Abstract: Chronic kidney disease (CKD) is a progressively worsening condition that erodes renal function over time, reduces quality of life, and can ultimately culminate in kidney failure with far-reaching systemic complications. In addition to reduced filtration, worsening kidney function disrupts mineral homeostasis and leads to CKD–mineral and bone disorder (CKD-MBD). Dysregulated calcium handling and maladaptive endocrine responses contribute to bone pathology and increase cardiovascular calcification risk; therefore, serial calcium monitoring remains clinically relevant for longitudinal CKD management. Conventional calcium measurements are typically obtained with centralized analyzers or laboratory assays (e.g., colorimetry and electrode/optical readouts). Despite high accuracy, the required instrumentation, controlled operating conditions, and pretreatment steps complicate rapid point-of-care deployment, especially when only microliter-scale biofluids are available. Accordingly, this study develops a finger-actuated microfluidic colorimetric platform capable of determining calcium ion concentrations in human biofluids, such as whole blood, serum, and urine. The platform integrates a three-dimensional PMMA/paper microchip with a compact reader that maintains stable temperature control while enabling CMOS-based optical detection. With just 6 μL of sample, a brief finger press propels the biofluid across an internal filtration layer, generating serum or cleaned urine that subsequently reacts with a pre-deposited murexide reagent. Under optimized conditions (1.6% reagent, 50°C, 3 min), the signal follows a strong logarithmic relationship with calcium concentration (Y = 47.273 ln X + 28.890; R² = 0.9905), supporting quantification over 1–40 mg/dL and a detection limit of 0.2 mg/dL. Across 80 clinical CKD specimens spanning serum, whole blood, and urine, results aligned closely with the NM-BAPTA reference assay, with R² values exceeding 0.97.

Abstract: A laboratory–scale mechanical draft cooling tower equipped with eight sections of perforated inclined plates was designed to determine the effect of operating conditions on the volumetric mass transfer coefficient (kya) between water and air. A three–factor, three–level design of experiments (DOE) was implemented, considering liquid mass flow rate L (120, 240, and 360 kg/h), gas mass flow rate G (36, 57, and 75 kg/h), and top water temperature TL2 (50, 60, and 70◦C). A total of 54 runs were performed, and the global volumetric mass transfer coefficient was calculated by combining energy and mass balances with the Mickley method. The experimental data were fitted to a power–law correlation using multivariable regression. The ANOVA showed that TL2 is the dominant factor, followed by L, whereas the influence of G is comparatively small in the studied range. The selected correlation, based on the nominal gas flow rate, achieved R2=0.869 and a RMSE of 5930 kg/(m3h). The kya values were found in the range from 4600 to 62000 kg/(m3h). Vertical temperature profiles of water and air along the column revealed that, for high liquid flow rates, most of the cooling occurs in the lower stages, suggesting that the upper sections are underutilized.

Abstract: Water pollution due to insufficient wastewater treatment is a global concern. In this paper coagulation and flocculation as a tertiary unit process was investigated to find the solution for a non-complying wastewater treatment facility. The Palapye Pond Enhanced Treatment and Operation (PETRO) system has not been compliant for a long time with effluent characterised by high turbidity, Biological Oxygen Demand/Chemical Oxygen Demand (BOD/COD), Total Suspended Solids (TSS), Nitrates (NO3), and Phosphates (PO4.) The effluent from the plant is released into the stream that drains into the nearby Lotsane dam, posing a lot of danger to the water quality of the dam. The main objective of the project was to investigate the effect of coagulation and flocculation processes at the secondary stage of the wastewater treatment. Response Surface Methodology (RSM), Central Composite Design (CCD) and Multi Response Surface (MRS) were used to optimize the coagulation process and generate regression models to predict the coagulation and flocculation. The performance was evaluated using turbidity, Colour, COD and TSS as response variables. Response surface analysis indicated that the experimental data could be adequately Fitted to quadratic polynomial models. Under optimum conditions the removal efficiency for Al2(SO4)3·18H2O: 91.1% (turbidity), 88.2% (colour), 58.9% (COD), 83.0% (TSS); for FeCl3·6H2O: 93.2%, 88.7%, 63.8%, 91.3%; for Moringa: 91.8%, 85.4%, 56.6%, 83.7%. The optimal removals based on MRS for Al2(SO4)3.18H2O, FeCl3.6H2O and Moringa were 90.7%, 89.7%, 59.9% and 88.5%; 94.7%, 90.8%, 58.1% and 93.8%; 94.0%, 87.2%, 60.1% and 82.1% for turbidity, colour, COD and TSS respectively. This research has demonstrated that the coagulation/flocculation process can be incorporated into the secondary stage of the wastewater treatment facility and the treatment process optimized using RSM, CCD and MRS. The study introduces comparative evaluation of three coagulants within a single RSM-CCD optimization framework, employing desirability functions for multi-response optimization.

Abstract: This study presents the development and evaluation of surface functionalized solidly mounted resonators (SMRs), including custom UWAR devices and commercial Sorex sensors, for the detection and classification of plant-emitted volatile organic compounds (VOCs). The sensors were tested against linalool, trans-2-hexenal (T2H), and D-limonene at different concentrations under both dry and humid conditions (up to 33% RH). A Python-based signal-processing workflow was established to filter frequency responses and extract key features, such as baseline, saturation point, and frequency shift (Δf). Adsorption behaviour was modelled using the Freundlich isotherm, showing good agreement with experimental data and suggesting heterogeneous, multilayer adsorption on CH₃-terminated EC surfaces. A 2D polar classification framework combining vector-normalized Δf values from UWAR and Sorex sensors enabled clear separation of the VOCs. The results highlight the complementary performance of the two types of SMR sensors and demonstrate that feature-engineered resonant devices, combined with computational classification, offer strong potential for future use in plant health monitoring systems.

Abstract: This review examines implementation dimensions of integrated lemon biorefinery systems, including cascade valorisation design, circular-economy integration, life-cycle assessment, techno-economic feasibility, and regulatory frameworks. Bibliometric analysis of Web of Science data (2015–2025) reveals exponential growth in citrus-biorefinery research, with lemon representing a burgeoning subset. Techno-economic assessments indicate that cascade biorefineries recovering essential oils, pectin, polyphenols, nanocellulose, and bioenergy can achieve cumulative revenues of USD 400–650 per tonne of dry peel. Whilst small-scale units (<500 tonnes/year) struggle to achieve viability, industrial simulations demonstrate Internal Rates of Return exceeding 18% at processing scales above 100,000 tonnes annually (2025 basis). Life-cycle assessments confirm environmental benefits, with greenhouse gas reductions of 60–85% relative to conventional disposal. Critical success factors include adopting green extraction technologies to preserve bioactive integrity and mitigating D-limonene inhibition in downstream anaerobic digestion. These findings establish lemon biorefineries as technically mature, economically viable pathways for circular bioeconomy transitions, provided regulatory hurdles—Novel Foods authorisation (EU) and GRAS determination (US)—are effectively navigated.

Abstract: This article presents a dataset generated for a techno-economic assessment (TEA) of the methanol-to-jet (MtJ) fuel production pathway. The dataset was produced using a large-scale Monte Carlo (MC) sampling approach applied to a steady-state process model implemented in Aspen Plus V14. The techno-economic evaluation was conducted using an external cost model, with subsequent data processing performed in Python. In total, three million individual data points were generated by varying key technical and economic input parameters within predefined ranges and are under public access. For each MC sample, the net production cost on a mass basis (NPC_m, EUR _kgjet-fuel^-1) of synthetic jet fuel was calculated as the primary economic performance indicator. The dataset comprises both the sampled input parameters and the corresponding techno-economic output variables and is intended to support transparency, reproducibility, and further uncertainty analysis of MtJ fuel production pathways.

Abstract: The Zeta-Minimizer Theorem (ZMT) provides a variational framework deriving num-ber-theoretic structures (primes, zeta) as shadows of optimization in measure spaces with helical symmetries. Here, we apply ZMT heuristics to superconductivity phase diagrams under pressure, modeling Tc(P) behaviors as projections of Gibbs-like free energy landscapes. Ideal mixtures (convex weighted exponential decays) capture monotonic suppression or activation, while non-ideal excess quadratic terms (from Hessian distortions) generate domes via non-convexity and phase coexistence shadows. Across seven diverse case studies—iron pnictides, cuprates, hydrides (H₃S/Y-H), nickelates, borides (ZrB₁₂), and chalcogenides (PdSSe)—ZMT-inspired fits (e.g., Margules-like excess) achieve R² > 0.95, unifying classical thermodynamics (Gibbs-Duhem equilibria) with quantum scales (frequency embedding via spectral minima). This heuristic bridge complements Cooper pair theory, offering predictive insights for material design without overfitting. ZMT demotes microscopic mechanisms to derived artifacts of deeper variational principles, suggesting new avenues for room-temperature superconductivity.

Abstract: This study aimed at producing galactooligosaccharides (GOS) from Porungo cheese whey in immobi-lized enzyme bioreactors. The β-galactosidase was produced, concentrated, and immobilized on chi-tosan-genipin supports. Initially, GOS production was conducted in conical flasks, investigating three different variables: enzyme concentration (50 U/mL - 150 U/mL), Porungo cheese whey concentration (200 g/L - 400 g/L), and temperature (37 – 43 ºC). The highest GOS yields (15.24 %) occurred under intermediate process conditions (100 U/mL, 300 g/L, 40 ºC), reaching a GOS concentration of 27.04 g/L. These conditions were then used in a packed-bed column bioreactor operated in batch mode, achieving yields of 19.72 %. Repeated batches were carried out, and the system was stable until the fifth cycle, with enzyme activity remaining at 83.56 % of the initial level. Continuous bioreactors were conducted, varying feed flow rates (1 mL/h - 3 mL/h), with the highest yields and lactose conversion occurred for the longest residence time (24.63 % and 68.38 %), respectively, with high GOS concentra-tion (44.14 g/L). Microorganisms isolated from Porungo cheese showed the ability to metabolize the GOS produced, demonstrating its prebiotic potential. This work can contribute to optimizing the production of GOS, an important product for pharmaceuticals and food industries.

Abstract: This study focused on simulating and optimizing the production of cumene (isopropylbenzene) through the alkylation of benzene with propylene using a Beta Zeolite catalyst. Two process configurations were evaluated: one without a transalkylation reactor and another incorporating a transalkylation unit to convert byproducts back into cumene. The process was modeled under steady-state conditions in Aspen HYSYS using plug flow reactors and the Peng-Robinson fluid package, with reaction kinetics derived from literature on zeolite-catalyzed systems. Optimization studies examined the effects of reactor temperature, pressure, and benzene-to-propylene molar ratio. Increasing the reactor temperature to 178°C improved propylene conversion to 96.20%, while raising the pressure from 3540 kPa to 3600 kPa further enhanced conversion to 96.24%. The fresh benzene feed flow rate was initially 127.7 kmol/h, which was reduced to 101 kmol/h, and optimizing the benzene-to-propylene molar feed ratio to approximately 0.75:1 increased cumene production to 135.792 kmol/h while minimizing byproduct formation. A comparative analysis revealed that the configuration without a transalkylation reactor produced 4.171 kmol/h of diisopropylbenzene (DIPB) as waste, representing both economic losses and environmental concerns due to its toxicity. In contrast, the transalkylation reactor enabled DIPB conversion into additional cumene, improving process efficiency and sustainability. These findings demonstrate that optimizing reaction conditions including temperature, pressure, and feed ratios along with integrating a transalkylation step, significantly enhances cumene yield while reducing waste generation, leading to a more viable and environmentally friendly process.

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: Rice husk ash is a valuable material produced from the thermal processing of rice husks, which generate over 100 million tonnes of waste annually worldwide. This review examines the production methods, chemical and physical properties, applications, and challenges associated with rice husk ash utilization across various industries. The production methods analyzed include uncontrolled burning, controlled combustion, fluidized bed combustion, gasification, and pyrolysis. Processing conditions, particularly combustion temperature, significantly influence the final properties of rice husk ash. The optimal temperature range of 500-700°C produces ash with predominantly amorphous silica content ranging from 80 to 95 percent, which provides excellent pozzolanic reactivity. The study evaluates key applications in construction, where rice husk ash serves as a supplementary cementitious material that enhances concrete strength and durability at replacement levels of 10-20 percent. Additional applications include high-purity silica extraction, ceramic manufacturing, environmental remediation through wastewater treatment and heavy metal adsorption, and soil amendment for agricultural improvement. Emerging applications in nanotechnology and advanced materials demonstrate the expanding scope of rice husk ash utilization. The environmental benefits are substantial, including agricultural waste reduction, lower carbon dioxide emissions compared to conventional cement production, and contribution to circular economy principles. However, several barriers limit widespread adoption. These include high variability in ash quality depending on rice variety and processing conditions, absence of standardized production protocols, limited infrastructure in rice-producing regions, and insufficient regulatory frameworks. Current research trends show increasing focus on geopolymer concrete, digital fabrication applications, and high-value silica products. Successful implementation requires coordinated efforts to develop quality standards, optimize processing technologies, establish efficient supply chains, and create supportive policies. Future research should prioritize process optimization for consistent quality, development of application-specific standards, exploration of high-value nanotechnology applications, and comprehensive techno-economic assessments to guide industrial adoption.

Abstract: A ceramic anode made of Sb-doped SnO2 and coated with a photoactive BiPO4 layer was tested for the (photo)electrochemical oxidation of three commonly used pharmaceuticals: atenolol, ibuprofen, and norfloxacin. Light-pulsed chronoamperometry showed that the photoanode responded immediately to illumination. The application of light and current enhanced degradation for all compounds when treated separately. Ibuprofen and nor-floxacin exhibited higher degradation than mineralization, which demonstrates their per-sistent nature. Electric current was essential to achieve efficient degradation and mineral-ization, demonstrating the effectiveness of the electrochemical approach. For multicom-ponent mixtures, applying light resulted in higher mineralization compared to dark con-ditions at low operation currents (0.2 A). At higher currents (0.4–0.8 A), the contribution of light was partially masked by the enhanced electrochemical production of hydroxyl radi-cals. Analysis of individual compounds within the mixture revealed significant im-provements in degradation under light exposure. Overall, these results demonstrate the potential of the Sb-doped SnO2 ceramic photoanode as a cost-effective and promising al-ternative to commercial materials for treating pharmaceutical contaminants.

Abstract: Wastewater-integrated microalgal cultivation offers a sustainable pathway to reduce biofuel production costs while simultaneously addressing nutrient-rich effluent management. In this study, matured compost leachate was systematically evaluated as a sole cultivation medium for Desmodesmus sp. under different dilution regimes, with emphasis on growth kinetics, wastewater remediation efficiency, lipid accumulation behavior, and biodiesel quality. Desmodesmus sp. successfully acclimatized to 100% undiluted matured compost leachate within four days and maintained stable mixotrophic growth without dilution or pre-treatment. Cultivation in undiluted leachate achieved a maximum biomass concentration of 2.69 ± 0.09 g L⁻¹, representing an approximately fourfold increase compared to Bold’s Basal Medium. Concurrently, high treatment efficiencies were obtained, with chemical oxygen demand removal of 82.6%, total nitrogen reduction of 60–72%, and total phosphorus removal of 65–66%, confirming effective integration of biomass production with wastewater remediation. Lipid biosynthesis was strongly governed by nitrogen availability, with lipid concentration increasing from 0.32 g L⁻¹ during exponential growth to 0.72 g L⁻¹ under nitrogen-depleted stationary conditions. Fatty acid methyl ester profiling revealed a stress-induced shift toward saturated and monounsaturated fatty acids, accounting for 75.6% of total fatty acids and dominated by palmitic acid (C16:0). This compositional restructuring resulted in biodiesel properties characterized by a high cetane number of 64.5, low iodine value, and oxidative stability exceeding 30 h, meeting or surpassing international biodiesel quality benchmarks.

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.