Energy Efficiency Rate Research Articles

Performance evaluation and multi-objective optimization of hydrogen-based integrated energy systems driven by renewable energy sources

This study proposes an integrated energy system using hydrogen storage to realize the efficient utilization of renewable energy sources and reduce the fluctuation when renewable energy is connected to grid. The system utilizes solar and wind energy to realize hydrogen production, desalination, and CCHP. First, the energy, exergy, and economic evaluations for the proposed system are carried out, and then an in-depth analysis of the key operating parameters is performed. The system achieves energy efficiency, exergy efficiency, and cost rate of 48.49%, 19.98%, and 7.969$/h, respectively. And the exergy analysis shows that the main exergy destructions are caused by the parabolic trough solar collector and the transcritical CO2 power cycle. The parametric analysis demonstrates when solar radiation flux and wind speed increase, the exergy efficiency and hydrogen production are increased, but the cost rate is increased accordingly. Finally, two sets of multi-objective optimization schemes are performed combining the artificial neural network with the non-dominant genetic algorithm-Ⅱ(NSGA-II). For the optimized fresh water output, cost rate, and exergy efficiency, it is achieving improvements of 51.73%, 8.4%, and 3.6%, and for the optimized hydrogen production, cost rate, and exergy efficiency, it is increased by 12.53%, 0.564%, and 0.75%, respectively.

Advanced spatial query processing in IoT through mobile agent integration

In IoT environments, various applications and protocols rely on spatial query processing to obtain, analyze, and interpret information from sensor nodes based on their location within a specific area. It enables the efficient monitoring and management of spatial data, which is crucial for applications such as environmental monitoring, smart cities, and disaster response. By querying the location and status of sensor nodes, spatial query processing facilitates real-time decision-making, optimizes resource allocation, and enhances the overall performance of the IoT system.This paper focuses on Window queries, a key spatial query type that retrieves data from sensors within a defined two-dimensional zone. The research aims to improve spatial query processing in IoT environments by proposing a Window query processing method that balances various performance factors such as energy efficiency, latency, accuracy, and query success rate. We introduce a novel approach using mobile agents to enhance spatial query processing by addressing the constraints of sensor networks. Simulation results from the NS-2 simulator demonstrate the effectiveness of our method across different parameters and environments.

Thermo-environmental analysis of a renewable Allam Cycle for generating higher power and less CO2 emissions

Although CO2 emissions from burning fossil fuels have caused numerous environmental problems, the utilization of oxy-fuel cycles has proven to be an effective method for generating more power while minimizing CO2 emissions. In this study, an innovative power generation system was presented that integrates renewable energies (solar and wind) with a small-scale oxy-fuel cycle (Allam Cycle). The system underwent modeling, simulation, and optimization from energy, exergy, exergoeconomic, and environmental perspectives. Generating higher power and emitting lower CO2 emissions compared to conventional gas turbines was the main goal of this study. Additionally, to present results meaningfully, comparisons were made between two different weather conditions: St. Petersburg in Russia (cold climate) and Tehran in Iran (normal climate). In this regard, dynamic analysis was performed using TRNSYS and Engineering Equation Solver (EES) tools. Furthermore, a multi-objective optimization was conducted using the TOPSIS multi-criteria method on functional parameters. The optimization results revealed that the maximum energy efficiency and minimum total cost rate were achieved at 75 % for Tehran and 1.84 $/h for St. Petersburg. Providing power for internal components, the highest power sold to the grid was reported to be 8365 kWh in December in St. Petersburg and 9646 kWh in May in Tehran. Additionally, the flat plate collector (FPC) demonstrated maximum efficiency and stored heat of 36 % in Tehran and 2.25 kWh in St. Petersburg, respectively. Ultimately, the results of pollutant emissions indicated that the Allam Cycle achieved a significant CO2 reduction compared to a conventional gas turbine, with a reduction ratio of 117 times in St. Petersburg and 119 times in Tehran.

A novel energy-efficiency framework for UAV-assisted networks using adaptive deep reinforcement learning

In the air-to-ground transmissions, the lifespan of the network is based on the “unmanned aerial vehicle's (UAV)” life span because of the limited battery capacity. Thus, the enhancement of energy efficiency and the outage of the ground candidate’s minimization are significant factors of the network functionality. UAV-aided transmission can highly enhance the spectrum efficacy and coverage. Because of their flexible deployment and the high maneuverability, the UAVs can be the best alternative for the situations where the “Internet of Things (IoT)” systems utilize more energy to attain the essential information rate, when they are far away from the terrestrial base station. Therefore, it is significant to win over the few troubles in the conventional UAV-aided efficiency approaches. Thus, this proposed work is aimed to design an innovative energy efficiency framework in the UAV-assisted network using a reinforcement learning mechanism. The energy efficiency optimization in the UAV offers better wireless coverage to the static and mobile ground user. Presently, reinforcement learning techniques effectively optimize the energy efficiency rate of the system by employing the 2D trajectory mechanism, which effectively removes the interference rate attained in the nearby UAV cells. The main objective of the recommended framework is to maximize the energy efficiency rate of the UAV network by performing the joint optimization using UAV 3D trajectory, with the energy utilized during interference accounting, and connected user counts. Hence, an efficient Adaptive Deep Reinforcement Learning with Novel Loss Function (ADRL-NLF) framework is designed to provide a better energy efficiency rate to the UAV network. Moreover, the parameter of ADRL is tuned using the Hybrid Energy Valley and Hermit Crab (HEVHC) algorithm. Various experimental observations are performed to observe the effectualness rate of the recommended energy efficiency model for UAV-based networks over the classical energy efficiency framework in UAV Networks.

Local pH Gradients Alter Product Selectivity of Electrocatalytic Oxygen Reduction Reaction on La‐Cr‐Co Perovskites

AbstractThe electrochemical production of hydrogen peroxide through the oxygen reduction reaction (ORR) presents the interesting situation where the fully reduced form, H2O, is significantly favored by thermodynamics. Perovskite oxide based catalysts with slow kinetics, or large overpotentials, have been shown to enable production of H2O2 with good selectivity, but this comes at the expense of energy efficiency and production rates. We analyze the structure and electrocatalytic ORR capabilities of a perovskite oxide series based on LaCr1‐xCoxO3. We find that the crystal lattice distorts in two ways as Co content increases – anisotropic compression of the lattice is accompanied by a pinching of angles within the structure, which gives way to compression of B−O bonds at higher Co contents. Product selectivity is observed to change as a function of both catalyst composition and rotation rates of rotating ring‐disk electrode. The rotation‐dependent behavior is attributed to accelerated displacement of surface‐bound peroxide intermediates by a pH gradient at the catalyst surface, and composition‐dependent changes in selectivity are found to correlate to the angular distortion of the crystal lattice. These results highlight the importance of local environments in electrocatalyst reactions and demonstrate that structure and local pH can be used to manipulate product selectivity.

Re-investigating the linkage between buildings’ energy efficiency and mortgage rates

ABSTRACT The energy use in residential buildings is one of the deciding factors in the transition to a carbon-neutral economy. Using data on Dutch residential mortgages from 2013 to 2022, we re-investigate the linkage between mortgage interest rates and the energy efficiency of buildings. The results are novel in that they are the first to empirically provide evidence in favour of a small sustainability discount of about 3 bps on mortgage interest rates for residential homes. Sensitivity checks suggest that younger-aged borrowers and those in the middle-income range benefit the most from a sustainability discount. From a policy perspective, our results are relevant as they show how banks ‘walk the talk’ and incorporate energy efficiency in mortgage terms.

An Energy-Balance Clustering Routing Protocol for Intra-Body Wireless Nanosensor Networks

Aims and Background: Numerous sensor nodes spread out across the surveillance region form the Wireless-Sensor Network (WSN), a smart, self-organizing network. Since the lumps can typically only be motorized by batteries, creating a WSN while maintaining an optimal energy balance and extending the network's lifetime is the biggest issue. Methods: A novel network architecture that integrates nanotechnology with sensor networks is known as a Wireless-NanoSensor-Network(WNSN). A new area of focus in research is intra-bodyiWNSNs, which are WNSNs with promising potential applications in biomedicine, damage detection, and intra-body health monitoring. We suggest an energy-balance-clustering-routing protocol (EBCR) for iSN nodes that have limited energy storage, short communication range, and low computation and processing capabilities. The protocol uses a novel hierarchical clustering approach to lessen the communication burden on nano-nodes. Results: Cluster nano-nodes can use one-hop routing to send data directly to the Cluster-Head(CH) nodes, and the CH-nodes can utilize multi-hop routing to send data to the nano control node. In addition, selecting the next hop node to minimize energy usage while guaranteeing successful data packet delivery involves balancing distance and channel capacity. The protocol's strengths in energy efficiency, network-lifetime extension, and data-packet transmission success rate were highlighted by the simulation results. Conclusion: It is clear that the EBCR protocol is a viable option for iWNSNs' routing system.

Non-orthogonal Multiple Access (NOMA) Channel Estimation for Mobile & PLC-VLC Based Broadband Communication System

Background: The paper focuses on enhancing the performance of 5G wireless mobile communication systems. Furthermore, it addresses the increasing demand for high data rates, improved channel capacity, and spectrum efficiency outlined by the 3rd Generation Partnership Project (3GPP) protocol. Objective: To develop an innovative Non-Orthogonal Multiple Access (NOMA)-based channel estimation (CE) model aimed at improving the performance of 5G wireless mobile communication systems Methods: A proportionate recursive least squares (PRLS) algorithm is utilized for estimating the characteristics of practical Rayleigh fading channels. The applicability of the PRLS algorithm is investigated in Lambertian channels for indoor broadband communication systems such as power line communication (PLC) and visual light communication (VLC) systems. Results: The assessment of evaluation metrics, including mean square error (MSE), bit error rate (BER), spectral efficiency (SE), energy efficiency (EE), capacity, and data rate, have been analysed. Faster convergence and higher accuracy compared to existing state-of-the-art approaches have been demonstrated. Conclusion: The NOMA-based channel estimation model presents significant promise in enhancing the performance of 5G wireless communication systems. The demands for higher data rates and improved spectral efficiency as per 3GPP standards have been addressed.

WMO Global Energy Resilience Atlas—Climate Risk Indices for Hydropower

The importance of energy transition was underlined at COP28 in Dubai, where governments committed to tripling renewables capacities and doubling the rate of energy efficiency by 2030. However, the power generated by climate-dependent energy sources exhibits greater vulnerability to potential climate changes in the long term. Therefore, climate models play a pivotal role in estimating the effects of climate change on renewables in the context of strategic planning for the development and operation of new renewable power plants. In this context, the World Meteorological Organization (WMO) developed a Global Energy Resilience Atlas aimed at providing insights into the climate change risks for the hydropower sector, the largest renewable electricity source for most countries, generating over 4300 TWh globally. This study focuses on defining four Hydro Climate Risk indices (HCRIs) using historical and climate projection precipitation data for three climate scenarios. The final product is a freely available and interactive tool. The developed methodology and tool address how climate changes have historically affected hydropower generation and how they will impact the future at national scales. The final product also addresses the needs of policymakers at national, regional, and global levels in crafting long-term planning for a more secure energy sector, accelerating the energy transition to more sustainable and reliable energies.

Wood Chipper Design for Biofuel Production in a Global Catastrophic Loss of Infrastructure Scenario

A variety of events such as high-altitude electromagnetic pulses, extreme solar storms, and coordinated cyber attacks could result in a catastrophic loss of infrastructure on a continental or global scale. The lengthy repair of critical infrastructure creates a need for alternative fuels such as wood gas. Wood gas is produced by heating wood in a low-oxygen environment and can be used to power combustion engines. This work investigates a novel wood chipper, designed as an energy-efficient tool for producing wood gas stock, wood chips, aiming to speed up the transition to alternative fuel. A prototype is built and tested to determine the energy efficiency and production rate of the device. The results suggest that the wood chipper could produce one cord of wood chips, 3.6 m3, in less than a day and is a viable alternative to other manual wood-processing methods. In addition, the global scaling up of production of the wood chipper is considered, indicating that the mass production of the wood chipper could accelerate the transition of wood gas production methods from manual to machine-driven immediately after a catastrophic event.

5G Wireless Technology: Spectrum Sharing and Heterogeneous Networks

Abstract: This paper addresses the critical challenge of 5G network cell planning, emphasizing the essential role of estimating the number of cells or base stations required for a given area and user bandwidth. The proposed model explores four scenarios with varying network parameters to determine the optimal base station deployment. The increasing demand for 5G networks necessitates a focus on small cells or femtocells, offering advantages such as consistent coverage, power adjustment, energy efficiency, and higher data rates. However, deploying excessive femtocells may lead to unnecessary handovers, requiring optimization strategies. The study introduces an analytical model for heterogeneous cellular networks, integrating fourth and fifth-generation systems. The model, represented by a two dimensional Markov Chain, employs a novel decomposition approach for analyzing system performance measures. Validation is conducted through simulation and simultaneous equation systems. The proliferation of mobile devices and data traffic necessitates dense 5G network deployments, with small cells gaining traction due to their versatile coverage, power adaptability, energy efficiency, and higher data rates. However, an excessive deployment of small cells can lead to frequent handovers, prompting the need for enhanced handover strategies and coexistence with other technologies in the 5G landscape. The paper concludes by highlighting the impending need for practical implementation testing of 5G systems, emphasizing the significant role of small cell deployments. It discusses an initiative focusing on testing small cellenabled operator business models in real-world scenarios, anticipating the pivotal role of small cells in future 5G systems.

Hybrid Radix-16 booth encoding and rounding-based approximate Karatsuba multiplier for fast Fourier transform computation in biomedical signal processing application

Multiplication is an essential biomedical signal processing function implemented in the Digital Signal Processing (DSP) cores. To enhance the speed, area and energy efficiency of DSP cores, approximate multiplication is used. Also, low power multiplier unit design is one of the requirements of DSP processor to meet the increasing demands. To balance both the design and error metrics of a multiplier design, an efficient Hybrid Radix-16 Booth Encoding and rounding-based approximate Karatsuba Multiplier (RBEKM-16) is proposed. This research introduces an Approximate Karatsuba multiplier based on rounding, utilizing rounding approximation to compute the least significant part of the product. Simple operators, like adders and multiplexers, replace complex and costly conventional Floating-Point (FP) multipliers in this process. Radix-4 logarithms are incorporated to further minimize hardware complexity and calculate the product's most significant part. Subsequently, an approximate 4-2 compressor is applied in the partial product reduction stage to generate the most significant bit result. In the experimental scenario, the efficiency of the multiplier is evaluated in terms of energy efficiency, area utilization and error rate by using Xilinx ISE 8.1i tool. The results from the experiments indicate that the suggested multiplier demonstrates improved energy efficiency, utilizes space more effectively, and performs well in applications related to biomedical signal processing. Further, the accomplished area utilization of the proposed 16-bit multiplier is 1068 μm2, delay is 3.01 ns, power consumption is 0.021 mW and power delay product is 119 fJ.

Enhancing economic and performance effectiveness of a geo-solar system using multi aspect artificial neural network

Geo-solar systems, comprising ground-source heat pumps (GSHP) and solar thermal collectors, offer promising solutions for sustainable space heating and hot water provision in residential buildings. However, optimizing these systems for maximum energy efficiency and cost-effectiveness remains a challenge. In this context, this study presents a comprehensive approach to enhancing the performance and cost-effectiveness of geo-solar systems. The proposed methodology integrates artificial neural network (ANN) techniques and multi-aspect optimization to optimize system parameters. The detailed of building mathematical model incorporating energy and exergy equations, thermodynamic, and economic relations for all system components. The neural network model inputs to the include five neurons represents Geothermal temperature, Solar radiation levels, System pressure levels, Ambient temperature, and Operational time, and the model outputs represent Energy efficiency and Exergy destruction rate. Through the training of ANN using simulation data and the application of a multi-objective genetic algorithm, the system is optimized for energy efficiency, cost, and environmental impact simultaneously. The results of analysis show that the exergy efficiency of system is 22.02 % and turbine generates 1963 kW. Changes in P10, ranging from 2000 to 3400 kPa leads to the exergy destruction reduction from 8350 kW to 8000 kW, while the other parameters exhibit an increasing trend.

Process design and simulation of methyl isobutyl ketone (MIBK) dehydration by batch distillation: A study on unit configuration and operational policies

Heteroazeotropic batch distillation of methyl isobutyl ketone-water binary mixture is investigated, seeking a reliable operation for dehydration of methyl isobutyl ketone (MIBK). The dynamic batch distillation module (BatchSep) of the commercial package Aspen Plus V.12.1® is applied for simulations. An initially fed unit at atmospheric pressure is simulated from the heating-up step until it reaches the desired MIBK purity of 99.8 wt%. Three configurations, namely conventional batch distillation unit (Mode I), batch distillation unit with decanter (Mode II), and a simple distillation unit (Mode III), are compared in a wide range of operating conditions. The effects of condenser temperature and the number of theoretical stages are also examined. According to the results, applying a decanter (Mode II) with a return fraction of over 0.75 for the MIBK-rich phase and below 0.4 for the aqueous phase provides higher MIBK recovery than the maximum achievable value in a conventional unit (Mode I), with almost no increase in process time. A perfect decanter offers an almost complete MIBK recovery, which is about 5% over the maximum value by conventional units (Mode I). Moreover, cutting the reflux (Mode III) offers the fastest way to the desired product but provides the lowest MIBK recovery value. The aqueous phase return fraction does not significantly impact MIBK recovery, but if it exceeds 0.5, it remarkably affects the process time/energy cost. When maximum MIBK recovery rate/minimum energy cost is desired, applying a decanter (Mode II) with a return fraction above 0.5 for the MIBK-rich phase and below 0.55 for the aqueous phase yields a higher production rate and a lower energy cost per unit quantity of product compared to the best achievable values for a conventional unit (Mode I). A perfect decanter improvesproductionrateandenergyefficiencyby8 %overthe best case in a conventionalunit (ModeI). Also, operation without reflux (Mode III) is preferred over a conventional operation (Mode I) with a total return fraction over 0.7 due to its superior energy efficiency and production rate.

Internet of Medical Things (IoMT) optimization for healthcare: A deep learning-based interference avoidance model

The Internet of Medical Things (IoMT) is a significant component of the broader Internet of Things (IoT), IoMT is responsible for monitoring, collecting, and transmitting critical medical data to central medical computer centers. IoMT faces significant challenges, including energy efficiency, interference from other devices in the spectrum, latency, and security and privacy concerns. As a result, in hospital settings, the development of a precise and dependable IoMT system is crucial. Consequently, this investigation offered an innovative, accurate, and dependable IoMT method. The suggested model intends to reduce or eliminate interference caused by other transmitting devices sharing the same IoMT spectrum within hospitals or medical institutions. It also presents an interference-avoidance distributed deep learning model for IoMT to medical receptionists. This model uses data from the Lagrange optimization technique to determine the ideal distance between interfering devices and medical receptions. As a result, the required system signal-to-interference-plus-noise (SINRth) is met while maintaining the highest energy efficiency (EE) and system achievable data rate (R). The proposed analytical model and deep learning model demonstrate the efficacy of the proposed approach by achieving the required system signal-to-interference-plus-noise (SINRth) with the best energy efficiency (EE) and system achievable data rate (R) while suppressing interference to any medical receptions.

Joint subchannel power allocation for downlink NOMA systems based on quantum carnivorous plant algorithm

Currently, most existing research on non-orthogonal multiple access (NOMA) systems converts power allocation into two independent procedures, i.e., inter-subchannel power allocation and intra-subchannel power allocation. However, improper inter-subchannel power allocation will adversely affect the power allocation process of intra-subchannel. If the power allocations of inter-subchannel and intra-subchannel are optimized simultaneously, it usually needs high computational complexity, and is hard to obtain an optimal solution. To tackle this issue, this paper studies the joint subchannel power allocation for NOMA systems and proposes a new intelligent algorithm called quantum carnivorous plant algorithm (QCPA) for simultaneously optimizing inter-subchannel and intra-subchannel power allocations. Analysis of the convergence of QCPA verified its superior performance on the test functions. By utilizing the optimal solution obtained by QCPA as the power allocation scheme, energy efficiency and sum transmission rate in NOMA systems are significantly increased compared to existing traditional power allocation methods. The results above demonstrate that QCPA effectively addresses test functions and the power allocation problem for NOMA. QCPA possesses favorable convergence in comparison to other comparison algorithms and methods.

Onboard pre-combustion carbon capture with combined-cycle gas turbine power plant architectures for LNG-fuelled ship propulsion

This paper investigates the concept of pre-combustion carbon capture (Pre-CCS) with a combined cycle gas turbine (CCGT) propulsion system to achieve energy efficient carbon capture onboard a Liquid Natural Gas fuelled vessel. A basic CCGT model with energy efficiency of 51.6 % when using LNG as fuel was integrated with a Pre-CCS system composed of a steam methane reformer, water–gas shift reactors, pressure swing adsorption (SMR-PSA), and liquid CO2 storage. Various waste heat utilisation schemes integrated with the CCGT-reformer were modelled in Aspen HYSYS. The modelling of the waste heat recovery networks as energy streams integrating between various processes and unit of operations has facilitated a closed-loop simulation of various systems. It was concluded that a proper utilisation of the high-grade heat from the gas turbine (GT) exhaust and from the reformer is crucial to improve the overall energy efficiency and carbon capture rate, exemplified by a scheme where the heating needs for the SMR and the cooling needs from the water–gas-shift reactors are fully integrated. When optimised, the integrated system had an overall energy efficiency of 41.5 % and 43.2 % (i.e. an efficiency loss of 10.2–10.5 percentage points from the base CCGT system) accompanied with specific GHG emission reduction of 51.2 % and 52.3 % for operation of the GT at a turbine entry temperature of 1700 K and 1850 K, respectively. Intermediate levels of reforming, where the GT uses a mix of LNG and reformate gases, were also simulated, showing an almost linear evolution from the 100 % LNG feed to the GT to the 100 % reformate case. Such a strategy could be used for gradual evolution to meet the required CO2 reduction timeline. It was determined that the 100 % reformate case provides the highest CO2 capture rate. Estimates of other emissions from the GT assuming typical combustor residence times for the whole range of fuel blends were also carried out, showing that the overall greenhouse gas emission is dominated by CO2, given that the GT emits negligible N2O and unburnt methane. Some capital cost estimates were also made to determine the unit price of hydrogen fuel produced onboard, suggesting that the CAPEX was not prohibitive. These findings support the pre-combustion CCS − CCGT integrated system as a promising long-term decarbonisation and propulsion strategy to comply with emissions regulations, displaying good performance in terms of cost, energy consumption, and CO2 reduction. The scalability and adaptability of the proposed system for existing and new-built ships across the decarbonisation timeline is also discussed.

TEE-AODV Trust-based Route Selection and Improving Energy Efficiency in MANET

Mobile ad hoc networks (MANETs) are the future communication infrastructure for all devices that operate without the elaborate design found in wired infrastructure networks. Constructing a routing protocol that helps MANETs to achieve (QoS). It is quite challenging to achieve energy-efficient routing, due to its limited resources and node mobility concerns, which would impact node resource stability and lead to congestion and lower user QoS. A novel Trustworthy Energy Efficient AODV protocol has been proposed for improving path selection and locating trusted stations to maximize QoS. This approach employs location data, transmission speed, and directions to estimate the energy usage and trustworthiness of distinct nodes. The proposed TEE-AODV protocol includes two new control information namely Trust Request packet and Trust Reply. (T-RREP) AODV's packet includes a neighbor list and channel trustworthiness. The number of working neighbors in every path is counted, and this information is used to efficiently choose paths. The TEE-AODV calculates a QoS supporting metric for each detected route to identify a traffic-free path at any moment and determines whether it is a trusted route. The NS-2 simulator has been used to assess the suggested approach in terms of specific characteristics, including energy consumption (EC), (PDR), end-to-end delay (), and number of dead nodes. According to the experimental results, the suggested framework enhances reliable route selection while lowering e nd-to-end latency and improving energy efficiency. The experimental findings indicate that the suggested TEE-AODV protocol achieves high energy efficiency rate in the selection of trustworthy route and minimizing the E D .

Advances in zero liquid discharge multigeneration plants: A novel approach for integrated power generation, supercritical desalination, and salt production

In light of the escalating worldwide demand for energy and water resources, the imperative for developing sustainable multigeneration systems has gained prominence in recent research and development. This study aims to investigate the feasibility and performance of a novel multigeneration plant operating at supercritical conditions to simultaneously generate freshwater, power, and dry salt as a by-product while eliminating liquid discharge. This approach offers a promising solution to address the interconnected challenges of energy and water sustainability. The theory contends that combining supercritical desalination with power generation increases energy efficiency and water production rates, surpassing conventional systems. Therefore, a model is developed employing a supercritical once-through boiler capable of supplying the necessary heat duty required for steam generation from the integrated desalination plant, producing power. Additionally, multiple heat exchangers and flash separators are implemented, enhancing freshwater productivity and obtaining dry salt, eliminating the need for brine waste disposal. A thorough sensitivity analysis is conducted utilizing a robust model developed on Aspen HYSYS to ascertain the optimal operational parameters of the proposed plant. The findings of this study underscore the potential of concurrent production of 1.65 million m3/year of freshwater, 302.5 GWh of electricity, and 64,845 ton/year of dry salt for 0.38 $/m3, 0.07 $/kWh, and 0.02 $/kg, respectively. In conclusion, this research highlights the prospects of revolutionary progress in multigeneration systems for sustainable development.

Nexus between technological innovation and environmental pollution in selected OECD countries

AbstractAddressing environmental pollution is fundamental to establishing sustainable development across the globe. While navigating the Fourth Industrial Revolution, it is of critical relevance for economies worldwide to come up with innovative measures that can withstand the factors driving environmental pollution. On that note, this study explores the technological innovation‐environmental pollution linkages in the context of 10 members of the Organization for Economic Cooperation and Development (OECD) using data spanning from 1994 to 2018. Notably, considering ecological footprints as the environmental proxy, the analysis controls for the corresponding levels of economic growth, financial development, and renewable energy consumption in the concerned countries. In addition, the long‐term estimates are investigated using Augmented Mean Group, Common Correlation Effects Mean Group, Fully Modified Ordinary Least Square, and Dynamic Ordinary Least Square techniques while the causality relationship is determined by Dumitrescu‐Hurlin panel bootstrapped causality test. The results establish the long‐term cointegrating linkages among the variables considered. It is also observed that economic growth increases the ecological footprint level, while technological innovation, renewable energy consumption, and financial development reduce it. Moreover, the results reveal that technological innovation and ecological footprint causally influence each other, while there are one‐way causalities moving from economic growth and financial development to ecological footprint. Furthermore, a two‐way causality concerning renewable energy consumption and ecological footprint is also detected. Considering these results, it is pertinent for the selected OECD countries to improve energy efficiency rates, scale investment for developing the renewable energy sector, and execute policies that support investments in initiatives concerning low‐carbon technological development. Additionally, these countries should look to implement policies that are compatible with the objectives of establishing green growth so that low‐emission development can take place to tackle climate change‐related problems.