Empirical Investigation of Treatment of Sour Gas by Novel Technology: Energy Optimization

The sour gas sweetening is one of the main processes in gas industries. Gas sweetening is done through chemical processes. Therefore, it requires high cost and energy. The results show that increasing the operating temperature increases the mass transfer coefficient and increases the mass transfer rate. Theoretical and experimental data show that sulfur removal in 4.5 W magnetic field is desirable. The increase in sulfur removal percentage in the magnetic field of 4.5 W and 6.75 W is about 16.4% and 15.2%, respectively. According to the obtained results, the effect of temperature increase from 18.8°C to 23.4°C is more evident than the effect of temperature change from 23.4°C to 32.2°C. Because more thermal energy is needed to provide higher temperatures. Therefore, the temperature of 23.4°C is reported as the optimal temperature. The results of this research show that the percentage of sulfur removal is also high at this temperature.

Scheduling an Energy-Aware Parallel Machine System with Deteriorating and Learning Effects Considering Multiple Optimization Objectives and Stochastic Processing Time

Currently,energy conservation draws wide attention in industrial manufacturing systems.In recent years,many studies have aimed at saving energy consumption in the process of manufacturing and scheduling is regarded as an effective approach.This paper puts forwards a multi-objective stochastic parallel machine scheduling problem with the consideration of deteriorating and learning effects.In it,the real processing time of jobs is calculated by using their processing speed and normal processing time.To describe this problem in a mathematical way,amultiobjective stochastic programming model aiming at realizing makespan and energy consumption minimization is formulated.Furthermore,we develop a multi-objective multi-verse optimization combined with a stochastic simulation method to deal with it.In this approach,the multi-verse optimization is adopted to find favorable solutions from the huge solution domain,while the stochastic simulation method is employed to assess them.By conducting comparison experiments on test problems,it can be verified that the developed approach has better performance in coping with the considered problem,compared to two classic multi-objective evolutionary algorithms.

A review of uncertain factors and analytic methods in long-term energy system optimization models

A larger number of uncertain factors in energy systems influence their evolution.Owing to the complexity of energy system modeling,incorporating uncertainty analysis to energy system modeling is essential for future energy system planning and resource allocation.This study focusses on long-term energy system optimization model.The important uncertain parameters in the model are analyzed and divided into policy,economic,and technical factors.This study specifically addresses the challenges related to carbon emission reduction and energy transition.It involves collecting and organizing relevant research on uncertainty analysis of long-term energy systems.Various energy system uncertainty modeling methods and their applications from the literature are summarized in this review.Finally,important uncertainty factors and uncertainty modeling methods for long-term energy system modeling are discussed,and future research directions are proposed.

Hybrid Power Bank Deployment Model for Energy Supply Coverage Optimization in Industrial Wireless Sensor Network

Energy supply is one of the most critical challenges of wireless sensor networks(WSNs)and industrial wireless sensor networks(IWSNs).While research on coverage optimization problem(COP)centers on the network’s monitoring coverage,this research focuses on the power banks’energy supply coverage.The study of 2-D and 3-D spaces is typical in IWSN,with the realistic environment being more complex with obstacles(i.e.,machines).A 3-D surface is the field of interest(FOI)in this work with the established hybrid power bank deployment model for the energy supply COP optimization of IWSN.The hybrid power bank deployment model is highly adaptive and flexible for new or existing plants already using the IWSN system.The model improves the power supply to a more considerable extent with the least number of power bank deployments.The main innovation in this work is the utilization of a more practical surface model with obstacles and training while improving the convergence speed and quality of the heuristic algorithm.An overall probabilistic coverage rate analysis of every point on the FOI is provided,not limiting the scope to target points or areas.Bresenham’s algorithm is extended from 2-D to 3-D surface to enhance the probabilistic covering model for coverage measurement.A dynamic search strategy(DSS)is proposed to modify the artificial bee colony(ABC)and balance the exploration and exploitation ability for better convergence toward eliminating NP-hard deployment problems.Further,the cellular automata(CA)is utilized to enhance the convergence speed.The case study based on two typical FOI in the IWSN shows that the CA scheme effectively speeds up the optimization process.Comparative experiments are conducted on four benchmark functions to validate the effectiveness of the proposed method.The experimental results show that the proposed algorithm outperforms the ABC and gbest-guided ABC(GABC)algorithms.The results show that the proposed energy coverage optimization method based on the hybrid power bank deployment model generates more accurate results than the results obtained by similar algorithms(i.e.,ABC,GABC).The proposed model is,therefore,effective and efficient for optimization in the IWSN.

Mission-oriented cooperative 3D path planning for modular solar-powered aircraft with energy optimization

Modular Solar-Powered Aircraft(M-SPA)is a kind of High-Altitude Long-Endurance(HALE)aircraft which exploits the mission advantage of swarm UAV and the HALE advantage of large aspect-ratio SPA.M-SPA’s separated mode and combined mode give it the potential to maximize the mission efficiency with limited solar energy.In this paper,firstly,oriented by the mission of maximizing the cruise area,the overall design of the M-SPA is modeled,including the energy model,the aerodynamic model and the flight environment settings.Secondly,by analyzing the energy consumption of the flight modes,we design a multi-phase flight mission strategy.Then,a 24-hour three-dimensional(3D)flight profile of the M-SPA is optimized,including the sub-SPA cooperative path planning in the separation mode.Finally,inspired by the Traveling Salesman Problem(TSP),an improved Ant Colony Algorithm(ACA)is exploited to find the optimal path for each sub-SPA,which is further developed into a dynamic separation and combination scheme for the M-SPA.The simulation results show that the mission performance of the M-SPA outperforms that of the conventional SPA,and explicitly,the mission coverage of the M-SPA is slightly less than a linear increase under comparable simulation conditions.

An Energy Efficient Control Strategy for Electric Vehicle Driven by In-Wheel-Motors Based on Discrete Adaptive Sliding Mode Control

This paper presents an energy-efficient control strategy for electric vehicles(EVs)driven by in-wheel-motors(IWMs)based on discrete adaptive sliding mode control(DASMC).The nonlinear vehicle model,tire model and IWM model are established at first to represent the operation mechanism of the whole system.Based on the modeling,two virtual control variables are used to represent the longitudinal and yaw control efforts to coordinate the vehicle motion control.Then DASMC method is applied to calculate the required total driving torque and yaw moment,which can improve the tracking performance as well as the system robustness.According to the vehicle nonlinear model,the additional yaw moment can be expressed as a function of longitudinal and lateral tire forces.For further control scheme development,a tire force estimator using an unscented Kalman filter is designed to estimate real-time tire forces.On these bases,energy efficient torque allocation method is developed to distribute the total driving torque and differential torque to each IWM,considering the motor energy consumption,the tire slip energy consumption,and the brake energy~?recovery.Simulation results of the proposed control strategy using the co-platform of Matlab/Simulink and CarSim way.

Energy Estimation and Optimization Platform for 4G and the Future Base Station System Early-Stage Design

There are already several power models to estimate the power consumption of base stations at system level. However, there is so far no model that can predict power consumption of the future base station designs based on algorithms and hardware selections with insufficient physical information. We present such an energy model for typical base stations. This model can help designers in estimating, evaluating and optimizing energy/power consumption of candidate designs in early design stages. The proposed model is verified by an LTE extreme scenario. The estimated results show that digital front-end, channel equalization and channel decoding are three major power greedy modules(consuming 39.4%, 16.3%, 13.4%) in a digital baseband subsystem. The power estimation error of the proposed power amplifier(PA) power model is 3.5%(macro cell). The major contribution of this paper is that the proposed models can rapidly estimate energy/power consumption of 4G and the future base stations(such as 5G) in early design stages with well acceptable precision, even without sufficient implementation information.

AN IMPROVED LP MODEL FOR ENERGY OPTIMIZATION OF THE INTEGRATED IRON AND STEEL PLANT WITH A COGENERATION SYSTEM IN CHINA

In an integrated iron and steel plant with a cogeneration system, recycled energy is continuously transported into the cogeneration system and the electricity is continuously generated, and both of them could not be stored for a long time. Moreover, thegeneration and consumption of electricity is irregular, which may bring about more unexpected imbalances. Therefore, it is a crucial issue to schedule the entire energy system by optimizing the operation of energy utilization, which includes the raw energy in the production system, the generation electricity in the cogeneration system and the recycled energy in these two systems. In this paper, an improved Linear Programming model for energy optimization in the integrated iron and steel plant with a cogeneration system is established. The improved model focuses on controlling the whole energy flow and scheduling the whole energy consumption in the entire energy system between the production system and cogeneration system through optimizing all kinds of energy distribution and utilization in an integrated iron and steel plant with a cogeneration system. Case study shows that the improved model offers the optimal operation conditions at the higher energy utilization, lower energy cost and lower pollution emissions.

An Efficient Method for Heat Recovery Process and Temperature Optimization

Flue gas heat loss accounts for a significant component of theoverall heat loss for coal-fired boilers in power plants. The flue gas absorbsmore heat as the exhaust gas temperature rises, which reduces boiler efficiencyand raises coal consumption. Additionally, if the exhaust gas temperatureis too high, a lot of water must be used to cool the flue gas for the wetflue gas desulfurization system to function well, which has an impact onthe power plant’s ability to operate profitably. It is consequently vital totake steps to lower exhaust gas temperatures in order to increase boilerefficiency and decrease the amount of coal and water used. Desulfurizationperformance may be enhanced and water use can be decreased by reasonableflue gas characteristics at the entry. This study analyzed the unit’s energyconsumption, investment, and coal savings while proposing four couplingstrategies for regulating flue gas temperature and waste heat recovery. Agraded flue gas conditioning and waste heat recovery plan was presentedunder the condition of ensuring high desulfurization efficiency, along withthe notion of minimizing energy loss owing to energy inflow temperaturedifference. Numerical results show that the proposed methods improved thesystem performance and reduced the water consumption and regulated theboiler temperature.

Improved Metaheuristic Based Failure Prediction with Migration Optimization in Cloud Environment

Cloud data centers consume high volume of energy for processing and switching the servers among different modes.Virtual Machine(VM)migration enhances the performance of cloud servers in terms of energy efficiency,internal failures and availability.On the other end,energy utilization can be minimized by decreasing the number of active,underutilized sources which conversely reduces the dependability of the system.In VM migration process,the VMs are migrated from underutilized physical resources to other resources to minimize energy utilization and optimize the operations.In this view,the current study develops an Improved Metaheuristic Based Failure Prediction with Virtual Machine Migration Optimization(IMFP-VMMO)model in cloud environment.The major intention of the proposed IMFP-VMMO model is to reduce energy utilization with maximum performance in terms of failure prediction.To accomplish this,IMFPVMMO model employs Gradient Boosting Decision Tree(GBDT)classification model at initial stage for effectual prediction of VM failures.At the same time,VMs are optimally migrated using Quasi-Oppositional Artificial Fish Swarm Algorithm(QO-AFSA)which in turn reduces the energy consumption.The performance of the proposed IMFP-VMMO technique was validated and the results established the enhanced performance of the proposed model.The comparative study outcomes confirmed the better performance of the proposed IMFP-VMMO model over recent approaches.

Design of piezoelectric energy harvesting devices subjected to broadband random vibrations by applying topology optimization

Converting ambient vibration energy into electrical energy by using piezoelectric energy harvester has attracted a lot of interest in the past few years.In this paper,a topology optimization based method is applied to simultaneously determine the optimal layout of the piezoelectric energy harvesting devices and the optimal position of the mass loading.The objective function is to maximize the energy harvesting performance over a range of vibration frequencies.Pseudo excitation method （PEM） is adopted to analyze structural stationary random responses,and sensitivity analysis is then performed by using the adjoint method.Numerical examples are presented to demonstrate the validity of the proposed approach.

Attitude control of flapping wing aircraft based on energy optimization and ESO

Aiming at the problem of insufficient endurance performance of flapping wing aircraft,a stable attitude control algorithm based on energy optimization and ESO(extended state observer)is designed,which effectively reduces the energy consumption in cruise phase.Firstly,the longitudinal dynamic model of flapping wing aircraft is established,and then the uncertain part of the system and various unknown external disturbances are taken as the total disturbance.ESO module is introduced to observe and track the total disturbance in real time.Therefore,the system is transformed into a series integral system through the total disturbance feedback,and then the energy optimal control law is designed on the base of the transformed system.The numerical simulation results show that,compared with the traditional PID control method,the designed energy optimal control method reduces the average energy consumption by 35.28%.

Guest Editorial for Special Issue on Control and Optimization in Renewable Energy Systems

I.I NTRODUCTION W ITH the advent of low-carbon economy,there has been a growing interest in harnessing renewable energy resources particularly for electricity generation.Renewable energy resources are advocated for the economic and environ-

Based on the water source heat pump system operation control and energy consumption optimization

The contradiction between the increasing material demand and resource,is the country has faced problems,to better solve the material demand and the contradiction between the environment and resources,is applied to the development of new energy,new energy,not only can alleviate people and resources,environment and resources,the contradiction between people and the environment,also can promote the sustainable development of world economy,HVAC technology has emerged a new generation of energysaving technology,HVAC has the characteristics of low consumption,low pollution,is a development of technology,to be promoted for environmentfriendly,resource-conserving society has an important role in promoting.This paper focuses on the HVAC technology,water source heat pump system operation control and energy consumption optimization,for the relevant personnel reference.

An OP-TEE Energy-Efficient Task Scheduling Approach Based on Mobile Application Characteristics

Trusted Execution Environment(TEE)is an important part of the security architecture of modern mobile devices,but its secure interaction process brings extra computing burden to mobile devices.This paper takes open portable trusted execution environment(OP-TEE)as the research object and deploys it to Raspberry Pi 3B,designs and implements a benchmark for OP-TEE,and analyzes its program characteristics.Furthermore,the application execution time,energy consumption and energy-delay product(EDP)are taken as the optimization objectives,and the central processing unit(CPU)frequency scheduling strategy of mobile devices is dynamically adjusted according to the characteristics of different applications through the combined model.The experimental result shows that compared with the default strategy,the scheduling method proposed in this paper saves 21.18%on average with the Line Regression-Decision Tree scheduling model with the shortest delay as the optimization objective.The Decision Tree-Support Vector Regression(SVR)scheduling model,which takes the lowest energy consumption as the optimization goal,saves 22%energy on average.The Decision Tree-K-Nearest Neighbor(KNN)scheduling model with the lowest EDP as the optimization objective optimizes about 33.9%on average.

Research on Optimal Configuration of Energy Storage in Wind-Solar Microgrid Considering Real-Time Electricity Price

Capacity allocation and energy management strategies for energy storage are critical to the safety and economical operation of microgrids.In this paper,an improved energymanagement strategy based on real-time electricity price combined with state of charge is proposed to optimize the economic operation of wind and solar microgrids,and the optimal allocation of energy storage capacity is carried out by using this strategy.Firstly,the structure and model of microgrid are analyzed,and the outputmodel of wind power,photovoltaic and energy storage is established.Then,considering the interactive power cost between the microgrid and the main grid and the charge-discharge penalty cost of energy storage,an optimization objective function is established,and an improved energy management strategy is proposed on this basis.Finally,a physicalmodel is built inMATLAB/Simulink for simulation verification,and the energy management strategy is compared and analyzed on sunny and rainy days.The initial configuration cost function of energy storage is added to optimize the allocation of energy storage capacity.The simulation results show that the improved energy management strategy can make the battery charge-discharge response to real-time electricity price and state of charge better than the traditional strategy on sunny or rainy days,reduce the interactive power cost between the microgrid system and the power grid.After analyzing the change of energy storage power with cost,we obtain the best energy storage capacity and energy storage power.

Energy-Optimal Braking Control Using a Double-Layer Scheme for Trajectory Planning and Tracking of Connected Electric Vehicles

Most researches focus on the regenerative braking system design in vehicle components control and braking torque distribution,few combine the connected vehicle technologies into braking velocity planning.If the braking intention is accessed by the vehicle-to-everything communication,the electric vehicles(EVs)could plan the braking velocity for recovering more vehicle kinetic energy.Therefore,this paper presents an energy-optimal braking strategy(EOBS)to improve the energy efficiency of EVs with the consideration of shared braking intention.First,a double-layer control scheme is formulated.In the upper-layer,an energy-optimal braking problem with accessed braking intention is formulated and solved by the distance-based dynamic programming algorithm,which could derive the energy-optimal braking trajectory.In the lower-layer,the nonlinear time-varying vehicle longitudinal dynamics is transformed to the linear time-varying system,then an efficient model predictive controller is designed and solved by quadratic programming algorithm to track the original energy-optimal braking trajectory while ensuring braking comfort and safety.Several simulations are conducted by jointing MATLAB and CarSim,the results demonstrated the proposed EOBS achieves prominent regeneration energy improvement than the regular constant deceleration braking strategy.Finally,the energy-optimal braking mechanism of EVs is investigated based on the analysis of braking deceleration,battery charging power,and motor efficiency,which could be a guide to real-time control.

A Stochastic Programming Strategy in Microgrid Cyber Physical Energy System for Energy Optimal Operation

This paper focuses on the energy optimal operation problem of microgrids(MGs) under stochastic environment.The deterministic method of MGs operation is often uneconomical because it fails to consider the high randomness of unconventional energy resources.Therefore,it is necessary to develop a novel operation approach combining the uncertainty in the physical world with modeling strategy in the cyber system.This paper proposes an energy scheduling optimization strategy based on stochastic programming model by considering the uncertainty in MGs.The goal is to minimize the expected operation cost of MGs.The uncertainties are modeled based on autoregressive moving average(ARMA) model to expose the effects of physical world on cyber world.Through the comparison of the simulation results with deterministic method,it is shown that the effectiveness and robustness of proposed stochastic energy scheduling optimization strategy for MGs are valid.

Optimal array layout of cylindrical baffles to reduce energy of rock avalanche

The array of baffles protection structure is a flow resistance structure with good drainage,blocking,and intercepting effects on the rock avalanches.In this research,the physical model test on rock avalanches was conducted.Three parameters(column spacing,row spacing,and a number of baffles)were used as indicators to determine the optimal layout of the array of baffles for energy efficiency consumption blocking.Then,the lattice Boltzmann numerical simulation method was used to build a numerical simulation model of rock avalanches-array of the baffles-hazard-bearing body to obtain the rock’s velocity attenuation and flow trajectory avalanches in the impact baffle protection structure.Finally,the results of the physical model test and the numerical simulation were mutually confirmed.The following conclusions were drawn through two methods of physical model test and numerical simulation.(1)The optimal layout parameters of array of baffles were determined as three rows of array of baffles(The number of baffles in each row is 7,8,9),column spacing Sc=3.5,and row spacing Sr=4.5.(2)Under the conditions of high baffle density(such as Sc=1.5,2.5),the rock avalanches would produce a certain degree of circumfluence,which would increase the fluid velocity by at least 24.5% over the average velocity,so the column spacing density should be increased appropriately to achieve the optimal effect of reducing the energy of rock avalanches.(3)In the event of a prototype grooved rock avalanches with a velocity close to 24.5 m/s and a flow depth of about 1.5 m,the three-row array of baffles protection with the parameters Sc^(*)=1.18 m and Sr^(*)=1.51 m could be arranged,playing the role of optimizing the array of baffles to guide the flow and block the energy consumption.LBM experiments can be used to replace laboratory experiments to some extent.Further Lattice Boltzmann method-Discrete element method(LBM-DEM)studies are required before applications to practical engineering.

Energy and Bandwidth Based Link Stability Routing Algorithm for IoT

Internet of Things(IoT)is becoming popular nowadays for collecting and sharing the data from the nodes and among the nodes using internet links.Particularly,some of the nodes in IoT are mobile and dynamic in nature.Hence maintaining the link among the nodes,efficient bandwidth of the links among the mobile nodes with increased life time is a big challenge in IoT as it integrates mobile nodes with static nodes for data processing.In such networks,many routing-problems arise due to difficulties in energy and bandwidth based quality of service.Due to the mobility and finite nature of the nodes,transmission links between intermediary nodes may fail frequently,thus affecting the routing-performance of the network and the accessibility of the nodes.The existing protocols do not focus on the transmission links and energy,bandwidth and link stability of the nodes,but node links are significant factors for enhancing the quality of the routing.Link stability helps us to define whether the node is within or out of a coverage range.This paper proposed an Optimal Energy and bandwidth based Link Stability Routing(OEBLS)algorithm,to improve the link stable route with minimized error rate and throughput.In this paper,the optimal route from the source to the sink is determined based on the energy and bandwidth,link stability value.Among the existing routes,the sink node will choose the optimal route which is having less link stability value.Highly stable link is determined by evaluating link stability value using distance and velocity.Residual-energy of the node is estimated using the current energy and the consumed energy.Consumed energy is estimated using transmitted power and the received power.Available bandwidth in the link is estimated using the idle time and channel capacity with the consideration of probability of collision.