Real-time Optimization Algorithm Research Articles

Optimization of Injection-Production Relationship of Offshore Unconsolidated Sandstone Reservoirs Based on Inter-well Connectivity

Abstract The dynamic heterogeneity of water drive sandstone reservoir in high water cut stage is strong, which leads to the differential enrichment and distribution of remaining oil. The adjustment of injection-production relationship can effectively guide the work of stabilizing oil and controlling water. Based on the principle of inter-well connectivity, this paper comprehensively considers the geological characteristics and development dynamics, and constructs the multi-layer reservoir joint well network inter-well connectivity model. By integrating the automatic history matching and the real-time injection-production optimization algorithms, the accurate inversion of connectivity parameters and real-time optimization prediction of production dynamics are realized. Field applications in the block X demonstrated that this method resulted in an annual increase in oil production of 16,000 cubic meters and a reduction in water cut by 0.87%, showing significant enhancement in oil recovery and water control. This method can be applied to guide the adjustment of the planar injection-production structure in similar reservoirs.

Robust Switching Time Optimization for Networked Switched Systems via Model Predictive Control.

This article presents a model predictive control (MPC) strategy to find the optimal switching time sequences of networked switched systems with uncertainties. First, based on predicted trajectories under exact discretization, a large-scale MPC problem is formulated; second, a two-level hierarchical optimization structure coupled with a local compensation mechanism is established to solve the formulated MPC problem, where the proposed hierarchical optimization structure is actually a recurrent neural network consisting of a coordination unit (CU) at the upper level and a series of local optimization units (LOUs) related to each subsystem at the lower level. Finally, a real-time switching time optimization algorithm is designed to calculate the optimal switching time sequences.

Research on Multi-type and Multi-level Real-time Simulation and Optimization of New Power System

Abstract Aiming at the problem that the current real-time simulation algorithm is difficult to improve the performance and accuracy of multi-type and multi-level real-time simulation when dealing with new power systems, we propose a new multi-type and multi-level real-time simulation and optimization algorithm for power systems. Firstly, the efficiency and accuracy of the solution are improved by directly updating the conductance nodes (Gnodes) and current nodes (Inodes) on power system components, and the updating of Gnodes and Inodes is replaced by the prior calculation based on the inverse matrix (Ginv) of admittance matrix, thus further optimizing the simulation solution process and reducing the computational complexity. Then, the efficient numerical method, data structure optimization, and parallel computing technology are used to optimize the construction process of the inverse matrix (Ginv) of the admittance matrix to reduce the computational complexity and memory consumption of electromagnetic transient simulation. Finally, the idea of the sparse matrix is introduced to optimize the operation process of Gstruct and improve the calculation speed of the simulation solution. The research proves that the multi-type and multi-level real-time simulation solution and its optimization method of new power systems can be studied more systematically through the above technical route, which can improve the efficiency and accuracy of power system simulation solutions and provide strong support for practical application.

Social Elastic Band with Prediction and Anticipation: Enhancing Real-Time Path Trajectory Optimization for Socially Aware Robot Navigation

AbstractAs social robots are projected to become an integral part of human life in the coming decades, their ability to adapt movement and trajectory when in proximity to people is essential for ensuring social acceptance during human-robot interaction. A key aspect of this adaptability involves predicting and anticipating human intents during robot navigation. Despite significant strides in the social navigation of autonomous robots within human environments, opportunities for advancements in related algorithms persist. This paper presents a novel real-time path trajectory optimization algorithm for socially aware robot navigation, grounded in the social elastic band concept, incorporating prediction and anticipation of human movements to adapt its forward velocity. Building upon the elastic band framework introduced in the 1990s for adapting robot trajectories in dynamic environments, our proposal of social elastic band differentiates between objects and human presence. This distinction allows for the definition of social interaction spaces and their relationship to the elastic band, facilitating the generation of socially accepted paths that rapidly adapt to environmental changes without causing a disturbance. Integrated into the SNAPE social navigation framework, the algorithm has been tested and validated through simulations and real-world experiments in various environments.

Comprehensive Travel Companion: A Hybrid Recommendation and Route Planning System for Globetrotting

This paper presents a comprehensive AI-powered travel companion system designed to enhance the travel experience for globetrotters. Leveraging cutting-edge artificial intelligence (AI) techniques, the system combines image recognition, recommendation algorithms, and route planning functionality to provide personalized travel recommendations and seamless navigation. The integration of collaborative filtering, content-based recommendation, and real-time route optimization algorithms ensures that users receive tailored suggestions and efficient travel routes based on their preferences, interests, and current context. The architecture, implementation details, and evaluation of the proposed system are discussed, highlighting its effectiveness in facilitating immersive and enjoyable travel experiences. Keywords: Travel Companion, Recommendation Systems, Route Planning, Image Recognition, Collaborative Filtering, Content-Based Recommendation, Dijkstra's Algorithm.

Efficiency Comparison CRAFT vs. SLP Methods in Production Optimization

The research conducted at PT. Carma Wira Jatim aimed to optimize the production area layout due to significant challenges stemming from machine delays and material handling inefficiencies. Through systematic layout planning (SLP) and computerized relative allocation of facilities technique (CRAFT), the study proposed improved layouts using Win QSB software. The results indicated differing efficiencies between the CRAFT and SLP methods, with CRAFT demonstrating a 4.5% reduction in total material handling moments compared to the initial layout, suggesting its superiority in this context. These findings underscore the importance of efficient layout design in mitigating production delays and enhancing operational effectiveness. Further research could explore real-time optimization algorithms to adapt layouts dynamically and investigate the impact of layout improvements on overall productivity and resource utilization. Highlight: Improved layout design enhances production efficiency. CRAFT demonstrates superior material handling optimization. SLP offers systematic planning for layout enhancement. Keywoard: Production Layout Optimization, SLP, CRAFT, Material Handling Efficiency, Operational Effectiveness

AI Enabled MED Drone for Healthcare Application

The project proposes the development of an AI-enabled MedDrone for medication delivery, introducing an innovative and efficient solution to enhance healthcare logistics. The MedDrone, equipped with artificial intelligence capabilities, aims to revolutionize the process of medication delivery by leveraging autonomous aerial technology. The system integrates advanced algorithms to optimize route planning, ensuring timely and accurate delivery of medications to designated locations. The AI component enables the drone to adapt to real-time variables such as weather conditions and traffic, enhancing the reliability and responsiveness of the delivery process. This project addresses the challenges of traditional medication distribution, offering a futuristic and intelligent approach to healthcare logistics through the deployment of AI-enabled autonomous drones for efficient and timely medication delivery. The proposed system integrates AI algorithms for real-time route optimization, obstacle avoidance, and decision-making, enabling the drone to autonomously navigate complex environments and deliver essential medical supplies such as first aid kits, defibrillators, and medications to the point of need. Through the utilization of machine learning techniques, the drone continuously learns from its interactions with the environment, enhancing its adaptability and performance over time. Additionally, the system incorporates advanced communication technologies to enable seamless coordination with emergency responders and healthcare professionals, ensuring timely delivery and proper utilization of resources. The efficacy of the proposed AI-based medical drone system is demonstrated through simulations and real-world trials, highlighting its potential to revolutionize emergency response efforts and improve healthcare accessibility, particularly in challenging or resource-constrained environments.

Evaluating machine learning algorithm for real-time heat exchanger optimization and automatic issue detection device: experimental analysis

Evaluating machine learning algorithm for real-time heat exchanger optimization and automatic issue detection device: experimental analysis

Retracted: Real-Time Collision Detection Optimization Algorithm Based on Snake Model in the Field of Big Data

Retracted: Real-Time Collision Detection Optimization Algorithm Based on Snake Model in the Field of Big Data

Optimal Computation Resource Allocation in Energy-Efficient Edge IoT Systems With Deep Reinforcement Learning

This paper investigates a computation resource optimization problem of mobile edge computing (MEC)-aided Internet-of-Things (IoT) devices with a reinforcement learning (RL) solution. Specifically, we leverage the stochastic optimization method and formulate the Lyapunov optimization technique to maximize the long-term energy efficiency, taking into account the transmission power, network stability, and transmission latency. Based on the Markov decision process and model-free deep RL (DRL) approach, we propose a double DRL-based online computation offloading method to implement a deep neural network that learns from interactions to solve the computation offloading and transmission latency problem in the dynamic MEC-aided IoT environments. Furthermore, we design an adaptive method for continuous action-state spaces to minimize the completion time and total energy consumption of the IoT devices for stochastic computation offloading tasks. The proposed real-time Lyapunov optimization and DRL algorithms achieve low computational complexity and optimal processing time. Simulation results demonstrate that the proposed algorithm can achieve near-optimal control performance with enhanced energy efficiency performance compared to the baseline policy control algorithms.

Real-Time structure topology optimization using CNN driven Moving Morphable component method

Classical optimization methods require finite element analysis in iterations, which increase the computing time and decrease the algorithmic efficiency. The deep learning model can potentially realize real-time topology optimization design, but it normally requires large training set. This paper presents a real-time topology optimization algorithm based on the Moving Morphable Component (MMC) method using a Convolutional Neural Network (CNN). The optimization algorithm uses a new data pre-processing method, which can preserve the numerical characteristics and smoothness of the structure boundary, hence it can help CNN to capture data features with a limited sample set. The topology optimization boundary information of the optimized result is used as the sample set label to avoid the components dislocation phenomenon. The new algorithm effectiveness has been verified with several examples. The trained model can significantly improve the optimization efficiency of the MMC method and offer accurate results with a clear structure boundary.

Real-Time Collision Detection Optimization Algorithm Based on Snake Model in the Field of Big Data

Big data processing includes multiple processing flows of data. But data quality is the most important part of the entire process. Every data processing link will have an impact on the quality of big data. Collision detection is an important research content in many fields such as computer graphics and computer virtual reality. In layman's terms, it means that the computer detects the signal voltage on the channel while sending data. When the signal voltage swing value detected by a station exceeds a certain threshold value, it will be considered that at least two stations on the bus are sending data at the same time, indicating that a collision has occurred. As the number of questions increases, the level becomes higher, the target information becomes more diverse, and the algorithm becomes more complex. The traditional evolutionary algorithm is far from being able to deal with this situation effectively, and the optimization target algorithm emerges as the times require. Evolutionary computing is a mature global optimization method with high robustness and wide applicability. It has the characteristics of self-organization, self-adaptation, and self-learning. It is not limited by the nature of the problem and can effectively deal with complex problems that are difficult to solve by traditional optimization algorithms. This paper aims to study the optimization algorithm of real-time collision detection based on Snake model in the field of big data. It is expected that, with the support of big data technology, the efficiency of real-time collision detection of Snake model will be improved and time will be saved. This paper proposes a multiline swarm particle swarm algorithm and combines it with the Snake model to improve the detection efficiency of the collision detection algorithm. It verifies the detection performance of traditional algorithms and tests their effectiveness in detection. The experimental results of this paper show that the frame rate of the Snake model algorithm is 15, the frame rate of the K-DOPs algorithm is 6.7, and the error of the algorithm is 1.04. It shows that the frame rate of Snake model algorithm is better.

Cooperative Navigation for Heterogeneous Air-Ground Vehicles Based on Interoperation Strategy

This paper focuses on the cooperative navigation of heterogeneous air-ground vehicle formations in a Global Navigation Satellite System (GNSS) challenged environment and proposes a cooperative navigation method based on motion estimation and a regionally optimal path planning strategy. In air-ground vehicle formations, unmanned ground vehicles (UGVs) are equipped with low-precision inertial navigation measurement units and wireless range sensors, which interact with unmanned aerial vehicles (UAVs) equipped with high-precision navigation equipment for cooperative measurement information and use the UAVs as aerial benchmarks for cooperative navigation. Firstly, the Interacting Multiple Model (IMM) algorithm is used to predict the next moment’s motion position of the UGVs. Then regional real-time path optimization algorithms are introduced to design the motion position of the high-precision UAVs so as to improve the formation’s configuration and reduce the geometric dilution of precision (GDOP) of the configuration. Simulation results show that the Dynamic Optimal Configuration Cooperative Navigation (DOC-CN) algorithm can reduce the GDOP of heterogeneous air-ground vehicle formations and effectively improve the overall navigation accuracy of the whole formation. The method is suitable for the cooperative navigation environment of heterogeneous air-ground vehicle formations under GNSS-challenged conditions.

Real-Time Performance Optimization of Electromagnetic Levitation Systems and the Experimental Validation

The electromagnetic levitation (EML) system serves as a key subsystem in maglev trains for the purpose of levitation. It is highly dynamic, open-loop unstable, and safety-critical. The expense of establishing an accurate model out of the Maglev train, in addition to the varying operating conditions, results in an imperfectly known model in engineering practice. Thus high-performance levitation control, w.r.t. an imperfectly known model, is of considerable practical interest. Motivated by such an observation, this article investigates real-time levitation performance optimization of the EML system, with an imperfectly known model. The EML system is first modeled and an equivalent demonstration benchmark is developed. Then, the structure for levitation performance optimization is presented on top of the coprime factorization technique. Furthermore, the real-time levitation performance optimization algorithm is developed, utilizing only the input and output data. In the end, the proposed methods are validated on the benchmark.

Real-time optimization for a Digital Twin of a robotic cell with human operators

In this paper, we develop a Digital Twin (DT) for a cell with a robotic arm serving seven stations and assisted by a human operator. The lack of reliable predictions of human behaviours requires constant monitoring of the cell in order not to affect the degree of context-awareness, autonomy, and adaptability of a DT. This monitoring is carried out through a real-time simulation and optimization algorithm, which continuously produces near-optimal decisions for machines and efficient recommendations for human operators. The proposed approach is tested in a large computational campaign based on real-world data. The results show that the behaviour of the operator scarcely affects the performance of the cell at least in the considered settings.

Recursive Estimation of Modifiers for Real-Time Optimization with Gradient Adaptation

Because of the uncertainties during the design phase of processes, the drift of process parameters during the plant lifetime and exogenous factors affecting the supply or demand of materials, the operation of continuous processes needs to be updated to track the economically optimal conditions that maximize the profit. For this reason, the practice of RealTime Optimization (RTO) has been established in the process industry to assist in selecting the setpoints to be used in the process. Real Time Optimization works by updating the setpoints of a plant using a model. To cope with the inevitable inaccuracy of the available models, various techniques have been developed, most notably parameter adaptation and Modifier Adaptation (MA). MA corrects the process model by bias and gradient correction terms for the variables of interest (typically the cost function and constraints), such that the RTO algorithm is guaranteed to reach a true (local) plant optimum upon convergence. The most crucial part in RTO-MA schemes is the estimation of the gradients, which requires the evaluation of the plant response at several points and determines the overall complexity of the RTO-MA problem. The goal of this contribution is to show how a recursive estimator that shares some similarities with the Kalman filter can be used to estimate the modifiers.

DQN-Based Proactive Trajectory Planning of UAVs in Multi-Access Edge Computing

The main aim of future mobile networks is to provide secure, reliable, intelligent, and seamless connectivity. It also enables mobile network operators to ensure their customer’s a better quality of service (QoS). Nowadays, Unmanned Aerial Vehicles (UAVs) are a significant part of the mobile network due to their continuously growing use in various applications. For better coverage, cost-effective, and seamless service connectivity and provisioning, UAVs have emerged as the best choice for telco operators. UAVs can be used as flying base stations, edge servers, and relay nodes in mobile networks. On the other side, Multi-access Edge Computing (MEC) technology also emerged in the 5G network to provide a better quality of experience (QoE) to users with different QoS requirements. However, UAVs in a mobile network for coverage enhancement and better QoS face several challenges such as trajectory designing, path planning, optimization, QoS assurance, mobility management, etc. The efficient and proactive path planning and optimization in a highly dynamic environment containing buildings and obstacles are challenging. So, an automated Artificial Intelligence (AI) enabled QoS-aware solution is needed for trajectory planning and optimization. Therefore, this work introduces a well-designed AI and MEC-enabled architecture for a UAVs-assisted future network. It has an efficient Deep Reinforcement Learning (DRL) algorithm for real-time and proactive trajectory planning and optimization. It also fulfills QoS-aware service provisioning. A greedy-policy approach is used to maximize the long-term reward for serving more users with QoS. Simulation results reveal the superiority of the proposed DRL mechanism for energy-efficient and QoS-aware trajectory planning over the existing models.

Phase-Duty Modulated Loop Decoupling and Design Optimization for a Triple Active Bridge Converter for Light Electric Vehicle Charging

This article presents a comprehensive generalized harmonics approximation based modeling and design of a triple active bridge (TAB) dc–dc converter topology aimed to facilitate simultaneous—main and auxiliary—battery charging solutions for light electric vehicle charging applications. A three-loop control scheme, incorporating the duty and phase control parameters, is proposed in this article to address the issue of inherent coupling of two output bridges and their control loops (as observed in conventional phase modulation-based voltage control techniques), along with an objective to enhance the operational efficiency of the system, in affiliation with a real-time power flow optimization algorithm. Adhering to the objective of achieving minimized losses, the optimization algorithm provides insightful design limitations in terms of selecting the required leakage inductances to ensure desired power flow at the output bridges. Furthermore, to validate the effectiveness of the proposed three-loop control, a detailed set of simulation and experimental results are presented at various steady state and dynamic load change conditions. A detailed analytical loss model of the TAB topology is also presented along with a comparative study elucidating the effectiveness of the proposed three-loop control over the conventional control schemes. The analyses portray a peak efficiency of 96.24% at the rated load of 300 W, which is 2.94% and 4.97% higher than the decoupled and the conventional phase shift control schemes, respectively.

Parallel Enabled and Stability-Aware Self Optimizing Control With Globalized Constrained Nelder-Mead Optimization Algorithm

Parallel computation enhanced by Digital Twin can accelerate the execution of real time optimization algorithms for closed loop control systems. In this paper, a parallel implementation of the stability-aware Globalized Constrained Nelder-Mead Self Optimizing Control method is presented. It is based on the Analysis, Control, Parallel Execution approach, leveraging the use of Digital Twin instances for the simultaneous cost function evaluation during the most intensive function evaluation steps. Obtained results shows that the concurrent function evaluations using Digital Twins reduce the execution time and accelerate the convergence of the optimization algorithm.

Feasibility in real-time optimization under model uncertainty. The use of Lipschitz bounds

In real-time optimization (RTO), feedback information from the plant is used to compensate for model uncertainty. Feasibility upon convergence can be guaranteed by simply adding bias correction terms to the constraints predicted by the model. However, the RTO solutions obtained prior to convergence may violate the plant constraints in the presence of model uncertainty. The use of constraint upper-bounding functions based on Lipschitz continuity assumptions has been proposed as a means to ensure the satisfaction of constraints. This paper presents a comparative study between three different types of Lipschitz bounds for providing theoretical feasibility guarantees in different RTO schemes. Based on a novel Lipschitz bound on the constraint modeling error, robust RTO algorithms are proposed for the two model adaptation strategies that are most commonly employed in industrial RTO practice, which are the constraint–adaptation and parameter-adaptation schemes. A robust modifier-adaptation algorithm is also studied.