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•Demonstration of flotation to separate cesium (Cs) contaminated clays.•EDAB surface decoration of CS-MMT modifying particle size and wettability.•Increased Cs contamination led to ...increased Cs-MMT recovery.•Selective separation of Cs-MMT when blended with pristine-MMT.
The ongoing boom of industrialization is conflicted by concerns regarding increased levels of environmental contamination, in particular the uncontrolled release of heavy metal ions and radionuclides into soils and groundwater systems. The extent of contamination can be substantial, hence ways to remediate and reduce the volume of waste for further treatment and ultimate disposal are highly desired. In the current study, flotation has been considered as an engineering solution to rapidly separate cesium contaminated clays from low-level contaminated and pristine clays. Cesium (Cs+) sorption by montmorillonite clay particles was considered over a range of ionic concentrations (0.01–500 mM), showing a multistage sorption isotherm that can be interpreted using a two-site model, which considers both interlayer ion-exchange and specific ion sorption on the clay basal planes at higher cesium concentrations. Assessment by X-ray photoelectron spectroscopy (XPS) and zeta potential confirmed the increased surface contamination with increasing Cs+ concentration, with the surface enrichment sufficiently altering the surface chemistry of the contaminated clays for them to favourably interact with the flotation collector, ethylhexadecyldimethyl-ammonium-bromide (EDAB). Within a critical concentration range of EDAB, the cesium contaminated clays were separated from pristine clays using flotation, with recovery efficiencies of ∼75% for the contaminated clays, compared to <25% for the pristine clays. When contaminated and pristine clays were blended, separation by flotation once again demonstrated excellent selectivity for the contaminated clays. The current study highlights the potential for flotation to rapidly treat contaminated clay rich soils and significantly reduce the volume of contaminated solids for further treatment or ultimate disposal.
The objectives of this study are to evaluate the technical and cost implications of retrofitting post-combustion Carbon Capture and Storage (CCS) in existing coal-fired power plants in Thailand, with ...a special focus on the Mae Moh plant managed by the Electricity Generating Authority of Thailand (EGAT). We undertake a detailed analysis using AspenPlus simulation models to determine the optimum capture cost per ton of CO2 and to examine the effects of various flue gas loads on CO2 capture performance and cost-effectiveness. The research reveals a key operational insight: as the flow rate of flue gas increases, the cost to capture a ton of CO2 decreases, indicating economies of scale in CCS operations. Furthermore, the study explores the potential for integrating solar photovoltaic (PV) technology as a renewable energy source, which shows promise in lowering Thailand’s power sector emissions and operational costs. By comparing the levelized cost of electricity (LCOE) for solar PV against conventional coal-fired power generation and considering the country’s favorable geographic and climatic conditions, solar PV emerges as an economically viable and environmentally sustainable alternative. The findings of this research aim to inform strategic energy policy decisions in Thailand, advocating for a transition to more sustainable energy systems and emphasizing the balance between environmental responsibility and economic feasibility.
Mitigating CO2 emissions is an important clean energy research topic. Post-combustion carbon capture is a well established and vital carbon capture technology for existing fossil-fueled power plants. ...In this work, monoethanolamine (MEA) based carbon capturing unit was designed using AspenPlus V.10 software for a 300 MWe power unit of Mae Moh power plant in Thailand. Technical and economic analysis of retrofitting a lean aqueous MEA system was investigated. From the simulation study, it was revealed that the optimal lean CO2 feeding for the amine-based carbon separation plant was about 0.2 mol/mol using packed columns with Sulzer Mellapak 250Y product. The optimal liquid-to-gas ratio with a flue gas containing 15% CO2 was approximately 3.0. Furthermore, the optimal total costs of the plants were less than 55 $/ton of CO2 captured.
The new method to evaluate the contribution of the related factors to the oil recovery is proposed by using the desirability model. The related factors are re-scaled and combined to be a single ...parameter in order to correlate with an indicator of oil recovery. The correlated result could be able to predict a trend of the factors and oil recovery as an empirical approach if a good correlation is achieved. Three published works of the coreflooding experiments are examined the effectivenesses and limitations of the proposed model. The analysed plots of desirability and the oil recovery imply an insight into the oil recovery mechanisms by indicating the dominant factors. The results meet a good agreement with the published works. Although the dominant factors are indicated and the correlation trend is able to be determined, the accuracy of the proposed method needs a high number of data sets to increase the statistical reliability.
Exploiting unconventional petroleum is needed in this era, and thus more enhancement has been introduced including chemical additives to manipulate the fluid flow characteristics. Although most of ...the examinations were at the laboratory scale, the scaling-up to reservoir applicability is in question and needs further studies. The current study examined laboratory-derived relative permeability (k r) at various wettabilities and the oil–water interfacial tension on reservoir-scale numerical simulation, utilizing a huff-n-puff technique, aiming to emphasize suitability and limitations of k r. The simulated results emphasized the important character of k r, which dictates the oil recovery in tight reservoirs where the huff-n-puff technique is normally implemented. Although derived from laboratory tests, such a k r character has to be taken caution when considering EOR design in field deployment at the reservoir scale since there are suitability and limitations to be concerned for each k r character as well as the economic aspect. Not all reservoir parameters or producing factors could practically induce substantial changes in oil production; hence, further techno-economic study should be executed prior to implementation toward field implementation of tight reservoirs. Simulations of primary oil production agreed with those of the experiment, confirming the model validity. Following the secondary six-cycle huff-n-puff process also yielded the same trend of oil production performances, dictated by k r as a primary fluid flow characteristic. Four influencing factors of reservoir properties and producing design were further investigated for their sensitivity to the oil production performances. At various k r investigated in the current study, the porosity factor was distinctively found to have limited suitability to low values if high oil production is anticipated, owing to the nature of tight reservoirs pe se. On the contrary, reservoir permeability likely did not have much influence since more oil was produced with higher value of permeability, albeit no sensitivity at much higher values. The shut-in period for the huff-n-puff process was not sensitive to the oil production of all fluids. Interestingly, the critical water saturation where the water phase starts to flow (k rw > 0) had a strong influence on the oil production in two approaches, either demoting the oil production or having no contribution, depending on the k rw–S w character. Although derived from laboratory tests, such a k r character has to be taken caution when considering enhanced oil recovery design in reservoir-scale deployment since there are suitability and limitations to be concerned for each k r character.
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•Rapid dewatering of cesium contaminated clinoptilolite was studied using flotation.•Adsorption of EHDa-Br and CPC surfactant collectors were analysed.•<8% of contaminated cesium was ...removed with surfactant co-adsorption.•Optimum conditions were found with 0.5 mM CPC and 30 μL of MIBC frother.•Flotation achieved ∼90% clinoptilolite recovery and > 4 water reduction ratio.
Flotation using cationic surfactants has been investigated as a rapid separation technique to dewater clinoptilolite ion exchange resins, for the decontamination of radioactive cesium ions (Cs+) from nuclear waste effluent. Initial kinetic and equilibrium adsorption studies of cesium, suggested the large surface area to volume ratio of the fine zeolite contributed to fast adsorption kinetics and high capacities (qc = 158.3 mg/g). Adsorption of ethylhexadecyldimethylammonium bromide (EHDa-Br) and cetylpyridinium chloride (CPC) surfactant collectors onto both clean and 5 ppm Cs+ contaminated clinoptilolite was then measured, where distribution coefficients (Kd) as high as 10,000 mL/g were evident with moderate concentrations CPC. Measurements of particle sizes confirmed that adsorption of surfactant monolayers did not lead to significant aggregation of the clinoptilolite, while < 8% of the 5 ppm contaminated cesium was remobilised. Importantly for flotation, both the recovery efficiency and dewatering ratios were measured across various surfactant concentrations. Optimum conditions were found with 0.5 mM of CPC and addition of 30 μL of MIBC frother, giving a recovery of ∼90% and a water reduction ratio > 4, highlighting the great viability of flotation to separate and concentrate the contaminated powder in the froth phase.
Surfactant flooding is one technique of chemical enhanced oil recovery (EOR) aimed at improving the microscopic displacement efficiency of trapped residual oil via reducing the oil–water interfacial ...tension and wettability alteration. Success of surfactant flooding strongly relies on surfactant loss through its adsorption onto reservoir minerals to ensure maximum transfer to target reservoir. The current study examines the adsorption behavior of saponin natural surfactant onto carbonate rock outcrops. As an environmentally friendly extract from plants, saponins have shown the potential to increase oil recovery, although saponin loss or adsorption on surfaces is yet to be studied. Common synthetic surfactants of various types (i.e., cationic and anionic) and different molecular structures (other nonionic surfactants) have also been studied to provide comparisons to saponin. The surfactant adsorption onto carbonate samples was studied by batch adsorption experiments, with the residual surfactant concentration determined by the surface tension technique. It was found that saponin, a natural nonionic surfactant, adsorbed less than the ionic surfactants, since saponin adsorption is not governed by electrostatic interactions but weaker hydrogen bonding. Such data concludes that saponins are likely to yield less retention than the ionic surfactants, but compared to other nonionic surfactants its retention is greater. This is likely attributed to differing surfactant molecular structures. Due to its branch-like structure with more terminal functional groups, saponin adsorbs more on the rock surface compared to other long-chain nonionic surfactants. The findings of the current study provide a useful guide in surfactant selection for EOR and highlight a potential of natural and environmentally friendly surfactants.
Hydrogen is a clean and sustainable renewable energy source with significant potential for use in energy storage applications because of its high energy density. In particular, underground hydrogen ...storage via the dissolution of hydrogen gas in an aqueous solution has been identified as a promising strategy to address the difficulties associated with large-scale energy storage. However, this process requires the accurate prediction of the solubility of hydrogen in aqueous solutions, which is affected by a range of factors, including temperature, pressure, and the presence of solutes. The present study thus aimed to effectively predict the solubility of hydrogen in aqueous solutions that vary in their salinity by employing a machine learning approach. Four machine learning models were developed and tested: adaptive gradient boosting (Adaboost), gradient boosting, random forest, and extreme gradient boosting. The performance of each model was quantified in terms of their coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). The Adaboost algorithm exhibited superior performance across all metrics, with an R2 of 0.994, MAE of 0.006, and RMSE of 0.018. A Williams plot detected only 18 outliers in the Adaboost predictions from a total of 255 data points. These results indicate that machine learning techniques have the potential to serve as a valuable tool in the prediction of hydrogen solubility in aqueous solutions for underground hydrogen storage, facilitating the development of smart, cost-effective, and safe hydrogen storage technologies.
•This study predicts the solubility of hydrogen in aqueous solutions with salinity using machine learning approaches.•Adaptive gradient boosting (Adaboost) outperformed gradient boosting, random forest, and extreme gradient boosting.•The results show that the approach can facilitate the development of cost-effective and safe hydrogen storage technologies.
The interfacial activity of poly(N-isopropylacrylamide) (pNIPAM) nanoparticles in the absence and presence of an anionic surfactant (sodium dodecyl sulfate, SDS) was studied at a crude oil–water ...interface. Both species are interfacially active and can lower the interfacial tension, but when mixed together, the interfacial composition was found to depend on the aging time and total component concentration. With the total component concentration less than 0.005 wt %, the reduced interfacial tension by pNIPAM was greater than SDS; thus, pNIPAM has a greater affinity to partition at the crude oil–water interface. However, the lower molecular weight (smaller molecule) of SDS compared to pNIPAM meant that it rapidly partitioned at the oil–water interface. When mixed, the interfacial composition was more SDS-like for low total component concentrations (≤ 0.001 wt %), while above, the interfacial composition was more pNIPAM-like, similar to the single component response. Applying a weighted arithmetic mean approach, the surface-active contribution (%) could be approximated for each component, pNIPAM and SDS. Even though SDS rapidly partitioned at the oil–water interface, it was shown to be displaced by the pNIPAM nanoparticles, and for the highest total component concentration, pNIPAM nanoparticles were predominantly contributing to the reduced oil–water interfacial tension. These findings have implications for the design and performance of fluids that are used to enhance crude oil production from reservoirs, particularly highlighting the aging time and component concentration effects to modify interfacial tensions.
Immiscible fluid–fluid displacement dynamics is a crucial element to understanding and engineering many subsurface flow applications, including enhanced oil recovery and carbon dioxide geological ...sequestration. Although there are several interfacial properties that govern such a displacement dynamic, the wettability has been considered a dominant factor. Owing to its complex coaffinity among the three phases (i.e., solid–fluid–fluid) and difficulty to be characterized accurately and efficiently, the wettability (defined as the contact angle: θ) determination is of interest in the current study with aim toward machine learning (ML) approach. In the current research, four experimental packages of fluid displacement at 1D capillary scale served as data sets for ML examination on the θ predictability. Via digital image processing, fluid traveling length at a given time was extracted, and the theoretical θ was calculated as ground truth for the modeling, with input features being fluid traveling length, displacing velocity, and the interfacial tension. Random forest (RF) and multilayer perception (MLP) were selected for the modeling due to their appropriate characteristics to the investigated data (being nonlinear relation). The prediction results showed that RF apparently outperformed MLP on the θ prediction, reflecting its best ability to manage missing values and outliers. Although more input features analyzed (from two to three features) did yield a better prediction, the best model remains RF. Sensitivity on the key parameters of displacing velocity and the interfacial tension was also analyzed, where the results confirm the model prediction agreement with theories. The study demonstrated how ML model can be an alternative tool to elucidate the fluid displacement in subsurface, with additional potential for autonomously improving the deep underground flows, converging a new concept of “augmented” artificial intelligence.
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