The new generation of reactor technology: Fluoride-Salt-cooled high-Temperature Advanced Reactor (FuSTAR), mainly applied to the steady supply of stable electricity and high-temperature process heat for remote, under-...
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The new generation of reactor technology: Fluoride-Salt-cooled high-Temperature Advanced Reactor (FuSTAR), mainly applied to the steady supply of stable electricity and high-temperature process heat for remote, under-ground, and arid areas. So far, the design of neutron physics and thermal hydraulics of the core have been finished, but the heat transport system and Passive Residual Heat Removal System of FuSTAR still need to be designed and optimized. Given the complexity and consistency of the design, a unified design and optimization method is required, which can directly obtain detailed configurations and dimensions. In this paper, a design and optimization method -Multi-Layer nonlinear programming was proposed to obtain the optimal parameters, including vital thermodynamic values, the dimensions of heat exchangers, the configurations of pipelines, and the optimal equivalent parameters in the transient simulation. Finally, based on Multi-Layer nonlinear Pro-gramming, the effectiveness of the Passive Residual Heat Removal System was demonstrated by the deterministic safety analysis. The detailed parameters of the Passive Residual Heat Removal System in this paper can provide references for subsequent experimental verification. Moreover, for any reactor relying on a heat transport system, researchers can directly obtain its excellent preliminary configurations by this method avoiding a lot of iteration and conflict design, which is conducive to the integrated modeling and simulation of a new generation of reactors.
Linear two-stage deformation trajectories with zero initial and final strain rates are considered using a hereditary-type damage accumulation model. Several problems intended to find the largest value of accumulated p...
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Linear two-stage deformation trajectories with zero initial and final strain rates are considered using a hereditary-type damage accumulation model. Several problems intended to find the largest value of accumulated plastic strain are formulated and analyzed. All problems are reduced to nonlinear programming problems with three unknowns, a linear objective function, and constraints in the form of nonlinear inequalities and/or strict equalities. The existence and uniqueness of the solutions of the problems are analyzed. The analytical dependence of the accumulated strain on the parameters of the model and the trajectory of deformation is obtained and analyzed. It is shown that the largest accumulated strain corresponds to the left limiting trajectory, which, in fact, is a single-link trajectory with linearly decreasing strain rate.
For the objective of carbon neutrality stated by the global climate conference, the iron and steel industry, a major carbon emitter, must transition to green and low-carbon development as quickly as feasible. Hydrogen...
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For the objective of carbon neutrality stated by the global climate conference, the iron and steel industry, a major carbon emitter, must transition to green and low-carbon development as quickly as feasible. Hydrogen metallurgy direct reduction technology has attracted much attention because of its low emission. However, the current study on hydrogen metallurgy shaft furnace lacks a comprehensive in-depth analysis of gas consumption, gas utilization, and exergy intensity. To tackle these problems, a material and energy optimization model including intermolecular chemical reaction is established. The original process is optimized by this model, the gas utilization is enhanced by 26.7%, gas consumption is decreased by 906.34 m3/t-DRI, and exergy intensity is reduced by 8.8 GJ/t-DRI. Furthermore, the impacts of gas composition, gas temperature, and ore temperature on gas consumption, gas utilization, exergy structure, and exergy intensity, as well as their interactions, are investigated thoroughly. The analysis highlighted that properly reducing hydrogen content and increasing gas and ore temperature can improve gas utilization and reduce gas consumption. Simultaneously, lowering gas consumption can effectively lower exergy intensity. And furnace top gas exergy is closely related to gas utilization. On the whole, it is advantageous to promote the development of hydrogen metallurgy by conducting in-depth and systematic analyses.
This paper presents an efficient NMPC-based multi-task control toolkit for complex robotic systems used in remote handling applications. Instead of resolving multi-task problem by solving instantaneous optimization pr...
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This paper presents an efficient NMPC-based multi-task control toolkit for complex robotic systems used in remote handling applications. Instead of resolving multi-task problem by solving instantaneous optimization problems like some existing controllers, in our toolkit a NMPC based control method is proposed which computes the control commands by solving a finite time horizon optimal control problem. The predictive feature of the controller enhances the intelligence and safety of robotic systems. However, it causes the computation more time-consuming. To improve the computational efficiency, we develop a two-step primal- dual interior-point solver ensuring the NMPC problem to be solved in real-time by exploiting the special structure of KKT matrix. Furthermore, our toolkit supports symbolic computation and C-code generation, which greatly reduces the effort in programming and deploying the controller on different computing platforms. The effectiveness and efficiency of our control method are demonstrated by numerical and dynamic simulations on different types of robotic systems conducting diverse RH applications.
Testing is crucial for early detection, isolation, and treatment of coronavirus disease (COVID-19)-infected individuals. However, in resource-constrained countries such as the Philippines, test kits have limited avail...
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Testing is crucial for early detection, isolation, and treatment of coronavirus disease (COVID-19)-infected individuals. However, in resource-constrained countries such as the Philippines, test kits have limited availability. As of 11 April 2020, there are 11 testing centers in the country that have been accredited by the Department of Health (DOH) to conduct testing. In this paper, we use nonlinear programming (NLP) to determine the optimal percentage allocation of COVID-19 test kits among accredited testing centers in the Philippines that gives an equitable chance to all infected individuals to be tested. Heterogeneity in testing accessibility, population density of municipalities, and the capacity of testing facilities are included in the model. Our results show that the range of optimal allocation per testing center are as follows: Research Institute for Tropical Medicine (4.17-6.34%), San Lazaro Hospital (14.65-24.03%), University of the Philippines-National Institutes of Health (16.25-44.80%), Lung Center of the Philippines (15.8-26.40%), Baguio General Hospital Medical Center (0.58-0.76%), The Medical City, Pasig City (5.96-25.51%), St. Luke's Medical Center, Quezon City (1.09-6.70%), Bicol Public Health Laboratory (0.06-0.08%), Western Visayas Medical Center (0.71-4.52%), Vicente Sotto Memorial Medical Center (1.02-2.61%), and Southern Philippines Medical Center (approximate to 0.01%). Our results can serve as a guide to the authorities in distributing the COVID-19 test kits. These can also be used for proposing additional testing centers and utilizing the available test kits properly and equitably, which helps in "flattening" the epidemic curve.
Model predictive control (MPC) is widely accepted as a generic multivariable controller with constraint handling. More recently, MPC has been extended to nonlinear model predictive control (NMPC) in order to realize h...
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Model predictive control (MPC) is widely accepted as a generic multivariable controller with constraint handling. More recently, MPC has been extended to nonlinear model predictive control (NMPC) in order to realize high-performance control of highly nonlinear processes. In particular, NMPC allows incorporation of detailed process models (validated by off-line analysis) and also integrates with on-line optimization strategies consistent with higherlevel tasks, such as scheduling and planning. NMPC for tracking and so-called "economic" stage costs has been developed, and fundamental stability and robustness properties of NMPC have been analyzed. This perspective provides an overview of NMPC concepts and approaches, as well as the underlying optimization strategies that support the solution strategies. In addition, three challenging process case studies are presented to demonstrate the effectiveness of NMPC.
Potential supply-net risk factors include capacity issues, currency volatility, design changes, frequent changes in tax regulations, unsafe information systems, and port shutdowns. Such risk factors make it challengin...
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Potential supply-net risk factors include capacity issues, currency volatility, design changes, frequent changes in tax regulations, unsafe information systems, and port shutdowns. Such risk factors make it challenging for decision makers to design efficient risk-management procedures and minimize the operational costs associated with mitigating these risks. Since complete information about all risks is generally not available, we utilize an axiomatic approach, based on the notion of entropy, to develop an efficient solution procedure. The strategy involves developing a stopping rule that enables the decision maker to decide online whether or not to continue investing in the acquisition of information about risk factors. The problem is formulated as a non-linear integer optimization model. We develop sufficient conditions for the entropy function such that a unique global solution is obtained.A case study that addresses risks associated with the transportation of aquatic products in refrigerated containers demonstrates the superior performance of the stopping rule relative to a standard risk-assessment procedure. In addition, numerous computerized experiments are carried out, under different problem settings, to compare the stopping rule with an anticipative optimum. The cost incurred when using the stopping rule is found to be no more than 0.4% higher than the cost of the anticipative optimum (the lower bound for the objective). These findings clearly demonstrate the efficiency of the proposed stopping rule for a wide range of problem sizes.
Predictions and avoidance of cascading branch failures are of great importance in power system operations and have long been research topics drawing much attention. Power flows will redistribute each time when the gri...
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Predictions and avoidance of cascading branch failures are of great importance in power system operations and have long been research topics drawing much attention. Power flows will redistribute each time when the grid topology changes in the cascading branch failure process. In order to obtain the exact power flows on all branches, power flow equations with different grid topologies must be solved frequently and heavy computational burden is thus introduced. Therefore, an efficient prediction and avoidance method can only be established when power flows can be quickly obtained under all possible topologies in the cascading process without solving the complicated ac power flow equations. To this end, an artificial neural network (ANN) based method is proposed in this paper to approximate the branch flows. A salient feature of the proposed method is that apart from the nodal power injections, the branch states (0/1 variable) are also the inputs of the ANN to consider the topology changes. The power flows are the outputs of the ANN and the corresponding overload states of all branches are then fed back to the ANN as inputs (the branch states). An iterative method is thus established. Case studies are performed and the results are encouraging.
Planning of sewer systems typically involves limitations and problems, regardless of whether traditional planning methods or optimization models are used. Such problems include non-quantifiability, fuzzy objectives, a...
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Planning of sewer systems typically involves limitations and problems, regardless of whether traditional planning methods or optimization models are used. Such problems include non-quantifiability, fuzzy objectives, and uncertainties in decision-making variables which are commonly applied in the planning of any process. Particularly, uncertainties have prevented the inclusion of these variables in models. Consequently, the theoretical optional solution of the mathematical models is not the true optimum solution to practical problems. In this study, to solve the above problems for regional sewer system planning, multi-objective programming (MOP), nonlinear programming, mixed-integer programming, and compromise fuzzy programming were used. The objectives of this study were two-fold: (1) determination of the necessary decision-making variables or parameters, such as the optimum number of plants, piping layout, size of the plant, and extent of treatment;(2) establishment of a framework and methodology for optimal planning for designing a regional sewer system, matching demanded targets with the lowest cost, which would achieve the aim of lower space and energy requirements as well as consumption and high treatment efficiency for the purpose of meeting effluent standards. The findings of this study revealed that individual regional sewage treatment plants could be merged to form a centralized system. Land acquisition was difficult;thus, reducing the number of plants was required. Therefore, the compromise-fuzzy-based MOP method could effectively be used to build a regional sewer system plan, and the amount of in-plant establishment reached its maximized value with a minimized cost.
This project aims to model the wastewater regulation problem as a bilevel optimization problem. Due to human activities such as industry, agriculture, and domestic use, many bodies of water have been affected by pollu...
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This project aims to model the wastewater regulation problem as a bilevel optimization problem. Due to human activities such as industry, agriculture, and domestic use, many bodies of water have been affected by pollution. To remedy the problem of water quality Environmental Protection Agencies use environmental penalty functions. This enforces managers of wastewater treatment plants to find treatment strategies that meet water quality standards before discharging pollution to the environment. In the case of shallow bodies of water, the behavior of the wastewater dispersion is governed by the Navier-Stokes equation;therefore, the objective functions of the decision-makers have a nonlinear behavior relative to a leader-follower dynamic. To find the optimal penalty function we construct the emission concentration system solution and define the wastewater regulation problem as a bilevel optimization problem. The aim of this paper is threefold: First, it formulates the wastewater regulation problem as a bilevel optimization problem;second, it provides theoretical insight when the problem is reformulated to a single-level formulation using a Karush-Kuhn-Tucker condition approach;third, it develops discretization techniques that allow finding numerical solutions of the location of the wastewater regulation problem.
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