Search Results (57)

Routine model-based diagnosis of sulfuric acid converters can attribute performance loss to catalyst deactivation, fouling, bypass, or maldistribution before establishing whether routine measurements distinguish these causes. This study develops the Converter Condition Inference Framework (CCIF) for four-bed double-contact SO₂ converters, placing a practical identifiability gate before mechanism-specific interpretation. A reported-parameter reaction and energy balance kernel using published feed, kinetics, and physical reaction enthalpy reproduces the industrial benchmark only approximately. An energy balance check indicates that the reported first-bed conversion and temperature require an effective enthalpy of about 1.6 times the physical value under the adopted thermochemical basis. At the reference condition, the uncertainty-scaled local sensitivity matrix has rank 1 for a six-state vector; this result persists across the tested inlet temperature range and rate and heat release perturbations. In the equilibrium-limited reduced model, catalyst activity loss and fouling down to 40% of fresh activity produce no resolvable change in conversion, outlet SO₂ slip, or bed temperatures, whereas bypass alters the temperature signature. Even idealized bed-resolved catalyst condition observations raise the rank only to 5. CCIF therefore reports the evidence-supported state class and required measurement upgrades rather than a validated diagnosis. Unit-specific calibration and catalyst-side evidence remain necessary before mechanism-specific maintenance decisions. Full article

The rising demand for low-carbon hydrogen has intensified interest in biogas-reforming as a renewable, decentralized alternative to fossil-based production. However, published assessments of the competing reforming routes rely on inconsistent system boundaries and assumptions, which prevents a reliable comparison between them. This study addresses that gap by evaluating four biogas-reforming pathways, steam reforming (SR), dry reforming (DR), partial oxidation (POX), and autothermal reforming (ATR), within a single, consistent techno-economic and environmental assessment framework. Thermodynamic performance, life-cycle global warming potential, and the levelized cost of hydrogen (LCOH) are analyzed together, with cost uncertainty quantified through a 10,000-iteration Monte Carlo simulation and a six-parameter sensitivity analysis. The harmonized comparison shows that steam reforming is simultaneously the most favorable route for hydrogen yield, energy efficiency, levelized cost, and life-cycle carbon intensity, establishing it as the benchmark, whereas dry reforming, although it uniquely consumes CO₂, incurs the highest cost and the greatest catalyst-deactivation risk. Across all pathways, plant scale and capacity factor emerge as the dominant cost drivers, and biogas-derived hydrogen remains more expensive than conventional gray hydrogen, so its deployment depends on impurity-tolerant catalysts, improved heat integration, life-cycle-verified CO₂ management, and supportive low-carbon incentives. The novelty of this work lies in its unified, multi-criteria framework, which enables a like-for-like ranking of biogas-reforming routes and clarifies the conditions under which each becomes competitive. Full article

The circular economy increases the importance of quality and traceability in functional materials produced from industrial, municipal, and agricultural waste. However, the selection of Industry 4.0 (I4.0) technologies in waste valorization remains unsystematic and weakly linked to material and process requirements. This study develops a decision-support model for selecting I4.0 solutions for quality assurance and traceability in the production of waste-derived functional materials. The model was grounded in a two-track scoping review reported according to PRISMA-ScR. Track A mapped the functions of I4.0 technologies in quality control and manufacturing traceability, while Track B identified challenges related to waste-derived biopolymers, composites, sorbents, catalysts, and nanomaterials. Among 322 primary material studies, microstructure, morphology, and porosity dominated (69.9%), whereas traceability was underrepresented and requires further validation. In Track A, 629 studies were unambiguously mapped, with inspection and monitoring as the dominant functions. Material-specific challenges were then linked to required functions and candidate technologies, which were ranked by their fit to the decision situation and literature support. The resulting model covered 48 material situations and 95 function–technology pairs. It supports technology selection based on material category, waste type, process stage, and quality or traceability requirements rather than technology availability alone. The proposed approach may be extended to waste-derived materials for other application sectors, including construction and transport, subject to sector-specific, material-specific, and industrial validation. Full article

Rapid estimation of the melt flow index (MFI) is essential for the timely control of industrial polypropylene polymerization because conventional plastometer measurements require offline sampling and introduce analytical delays. This work investigates the physicochemical limits of MFI model transferability using 425 reactor-grade polypropylene production runs spanning homopolymer, random copolymer, and impact copolymer architectures. Principal component analysis (PCA), partial least squares (PLS), PCR-Ridge, Random Forest, Extra Trees, and Gradient Boosting were evaluated using within-pool, repeated five-fold, and product group validation. The latent structure, predictive performance, and dominant process descriptors were strongly architecture-dependent. Hydrogen-related variables remained central to molecular weight control, whereas comonomer descriptors, catalyst and donor variables, hydrodynamic conditions, and second reactor variables gained importance as compositional and morphological complexity increased. Nonlinear ensembles improved prediction within heterogeneous pools, but product group validation still revealed transferability losses when models crossed architecture-dependent process–structure–property domains. Repeated cross-validation and preprocessing sensitivity analyses confirmed that the principal model rankings and architecture-dependent conclusions were stable under resampling and correlation filtering. These results show that the polymer architecture defines a practical applicability boundary for industrial MFI soft sensors and supports architecture-specific or architecture-routed calibration when universal models fail cross-family validation. Full article

In the renewable energy-driven “green electricity–green hydrogen–green ammonia” pathway, the development of low-temperature and low-energy-consumption ammonia synthesis technologies is of great significance. In this work, a plasma-catalytic ammonia synthesis system was established using a coaxial dielectric barrier discharge (DBD) reactor. The effects of different catalysts, including Ag, Cu, γ-Al₂O₃, BaTiO₃ and Co/BaTiO₃, Ni/BaTiO₃ on ammonia synthesis performance were systematically investigated. The reaction process was analyzed using voltage–current waveforms, Lissajous figures, and optical emission spectroscopy (OES). The results show that different catalytic systems have a significant influence on ammonia synthesis performance, with the promotional effect ranked as follows: Ni/BaTiO₃ > Co/BaTiO₃ > BaTiO₃ > Ag > γ-Al₂O₃ > Cu. Among them, Ni/BaTiO₃ exhibited the best performance. Under the conditions of N₂:H₂ = 1:1 and a gas flow rate of 2.5 L/min, the NH₃ synthesis rate reached 259.48 μmol/min, and the maximum energy efficiency reached 1.40 g-NH₃/kWh. Catalyst characterization results indicate that the BaTiO₃ support maintained a stable crystal structure, while the loaded metal species were highly dispersed and uniformly distributed on the support surface, which is beneficial for the adsorption and conversion of reactive species on the catalyst surface. Discharge characteristic analysis shows that the introduction of BaTiO₃ enhanced the local electric field and improved the uniformity of micro-discharges, while the further incorporation of metal active components strengthened the micro-discharge behavior. OES results reveal that the intensities of characteristic emission lines, such as NH, N₂⁺, and Hα, were significantly enhanced in the Ni/BaTiO₃ system, facilitating the formation and conversion of NH_x intermediates. The superior performance of Ni/BaTiO₃ is attributed to the coupling between BaTiO₃-induced dielectric enhancement and Ni-promoted surface hydrogenation and NH₃ desorption. This work provides mechanistic insight into catalyst-dependent DBD plasma-catalytic ammonia synthesis and offers an experimental basis for the further optimization of plasma-based ammonia production. Full article

The sluggish kinetics of the hydrogen oxidation reaction (HOR) in alkaline media remain a primary bottleneck for anion exchange membrane fuel cells (AEMFCs), necessitating catalysts that synergistically optimize the adsorption of hydrogen (*H) and hydroxide (*OH) intermediates. Herein, we construct a well-defined heterointerface between Pd clusters and CeO₂ on nitrogen-doped carbon (Pd-CeO₂/NC) to electronically engineer the active sites. Spectroscopic studies and theoretical calculations collectively reveal that CeO₂ acts as an electron acceptor, drawing electrons from Pd via interfacial Pd-O-Ce bridges. This charge transfer induces a downshift of the Pd d-band center, which optimally tunes the adsorption strength of both *H and *OH at the interface, thereby breaking the scaling relationship that limits HOR activity. The resulting Pd-CeO₂/NC catalyst achieves an exceptional exchange current density of 3.66 mA cm⁻², surpassing that of commercial Pt/C by a factor of two and ranking among the best reported noble metal catalysts. Furthermore, it exhibits outstanding long-term stability and remarkable CO tolerance, retaining high activity in an atmosphere containing 1000 ppm CO. This work underscores the profound efficacy of metal–oxide heterointerface engineering in regulating electronic structures for multi-intermediate optimization, offering a viable design principle for advanced alkaline HOR electrocatalysts. Full article

Methane steam reforming (MSR) is the most widely used industrial process for hydrogen production. However, catalyst deactivation, carbon emissions, and energy inefficiencies limit its sustainable performance. Therefore, improving catalyst selection and optimizing operating conditions are essential for efficient hydrogen generation. This study proposes an artificial intelligence-driven framework to optimize catalyst–condition combinations in MSR systems. The framework integrates Hybrid Golden Beetle Optimization (HGBO), VIKOR-based multi-criteria decision making, and Convolutional Long Short-Term Memory (ConvLSTM) modeling. HGBO explores the solution space and generates Pareto-optimal combinations of catalysts and operating conditions. These solutions are then ranked using the VIKOR method. The ranking considers hydrogen yield, methane conversion, energy efficiency, CO₂ emissions, and catalyst lifetime. Economic feasibility is also included in the decision process. ConvLSTM modeling captures spatiotemporal relationships in catalyst and process data and predicts catalyst degradation under different operating conditions. The framework is evaluated using 620 experimentally reported MSR cases collected from the published literature within industrial ranges of 600–1200 °C, 1–40 bar, and H₂O/CH₄ ratios of 1–6. The optimized configurations achieve hydrogen yields up to 98.5%, energy efficiency approaching 99%, and reduced CO₂ emissions of about 0.85 kg h⁻¹. The results provide practical guidance for catalyst selection and process optimization in industrial hydrogen production systems. Full article

In order to reach net-zero by 2050, we need to have strong decarbonization policies, especially in hard-to-abate clean-ups like steel (8% of the global emissions), cement (7%), and power generation (30%), and negative emissions through direct air capture (DAC) and bioenergy with carbon capture and storage (BECCS). This review paper summarizes the progress in CO₂ capture, compression, transportation, and storage technologies between 2020 and 2025, including energy penalty (20–40%) and cost (15–30%) reductions, with innovations such as metal–organic frameworks (MOFs), bio-inspired catalysts, ionic liquids, and artificial intelligence (AI)-based optimization. This paper, as a new input into the carbon capture and storage (CCS) field, uses the Weighted Sum Model (WSM) as a multi-criteria decision-making tool to rank the best technologies in the capture, storage, monitoring, and transportation sectors. The weights of the criteria are calculated based on Shannon entropy, and the assessment is performed in three conditions, namely, optimistic, pessimistic, and expected. The weights are computed with sensitivity analysis to make the assessment robust. The viability of key projects, such as Northern Lights (Norway, 1.5 MtCO₂/year), Porthos (The Netherlands, 2.5 MtCO₂/year), Quest (Canada, 1 MtCO₂/year), and Petra Nova (USA, 1.6 MtCO₂/year), is evident, and it is projected that, globally, CCS will reach 49 MtCO₂/year across 43 plants in 2025. The review incorporates socio-economic and environmental justice, including barriers such as high costs ($30–600/MtCO₂), energy penalties (1–10 GJ/tCO₂), and opposition between people (20–40% in EU/US). In comparison with previous reviews, this article has a more comprehensive focus, provides quantitative synthesis through WSM, and discusses the implications for researchers, policymakers, and stakeholders towards achieving faster CCS implementation on the path to net-zero. Full article

Traditional oxidative dehydrogenation of n-butene has typically required relatively high operating temperatures (400–500 °C), which has driven increasing interest in the development of catalysts capable of delivering high activity at lower temperatures. In this study, zinc ferrite (ZnFe₂O₄-ST) was successfully synthesized via hydrothermal hydrolysis of Zn–Fe oleate and demonstrated remarkable catalytic performance for the oxidative dehydrogenation of n-butene under mild conditions. At 300 °C, ZnFe₂O₄-ST achieved a conversion of 72.9% with 92.1% selectivity toward 1,3-butadiene, a result that, to the best of our knowledge, ranks among the best reported in the literature. By contrast, ZnFe₂O₄ prepared by conventional coprecipitation (17.2% conversion with 91.3% selectivity) and sol-gel (10.1% conversion with 86.4% selectivity) methods showed much lower activities, highlighting the critical influence of synthesis strategy on catalytic performance. To better understand the origin of these differences, a detailed structural and physicochemical characterization was undertaken using X-ray diffraction (XRD), thermogravimetric analysis (TGA), transmission electron microscopy (TEM), N₂ adsorption–desorption, X-ray photoelectron spectroscopy (XPS), H₂-temperature-programmed reduction (H₂-TPR), temperature-programmed re-oxidation (TPRO), and NH₃-temperature-programmed desorption (NH₃-TPD). These analyses revealed that the as-synthesized ZnFe₂O₄-ST possessed a significantly smaller average particle size, a larger specific surface area, and superior reducibility compared with the other samples. These properties are believed to be the key factors underpinning its outstanding catalytic behavior and provide important insights into the design of efficient low-temperature catalysts for selective oxidative dehydrogenation. Full article

In Philippine state universities and colleges (SUCs), faculty are mandated to balance instruction, research, and extension as “trifocal” functions. Yet, research often competes with heavy teaching loads, administrative work, and community engagement, especially in science disciplines that demand laboratory-based and fieldwork. This qualitative multiple-case study examined how twelve science faculty members across academic ranks in a Philippine SUC system enact the research function within their trifocal roles. Drawing on semi-structured interviews, institutional and policy documents, and cross-case analysis, this study employed a case study design through the lens of systems thinking to identify how research function is embedded in institutional structures and professional life-worlds. Findings show that faculty construct research as (1) a catalyst that propels instruction and anchors extension programs; (2) a strategic requirement intertwined with promotion and career progression; and (3) a relational and infrastructural practice dependent on collegial networks, mentoring, and institutional support systems. Feedback loops link these themes wherein research output fuels promotion and time protection, which, in turn, shape opportunities for further research and mentoring. Additionally, verbatim accounts reveal how faculty members navigate structural pressures, such as bureaucratic processes and workload policies, while framing research as a moral and professional responsibility. This article argues that designing research support in SUCs requires moving beyond compliance-driven metrics to system-level arrangements that honor research as a form of scholarly work deeply connected with teaching quality and community impact. Implications are suggested for workload policy, mentoring, and research-capable learning environments in the Philippines and comparable higher education contexts. Full article

This work presents an integrated Python-based framework to optimize the ethanol-to-1,3-butadiene conversion over a K₂O:ZrO₂:ZnO/MgO–SiO₂ catalyst, a sustainable alternative in decarbonizing plastics and rubber manufacturing. Thermodynamic evaluations confirmed the feasibility of all elementary steps, while kinetic modeling identified the butadiene-forming reaction as the most sensitive step. Experimental data were analyzed using multivariate surface-response methods, revealing an optimal operating window of 350–375 °C and 0.93–1.24 h⁻¹. A Random Forest model (R² = 0.91) ranked weight hourly space velocity (WHSV) and selectivity descriptors as the most dominant variables, providing a quantitative basis for data-driven process intensification. Full article

In this work, the kinetics of the redistribution of oils, resins, and asphaltenes in high-viscosity oil from the Karazhanbas field (Republic of Kazakhstan) were investigated. This was achieved with an ultrasonic treatment (22 kHz, 50 W) in the presence of a zeolite catalyst (1.0 wt%). The parameters of the technological process were established as a temperature range from 30 to 70 °C and an exposure time of 3 to 11 min. This allowed us to increase the oil content by 14.8% and decrease the concentration of resins by 12.2% and asphaltenes by 2.6%. Conversion schemes (“oils ↔ resins” and “resins ↔ asphaltenes”) were developed, which made it possible to determine the main direction of the reaction processes. The most rapid process is the conversion of resins to oils (k₂ = 0.1148–0.1860 min⁻¹). The process of the cracking of asphaltenes with the formation of resins (k₄ = 0.1023–0.1413 min⁻¹) ranks second in rates. Condensation reactions, including the transition of oils to resins (k₁ = 0.0175–0.0252 min⁻¹) and resins to asphaltenes (k₃ = 0.0139–0.0194 min⁻¹), occur significantly more slowly. The calculated activation energies (7.0–10.4 kJ/mol) show that the cavitation treatment of high-viscosity oil in the presence of a catalyst effectuates the processing of heavy oil with minimal energy consumption. A group composition analysis of the light and middle oil fractions demonstrated an increase in paraffinic, naphthenic, benzenic, and olefinic hydrocarbons, with a simultaneous decrease in naphthalenes and heteroatomic compounds. The results obtained confirm the effectiveness of ultrasonic–catalytic treatment for the structural cracking of high-viscosity oil and the formation of lighter hydrocarbon fractions. Full article

Lignin pyrolysis is a pivotal route for biomass valorization, yet the intricate radical reaction network involved results in ambiguous hydrogen/oxygen transfer pathways and product formation mechanisms, severely impeding precise control over directed conversion processes. This study employed a combination of multi-isotope tracing techniques and GC-MS analysis to elucidate the formation mechanisms of four phenolic products during the 500 °C hydrothermal pyrolysis of dealkaline lignin. Experiments using D₂O and H₂¹⁸O revealed that the M + 2 signal was predominantly derived from double deuterium substitution, with an abundance difference spanning 13–81 folds. Phenol exhibited the highest M + 1 abundance (3.947) due to the full exposure of its exchangeable hydrogen sites, while its M + 2 abundance ranked second only to that of 2-methylphenol. For 2-methylphenol, the hyperconjugation effect of the ortho-methyl group activated the phenolic structure, leading to the highest M + 2 abundance among all products (M + 2/M + 1 = 2.3). In contrast, 3-methylphenol showed relatively low abundances (M + 2/M + 1 = 1.67) because the meta-methyl group lacked activating effects and introduced steric hindrance. For guaiacol, the steric hindrance of the methoxy group completely overshadowed its electronic activation effect, resulting in the lowest M + 2 abundance (1.545). CD₃OD tracing experiments and the absence of detectable M + 3 peaks confirmed that the methyl groups in 2-methylphenol and 3-methylphenol were entirely endogenous to the structural units of lignin itself. By precisely tracking the migration pathways of hydrogen and oxygen, this study revealed that hydrogen transfer dominated the pyrolysis process, while oxygen transfer was hindered and methyl groups exhibited endogenous characteristics. These findings establish a mechanistic foundation for designing efficient catalysts tailored to lignin pyrolysis and for rationally steering product selectivity. Full article

In this work, four different magnetic Fe₃O₄ nanoparticles are prepared via solvothermal method. According to the morphology, the products can be divided into flower-like Fe₃O₄ (F-Fe₃O₄), solid spherical Fe₃O₄ (S-Fe₃O₄), hollow spherical Fe₃O₄ (HO-Fe₃O₄), and hexahedral Fe₃O₄ (HE-Fe₃O₄). A set of measurements is performed to confirm the structure, composition, and pore properties of the obtained materials. The catalytic activities of the prepared materials are examined and compared. The results prove that the four materials have an intrinsic catalytic property. HO-Fe₃O₄ ranks first in the catalytic activity mainly due to its large surface area and reasonable element composition. The maximum specific saturation magnetization and specific surface area of HO-Fe₃O₄ are 72.94 emu/g and 42.60 m²/g. Fe²⁺/Fe³⁺ in HO-Fe₃O₄ is 51.5%. It is found that HO-Fe₃O₄ possesses fantastic stability and perfect reproducibility as it is used as a catalyst several times without significant loss in its activity. Full article

The synthesis and electrocatalytic properties of amorphous first- and third-row transition metal sulfides (a-TMS) for green hydrogen generation have been comprehensively reviewed. These electrocatalysts can be prepared by several solution processes, including chemical bath deposition, electrodeposition, sol–gel, hydrothermal reaction and thermolysis. The deposition method strongly influences the electrochemical properties of the synthesized a-TMS electrocatalyst. Based on overpotential at 10 mA/cm², the electrocatalytic activity of mono-metallic a-TMS for hydrogen evolution is ranked as follows: a-NiS_x > a-CuS_x > a-CoS_x > a-WS_x > a-FeS_x. The best performing a-NiS_x prepared by chemical bath deposition has an overpotential at 10 mA/cm² of 53 mV and Tafel slope of 68 mV/dec in 1 M KOH electrolyte. The integration of Ni into the a-TMS network structure is crucial to achieving high activity in multi-metallic a-TMS electrocatalyst, as demonstrated by the bifunctional (NiFe)S_x/NiFe(OH)_y nanocomposite catalyst. The critical role of Ni in a-TMS catalyst design can be attributed to the lower free energy change for hydrogen adsorption on Ni. Finally, the emerging catalyst design strategy of amorphous–crystalline heterostructures with a three-dimensional morphology will be discussed together with the need to identify hydrogen adsorption sites on a-TMS electrocatalysts in future. Full article