Optimal Control Plan Research Articles

Comprehensive risk management of reservoir landslide-tsunami hazard chains: a case study of the Liangshuijing landslide in the Three Gorges Reservoir area

AbstractLandslide-induced tsunamis in reservoirs often pose greater risks than landslides themselves. Existing studies and mitigation practices have prioritized increasing landslide stability to meet safety standards, incurring high costs and lacking sustainability. More than 5000 landslides have developed along the Three Gorges Reservoir banks in China, and some may trigger tsunamis. To effectively and sustainably manage risk of landslide-tsunami hazard chains (LTHC), we propose a new framework (based on quantitative risk analysis) involves decision-making based on risk characteristics and local economies, a strategy for selecting optimal risk control plan through cost–benefit analyses that consider long-term effectiveness and adverse effects, and dynamic management through iterative risk reassessment-recontrol cycles. The Liangshuijing landslide accelerated in 2009, posing a tsunami threat to the Yangtze River and coastal residents. Post-mitigation, the risks under four mitigation plans and combined scenarios were quantitatively evaluated. The mass removal plan reduces total risk by approximately 68%, less than the 80% reduction from the anti-slip pile plan, but with a 30-fold greater benefit–cost ratio (BCR). The surface drainage and monitoring network plans show limited long-term cost-effectiveness due to short measure lifetimes, however, their short-term BCRs exceed those of the mass removal plan due to lower direct costs. According to comprehensive long and short-term analyses, combined plans involving mass removal are the most cost-effective. Our results reveal how to select appropriate strategies for managing LTHC risk, how landslide mitigation controls tsunami risks, and how time-related parameters affect solution effectiveness. Our findings show that partially mitigating risks may provide more cost-effective and sustainable management of LTHCs than eliminating risks to negligible levels.

Optimal emission control plan aiming to basically eliminate PM2.5 heavy pollution in North and Northwest China in 2018

The fundamental eradication of heavy air pollution is the core objective during China's "14th Five-Year Plan" period. The purpose of this study was to basically eliminate fine particulate matter (PM2.5) heavy pollution in North and Northwest China during all PM2.5 heavy pollution process throughout 2018 through optimal emission control model based on linear programming technique. The results showed that the sensitivity coefficient of PM2.5 concentration to precursor emissions in North China increased with emissions disturbances, ranging from 0.23 to 0.93 with −80% disturbances. Compared to non-optimal control (NOC) scenario in North China, the optimal control (OC) scenario implemented in various precursors in various regions not only resulted in less precursors mitigation (with emission reduction of 4.1%, 5.9%, 16.6%, 3.5%, and 4.5% for primary PM2.5, SO2, NOx, NH3, and VOCs, respectively), but also led to greater air quality improvements (with the number of PM2.5 heavy pollution days (HPDs) reduced by 37 days, achieving the goal of basically eliminate PM2.5 heavy pollution in North China). If the polluting industries in North China were transferred to Northwest China and the synergistic control (SC scenario) measures were applied, both regions could achieve basically elimination of PM2.5 heavy pollution during the whole simulation periods. However, without implementing optimal control measures in Northwest China (FU scenario), the air quality in that region was poised to deteriorate. Specifically, the number of HPDs in Northwest China changed from 95 days under business as usual (BAU) scenario to 376 days under FU scenario and 29 days under SC scenario, and no new cities with PM2.5 heavy pollution emerged under SC scenario compared to BAU scenario. This study could provide scientific support for the battle against heavy pollution weather in China by redistributing the domestic industrial layout, rather than solely reducing local emissions.

Patient Preferences for Pain Control After Mohs Micrographic Surgery

Patient preferences for pain medications after Mohs micrographic surgery are important to understand and have not been fully studied. To evaluate patient preferences for pain management with only over-the-counter medications (OTCs) or OTCs plus opioids after Mohs micrographic surgery given varying levels of theoretical pain and opioid addiction risk. This prospective discrete choice experiment was conducted in a single academic medical center from August 2021 to April 2022 among patients undergoing Mohs surgery and their accompanying support persons (≥18 years). A prospective survey was administered to all participants using the Conjointly platform. Data were analyzed from May 2022 to February 2023. The primary outcome was the pain level at which half of the respondents chose OTCs plus opioids equally to only OTCs for pain management. This pain threshold was determined for varying opioid addiction risk profiles (low, 0%; low-moderate, 2%; moderate-high, 6%; high, 12%) and measured via a discrete choice experiment and linear interpolation of associated parameters (pain levels and risk of addiction). Of the 295 respondents (mean [SD] age, 64.6 [13.1] years; 174 [59%] were female; race and ethnicity were not considered) who completed the discrete choice experiment, 101 (34%) stated that they would never consider opioids for pain management regardless of the pain level experienced, and 147 (50%) expressed concern regarding possible opioid addiction. Across all scenarios, 224 respondents (76%) preferred only OTCs vs OTCs plus opioids after Mohs surgery for pain control. When the theoretical risk of addiction was low (0%), half of the respondents expressed a preference for OTCs plus opioids given pain levels of 6.5 on a 10-point scale (90% CI, 5.7-7.5). At higher opioid addiction risk profiles (2%, 6%, 12%), an equal preference for OTCs plus opioids and only OTCs was not achieved. In these scenarios, patients favored only OTCs despite experiencing high levels of pain. The findings of this prospective discrete choice experiment indicate that the perceived risk of opioid addiction affects the patient's choice of pain medications after Mohs surgery. It is important to engage patients undergoing Mohs surgery in shared decision-making discussions to determine the optimal pain control plan for each individual. These findings may encourage future research on the risks associated with long-term opioid use after Mohs surgery.

Flow-level coordination of connected and autonomous vehicles in multilane freeway ramp merging areas

On-ramp merging areas are deemed to be typical bottlenecks for freeway networks due to the intensive disturbances induced by the frequent merging, weaving, and lane-changing behaviors. The Connected and Autonomous Vehicles (CAVs), benefited from their capabilities of real-time communication and precise motion control, hold an opportunity to promote ramp merging operation through enhanced cooperation. The existing CAV cooperation strategies are mainly designed for single-lane freeways, although multilane configurations are more prevailing in the real-world. In this paper, we present a flow-level CAV coordination strategy to facilitate merging operation in multilane freeways. The coordination integrates lane-change rules between mainstream lanes, proactive creation of large merging gaps, and platooning of ramp vehicles for enhanced benefits in traffic flow stability and efficiency. The strategy is formulated under an optimization framework, where the optimal control plan is determined based on real-time traffic conditions. The impacts of tunable model parameters on the produced control plan are discussed in detail. The efficiency of the proposed multilane strategy is demonstrated in a micro-simulation environment. The results show that the coordination can substantially improve the overall ramp merging efficiency and prevent recurrent traffic congestions, especially under high traffic volume conditions.

Multi-cycle reactive voltage rolling optimization control method supporting photovoltaic absorption of regional grid

To achieve reactive voltage control and resolve the problem of photovoltaic in regional grids with high-proportion photovoltaic access, a multi-cycle reactive voltage rolling optimization control method supporting photovoltaic absorption was proposed, and a multi-cycle reactive voltage rolling optimization control framework was constructed based on the sensitivity-based mathematical model of allowed maximum photovoltaic capacity. First, a reactive power optimization model was built day ahead to minimize the actions of the discrete reactive regulating equipment and the resistance of photovoltaic absorption, and the action plan for discrete reactive power regulating equipment was thus determined. After that, with the determined day-ahead control plan for discrete reactive equipment as the constraint, and with the maximization of photovoltaic absorption and improvement of the dynamic reactive reserve margin of the system as the objectives within day, a reactive voltage rolling optimization control model was built for the optimal control plan for the discrete and continuous reactive equipment; the rolling optimization control model performed optimization once every five minutes, with two hours as a cycle. At last, with a regional grid system as a study case, a simulating calculation was carried out and the control effects after optimization by the proposed multi-cycle reactive voltage rolling optimization control method showed that the maximum voltage deviation of two key nodes had been dropped by 34.21% and 45.98%, respectively, the action times of capacitors and the regulation times of main transformers had been reduced by 61.29% and 64%, respectively, the maximum regulation capacity requirement of continuous reactive equipment in photovoltaic stations had been dropped by 62.17%, and the forecast photovoltaic output could be fully absorbed in this system. These findings verified the feasibility and effectiveness of the proposed method in improving voltage control and promoting maximum photovoltaic absorption of regional grid. The case study showed that the day-ahead optimization control method could alleviate the large fluctuations of photovoltaics by arranging a discrete reactive power equipment adjustment plan, and the within-day rolling optimization control could adjust the output of continuous reactive power equipment to alleviate short-term random fluctuations of photovoltaics after simulating calculation.

Application of machine learning for wind energy from design to energy-Water nexus: A Survey

The world’s dependency on fossil fuels is decreasing swiftly, and countries rely more on renewable energies. Among renewable energies, wind energy has become one of the most significant ones. Increasing its production and reducing energy and water costs has attracted many attentions. Moreover, due to the uncertainty of wind speed, power generation of wind farms is facing high volatility, which is affecting the electricity markets. Thus, in light of the rapid growth of wind energy technologies, new approaches based on advanced analytic are required. This paper presents a comprehensive review of artificial intelligence employed in wind energy systems, surveying the studies most applied in various applications and resulting from artificial neural networks (ANN) could be a sustainable approach instead of conventional methods in many cases. A large number of research studies associated with this topic are published since 2015, and based on the application could be categorized in five main groups: wind speed prediction, design optimization, fault detection, optimal control and maintenance planning. A statistical analysis of ANN application in these fields is carried out for the present time and future trends.

Passenger Flow Control Method for Network-Wide Operation of Urban Rail Transit System: a case study in Guangzhou

A passenger flow control method for network-wide operation of urban rail transit system is proposed in this paper. In order to find a better trade-off between the delayed time and the increasing number of controlled passengers, the objective function of passenger flow control is formulated based on the problem description. The constraints about the passenger behaviour and capacity limitation are described and the model is formulated successfully. Finally, the proposed method is applied to control the passenger flow of GuangZhou Metro Line 1. The comparison results show that the proposed control model can be used to produce the optimal control plan.

Dynamic prediction and regulation of water resource carrying capacity: a case study on the Yellow River basin

This study aimed to established the water resource carrying capacity of the Yellow River basin (YRB), China in respect to water quantity and quality to facilitate accurate prediction of changes in regional water resources carrying capacity and to formulate an optimal control plan based on the economic society-water resources-ecological environment mutual feedback mechanism. Various methods, including element diagnosis, identifying indicators of key drivers, integrating support vector machines and system dynamics were applied to establish a quantitative dynamic water resources carrying capacity simulation model for the YRB by formulating control objectives and analyzing the difficulty and cost of managing drivers of carrying capacity. The orthogonal test method was applied to improve the capacity and reduce load within the YRB. The six indicators of key drivers chosen were:① unconventional water utilization; ② seawater intake; ③ average irrigation water consumption; ④ irrigation area; ⑤ industrial value-added water consumption; ⑥ 10, 000 yuan industrial value-added water consumption. The simulation results showed that the upstream, middle and downstream areas of the YRB are currently in an overloaded or severely overloaded state at all planned levels before water transfer projects such as the West Route of South-to-North Water Transfer Project become effective. The study identified the optimal control plan to avoid overloading of the annual water carrying capacity of the YRB at all levels and provides scientific guidance for water resources management of the YRB.

Fuzzy model based on local injection of MTX combined with traditional Chinese medicine guided by ultrasound intervention to treat CSP

In view of pregnancy at the uterine scar after cesarean section, there is an urgent need to find a conservative treatment mode with drugs that can improve the success rate of treatment, shorten the conservative treatment time, and cause little harm to the body. This research combines ultrasound intervention technology to construct a fuzzy model to comprehensively evaluate the clinical effects of traditional Chinese medicine combined with western medicine in the treatment of CSP. Moreover, this research combines the current application algorithms of machine vision to explain the relevant principles of ultrasound interventional vision. In addition, this research uses the background subtraction method to extract the target, and uses the machine vision algorithm to extract the ultrasonic detection target object. Finally, this research adjusts the control parameters according to the fuzzy control, and outputs the optimal control plan with BP network training. The results of this study show that the combination of Chinese and Western medicines in the treatment of uterine scars after cesarean section has the advantages of small gestational sacs, short pregnancy, and low blood β-HCG, and it has the advantages of less pain, economy, fewer side effects, and successfully preserves the uterus. Therefore, it is worthy of clinical application.

Energy management of intelligent building based on deep reinforced learning

In the context of the ubiquitous power Internet of Things (UPIoT), this paper attempts to make full use of distributed new energy, and rationalize the energy management strategy of households. Inspired by the energy management system (EMS) of intelligent buildings, the authors searched for the optimal control plan for energy based on deep reinforcement learning (DRL) algorithm. Under the overall architecture of the system, a distributed new energy generation system was modelled for consumers in intelligent buildings, including energy storage, household electrical loads, new energy vehicles, etc. Next, a Q-learning-based energy management model was established for intelligent buildings, and the corresponding constraints were set up. After that, the reward and penalty functions of the EMSs for households and the intelligent building were designed based on the daily economic dispatch (DED) model. Finally, the energy management strategy was optimized, creating the real-time optimization control process. The proposed energy management strategy was proved effective for intelligent buildings through simulations. The research results provide a reference for energy management in other microgrids.

Optimal control strategy of COVID-19 spread in Morocco using SEIRD model

Abstract This paper aims to predict the development of the COVID-19 pandemic in Morocco from a mathematical approach. Based on the reliability of the data and the nature of confirmed cases, the SEIRD model is employed to provide a theoretical framework to forecast COVID-19 ongoing epidemic. Findings suggest that the structure and parameters of the proposed model give insights into the dynamics of the virus. Hence, this study contributes to the conceptual areas of knowledge on COVID-19 in proposing an optimal control plan to help decrease the number of confirmed cases by applying preventive measures such as social distancing, wearing facial masks. Matlab/Simulink TM simulations are used to illustrate the findings.

Real-time signal queue length prediction using long short-term memory neural network

Optimal traffic control and signal planning can significantly reduce traffic congestion and potential delays at intersections. However, a major challenge to optimize traffic signal timing is to accurately predict traffic before commencing the next cycle. An optimal strategy cannot be achieved with a poor prediction of future traffic. In this study, using a deep learning approach, we develop a data-driven real-time queue length prediction technique. We consider a connected corridor where information from vehicle detectors (located at the intersection) will be shared to consecutive intersections. We assume that the queue length of an intersection in the next cycle will depend on the queue length of the target and two upstream intersections in the current cycle. We use InSync adaptive traffic control system data to train a long short-term memory neural network model capturing time-dependent patterns of a queue of a signal. To avoid overfitting and select the best combination of hyperparameters, we use sequential model-based optimization technique. Our experiment results show that the proposed model performs very well to predict the queue length. Although we run our experiments predicting the queue length for a single movement, the proposed method can be applied for other movements as well.

Study on Optimization of Active Control Schemes for Considering Transient Processes in the Case of Pipeline Leakage

Pipeline leakage of crude oil, refined oil or other petroleum derivatives can cause serious damage to the environment, soil, and more importantly, pose a serious threat to personal safety. The losses can be minimized to a degree by active control. Therefore, timely and effective control measures should be taken to minimize the leak volume whenever a pipeline leaks. However, the complexity of pipeline hydraulic systems makes it difficult to optimize control schemes for pipeline hydraulic devices under leak conditions, and existing studies rarely consider complex transient processes. This paper aims to establish a mixed integer linear programming model considering transient processes, hydraulic constraints, equipment constraints and flow constraints, and develop a detailed control scheme of the devices by the branch and bound algorithm. Moreover, it is the objective of the model to figure out the most optimal control plan to minimize the leakage. Experiments on a real-world liquid pipeline have proved the practicability and high reliability of the model.

Optimal Control and Path Planning of a 3PRS Robot Using Indirect Variation Algorithm

SUMMARYIn this paper, optimal control of a 3PRS robot is performed, and its related optimal path is extracted accordingly. This robot is a kind of parallel spatial robot with six DOFs which can be controlled using three active prismatic joints and three passive rotary ones. Carrying a load between two initial and final positions is the main application of this robot. Therefore, extracting the optimal path is a valuable study for maximizing the load capacity of the robot. First of all, the complete kinematic and kinetic modeling of the robot is extracted to control and optimize the robot. As the robot is categorized as a constrained robot, its kinematics is studied using a Jacobian matrix and its pseudo inverse whereas its kinetics is studied using Lagrange multipliers. The robot is then controlled using feedforward term of the inverse dynamics. Afterward, the extracted dynamics equation of the robot is transferred to state space to be employed for calculus of variations. Considering the constrained entity of the robot, null space of the robot is employed to eliminate the Lagrange multipliers to provide the applicability of indirect variation algorithm for the robot. As a result, not only are the optimal controlling signals calculated but also the corresponding optimal path of the robot between two boundary conditions is extracted. All the modeling, controlling, and optimization process are verified using MATLAB simulation. The profiles are then double-checked by comparing the results with SimMechanics. It is proved that with the aid of the proposed controlling and optimization method of this article, the robot can be controlled along its optimal path through which the maximum load can be carried.

Simulation of seawater intrusion and optimization of cutoff wall schemes based on surrogate model

In the present study, a 3-D variable density mathematical model for seawater intrusion (SI) was built in Longkou City, China. Based on the model, a cutoff wall construction scheme suitable for the study area was proposed, and the mathematical model was used to simulate and predict the effects of the cutoff wall. By introducing the seawater desalination technology into the construction of the cutoff wall, the optimization model was built with the minimum cost as the objective function, and the simulation model was embedded into the optimization model for calculation. To reduce the huge amounts of computations caused by calling the simulation model repeatedly, a surrogate model of the simulation model was built using the Kriging method. The optimal control plan was obtained by solving the optimization model. The results show the following: 1. The 3-D variable density groundwater intrusion model built with calibration and verification is a reliable method of risk assessment of SI. 2. The surrogate model can significantly reduce the computational load while ensuring accuracy. 3. Using the optimization model based on the surrogate model, the optimal solution can be quickly yielded for the design of the cutoff wall, and it is an effective way for the risk management of SI.

Solving an Integrated Sales-Leasing Problem With Remanufacturing and Inventory Shortage Using Differential Evolution

In this article, an integrated sales and leasing company is considered. This company remanufactures leased products at the end of operating lease contracts to make them as good as new ones and sell them to the customers. In order to satisfy customers' demand, required products are provided from a third-party when the company meets inventory shortage. Non-linear competitive demand functions are used which are sensitive to manufacturer suggested retail price (MSRP) and inflation rate. A mixed integer non-linear mathematical model (MINLP) is developed to determine optimal price of selling products, optimal amount of monthly payments in leasing contracts, and optimal inventory control planning, i.e. the optimal amount of manufacturing and remanufacturing products and optimal inventory levels. The main objective is to maximize net profit of the company. Small, medium and large-scale sizes of the model are solved to show the applicability of the model. To solve the large-scale problem, differential evolution (DE) algorithm is applied as a meta-heuristic solution approach. Numerical results show high sensitivity of model to demands. Also, optimal trend behaviors of some main variables of the problem seem similar to the competitive behavior of demands.

Multi-objective traffic signal optimization using 3D mesoscopic simulation and evolutionary algorithms

Modern cities are currently facing rapid urban growth and struggle to maintain a sustainable development. In this context, “eco-neighbourhoods” became the perfect place for testing new innovative ideas that would reduce congestion and optimize traffic flow. The main motivation of this work is a true and stated need of the Department of Transport in Nancy, France, to improve the traffic flow in a central eco-neighbourhood currently under reconfiguration, reduce travel times and test various traffic control scenarios for a better interconnectivity between urban intersections. Therefore, this paper addresses a multi-objective simulation-based signal control problem through the case study of “Nancy Grand Cœur” (NGC) eco-neighbourhood with the purpose of finding the optimal traffic control plan to reduce congestion during peak hours. Firstly, we build the 3D mesoscopic simulation model of the most circulated intersection (C129) based on specifications from the traffic management centre. The simulation outputs from various scenario testing will be then used as inputs for the optimisation and comparative analysis modules. Secondly, we propose a multi-objective optimization method by using evolutionary algorithms and find the optimal traffic control plan to be used in C129 during morning and evening rush hours. Lastly, we take a more global view and extend the 3D simulation model to three other interconnected intersections, in order to analyse the impact of local optimisation on the surrounding traffic conditions in the eco-neighbourhood. The current proposed simulation-optimisation framework aims at supporting the traffic engineering decision-making process and the smart city dynamic by favouring a sustainable mobility.

Continuous and Discrete-Time Optimal Controls for an Isolated Signalized Intersection

A classical control problem for an isolated oversaturated intersection is revisited with a focus on the optimal control policy to minimize total delay. The difference and connection between existing continuous-time planning models and recently proposed discrete-time planning models are studied. A gradient descent algorithm is proposed to convert the optimal control plan of the continuous-time model to the plan of the discrete-time model in many cases. Analytic proof and numerical tests for the algorithm are also presented. The findings shed light on the links between two kinds of models.

Optimal production control and marketing plan in two-machine unreliable flexible manufacturing systems

In this paper, we have developed a production planning and marketing model in unreliable flexible manufacturing systems with inconstant demand rate that its rate depends on the level of advertisement on that product. The proposed model is more realistic and useful from a practical point of view. The flexible manufacturing system is composed of two machines that produce a single product. Markovian models frequently have been used in modeling a wide variety of real-world systems under uncertainties. Therefore, in this paper, the inventory balance equation is represented by a continuous-time model with Markov jump process to take into account machines breakdown. The objective is to minimize the expected total cost of the firm over an infinite time horizon. While the total cost consists of the cost of the product surplus, the cost of the production, and the cost of the advertisement. In the process of finding a solution to the problem, we first characterize an optimal control by a class of linear stochastic system where some parameter values are subject to random jump. By defining quadratic cost functions and characterizing the associated limiting optimal control problem, a discrete-time approximation model and an asymptotic optimal control model are developed. It is clear that such a solution exists and can be obtained as a limit of a monotonic sequence with solving the steady-state Riccati equation.

Research on minimizing electric power usage in water distribution system

The purpose of this study is to propose the optimal water supply control and management plan for minimizing power usage by water distribution pumps. The key topic of this study is to determine from which treatment plants water should be supplied, and through which supply stations it should be distributed. This study devised an optimal model of the ‘route/flow decision problem’ for minimizing power use in the water distribution system while meeting demand in the distributing areas, and formulated the model by means of mixed integer linear programming (MILP). Then to validate the MILP model we attempted a case study in an actual water distribution system. The research demonstrated the effectiveness of the proposed optimization when applied to minimizing power usage in water operation.