Abstract: Ammonia decomposition is one of the most used pathways for a carbon-free hydrogen production, particularly in systems where ammonia is used as a hydrogen carrier. Modelling and simulation are critical for the general quantification of reaction kinetics, transport limitations, reactor performance, and system-level integration; however, simulation-based studies remain disjointed across modelling scales and synthesis routes. This systematic review examines modelling and simulation studies on ammonia decomposition published in the period between 2014 and 2025, identified through a structured Scopus search and screened using PRISMA methodology. A total of 70 model-ling-focused studies were classified into five modelling categories: reactor-scale numerical and CFD modelling; kinetic and thermochemical mechanism modelling; thermodynamic, energy, and exergy-based process simulation; multiscale or cross-scale modelling; and conceptual or dimensionless modelling frameworks. The results show that reactor-scale CFD and kinetic models constitute most published studies, while integrated multiscale frameworks linking catalyst-scale phenomena to reactor and process-level performance remain limited. Furthermore, the inclusion of technoeconomic analysis (TEA) and life-cycle assessment (LCA) is limited, restricting quantitative evaluation of scalability and system viability. Based on the reviewed literature, key methodological gaps are identified, and a multiscale modelling roadmap is proposed to support the design, optimisation, and scale-up of ammonia-to-hydrogen conversion systems.

Abstract: The objective of this work is to propose a simulation strategy for production planning that is compatible with the dynamism of natural gas processing, especially under open-market arrangement, in which several scheduling simulations must be performed within short time horizons. In such contexts, traditional first-principles-based ap-proaches, although accurate, require prohibitive computational times, motivating the need for an alternative simulation strategy. This work thus proposes a data-driven model built with the aid of machine learning and applied in a case study with historical data from the largest gas processing site in Brazil: Cabiúnas Petrobras asset. Main plant flowrates were selected: 18 targets and 44 input candidates – 1282 observations from three and a half years of operation. Principal Component Analysis was used for order reduction, keeping the 22 main principal components. A forward neural network (2 hidden layers and 225 neurons per layer) was built from training/test sets randomly selected and optimized hyperparameters – learning rate (0.001533) and batch size (8). Training converged in roughly 200 epochs (Adam optimizer), with early stop triggered by validation set. A mean absolute error of 0.0017 (test set) and R2=0.72 were found, a promising result considering plant complexity and data simplicity. Results showed particularly good fit for lighter products (sales gas, natural gas liquid), also indicating an opportunity for further work by including inputs related to liquid fractionation.

Abstract: Filter cake (or cachaza), a residue generated in the artisanal production of panela, represents an under-explored source of renewable energy in the Ecuadorian Amazon. Valorizing filter cake could reduce the use of solid biomass and emissions associated with traditional combustion. Our objective was to determine the energy potential of the biogas obtained and its contribution to the sustainability of the panela (unrefined cane sugar) production system. A sequential procedure was applied that included the physicochemical characterization of filter cake, feed flow modeling, and stoichiometric simulation under mesophilic conditions. The anaerobic digestion of filter cake with the optimal Composition 6 generated up to 1,736.40 m³·day⁻¹ of biogas with 40.7% methane and a calorific value of 14,350 kJ·m⁻³. This was enough to replace 1.24 t·day⁻¹ of wood or 2.38 t·day⁻¹ of bagasse in the production system. This represents an annual saving of 631.08 t of solid biomass, equivalent to conserving 3.63 ha·year⁻¹ of the Amazon rainforest. The TRACI analysis showed impacts on climate change (17.40 kg CO₂ eq/m³) and acidification (0.00516 kg SO₂ eq/m³), attributable to unburned methane and residual H₂S. Meanwhile, the social assessment using the OHSP indicator showed high risks in terms of handling filter cake and cleaning the digestate.

Abstract: Sulfur compounds are extremely toxic and highly corrosive (e.g. mercaptans and hy-drogen sulfide) and are commonly found in natural gas streams and can be damaging even if only minute amounts are present in natural gas streams because it can affect the quality of fuels and cause failure of downstream equipment. Many metal oxides have been used as adsorbent/catalyst for the removal of sulfur compounds from natural gas; however, they vary greatly in how well they can remove sulfur compounds, and the underlying mechanisms of these processes are still not fully understood. Therefore, the purpose of this study was to examine the adsorption/removal performance of many metal oxides on halloysite support at the same conditions to identify the relationship between the electronic properties (specifically bandgap energy) and breakthrough time (a measure of removal/adsorption efficiency). The experimental results indicate large differences in the adsorption performance of the studied oxides and some commercial metal oxides had lower than expected adsorption performance. Conversely, all the studied oxides with the lowest bandgap energies showed higher sulfur compound (e.g. ethyl mercaptan) uptake and longer breakthrough times indicating that the electronic properties of the oxides are important in determining the strength of interaction be-tween the sulfur compounds and the metal oxide. The experimental results from this study will provide understanding of why certain metal oxides may not perform as good as others during natural gas desulfurization and assist in developing a systematic method for selecting adsorbents/catalysts that will improve the overall natural gas desulfurization process. Furthermore, incorporating palladium oxides into the base catalyst formulation achieved a maximum breakthrough time of 630 minutes at 25°C 200 psi, and 36 mL/min. These findings provide critical insights for developing catalysts that integrate metal oxides to enhance adsorption efficiency while reducing hazardous byproducts during sulfur compounds (e.g. mercaptans and hydrogen sulfide) removal from natural gas.

Abstract: The development and deployment of robust technical solutions for sustainability improvement have become increasingly critical in response to growing environmental and social pressures, while maintaining economic viability, particularly in industrial systems that require multi-stage technology implementation. Identifying such solutions requires the systematic treatment of significant uncertainties that affect sustainability-related decision making. Among these, epistemic uncertainty, arising from incomplete or imperfect knowledge, is inherently subjective and, in principle, reducible. Fuzzy set theory provides an effective and well-established framework for representing and managing epistemic uncertainty in sustainability analysis. In this work, a fuzzy decision-making framework is proposed to support multi-stage technology development and deployment for dynamic sustainability performance improvement in industrial systems. The framework integrates comprehensive sustainability assessment with fuzzy representations of epistemic uncertainty to enable consistent comparison of alternative strategies at each implementation stage. It identifies the most appropriate strategy at each stage while ensuring alignment with long-term sustainability objectives. The proposed approach functions as a decision-support tool for guiding adaptive, stage-wise technology deployment under uncertainty. A case study of a nickel electroplating system is presented to demonstrate the applicability and effectiveness of the methodology.

Abstract: With the increasing demand for clean energy and the uncertainty surrounding the application of renewables, recent years have seen ammonia emerging as a viable way to store and transport hydrogen on a large scale. Its increasing importance in national hydrogen policies, as in the case of Brunei, highlights the need to look into technological readiness and global paths of innovation for this novel fuel. This study analyzes the global development of ammonia-based hydrogen production technologies from a methodological perspective and has shown that 708 granted patents to that were systematically screened, sorted and analyzed. A statistically sound retrieval method and screening process, following the PRISMA guidelines, have been employed to categorize the patents by synthesis processes, types of catalyst, and technological field. The results indicate that electrochemical, plasma-based, photocatalysis, and hybrid systems are becoming common paths as low-temperature alternatives, while thermal catalytic breakdown remains the most popular and well-known path to pursue. A range of reactor engineering, system integration, and catalyst design efforts have been undertaken, particularly in Asia. This indicates a high level of industrial and research interest in advancing ammonia-to-hydrogen technologies. These findings offer a clear overview of current technological maturity and emerging innovation trends, supporting long-term transitions toward cleaner hydrogen pathways.

Abstract: Safety management in the chemical process industry remains a critical challenge due to recurring high impact industrial accidents and the limited predictive capability of conventional threshold based safety systems. Traditional PLC–SCADA frameworks rely on static alarm limits and reactive shutdown logic, which often fail to detect early stage nonlinear deviations in complex, multivariate processes. This study presents ChemSafeAI+, a machine learning driven dynamic safety and optimization framework designed to augment existing industrial control architectures. The system integrates real-time anomaly detection using gradient-boosting models, predictive analytics, safety action processing, operator aware visualization dashboards, and traceable console logging within a unified, modular architecture. The framework is evaluated using a validated synthetic dataset derived from the Haber–Bosch ammonia synthesis process, capturing realistic thermodynamic, kinetic, and operational variability across 5000 operating scenarios. Experimental results demonstrate strong anomaly detection capability and consistent early warning behavior across multiple abnormal operating conditions. SHAP-based explainability provides both global and local interpretability, aligning model decisions with domain relevant process variables. By combining predictive intelligence with safety oriented decision logic and operator traceability, ChemSafeAI+ demonstrates the feasibility of ML driven supervisory safety systems for proactive risk mitigation and improved operational resilience in industrial chemical environments.

Abstract: This study investigated the kinetics of aptamer-cardiac troponin I (cTnI) interaction to establish a new dynamic quantitative indicator for the rapid, highly sensitive detection of cTnI, a critical myocardial infarction biomarker. The goal was to overcome the limitations of conventional diagnosis based on saturated binding amounts, which takes excessive time for point-of-care testing (POCT). Cyclic voltammetry (CV) was performed on a gold electrode immobilized with double-stranded aptamers, and the interaction kinetics were rigorously analyzed across cTnI concentrations from 10 pg/mL to 90 pg/mL. The adsorption process, quantified by changes in charge amount, was found to follow a similar first-order interaction model. The most significant findings were the establishment of a robust power function (R2=0.9515) relating the cTnI concentration to the derived interaction rate constant. This high explanatory power confirms the predictable and quantitative relationship between concentration and reaction speed. In conclusion, the interaction rate constant is proposed as a novel dynamic indicator for predicting cTnI concentration, providing a crucial technological foundation for developing next-generation, high-speed, high- sensitivity aptamer-based biosensors essential for time-critical POCT applications.

Abstract: Zeolite Y has been considered as one of the most versatile materials that are used in catalysis, adsorption, and separation. However, its inherent microporosity often impedes the diffusion of reactants and products, thus constraining overall performance. This review systematically investigates the major post-synthetic modification strategies intended to mitigate these limitations and to refine the structural and physicochemical properties of zeolite Y. Particular focus is placed on the mechanisms and structural consequences of dealumination, desilication, ion exchange, and surface functionalization, each of which uniquely influences acidity, porosity, and framework stability. The synergistic combination of dealumination and desilication is especially highlighted for its capacity to generate hierarchical structures containing mesoporosity with optimal acidity robustness. Recent developments that integrate the use of microwave and ultrasound-enhanced methods are considered sustainable and energy-efficient solutions that offer accurate control over the framework transformation and shorten processing times. These post-synthetic advancements have led to hierarchical, multifunctional zeolite Y materials that show high levels of catalytic activity, enhanced adsorption capacity, and improved selectivity over a wide range of industrially related reactions. This review concludes how such modification techniques expand the functional range of zeolite Y, thereby enabling its use in new areas of application, including CO2 capture, biofuels production, and environmentally friendly catalytic processes. Future perspectives emphasize ongoing refinement of structure-function relationships, scalability of processes, and integration of modification methodologies to reinforce zeolite Y’s pivotal role in sustainable chemical manufacturing.

Abstract: Aflatoxin B1 is one of the most toxic mycotoxins contaminating animal feed, and bentonite clays are widely used as adsorbents to reduce its bioavailability. The influence of particle size on bentonite adsorption performance, particularly regarding cost-effectiveness of fine fractionation, remains underexplored. This study investigated natural bentonite from the Bijelo Polje deposit (Montenegro) containing ~55% montmorillonite and its size fractions: <0.200 mm (~72% smectite), <0.037 mm (~75% smectite), and <0.005 mm (~91% smectite), obtained by sieving and centrifugation. Fractions were characterized by laser diffraction, chemical composition, cation exchange capacity, and quantitative XRD (Rietveld refinement). In vitro AFB1 adsorption (2–50 mg/L initial concentration, pH 3.0, 0.02% w/v adsorbent) simulated monogastric gastrointestinal conditions. Particle size reduction progressively increased smectite content, CEC (44–70 meq/100 g), and purity, reducing heavy metals to undetectable levels. All purified fractions achieved satisfactory AFB1 binding (>90% at 4 mg/L). The finest <0.005 mm fraction exhibited the highest maximum adsorption capacity (qmax ≈ 240 mg/g) due to superior specific surface area and site accessibility. However, only the <0.005 mm fraction meets EU regulatory requirements (Commission Implementing Regulation (EU) No 1060/2013) for AFB1-binding feed additives (≥70% dioctahedral smectite, low accompanying minerals, >90% binding), as coarser fractions retain excess quartz and calcite. Extensive fractionation, despite higher costs, is essential for regulatory-compliant, high-performance natural bentonite adsorbents.

Abstract: This study presents a systematic investigation of silica extraction from rice husk ash (RHA) using Taguchi L27 orthogonal array optimization methodology. With global rice production generating 31-39 million tonnes of RHA annually, valorization of this agricultural waste addresses both environmental disposal challenges and sustainable silica production needs. The extraction process involved controlled calcination, acid leaching with hydrochloric acid, alkali solubilization using sodium hydroxide, and acid precipitation to produce high-purity amorphous silica. Three critical process parameters—heating temperature (600-800°C), heating time (2-6 hours), and chemical concentration (1-3 M)—were systematically optimized across 27 experimental runs. Statistical analysis identified optimal conditions of 700°C calcination temperature, 4-hour processing time, and 3 M chemical concentration, achieving maximum silica yield of 7.02 g from 10 g RHA (70.2% extraction efficiency). Main effects analysis revealed chemical concentration as the most influential parameter, followed by temperature exhibiting volcano-shaped behavior with peak efficiency at 700°C, and heating time showing positive linear correlation with yield. Characterization confirmed successful extraction of high-purity silica with white appearance, near-neutral pH, bulk density of 180-200 kg/m³, and 3.1% moisture content. The NaOH/CuSO₄ confirmatory test validated silica presence, while absence of HCl reaction confirmed purity. Results demonstrated superior performance compared to conventional methods, with yields exceeding reported alkali hydrothermal extraction (52.8%) and approaching optimized acid leaching ranges (70-90%). The Taguchi optimization approach reduced experimental requirements by 66% compared to full factorial design while maintaining statistical rigor. This research establishes an efficient, scalable methodology for converting agricultural waste into value-added industrial material suitable for construction, ceramics, and environmental remediation applications, contributing to circular economy principles and sustainable materials development.

Abstract: This study investigates the chemical composition, liquefaction behavior, and polyurethane foam (PU) properties of two lignocellulosic biomasses, Red Angico (Anadenanthera colubrina) and Mahogany (Swietenia macrophylla), as potential sources of bio-based polyols. Detailed chemical characterization revealed that Red Angico has high α-cellulose (48.44%) and moderate hemicellulose (25.68%) content, while Mahogany shows the inverse, with high hemicellulose (56.11%) and low cellulose (18.24%), influencing their reactivity during liquefaction. Liquefaction trials using a polyalcohol system (glycerol:ethylene glycol) demonstrated higher conversion efficiency for Mahogany, reaching 93.4% at 180 °C in 60 minutes, compared to 73.9% for Red Angico. Hydroxyl value analysis revealed increasing functionality for Mahogany polyols with time, whereas Red Angico showed declining values, indicating possible recondensation reactions. PU foams were synthesized using the resulting polyols, with compressive strength and modulus increasing with isocyanate index. Red Angico foams, despite lower OH values, displayed superior mechanical performance, attributed to their lower hydroxyl content favoring optimal crosslinking. Water content, used as a chemical blowing agent, negatively impacted compressive strength for both foams due to increased porosity. Results highlight the species-specific influence of chemical composition on liquefaction behavior and foam performance, suggesting tailored processing conditions are essential for maximizing bio-based PU properties.

Abstract: Transitioning to more efficient and digital industrial processes requires plant design methodologies that go beyond traditional approaches and respond to the operational challenges of Industry 4.0. The objective of this study was to integrate Artificial Intelligence (AI) and Augmented Reality (AR) into SLP methodology for the design of a cosmetic emulsion production plant. A case study was developed based on the layout of a previously reported cosmetic plant by creating a preliminary layout using SLP and evaluating it using AI based on technical prompts. Subsequently, the refined model was represented in three dimensions and validated in a real environment using AR. The results show that AI identified opportunities for improvement in operational flows, relationships between critical areas, and space proportions, allowing for precise adjustments without altering the original design logic. Likewise, AI verification and immersive validation using AR confirmed the spatial compatibility of the layout with the selected site, facilitating the early assessment of circulation, access, and volumetric behavior. Thus, the sequential integration of SLP + AI + AR demonstrated its potential to reduce uncertainty in the early stages and move toward modernizing plant design in line with Industry 4.0 principles.

Abstract: Additive manufacturing is one of the most efficient approaches for fabricating components with complex geometries. Among the wide variety of additive manufacturing technologies, extrusion-based processes using gel materials are attracting increasing attention from researchers. In this work, we consider the extrusion of gel materials for 3D printing and propose a method for calibrating additive manufacturing equipment based on optimization of the pre-extrusion and retraction parameters. Experimental studies were carried out on the extrusion of materials with different rheological properties. The capabilities of the proposed calibration method to improve printing quality are demonstrated using gel materials based on partially crosslinked sodium alginate with a viscosity of 1053 Pa·s. A multi-material 3D printing process was also implemented, enabling the combination of different materials within a single fabrication process. To realize the proposed 3D printing approach, we present a custom-built setup that allows the use of two extrusion modules for materials with different physicochemical properties. The capabilities of the developed system are demonstrated by fabricating a structure with an internal hollow channel and a structure based on a sodium alginate–chitosan polyelectrolyte complex.

Abstract: In this study, the dyeing kinetics of polyamide fabrics with acid dyes, Telon Blue M2R, under both conventional and microwave-assisted heating conditions were comprehensively investigated. While the conventional dyeing reaction was completed in 30 minutes, microwave-assisted dyeing was performed in the microwave device for 10 minutes. Dyeing kinetics were investigated as a function of reaction time, reaction concentration and dyeing temperatures. The K/S values (color depth) of the dyed fabrics were correlated with the concentration. A significant reduction in the dyeing process time for polyamide fabric was observed with microwave heating compared to the conventional method. Kinetic analysis revealed that the PSO kinetic model provides a better fit to the experimental data on the diffusion process of acid dye in polyamide fabrics, as evidenced by higher correlation coefficients (R²) compared to the PFO model. The activation energy of the reaction in dyeing was found to be 63.27 kJ/mol, and the Arrhenius constant was determined as 7,20 x 10¹⁰ L/g.min in conventional media and 18,70 x 10¹⁰ L/g.min in microwave media. The Arrhenius factor in the microwave medium was more than two times higher than in the conventional one.

Abstract: Trichloroethylene (TCE) and tetrachloroethylene (PCE) are chlorinated organic liquids widely employed in various industrial processes. However, due to their high toxicity and cancerogenic proprieties, these compounds are recognized as environmental pollutants. Therefore, the removal of TCE and PCE from wastewater is a crucial objective for environmental protection. This work investigated the adsorption capacity of syndiotactic polystyrene (sPS) fibers, activated in the nanoporous crystalline δ form, to remove volatile organic compounds from aqueous solutions. TCE can be adsorbed in the nanoporous crystalline δ form of sPS, leading to the formation of a clathrate structure, in which it acts as the guest molecule. This adsorption mechanism allows for high process selectivity, as well as the capture of even trace amounts (in the ppb range) of the pollutants under consideration, in relatively short times (e.g., 67 hours). Also, a process with two successive adsorption tests was performed replacing the solid used for the first contact with the contaminated solution with fresh δ-sPS fibers. This approach allowed the reduction of TCE concentration down to 8 ppb. In conclusion, δ-sPS nanoporous fibers demonstrated a great potential for the efficient removal of chlorinated organic compounds from wastewater, providing a promising alternative to conventional adsorption processes.

Abstract:

The Bayer process, the dominant method of alumina production for over a century, faces several challenges, including low iron content in bauxite residue, increased caustic alkali consumption and low alumina recovery rates. This article focuses on studying electrolytic reduction processes of bauxite iron minerals in alkaline solutions as a potential improvement to the traditional Bayer process for producing alumina. The research employs a metal mesh cathode at the bottom of an electrochemical cell to simultaneously reduce iron minerals and leach aluminium and silica from coarse boehmite bauxite before milling and high-pressure leaching. Preliminary thermodynamic research indicates that the presence of both hematite (α-Fe₂O₃) and chamosite ((Fe²⁺,Mg,Al,Fe³⁺)₆(Si,Al)₄O₁₀(OH,O)₈) in this type of bauxite helps to achieve a higher iron concentration in the solution. Cyclic voltammetry revealed that, in the initial stage of electrolysis, overvoltage at the cathode decreases as metallic iron deposited and conductive magnetite form on the surface of the particles. After 60 min, the reduction efficiency begins to decrease. The proportion of the current used for magnetization and iron deposition on the cathode decreased from 89.5% after 30 min to 67.5% after 120 min. Studying the electrolysis product using SEM-EDS revealed the formation of a dense, iron-containing reaction product on the particles' surface, preventing diffusion of the reaction products. Mössbauer spectroscopy of the high-pressure leaching product revealed that the primary iron-containing phases of bauxite residue are maghemite (Fe₃O₄), formed during the hydrolysis of sodium ferrite (Na₂FeO₄).

Abstract: Effective management of discharged wastewater quality is crucial for maintaining public health, preserving aquatic ecosystems, and ensuring compliance with environmental regulations. However, spatial and temporal data sparsity remains a fundamental constraint. This review critically examines the role of Geographic Information Systems (GIS) and statistical interpolation techniques in bridging these data gaps to create continuous maps of wastewater quality parameters (e.g., BOD₅, COD, TSS, nutrients). Moving beyond a simple compilation of methods, this paper presents a comprehensive framework that categorizes and evaluates interpolation techniques, ranging from deterministic and geostatistical approaches to emerging machine learning (ML) and hybrid models, based on their ability to address specific challenges in wastewater systems. A key contribution is a meta-analysis of 28 comparative studies, which quantitatively synthesizes evidence on the prediction accuracy (RMSE) of different methods. The results indicate that machine learning and hybrid models significantly outperform deterministic and basic geostatistical methods, with a pooled reduction in RMSE of 18.4% (95% CI: 12.1-24.3%) compared to Ordinary Kriging. We explore applications in pollutant tracking, impact assessment, and infrastructure planning, highlighting how the integration of real-time sensor data (IoT) and remote sensing is transforming static maps into dynamic monitoring tools. Finally, we present a forward-looking roadmap for research, informed by our quantitative findings, emphasizing the need for hybrid modeling frameworks that leverage AI, the development of digital twins for wastewater networks, and the integration of uncertainty quantification into decision-support systems. By quantitatively synthesizing the current state-of-the-art and identifying critical knowledge gaps, this review aims to guide future research towards more intelligent, adaptive, and reliable spatial assessments of wastewater quality.

Abstract: The commitment to the Sustainable Development Goals and the need for increasing circularity of industrial processes call for the exploitation of byproducts to generate value-added chemicals in cost- and energy-advantageous processes. In this process simulation-based research, two technologies were evaluated for the synthesis of isoamyl acetate from fusel oil: A) an indirect process, and B) a direct process using reactive distillation. Aspen Hysys v14.0 was used for the simulation. A sensitivity analysis was performed to identify the influence of operating parameters on product purity, isoamyl acetate recovery and productivity, and energy consumption. Technology B was found to be the most favorable, obtaining 22.27 kg/h of isoamyl acetate with a purity of 98%. The total consumption of cooling water and heating was 87.6 MJ/h and 88.22 MJ/h, respectively. Based on the best conditions, a technical-economic analysis was performed that demonstrated the viability of the process, obtaining a net present value (NPV) of US$3,587,110/year, an internal rate of return (IRR) of 38.95% and a payback period (PP) of 5.05 years. If acid recirculation is considered in the process, an NPV of US$7,232,950, an IRR of 56.34%, and a PP of 3.56 years are obtained.

Abstract: This research optimized the parameters of Ohmic Heating Pasteurization (OHP) for passion fruit juice utilizing a Box–Behnken design. Researchers assessed how temperature (75–95°C), holding time (15–45 s), and voltage gradient (10–30 V/cm) influence the system performance coefficient (SPC), total color difference (ΔE), and vitamin C retention. The op-timal conditions were 82.5°C, 25 s, and 18.5 V/cm, achieving a microbial reduction exceeding 5 log CFU/mL, 45% retention of vitamin C, minimal color alteration (ΔE = 7.56), and an SPC of 0.85. Traditional pasteurization (85°C, 25 s) preserved merely 10% of vitamin C, induced a more significant color alteration (ΔE = 14.87), and resulted in a reduced SPC (0.54). The OHP-treated juice demonstrated superior antioxidant activity and prolonged shelf life (70 days at 8°C) in comparison to conventionally processed juice (28 days). The research as-sessed enzymatic activity (POD, PPO), demonstrating that OHP achieved superior inactiva-tion, thereby enhancing color stability and long-term product quality. These results indicate that OHP is a promising and sustainable thermal technology for high-acid fruit juice pas-teurization, combining energy efficiency with superior quality retention and enzyme inac-tivation.