Ozone application in different industries:a review of recent developments

Ozone – a powerful antimicrobial agent, has been extensively applied for decontamination purposes in several industries (including food, water treatment, pharmaceuticals, textiles, healthcare, and the medical sectors). The advent of the COVID-19 pandemic has led to recent developments in the deployment of different ozone-based technologies for the decontamination of surfaces, materials and indoor environments. The pandemic has also highlighted the therapeutic potential of ozone for the treatment of COVID-19 patients, with astonishing results observed. The key objective of this review is to summarize recent advances in the utilisation of ozone for decontamination applications in the above-listed industries while emphasising the impact of key parameters affecting microbial reduction efficiency and ozone stability for prolonged action. We realise that aqueous ozonation has received higher research attention, compared to the gaseous application of ozone. This can be attributed to the fact that water treatment represents one of its earliest applications. Furthermore, the application of gaseous ozone for personal protective equipment (PPE) and medical device disinfection has not received a significant number of contributions compared to other applications. This presents a challenge for which the correct application of ozonation can mitigate. In this review, a critical discussion of these challenges is presented, as well as key knowledge gaps and open research problems/opportunities

Metal Organic Frameworks as Biosensing Materials for COVID-19

The novel coronavirus disease (COVID-19) pandemic outbreak is the most startling public health crises with attendant global socio-economic burden ever experienced in the twenty-first century. The level of devastation by this outbreak is such that highly impacted countries will take years to recover. Studies have shown that timely detection based on accelerated sample testing and accurate diagnosis are crucial steps to reducing or preventing the spread of any pandemic outbreak. In this opinionated review, the impacts of metal organic frameworks (MOFs) as a biosensor in a pandemic outbreak is investigated with reference to COVID-19. Biosensing technologies have been proven to be very effective in clinical analyses, especially in assessment of severe infectious diseases. Polymerase chain reactions (PCR, RT-PCR, CRISPR) - based test methods predominantly used for SARS-COV-2 diagnoses have serious limitations and the health scientists and researchers are urged to come up with a more robust and versatile system for solving diagnostic problem associated with the current and future pandemic outbreaks. MOFs, an emerging crystalline material with unique characteristics will serve as promising biosensing materials in a pandemic outbreak such as the one we are in. We hereby highlight the characteristics of MOFs and their sensing applications, potentials as biosensors in a pandemic outbreak and draw the attention of researchers to a new vista of research that needs immediate action

A study on the composition of heavy organic precipitates at various locations of a petroleum production line: wellhead, separator, and flowline

The heavy fractions from crude oil samples from different locations of a petroleum production line was investigated by gravimetric precipitation technique through the varying of n-alkane precipitant(s) type, volume, and volume ratios. The type of heavy organics (HOs) obtained at the different locations was studied using chromatographic fractionation into saturates, aromatics, resins and residual asphaltenes. Saturates and aromatics compositions were qualitatively and quantitatively determined by Gas Chromatographic-Flame Ionization Detection (GC-FID), while Ultraviolet-Visible spectroscopy was used for the resins. The results obtained show that the amounts of HOs precipitated changes with precipitants type, volume, and volume ratios and are in the order: wellhead (WH) > flowline (FL) > separator (SR). With changes in the total volume of precipitant binary mixtures, maximum precipitation is obtained at 40 mL/g of oil. Between 70–80 mL/g of oil, the amount of precipitate produced remain constant for all samples. There is no clear-cut trend in the concentration of individual and total saturate and aromatic compositions of the heavy organics along the different locations of the production system. However, the concentration of resins increases in the order: separator > flowline > wellhead

Multi-criteria decision analysis for the evaluation and screening of sustainable aviation fuel production pathways

The aviation sector, a significant greenhouse gas emitter, must lower its emissions to alleviate the climate change impact. Decarbonization can be achieved by converting low-carbon feedstock to sustainable aviation fuel (SAF). This study reviews SAF production pathways like hydroprocessed esters and fatty acids (HEFA), gasification and Fischer–Tropsch Process (GFT), Alcohol to Jet (ATJ), direct sugar to hydrocarbon (DSHC), and fast pyrolysis (FP). Each pathway's advantages, limitations, cost-effectiveness, and environmental impact are detailed, with reaction pathways, feedstock, and catalyst requirements. A multi-criteria decision framework (MCDS) was used to rank the most promising SAF production pathways. The results show the performance ranking order as HEFA > DSHC > FP > ATJ > GFT, assuming equal weight for all criteria

Is the Public willing to help the Nigerian Police during the Boko Haram crisis? A look at moderating factors.

This paper sought the opinion of 200 Nigerians on their willingness to cooperate with the Police during the Boko Haram crisis. Public perceptions of Police effectiveness during the crisis, residence location, gender and religious affiliation were used as moderators. Data was analysed using an explanatory factor analysis and structural equation modelling. Results indicated a strong association between perceived effectiveness and willingness to report to the Police with respondents who question the effectiveness of the Police being less likely to be willing to report criminal activity about Boko Haram. Further to this, the impact of religion on willingness to report was at least partially mediated by perceived effectiveness of the Police with the results showing that Christian respondents perceived the Police as less effective. Females and those living in the North were significantly less willing to report criminal activity to the Police The findings are then discussed in relation to the BH crises and directions for future research are given

Data-Driven Machine Learning Approach for Predicting the Higher Heating Value of Different Biomass Classes

Higher heating values (HHV) is a very useful parameter for assessing the design and large-scale operation of biomass-driven energy systems. HHV is conventionally measured experimentally with an adiabatic oxygen bomb calorimeter. This procedure is often time-consuming and expensive. Furthermore, limited access to the required facilities is the main bottleneck for researchers. Empirical linear and nonlinear models have initially been proposed to address these concerns. However, most of the models showed discrepancies with experimental results. Data-driven machine learning (ML) methods have also been adopted for HHV predictions due to their suitability for nonlinear problems. However, most ML correlations are based on proximate or ultimate analysis. In addition, the models are only applicable to either the originator biomass or one specific type. To address these shortcomings, a total of 227 biomass datasets based on four classes of biomass, including agricultural residue, industrial waste, energy crop, and woody biomass, were employed to develop and verify three different ML models, namely artificial neural network (ANN), decision tree (DT) and random forest (RF). The model incorporates proximate and ultimate analysis data and biomass as input features. RF model is identified as the most reliable because of its lowest mean absolute error (MAE) of 1.01 and mean squared error (MSE) of 1.87. The study findings can be used to predict HHV accurately without performing experiments

Process modelling integrated with interpretable machine learning for predicting hydrogen and char yield during chemical looping gasification

Chemical looping gasification (CLG) is a promising thermochemical process for the production of H2. CLG process is mainly based on oxygen transfer from an air reactor to a gasification reactor using solid metal oxides (also called oxygen carriers, (OC)) as oxidants. The unique oxygen separation system of CLG makes it an advanced process with a smaller carbon footprint compared to the conventional gasification process. The other advantages of CLG includes increased efficiency, reduced greenhouse gas emissions, and improved process stability compared to conventional biomass gasification. Although CLG is a promising technology, it still faces several challenges such as high capital cost, OC durability, complex reaction mechanism and scalability issues. Some of these challenges can be addressed by understanding the impact of various process conditions on H2 yield and char formation during CLG. The present study proposes a novel integrated process simulation and experimental studies to generate large dataset used for interpretable machine learning (ML) analysis. Three different ML models including support vector machine (SVM), random forest (RF), and gradient boost regression (GBR) were used to develop models for predicting the H2 and char yield during CLG. The GBR outperformed other models for the prediction of H2 and char yield during CLG with R2 value > 0.9. Among the experimental conditions, the temperature (T) and steam to biomass ratio (SBR) were the most relevant parameters affecting H2 and char production. Biomass ash, C, volatile matter (VM) and H content also influenced H2 and char formation. Overall, a combination of SHAP and partial dependence plot helped address the black box challenges of ML models

CFD modelling and simulation of drill cuttings transport efficiency in annular bends: Effect of particle size polydispersity

This study analyses the impact of particle polydispersity using the Eulerian-Eulerian (EE) and Lagrangian-Eulerian (LE) modelling approaches in the context of wellbore cleaning operations in the drilling industry. Spherical particles of sizes 0.5 mm, 0.75 mm and 1 mm are considered, whereas a Power Law rheological model is used for the fluid phase description. The EE approach implemented herein applies the Kinetic Theory of Granular Flow (KTGF) in ANSYS Fluent® and accounts for the particle size differences by representing them as different phases within the computational domain. With the LE approach, we employ the Dense Discrete Phase Model (DDPM) and capture this difference with the aid of a size distribution model (the Rosin-Rammler model). The findings of our computational experiments show considerable differences in key variables (the pressure drop, and particle deposition tendencies) between monodispersed and polydispersed transport scenarios