Process intensification of biomass pyrolysis by fixed bed reactor with internals for producing hydrogen-rich gas and mononuclear aromatics

Fixed-bed biofilm reactor for single-stage bioconversion of organic waste to medium-chain carboxylic acids.

Tuning hydroconversion performance of n-hexadecane over Ni/silica-alumina catalysts: Role of Si/Al ratio and nickel loading

• A moving-bed thermochemical energy storage system is experimentally investigated. • Pumice-based salt-in-matrix composite sorbents are developed and implemented. • The system achieves an energy storage density of 189.7 kWh/m 3 . • Maximum effective energy and exergy efficiencies are 52.7% and 6.8%, respectively. • A correlation between air humidity difference and temperature lift is established. In the last decade, low-grade thermochemical energy storage systems have been gaining interest due to their long-term heat storage potential and high energy storage density. Despite the advantageous aspects of this heat storage method, previously investigated fixed-bed reactors suffer from low heat and mass transfer performance and offer limited process control. In order to overcome these challenges, a new multi-layer moving bed reactor was designed, manufactured, and tested in this study. The proposed reactor consists of reaction and storage sections where eight independent sorption beds have freedom of movement between the two sections. Such a design enables a modular concept, where each sorption bed could be charged or discharged individually, while the remaining sorption beds are stored inside their own hermetically insulated chambers. In the system, two different sizes of pumice stones, namely PM1 and PM2, were used as the host matrix, and three different thermochemical materials were synthesized by impregnation of the LiCl-CaCl 2 mixture and CaCl 2 as salts into pumice. During the experiments, comparative analyses of different materials, short-cycle full-system analyses, long-cycle energy density analyses, and multi-bed performance analyses have been performed. Additionally, the impact of air velocity was investigated. The evaluations were performed based on the First and Second Laws of Thermodynamics. Study results demonstrated that each sorption bed provides an average heat output between 0.58 and 1.07 kW depending on the inlet air conditions and the composition of thermochemical material. According to the study results, the energy storage density of the system was obtained as 189.7 kWh/m 3 with the use of PM2-CaCl 2 . On the other hand, 4.2 m/s was found as the most optimal air velocity, proving the highest average heat output during the discharging process and the highest moisture desorption rate per unit of heat consumed during the charging process. A linear correlation between the air absolute humidity difference and the air temperature lift for the discharging process was also obtained, which could provide useful insights for the performance prediction of thermochemical energy storage systems.

Thermocatalytic CO2 hydrogenation to high-yield ethanol and C3–C5 alcohols over promoted Fe-based catalysts

Metal sulphate catalysts are widely used in biomass conversion to produce value-added chemicals, yet their stability in the reaction environment has not been studied. Most often, based on the inherent thermal stability of the pure species, sulphate catalysts are simply assumed stable when the reaction temperatures are low. However, in this paper, through studying the catalytic pyrolysis of cellulose ((C 6 H 10 O 5 ) n ) by experimental investigation, advanced characterisation and density functional theory (DFT) calculation, we prove that the ZnSO 4 - supported MCM-41 (with the inclusion of 1 wt% Pd in its matrix) is unstable at the pyrolysis temperatures of 400-450 °C, which is far below the thermal decomposition temperature of 646 °C for pure ZnSO 4 . This is due to the strong reaction between ZnSO 4 and formaldehyde (HCHO), an intermediate produced from the Grob fragmentation of the methyl group on glucose. It induces the loss of Brønsted acidity and reactivity of catalysts, along with the release of gaseous SO 2 that is environmentally concerning. Nevertheless, upon the inclusion of only 3.5 wt% Al into the MCM-41 matrix, ZnSO 4 was confirmed to remain stable during the cyclic and continuous tests, due to the formation of a strong covalent Zn-SO 4 -Al bond that enhances the dispersion of ZnSO 4 within the MCM mesoporous framework, and the energy demand for the desorption of SO 2 from the catalyst surface . Most significantly, such a hidden benefit of Al is complementary to its primary role in moderating the total acidity and Brønsted/Lewis acid ratio, leading to a record - high furfural (C 5 H 4 O 2 ) selectivity of 44-49.5% and a mass yield of 27.7-31.2 wt% from the pyrolysis of cellulose in batch-scale fixed-bed reactor. The hidden benefit of Al is also applicable to other sulphates, including CuSO 4 and Fe 2 (SO 4 ) 3 , although the extent for the improvement on their stability varies. These findings are expected to offer new insights for the design and use of sulphate-based acidic catalysts in practical applications. • ZnSO 4 @Pd-Al-MCM achieves furfural selectivity of ~49.5% and yield of ~31.2 wt%. • ZnSO 4 @Pd-MCM suffers from instability during cellulose pyrolysis (400-450°C). • HCHO strongly reacts with ZnSO 4 , driving SO 2 release and Brønsted acidity loss. • Al incorporation (3.5 wt%) forms Zn-SO 4 -Al bonds, enhancing sulphate stability. • Al-induced stabilisation is applicable to CuSO 4 and Fe 2 (SO 4 ) 3 sulphates.

Abstract In this study, BN/TiO 2 was evaluated for the photocatalytic removal of ciprofloxacin (CIP) under UV irradiation in real water collected from a drinking water treatment plant (DWTP) at the following treatment points: influent, clarifier, and effluent. Comparative photocatalytic experiments were conducted in both synthetic and real water matrices using TiO 2 , BN, and BN/TiO 2 . The optimized parameters were pH 5, CIP concentration 5 mg/L, and photocatalyst dosage 20 mg. The highest removal efficiency of 97.04% was determined under these optimised parameters. BN/TiO 2 outperformed both the synthesized TiO 2 and BN due to synergistic effects arising from strong interfacial contact, which facilitated efficient charge separation, where BN acted as an electron sink. Superoxide radicals (O 2 •− ) were the primary species responsible for CIP photocatalytic removal, with the contribution of the other scavengers in the order: holes > electrons > hydroxyl radicals. The photocatalytic removal efficiencies of CIP using BN/TiO 2 were 69.3%, 83.3%, and 90.2% in the influent, clarifier, and effluent, respectively. Enhanced removal in the effluent was attributed to lower turbidity, which improved light transmission; higher dissolved oxygen that promoted superoxide radical formation; and increased ionic strength, which facilitated charge transport while suppressing electron–hole recombination. One-way ANOVA confirmed that both the operational parameters and the water matrix significantly influenced BN/TiO 2 photocatalytic removal of CIP. These findings position BN/TiO 2 as a promising photocatalyst for DWTP integration, ideally in fixed-bed reactors post-clarifier stage for easy recovery. For practical use, UV-powered BN/TiO 2 system proves most effective between clarifier and disinfection stages under near-neutral pH conditions.

Chemical looping gasification is a promising technology for transforming biomass to value-added hydrogen-rich syngas; however, the existing oxygen carriers face the challenges of low catalytic activity and stability, hindering their conversion performance at a wide range of biomass fuels and reaction temperatures. Herein, Sr0.75Ba0.25FeO3-δ was synthesized and applied to hydrogen-rich syngas production, and experiments were conducted in a fixed-bed reactor. The effects of ratios of oxygen carrier and biomass, temperatures, and biomass types on cyclic performance were investigated. Results revealed that the H2 concentration and H2 yield could reach ~80% and ~13 mmol g-1 for pine sawdust, ~85% and ~4 mmol g-1 for cellulose, and ~70% and ~25 mmol g-1 for lignin at 850°C. At 750-900°C, the H2 concentration stabilized at above 60%. In addition, Sr0.75Ba0.25FeO3-δ maintained a H2 yield of 16 mmol g-1 after multiple cycles. Compared with traditional oxygen carriers, Sr0.75Ba0.25FeO3-δ shows at least 30% enhancement in H2 production rate. The outperformed H2 production is attributed to the high lattice oxygen activity and catalytic ability. This work demonstrated that Sr0.75Ba0.25FeO3-δ is a promising oxygen carrier candidate for biomass chemical looping gasification with fuel adaptability, temperature flexibility, and catalytic stability.

This work establishes a generalized framework for predicting effectiveness factors (E) of catalysts with any types of kinetics using a modified Thiele modulus λp for catalysts of arbitrary geometries and assemblages with mixed properties. It is verified that the most appropriate definition of the Thiele modulus is given by λ p = V p A p R ( C AS ) 2 D e ∫ C Ae C AS R ( C A ) d C A which unifies diffusion-reaction phenomena for catalyst pellets of arbitrary shapes and property distributions. Numerical validations for various catalyst geometries (spheres, finite cylinder, hollow cylinders, cones, parallelepipeds) confirmed that E = 1 1 + λ p 2 closely predicts the effectiveness factor for different values of λp . The model resolves the limitations of classical approaches that are difficult and complex for pellets of non-basic shapes. For catalyst pellet assemblages of mixed properties, the overall modulus λb defined as follows was proved to be appropriate for the above equation for E: λ b = ( ∑ i = 1 N c v c i 1 + λ p c i 2 ) − 2 − 1 Validated for catalyst assemblages and fixed-bed reactor descriptions, this general definition of the Thiele modulus for any kinetics expression, together with the simple relation E = 1 1 + λ p 2 , enables optimization of industrial catalysts with complex geometries or property distributions. It also provides unified criteria for the asymptotic regimes of effectiveness factor; one for the regime in which pore diffusion does not affect the overall rate (λp < 0.1 within ∼ 1% error and λp < 0.3 within ∼ 10% error) so that E ≈ 1 and the other in which pore diffusion is strong enough to make the reaction occur mainly near the external surface (λp > 10 within ∼ 1% error and λp > 3 within ∼ 10% error) so that E ≈ 1 / λ p .

CuCo-based catalysts are promising candidates for higher alcohol synthesis from syngas, yet their performance is often limited by poor metal dispersion and insufficient Cu-Co synergy. In this work, a series of ordered mesoporous CuCoAl catalysts with varying Cu/Co atomic ratios were synthesized via the evaporation-induced self-assembly (EISA) method. The structural, electronic, and catalytic properties were systematically investigated using N2 physisorption, XRD, TEM, H2-TPR, CO-TPD, XPS, and fixed-bed reactor evaluation. The results show that all CuCoAl catalysts prepared by the EISA method possess well-ordered mesoporous structures with high surface areas (up to 235 m2/g) and narrow pore size distributions. The interaction between Cu and Co stabilizes the mesoporous framework, inhibits Cu particle growth, and induces electron transfer from Cu to Co as evidenced by XPS. Among the catalysts tested, Cu1Co1Al (Cu/Co = 1:1) exhibits the highest strong CO adsorption capacity (1.54 mmol/g) and surface hydroxyl content (63.29%), achieving a CO conversion of 32.9% with a C2+ alcohol space-time yield of 20.5 mg·gcat-1·h-1. These findings establish clear structure-performance relationships for ordered mesoporous CuCoAl catalysts and provide fundamental guidance for the rational design of efficient catalysts for higher alcohol synthesis.

This data article presents a dataset of miniplant-scale reverse water-gas shift (rWGS) experiments conducted in a heated fixed-bed reactor under systematically varied operating conditions. The dataset contains processed measurements including reactor temperature, molar fractions of CO2, CO, H2, CH4, and derived quantities such as CO2 conversion and CO selectivity. The experiments cover a wide parameter space, including gas hourly space velocities of 8000, 14,000 and 20,000 h-1 with temperatures between 550 and 950 °C (increment of 50 K), and H2:CO2 feed ratios of 2:1, 2.5:1 and 3:1. The dataset presents the steady-state values and links to the reproductible data processing step, based on a prior study, enabling Fairness of all steps from the initial measurements to the final processed variables. The processing workflow includes calibration of gas analysis signals, smoothing, dry-gas calculation, and uncertainty estimation. These data provide value for validating mechanistic kinetic models, benchmarking computational fluid dynamics (CFD) reactor simulations, training machine learning models including physics-informed machine learning frameworks, and supporting thermodynamic model assessments. All raw and processed data are made publicly available in a long-term repository, ensuring FAIR access and enabling reuse by the scientific community.

Three-dimensional evaluation of operating conditions and channel design in a wall-coated microreactor for dry reforming of methane

Valorization of oily sludge with coal gasification slag via catalytic pyrolysis.

Recovery of fuel liquid from plastic waste using Ni/ZSM-5 catalyst by distillation bubble cap plate column

Modelling of a surface-absorption fixed-bed solar-driven reactor for waste plastics pyrolysis

Oil hydrogenation in a HiGee-aided fixed bed reactor: Process intensification and macro-kinetic model

In this study, supervised machine learning (ML) regression models are employed to predict water uptake during the sorption process in a sorption reactor for thermal energy storage applications. Two main methods are used to study sorption storage systems: experimental studies and numerical simulations. Experimental studies involve physical testing and measurements but are often costly and time-consuming. Numerical simulations are more flexible and cost-effective, though they can require significant computational resources for large or complex systems. To address these challenges, researchers are increasingly employing various machine learning techniques, which offer strong potential for data analysis and predictive modeling. In this study, CFD-based sorption simulations are integrated with machine learning models to predict the spatiotemporal evolution of water uptake. Several ML techniques including support vector regression (SVR), Random Forest, XGBoost, CatBoost (gradient boosting decision trees), and multilayer perceptron neural networks (MLPs) are evaluated and compared. A fixed-bed reactor equipped with fins and tubes is considered within a closed adsorption thermal storage system. Numerical simulations are conducted for three different fin lengths (10 mm, 25 mm, and 35 mm) to generate a comprehensive dataset for training the ML models and capturing the complex temporal evolution of water uptake, thereby enabling predictions for unseen fin geometries. The results indicate that neural network-based models achieve superior predictive performance compared to the other methods. For water uptake training, the mean absolute error (MAE), root mean squared error (RMSE), and coefficient of determination R2 are approximately 2.83, 4.37, and 0.91, respectively. The predicted water uptake shows close agreement with the numerical simulation results. For the prediction cases, the MAE, MSE, and R2 values are approximately 1.13, 1.2, and 0.8, respectively. Overall, the study demonstrates that machine learning models can accurately predict water uptake beyond the training dataset, indicating strong generalization capability and significant potential for improving thermal management system design. Additionally, the proposed approach reduces simulation time and computational cost while providing an efficient and reliable framework for modeling complex sorption processes in thermal energy storage systems.

This present work investigates the potential advantages of combining exothermic catalytic methane oxidation with endothermic catalytic reforming of methane over a dual-catalyst bed to produce syngas, partly similar to the autothermal reforming (ATR) of methane. Noncatalytic oxidation of methane, hot-spot formation, coking, and catalyst stability are the major challenges in the traditional ATR process, and using two catalysts to sequentially combine the oxidation and reforming was shown to address the aforementioned challenges. An oxidation catalyst (Pd/CeO2/Al2O3) and mesoporous alumina-supported (MAl) bimetallic RhNi and NiCo catalysts as reforming catalysts were chosen and packed in a fixed-bed catalytic reactor as layers and in a blended form. The sequential layer of Pd/CeO2/Al2O3 and NiCo/MAl, as well as the blended form of Pd/CeO2/alumina and RhNi/MAl, was able to reform methane using steam and oxygen as the oxidant feed to produce syngas with excellent catalyst stability. The Pd/CeO2/Al2O3 catalyst demonstrated methane oxidation capability, achieving high activity at temperatures as low as 290 °C and generating substantial heat at higher temperatures, sufficient to initiate the downstream reforming reactions over the reforming catalyst. The H2/CO ratio present in the as-produced syngas was higher than that of the CO-rich syngas obtained by dry methane reforming (DRM) and catalytic partial oxidation (CPOX) but lower than the ratio obtained from steam reforming of methane (SRM) and ATR processes. DFT studies revealed that the combination of exothermic oxidation and endothermic reforming in a dual-catalyst bed improved the activity of reforming and enhanced the syngas production as well as the catalyst stability.

ABSTRACT In fixed-bed reactors, the use of large particles in thermally thick regimes is common, which underscores the need for a comprehensive understanding of intraparticle processes. However, integrating detailed particle models into fixed-bed simulations is computationally expensive. This study introduces a tabulation method for biomass conversion’s drying and pyrolysis stages, where significant mass exchange to the gas phase occurs. The particle mass, particle surface temperature, and heat transfer rate to the particle are selected as control parameters to tabulate the particle conversion rate. The drying and pyrolysis of particles are pre-simulated under various conditions, including different moisture and ash content, and all the relevant variables are stored in tables as a function of the controlling parameters. This approach replaces the detailed, computation-heavy particle model in the bed simulation, enhancing computational efficiency. The method’s validity is confirmed by comparing stacked particle conversion results against those from a detailed model. The tabulation method can achieve a speed improvement of two to three orders of magnitude compared to the detailed particle model, a significant advantage when modeling a bed with a substantial number of particles. Additionally, the bed model is integrated with a CFD solver, and a set of tar-cracking reactions are proposed to model a batch reactor’s bed and freeboard. Model predictions align reasonably with experimental data, including maximum temperature and flame propagation speed.