Energy-Saving Mechanism of Wastewater Treatment Process Adaptation on Natural Temperature Variation: The Case from Coking Wastewater.

The cyclical variations in environmental temperature generated by natural rhythms constantly impact the wastewater treatment process through the aeration system. Engineering data show that fluctuations in environmental temperature cause the reactor temperature to drop at night, resulting in increased dissolved oxygen concentration and improved effluent wastewater quality. However, the impact of natural temperature variation on wastewater treatment systems and the energy-saving potential has yet to be fully recognized. Here, we conducted a comprehensive study, using a full-scale oxic-hydrolytic and denitrification-oxic (OHO) coking wastewater treatment process as a case and developed a dynamic aeration model integrating thermodynamics and kinetics to elucidate the energy-saving mechanisms of wastewater treatment systems in response to diurnal temperature variations. Our case study results indicate that natural diurnal temperature variations can cut the energy consumption of 660,980 kWh annually (up to 30%) for the aeration unit in the OHO system. Wastewater treatment facilities located in regions with significant environmental temperature variation stand to benefit more from this energy-saving mechanism. Methods such as flow dynamic control, load shifting, and process unit editing can be fitted into the new or retrofitted wastewater treatment engineering.

Green Intrusion Detection Systems: A Comprehensive Review and Directions.

Intrusion detection systems have proliferated with varying capabilities for data generation and learning towards detecting abnormal behavior. The goal of green intrusion detection systems is to design intrusion detection systems for energy efficiency, taking into account the resource constraints of embedded devices and analyzing energy-performance-security trade-offs. Towards this goal, we provide a comprehensive survey of existing green intrusion detection systems and analyze their effectiveness in terms of performance, overhead, and energy consumption for a wide variety of low-power embedded systems such as the Internet of Things (IoT) and cyber physical systems. Finally, we provide future directions that can be leveraged by existing systems towards building a secure and greener environment.

Energy-Efficient Anomaly Detection and Chaoticity in Electric Vehicle Driving Behavior.

Detection of abnormal situations in mobile systems not only provides predictions about risky situations but also has the potential to increase energy efficiency. In this study, two real-world drives of a battery electric vehicle and unsupervised hybrid anomaly detection approaches were developed. The anomaly detection performances of hybrid models created with the combination of Long Short-Term Memory (LSTM)-Autoencoder, the Local Outlier Factor (LOF), and the Mahalanobis distance were evaluated with the silhouette score, Davies-Bouldin index, and Calinski-Harabasz index, and the potential energy recovery rates were also determined. Two driving datasets were evaluated in terms of chaotic aspects using the Lyapunov exponent, Kolmogorov-Sinai entropy, and fractal dimension metrics. The developed hybrid models are superior to the sub-methods in anomaly detection. Hybrid Model-2 had 2.92% more successful results in anomaly detection compared to Hybrid Model-1. In terms of potential energy saving, Hybrid Model-1 provided 31.26% superiority, while Hybrid Model-2 provided 31.48%. It was also observed that there is a close relationship between anomaly and chaoticity. In the literature where cyber security and visual sources dominate in anomaly detection, a strategy was developed that provides energy efficiency-based anomaly detection and chaotic analysis from data obtained without additional sensor data.

Variational Autoencoders for Network Lifetime Enhancement in Wireless Sensors.

Wireless sensor networks (WSNs) are structured for monitoring an area with distributed sensors and built-in batteries. However, most of their battery energy is consumed during the data transmission process. In recent years, several methodologies, like routing optimization, topology control, and sleep scheduling algorithms, have been introduced to improve the energy efficiency of WSNs. This study introduces a novel method based on a deep learning approach that utilizes variational autoencoders (VAEs) to improve the energy efficiency of WSNs by compressing transmission data. The VAE approach is customized in this work for compressing WSN data by retaining its important features. This is achieved by analyzing the statistical structure of the sensor data rather than providing a fixed-size latent representation. The performance of the proposed model is verified using a MATLAB simulation platform, integrating a pre-trained variational autoencoder model with openly available wireless sensor data. The performance of the proposed model is found to be satisfactory in comparison to traditional methods, like the compressed sensing technique, lightweight temporal compression, and the autoencoder, in terms of having an average compression rate of 1.5572. The WSN simulation also indicates that the VAE-incorporated architecture attains a maximum network lifetime of 1491 s and suggests that VAE could be used for compression-based transmission using WSNs, as its reconstruction rate is 0.9902, which is better than results from all the other techniques.

An improved transient search optimization algorithm for building energy optimization and hybrid energy sizing applications.

In this paper, a new algorithm named the improved transient search optimization algorithm (ITSOA) is utilized to solve classical test functions, optimize the consumption of building energy, and optimize hybrid energy system production. The conventional TSOA draws inspiration from the fleeting behavior of electrical circuits with energy storage components. Rosenbrock's direct rotation technique is used to improve the traditional TSOA performance against exploration and exploitation unbalance. First, the ITSOA performance is investigated in solving 23 classical benchmark functions, and the outcomes have shown the superior capability of the recommended algorithm in comparison with the conventional TSOA, DMO, SHO, GA, MRFO, and PSO methods. Also, the ITSOA proficiency is verified in solving the building energy optimization (BEO) problem for minimizing the energy usage of two simple and detailed buildings. The optimization results showed that the optimized solutions of ITSOA in single and multi-objective optimizations compared to conventional TSOA, DMO, SHO, GA, MRFO, and PSO obtained a lower value of the cost function. Also, the superiority of ITSOA has been confirmed to solve the BEO compared to previous methods. Moreover, the multi-objective optimization results have shown that ITSOA is able to determine the ultimate solution among the Pareto front set based on the fuzzy decision-making approach and building energy utilization decisions.

Energy-Efficient and Highly Reliable Geographic Routing Based on Link Detection and Node Collaborative Scheduling in WSN.

Energy efficiency and data reliability are important indicators to measure network performance in wireless sensor networks. In existing research schemes of routing protocols, the impact of node coverage on the network is often ignored, and the possibility that multiple sensor nodes may sense the same spatial point is not taken into account, which results in a waste of network resources, especially in large-scale networks. Apart from that, the blindness of geographic routing in data transmission has been troubling researchers, which means that the nodes are unable to determine the validity of data transmission. In order to solve the above problems, this paper innovatively combines the routing protocol with the coverage control technique and proposes the node collaborative scheduling algorithm, which fully considers the correlation characteristics between sensor nodes to reduce the number of active working nodes and the number of packets generated, to further reduce energy consumption and network delay and improve packet delivery rate. In order to solve the problem of unreliability of geographic routing, a highly reliable link detection and repair scheme is proposed to check the communication link status and repair the invalid link, which can greatly improve the packet delivery rate and throughput of the network, and has good robustness. A large number of experiments demonstrate the effectiveness and superiority of our proposed scheme and algorithm.

Energy optimization for wireless sensor network using minimum redundancy maximum relevance feature selection and classification techniques.

In wireless sensor networks (WSN), conserving energy is usually a basic issue, and several approaches are applied to optimize energy consumption. In this article, we adopt feature selection approaches by using minimum redundancy maximum relevance (MRMR) as a feature selection technique to minimize the number of sensors thereby conserving energy. MRMR ranks the sensors according to their significance. The selected features are then classified by different types of classifiers; SVM with linear kernel classifier, naïve Bayes classifier, and k-nearest neighbors classifier (KNN) to compare accuracy values. The simulation results illustrated an improvement in the lifetime extension factor of sensors and showed that the KNN classifier gives better results than the naïve Bayes and SVM classifier.

Unleashing the potential of Cajanus cajun biochar polymer composite for Cu (II) removal: mechanism, modification, and application.

This study introduces a cost-effective approach to fabricating a porous and ionically surface-modified biochar-based alginate polymer networks composite (SBPC) through air drying. The study critically analyzes the role and concentrations of various components in the success of SBPC. Characterization techniques were employed to evaluate the microstructure and adsorption mechanism, confirming the ability of the adsorbent's carboxyl and hydroxyl groups to eliminate various heavy metal ions in water simultaneously. The SBPC demonstrated high copper binding capacities (937.4 mg/g and 823.2 mg/g) through response surface methodology (RSM) and column studies. It was also influential in single and natural systems, exhibiting competitive behavior and efficient removal of Cu2+. The Langmuir isotherm and pseudo-second-order kinetics strongly correlate with experimental data, with R2 values of 0.98 and 0.99, respectively. SBPC showed remarkable stability, up to 10 desorption cycles, and achieved 98% Cu2+ adsorption efficiency and 91.0% desorption. Finally, the cost analysis showed a cost of 125.68 INR/kg or 1.51 USD/kg, which is very low compared to the literature. These results highlight the potential of SPBC and show that it provides an efficient and cost-effective solution for removing Cu2+ from a real system.

Evaluating the effect of pulse energy on femtosecond laser trabeculotomy (FLT) outflow channels for glaucoma treatment in human cadaver eyes.

BACKGROUND AND OBJECTIVES: Femtosecond laser trabeculotomy (FLT) creates aqueous humor outflow channels through the trabecular meshwork (TM) and is an emerging noninvasive treatment for open-angle glaucoma. The purpose of this study is to investigate the effect of pulse energy on outflow channel creation during FLT. MATERIALS AND METHODS: An FLT laser (ViaLase Inc.) was used to create outflow channels through the TM (500 µm wide by 200 µm high) in human cadaver eyes using pulse energies of 10, 15, and 20 µJ. Following treatment, tissues were fixed in 4% paraformaldehyde. The channels were imaged using optical coherence tomography (OCT) and assessed as full thickness, partial thickness, or not observable. RESULTS: Pulse energies of 15 and 20 µJ had a 100% success rate in creating full-thickness FLT channels as imaged by OCT. A pulse energy of 10 µJ resulted in no channels (n = 6), a partial-thickness channel (n = 2), and a full-thickness FLT channel (n = 2). There was a statistically significant difference in cutting widths between the 10 and 15 µJ groups (p < 0.0001), as well as between the 10 and 20 µJ groups (p < 0.0001). However, there was no statistically significant difference between the 15 and 20 µJ groups (p = 0.416). CONCLUSIONS: Fifteen microjoules is an adequate pulse energy to reliably create aqueous humor outflow channels during FLT in human cadaver eyes. OCT is a valuable tool when evaluating FLT.

Multiple objective energy optimization of a trade center building based on genetic algorithm using ecological materials.

It is crucial to optimize energy consumption in buildings while considering thermal comfort. The first step here involved an EnergyPlus simulation on a trade center building located in Tehran, Bandar Abbas, and Tabriz, Iran. A multi-objective optimization was then performed based on non-dominated sorting genetic algorithm II (NSGA-II) in jEPlus + EA to establish the building in the selected city where would benefit the most from implementing the radiant ceiling cooling system. Efforts were undertaken to choose environmentally-friendly materials. The final solution by Pareto charts resulted in a 52% reduction in energy consumption, a 37.3% decrease in cooling load, and a 17.4% improvement in comfort hours compared to the original design. Annual emission of greenhouse gas reduced as 167.67 tone of CO2 equivalent emission, 25.77 ton of CH4, and 0.2 ton of NO2. The mentioned algorithm was conducted for the first time on a trade center, including a DOAS system and radiant ceiling cooling system. Simultaneously, the environmental-friendly materials were dealt with. The procedure holds significant relevance for the design and optimization of buildings in Iran, especially wherever the climate is hot and humid. This approach offers advantages to the environment by reducing the impact on energy resources and utilizing environmentally-friendly materials.

A techno-economic analysis of diesel exhaust injection into mine tailings for carbon sequestration.

The environmental impact of off-grid mines in remote, cold climates is significantly intensified by their dependence on fossil fuels for power and heating. A promising solution lies in the potential to capture and permanently store carbon within mine tailings, thus allowing the mining industry to take a leading role in carbon removal initiatives and provide sustainable solutions. This study explores energy-optimal design scenarios for flue gas injection into mine waste to capture carbon. The approach involves installing perforated pipes within dry stack tailings. The established reduced-order model in this research serves as a novel tool for decision-making, aiding in the selection of an appropriate perforation scheme for the injection pipes embedded in the tailings. A cost analysis is also performed to assess the financial viability of the proposed concept under different operating parameters. Operational expenses, particularly energy costs, are found to be influenced by the permeability of the tailings. In instances of lower permeabilities, larger injection pipes are required. The findings indicate that achieving viable operating costs for sequestering one tonne of carbon dioxide hinges on amenable pipe sizing and engineering. Additionally, the study estimates that maintaining a reasonable level (around 1%) between the power being decarbonized and the power required for the carbon sequestration operation is crucial.

Building integrated diffusers' area ratio optimization.

This paper presents an investigation into the effect of area ratio parameter of diffusers on its energy output through power coefficient Cp. This parameter has effect both on diffusers' energy yield, besides diffuser's size for architectural integration prospects. A systematic increase in diffusers area ratio is adopted following standardized diffuser profile presented by NACA 1244 aerofoil. A series of area ratios were investigated (i.e., 1.25, 1.5, 1.75, 2, 2.5, 3 and 3.5). Area ratio of 1.5 (i.e., outlet/inlet, 0.75 m/0.50 m) exhibited the highest power coefficient Cp of 4.2, in addition to achieving highest resulting velocity of 25.8 m/s under incident velocity of 16m/s. Considerable wind separation inside inner walls of diffusers occurred from area ratio 1.75 onwards, which impacted resulting velocities. Simulations performed with ANSYS CFD Academic to standalone diffusers. A series of incident velocities employed from 1 to 16 m/s that resulted in velocity increase by 120-156% respectively.

Can We Boost N-Glycopeptide Identification Confidence? Smart Collision Energy Choice Taking into Account Structure and Search Engine.

High confidence and reproducibility are still challenges in bottom-up mass spectrometric N-glycopeptide identification. The collision energy used in the MS/MS measurements and the database search engine used to identify the species are perhaps the two most decisive factors. We investigated how the structural features of N-glycopeptides and the choice of the search engine influence the optimal collision energy, delivering the highest identification confidence. We carried out LC-MS/MS measurements using a series of collision energies on a large set of N-glycopeptides with both the glycan and peptide part varied and studied the behavior of Byonic, pGlyco, and GlycoQuest scores. We found that search engines show a range of behavior between peptide-centric and glycan-centric, which manifests itself already in the dependence of optimal collision energy on m/z. Using classical statistical and machine learning methods, we revealed that peptide hydrophobicity, glycan and peptide masses, and the number of mobile protons also have significant and search-engine-dependent influence, as opposed to a series of other parameters we probed. We envisioned an MS/MS workflow making a smart collision energy choice based on online available features such as the hydrophobicity (described by retention time) and glycan mass (potentially available from a scout MS/MS). Our assessment suggests that this workflow can lead to a significant gain (up to 100%) in the identification confidence, particularly for low-scoring hits close to the filtering limit, which has the potential to enhance reproducibility of N-glycopeptide analyses. Data are available via MassIVE (MSV000093110).

Modeling thermal energy distribution and transmission networks for household consumption through comparative analysis.

The high costs of energy supply and variable energy demands in consumption units, especially domestic consumption in different time frames, have accelerated technological developments for the proper use of energy resources to reduce energy consumption. The design of a distribution network for consumption depends on environmental conditions, equipment locations, consumer demands, consumption simultaneity factor, and some other parameters. These factors can mitigate energy loss in transmission networks. This study analyzes effective factors in the thermal energy distribution and transmission systems from generators to household consumers by considering the energy consumption rates in units based on a mathematical model to increase energy consumption in teams and rely on consumption during transmission. For this purpose, energy demands were evaluated in consumption units in a sample one-year project. The results were employed to design an optimal network for transferring energy from generators to consumers by modeling the distribution system. In this study, the thermal energy distribution and transmission network for domestic consumption was assessed and ranked have been assessed and ranked through single-stage distribution (SSD), multistage distribution (MSD), and MSD with the flow bypass method. The results of simulating the MSD system with the flow bypass method indicated the optimal performance of the proposed system in both consumer and generator sectors. This method also reduced fuel consumption by 6.09% and increased electricity consumption of the transmission network by 95% compared with single-stage transmission networks. Moreover, the method yielded a 6.03% reduction in the total cost of energy consumed to provide the thermal load of the building compared with SSD on a yearly basis.

The clean energy development path and sustainable development of the ecological environment driven by big data for mining projects.

Clean energy is urgently needed to realize mining projects' sustainable development (SD). This study aims to discuss the clean energy development path and the related issues of SD in the ecological environment driven by big data for mining projects. This study adopts a comprehensive research approach, including a literature review, case analysis, and model construction. Firstly, an in-depth literature review of the development status of clean energy is carried out, and the existing research results and technology applications are explored. Secondly, some typical mining projects are selected as cases to discuss the practice and effect of their clean energy application. Finally, the corresponding clean energy development path and the SD analysis model of the ecological environment are constructed based on big data technology to evaluate the feasibility and potential benefits of promoting and applying clean energy in mining projects. (1) It is observed that under different Gross Domestic Product (GDP) growth rates, the new and cumulative installed capacities of wind energy show an increasing trend. In 2022, under the low GDP growth rate, the cumulative installed capacity of global wind energy was 370.60 Gigawatt (GW), and the new installed capacity was 45 GW. With the high GDP growth rate, the cumulative and new installed capacities were 367.83 GW and 46 GW. As the economy grows, new wind energy capacity is expected to increase significantly by 2030. In 2046, 2047, and 2050, carbon dioxide (CO2) emissions reductions are projected to be 8183.35, 8539.22, and 9842.73 Million tons (Mt) (low scenario), 8750.68, 9087.16, and 10,468.75 Mt (medium scenario), and 9083.03, 9458.86, and 10,879.58 Mt (high scenario). By 2060, it is expected that CO2 emissions reduction will continue to increase. (2) The proposed clean energy development path model has achieved a good effect. Through this study, it is hoped to provide empirical support and decision-making reference for the development of mining projects in clean energy, and promote the SD of the mining industry, thus achieving a win-win situation of economic and ecological benefits. This is of great significance for protecting the ecological environment and realizing the sustainable utilization of resources.

Q-learning-based UAV-mounted base station positioning in a disaster scenario for connectivity to the users located at unknown positions.

Due to its flexibility, cost-effectiveness, and quick deployment abilities, unmanned aerial vehicle-mounted base station (UmBS) deployment is a promising approach for restoring wireless services in areas devastated by natural disasters such as floods, thunderstorms, and tsunami strikes. However, the biggest challenges in the deployment process of UmBS are ground user equipment's (UE's) position information, UmBS transmit power optimization, and UE-UmBS association. In this article, we propose Localization of ground UEs and their Association with the UmBS (LUAU), an approach that ensures localization of ground UEs and energy-efficient deployment of UmBSs. Unlike existing studies that proposed their work based on the known UEs positional information, we first propose a three-dimensional range-based localization approach (3D-RBL) to estimate the position information of the ground UEs. Subsequently, an optimization problem is formulated to maximize the UE's mean data rate by optimizing the UmBS transmit power and deployment locations while taking the interference from the surrounding UmBSs into consideration. To achieve the goal of the optimization problem, we utilize the exploration and exploitation abilities of the Q-learning framework. Simulation results demonstrate that the proposed approach outperforms two benchmark schemes in terms of the UE's mean data rate and outage percentage.

Energy Consumption Optimization of a Fluid Bed Dryer in Pharmaceutical Manufacturing Using EDA (Exploratory Data Analysis).

In this paper, a data preprocessing methodology, EDA (Exploratory Data Analysis), is used for performing an exploration of the data captured from the sensors of a fluid bed dryer to reduce the energy consumption during the preheating phase. The objective of this process is the extraction of liquids such as water through the injection of dry and hot air. The time taken to dry a pharmaceutical product is typically uniform, independent of the product weight (Kg) or the type of product. However, the time it takes to heat up the equipment before drying can vary depending on different factors, such as the skill level of the person operating the machine. EDA (Exploratory Data Analysis) is a method of evaluating or comprehending sensor data to derive insights and key characteristics. EDA is a critical component of any data science or machine learning process. The exploration and analysis of the sensor data from experimental trials has facilitated the identification of an optimal configuration, with an average reduction in preheating time of one hour. For each processed batch of 150 kg in the fluid bed dryer, this translates into an energy saving of around 18.5 kWh, giving an annual energy saving of over 3.700 kWh.

Energy Prediction and Optimization for Smart Homes with Weather Metric-Weight Coefficients.

Home appliances are considered to account for a large portion of smart homes' energy consumption. This is due to the abundant use of IoT devices. Various home appliances, such as heaters, dishwashers, and vacuum cleaners, are used every day. It is thought that proper control of these home appliances can reduce significant amounts of energy use. For this purpose, optimization techniques focusing mainly on energy reduction are used. Current optimization techniques somewhat reduce energy use but overlook user convenience, which was the main goal of introducing home appliances. Therefore, there is a need for an optimization method that effectively addresses the trade-off between energy saving and user convenience. Current optimization techniques should include weather metrics other than temperature and humidity to effectively optimize the energy cost of controlling the desired indoor setting of a smart home for the user. This research work involves an optimization technique that addresses the trade-off between energy saving and user convenience, including the use of air pressure, dew point, and wind speed. To test the optimization, a hybrid approach utilizing GWO and PSO was modeled. This work involved enabling proactive energy optimization using appliance energy prediction. An LSTM model was designed to test the appliances' energy predictions. Through predictions and optimized control, smart home appliances could be proactively and effectively controlled. First, we evaluated the RMSE score of the predictive model and found that the proposed model results in low RMSE values. Second, we conducted several simulations and found the proposed optimization results to provide energy cost savings used in appliance control to regulate the desired indoor setting of the smart home. Energy cost reduction goals using the optimization strategies were evaluated for seasonal and monthly patterns of data for result verification. Hence, the proposed work is considered a better candidate solution for proactively optimizing the energy of smart homes.

UAV-Aided Dual-User Wireless Power Transfer: 3D Trajectory Design and Energy Optimization.

Unmanned aerial vehicles (UAVs) have been widely considered to enhance the communication coverage, as well as the wireless power transfer (WPT) of energy-constrained communication networks to prolong their lifetime. However, the trajectory design of a UAV in such a system remains a key problem, especially considering the three-dimensional (3D) feature of the UAV. To address this issue, a UAV-assisted dual-user WPT system was investigated in this paper, where a UAV-mounted energy transmitter (ET) flies in the air to broadcast wireless energy to charge the energy receivers (ERs) on the ground. By optimizing the UAV's 3D trajectory toward a balanced tradeoff between energy consumption and WPT performance, the energy harvested by all ERs during a given mission period was maximized. The above goal was achieved through the following detailed designs. On the one hand, on the basis of previous research results, there is a one-to-one correspondence between the UAV's abscissa and height, so only the relationship between the height and time was focused on in this work to obtain the UAV's optimal 3D trajectory. On the other hand, the idea of calculus was employed to calculate the total harvested energy, leading to the proposed high-efficiency trajectory design. Finally, the simulation results demonstrated that this contribution is capable of enhancing the energy supply by carefully designing the 3D trajectory of the UAV, compared to its conventional counterpart. In general, the above-mentioned contribution could be a promising way for UAV-aided WPT in the future Internet of Things (IoT) and wireless sensor networks (WSNs).

Energy Optimization of Wireless Sensor Embedded Cloud Computing Data Monitoring System in 6G Environment.

With the construction and development of modern and smart cities, people's lives are becoming more intelligent and diversified. Surveillance systems increasingly play an active role in target tracking, vehicle identification, traffic management, etc. In the 6G network environment, facing the massive and large-scale data information in the monitoring system, it is difficult for the ordinary processing platform to meet this computing demand. This paper provides a data governance solution based on a 6G environment. The shortcomings of critical technologies in wireless sensor networks are addressed through ZigBee energy optimization to address the shortage of energy supply and high energy consumption in the practical application of wireless sensor networks. At the same time, this improved routing algorithm is combined with embedded cloud computing to optimize the monitoring system and achieve efficient data processing. The ZigBee-optimized wireless sensor network consumes less energy in practice and also increases the service life of the network, as proven by research and experiments. This optimized data monitoring system ensures data security and reliability.