A deep reinforcement learning approach to gasoline blending real-time optimization under uncertainty

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.

A Novel Real-time Optimization Methodology for Chemical Plants

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.

Real-Time Optimization Model for Continuous Reforming Regenerator

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.

A Real-time Lithological Identification Method based on SMOTE-Tomek and ICSA Optimization

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.

Real-time optimization of energy consumption under adaptive cruise control for connected HEVs

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.

Evolutionary-assisted reinforcement learning for reservoir real-time production optimization under uncertainty

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.

Maneuver control at high angle of attack based on real-time optimization of integrated aero-propulsion

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.

Real-time optimization using gradient adaptive selection and classification from infrared sensors measurement for esterification oleic acid with glycerol

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.

ROS2 Real-time Performance Optimization and Evaluation

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.

Real-time optimization power-split strategy for hybrid electric vehicles

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.

Data network traffic analysis and optimization strategy of real-time power grid dynamic monitoring system for wide-frequency measurements

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.

Multi-objective Optimization of Multi-Agent Elevator Group Control System Based on Real-time Particle Swarm Optimization Algorithm

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.

An optimized protocol for stepwise optimization of real-time RT-PCR analysis

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.

General Method of Building a Real-Time Optimization Policy for Dynamic Vehicle Routing Problem

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.

Real-time object segmentation based on convolutional neural network with saliency optimization for picking

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.

A Multi-objective QoS Optimization with Fuzzy Based Parameter Setting for Real-Time Multicasting

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.

Optimization of Quantitative Real-time PCR System on Amplification of Beta-glucosidase Gene Os1bglu4

[Objective] This study aimed to establish a quantitative real-time PCR （qRT-PCR） system for detecting the expression of rice beta-glucosidase gene Os1bglu4.[Method] The PCR was conducted with SYBR Green Ⅰ method,using the primers of reference gene actin or ubiquitin.[Result] Actin was more suitable to be the reference gene than ubiquitin.More accurate results were obtained when the 100 ng cDNA template was added at a large volume and a lower concentration.The primer concentration in the range from 0.2 to 0.8 μmol/L we set had no significant influence on the results,so,0.4 μmol/L was selected as the optimal primer concentration in this study.The amplification efficiency was greatly reduced when the annealing temperature was set at 64 ℃,therefore,annealing temperature was set at 60 ℃.Compared with the reaction system of 25 μl,the fluorescence intensity was significantly lower but the CT value did not change greatly in 10 μl system.So,the 10 μl reaction system was selected,which significantly reduces the research costs for the detection of a large amount of samples in future study.

Conception of a Web Operation System for Processing Petroleum Related Drilling Data: A Focus on Pre-Salt Real-Time Automation and Optimization

Petroleum and Natural Gas still represent a considerable share in terms of energy consumption in the current global matrix, so that its exploration/exploitation is present in the market and driving activities in locations of specific complexities, as the ones along unconventional hydrocarbon resources from the Brazilian pre-salt. The daily cost of well drilling under harsh conditions can exceed US $1 million a day, turning any type of downtime or necessary maintenance during the activities to be very costly, moment in which processes optimization starts to be a key factor in costs reduction. Thus, new technologies and methods in terms of automating and optimizing the processes may be of great advantages, having its impact in total related project costs. In this context, the goal of this research is to allow a computation tool supporting achieving a more efficient drilling process, by means of drilling mechanics parameters choosiness aiming rate of penetration (ROP) maximization and mechanic specific energy (MSE) minimization. Conceptually, driven by the pre-operational drilling test curve trends, the proposed system allows it to be performed with less human influences and being updateable automatically, allowing more precision and time reduction by selecting optimum parameters. A Web Operating System (Web OS) was designed and implemented, running in online servers, granting accessibility to it with any device that has a browser and internet connection. It allows processing the drilling parameters supplied and feed into it, issuing outcomes with optimum values in a faster and precise way, allowing reducing operating time.

Real-Time Memory Data Optimization Mechanism of Edge IoT Agent

With the full development of disk-resident databases(DRDB)in recent years,it is widely used in business and transactional applications.In long-term use,some problems of disk databases are gradually exposed.For applications with high real-time requirements,the performance of using disk database is not satisfactory.In the context of the booming development of the Internet of things,domestic real-time databases have also gradually developed.Still,most of them only support the storage,processing,and analysis of data values with fewer data types,which can not fully meet the current industrial process control system data types,complex sources,fast update speed,and other needs.Facing the business needs of efficient data collection and storage of the Internet of things,this paper optimizes the transaction processing efficiency and data storage performance of the memory database,constructs a lightweight real-time memory database transaction processing and data storage model,realizes a lightweight real-time memory database transaction processing and data storage model,and improves the reliability and efficiency of the database.Through simulation,we proved that the cache hit rate of the cache replacement algorithm proposed in this paper is higher than the traditional LRU(Least Recently Used)algorithm.Using the cache replacement algorithm proposed in this paper can improve the performance of the system cache.

Eureka Webair： Web-Based Air Quality Management, Real-Time Forecasting and Emission Control Optimization

For the assessment and management of regional to local air quality, an integrated environmental management information system was built within the multi national Eureka project 3266 Webair, http：//www.ess.co.at/WEBAIR. The system combines data bases and GIS and a range of coupled models and analytical tools that address a range of typical management problems and cover several levels of nesting from regional to city level and street canyons. The main functions are to support regulatory tasks, compliance monitoring, operational forecasting and reporting, impact assessment EIA （environmental impact assessment）, SEA （strategic environmental assessment） and public information within one consistent framework. A major objective is the improvement of air quality through emission control. The integrated model system together with its shared data bases provides a reliable, consistent basis for the non-linear techno-economic and multi-criteria optimization of emission control strategies （including greenhouse gases and energy efficiency）. A real-time expert system drives, supports and monitors the autonomous and interactive operations, and provides embedded QA/QC （quality assurance/quality control） functions for reliable operations and ease of use.