Energy Consumption Efficiency Research Articles

Research On Target And Path Planning Of Regional Carbon Peaking And Carbon Neutrality

To achieve peak carbon neutrality is a broad and profound economic and social systemic change. At present, the economic and social development of our country has entered the stage of accelerating green and low-carbon high-quality development. In this paper, under no less than three scenarios, the regional dual carbon target is associated with the time node of carbon peak and carbon neutrality, and is associated with the improvement of energy efficiency and the increase of the proportion of non-fossil energy consumption. In various scenarios, if the GDP in 2035 doubles over the base period and quadruples in 2060, the method of calculating carbon emissions is found. If the carbon absorption amount of ecological carbon sink in 2060 is 10% of the carbon emissions of the base period, the calculation method of carbon emissions at this time is found; To find the targets and paths that can determine the GDP, population, energy consumption, energy efficiency and non-fossil energy consumption in 2025, 2030, 2035, 2050 and 2060, and finally complete the qualitative and quantitative analysis of the four major projects.

Optimizing Operational Parameters for Lithium Hydroxide Production via Bipolar Membrane Electrodialysis

Traditional lithium hydroxide production techniques, like lithium sulfate and lithium carbonate causticizing methods, suffer from drawbacks including high specific energy consumption, time-consuming processes, and low recovery rates. The conversion of lithium chloride to lithium hydroxide using bipolar membrane electrodialysis is straightforward; however, the influence of operational parameters on bipolar membrane electrodialysis performance have not been investigated. Herein, the impact of the current density (20 mA/cm2~80 mA/cm2), feed concentration (0.5 M~2.5 M), initial feed pH (2.5, 3.5 and 4.5), and the volume ratio of the feed and base solution (1:1, 2:1 and 3:1) on the current efficiency and specific energy consumption in the bipolar membrane electrodialysis was systematically investigated. The bipolar membrane electrodialysis process showed promising results under optimal conditions with a current density of 50 mA/cm2 and an initial lithium chloride concentration of 1.5 M. This process achieved a current efficiency of 75.86% with a specific energy consumption of 3.65 kwh/kg lithium hydroxide while also demonstrating a lithium hydroxide recovery rate exceeding 90% with a purity of about 95%. This work will provide valuable guidance for hands on implementation of bipolar membrane electrodialysis technology in the production of LiOH.

Predicting Liquid Natural Gas Consumption via the Multilayer Perceptron Algorithm Using Bayesian Hyperparameter Autotuning

Reductions in energy consumption and greenhouse gas emissions are required globally. Under this background, the Multilayer Perceptron machine-learning algorithm was used to predict liquid natural gas consumption to improve energy consumption efficiency. Setting hyperparameters remains challenging in machine-learning-based prediction. Here, to improve prediction efficiency, hyperparameter autotuning via Bayesian optimization was used to identify the optimal combination of the eight key hyperparameters. The autotuned model was validated by comparing its predictive performance with that of a base model (with all hyperparameters set to the default values) using the coefficient of variation of root-mean-square error (CvRMSE) and coefficient of determination (R2) based on the Measurement and Verification Guideline evaluation metrics. To confirm the model’s industrial applicability, its predictions were compared with values measured at a small-to-medium-sized food factory. The optimized model performed better than the base model, achieving a CvRMSE of 12.30% and an R2 of 0.94, and achieving a predictive accuracy of 91.49%. By predicting energy consumption, these findings are expected to promote the efficient operation and management of energy in the food industry.

Sulfonated Chitosan Gel Membrane with Confined Amine Carriers for Stable and Efficient Carbon Dioxide Capture.

Capturing carbon dioxide (CO2) from flue gases is a crucial step towards reducing CO2 emissions. Among the various carbon capture methods, facilitated transport membranes (FTMs) have emerged as a promising technology for CO2 capture owing to their high efficiency and low energy consumption in separating CO2. However, FTMs still face the challenge of losing mobile carriers due to weak interaction between the carriers and membrane matrix. Herein, we report a sulfonated chitosan (SCS) gel membrane with confined amine carriers for effective CO2 capture. In this structure, diethylenetriamine (DETA) as a CO2-mobile carrier is confined within the SCS gel membrane via electrostatic forces, which can react reversibly with CO2 and thus greatly facilitate its transport. The SCS ion gel membrane allows for the fast diffusion of amine carriers within it while blocking the diffusion of nonreactive gases, like N2. Thus, the prepared membrane exhibits exceptional CO2 separation capabilities when tested under simulated flue gas conditions with CO2 permeance of 1155 GPU and an ultra-high CO2/N2 selectivity of above 550. Moreover, the membrane retains a stable separation performance during the 170 h continuous test. The excellent CO2 separation performance demonstrates the high potential of gel membranes for CO2 capture from flue gas.

Multi-objective optimization of key parameters of stirred tank based on ANN-CFD

In order to balance the maximum mixing efficiency and minimum energy consumption of stirred tanks, this study proposes a four-stage optimization framework, integrating all: CFD model, ANN data-prediction model, multi-objective optimization model, and multi-criteria decision making model. With the stirred tank reactor as the modeling and simulation context, a data-prediction model, GA-GABP, is developed firstly. Second, an optimization model is established targeting energy consumption, fluid mixing degree, and suspension uniformity: the predictions of GA-GABP are optimized using the NSGA II, and finally, based on the Pareto front, weights are determined using the entropy weighting method, and the TOPSIS algorithm is employed to decide the final optimization scheme. Compared to the base case, the optimized scheme Opt1 shows a 52.49% reduction in energy consumption, a 1.35% increase in fluid mixing degree, and a 72.31% improvement in suspension uniformity. This demonstrates the framework's effectiveness in balancing multiple conflicting objectives.

The linkages between energy efficiency, renewable electricity, human capital and inclusive growth: The role of technological development

This study highlights the important role of strategic policy interventions in promoting sustainable economic prosperity and social equity in OECD economies. It specifically focuses on the positive effects of technological development, energy efficiency, renewable electricity, and human capital on inclusive growth across 35 OECD economies from 1990 to 2019. We employed cross-sectional augmented distributed lag, fully modified ordinary least squares, and dynamic ordinary least squares to analyze the data. The long-run and short-run estimates show that technological development, energy efficiency, renewable electricity, human capital, and renewable energy consumption have positive effects on inclusive growth. Based on these findings, we propose that the government should provide funds for innovation and technological development, and it improves access to information and services, it increases productivity and efficiency, creates new job opportunities, and promotes inclusive growth. Moreover, the government should promote energy efficiency, which will help in cost savings, job creation, improved access to energy, inclusive growth, and environmental benefits.

Water-induced controllable deswelling strategy enabled rapid fabrication of transparent cellulose film for plastics replacement

Plastic pollution has posed significant impacts on the ecosystem. While biodegradable transparent cellulose films are considered ideal alternatives, they still grapple with issues of high energy consumption and low production efficiency. In this study, we proposed a water-induced fiber deswelling strategy to expedite the fabrication of transparent cellulose films from wood pulp. Raw cellulose fibers were firstly disintegrated into micro-sized fibers via cold alkali swelling and mild mechanical blending. Subsequently, water was introduced to govern the swelling and aggregation behavior of these treated cellulose fibers, reducing their water retention and facilitating the rapid preparation of cellulose films. By regulating the amount of water added, cellulose film can be prepared within 1 min using vacuum filtration. Furthermore, the resulting film exhibits good wet strength, surpassing most previously reported cellulose-based films, with a wet strength exceeding 30 MPa and a wet strength retention of up to 65.7 %. With these advantageous features, the cellulose film was demonstrated for packaging and fruits preservation, showing a great potential to replace conventional plastics in the packaging applications. In addition, the life cycle environmental impacts of 1.0 kg cellulose film production (cradle-to-gate system boundary) were 12.63 kg CO2-eq, 0.04 kg SO2-eq, and 397 MJ energy use.

Numerical simulation and optimization of Pervaporation process based on Heat-Mass-Flow coupling

Pervaporation (PV) has the advantages of low energy consumption and high separation efficiency, but the trade-off effect between flux and separation factor restricts its large-scale application. Based on the conservation equations of mass, momentum and heat, a transient model of the whole PV process coupled with heat-mass-flow was established in this paper. The effects of feed temperature, feed concentration and feed flow rate on heat and mass transfer in PV process were analyzed. TEOS crosslinked PDMS/PTFE-PVDF membrane was prepared by scraping coating method. The reliability of the model was verified by taking n-butanol/water separation system as an example. The concentration polarization and temperature boundary layer under different operating conditions are discussed. The effect of operating conditions on the separation performance was evaluated by response surface method, and the optimal operating conditions are obtained. The results show that the concentration polarization of the membrane surface becomes more obvious with the increase of feed concentration. The temperature boundary layer decreases with the increase of feed flow rate. Surprisedly, there is a significant temperature gradient in the membrane, and the maximum temperature difference between the two sides of the membrane is as high as 1.02 K. Diffusion in the membrane is the rate controlling factor of PV process. The contribution of operating conditions to the separation performance is in the order of feed temperature > feed concentration > feed flow rate. The best operating conditions are: feed temperature 350.2 K, feed concentration 3.8 % n-butanol/water and feed flow rate 23.6L/h. This work provides a theoretical basis for the design of PV membrane materials and the optimization of PV operating conditions.

Photoelectrocatalysis/photoelectro-fenton system based on cone-like TiO2/nickel foam photoanode for efficient degradation of carbamazepine: Comparison with DSA

To degrade refractory organics in wastewater more efficiently and with lower energy consumption, cone-like TiO2 was grown on nickel foam (NF) and coupled with natural air-diffusion electrode (NADE) to construct novel photoelectrocatalysis/photoelectro-Fenton (PEC/PEF) system, which could completely remove 10 mg/L of carbamazepine (CBZ) within 30 min (with a pseudo-first-order rate constant (k) of 0.1939 min−1). And the system was driven by only 2 mA of current (1.7 V cell potential) with an electrical energy per order (EEO) of only 0.005 kWh m−3 order-1. It took the lead in contaminant removal and energy consumption when compared with conventional PEF systems based on dimensionally stable anode (DSA). The synergy coefficient of PEC/PEF system was as high as 6.14, which was 6.5 times that of PEF system. The reaction mechanism different from PEF system was revealed, in which e--h+ pairs were generated at TiO2/nickel foam (TiO2/NF) under UV irradiation, e- transferred to NADE enhanced current density thus promoted H2O2 accumulation much more than that using DSA. The direct oxidation of h+ and the more •OH (6.17 μM vs. 5.82 μM) produced by H2O2 utilization were the reasons for higher degradation efficiency in PEC/PEF process. Our PEC/PEF system provides a new idea for low energy consumption and high efficiency removal of pollutants, it is promising for the application to the treatment of refractory organics in wastewater

Planning and resource allocation of a hybrid IoT network using artificial intelligence

This paper introduces a pioneering hybrid topology tailored for Internet of Things (IoT) applications, integrating mesh and star wireless sensor configurations. This hybridized approach aims to optimize energy consumption efficiency while ensuring comprehensive network coverage for sensor deployment. The formulation of the network strategy is rooted in empirical data collected from real sensors deployed across two neighboring municipalities within the State of Espírito Santo, Brazil. Specifically, our analysis encompasses 380 strategically positioned sensors throughout Vitória city, all intended for connectivity to a central gateway located in Vila Velha. To establish mesh network clusters, we employed the k-Medoids algorithm for clustering fusion, while the GA with a binary solution was utilized to determine the star network points. In this approach, Dijkstra and genetic algorithms with real solutions are incorporated to facilitate efficient resource allocation within the mesh (utilizing ZigBee) and star (utilizing LoRa) networks. These resource allocation strategies are devised with the overarching objective of minimizing energy consumption. The findings of this investigation demonstrate that through the implementation of planning and resource allocation algorithms, we were able to effectively reduce the number of mesh networks and allocate resources to each designated end-point sensor.

Evaluating renewable energy consumption efficiency and impact factors in Asia-pacific economic cooperation countries: A new approach of DEA with undesirable output model

Balancing economic growth with environmental protection is a global priority. Both developed and developing nations face the challenge of optimizing renewable energy use to achieve sustainable development while minimizing ecological harm. This study employs the Data Envelopment Analysis (DEA) model, incorporating undesirable output, to assess the efficiency of renewable energy consumption across 21 member countries of the Asia-Pacific Economic Cooperation (APEC) from 2011 to 2020. The main variables include foreign direct investment, total energy consumption, total renewable energy capacity as inputs, and gross domestic product growth and greenhouse gas emissions as desired outputs. Results reveal varying levels of renewable energy consumption efficiency, with Brunei, Japan, South Korea, and Hong Kong exhibiting the highest efficiency, while Russia, China, and Vietnam demonstrate the lowest. Notably, developed nations display greater efficiency in utilizing renewable energy compared to their developing counterparts. Moreover, optimizing foreign direct investment, renewable energy consumption, and total renewable energy capacity can enhance overall energy efficiency. By leveraging empirical evidence and estimation techniques, the study provides actionable recommendations for policymakers, emphasizing the pivotal role of financial development and green finance initiatives in promoting sustainable energy development across APEC member countries.

Review of Cloud-Based Smart Energy Meters

Abstract: Smart meters represent a pivotal advancement inenergy management, offering numerous benefits to consumers and utility providers. This report provides an overview of smart meters, their functionalities, advantages, challenges, and broader implications for the energy industry. A smart meter is an advanced device that accurately measures electricity consumption. It's better than traditional meters, providing efficient tracking and insights into energy usage habits. Upgrade to a smart meter to make informed decisions and save money on electricity bills. These devices are a vital component of smart grid technology, which aims to enhance energy distribution and consumption efficiency, reliability, and sustainability. Modern technologies, such as the Internet of Things (IoT), have many applications in the energy sector. IoT can help to improve energy efficiency, increase the utilization of renewable energy sources, and reduce the environmental impact of energy use. It can achieve these goals in energy supply, transmission, distribution, and demand. This paper reviews the literature on IoT in energy metering systems and its essential components, functionality, and advantages over conventional metering systems.

Dynamic path planning for mobile robots based on artificial potential field enhanced improved multiobjective snake optimization (APF‐IMOSO)

AbstractWith the widespread adoption of mobile robots, effective path planning has become increasingly critical. Although traditional search methods have been extensively utilized, meta‐heuristic algorithms have gained popularity owing to their efficiency and problem‐specific heuristics. However, challenges remain in terms of premature convergence and lack of solution diversity. To address these issues, this paper proposes a novel artificial potential field enhanced improved multiobjective snake optimization algorithm (APF‐IMOSO). This paper presents four key enhancements to the snake optimizer to significantly improve its performance. Additionally, it introduces four fitness functions focused on optimizing path length, safety (evaluated via artificial potential field method), energy consumption, and time efficiency. The results of simulation and experiment in four scenarios including static and dynamic highlight APF‐IMOSO's advantages, delivering improvements of 8.02%, 7.61%, 50.71%, and 12.74% in path length, safety, energy efficiency, and time‐savings, respectively, over the original snake optimization algorithm. Compared with other advanced meta‐heuristics, APF‐IMOSO also excels in these indexes. Real robot experiments show an average path length error of 1.19% across four scenarios. The results reveal that APF‐IMOSO can generate multiple viable collision‐free paths in complex environments under various constraints, showcasing its potential for use in dynamic path planning within the realm of robot navigation.

A techno-economic scheduling of distribution system including multi-microgrids considering system reconfiguration and bus selector switches

The Microgrid (MG) as an alternative energy system, would reduce power loss, enhance energy consumption efficiency, and improve the system reliability for a sustainable city. Energy scheduling of distribution systems (DSs) including multi-microgrids (MMGs), would result in a multi-objective problem from DS operators’ point of view for a techno-economic operation. In this regard, a multi-objective model for operation scheduling of DS and connected MGs, is proposed in this paper, which also considers the system management solutions such as reconfiguration and determination of the energy trading points for MGs connection. The proposed model is applied to a modified IEEE 33-bus distribution system including 4 MGs. Simulation results and analysis based on the fuzzy satisfying method indicate that the simultaneous consideration of distribution system reconfiguration and Bus Selector Switches (BSSs) for MGs can improve the social welfare function by 4.58 % and reduces the energy losses by 15.82 %.

The Impurity Removal and Comprehensive Utilization of Phosphogypsum: A Review.

Phosphogypsum (PG), a byproduct during the phosphoric acid production process, also known as the wet process, contains complex and diverse impurities, resulting in low utilization and considerable accumulation. This leads to a massive waste of land resources and a series of environmental pollution problems. Given the current urgent ecological and environmental situation, developing impurity removal processes with low energy consumption and high efficiency, exploring valuable resource recovery, preparing high value-added PG products, and broadening the comprehensive utilization ways of PG are significant strategies to promote the sustainable consumption of PG and sustainable development of the phosphorus chemical industry. This review comprehensively summarizes the advantages and disadvantages of existing PG impurity removal and utilization technologies and probes into the future development direction, which provides references and ideas for subsequent PG research.

Efficient removal of short-chain perfluoroalkyl substances (PFAS) using asymmetric membrane capacitive deionization

Concerns have been raised over the persistence and potential health risks of perfluoroalkyl substances (PFAS). PFAS pose significant challenges in water treatment owing to their chemical stability and the difficulty of treating them with conventional purification methods. Although traditional water treatment methods for contaminant removal have been explored, their effectiveness against PFAS remains limited. This gap in knowledge highlights the need for innovative approaches. This study introduces asymmetric membrane capacitive deionization (ACDI) as a novel method for short-chain PFAS removal to address the shortcomings of existing techniques. ACDI refers to a configuration in which an anion exchange membrane is removed from a conventional membrane capacitive deionization with both a cation exchange membrane and an anion exchange membrane. Among the PFAS, perfluorobutanesulfonic acid (PFBS), which is known to be difficult to remove because of its short chain, has been the focus. The ACDI system demonstrated superior deionization capacity (28.75 mg/g) and energy efficiency (energy consumption of 0.46 Wh/g) compared to conventional CDI and membrane CDI systems. In addition, the effects of key operating parameters, such as voltage (0.5 ∼ 1.2 V), initial concentration (50 ∼ 500 mg/L), and flow rate (1 ∼ 5 mL/min), on the PFBS removal were systematically investigated and superior at 1.2 V, 500 mg/L, and 2 mL/min. This study suggests the need for further exploration of real-world applications considering the presence of competing ions in wastewater. This study provides valuable insights into advancing CDI technology for the sustainable treatment of industrial wastewater containing PFAS.

Construction of new energy consumption optimization model based on improved pathfinder algorithm

As traditional energy reserves continue to decline, the importance of new energy sources increases. However, the current traditional power system often fails to consider new energy sources, particularly in power supply systems that integrate multiple new energy sources. The cost, efficiency, and environmental factors seriously affect the energy system’s efficiency. Therefore, this proposal presents a multi-objective optimization discrete assignment pathfinder algorithm. The algorithm can handle multi-objective optimization problems and adapt to various constraints, providing a more precise optimization scheme for new energy systems. The experimental results indicated that the proposed research method exhibits better performance compared to other algorithms of the same type. Compared with the multi-objective multivariate universe optimization algorithm and the multi-objective sparrow search algorithm, the research method was ahead in terms of fitness value by 9.54% and 14.67%, respectively. Meanwhile, in the grid simulation, the research method achieved an average efficiency of 96.16%, which is better than the comparative algorithms by 6.57–14.02%. The study not only improves the optimization efficiency of new energy consumption, but also provides a powerful decision support tool for the planning and operation of wind farms. It is of great significance for the improvement of power system efficiency and decarbonization, and helps to promote the large-scale integration and sustainable development of new energy.

Energy and exergy analysis for fast drying of ribbed smoked sheets in a novel forced-convection rubber smoking room

Rubber sheet smoking rooms are widely used by small farmers in rubber growing regions: sheets of raw rubber latex are ‘smoked’ in them to remove moisture, preserve and prepare the rubber for transport. Energy and exergy for a novel forced-convection rubber sheet smoking room were analyzed. Inlet hot air velocity varied between 12 and 14 m/s and the fuel-wood moisture content was between 24–55 % dry basis. Thermal efficiency for drying ribbed smoked sheet (RSS) rubber was 13.4–16.7 %. The specific energy consumption (SEC) was between 13.5–16.9 MJ/kg of water evaporated. The highest thermal efficiency or lowest specific energy consumption was achieved when using the dry fuel-wood (inlet velocity 14 m/s, 53 % db fuel-wood), while the RSS quality was maximum (100 %). The overall second law (exergy) efficiency was from 2.2-5.8 %. The highest exergy efficiency was obtained for air at 12 m/s and 24 % db fuel-wood. However, in this condition, only 55 % of sheets showed good quality. The exergy efficiency for the most suitable condition (100 % high quality sheets) was 2.6 %. Compared to an earlier forced-convection smoking room, the new room reduced the drying time from 48 h to 36 h, while thermal efficiency increased from 14.3 % to 16.5 %. When compared to the original smoking room, 65 % fuel-wood and 50 % drying time were saved and the rubber sheet quality increased from ∼90 % to 100 %. Overall, our forced-convection room was more efficient, dried faster and produced better quality than all previous types.

Efficiency improvement in energy consumption: A novel deep learning based model for leading a greener Economic recovery

This research focuses on elevating energy consumption efficiency through an innovative predictive model designed for urban industrial and residential sectors. Leveraging the Deep Q Network (DQN) framework, the study introduces the Dueling DQN architecture, a breakthrough approach that separates state value and advantage function estimations. This segregation significantly enhances learning efficiency, providing a more precise representation of the state's value and, consequently, optimizing energy consumption predictions. In tandem with architectural advancements, the research proposes a unique fuzzy Particle Swarm Optimization (PSO) approach to fine-tune DQN setting parameters during training. This integration of fuzzy logic into PSO ensures adaptability and robustness, addressing challenges associated with diverse environmental and operational conditions. For the goal of efficiency improvement in energy consumption, this study not only introduces a state-of-the-art solution for energy consumption prediction and optimization but also aligns with the broader goal of fostering a greener economic recovery. The model's promising results signify a significant step toward more sustainable and resource-efficient urban development, contributing to a resilient and environmentally conscious economic future. This research lays the foundation for a transformative approach to energy consumption, vital for steering economic recovery toward a greener and more sustainable trajectory. The simulation results advocate the high performance of the proposed deep learning prediction model.

Harmonizing smart technologies with building resilience and sustainable built environment systems

Given the escalating concerns posed by climate change-related disasters, there is an urgent need for a more sustainable and resilient built environment. The developing digital revolution in recent years has the potential to harness its superior features to achieve sustainable and resilient building sector goals. This paper aims to present a futuristic perspective on the three basic pillars of a more sustainable and resilient built environment. These pillars comprise sustainability, resilience, and the utilization of Artificial Intelligence (AI) for monitoring and managing structural health and performance. This is evident in the context of smart buildings and their ability to enhance energy consumption efficiency, improve quality of life, and strengthen structural health monitoring in terms of reducing damage and facilitating timely maintenance operations. Through comparing the components of the virtual city designed from two types of alkali-activated binder concrete (AABC) and ordinary Portland cement (OPC) concrete, all components of the city designed from AABC showed a reduction in the amount of emitted CO2. The highest decrease was observed in the schools designed from AABC, where the reduction was about 25 %, while the lowest decrease was for the villas and infrastructure; about 11 % and 10 % respectively, compared to those designed from OPC concrete. The results also indicated that the utilization of sustainable concrete instead of traditional varieties has the potential to decrease CO2 emissions by up to 25 %. The importance of this work resides in establishing a basis for forthcoming research that combines intelligent, environmentally friendly, and resilient building practices.