This paper presents a real-time energy optimization algorithm for a hybrid electric vehicle(HEV)that operates with adaptive cruise control(ACC).Real-time energy optimization is an essential ssue such that the HEV powe...This paper presents a real-time energy optimization algorithm for a hybrid electric vehicle(HEV)that operates with adaptive cruise control(ACC).Real-time energy optimization is an essential ssue such that the HEV powertrain system is as efficient as possible.With connected vehice technique,ACC system shows considerable potential of high energy eficiency.Combining a classical ACC algorithm,a two-level cooperative control scheme is constructed to realize real-time power distribution for the host HEV that operates in a vehicle platoon.The proposed control strategy actually provides a solution for an optimal control problem with multi objectives in terms of string stable of vehicle platoon and energy consumption minimization of the individual following vehicle.The string stability and the real-time optimization performance of the cooperative control system are confirmed by simulations with respect to several operating scenarios.展开更多
The gasoline inline blending process has widely used real-time optimization techniques to achieve optimization objectives,such as minimizing the cost of production.However,the effectiveness of real-time optimization i...The gasoline inline blending process has widely used real-time optimization techniques to achieve optimization objectives,such as minimizing the cost of production.However,the effectiveness of real-time optimization in gasoline blending relies on accurate blending models and is challenged by stochastic disturbances.Thus,we propose a real-time optimization algorithm based on the soft actor-critic(SAC)deep reinforcement learning strategy to optimize gasoline blending without relying on a single blending model and to be robust against disturbances.Our approach constructs the environment using nonlinear blending models and feedstocks with disturbances.The algorithm incorporates the Lagrange multiplier and path constraints in reward design to manage sparse product constraints.Carefully abstracted states facilitate algorithm convergence,and the normalized action vector in each optimization period allows the agent to generalize to some extent across different target production scenarios.Through these well-designed components,the algorithm based on the SAC outperforms real-time optimization methods based on either nonlinear or linear programming.It even demonstrates comparable performance with the time-horizon based real-time optimization method,which requires knowledge of uncertainty models,confirming its capability to handle uncertainty without accurate models.Our simulation illustrates a promising approach to free real-time optimization of the gasoline blending process from uncertainty models that are difficult to acquire in practice.展开更多
In this paper, a novel approach termed process goose queue (PGQ) is suggested to deal with real-time optimization (RTO) of chemical plants. Taking advantage of the ad-hoc structure of PGQ which imitates biologic natur...In this paper, a novel approach termed process goose queue (PGQ) is suggested to deal with real-time optimization (RTO) of chemical plants. Taking advantage of the ad-hoc structure of PGQ which imitates biologic nature of flying wild geese, a chemical plant optimization problem can be re-formulated as a combination of a multi-layer PGQ and a PGQ-Objective according to the relationship among process variables involved in the objective and constraints. Subsequently, chemical plant RTO solutions are converted into coordination issues among PGQs which could be dealt with in a novel way. Accordingly, theoretical definitions, adjustment rule and implementing procedures associated with the approach are explicitly introduced together with corresponding enabling algorithms. Finally, an exemplary chemical plant is employed to demonstrate the feasibility and validity of the contribution.展开更多
To reduce the propulsion system installation thrust loss under high angle of attack maneuvering,a control method based on real-time optimization of the integrated aeropropulsion is proposed.Firstly,based on data fitti...To reduce the propulsion system installation thrust loss under high angle of attack maneuvering,a control method based on real-time optimization of the integrated aeropropulsion is proposed.Firstly,based on data fitting and physical principle,an integrated onboard model of propulsion system is established,which can calculate various performance parameters of the propulsion system in real time,and has high accuracy and real-time performance.Secondly,to improve the compatibility of optimization real-time performance and search accuracy,the online optimization control of aero-propulsion system is realized based on an improved trust region algorithm.Finally,by controlling the auxiliary intake valve,a good match between inlet and engine is realized,which solves the problems of intake flow reducing and total pressure recovery coefficient declining,and improves the installation performance of propulsion system.The simulation results indicate that,compared with the conventional independent engine control,the real-time integrated optimization method reduces the installed thrust loss by 3.61%under the design condition,and 4.58%under the off-design condition.Furthermore,the simulation on HIL(Hardware-In-theLoop)platform verifies the real-time performance of integrated optimization method.展开更多
An approach for the simulation and optimization of continuous catalyst-regenerative process of reforming is proposed in this paper.Compared to traditional method such as finite difference method,the orthogonal colloca...An approach for the simulation and optimization of continuous catalyst-regenerative process of reforming is proposed in this paper.Compared to traditional method such as finite difference method,the orthogonal collocation method is less time-consuming and more accurate,which can meet the requirement of real-time optimization(RTO).In this paper,the equation-oriented method combined with the orthogonal collocation method and the finite difference method is adopted to build the RTO model for catalytic reforming regenerator.The orthogonal collocation method was adopted to discretize the differential equations and sequential quadratic programming(SQP)algorithm was used to solve the algebraic equations.The rate constants,active energy and reaction order were estimated,with the sum of relative errors between actual value and simulated value serving as optimization objective function.The model can quickly predict the fields of component concentration,temperature and pressure inside the regenerator under different conditions,as well as the real-time optimized conditions for industrial reforming regenerator.展开更多
The rapid proliferation of renewable energy integration and escalating grid operational complexity have intensified demands for resilient self-healing mechanisms in modern power systems.Conventional approaches relying...The rapid proliferation of renewable energy integration and escalating grid operational complexity have intensified demands for resilient self-healing mechanisms in modern power systems.Conventional approaches relying on static models and heuristic rules exhibit limitations in addressing dynamic fault propagation and multimodal data fusion.This study proposes a Transformer-enhanced intelligent microgrid self-healing framework that synergizes large languagemodels(LLMs)with adaptive optimization,achieving three key innovations:(1)Ahierarchical attention mechanism incorporating grid impedance characteristics for spatiotemporal feature extraction,(2)Dynamic covariance estimation Kalman filtering with wavelet packet energy entropy thresholds(Daubechies-4 basis,6-level decomposition),and(3)A grouping-stratified ant colony optimization algorithm featuring penalty-based pheromone updating.Validated on IEEE 33/100-node systems,our framework demonstrates 96.7%fault localization accuracy(23%improvement over STGCN)and 0.82-s protection delay,outperforming MILP-basedmethods by 37%in reconfiguration speed.The system maintains 98.4%self-healing success rate under cascading faults,resolving 89.3%of phase-toground faults within 500 ms through adaptive impedance matching.Field tests on 220 kV substations with 45%renewable penetration show 99.1%voltage stability(±5%deviation threshold)and 40%communication efficiency gains via compressed GOOSE message parsing.Comparative analysis reveals 12.6×faster convergence than conventional ACO in 1000-node networks,with 95.2%robustness against±25%load fluctuations.These advancements provide a scalable solution for real-time fault recovery in renewable-dense grids,reducing outage duration by 63%inmulti-agent simulations compared to centralized architectures.展开更多
Production optimization has gained increasing attention from the smart oilfield community because it can increase economic benefits and oil recovery substantially.While existing methods could produce high-optimality r...Production optimization has gained increasing attention from the smart oilfield community because it can increase economic benefits and oil recovery substantially.While existing methods could produce high-optimality results,they cannot be applied to real-time optimization for large-scale reservoirs due to high computational demands.In addition,most methods generally assume that the reservoir model is deterministic and ignore the uncertainty of the subsurface environment,making the obtained scheme unreliable for practical deployment.In this work,an efficient and robust method,namely evolutionaryassisted reinforcement learning(EARL),is proposed to achieve real-time production optimization under uncertainty.Specifically,the production optimization problem is modeled as a Markov decision process in which a reinforcement learning agent interacts with the reservoir simulator to train a control policy that maximizes the specified goals.To deal with the problems of brittle convergence properties and lack of efficient exploration strategies of reinforcement learning approaches,a population-based evolutionary algorithm is introduced to assist the training of agents,which provides diverse exploration experiences and promotes stability and robustness due to its inherent redundancy.Compared with prior methods that only optimize a solution for a particular scenario,the proposed approach trains a policy that can adapt to uncertain environments and make real-time decisions to cope with unknown changes.The trained policy,represented by a deep convolutional neural network,can adaptively adjust the well controls based on different reservoir states.Simulation results on two reservoir models show that the proposed approach not only outperforms the RL and EA methods in terms of optimization efficiency but also has strong robustness and real-time decision capacity.展开更多
Purpose-The production of glycerol derivatives by the esterification process is subject to many constraints related to the yield of the production target and the lack of process efficiency.An accurate monitoring and c...Purpose-The production of glycerol derivatives by the esterification process is subject to many constraints related to the yield of the production target and the lack of process efficiency.An accurate monitoring and controlling of the process can improve production yield and efficiency.The purpose of this paper is to propose a real-time optimization(RTO)using gradient adaptive selection and classification from infrared sensor measurement to cover various disturbances and uncertainties in the reactor.Design/methodology/approach-The integration of the esterification process optimization using self-optimization(SO)was developed with classification process was combined with necessary condition optimum(NCO)as gradient adaptive selection,supported with laboratory scaled medium wavelength infrared(mid-IR)sensors,and measured the proposed optimization system indicator in the batch process.Business Process Modeling and Notation(BPMN 2.0)was built to describe the tasks of SO workflow in collaboration with NCO as an abstraction for the conceptual phase.Next,Stateflow modeling was deployed to simulate the three states of gradient-based adaptive control combined with support vector machine(SVM)classification and Arduino microcontroller for implementation.Findings-This new method shows that the real-time optimization responsiveness of control increased product yield up to 13 percent,lower error measurement with percentage error 1.11 percent,reduced the process duration up to 22 minutes,with an effective range of stirrer rotation set between 300 and 400 rpm and final temperature between 200 and 210℃ which was more efficient,as it consumed less energy.Research limitations/implications-In this research the authors just have an experiment for the esterification process using glycerol,but as a development concept of RTO,it would be possible to apply for another chemical reaction or system.Practical implications-This research introduces new development of an RTO approach to optimal control and as such marks the starting point for more research of its properties.As the methodology is generic,it can be applied to different optimization problems for a batch system in chemical industries.Originality/value-The paper presented is original as it presents the first application of adaptive selection based on the gradient value of mid-IR sensor data,applied to the real-time determining control state by classification with the SVM algorithm for esterification process control to increase the efficiency.展开更多
In this paper,an adaptive cubic regularisation algorithm based on affine scaling methods(ARCBASM)is proposed for solving nonlinear equality constrained programming with nonnegative constraints on variables.From the op...In this paper,an adaptive cubic regularisation algorithm based on affine scaling methods(ARCBASM)is proposed for solving nonlinear equality constrained programming with nonnegative constraints on variables.From the optimality conditions of the problem,we introduce appropriate affine matrix and construct an affine scaling ARC subproblem with linearized constraints.Composite step methods and reduced Hessian methods are applied to tackle the linearized constraints.As a result,a standard unconstrained ARC subproblem is deduced and its solution can supply sufficient decrease.The fraction to the boundary rule maintains the strict feasibility(for nonnegative constraints on variables)of every iteration point.Reflection techniques are employed to prevent the iterations from approaching zero too early.Under mild assumptions,global convergence of the algorithm is analysed.Preliminary numerical results are reported.展开更多
Real-time interaction with uncertain and dynamic environments is essential for robotic systems to achieve functions such as visual perception,force interaction,spatial obstacle avoidance,and motion planning.To ensure ...Real-time interaction with uncertain and dynamic environments is essential for robotic systems to achieve functions such as visual perception,force interaction,spatial obstacle avoidance,and motion planning.To ensure the reliability and determinism of system execution,a flexible real-time control system architecture and interaction algorithm are required.The ROS framework was designed to improve the reusability of robotic software development by providing a distributed structure,hardware abstraction,message-passing mechanism,and application prototypes.Rich ecosystems for robotic development have been built around ROS1 and ROS2 architectures based on the Linux system.However,because of the fairness scheduling principle of the default Linux system design and the complexity of the kernel,the system does not have real-time computing.To achieve a balance between real-time and non-real-time computing,this paper uses the transmission mechanism of ROS2,combines it with the scheduling mechanism of the Linux operating system,and uses Preempt_RT to enhance the real-time computing of ROS1 and ROS2.The real-time performance evaluation of ROS1 and ROS2 is conducted from multiple perspectives,including throughput,transmission mode,QoS service quality,frequency,number of subscription nodes and EtherCAT master.This paper makes two significant contributions:firstly,it employs Preempt_RT to optimize the native ROS2 system,effectively enhancing the real-time performance of native ROS2 message transmission;secondly,it conducts a comprehensive evaluation of the real-time performance of both native and optimized ROS2 systems.This comparison elucidates the benefits of the optimized ROS2 architecture regarding real-time performance,with results vividly demonstrated through illustrative figures.展开更多
This study introduces a real-time data-driven battery management scheme designed to address uncertainties in load and generation forecasts,which are integral to an optimal energy storage control system.By expanding on...This study introduces a real-time data-driven battery management scheme designed to address uncertainties in load and generation forecasts,which are integral to an optimal energy storage control system.By expanding on an existing algorithm,this study resolves issues discovered during implementation and addresses previously overlooked concerns,resulting in significant enhancements in both performance and reliability.The refined real-time control scheme is integrated with a day-ahead optimization engine and forecast model,which is utilized for illustrative simulations to highlight its potential efficacy on a real site.Furthermore,a comprehensive comparison with the original formulation was conducted to cover all possible scenarios.This analysis validated the operational effectiveness of the scheme and provided a detailed evaluation of the improvements and expected behavior of the control system.Incorrect or improper adjustments to mitigate forecast uncertainties can result in suboptimal energy management,significant financial losses and penalties,and potential contract violations.The revised algorithm optimizes the operation of the battery system in real time and safeguards its state of health by limiting the charging/discharging cycles and enforcing adherence to contractual agreements.These advancements yield a reliable and efficient real-time correction algorithm for optimal site management,designed as an independent white box that can be integrated with any day-ahead optimization control system.展开更多
Energy management strategies based on optimal control theory can achieve minimum fuel consumption for hybrid electric vehicles, but the requirement for driving cycles known in prior leads to a real-time problem. A rea...Energy management strategies based on optimal control theory can achieve minimum fuel consumption for hybrid electric vehicles, but the requirement for driving cycles known in prior leads to a real-time problem. A real-time optimization power-split strategy is proposed based on linear quadratic optimal control. The battery state of charge sustainability and fuel economy are ensured by designing a quadratic performance index combined with two rules. The engine power and motor power of this strategy are calculated in real-time based on current system state and command, and not related to future driving conditions. The simulation results in ADVISOR demonstrate that, under the conditions of various driving cycles, road slopes and vehicle parameters, the proposed strategy significantly improves fuel economy, which is very close to that of the optimal control based on Pontryagin's minimum principle, and greatly reduces computation complexity.展开更多
The application and development of a wide-area measurement system(WAMS)has enabled many applications and led to several requirements based on dynamic measurement data.Such data are transmitted as big data information ...The application and development of a wide-area measurement system(WAMS)has enabled many applications and led to several requirements based on dynamic measurement data.Such data are transmitted as big data information flow.To ensure effective transmission of wide-frequency electrical information by the communication protocol of a WAMS,this study performs real-time traffic monitoring and analysis of the data network of a power information system,and establishes corresponding network optimization strategies to solve existing transmission problems.This study utilizes the traffic analysis results obtained using the current real-time dynamic monitoring system to design an optimization strategy,covering the optimization in three progressive levels:the underlying communication protocol,source data,and transmission process.Optimization of the system structure and scheduling optimization of data information are validated to be feasible and practical via tests.展开更多
In order to get a globally optimized solution for the Elevator Group Control System (EGCS) scheduling problem, an algorithm with an overall optimization function is needed. In this study, Real-time Particle Swarm Opti...In order to get a globally optimized solution for the Elevator Group Control System (EGCS) scheduling problem, an algorithm with an overall optimization function is needed. In this study, Real-time Particle Swarm Optimization (RPSO) is proposed to find an optimal solution to the EGCS scheduling problem. Different traffic patterns and controller mechanisms for EGCS are analyzed. This study focuses on up-peak traffic because of its critical importance to modern office buildings. Simulation results show that EGCS based on Multi-Agent Systems (MAS) using RPSO gives good results for up-peak EGCS scheduling problem. Besides, the elevator real-time scheduling and reallocation functions are realized based on RPSO in case new information is available or the elevator becomes busy because it is unavailable or full. This study contributes a new scheduling algorithm for EGCS, and expands the application of PSO.展开更多
Computational tool-assisted primer design for real-time reverse transcription(RT)PCR(qPCR)analysis largely ignores the sequence similarities between sequences of homologous genes in a plant genome.It can lead to false...Computational tool-assisted primer design for real-time reverse transcription(RT)PCR(qPCR)analysis largely ignores the sequence similarities between sequences of homologous genes in a plant genome.It can lead to false confidence in the quality of the designed primers,which sometimes results in skipping the optimization steps for qPCR.However,the optimization of qPCR parameters plays an essential role in the efficiency,specificity,and sensitivity of each gene’s primers.Here,we proposed an optimized approach to sequentially optimizing primer sequences,annealing temperatures,primer concentrations,and cDNA concentration range for each reference(and target)gene.Our approach started with a sequence-specific primer design that should be based on the single-nucleotide polymorphisms(SNPs)present in all the homologous sequences for each of the reference(and target)genes under study.By combining the efficiency calibrated and standard curve methods with the 2−ΔΔCt method,the standard cDNA concentration curve with a logarithmic scale was obtained for each primer pair for each gene.As a result,an R 2≥0.9999 and the efficiency(E)=100±5% should be achieved for the best primer pair of each gene,which serve as the prerequisite for using the 2^(−ΔΔCt) method for data analysis.We applied our newly developed approach to identify the best reference genes in different tissues and at various inflorescence developmental stages of Tripidium ravennae,an ornamental and biomass grass,and validated their utility under varying abiotic stress conditions.We also applied this approach to test the expression stability of six reference genes in soybean under biotic stress treatment with Xanthomonas axonopodis pv.glycines(Xag).Thus,these case studies demonstrated the effectiveness of our optimized protocol for qPCR analysis.展开更多
Traditionally,numerical trajectory integration for shooting equation calculation and iterations for shooting with randomly guessed initial solutions deteriorate the real-time performance of indirect methods for on-boa...Traditionally,numerical trajectory integration for shooting equation calculation and iterations for shooting with randomly guessed initial solutions deteriorate the real-time performance of indirect methods for on-board applications.In this study,the indirect method is improved to achieve real-time trajectory optimization of fuel-optimal powered planetary landings with the help of analytical shooting equation derivations and a practical homotopy technique.Specifically,the contributions of this paper are threefold.First,the analytical expressions for shooting equation calculation are derived to replace the traditional time-consuming trajectory integration.Consequently,the computational efficiency is significantly improved.Second,the original three-dimensional landing problem is connected with a simplified one-dimensional problem that only involves the vertical dynamics,and its analytical solution is obtained based on Pontryagin’s minimum principle.Third,starting with the analytical solution,the accurate solution of the original landing problem can be obtained through an adaptive homotopy process.Simulation results of Earth landing scenarios are given to substantiate the effectiveness of the proposed techniques and illustrate that the developed method can obtain a fuel-optimal landing trajectory in 5 ms with 100%success rate.展开更多
In petroleum engineering,real-time lithology identification is very important for reservoir evaluation,drilling decisions and petroleum geological exploration.A lithology identification method while drilling based on ...In petroleum engineering,real-time lithology identification is very important for reservoir evaluation,drilling decisions and petroleum geological exploration.A lithology identification method while drilling based on machine learning and mud logging data is studied in this paper.This method can effectively utilize downhole parameters collected in real-time during drilling,to identify lithology in real-time and provide a reference for optimization of drilling parameters.Given the imbalance of lithology samples,the synthetic minority over-sampling technique(SMOTE)and Tomek link were used to balance the sample number of five lithologies.Meanwhile,this paper introduces Tent map,random opposition-based learning and dynamic perceived probability to the original crow search algorithm(CSA),and establishes an improved crow search algorithm(ICSA).In this paper,ICSA is used to optimize the hyperparameter combination of random forest(RF),extremely random trees(ET),extreme gradient boosting(XGB),and light gradient boosting machine(LGBM)models.In addition,this study combines the recognition advantages of the four models.The accuracy of lithology identification by the weighted average probability model reaches 0.877.The study of this paper realizes high-precision real-time lithology identification method,which can provide lithology reference for the drilling process.展开更多
This paper concerns the problem of object segmentation in real-time for picking system. A region proposal method inspired by human glance based on the convolutional neural network is proposed to select promising regio...This paper concerns the problem of object segmentation in real-time for picking system. A region proposal method inspired by human glance based on the convolutional neural network is proposed to select promising regions, allowing more processing is reserved only for these regions. The speed of object segmentation is significantly improved by the region proposal method.By the combination of the region proposal method based on the convolutional neural network and superpixel method, the category and location information can be used to segment objects and image redundancy is significantly reduced. The processing time is reduced considerably by this to achieve the real time. Experiments show that the proposed method can segment the interested target object in real time on an ordinary laptop.展开更多
Currently,most of the policies for the dynamic demand vehicle routing problem are based on the traditional method for static problems as there is no general method for constructing a real-time optimization policy for ...Currently,most of the policies for the dynamic demand vehicle routing problem are based on the traditional method for static problems as there is no general method for constructing a real-time optimization policy for the case of dynamic demand.Here,a new approach based on a combination of the rules from the static sub-problem to building real-time optimization policy is proposed.Real-time optimization policy is dividing the dynamic problem into a series of static sub-problems along the time axis and then solving the static ones.The static sub-problems’transformation and solution rules include:Division rule,batch rule,objective rule,action rule and algorithm rule,and so on.Different combinations of these rules may constitute a variety of real-time optimization policy.According to this general method,two new policies called flexible G/G/m and flexible D/G/m were developed.The competitive analysis and the simulation results of these two policies proved that both are improvements upon the best existing policy.展开更多
We propose a multi-objective Pareto-optimal technique using Genetic Algorithm (GA) for group communication, which determines a min-cost multicast tree satisfying end-to-end delay, jitter, packet loss rate and blocking...We propose a multi-objective Pareto-optimal technique using Genetic Algorithm (GA) for group communication, which determines a min-cost multicast tree satisfying end-to-end delay, jitter, packet loss rate and blocking probability constraints. The model incorporates a fuzzy-based selection technique for initialization of QoS parameter values at each instance of multicasting. The simulation results show that the proposed algorithm satisfies on-demand QoS requirements (like high availability, good load balancing and fault-tolerance) made by the hosts in varying topology and bursty data traffic in multimedia communication networks.展开更多
基金supported by the National Natural Science Foundation(NNSF)of China(No.61973053).
文摘This paper presents a real-time energy optimization algorithm for a hybrid electric vehicle(HEV)that operates with adaptive cruise control(ACC).Real-time energy optimization is an essential ssue such that the HEV powertrain system is as efficient as possible.With connected vehice technique,ACC system shows considerable potential of high energy eficiency.Combining a classical ACC algorithm,a two-level cooperative control scheme is constructed to realize real-time power distribution for the host HEV that operates in a vehicle platoon.The proposed control strategy actually provides a solution for an optimal control problem with multi objectives in terms of string stable of vehicle platoon and energy consumption minimization of the individual following vehicle.The string stability and the real-time optimization performance of the cooperative control system are confirmed by simulations with respect to several operating scenarios.
基金supported by National Key Research & Development Program-Intergovernmental International Science and Technology Innovation Cooperation Project (2021YFE0112800)National Natural Science Foundation of China (Key Program: 62136003)+2 种基金National Natural Science Foundation of China (62073142)Fundamental Research Funds for the Central Universities (222202417006)Shanghai Al Lab
文摘The gasoline inline blending process has widely used real-time optimization techniques to achieve optimization objectives,such as minimizing the cost of production.However,the effectiveness of real-time optimization in gasoline blending relies on accurate blending models and is challenged by stochastic disturbances.Thus,we propose a real-time optimization algorithm based on the soft actor-critic(SAC)deep reinforcement learning strategy to optimize gasoline blending without relying on a single blending model and to be robust against disturbances.Our approach constructs the environment using nonlinear blending models and feedstocks with disturbances.The algorithm incorporates the Lagrange multiplier and path constraints in reward design to manage sparse product constraints.Carefully abstracted states facilitate algorithm convergence,and the normalized action vector in each optimization period allows the agent to generalize to some extent across different target production scenarios.Through these well-designed components,the algorithm based on the SAC outperforms real-time optimization methods based on either nonlinear or linear programming.It even demonstrates comparable performance with the time-horizon based real-time optimization method,which requires knowledge of uncertainty models,confirming its capability to handle uncertainty without accurate models.Our simulation illustrates a promising approach to free real-time optimization of the gasoline blending process from uncertainty models that are difficult to acquire in practice.
文摘In this paper, a novel approach termed process goose queue (PGQ) is suggested to deal with real-time optimization (RTO) of chemical plants. Taking advantage of the ad-hoc structure of PGQ which imitates biologic nature of flying wild geese, a chemical plant optimization problem can be re-formulated as a combination of a multi-layer PGQ and a PGQ-Objective according to the relationship among process variables involved in the objective and constraints. Subsequently, chemical plant RTO solutions are converted into coordination issues among PGQs which could be dealt with in a novel way. Accordingly, theoretical definitions, adjustment rule and implementing procedures associated with the approach are explicitly introduced together with corresponding enabling algorithms. Finally, an exemplary chemical plant is employed to demonstrate the feasibility and validity of the contribution.
基金supported in part by the National Natural Science Foundation of China (Nos. 51906102 and 52176009)the National Science and Technology Major Project, China (Nos. J2019-II-0009-0053, J2019-I-0020-0019 and 2019III-0014-0058)+2 种基金the Innovation Centre for Advanced Aviation Power, China (Nos. HKCX2020-02-022 and HKCX2020-02-027)the Research on the Basic Problem of Intelligent Aero-engine, China (No. 2017-JCJQZD-047-21)the Fundamental Research Funds for the Central Universities, China (No. NZ2020002)
文摘To reduce the propulsion system installation thrust loss under high angle of attack maneuvering,a control method based on real-time optimization of the integrated aeropropulsion is proposed.Firstly,based on data fitting and physical principle,an integrated onboard model of propulsion system is established,which can calculate various performance parameters of the propulsion system in real time,and has high accuracy and real-time performance.Secondly,to improve the compatibility of optimization real-time performance and search accuracy,the online optimization control of aero-propulsion system is realized based on an improved trust region algorithm.Finally,by controlling the auxiliary intake valve,a good match between inlet and engine is realized,which solves the problems of intake flow reducing and total pressure recovery coefficient declining,and improves the installation performance of propulsion system.The simulation results indicate that,compared with the conventional independent engine control,the real-time integrated optimization method reduces the installed thrust loss by 3.61%under the design condition,and 4.58%under the off-design condition.Furthermore,the simulation on HIL(Hardware-In-theLoop)platform verifies the real-time performance of integrated optimization method.
基金This work was supported by the Science and Technology Development Project of SINOPEC,China(No.319026).
文摘An approach for the simulation and optimization of continuous catalyst-regenerative process of reforming is proposed in this paper.Compared to traditional method such as finite difference method,the orthogonal collocation method is less time-consuming and more accurate,which can meet the requirement of real-time optimization(RTO).In this paper,the equation-oriented method combined with the orthogonal collocation method and the finite difference method is adopted to build the RTO model for catalytic reforming regenerator.The orthogonal collocation method was adopted to discretize the differential equations and sequential quadratic programming(SQP)algorithm was used to solve the algebraic equations.The rate constants,active energy and reaction order were estimated,with the sum of relative errors between actual value and simulated value serving as optimization objective function.The model can quickly predict the fields of component concentration,temperature and pressure inside the regenerator under different conditions,as well as the real-time optimized conditions for industrial reforming regenerator.
基金the project“Research on Power SafetyDecision Support SystemBased on Large Language Models”(Science and Technology Project of Huaian Hongneng Group Co.,Ltd.)under Contract No.SGTYHT/23-JS-001.
文摘The rapid proliferation of renewable energy integration and escalating grid operational complexity have intensified demands for resilient self-healing mechanisms in modern power systems.Conventional approaches relying on static models and heuristic rules exhibit limitations in addressing dynamic fault propagation and multimodal data fusion.This study proposes a Transformer-enhanced intelligent microgrid self-healing framework that synergizes large languagemodels(LLMs)with adaptive optimization,achieving three key innovations:(1)Ahierarchical attention mechanism incorporating grid impedance characteristics for spatiotemporal feature extraction,(2)Dynamic covariance estimation Kalman filtering with wavelet packet energy entropy thresholds(Daubechies-4 basis,6-level decomposition),and(3)A grouping-stratified ant colony optimization algorithm featuring penalty-based pheromone updating.Validated on IEEE 33/100-node systems,our framework demonstrates 96.7%fault localization accuracy(23%improvement over STGCN)and 0.82-s protection delay,outperforming MILP-basedmethods by 37%in reconfiguration speed.The system maintains 98.4%self-healing success rate under cascading faults,resolving 89.3%of phase-toground faults within 500 ms through adaptive impedance matching.Field tests on 220 kV substations with 45%renewable penetration show 99.1%voltage stability(±5%deviation threshold)and 40%communication efficiency gains via compressed GOOSE message parsing.Comparative analysis reveals 12.6×faster convergence than conventional ACO in 1000-node networks,with 95.2%robustness against±25%load fluctuations.These advancements provide a scalable solution for real-time fault recovery in renewable-dense grids,reducing outage duration by 63%inmulti-agent simulations compared to centralized architectures.
基金This work is supported by the National Natural Science Foundation of China under Grant 52274057,52074340 and 51874335the Major Scientific and Technological Projects of CNPC under Grant ZD2019-183-008the Science and Technology Support Plan for Youth Innovation of University in Shandong Province under Grant 2019KJH002,111 Project under Grant B08028.
文摘Production optimization has gained increasing attention from the smart oilfield community because it can increase economic benefits and oil recovery substantially.While existing methods could produce high-optimality results,they cannot be applied to real-time optimization for large-scale reservoirs due to high computational demands.In addition,most methods generally assume that the reservoir model is deterministic and ignore the uncertainty of the subsurface environment,making the obtained scheme unreliable for practical deployment.In this work,an efficient and robust method,namely evolutionaryassisted reinforcement learning(EARL),is proposed to achieve real-time production optimization under uncertainty.Specifically,the production optimization problem is modeled as a Markov decision process in which a reinforcement learning agent interacts with the reservoir simulator to train a control policy that maximizes the specified goals.To deal with the problems of brittle convergence properties and lack of efficient exploration strategies of reinforcement learning approaches,a population-based evolutionary algorithm is introduced to assist the training of agents,which provides diverse exploration experiences and promotes stability and robustness due to its inherent redundancy.Compared with prior methods that only optimize a solution for a particular scenario,the proposed approach trains a policy that can adapt to uncertain environments and make real-time decisions to cope with unknown changes.The trained policy,represented by a deep convolutional neural network,can adaptively adjust the well controls based on different reservoir states.Simulation results on two reservoir models show that the proposed approach not only outperforms the RL and EA methods in terms of optimization efficiency but also has strong robustness and real-time decision capacity.
基金the financial support of Ministry of Research Technology and Higher Education Republic of Indonesia with contract number:095/K3/KM/2015.
文摘Purpose-The production of glycerol derivatives by the esterification process is subject to many constraints related to the yield of the production target and the lack of process efficiency.An accurate monitoring and controlling of the process can improve production yield and efficiency.The purpose of this paper is to propose a real-time optimization(RTO)using gradient adaptive selection and classification from infrared sensor measurement to cover various disturbances and uncertainties in the reactor.Design/methodology/approach-The integration of the esterification process optimization using self-optimization(SO)was developed with classification process was combined with necessary condition optimum(NCO)as gradient adaptive selection,supported with laboratory scaled medium wavelength infrared(mid-IR)sensors,and measured the proposed optimization system indicator in the batch process.Business Process Modeling and Notation(BPMN 2.0)was built to describe the tasks of SO workflow in collaboration with NCO as an abstraction for the conceptual phase.Next,Stateflow modeling was deployed to simulate the three states of gradient-based adaptive control combined with support vector machine(SVM)classification and Arduino microcontroller for implementation.Findings-This new method shows that the real-time optimization responsiveness of control increased product yield up to 13 percent,lower error measurement with percentage error 1.11 percent,reduced the process duration up to 22 minutes,with an effective range of stirrer rotation set between 300 and 400 rpm and final temperature between 200 and 210℃ which was more efficient,as it consumed less energy.Research limitations/implications-In this research the authors just have an experiment for the esterification process using glycerol,but as a development concept of RTO,it would be possible to apply for another chemical reaction or system.Practical implications-This research introduces new development of an RTO approach to optimal control and as such marks the starting point for more research of its properties.As the methodology is generic,it can be applied to different optimization problems for a batch system in chemical industries.Originality/value-The paper presented is original as it presents the first application of adaptive selection based on the gradient value of mid-IR sensor data,applied to the real-time determining control state by classification with the SVM algorithm for esterification process control to increase the efficiency.
基金Supported by the National Natural Science Foundation of China(12071133)Natural Science Foundation of Henan Province(252300421993)Key Scientific Research Project of Higher Education Institutions in Henan Province(25B110005)。
文摘In this paper,an adaptive cubic regularisation algorithm based on affine scaling methods(ARCBASM)is proposed for solving nonlinear equality constrained programming with nonnegative constraints on variables.From the optimality conditions of the problem,we introduce appropriate affine matrix and construct an affine scaling ARC subproblem with linearized constraints.Composite step methods and reduced Hessian methods are applied to tackle the linearized constraints.As a result,a standard unconstrained ARC subproblem is deduced and its solution can supply sufficient decrease.The fraction to the boundary rule maintains the strict feasibility(for nonnegative constraints on variables)of every iteration point.Reflection techniques are employed to prevent the iterations from approaching zero too early.Under mild assumptions,global convergence of the algorithm is analysed.Preliminary numerical results are reported.
基金Supported by National Key Research and Development Program of China(Grant No.2019YFB1309900)Institute for Guo Qiang,Tsinghua University of China(Grant No.2019GQG0007).
文摘Real-time interaction with uncertain and dynamic environments is essential for robotic systems to achieve functions such as visual perception,force interaction,spatial obstacle avoidance,and motion planning.To ensure the reliability and determinism of system execution,a flexible real-time control system architecture and interaction algorithm are required.The ROS framework was designed to improve the reusability of robotic software development by providing a distributed structure,hardware abstraction,message-passing mechanism,and application prototypes.Rich ecosystems for robotic development have been built around ROS1 and ROS2 architectures based on the Linux system.However,because of the fairness scheduling principle of the default Linux system design and the complexity of the kernel,the system does not have real-time computing.To achieve a balance between real-time and non-real-time computing,this paper uses the transmission mechanism of ROS2,combines it with the scheduling mechanism of the Linux operating system,and uses Preempt_RT to enhance the real-time computing of ROS1 and ROS2.The real-time performance evaluation of ROS1 and ROS2 is conducted from multiple perspectives,including throughput,transmission mode,QoS service quality,frequency,number of subscription nodes and EtherCAT master.This paper makes two significant contributions:firstly,it employs Preempt_RT to optimize the native ROS2 system,effectively enhancing the real-time performance of native ROS2 message transmission;secondly,it conducts a comprehensive evaluation of the real-time performance of both native and optimized ROS2 systems.This comparison elucidates the benefits of the optimized ROS2 architecture regarding real-time performance,with results vividly demonstrated through illustrative figures.
基金supported by the Israeli Ministry of Infrastructure,Energy and Water Resources.
文摘This study introduces a real-time data-driven battery management scheme designed to address uncertainties in load and generation forecasts,which are integral to an optimal energy storage control system.By expanding on an existing algorithm,this study resolves issues discovered during implementation and addresses previously overlooked concerns,resulting in significant enhancements in both performance and reliability.The refined real-time control scheme is integrated with a day-ahead optimization engine and forecast model,which is utilized for illustrative simulations to highlight its potential efficacy on a real site.Furthermore,a comprehensive comparison with the original formulation was conducted to cover all possible scenarios.This analysis validated the operational effectiveness of the scheme and provided a detailed evaluation of the improvements and expected behavior of the control system.Incorrect or improper adjustments to mitigate forecast uncertainties can result in suboptimal energy management,significant financial losses and penalties,and potential contract violations.The revised algorithm optimizes the operation of the battery system in real time and safeguards its state of health by limiting the charging/discharging cycles and enforcing adherence to contractual agreements.These advancements yield a reliable and efficient real-time correction algorithm for optimal site management,designed as an independent white box that can be integrated with any day-ahead optimization control system.
文摘Energy management strategies based on optimal control theory can achieve minimum fuel consumption for hybrid electric vehicles, but the requirement for driving cycles known in prior leads to a real-time problem. A real-time optimization power-split strategy is proposed based on linear quadratic optimal control. The battery state of charge sustainability and fuel economy are ensured by designing a quadratic performance index combined with two rules. The engine power and motor power of this strategy are calculated in real-time based on current system state and command, and not related to future driving conditions. The simulation results in ADVISOR demonstrate that, under the conditions of various driving cycles, road slopes and vehicle parameters, the proposed strategy significantly improves fuel economy, which is very close to that of the optimal control based on Pontryagin's minimum principle, and greatly reduces computation complexity.
文摘The application and development of a wide-area measurement system(WAMS)has enabled many applications and led to several requirements based on dynamic measurement data.Such data are transmitted as big data information flow.To ensure effective transmission of wide-frequency electrical information by the communication protocol of a WAMS,this study performs real-time traffic monitoring and analysis of the data network of a power information system,and establishes corresponding network optimization strategies to solve existing transmission problems.This study utilizes the traffic analysis results obtained using the current real-time dynamic monitoring system to design an optimization strategy,covering the optimization in three progressive levels:the underlying communication protocol,source data,and transmission process.Optimization of the system structure and scheduling optimization of data information are validated to be feasible and practical via tests.
文摘In order to get a globally optimized solution for the Elevator Group Control System (EGCS) scheduling problem, an algorithm with an overall optimization function is needed. In this study, Real-time Particle Swarm Optimization (RPSO) is proposed to find an optimal solution to the EGCS scheduling problem. Different traffic patterns and controller mechanisms for EGCS are analyzed. This study focuses on up-peak traffic because of its critical importance to modern office buildings. Simulation results show that EGCS based on Multi-Agent Systems (MAS) using RPSO gives good results for up-peak EGCS scheduling problem. Besides, the elevator real-time scheduling and reallocation functions are realized based on RPSO in case new information is available or the elevator becomes busy because it is unavailable or full. This study contributes a new scheduling algorithm for EGCS, and expands the application of PSO.
基金The authors thank the USDA National Institute of Food and Agriculture Hatch project 02685 and North Carolina State University for the startup funds to the Liu laboratorythe NSFC fund 31871646 to the Zhao laboratory。
文摘Computational tool-assisted primer design for real-time reverse transcription(RT)PCR(qPCR)analysis largely ignores the sequence similarities between sequences of homologous genes in a plant genome.It can lead to false confidence in the quality of the designed primers,which sometimes results in skipping the optimization steps for qPCR.However,the optimization of qPCR parameters plays an essential role in the efficiency,specificity,and sensitivity of each gene’s primers.Here,we proposed an optimized approach to sequentially optimizing primer sequences,annealing temperatures,primer concentrations,and cDNA concentration range for each reference(and target)gene.Our approach started with a sequence-specific primer design that should be based on the single-nucleotide polymorphisms(SNPs)present in all the homologous sequences for each of the reference(and target)genes under study.By combining the efficiency calibrated and standard curve methods with the 2−ΔΔCt method,the standard cDNA concentration curve with a logarithmic scale was obtained for each primer pair for each gene.As a result,an R 2≥0.9999 and the efficiency(E)=100±5% should be achieved for the best primer pair of each gene,which serve as the prerequisite for using the 2^(−ΔΔCt) method for data analysis.We applied our newly developed approach to identify the best reference genes in different tissues and at various inflorescence developmental stages of Tripidium ravennae,an ornamental and biomass grass,and validated their utility under varying abiotic stress conditions.We also applied this approach to test the expression stability of six reference genes in soybean under biotic stress treatment with Xanthomonas axonopodis pv.glycines(Xag).Thus,these case studies demonstrated the effectiveness of our optimized protocol for qPCR analysis.
基金supported by the National Natural Science Foundation of China(Nos.11872223,11902174,11772167)。
文摘Traditionally,numerical trajectory integration for shooting equation calculation and iterations for shooting with randomly guessed initial solutions deteriorate the real-time performance of indirect methods for on-board applications.In this study,the indirect method is improved to achieve real-time trajectory optimization of fuel-optimal powered planetary landings with the help of analytical shooting equation derivations and a practical homotopy technique.Specifically,the contributions of this paper are threefold.First,the analytical expressions for shooting equation calculation are derived to replace the traditional time-consuming trajectory integration.Consequently,the computational efficiency is significantly improved.Second,the original three-dimensional landing problem is connected with a simplified one-dimensional problem that only involves the vertical dynamics,and its analytical solution is obtained based on Pontryagin’s minimum principle.Third,starting with the analytical solution,the accurate solution of the original landing problem can be obtained through an adaptive homotopy process.Simulation results of Earth landing scenarios are given to substantiate the effectiveness of the proposed techniques and illustrate that the developed method can obtain a fuel-optimal landing trajectory in 5 ms with 100%success rate.
基金supported by CNPC-CZU Innovation Alliancesupported by the Program of Polar Drilling Environmental Protection and Waste Treatment Technology (2022YFC2806403)。
文摘In petroleum engineering,real-time lithology identification is very important for reservoir evaluation,drilling decisions and petroleum geological exploration.A lithology identification method while drilling based on machine learning and mud logging data is studied in this paper.This method can effectively utilize downhole parameters collected in real-time during drilling,to identify lithology in real-time and provide a reference for optimization of drilling parameters.Given the imbalance of lithology samples,the synthetic minority over-sampling technique(SMOTE)and Tomek link were used to balance the sample number of five lithologies.Meanwhile,this paper introduces Tent map,random opposition-based learning and dynamic perceived probability to the original crow search algorithm(CSA),and establishes an improved crow search algorithm(ICSA).In this paper,ICSA is used to optimize the hyperparameter combination of random forest(RF),extremely random trees(ET),extreme gradient boosting(XGB),and light gradient boosting machine(LGBM)models.In addition,this study combines the recognition advantages of the four models.The accuracy of lithology identification by the weighted average probability model reaches 0.877.The study of this paper realizes high-precision real-time lithology identification method,which can provide lithology reference for the drilling process.
基金supported by the National Natural Science Foundation of China(61233010 61305106)+2 种基金the Shanghai Natural Science Foundation(17ZR1409700 18ZR1415300)the basic research project of Shanghai Municipal Science and Technology Commission(16JC1400900)
文摘This paper concerns the problem of object segmentation in real-time for picking system. A region proposal method inspired by human glance based on the convolutional neural network is proposed to select promising regions, allowing more processing is reserved only for these regions. The speed of object segmentation is significantly improved by the region proposal method.By the combination of the region proposal method based on the convolutional neural network and superpixel method, the category and location information can be used to segment objects and image redundancy is significantly reduced. The processing time is reduced considerably by this to achieve the real time. Experiments show that the proposed method can segment the interested target object in real time on an ordinary laptop.
基金Supported by the National Natural Science Foundation of China(71461006,71461007,71761009)Hainan Province Planning Program of Philosophy and Social Science(HNSK(YB)19-06,HNSK(YB)19-11)a Key Program of Hainan Educational Committee(hnky2019ZD-10).
文摘Currently,most of the policies for the dynamic demand vehicle routing problem are based on the traditional method for static problems as there is no general method for constructing a real-time optimization policy for the case of dynamic demand.Here,a new approach based on a combination of the rules from the static sub-problem to building real-time optimization policy is proposed.Real-time optimization policy is dividing the dynamic problem into a series of static sub-problems along the time axis and then solving the static ones.The static sub-problems’transformation and solution rules include:Division rule,batch rule,objective rule,action rule and algorithm rule,and so on.Different combinations of these rules may constitute a variety of real-time optimization policy.According to this general method,two new policies called flexible G/G/m and flexible D/G/m were developed.The competitive analysis and the simulation results of these two policies proved that both are improvements upon the best existing policy.
文摘We propose a multi-objective Pareto-optimal technique using Genetic Algorithm (GA) for group communication, which determines a min-cost multicast tree satisfying end-to-end delay, jitter, packet loss rate and blocking probability constraints. The model incorporates a fuzzy-based selection technique for initialization of QoS parameter values at each instance of multicasting. The simulation results show that the proposed algorithm satisfies on-demand QoS requirements (like high availability, good load balancing and fault-tolerance) made by the hosts in varying topology and bursty data traffic in multimedia communication networks.
