Predictive control for greenhouse temperature and humidity and energy optimization by improved NMPC objective function algorithm

Persistent low temperatures in autumn and winter have a huge impact on crops,and greenhouses rely on solar radiation and heating equipment to meet the required indoor temperature.But the energy cost of frequent operation of the actuators is exceptionally high.The relationship between greenhouse environmental control accuracy and energy consumption is one of the key issues faced in greenhouse research.In this study,a non-linear model predictive control method with an improved objective function was proposed.The improved objective function used tolerance intervals and boundary constraints to optimize the objective evaluation.The nonlinear model predictive control(NMPC)controller design was based on the wavelet neural network(WNN)data-driven model and applied the interior point method to solve the optimal solution of the objective function control,thus balancing the contradiction between energy consumption and control precision.The simulation results showed that the improved NMPC method reduced energy consumption by 21.02%and 9.54%compared with the model predictive control and regular NMPC,which proved the method achieved good results in a low-temperature environment.This research can provide an important reference for the field as it offers a more efficient approach to managing greenhouse climates,potentially leading to substantial energy savings and enhanced sustainability in agricultural practices.

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.

Energy Optimization of the Fin/Rudder Roll Stabilization System Based on the Multi-objective Genetic Algorithm （MOGA）

Energy optimization is one of the key problems for ship roll reduction systems in the last decade. According to the nonlinear characteristics of ship motion, the four degrees of freedom nonlinear model of Fin/Rudder roll stabilization can be established. This paper analyzes energy consumption caused by overcoming the resistance and the yaw, which is added to the fin/rudder roll stabilization system as new performance index. In order to achieve the purpose of the roll reduction, ship course keeping and energy optimization, the self-tuning PID controller based on the multi-objective genetic algorithm （MOGA） method is used to optimize performance index. In addition, random weight coefficient is adopted to build a multi-objective genetic algorithm optimization model. The objective function is improved so that the objective function can be normalized to a constant level. Simulation results showed that the control method based on MOGA, compared with the traditional control method, not only improves the efficiency of roll stabilization and yaw control precision, but also optimizes the energy of the system. The proposed methodology can get a better performance at different sea states.

Simulation and analysis of energy optimization for PEMFC hybrid system

The control objective and several key parameters of PEMFC hybrid system are analyzed. Control strategy design and energy optimization simulation are made individually for given cycle case and realtime operating case. For the given cycle case, genetic algorithm is adopted to solve the multi-constraint combinatorial optimization problem. Simulation result showed the algorithm＇s feasibility. As far as the realtime operation is concerned, based on the original fuzzy control strategy, the fuel cell voltage and voltage variance parameters are introduced to apply result reveals that the improved fuzzy control strategy can enhance the two-level modification on the fuzzy control output. The fuel cell efficiency and reduce the power fluctuations.

A game-theoretic approach for federated learning:A trade-off among privacy,accuracy and energy

Benefiting from the development of Federated Learning(FL)and distributed communication systems,large-scale intelligent applications become possible.Distributed devices not only provide adequate training data,but also cause privacy leakage and energy consumption.How to optimize the energy consumption in distributed communication systems,while ensuring the privacy of users and model accuracy,has become an urgent challenge.In this paper,we define the FL as a 3-layer architecture including users,agents and server.In order to find a balance among model training accuracy,privacy-preserving effect,and energy consumption,we design the training process of FL as game models.We use an extensive game tree to analyze the key elements that influence the players’decisions in the single game,and then find the incentive mechanism that meet the social norms through the repeated game.The experimental results show that the Nash equilibrium we obtained satisfies the laws of reality,and the proposed incentive mechanism can also promote users to submit high-quality data in FL.Following the multiple rounds of play,the incentive mechanism can help all players find the optimal strategies for energy,privacy,and accuracy of FL in distributed communication systems.

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.

Energy and Water Optimization in Biofuel Plants

In this paper we address the topic of energy and water optimization in the production of bioethanol from corn and switchgrass. We show that in order for these manufacturing processes to be attractive,there is a need to go beyond traditional heat integration and water recycling techniques. Thus,we propose a strategy based on mathe-matical programming techniques to model and optimize the structure of the processes,and perform heat integration including the use of multi-effect distillation columns and integrated water networks to show that the energy effi-ciency and water consumption in bioethanol plants can be significantly improved. Specifically,under some circum-stances energy can even be produced and the water consumption can be reduced below the values required for the production of gasoline.

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.

Run-to-run Optimization for Fed-batch Fermentation Process with Swarm Energy Conservation Particle Swarm Optimization Algorithm

An iterative optimization strategy for fed-batch fermentation process is presented by combining a run-to-run optimization with swarm energy conservation particle swarm optimization (SEC-PSO). SEC-PSO, which is designed with the concept of energy conservation, can solve the problem of premature convergence frequently appeared in standard PSO algorithm by partitioning its population into several sub-swarms according to the energy of the swarm and is used in the optimization strategy for parameter identification and operation condition optimization. The run-to-run optimization exploits the repetitive nature of fed-batch processes in order to deal with the optimal problems of fed-batch fermentation process with inaccurate process model and unsteady process state. The kinetic model parameters, used in the operation condition optimization of the next run, are adjusted by calculating time-series data obtained from real fed-batch process in the run-to-run optimization. The simulation results show that the strategy can adjust its kinetic model dynamically and overcome the instability of fed-batch process effectively. Run-to-run strategy with SEC-PSO provides an effective method for optimization of fed-batch fermentation process.

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.

Energy saving analysis of segment positioning in shield tunneling machine considering assembling path optimization

A motion parameter optimization method based on the objective of minimizing the total energy consumption in segment positioning was proposed for segment erector of shield tunneling machine. The segment positioning process was decomposed into rotation, lifting and sliding actions in deriving the energy calculation model of segment erection. The work of gravity was taken into account in the mathematical modeling of energy consumed by each actuator. In order to investigate the relationship between the work done by the actuator and the path moved along by the segment, the upward and downward directions as well as the operating quadrant of the segment erector were defined. Piecewise nonlinear function of energy was presented, of which the result is determined by closely coupled components as working parameters and some intermediate variables. Finally, the effectiveness of the optimization method was proved by conducting a case study with a segment erector for the tunnel with a diameter of 3 m and drawing comparisons between different assembling paths. The results show that the energy required by assembling a ring of segments along the optimized moving path can be reduced up to 5%. The method proposed in this work definitely provides an effective energy saving solution for shield tunneling machine.

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-