Microreactors have emerged as effective platforms for photocatalytic advanced oxidation processes targeting the removal of persistent emerging contaminants, such as steroid hormones, from hospital wastewater. Steroidal endocrine-disrupting compounds, particularly estrogens and progestins, are commonly present in hospital effluents and are known for their resistance to conventional treatment technologies. Photocatalytic systems based on microreactors offer several advantages over traditional batch photoreactors, including higher surface-area-to-volume ratios, enhanced mass transfer, and reduced diffusion limitations. The continuous microflow facilitates uniform photon distribution and accelerates reaction kinetics. By enabling efficient fluid transport under laminar flow through microchannels, microreactor technology significantly mitigates the mass transfer constraints often associated with catalyst immobilization, owing to the shortened diffusion path from the aqueous phase to the photocatalyst surface. In this study, the degradation efficiency of two representative steroidal hormones, norethindrone and oestrogen, was evaluated using a continuous-flow microreactor system with TiO₂ immobilized and irradiated by UVA-LED light. The key optimized process variables were pH, temperature, hydrogen peroxide concentration, and volumetric flow rate. The reaction system was equipped with piston-driven injection pumps, a Peltier thermal control module, pressure regulation, and online monitoring 1H NMR.

Photocatalytic degradation efficiency was assessed through spectral analysis using 1H NMR, IR, and UV–Vis spectroscopy. The optimal operational conditions for norethindrone degradation (79.3%) were pH 10, 20 °C, 10 mL/min, and 2 ppm H₂O₂. Under the same conditions, oestrogen degradation reached 68%. These results highlight the potential of integrating microreactor-based photocatalysis into hospital wastewater treatment frameworks as a sustainable and efficient solution to eliminate recalcitrant endocrine-disrupting contaminants, thereby contributing to improved environmental and public health protection.

in the form of a metal hydride has come forth as a safe and low-pressure storage solution with competitive volumetric energy density. In this technology, hydrogen is stored in a hydride-forming metal, in this study specifically AB5-type metal hydride, through exothermic absorption, which can then be discharged through endothermic desorption. This results in a complex batch system where hydrogen discharge is caused by high-temperature fluid heating the reactor and by extension the metal hydride bed. This in turn increases the hydrogen pressure of the gas surrounding the metal hydride bed which then is released through a regulator to achieve the desired pressure of the discharge hydrogen. This discharge dynamic system as a result is notoriously hard to model and predict. This paper reports the modelling of a metal hydride reactor during its discharge state using neural network regression. This was done by generating a validated finite element model of the reactor which was then used to generate dynamic operational data based on the desired pressure outlet and heating fluid temperature as independent variables. This data was then used to train an artificial neural network using the desired gas pressure, heating fluid temperature, and time as inputs and concentration as the variables the neural network would predict. Regarding model performance, the best-performing artificial neural network model achieved a regression coefficient of 0.9999 and a mean squared error of less than 10^-5 during training. Likewise, the best-performing neural network model validation using the experimentally observed data achieved a regression coefficient of 0.99 and a mean squared error of less than 10^-4. This proves that neural networks can model the complexity of metal hydride reactors during discharge, specifically the HySA-systems Metal Hydride reactor prototype.

The objective of this project is to eliminate permanent flaring and provide a sink for associated gas from the Main Marmul Production Station (MMPS) bulk separators (approximately 210,000 sm³/d), which is currently supplied to Marmul Power Station.

The associated gas from MMPS, with an H2S level of ~690 ppmv, is compressed and sent as export gas to Marmul Power Station, Marmul-A station, a water treatment plant (WTP), and Qaharir and Thuleilat stations. The high-H2S flash gas from the MMPS tanks is currently flared via the Gas Recovery Compressor (GRC) K-2412 to prevent contamination of the MMPS gas system. This is necessary due to the incompatibility of the power plant with sour gas.

Marmul Power Station, nearing the end of its design life, is planned for retirement starting in Q2 2022. After its closure, there will be no route for evacuating MMPS export gas.

To address this, associated gas from production separators will be mixed with high-H2S flash gas recovered from various tanks via a new Gas Recovery System. The combined gas will be treated in the Gas Sweetening Unit (GSU) using Thiopaq Technology. The sweet gas will be compressed, dehydrated, and sent to the South Oman Gas Line (SOGL).

Condensate from the new facility will be stabilized in a new Condensate Stabilizer Unit (CSU) before being routed to the MOL pump suction. Off-gas from the CSU and the condensate flash vessel will be routed to existing fired heaters for fuel gas use. Excess gas will be routed to the flare and managed under the separate "MMPS Excess Flare Gas Recovery Project".

The project will also include utilities such as an instrument air system, nitrogen generation, and flare system rationalization.

Incidents of explosions in ash treatment facilities at waste incineration plants have been reported. The cause is believed to be the reaction between cooling water and metallic aluminum contained in the ash, which generates hydrogen gas.

The authors have been investigating methods to suppress hydrogen generation through temperature changes during the conveyor transport process of ash treatment equipment. In this study, to replicate the temperature changes during conveyor transport, an experiment was conducted in which heated simulated incineration ash was immersed in water within a container for cooling. After cooling, the simulated ash was removed from the water, and the hydrogen gas concentration around the simulated ash was measured. The simulated incineration ash was composed of a mixture of aluminum powder, alumina powder, calcium oxide powder, and silicon dioxide powder, which are the main components of incineration ash.

The experimental results showed that as the temperature of the water during immersion increased, the amount of hydrogen gas generated during immersion decreased, and the hydrogen gas concentration around the simulated ash also decreased.

Based on these results, it was found that by maintaining the high-temperature incineration ash discharged from the furnace and the cooling water above 70°C and allowing natural cooling during transport on the conveyor, it may be possible to suppress hydrogen explosions in the conveyor and ash pit.

The demand for recovered carbon black (rCB) is growing rapidly; the main reason for this growth is the rising demand for sustainable alternatives to carbon black. This growth aligns with the United Nations' sustainable development goals (SDGs) 9, 11, 12, and 13 because it promotes the recycling of waste tires through pyrolysis into products, hence reducing the reliance on fossil fuels used to make carbon black. It also supports the development of sustainable products, thus reducing emissions and supporting the development of circular economy products. This expansion, however, will incur critical challenges associated with the large production, handling, and transportation requirements.

A new rCB beneficiation plant in South Africa aims to enter into the rCB manufacturing market, but to do this, it needs to not only optimize its process but also the products for ease of handling and transportation. However, the inherent variability and presence of contaminants in and low bulk density of rCB contribute to inconsistent material properties and significant dust generation during handling. Therefore, this new plant aims to standardize its rCB product through the development of proprietary treatment methods and further aims to reduce the dust by densifying the rCB through pelletization.

The objective of this study is to investigate the influence of these treatments and pelletization parameters on rCB quality. Raw and treated rCB samples were collected from the plant, and pellets were produced under varying binder conditions. Analytical characterization included bulk density measurements, proximate analysis (ash, volatile, and moisture content), ash composition (XRF), laser particle size analysis, and gas adsorption analysis (BET).

These tests aimed to assess how treatment and densification influence surface area, particle distribution, compositional stability, and pellet quality. Preliminary findings show a lower volatile content at a treatment temperature above 250 °C, with an average of 20% volatile matter removal when compared to the raw material.

This investigation aims to assess the performance of a green adsorbent in a laboratory-scale reactor for removing chromium (VI) from wastewater. Cellulose nanocrystals are excellent materials for removing heavy metal ions because of their biodegradability, availability and adaptability in dynamic and static adsorption processes. The green adsorbent was characterised using FTIR and SEM. A deep machine learning approach (artificial neural network and adaptive neuro-fuzzy inference system) was used to forecast the adsorption capacity of green and biodegradable adsorbent to remove chromium (VI) from wastewater. Four operational variables were input: pH, starting concentration of Cr (VI), contact duration, adsorbent dose, and the adsorption capacity output. The network was trained using feedforward propagation using the Levenberg–Marquardt algorithm (LM). ANN models with three algorithms (purelin, logsig, and transig) and ANFIS models were tested to optimise, develop, and forecast the chromium (VI) adsorption using a green and biodegradable adsorbent. The optimum conditions were pH 6, concentration 50 mg/L, time 120 min, and adsorbent dosage 10 g/ 100 mL. The findings show that artificial neural network models effectively predict chromium (VI) adsorption. In the training dataset, R² was 0.979, Mean Square Error (MSE), absolute average relative error (AARE) was 0.053, root mean square error (RMSE) was 0.077, and absolute average relative error (AARE) was 0.053 for the artificial neural network. For the adaptive neuro-fuzzy system, an RMSE of 0.021, AARE of 0.015, ARE of 0.01, MSE of 0.017, and R² of 0.998 were obtained.

Hydrogen is considered a promising solution for mitigating global warming due to its potential to provide CO₂-free power when used in fuel cells and power generation systems. Traditionally, hydrogen production relies heavily on fossil fuel reforming, which emits CO₂. This study investigates an alternative hydrogen generation method by stirring aluminum alloy powder in water. The aluminum alloy powder is derived from waste aluminum, and the process is powered by renewable energy, aiming to combine waste recycling with carbon-neutral hydrogen production. This approach explores the feasibility of establishing a sustainable hydrogen production plant.

Previous experiments using a small stirred reactor (100 mL) compared pure aluminum powder with Al-Sn alloy powders. The alloy powders produced significantly more hydrogen, likely because stirring causes particle fragmentation, increasing the powders’ specific surface area and enhancing the reaction rate.

To assess scalability for industrial application, experiments were conducted in a larger 500 mL reactor. Hydrogen generation was tested with pure aluminum and Al-Sn alloy powders of varying compositions. The results demonstrated that hydrogen production using alloy powders in the larger reactor was six times greater than in the smaller reactor—exceeding the expected increase based on volume scaling alone. This discrepancy was attributed to the increased power input from the stirrer in the larger reactor, which likely intensified particle fragmentation and reaction kinetics.

These findings offer valuable insights into the scale-dependent behavior of the reaction system, providing essential data for the design and practical implementation of a hydrogen production plant based on this stirring-induced aluminum–water reaction method.

The aviation industry is currently confronted with unprecedented challenges in mitigating its environmental footprint and attaining carbon neutrality. This review paper offers a succinct summary of the present obstacles impeding the attempts to reduce carbon emissions in the aviation industry. The text explores the constraints linked to conventional aviation fuels and emphasizes the pressing requirement for sustainable alternatives to fulfill strict emission reduction objectives. The subsequent emphasis is placed on the auspicious prospects of synthetic fuels produced from carbon dioxide (CO₂) and hydrogen (H₂) as a feasible means to reduce carbon emissions in aviation. An innovative strategy involves synthesizing fuels by capturing CO₂ from industrial processes and obtaining H₂ from renewable sources. The article examines the fundamental procedures utilized in the production of synthetic aviation fuels, including electrochemical and thermochemical techniques. In addition, the analysis explores the environmental advantages of synthetic fuels, highlighting their capacity to substantially diminish greenhouse gas emissions and other contaminants. These fuels exhibit both sustainability and versatility, rendering them a highly viable contender for fuelling future aviation fleets. To summarize, this analysis emphasizes the significant capacity of synthetic fuels produced from carbon dioxide (CO₂) and hydrogen (H₂) to function as environmentally friendly aircraft fuels. Examining these possibilities is essential to accomplish the lofty decarbonization objectives established for the aviation sector. This article seeks to provide a thorough understanding that can help to further ongoing research and initiatives aimed at transitioning the aviation sector towards a more sustainable and environmentally friendly future.

Hydrogen is increasingly recognised as a key element in the global transition towards sustainable and clean energy, offering a promising alternative to fossil fuels. Its production from renewable sources via electrolysis aligns with the goals of reducing carbon emissions and achieving energy security. However, despite its advantages, hydrogen poses significant challenges, particularly in terms of safe and efficient storage. Due to its low molecular weight, high diffusivity, and extreme flammability, hydrogen storage systems require rigorous risk assessment to prevent accidents and ensure operational reliability. This study focuses on evaluating the unavailability and safety of green hydrogen storage systems using two well-established risk analysis methods: Fault Tree Analysis (FTA) and Bayesian Networks (BNs). These complementary approaches enable the identification of critical failure modes and the quantification of system reliability under various scenarios. The comparative analysis reveals that both methods yield nearly identical results, underscoring the robustness and validity of the risk assessment. Based on these findings, the study proposes targeted safety measures aimed at mitigating identified risks and enhancing the secure deployment of hydrogen storage infrastructure. Ultimately, this work contributes to the advancement of safer hydrogen technologies and supports the broader energy transition by addressing key safety concerns associated with green hydrogen storage.

This study investigates the potential of kitchen waste sourced from five restaurants and traditional halls within Kwame Nkrumah University of Science and Technology (KNUST) for biogas production via anaerobic digestion for the first time. A total of 6,868 kg of waste, including cassava peels (39.7%), plantain peels (36.1%), and yam peels (24.2%), were collected over three weeks. Laboratory analysis assessed total solids (TSs), volatile solids (VSs), and ash content of representative waste samples. The TS values ranged from 14.82% to 35.26%, while VS values, indicative of biodegradable potential, spanned 53.3% to 83.7%, confirming high methane yield potential. Ash content remained below 10%, reinforcing the organic-rich composition.

Biogas yield was measured per kilogram of feedstock, with cassava and plantain peels yielding an average of 0.45–0.52 m³ CH₄/kg, outperforming yields reported in similar Ghanaian municipal waste studies by over 20%. Compared to traditional reliance on livestock manure with typical methane yields of 0.20–0.30 m³ CH₄/kg, the kitchen waste feedstock demonstrated superior energy conversion efficiency.

This research concludes that kitchen waste in high-density institutional environments provides a sustainable and efficient substrate for decentralized biogas generation, aligning with circular economy models. By diverting organic waste from landfills and reducing methane emissions, this method offers an affordable and environmentally sound solution to campus energy and sanitation challenges. The findings support broader integration of kitchen-waste-based digesters in urban institutions, with potential replication across West Africa to improve waste valorization and reduce reliance on fossil fuels.