Energy Efficiency Performance Research Articles

Optimization of an Industrial Circulating Water System Based on Process Simulation and Machine Learning

As an important part of industrial production, the optimization of circulating water systems is of great significance for improving energy efficiency and reducing operating costs. However, traditional optimization methods lack real-time and dynamic adjustment capabilities and often cannot fully cope with the complex and changeable industrial environment and energy demands. Advances in computer technology can enable people to use machine learning models to process information and data and ultimately help simplify simulation and optimization. In this paper, the circulating water system of a Fluid Catalytic Cracking (FCC) unit is optimized and evaluated based on process simulation and machine learning, adopting 284 sets of industrial operating data. The cooler network of the system is modified from a parallel structure to a series mode, and the effect is clarified using the ASPEN HYSYS software V12. Meanwhile, the fan power of the cooling tower is predicted by employing an optimized Gradient Boosting Regression (GBR) model, and the influence of the parallel-to-series transformation on the fan power is discussed. It is shown that the computer modeling results are in coincidence with the industrial data. Converting the parallel design to a series arrangement of the cooler network can significantly decrease the water consumption, with a reduction of 11%. The fan power of the cooling tower is also reduced by 8% after the optimization. Considering the changes in both water consumption and fan power, the saved total economic cost is 8.65%, and the decreased gas emission is 2142.06 kg/h. By building the optimization prediction system, the real-time sequencing and monitoring of equipment parameters are realized, which saves costs and improves process safety.

Green building evolution: enhancing energy efficiency and structural performance through innovative rice water and grey clay composite material

Green building evolution: enhancing energy efficiency and structural performance through innovative rice water and grey clay composite material

Introducing digital air-traffic controllers for urban-air mobility to ensure safe and energy-efficient flight operations

Abstract Facing the continued growth of cities, the introduction of urban-air mobility intends to reduce traffic congestion and improve the quality of services such as on-demand-transport, reduced travel times and increased connectivity. Nevertheless, its integration into existing air-traffic flows remains one of the biggest challenges ahead, especially once controlled airspace overlaps with urban-air mobility areas, such as at airport control zones. Here, air-traffic control needs to coordinate amobng urban-air mobility vehicles and conventional air traffic. In 2022, DLR conducted a human-in-the-loop simulation with ten air-traffic controllers to validate previously developed workflows for that coordination task applied for Hamburg airport. The simulation results revealed that additional urban-air mobility traffic increases controllers’ experienced workload up to 30% while slightly reducing their perceived situation awareness. Thus, a majority of controllers participating in the trials suggested to introduce an additional controller working position to exclusively control airtaxis and traffic following visual flight rules. This option is assessed as owning a high potential as cost intensive adaptions of regulations and procedures can be omitted. Nevertheless, the feasibility of this option is rather low due to the limited availability of endorsed human controllers. This study proposes a concept for a digital controller taking the responsibility of guidance for airtaxis under visual flight rules traffic (abbr. VFR) within a control zone. This digital controller is named “UAM digital controller” (abbr. UDC) and based on algorithms calculating slots and trajectories which have been validated in previous DLR projects. The UDC coordinates with the human controller once a potential conflict is detected. Within the study, first the concept of the digital controller is defined, following existing work. As a second step, a theoretical evaluation based on an air-traffic control task model and an operational concept for airtaxi integration will analyze the task load reduction for the human controller. Last but not least, the expected energy saving of flight operations by the digital controllers will be assessed.

Evaluating energy consumption patterns in novel foamed ternary alkali-activated masonry blocks

This study endeavors to tackle the energy requirements of the building sector by employing passive design strategies. However, there exists a dearth of comprehension regarding the energy efficiency performance of foamed alkali-activated materials. To bridge this research gap, the study proposes a solution in the form of a thermally proficient wall material crafted from ceramic tile dust (CTD), class C fly ash (FA), and Ground Granulated Blast-Furnace Slag (GGBS), all of which are industrial by-products. The foamed ternary alkali-activated (FTAA) blocks, developed as a result of this research, exhibited commendable performance in terms of mechanical strength of 18.6 MPa, lower density of 1200 kg/m3, porosity of 15.95%, lower specific heat capacity (SHC) of 831 J/(Kg·K), and thermal conductivity (TC) of 0.38 W/(m·K). The thermal efficiency of FTAA blocks curtails the transfer of heat from the external environment to the interior, thereby engendering a more agreeable indoor milieu for occupants. A simulation study utilizing the eQuest tool was executed to evaluate the thermal attributes of the developed blocks and their consequential impact on energy requirements. The findings revealed that in comparison to clay bricks, employing FTAA blocks could yield potential annual energy savings of approximately 4%. Furthermore, notable cost savings of about 4.94% during peak summer months and 5.51% annually were observed. The significance of utilizing these ternary blocks, derived from industrial waste, resides in their affirmative contribution to environmental preservation, augmented indoor thermal comfort, and diminished energy consumption for end users. Consequently, this research makes a meaningful stride towards diminishing operational energy in buildings, harmonizing with sustainability objectives.

Bio-Concrete and Its Self-Healing Capacity to Next-generation Sustainable Architecture: A Comprehensive Review

Concrete is commonly used as a foundation material in the construction sector. It's been around for over two centuries, and it looks like it'll keep being the material of choice for construction for the foreseeable future. Structural materials, long-term degradation creates microcracks which increases permeability and eventually lead to failure. Moreover, the identification and remediation of these fissures provide a formidable challenge, as they jeopardize the integrity and longevity of concrete buildings, particularly those subject to rigorous sealing criteria. Cement manufacturing and widespread usage both contribute to climate imbalance, with the former responsible for 5–7% of global carbon dioxide emissions. Biotechnology provides a viable alternative by allowing the culture of bacteria to produce calcium carbonate, a key ingredient in cement. Biomineralization and crystallization processes play a crucial role in the synthesis and transportation of concrete-compatible chemicals, enabling their delivery to fissure within concrete structures. This review paper discusses a methodology for advancing sustainable architecture in the future, utilizing the adaptable metabolic processes of microorganisms as a foundation. This involves integrating microbial technologies and materials created by microorganisms into the field of built environment design and construction. Currently, there is a growing presence in the market of innovative materials such as Bio-cement, which exhibit lower levels of embodied carbon compared to traditional materials. Furthermore, it discusses advancements in the synthesis and optimization of bio-concrete to address challenges in energy efficiency, structural integrity, and lifecycle performance. By bridging sustainability goals with cutting-edge technology, bio-concrete emerges as a cornerstone for resilient and eco-friendly infrastructure, laying the foundation for innovative architectural paradigms.

Desain dan Prototipe Integrasi IoT dalam Pertanian Hidroponik Cerdas Berbasis Energi Terbarukan

This research aims to develop a prototype for an Internet of Things-based automation system powered by renewable energy, specifically solar panels, to enhance the efficiency and productivity of hydroponic farming. The study addresses critical challenges, including high energy costs and the intensive monitoring demands of conventional hydroponic systems. The experimental methodology involves designing hardware featuring Arduino Uno as the central control unit, environmental sensors (DHT22) for real-time monitoring, and automated actuators such as water pumps, fans, and LED lights. The system integration process encompasses linking the hardware to an IoT interface, followed by rigorous functional testing to evaluate system stability, energy efficiency, and operational performance under varying environmental conditions. The results demonstrate that the prototype successfully operates the entire hydroponic system using energy exclusively from solar panels, eliminating reliance on conventional electricity. The system achieves precise control over critical plant growth parameters such as temperature, humidity, and lighting, with high operational stability observed during trials. This research makes a notable contribution by innovatively integrating IoT, automation, and renewable energy, thereby enhancing energy efficiency while delivering a sustainable and environmentally friendly hydroponic farming solution. The findings have significant practical implications, particularly for implementation in remote areas with limited access to electricity. Furthermore, the study paves the way for future developments, including applications in other agricultural systems and the integration of artificial intelligence to enhance predictive analytics and automated control.

Pass-Transistor-Enabled Split Input Voltage Level Shifter for Ultra-Low-Power Applications.

In modern ICs, sub-threshold voltage management plays a significant role due to its perspective on energy efficiency and speed performance. Level shifters (LSs) play a critical role in signal exchange among multiple voltage domains by ensuring signal integrity and the reliable operation of ICs. In this article, a Pass-Transistor-Enabled Split Input Voltage Level Shifter (PVLS) is designed for area, delay, and power-efficient applications with a wide voltage conversion range. The represented low-power LS structure is a general blend of both pull-up and pull-down networks that perform level-up or level-down shifts. The proposed PVLS is incorporated with the multi-threshold CMOS technique and a load-balancing driving split inverter to limit high static current, leakage power, and performance degradation. The schematic structure could be able to convert voltages from low to high as well as high to low. The architecture design has the lowest silicon area. The implementation of the proposed design was taken under 55 nm CMOS technology. The represented LS could be able to convert voltage ranges between 0.3 V and 1.3 V, which has a dynamic power of 2.00 nW. The overall propagation delay of the LS is 90 ps and an area of 7.66 µm2 for an input frequency of 1 MHz.

Soft Artificial Synapse Electronics.

Soft electronics, known for their bendable, stretchable, and flexible properties, are revolutionizing fields such as biomedical sensing, consumer electronics, and robotics. A primary challenge in this domain is achieving low power consumption, often hampered by the limitations of the conventional von Neumann architecture. In response, the development of soft artificial synapses (SASs) has gained substantial attention. These synapses seek to replicate the signal transmission properties of biological synapses, offering an innovative solution to this challenge. This review explores the materials and device architectures integral to SAS fabrication, emphasizing flexibility and stability under mechanical deformation. Various architectures, including floating-gate dielectric, ferroelectric-gate dielectric, and electrolyte-gate dielectric, are analyzed for effective weight control in SASs. The utilization of organic and low-dimensional materials is highlighted, showcasing their plasticity and energy-efficient operation. Furthermore, the paper investigates the integration of functionality into SASs, particularly focusing on devices that autonomously sense external stimuli. Functionalized SASs, capable of recognizing optical, mechanical, chemical, olfactory, and auditory cues, demonstrate promising applications in computing and sensing. A detailed examination of photo-functionalized, tactile-functionalized, and chemoreception-functionalized SASs reveals their potential in image recognition, tactile sensing, and chemosensory applications, respectively. This study highlights that SASs and functionalized SAS devices hold transformative potential for bioelectronics and sensing for soft-robotics applications; however, further research is necessary to address scalability, long-time stability, and utilizing functionalized SASs for prosthetics and invivo applications through clinical adoption. By providing a comprehensive overview, this paper contributes to the understanding of SASs, bridging research gaps and paving the way toward transformative developments in soft electronics, biomimicking and biointegrated synapse devices, and integrated systems.

Self-Locking Domino Logic Pipelines: Application in RISC-V Architectures in FPGA

This paper presents the design and implementation of a self-locking domino logic pipeline controller for a RISC-V processor implemented on an FPGA. The emphasis is on asynchronous circuit design, which offers advantages such as enhanced resilience to supply voltage fluctuations, optimized power efficiency, and the elimination of clock-related issues such as skew and single-point failures. By leveraging the asynchronous Globally Asynchronous Locally Synchronous (GALS) systems and domino logic, the controller ensures hazard-free operation while maintaining race-free processing. The asynchronous approach, integrated into a 32- bit RISC-V processor, allows for flexible and energy-efficient operation, thereby demonstrating its potential for performance-critical applications. This paper high- lights the contrasts between the asynchronous design and the traditional synchronous multicycle processor, demonstrating the benefits of asynchronous systems in terms of power consumption and performance. A significant contribution of this design is the pipeline’s completion detection mechanism, which ensures that each processing stage locks until valid results are obtained, thereby markedly enhancing system stability. Furthermore, the paper investigates the parallelization of domino gates and introduces an asynchronous Arithmetic Logic Unit (ALU), which further optimizes performance through self-locking mechanisms. The power, performance, and area (PPA) analysis of the design demonstrates considerable improvements in throughput (up to 10%) and reduced latency per instruction in comparison to its synchronous counterpart, while maintaining moderate resource utilization on an FPGA. The results indicate that asynchronous domino logic pipelines may offer a promising approach for achieving energy-efficient and high-performance processors in future computing architectures.

Integrating Advanced Control Strategies for Enhanced Motor Efficiency in Robotics

Motor efficiency requires diversified paradigm integration to facilitate the advancement of robotics applications through precision, energy optimization, and reliability. Advanced control strategies like AI-driven predictive mechanisms, harmonic drive systems, and real-time feedback tools in motor performance underline the efficiency required in robotic technologies. As a collective role for robotic motors, the approaches enable precise torque and speed modulation, dynamic adaptability to environmental changes, and energy-efficient operation in controlled strategies. Understanding theoretical foundations in motor efficiency guides decisions on the selection and implementation of robotic technologies in the automation of industrial and manufacturing processes. Comparative robotic role analyses pinpoint optimization for the technology to harness dynamic responsiveness and energy utilization efficiency. The ways to implement advanced motor controls underscore the potential of combining intelligent algorithms with innovative motor designs to elevate robotics reliance in complex situations. The advanced control strategies showcase the need for efficiency, adaptability, and relevance of robotic solutions in trending technological applications.

Enhancing Energy Efficiency of Hydrodealkylation (HDA) Toluene for Benzene Production through Optimizing Utility Tool (Pinch Analysis)

Benzene is one of the chemicals widely used in domestic and industrial products. There have been many extensive studies conducted on hydrodealkylation processes to produce benzene from toluene to achieve the optimum result. Aspen hysys V.11 used to simulate hydrodealkylation process. In this study, this work aims to optimize energy efficiency and manage the heat generated by the highly exothermic hydrodealkylation reaction by adjusting the utility type of the heater and cooler. The results of the analysis and calculations, the utility modifications in the heat exchanger network system have proven to have a positive impact on energy efficiency and overall performance. With high energy efficiency, the system is able to reduce reliance on auxiliary utilities while improving the sustainability of the production process. This approach is not only beneficial from an operational perspective but also contributes significantly to cost savings and reduced environmental impact, making it a very viable solution to implement. Copyright © 2024 by Authors, Published by Universitas Diponegoro and BCREC Publishing Group. This is an open access article under the CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0).

Investigation of the annular pressure influence on the energy efficiency of electric submersible pump installations in cyclic operation

ABSTRACT One of the negative aspects that affect the energy efficiency of electric submersible pumps installations (ESPI) is the annular pressure increasing. To assess the effect of annular pressure on the energy efficiency of ESPI operation, a technique has been developed that allows determining the specific power consumption and flow rate of wells equipped with ESPI during cyclic operation. Based on the developed technique, an assessment of the gas pressure effect in the annular space on the energy efficiency of ESPI during cyclic operation at different well flow rates, as well as at different ratios of the duration of operation and downtime in the cycle was made. According to the calculation results, it was found that a decrease in annular pressure leads to an increase in the energy efficiency of ESPI operation. It was stated that the greatest reduction in specific power consumption during the operation of the ESPI relative to the continuous mode of operation is achieved with decreasing the operating time in the cycle. The developed technique and results can be used at oil-producing enterprises to reduce electricity costs of oil production.

An Analysis on Sustainable Automotive Car Body Material Using Weighted Sum method

Introduction: In order to reduce its influence on the environment, the automobile industry must overcome a significant obstacle and switch to sustainability car body materials. This overview examines the demand for cutting-edge materials that enhance vehicle performance, increase recycling, and minimize carbon emissions. The sector can meet expanding transportation demands while promoting a greener future by using sustainable options. The automotive industry plays a major role in the global carbon footprint, with car body products being a major contributor. As sustainability becomes a major concern, the need for eco-friendly alternatives to traditional materials is clear. This introduction explores the concept of sustainable automotive car body materials, highlighting their importance in reducing emissions, conserving resources and promoting a greener future. By exploring innovative solutions, the industry can embrace sustainability while maintaining high-performance standards. Research significance: Research on sustainable automotive car body materials is significant in addressing the pressing challenges facing the automotive industry. By focusing on the development and use of environmentally friendly alternatives, this research aims to reduce the environmental impact of the industry. By reducing carbon emissions and promoting resource conservation, sustainable car body materials can contribute to a greener and more sustainable future. Furthermore, this research has the potential to improve energy efficiency and vehicle performance, leading to safer and more efficient vehicles. Additionally, by meeting growing consumer demand for sustainable options, this research will help automakers gain a competitive edge in the market and align their products with the values and expectations of environmentally conscious consumers. Method: The weighted sum method is a decision-making technique that assigns weights to different criteria and calculates an overall performance score of the alternatives. By assigning relative importance to each criterion, this method allows decision makers to objectively evaluate options and make informed choices. The weighted sum method combines the weighted scores of each criterion to obtain a comprehensive performance measure that enables a systematic approach to decision making. The method provides a structured framework for analyzing complex decision-making problems, finding applications in fields as diverse as project selection, supplier evaluation, and product design. Alternate parameters: Steel Alloy (AHSS), Aluminium Alloy (A7075 T6), Carbon Fiber/ epoxy laminate (CL), Titanium Alloy (Ti6Al4 V). Evaluation parameters: Tensile Strength (Mpa), Stiffness (Gpa), Damping capacity, Cost (Rs), CO2 Emission (Ton/Ton), Results: The Steel Alloy (AHSS) is in 1st rank. The Aluminium Alloy (A7075 T6) is in 4th rank. Carbon Fiber/ epoxy laminate (CL) is in 2nd rank. Titanium Alloy (Ti6Al4V) is in 3rd rank. The result is done by using the WSM method. Conclusion: sustainable automotive car body material in the Steel Alloy (AHSS) is in 1st rank. The Aluminium Alloy (A7075 T6) is in 4th rank. Carbon Fiber/ epoxy laminate (CL) is in 2nd rank. Titanium Alloy (Ti6Al4V) is in 3rd rank. The result is done by using the WSM method.

CONVERSION OF AQUEOUS AMMONIA SOLUTIONS USING AN ADAPTIVE WATER PURIFICATION SYSTEM

Water purification from ammonium nitrogen is currently an urgent task for protecting drinking water sources and ensuring the required water quality for consumers in various sectors of the Ukrainian economy. The technological approaches covered in this article can be used to remove other ammonium-based compounds from water. The obtained results of the adaptive water purification system (AWPS) with a simulated highly concentrated aqueous ammonium solution (1,16 g/dm3) suggest their use in water purification technologies from hydrobionts and side-products of biogas systems (digestat purification). Pulsed electrochemical methods were used in the operation of the AWPS in combination with ultrasonic exposure on oxidation-reduction processes with controlled injection of gas mixtures. A high correlation between all the variables studied was established. The same was confirmed by the regression analysis made in the process of empirical modeling of the relationship between the treatment time and the change in pH and in the concentrations of ammonium nitrogen, nitrates and nitrites. That is, all the indicators of multiple correlation and determination in the constructed significant models indicate a very high correlation with the treatment time. The decrease in pH from 11 to 9.6 can be explained by the fact that acids were formed during the treatment of the model solution, which caused the decrease. The energy efficiency of the AWPS operation was assessed by analyzing changes in the concentrations of the main component of the simulated solution (ammonium) and its derivatives using the example of nitrites and nitrates and a fixed operating time of 60 min. The use of electrolysis methods allowed the conversion of ammonium in an aqueous solution into derivatives - an aqueous solution of nitrites and nitrates, to record changes in their concentrations, and when using membrane electrolysis to obtain them in an ionic form, which is optimally suitable for plant nutrition and direct synthesis of nitrogen-containing fertilizers with simultaneous application by irrigation or spraying.

An African vulture optimization algorithm based energy efficient clustering scheme in wireless sensor networks

Energy efficiency plays a major role in sustaining lifespan and stability of the network, being one of most critical factors in wireless sensor networks (WSNs). To overcome the problem of energy depletion in WSN, this paper proposes a new Energy Efficient Clustering Scheme named African Vulture Optimization Algorithm based EECS (AVOACS) using AVOA. The proposed AVOACS method improves clustering by including four critical terms: communication mode decider, distance of sink and nodes, residual energy and intra-cluster distance. Through mimicking the natural scavenging behavior of African vultures, AVOACS continuously balances energy consumption on nodes resulting in an increase in network stability and lifetime. For CH selection, we use AVOACS, which considers the following parameters: communication mode decider, the distance between sink and node, residual energy, and intra-cluster distance. In comparison to the OE2-LB protocol, simulation findings demonstrate that AVOACS enhances stability, network lifetime, and throughput by 21.5%, 31.4%, and 16.9%, respectively. The results show that AVOACS is an effective clustering algorithm for energy-efficient operation in heterogeneous WSN environments as it contributes to a large increase of network lifetime and significant enhancement of performance.

Enhancing building sustainability: A comprehensive review and methodological roadmap for retrofit strategies

A large body of research has been developed with the aim of assisting policymakers in setting ambitious and achievable environmental targets for the retrofit of current and future building types for energy-efficiency and in creating effective retrofit strategies to meet these targets. The aim of this research is to conduct a comprehensive study to identify the relationship between building typology and sustainability, with a particular emphasis on retrofitting and try to identify research gaps in the most effective energy-saving strategies for retrofitting various types of buildings. In this regard, this study conducts a systematic literature review (SLR) utilizes artificial intelligence (AI) and natural language processing (NLP). Sixty relevant papers are selected and reviewed, establishing a comprehensive searching scheme. The research highlights retrofitting strategies for improving energy efficiency in buildings and discusses the limitations of current practices in terms of physical and technical developments, such as building retrofit assessment according to the typology of the building and environmental factors. To address these limitations, this study proposes a methodology for future research with a focus on in-depth building classification, developing tailored retrofitting alternatives, and establishing an adaptive solution framework. This framework aligns cohesively with diverse typologies, adapts to changing environments, and enhances long-term energy-efficient performance. It proposes detailed building categorization to understand the interconnections between a building's physical characteristics, technology, and energy needs. Additionally, it suggests tailoring retrofit solutions for diverse building types and creating an adaptable framework for changing conditions. Using qualitative research, literature review, quantitative analysis, and case studies, the methodology ensures research credibility. Prototyping is employed to refine processes, considering building types and environmental factors.

Adaptive Performance Evaluation of Container Terminals Through Normalization and Parameter Analysis

Background: Container terminals are a pivotal part of global logistics networks, influencing supply chain reliability and port competitiveness. Traditional performance evaluation methods, such as KPI-based assessments or multi-criteria analyses, often fail in dynamic operational conditions with inherent uncertainty and variability. Methods: This study proposes a normalization-based framework to evaluate container terminal performance by standardizing operational parameters, including availability, non-productive operations, operation time, energy consumption, and throughput. The methodology involves parameter definition, normalization, weight assignment, index calculation, and performance classification. Results: The findings demonstrate that normalization ensures a transparent and adaptable evaluation framework. Sample calculations show how parameter weights influence terminal assessments across varied scenarios, confirming the robustness of the proposed method in capturing dynamic operational changes. Conclusions: Normalization offers a practical tool for enhancing container terminal efficiency and competitiveness. It enables decision-makers to adapt strategies to changing priorities, such as throughput maximization or energy efficiency, ensuring comprehensive and reliable performance assessments.

Solid-State Oxide-Ion Synaptic Transistor for Neuromorphic Computing.

Neuromorphic hardware facilitates rapid and energy-efficient training and operation of neural network models for artificial intelligence. However, existing analog in-memory computing devices, like memristors, continue to face significant challenges that impede their commercialization. These challenges include high variability due to their stochastic nature. Microfabricated electrochemical synapses offer a promising approach by functioning as an analog programmable resistor based on deterministic ion-insertion mechanisms. Here, an all-solid-state oxide-ion synaptic transistor is developed, employing Bi2V0.9Cu0.1O5.35 as a superior oxide-ion conductor electrolyte and La0.5Sr0.5FeO3-δ as a variable-resistance channel able to efficiently operate at temperatures compatible with conventional electronics. This transistor exhibits essential synaptic behaviors such as long- and short-term potentiation, paired-pulse facilitation, and post-tetanic potentiation, mimicking fundamental properties of biological neural networks. Key criteria for efficient neuromorphic computing are satisfied, including excellent linear and symmetric synaptic plasticity, low energy consumption per programming pulse, and high endurance with minimal cycle-to-cycle variation. Integrated into an artificial neural network (ANN) simulation for handwritten digit recognition, the presented synaptic transistor achieved a 96% accuracy on the Modified National Institute of Standards and Technology(MNIST) dataset, illustrating the effective implementation of the device in ANNs. These findings demonstrate the potential of oxide-ion based synaptic transistors for effective implementation in analog neuromorphic computing based oniontronics.

BLE Periodic Advertising-Based Energy-Efficient Sensor Node Operation for Transfer of Large Data in Monitoring Applications

BLE Periodic Advertising-Based Energy-Efficient Sensor Node Operation for Transfer of Large Data in Monitoring Applications

Enhancing Membrane Materials for Efficient Li Recycling and Recovery.

Rapid uptake of lithium-centric technology, e.g., electric vehicles and large-scale energy storage, is increasing the demand for efficient technologies for lithium extraction from aqueous sources. Among various lithium-extraction technologies, membrane processes hold great promise due to energy efficiency and flexible operation in a continuous process with potential commercial viability. However, membrane separators face challenges such as the extraction efficiency due to the limited selectivity toward lithium relative to other species. Low selectivity can be ascribed to the uncontrollable selective channels and inefficient exclusion functions. However, recent selectivity enhancements for other membrane applications, such as in gas separation and energy storage, suggest that this may also be possible for lithium extraction. This review article focuses on the innovations in the membrane chemistries based on rational design following separation principles and unveiling the theories behind enhanced selectivity. Furthermore, recent progress in membrane-based lithium extraction technologies is summarized with the emphasis on inorganic, organic, and composite materials. The challenges and opportunities for developing the next generation of selective membranes for lithium recovery are also pointed out.