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In order to improve the economy and safety of multi-microgrids (MMGs) scheduling, this paper proposes a research on MMGs scheduling strategy that takes into account dynamic electricity prices based on the blockchain. We first introduce the principle of blockchain, analyze the security and economy of the combination of blockchain and MMGs scheduling, and design the scheduling architecture and process based on the blockchain platform. Second, we set a dynamic electricity price model according to the total power supply and demand of MMGs, and set a load optimization model. Finally, we take optimal system economy and minimum environmental pollution as the objective function, then use the linear programming method and the improved krill herd algorithm (KHA) with nonlinear changes in weights to solve the problem. The simulation results show that: (1) The dynamic electricity prices can reflect the power supply and demand of microgrids and optimize the load; (2) Comparing the three scheduling schemes, the strategy in this paper can improve the economic and environmental protection of MMGs by 37.33% and 39.34%, while reduce the interactive power between the microgrid and the distribution network by 56.28%, and the curtailment rate by 63.22%; (3) The improved krill herd algorithm has higher convergence speed and convergence accuracy;(4) The blockchain technology can ensure the security of scheduling data.

Electric mobility seems to bring a paradigm shift in the road transport sector worldwide. Huge consumption of fossil fuels and ever-increasing traffic congestion have caused concerns over future energy consumption, economy growth, and greenhouse gas emissions in the Gulf Cooperation Council region’s member countries. The introduction of electric vehicles (EVs) in the two most populous countries of the region, i.e., the Kingdom of Saudi Arabia and UAE is considered a promising option to address environmental pollution and future economy-related fears region. This paper presents key drivers for the countries to adopt electric transportation. This research study investigates the impact of EVs penetration on energy, economy, and environment of KSA and UAE through EV stockpile forecasting using linear regression analysis. The obtained results suggest that expected growth in KSA and UAE’s power sector will enable them to keep up 5% and 30% EVs penetration by 2030, respectively. In this regard, a set of policies are proposed, which will enable the countries to pace up their efforts to achieve the intended greenhouse gases (GHG) emission reduction goals. Though the presented research is focused on the case study of KSA and UAE, the research findings are generalized enough to be applied to all other regions of the region. The suggested set of policies will serve as guidelines for the relevant stakeholders about the necessary measures required for sustainable road transport electrification in KSA and UAE.

Current development of Pakistan’s economy, transportation and industry with the improvement of urbanization, environmental pollution problems have gradually become prominent, but this is contrary to people’s vision of pursuing a high-quality life. Now the problem of haze, photochemical problems in the air, and global warming is already a key issue of global concern. This is focused on the ambient air quality of Lahore city of Pakistan. The study reveals that the particulate matter in the Lahore season (PM2.5, PM10) exceeds Pakistan’s National Environmental Quality Standards (NEQS). Correlation study suggests the positive correlation between the particulate matter and other mass concentration particles like Ozone (O3), Nitrogen Oxide (NO), Sulphur Dioxide (SO2). Higher values of CO/NO suggest that mobile sources are one of the major factors of this increase in NO. Further estimation of backward trajectory is done by the Hybrid-Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model which provides the path of those particles in the last year period and the source of origin is from Afghanistan. This study provides in depth analysis of all factors of air pollutants by correlation between those factors. Prediction of future concentration of PM2.5 is predicted using the Seasonal Autoregressive Integrated Moving Average (SARIMA) model which gives the increasing value of PM2.5 in next year and provides the lowest and highest predicts (more than $100~\mu \text{g}/\text{m}^{3}$ ).

Enhancing the monitoring capabilities of wastewater treatment plant (WWTP) key features can accomplish accurate prediction to help WWTPs develop a plan, which is of great significance to control regional water environmental pollution. Chemical oxygen demand (COD) is one of the key features of wastewater treatment. Traditional monitoring methods are time consuming and have high costs making it difficult to meet the needs of rapid monitoring in practical applications. To address this issue, a method for optimizing a long short term memory (LSTM) neural network model based on adaptive hybrid mutation particle swarm optimization (AHMPSO) and an attention mechanism (AM) is proposed. As the hyperparameters of the LSTM are difficult to select, AHMPSO is used to optimize the LSTM. A nonlinear variable inertia weight with random factors is introduced to balance the global search ability and the local search ability and to improve the convergence speed of the PSO algorithm. In addition, the hybrid mutation strategy is added in the search process to reduce the risk of particles falling into local optimal solutions. Finally, an AM is added to the LSTM model to mine local water quality features to improve the effluent COD prediction accuracy. Compared with other models (LSTM, LSTM-AM, and PSO-LSTM-AM), the RMSE(the root mean square error) of the optimized model decreased by 7.803%-19.499%, the MAE(the mean absolute error) of the optimized model decreased by 9.669%-27.551%, the MAPE(the mean absolute error) of the optimized model decreased by 8.993%-25.996%, and the R2 (the coefficient of determination) value of the optimized model increased by 3.313%-11.229%. The experimental results show that the optimized model has better performance and achieves a more accurate prediction of the effluent COD.

The dramatic increase in the amount of garbage and complex diversity of the materials in the garbage bring serious environmental pollution problems and wastes resources. Recycling reduces waste but manual pipeline waste sorting involves a harsh working environment at high labor intensity with low sorting efficiency. In our paper, a novel intelligent garbage classification system based on deep learning and an embedded Linux system is proposed. The system is divided into three parts. First, a Raspberry Pi 4B is utilized as the master board for the hardware system. The peripherals of the system consist of a touch panel, sensors, a 2-DOF (degree of freedom) servo, and a camera. Second, a new GNet model for garbage classification based on transfer learning and the improved MobileNetV3 model is proposed. Third, a GUI based on Python and QT is employed to build a human-computer interaction system to facilitate system manipulation and observation. A series of garbage classification experiments on the Huawei Garbage Classification Challenge Cup dataset were conducted. The proposed classification system’s prediction accuracy was 92.62% at 0.63 s efficiency. The experimental results in this paper demonstrate that the proposed intelligent garbage classification system delivers high performance both in terms of accuracy and efficiency.

Persistent organic pollutants (POPs) are highly toxic and difficult to degrade in the natural ecology, which has a severe negative impact on the ecological environment. Quantifying changes in the concentrations of persistent organic pollutants in the Great Lakes is challenging work. Machine learning (ML) methods are potent predictors that have recently achieved impressive performance on time series tasks. ARIMA model, Linear Regression methods, XGBoost algorithm, and Long Short-Term Memory (LSTM) are commonly used for estimating time-series changes. Traditionally Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) have been standard criteria to measure the error between the actual value and predicted value; however, Euclidean distance (ED) cannot effectively calculate the similarity between two-time series. We proposed an alternative criterion called Penalty Dynamic Time Wrapping (Penalty-DTW) based on Dynamic Time Wrapping (DTW). It can accurately measure the difference between the actual value and the predicted value. We study the benefits of Penalty-DTW vs. ED under the above ML algorithms. Further, considering the machine learning algorithm’s uncertainty, we proposed combining LSTM and deep ensemble methods to quantify algorithms uncertainty and make a confident prediction. We find improved LSTM model outperformed other predictive power models by comparing pollutant concentration prediction. The prediction results show that the concentration of pollutants has a stable downward trend in recent years. Simultaneously, we found that pollutants’ concentration correlates with seasons, which positively guides environmental pollution control in the Great Lakes.

Accurate and timely monitoring of leaf nitrogen concentration (LNC) in rice is crucial to optimize nitrogen fertilizer management and reduce environmental pollution. Existing vegetation indices (VIs) often perform well for high canopy cover conditions, but their performance becomes poor at early growth stages due to the significant exposure of background materials and the induced spectral mixing effect. This study proposed a novel approach to estimate the LNC at early and middle growth stages of paddy rice by using abundance adjusted VIs (AAVIs) from unmanned aerial vehicle (UAV) multispectral imagery. An AAVI was constructed by combining the traditional VI and the rice abundant from linear spectral mixture analysis of UAV imagery. Subsequently, the performance of AAVIs was evaluated in comparison with traditional VIs derived from all pixels or green pixels for individual growth stages or multiple stages. The results demonstrated that AAVIs exhibited better performance in LNC estimation, regardless of individual stages or across the entire early season. Specially, AACIred-edge showed the best performance among the AAVIs evaluated for LNC estimation. For universal modeling across early stages, the combination of AACIred-edge and AAEVI yielded the highest accuracy (R2 = 0.78, RMSE = 0.26%, and rRMSE = 10.4%) performed remarkably better than the traditional VIs from all pixels or green pixels (R2

The growing interest with the developments of renewable sources owing to less environmental pollution creates challenges for the grid operators. Virtual Power Plant (VPP) is a novel concept that will integrate the small distributed energy resources and will act as a single conventional power plant in the electricity market. As a core energy management system in VPP, the energy should be dispatched optimally for achieving the maximum profit. Therefore, smart energy management is developed in this article of VPP with Programmable Logic Controller (PLC) and Internet of Things (IoT) in a unified market environment that integrates the DA and RT market. The cost characteristics for the interruptible load, battery storage system are modelled individually. The PLC and IoT based automation is proposed for optimal dispatch strategy of VPP. The proposed scheme can efficiently handle the energy demand for the VPP domain. Four different scenarios are considered with different loading conditions for validation of the concept of smart energy management. The profitability for each scenario is shown with the experimental results.

International audience; In this paper, we will present a summary of recent studies made to model the atmospheric dispersion around a building using Computational Fluid Dynamics (CFD). So we will focus on the studies made by Bahlali et al, who uses the Lagrangian stochastic CFD model implemented in Code_Saturne to model our problem taking into account atmospheric stratification. Our work essentially consists in showing the importance of this model by commenting on the results of the work obtained by Bahlali et al.

Most of the household appliances and electronic office equipment commercially available today have low-power, sleep, idle or standby modes. The energy consumed by devices in these operating modes is usually small and neglected by users however, considering the multitude of such devices per household / office, over large areas (countries and continents) and for long time periods (months and years), these small energy losses add up to impressive amounts of wasted energy and large-scale pollution. A study conducted over a two-year period concluded that in the European Union about 43 TWh of standby electric energy are consumed in total per year and this generates about 19 million tonnes of CO2 greenhouse gas emissions. In this context it becomes necessary to take further actions to reduce the standby energy consumption and environmental pollution. This paper proposes one of the most affordable autonomous smart plug designs that is up to three times more energy-efficient than other similar smart plugs commercially available on the market.

In order to mitigate the environmental pollution problem, the fuel cell electric vehicle (FCEV), as one kind of renewable energy industry, is researched and developed. The performance of FCEV is deeply relied on the energy management strategy (EMS), an inappropriate EMS may cause the bad performance of FCEV. Strategies designed under specific work condition have poor adaptability to complex work conditions. What’s more, these strategies give little concern on the volatility of fuel cell’s output power, which will cause a loss of fuel cell’s life. Aiming at decreasing the hydrogen consumption of FCEV and the volatility of the output power of fuel cell, an optimized fuzzy logic control energy management strategy based on work condition recognition is proposed. In order to achieve a better performance, this strategy can recognize instant driving condition, and pick the corresponding parameters of fuzzy logic control system from the preset database of the parameters. The database is obtained by offline optimization with genetic algorithm. A simulation result based on the MATLAB/Simulink platform is obtained to demonstrate that this strategy has a better performance than a conventional fuzzy logic controller with fixed parameters under mixed work conditions.

Several studies have been conducted on Hybrid Power Generation Systems (HPGS) to reduce the existing gap in access to electricity, especially in isolated and difficult access areas. The appropriate use of HPGS allows to power a community, reduces environmental pollution, and improves the performance of a system that only uses Fossil Fuel Generators (FFG). However, despite the efforts made in several countries, there are still isolated areas that remain without electricity due to several factors, such as the high cost of generation, transmission, and energy distribution. For this reason, this work seeks to generate new viable solutions to improve access to electricity especially in isolated areas of Ecuador, although the tool can be applied to any community without constant electricity access. For this purpose, the modeling of an HPGS is proposed, such system is composed of an FFG, a photovoltaic generator, and a hybrid energy storage system (i.e., electrical and thermal). A control algorithm for the FFG is used to adapt the motor rotational speed by using a minimum fuel consumption to supply the load profile. The results are obtained from simulations with real data of three case studies. A reduction in fuel consumption, greenhouse gas emissions, and loss power from renewable energy is demonstrated.

The permanent magnet (PM) traction motor is one of the feasible solutions to the energy crisis and environmental pollution. Due to the high demands in power and torque density with overall high efficiency, the permanent magnet synchronous machine (PMSM) is mainly considered. Usage of soft magnetic composites (SMC) material in the machine parts helps to build a complex 3D structure and reduces the manufacturing cost. This paper presents the electromagnetic performance comparison analysis of a 7.12 kW radial flux PMSM with laminated steel and SMC stator core. Two different designs of 24/20 slot-pole and 12/8 slot-pole configuration using conventional surface permanent magnet rotor design are simulated using a 3D FEA software package. The comparison is mainly focused on the torque density, copper loss, core loss, and active weight of the machine. In addition, the ring-type Halbach-array PM rotor fabricated using cold spray additive manufacturing for 12/8 slot-pole is analyzed and optimized with the SMC stator core for high-speed electric vehicle application. The THD in phase back EMF and torque ripple is reduced by modifying the SMC stator tooth shape. Also, a scaled-down laboratory design of ring-type Halbach-array PM rotor with 12/10 slot-pole is simulated using FEA with different lamination thickness of rotor core.

As a consequence of global industrialization and the increased use of cars powered by gasoline, the level of environmental pollution (mostly by nitrogen dioxide) is increased year by year. There are a number of studies, where ambient nitrogen dioxide concentrations were measured, and currently, in many cities, smart air pollution sensors are installed. However, the problem of monitoring the nitrogen dioxide concentration on highways has not been solved. Herein, the preliminary results on the microwave-based NO 2 gas sensor for such the application are presented.

Heavy metals (HMs) are considered persistent pollutants due to their low degradation rate. They can be easily accumulated along the food chain and reach the higher trophic levels, posing a threat for both the environment and human health. Microalgae, being at the base of food webs in marine and fresh waters, are one of the first groups to be affected by HM pollution and can be considered potential HM indicators in aquatic environments. In this frame we propose QPM (quantitative phase microscopy) optical techniques as an innovative approach for rapid detection of HMs stress on microalgae. Furthermore, since some HMs are considered essential elements for cell growth and metabolism, microalgae have designed strategies for their uptake and have been thus considered attractive candidates for bioremediation purposes. In the present work, a strain of Skeletonema pseudocostatum isolated from the mouth of the Sarno river (Gulf of Naples) was exposed to several concentrations of copper to test its tolerance to the HM in 72 hours small-scale experiments. A lethal (25 μM) and a sub-lethal (10 μM) copper concentration were identified and used for 72 hours scale-up experiments. The rate of copper adsorption/compartmentalization was evaluated by ICP-MS analyses of microalgal pellets at the end of each experiment. S. pseudocostatum showed tolerance to copper up to a concentration of 10 μM, with a rate of removal of 14% of copper administered in solution in larger scale experiments. These results seem promising considering future possible applications of this species for HM removal. In addition, preliminary QPM investigations of copper effects on cellular and subcellular morphology seem to suggest that these techniques could aid in elucidating S. pseudocostatum potential as a sensor of HM environmental pollution.

As the rotorcraft technology has developed rapidly in recent years, more and more rotorcrafts have been employed in agricultural scenarios, for example, the remote sensing, plant protection and pesticide spraying. Benefit of its light weight and vertical take-off and landing, the agricultural rotorcrafts are used in the complex terrains, such as the hilly fields and terraced fields. In such context, operation routes are rather curvy to follow up the plants in irregular arrangement, but the curvy route tracking would cause maneuvering of the rotorcraft and its downwash swing. This paper aims to plan appropriate flight path for the UAV to decrease the unnecessary maneuvers and droplet deviation during agricultural operation in order to improve the spray efficiency and reduce environmental pollution as far as possible. The experimental results verified the effectiveness of the proposed method.

Commercialization of electric vehicles (EVs) and light electric vehicles (LEVs) play a major role in reduction of greenhouse gases and environmental pollution. In this paper, the main reasons for popularity of the LEVs are explained based on the statistics available in literature. The propulsion system of the LEVs (including the electric motor, transmission system, battery package, and inverter) aims to offer high efficiency over a wide range of torque-speed operating points. Hence, the optimal design and performance improvement of such vehicles have attracted attention in literature. The investigated drivetrain systems in various LEVs are reviewed in this paper to highlight their advantages and disadvantages. The popular electric motors used in the LEVs are discussed and their characteristics are emphasized. The available research gaps in this field are highlighted at the end of the paper to provide a guide for the new researchers in this field.

At present, China is the country with the largest amount of renewable energy utilization in the world, and is in the forefront of the world in the utilization of water energy, wind energy, solar energy and biomass energy. The Chinese government has set a grand goal for renewable energy development, with non fossil energy accounting for 15% of primary energy consumption in 2020 and increasing to about 20% in 2030. Recently, the renewable energy Internet has put forward higher requirements for electronic systems. In order to better meet the requirements, it is necessary to innovate the renewable energy Internet. However, microelectronics has been applied to different levels of renewable energy Internet and has become an important supporting technology. The traditional energy mode based on fossil fuels such as coal and petroleum will bring a series of major problems, such as environmental pollution, rapid increase of carbon emissions, and decline of fossil energy reserves, which will cause great obstacles to sustainable development. Therefore, renewable energy is an important way to solve the energy problem. Large-scale renewable energy includes solar energy, wind energy and tidal energy.

For petrochemical processes, the key questions to solve are: how to reduce the discharged organic tail gas; how to reuse and recycle the discarded organic streams; what is the optimal recovery ratio to ensure the most efficient use of resources and the minimum impact on the environment? The current study examines a polyethylene plant to solve these problems. Optimising the emission reduction of olefin tail gas in polyethylene synthesis can improve the utilisation rate of waste resources and bring practical economic benefits to the factory. It can significantly reduce environmental pollution. The annual economic revenue and payback period are considered. The objective function with the recovery ratio as the principal degree of freedom is optimised. The light hydrocarbons are recovered by membrane separation from olefin tail gas of polyethylene synthesis. A range of membrane area and recovery ratio are identified and analysed, suggesting an optimal recovery rate. The results of the case study show that the recovery ratio of light hydrocarbons (C2+) is 53.6 %, which has a significant economic benefit.

Garbage classification is an important part of resource recycling and utilization, which can improve the utilization rate of resource recycling and reduce environmental pollution. Due to the wide variety of MSW and the lack of a unified standard for specific classification, most people will have difficulty in choosing MSW in practical operation. Traditional image classification methods have been difficult to meet the requirements. We can build an accurate classification model based on deep learning technology and use technical means to improve the living environment. In this paper, deep learning algorithm is used to identify and classify garbage, which can not only improve the efficiency of garbage classification, but also improve the accuracy of garbage classification. Mainly studies the garbage classification model based on CNN, methods of using the migration study selected suitable for network model of garbage classification in this paper, and then use the model integration methods such as increase the generalization ability of the model, finally using distillation technology will big model learned knowledge transferred to small in the model, the loss of lesser accuracy at the same time, improve efficiency and prediction accuracy of model prediction, To quickly and accurately identify the garbage type effect.

With the exhaustion of oil resources and the aggravation of environmental pollution, electric vehicles, as the main force of new energy consumption, have a more and more promising development prospect. In China, the utilization rate of charging facilities in the public is very low, and there is a large number of redundant charging stations that waste resources. Charging station congestion, meanwhile, is one of the reasons why it is difficult to charge for electric vehicles. This paper proposes a data-driven approach to optimize the existing charging station network by eliminating redundant charging stations, and to identify the charging station congestion areas in the original charging network to provide suggestions for further solving the difficulty of charging electric vehicles. Firstly, we infer that the fine-grained charging situation (consisting of the waiting time and the visiting rate) at different stations. Using a 3D tensor, we model the charging behavior of the electric vehicle, in which the three dimensions represent stations, hours, and days respectively. Secondly, for times and stations with sparse data, we use a context-aware tensor collaborative decomposition method to estimate the situation. For charging stations in a specific period of time, we separately set up a queue system for them to estimate their visiting rate and detect the distribution characteristics of EV charging hotspots in the city. Finally, we introduce a flexible scoring function to evaluate the usage benefits of charging stations and propose a heuristic network expansion algorithm to optimize the network. Applying the data-driven approach to Wuhan city, the results show that using our method can eliminate redundant sites while increasing utilization and find charging station congestion area to guide the government to further charging station planning. Our approach can be adapted for other optimal problems such as chain supermarket layout, public facility planning, and resources configuration using trajectory data.

Tailings ponds are places for storing industrial waste. Once the tailings pond collapses, the villages nearby will be destroyed and the harmful chemicals will cause serious environmental pollution. There is an urgent need for a reliable forecasting model, which could investigate the tendency in saturation line and issue early warnings. In order to fill the gap, this work presents a hybrid network - Long-Short-Term Memory (LSTM) and Convolutional Neural Network (CNN), namely CNN-LSTM network for predicting the tailings pond risk. Firstly, the nonliear data processing method was composed to impute the missing value with the numerical inversion (NI) method, which combines correlation analysis, sensitivity analysis, and Random Forest (RF) algorithms. Secondly, a new forecasting model was proposed to monitor the saturation line, which is the lifeline of the tailings pond and can directly reflect the stability of the tailings pond. The CNN was used to identify and learn the spatial structures in the time series, then followed by LSTM cells for detecting the long-term dependence. Finally, different experiments were conducted to evaluate the effectiveness of the model by comparing it with other state-of-the-art algorithms. The results showed that combing CNN with LSTM layers achieves the best score in mean absolute error (MAE), root-mean-square error (RMSE) and coefficient of determination (R2).

Integration of electricity and gas distribution networks improves energy utilization and alleviates environmental pollution. In order to obtain a reasonable energy prices and optimize the day-ahead scheduling scheme under the premise of considering the interests of both supply and demand, an interactive equilibrium model considering electricity-gas energy distribution system and integrated load aggregators is established in this paper. In the upper level, the integrated energy distribution network (IEDN) maximizes the profit via solving the day-ahead optimal energy flow considering energy prices changing in the next day. In the lower level, integrated load aggregators (ILA) minimize charging costs, thus optimizing energy distribution based on integrated demand responses. By using the Karush-Kuhn-Tucker (KKT) optimality conditions and the duality theorem of linear programming, the master-slave game model is transformed into a single layer optimization problem. To solve this problem, second-order cone programming (SOCP) and penalty convex-concave procedure (PCCP) are used to deal with the non-convex functions of electricity-gas distribution system. Therefore, the proposed model is approximated by a mixed-integer second-order cone programming problem. Finally, case studies verify the effectiveness of the proposed model and method.

Economic Dispatch problem is just not related to minimizing the cost of power generation but also correlates with environmental pollution issues. The major part of generating units is fossil fuel-based and releases different kinds of pollutant gases. Every day a huge amount of fossil fuel is used in thermal power plants and a bulk amount of harmful gases are released into the environment. The thermal units based on fossil fuels are one of the main causes of air pollution and global warming. To overcome this issue many researchers proposed different solution methods. The present work is also focused on the solution of environmental/economic load dispatch. It is also called Economic Emission Load Dispatch (EELD). Evolutionary Algorithms (EAs) are the most popular solution technique adopted by researchers to solve mix-integer, non-linear and complex problems. EAs suffer from premature convergence and lack of diversity issues. The present work proposed an Adaptive Real Coded Quantum Inspired Evolutionary algorithm (ARQiEA) to solve the multi-objective EED problem. The constraints are satisfied using a repair-based constraint handling method. The proposed algorithm provided efficient and improved results during the test on IEEE 30 Bus system including six generators and the results are compared with well-known techniques and found competitive than others.

In recent years, environmental pollution has become more and more serious, mainly reflected in noise pollution, air quality decline, increasing greenhouse gases, global warming and so on. Therefore, many countries signed the 《The Paris Agreement》 launched by the United Nations in 2016, aiming at reducing the ecological risk of climate change to the earth and the survival crisis to human beings through various measures. At the same time, the aviation industry has responded positively to the call to reduce carbon emissions with a number of programs. The technology development of hybrid-electric and full-electric powered aircraft has been vigorously promoted. Due to the advantages of low noise, low pollution, high propulsion efficiency and other advantages that traditional aircraft do not have. The distributed leading edge electric power aircraft has improved the comfort and environmental protection of the aircraft, and has become a research hotspot for many research institutions, companies and universities. This paper firstly proposes a kind of distributed propeller electric general aviation aircraft aerodynamic configuration. Then on the basis of this design, Slip Grid Technology is used to complete numerical simulation of propeller slipstream. In this numerical simulation, the Reynolds-averaged Navier – Stokes equations(RANS) and standard $\mathrm{k}-\varepsilon$ turbulence model are used to complete simulation calculation of aerodynamic characteristics of the aircraft in the cruise state, take-off and landing state. Finally the effect of reducing the number of motors on the aerodynamic performance of the wing is studied when the total power of the leading edge motor is constant. The results show that: (1) The propeller slipstream increases the dynamic pressure on the wing surface, and increases the maximum lift-drag ratio and the maximum lift coefficient. (2) When the total power is constant, the slipstream generated by multiple small propellers is stronger than that generated by large propellers, and the lifting capacity is stronger.

With the increase in the population in metropolitan cities, vehicle density is also increasing. Due to this, vehicle parking has become a common issue in metro cities. This leads to wastage of time in search for parking. Moreover, large energy consumption, long processing time and also environmental pollution are highly concerned issues. Few introduced systems may lack the ability to detect the empty spots for parking. Many concepts and systems are introduced on IoT and cloud-based technology which is an expensive method for data transmission and are mostly dependable on factors such as climatic conditions and network availability. It requires high investment due to involvement of high-end technologies, which may not justify for small scale implementation. To overcome this issue a novel configuration is introduced which is cost effective, user friendly, less human dependent system which has access to real time data.

The boom in Internet-of-Things (IoT) applications in various sectors of the economy such as agriculture, commerce, healthcare, mining, etc, has also raised questions on how to power these IoT devices for long-term use without contributing to environmental pollution from discarded batteries. Energy harvesting for IoT, especially ambient backscatter communication, is a likely solution to support the long-term operation of green IoT networks. In this paper, we study a small cell cooperative ambient backscatter network comprised of a primary transmitter (PTx), a primary receiver (PRx), a passive backscatter device (BD) and an IoT receiver. Next, we investigate the outage performance of these devices when full-duplex users are employed in the backscatter system to improve the system transmission rate. Then, we derive exact and asymptotic outage probability (OP) expressions for both PRx, BD, and IoT receiver. Lastly, the obtained expressions are verified using Monte Carlo simulations.

As increase in the environmental pollution for the sustainable and rapid growth of the world, air and sound pollution is major threat to the developing nations for the clean and healthy environment. Air pollution can't be ignored as one of the environmental problems. The pollutants cause damage to human health for a long time. Traditional methods to monitor air quality, such as the construction of air quality monitoring stations, are usually costly. This project can be used for realtime air quality monitoring. Monitoring of air quality is famous and well-established science started in the 1980s. The technique was then quite limited and the solution was used to measure the complex and high-cost air pollution. Interestingly, the solutions used to monitor air quality are now not only becoming more accurate but also being measured faster with the latest and more modern technologies. Devices are getting smaller and more cost-effective than ever. The device presented uses one of the cheapest dust sensors from the young ‘DSM501A’ Sam. This sensor can detect particles such as PM2.5 and PM10. This article proposes a system for air pollution control and sound monitoring and analyses it. To control pollution, the system is fully based on the internet of things (IoT) and highly desirable. This framework allows us to detect pollution levels (air or sound) occasionally. The article explores the different units such as hardware and software used in the system proposed, and also explains how IOT and its operations and its Microcontroller(Arduino Uno R3) and its architecture, gas sensor, and features are modeling and working with the basic components used in the proposed system. The ESP8266 Wi-Fi Model and the sound sensors.

This study's main purpose that is a Cummins diesel engine simulation model using GT-power software assess the ability to convert the diesel engine to the dual-fuel engine and the potential use of dual-fuel (CNG-Diesel) to improve engine efficiency, save the cost of fuel and minimize environmental pollution. In the first stage, the simulation was conducted for the basic diesel engine, based on the engine manufacturer's experimental parameters used as the input in GT-Suite to predict the diesel engine performance. The elements are calibrated and optimized on the GT-Suite map to get results closest to the actual base diesel engine. The value has a maximum deviation of ±5% and validates the basic diesel engine model by GT-power. In the second stage, from the basic diesel engine model in GT-Power design DF (CNG-Diesel) engine. In particular, applying the combination of two distinct parallel combustion models (DIPulse and SITurb) allowed the modelling of premixed and diffuse sections of the dual-fuel combustion process. The DF engine simulation results are compared with based diesel engine simulation results. It is possible to reevaluate the performance parameters and predict the fuel consumption of the Cummin engine.

When the pressure in the high-pressure pipelines of the diesel engine’s fuel injection system decreases, the injection pressure at the injector decreases accordingly, making the formation of the fuel-air mixture uneven, which will reduce the efficiency of the fuel-air mixture. Low heat and combustion temperature, cause increased soot emissions. Therefore, it is necessary to determine the level of pressure reduction in the high-pressure pipelines during engine operation and compare it to the initial state (standard state) given by the manufacturer. Thereby giving measures to repair and maintain the fuel injection system, which will help improve combustion efficiency and reduce environmental pollution, and at the same time prevent problems with the diesel engines used as the main engine of fishing boats. In this paper, the experimental results on 4CHE-Yanmar diesel engines are presented, showing that: When the pressure in the high-pressure pipeline is reduced by up to 14% compared to the initial state of the manufacturer, the soot emissions increased by 5%, NOx emissions decreased by about 6%.

With the increase in applications related to smart home communication, intelligent health care, environmental monitoring and Internet of Things (IoT), the demand for low-power electronic devices have increased significantly. Usually, a replaceable battery would be selected as an energy source for these electronic devices, but it is impractical to replace batteries in some areas that may be difficult to reach or potentially dangerous. Also, batteries add to the size, and their disposal causes environmental pollution. This has raised a need for reliable, continuous and environment-friendly sources of powering and charging these devices. Considering the ubiquitous nature of Wireless Fidelity (Wi-Fi) in terms of its accessibility, widespread availability, and high-speed data access, one of the options of harvesting energy is from Wi-Fi signals. The ability to harvest Wi-Fi energy from ambient or dedicated sources enables wireless charging of low-power devices. This paper develops the framework for a Wi-Fi Energy Harvesting system based on the 7-stage Villard rectifier voltage multiplier circuit, which was analyzed and simulated using Agilent Design Systems (ADS). The design, simulation, and performance evaluation of the circuit are described in the paper. The output of the multiplier circuit is given to the power management circuit, and a final simulated output of 3.3 V / 5 V is achieved.

The use of internal combustion engine (ICE) vehicles over the years has contributed significantly to problems such as climate change, environmental pollution, and increased cost of gas. The automotive industry in South Africa is amongst the highest Carbon (CO 2 ) emitters, responsible for approximately 20% of total emissions and the third highest contributor to air pollution. This has given rise to the quest for alternative source of power for vehicular application. Some of such power sources are solar and battery electric vehicles (EVs) due to their simplicity and technology advancement. Therefore, this paper reviews the general state, overview and challenges hindering mass adoption of EVs in South Africa. It highlights potential opportunities that exist in the electric vehicle sector, barriers, material availability and stakeholder involvement. However, providing adequate charging infrastructure, tax incentives, and sensitizing the public will support mass adoption of EV as enshrined in the Automotive Production Development Programme (APDP).

Generation of energy from renewable energy sources has been gaining momentum and popularity in recent years. This is owing to the global commitment to decelerate global warming, reduce greenhouse gas emission and avoid environmental pollution by cutting down fossil fuel-based generation to the barest minimum. Solar energy generation as an alternative is one of the most common renewable energy generation technologies in the world today. The technology however, has its setbacks and requires constant maintenance in order to maintain high generation efficiency and avoid faults and generation downtime. This study reviews the strategies and methods for mitigating the various faults associated with solar photovoltaic systems. It also attempts examining the effects of these strategies on the overall performance of photovoltaic systems.

In the context of the rapid development of urbanization and motorization, road congestion, environmental pollution caused by traffic, and traffic accidents have become a “city disease” that attracts much attention. To solve these problems, the development of vehicle-to-vehicle, automatic driving technology, and other science and technology is constantly giving new connotation to traffic flow. The application of these technologies makes urban roads gradually transform from the traditional traffic flow composed of Human Driven Vehicles (HDVs) to the traffic flow composed of Intelligent and Connected Vehicles (ICVs). Domestic research on speed guidance is relatively late, and most of them are the influence of speed guidance on expressways, but there are few signal-controlled intersections. Therefore, this paper proposes a speed guidance model based on the Cooperative Adaptive Cruise Control (CACC) model calibrated by PATH Laboratory of the University of California, Berkeley through real car experiments, and compares it with the driving model based on Intelligent Driver Model (IDM) as the driving model of artificial driving vehicles. Aiming at the signalized intersection area of the urban road, the acceleration, speed and position change of vehicles are studied by simulation experiment. The results show that in the 100s simulation experiment, ICVs has an average increase of 16s on the road than HDVs, and an average of 16.8s less parking time. Therefore, the traffic flow composed of ICVs can effectively shorten the total travel time and the parking time of the vehicle in the vicinity of intersections, thereby improving the efficiency of road traffic, which provides a reference for the application and development of ICVs on the road in the future.

Demand response becomes an active research area in the current context. Various types of models and algorithms had been developing for load shifting and scheduling by flattening the demand curve while minimizing the energy cost. In Sri Lanka, approximately 700 MW peak energy demand is supplied by diesel engines and gas turbines in addition to the base power plants which creates extra cost and environmental pollution. However, during the period of off peak, demand is considerably lower than period of peak. This work is carried out to encourage domestic customers to reduce electricity consumption during peak times through optimize scheduler. The mobile application developed can propose a best scheduling for the flexible domestic appliances without effecting to user comfort while achieving a stable demand curve.

The logistics of agricultural products has always been a hot issue in the logistics industry. The waste of agricultural products caused by improper transportation and packaging is huge every year. In order to reduce the environmental pollution caused by the loss of agricultural products and packaging waste, the application of standard turnover baskets is of great significance. In this paper, according to the present situation of waste of fruits and vegetables, the advantages and challenges of sharing standard turnover baskets by green circulation were analyzed, and an effective incentive mechanism aimed at promoting the application of standard turnover basket in agricultural products logistics was put forward. This paper is of great practical significance to environmental protection and the realization of sustainable development goals in China.

The environmental pollution detection of gas has important practical significance in the environment-friendly society. The first principle was used to study the adsorption of NO 2 on the surface of CuO-MoS 2 heterojunction composite in our article. The optimal adsorption sites of NO 2 gas molecules were determined.by the adsorption energy, adsorption distance and charge transfer. The calculation results of density of states (DOS) and electron localization function showed the NO 2 molecule was chemical adsorption and the path of charge transfer was obtained. According to the the cloud image of electron localization density and the optimal adsorption distance of the center (S, Mo) site, bridge (Mo-S) site and hollow site, it is clear that the band gap of CuO-MoS 2 heterojunction can be effectively tuned and the optimal adsorption distance of B (Mo-S) is 2.484 Å. Moreover, the O atoms in the gas molecules and the Cu atoms in the heterojunction contribute greatly to the density of states and energy in the adsorption process. This article work provides theoretical guidance for improving CuO-MOS 2 to NO 2 gas adsorption in the future.

In recent years, the traditional power grid has revealed its intrinsic flaws. On the one hand, its power generation mainly relies on fossil energy, which inevitably emits carbon dioxide and other gases and causes environmental pollution; on the other hand, the structure of centralized power generation leads to its possible chain reaction of failures. Meanwhile, With the development of new energy technologies, distributed power generation systems represented by nano-grids (NGs) and micro-grids (MGs) and electric vehicles (EVs) have emerged. Charging and discharging are the two forms of EVs, which can play a driving role in relieving the pressure of power supply at peak hours of the grid and achieving peak shaving. In this paper, the electric vehicle (EV) travel characteristics are studied by using the residential community with the nano-grid (NG). For the problem of matching the supply-demand difference between the EV discharging process and the NG system, a reservation discharging mechanism is proposed, and an optimal scheduling strategy for the NG is developed on this basis. A small-scale NG system containing wind turbine, photovoitaic, energy storage and electric vehicle is simulated in MATLAB platform, and the effectiveness of this mechanism is verified through simulation cases.

Lithium battery (LiB) is widely used in electric vehicles (EVs), energy storage power stations and other fields. because of its high voltage, light weight, high energy density, good cycle performance, low self discharge rate and no memory effect, less environmental pollution and other significant advantages. However, due to the limited voltage and capacity of battery cells, battery is usually used as a battery pack composed of hundreds of single cells in parallel or series, The difference between batteries not only affect the charging and discharging capacity of batteries, but also lead to battery aging and potential safety problems. It is of great significance to study the influence of the differences between cells on the battery pack for battery grouping and battery balancing. This paper takes the coupling relationship among the initial state of charge (SOC), initial capacity and initial internal resistance into consideration, and establishes the relationship between the initial parameters. A model consists six cells connected in series is established based on the Rint equivalent circuit model, and simulations under different working conditions are conducted. The simulation results show that the consistency of the capacity of battery pack is mainly affected by the initial SOC and initial capacity. The influence of the two factors on capacity of battery pack consistency under different charging current rates is different. The initial internal resistance has little effect on capacity consistency under different charging current rates.

In order to solve the problems of sustainable energy supply and environmental pollution, integrated energy system has become the hot spot in the field of energy. Taking electric energy as the core, integrated energy system integrates solar energy, wind energy, biomass energy, ocean energy, geothermal energy and other clean and renewable energy at the source end, and realizes the generation combined cooling heating and power at the terminal. However, there exists the information interaction among resource individuals, integrated energy system and external network at different levels of horizontally and vertically. It is necessary to study how to build information model for integrated energy system. Therefore, this paper systematically summarizes the top-level architecture of integrated energy system information model, common schedulable resources in the integrated energy system and their standardized information model, and information interaction mechanism for integrated energy system. Finally, combined with the integrated energy strategy and future development status, the development prospects and typical application forms of integrated energy information modeling technology are discussed.

Because of its clean, safe, convenient and efficient characteristics, the photovoltaic power generation act as an important part to effectively improving the climate change, energy security as well as environmental pollution. To effectively use solar energy resources and increase the photovoltaic power generation, accurate solar radiation estimation is an indispensable basis and premise. A new estimation algorithm based on singular spectral analysis-deep belief network (SSA-DBN) is proposed in this paper. Firstly, considering meteorological factors, the air pollution data is mainly studied. A deep belief network model is established based on the input quantity selected by the maximum correlation minimum redundancy algorithm (mRMR). Afterwards, the singular spectrum analysis of historical data sequences, including singular value decomposition and reconstruction, is applied to the radiation estimation model improvement, and a daily solar radiation estimation model based on singular spectrum analysis-deep belief network (SSA-DBN) is established. It could improve the accuracy of radiation estimation and provide more effective information for photovoltaic grid-connection and power system operation and scheduling.

In recent years, as science and technology continue to advance, energy consumption has been constantly growing. As a result, environmental problems have been highlighted and become a hot topic. In contrast to coal, petroleum, natural gas, and other energy sources, electric energy has significant advantages in quickness, safety, and cleanliness. Thus, the implementation of electric energy substitution is capable of easing energy shortage and reducing environmental pollution to some extent. Study on electric energy potential provides a reference basis for electric energy programs. Therefore, in view of the quantization of electric energy substitution potential with electric energy substitution amount, this paper proposes a time series and back propagation (BP) neural networks-based combined prediction method. This means electric energy substitution amount can be predicted by the cubic exponential smoothing method-based time series model and the prediction results can be modified by BP neural network. In this study, the energy consumption from National Bureau of Statistics is used as data for forecasting and comparative analysis. The calculation results indicate that, compared to a single-method prediction, the prediction accuracy of electric energy substitution can be greatly enhanced via the combined prediction based on time series and BP neural networks. These findings have implications for analyzing electric energy substitution potential.

Faced with serious global environmental pollution and energy crisis, the integrated energy system(IES) is one of the promising solutions. However, the multi-time scale characteristics of the gas-electrothermal coupling IES present great difficulties for their unification and practical application. To address the issue, this paper provides physical validation models of gas thermal to verify the possibility of their unification. Then, the gas-thermal inertial power support model is established to participate in the standby configuration in a gas-electrothermal coupling IES park as a new standby form. Different schemes of standby configuration are analyzed in the end of the paper.

Uncertainties of renewable energy sources and load demand have a significant effect on the microgrid operation. In this paper, a robust optimal energy management approach is proposed for a grid-connected microgrid where the adjustable uncertain sets are used to represent uncertainties. A novel statistic index is developed to evaluate the effectiveness of the robust optimization and obtain reasonable model parameters. Moreover, the optimal economic cost and the least environmental pollution are achieved simultaneously. Simulation results in different scenarios are given to verify the effectiveness of the robust optimization approach.

As the most potential energy storage system at present, advanced adiabatic compressed air energy storage can better realize the regional comprehensive energy system’s absorption of new energy, efficient utilization of traditional energy and reduction of environmental pollution. In this paper, an energy supply optimization model of regional integrated energy system with advanced adiabatic compressed air energy storage is established to realize the economic optimization of the system’s electric heating energy in the daily operation cycle. By studying the energy storage characteristics of advanced adiabatic compressed air energy storage and its relationship with regional integrated energy system, the electric power balance, thermal power balance, operation constraints and environmental costs of various energy supply devices are analyzed. Gams optimization software is used to solve the operation costs of the traditional regional comprehensive energy system model and the model established in this paper in the daily operation cycle. Different models are compared from two aspects of environmental protection and economy, which verifies the effectiveness and feasibility of the model in reducing costs, promoting new energy consumption and energy conservation and emission reduction.

The development of renewable is becoming a new trend to cope with the shortage of fossil fuels and environmental pollution, but the inherent uncertainty and volatility pose great challenges to power system operation with high share of renewables. Energy hub which incorporates multiple energy production, conversion, and storage facilities can play a noteworthy role in renewable accommodation and energy saving. This paper proposes a multi-stage robust optimization model for day-ahead scheduling of energy hub composed of wind generation, advanced adiabatic compressed air energy storage (AA-CAES) system, and heat pump in the presence of wind uncertainty. To overcome the computational difficulty, the multistage model is decomposed into two stages. The first stage minimizes the operation cost under the predicted scenario, and the second stage determines real-time dispatch in response to the observed wind power generation. Dynamic lower and upper bound variables for two correlated state-of-charges (SoC) are added as the first-stage decision variables with ramping limit constraints, so that the second-stage decision can be made period-by-period without jeopardizing robust feasibility. This is an important difference compared to the two-stage robust optimization whose second-stage decision is anticipative and thus non-causal. Finally, the day-ahead scheduling problem is reformulated as a mixed-integer linear program (MILP) guaranteeing the robustness and non-anticipativity of real-time dispatch. Case studies verify the effectiveness of the proposed method.