Adsorption-type aluminium-based direct lithium extraction: The effect of heat, salinity and lithium content

Conventional lithium production through solar evaporation is considered a time-consuming procedure, taking a substantial 12 to 18 months with significant environmental impacts such as aquifer depletion and damaging the basin\u27s complex hydrological system. Direct Lithium Extraction (DLE) has emerged as a promising alternative for lithium extraction from brines, offering reduced environmental impact. Although adsorption-type DLE with aluminium-based adsorbents is the sole commercial technology of DLE, a debate persists concerning its Technology Readiness Level (TRL), which challenges the prevailing notion that adsorption-type DLE undeniably reaches a TRL of 9. Within this narrative, we propose that adsorption is capable of attaining its highest potential TRL in lithium recovery from brines when three critical conditions are met: the presence of a certain level of salinity, a minimum lithium content in the brine, and a heat source to heat up the brine. In this account, an attempt has been made to elucidate the role of these three minimum criteria during adsorption-type DLE

Artificial intelligence-based material discovery for clean energy future

Artificial intelligence (AI)-assisted materials design and discovery methods can come to the aid of global concerns for introducing new efficient materials in different applications. Also, a sustainable clean future requires a transition to a low-carbon economy that is material-intensive. AI-assisted methods advent as inexpensive and accelerated methods in the design of new materials for clean energies. Herein, the emerging research area of AI-assisted material discovery with a focus on developing clean energies is discussed. The applications, advantages, and challenges of using AI in material discovery are discussed and the future perspective of using AI in clean energy is studied. This perspective paves the way for a better understanding of the future of AI applications in clean energies

Harnessing the power of neural networks for the investigation of solar-driven membrane distillation systems under the dynamic operation mode

Accurate modeling of solar-driven direct contact membrane distillation systems (DCMD) can enhance the commercialization of these promising systems. However, the existing dynamic mathematical models for predicting the performance of these systems are complex and computationally expensive. This is due to the intermittent nature of solar energy and complex heat/mass transfer of different components of solar-driven DCMD systems (solar collectors, MD modules and storage tanks). This study applies a machine learning-based approach to model the dynamic nature of a solar-driven DCMD system for the first time. A small-scale rig was designed and fabricated to experimentally assess the performance of the system over 20 days. The predictive capabilities of two neural network models: multilayer perceptron (MLP) and long short-term memory (LSTM) were then comprehensively examined to predict the permeate flux, efficiency and gain-output-ratio (GOR). The results showed that both models can efficiently predict the dynamic performance of solar-driven DCMD systems, where MLP outperformed the LSTM model overall, especially in the prediction of efficiency. Additionally, it was indicated that the accuracy of the models for the prediction of GOR can be significantly improved by increasing the size of the dataset

Microplastics fouling mitigation in forward osmosis membranes by the molecular assembly of sulfobetaine zwitterion

Forward osmosis (FO) membranes have potential for the efficient water and wastewater treatment applications. However, their development has faced significant challenges due to their fouling propensity. In this study, FO membranes modified with sulfobetaine zwitterions (i.e., [2-(Methacryloyloxy)ethyl]dimethyl-(3-sulfopropyl) ammonium hydroxide) were fabricated and used for the first time to address microplastic (MP) fouling issue. Water flux, reverse salt flux (RSF), fouling, and flux recovery were evaluated for the membranes loaded with different quantities of the zwitterions ranging from 0.25 % to 2 %. The developed membranes were tested over 49 h with feed solutions containing polyethylene MPs and bovine serum albumin (BSA) to evaluate their fouling resistance. The synergistic effects of the two foulants indicated that the MPs were the primary cause of fouling. The presence of BSA effectively reduced the blocking effect of MPs and therefore lowers overall fouling. Additionally, improved water flux, structural parameter (S), and RSF were reported for the modified membranes. The zwitterion\u27s unique structure with hydrophilic groups (C[dbnd]O and O[dbnd]S[dbnd]O) resulted in high flux recovery rates of over 90 % for all modified membranes within only 30 min of physical cleaning upon fouling tests. The results demonstrate the high potential of the modification method for targeting the removal of MPs in TFC-based membranes

Experimental investigation of temperature polarisation by capturing the temperature profile development over DCMD membranes

Temperature polarisation (TP) is a major drawback limiting the global acceptance of membrane distillation (MD) technology. TP is typically quantified using a dimensionless index known as Temperature Polarisation Coefficient (TPC). TPC has significant limitations, whereby it cannot be used to compare different MD configurations or design conditions, nor to analyse the TP phenomenon along the membrane. In this research, the temperature profile over and along a lengthy DCMD membrane has been measured under various operational conditions, where its impact on TP has been explored for the first time. A specialised DCMD membrane cell was manufactured to capture temperature profiles, both along and over the membrane surfaces, using miniature thermocouples. The effects of flow rate and feed temperature were investigated on the temperature profiles. The results showed that the extent of TP was not constant along the membrane, and that the temperature profile was not symmetrical across the feed and permeate side, predominantly due to the effects of the inlet and outlet on the flow. The TPC value calculated using the conventional method was not able to accurately reflect the TP phenomenon along the membrane, indicating TPC to be an ineffective tool to study TP along the membrane

Experimental investigation of varying design parameters on the production rate and temperature polarisation of a DCMD system

Much of the research in the analysis of Temperature polarisation (TP) and the productivity of a membrane distillation (MD) system tends to concentrate on operational conditions. However, substantial enhancements in permeate flux can be realised through the incorporation of fundamental design modifications. This research showed that TP can be successfully mitigated almost to a level of non-existence, by manipulating the module orientation and flow channel height of an in-house designed direct contact membrane distillation (DCMD) system. Notably, at higher flow channel heights, changing the module orientation from the default horizontal position with the feed side on top (FST) to a sideway orientation led to a remarkable 90% increase in the permeate flux of the DCMD module. Permeate side on top (PST) and sideways orientations performed significantly better than FST for larger channel heights, while at low channel heights, the improvement was slight. Temperature measurements proved that thermal convective currents and secondary flows played a vital role in assisting or opposing TP and cannot be disregarded when investigating the hydrodynamics of a DCMD system. The impact of flow directions was insignificant with different channel heights, while the proximity of the flow inlets played a pivotal role in shaping the temperature profiles along the membrane

Beyond the surface: Understanding temperature polarisation in DCMD membranes through varied operational parameters

Conventionally, a single Temperature Polarisation Coefficient (TPC) value is calculated to quantify Temperature Polarisation (TP). In this research, the extent of polarisation is investigated by capturing temperature profiles at specific points along a MD membrane using miniature thermocouples, eliminating the need for TPC calculations. The extent of polarisation at a point is affected by two contributory factors, namely the proximity of flow inlets and the difference in vapour pressure across the membrane at that point. Under this direction, this work examined the influence of permeate temperature, feed salinity, and flow direction on the development of the temperature profiles. Our analysis revealed an elevation in TP on both sides of the membrane when the permeate temperature was increased. In addition, changes in feed salinity had a very minute impact on the development of the temperature profiles. By comparing the cocurrent and counter-current flow, the influence of the two contributory factors was further proved, with counter-current flow working better for long membrane modules. Furthermore, an investigation on the symmetricity of polarisation across the membrane revealed asymmetricity depends on the operating conditions, especially direction of flow. The asymmetricity was infinitesimal at low inlet temperature differences for cocurrent flow

Mechanism understanding of Li-ion separation using a perovskite-based membrane

Lithium ions play a crucial role in the energy storage industry. Finding suitable lithium-ion-conductive membranes is one of the important issues of energy storage studies. Hence, a perovskite-based membrane, Lithium Lanthanum Titanate (LLTO), was innovatively implemented in the presence and absence of solvents to precisely understand the mechanism of lithium ion separation. The ion-selective membrane’s mechanism and the perovskite-based membrane’s efficiency were investigated using Molecular Dynamic (MD) simulation. The results specified that the change in the ambient condition, pH, and temperature led to a shift in LLTO pore sizes. Based on the results, pH plays an undeniable role in facilitating lithium ion transmission through the membrane. It is noticeable that the hydrogen bond interaction between the ions and membrane led to an expanding pore size, from (1.07 Å) to (1.18 – 1.20 Å), successfully enriching lithium from seawater. However, this value in the absence of the solvent would have been 1.1 Å at 50 °C. It was found that increasing the temperature slightly impacted lithium extraction. The charge analysis exhibited that the trapping energies applied by the membrane to the first three ions (Li +, K +, and Na+) were more than the ions’ hydration energies. Therefore, Li +, K +, and Na + were fully dehydrated, whereas Mg2 + was partially dehydrated and could not pass through the membrane. Evaluating the membrane window diameter, and the combined effect of the three key parameters (barrier energy, hydration energy, and binding energy) illustrates that the required energy to transport Li ions through the membrane is higher than that for other monovalent cations

Investigating the impact of company announcements on stock prices: An application of machine learning on Australian lithium market

This paper investigates the effects of various types of announcements made by lithium producers on stock prices. We collected data from 40 lithium-producing companies listed on the world\u27s largest stock exchanges, spanning from May 2020 to September 2022. To analyze the impact of announcements such as quoted and unquoted securities, market announcements, company reports, public meetings and presentations, financial announcements, and technical announcements on stock prices, we employed an extreme gradient boosting (XGBoost) model. Our results indicate that stock exchange market announcements and announcements about public meetings and presentations significantly influenced the stock prices of all eight large-cap companies studied. Announcements about public meetings and presentations were crucial predictors of stock prices for 73% of all companies analyzed. Additionally, positive financial announcements were key predictors for 70% of the companies. These findings suggest that investors should consider these predictors when making investment decisions in the lithium-related stock market. This study contributes to the existing literature by providing empirical evidence on the impact of different types of announcements made by lithium producers on stock prices. Furthermore, the XGBoost model used in this study can be applied to other industries and markets to analyze the impact of various types of announcements on stock prices