The flexible operation of alkaline water electrolyzers enables power-to-x plants to react efficiently to different energy scenarios. In this work, a novel scheduling model for alkaline water electrolysis is formulated...
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The flexible operation of alkaline water electrolyzers enables power-to-x plants to react efficiently to different energy scenarios. In this work, a novel scheduling model for alkaline water electrolysis is formulated as a mixed-integer linear program. The model is constructed by implementing operational states (production, standby, idle) and transitions (cold/full startup, shutdown) as integer variables, while the power loading and hydrogen flowrate are set as continuous variables. The operational characteristics (load range, startup time, ramp rates) are included as model constraints. The proposed model allows finding optimal number of electrolyzers and production schedules when dealing with large data sets of intermittent energy and electricity price. The optimal solution of the case study shows a balance between hydrogen production, energy absorption, and operation and investment costs. The optimal number of electrolyzers to be installed corresponds to 54% of the ones required to absorb the highest energy peak, being capable of loading 89.7% of the available energy during the year of operation, with an overall plant utilization of 93.7% and 764 startup/shutdown cycles evenly distributed among the units. (c) 2020 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
This paper offers a new robust mathematical model for designing an efficient flood evacuation plan in disasters. The mathematical model takes a set of potential locations for establishing evacuation shelters with limi...
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This paper offers a new robust mathematical model for designing an efficient flood evacuation plan in disasters. The mathematical model takes a set of potential locations for establishing evacuation shelters with limited capacities. We suggest an innovative model to use helicopters to rescue people from the flood for the first time in this field of knowledge. Besides, the helicopters' capacity and maximum flying time are considered limited to adopt real-world conditions. Our goal is to maximize the number of rescued people and to minimize the total cost. By solving the model, we determine the allocation of people to the shelters, the optimal location of shelters, allocation of the helicopters to the evacuation shelters, and flying path and routes of the helicopters. Since the main parameters of the proposed model are due to uncertainty in real-world situations, we implemented robust optimization to formulate uncertainties. Due to the Np-hardness of the suggested formulation, we offer four algorithms to solve the mathematical model. We enhance the efficiency of the algorithms through a robust design of experiments and assess their performance considering several measures via post hoc analysis. At the end, we implement the robust model on real-world data from 2011 Japan's destructive tsunami in Ishinomaki city. The results reveal that the model is able to provide promising solutions compared to the classical models and leads to higher rescue rates and lower cost.
This article establishes an intuitionistic fuzzy linear regression model (IFLRM) under the consideration that the explanatory and response variables in the observation data set as well as the parameters of the model a...
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This article establishes an intuitionistic fuzzy linear regression model (IFLRM) under the consideration that the explanatory and response variables in the observation data set as well as the parameters of the model are intuitionistic fuzzy numbers (IFNs). The weakest T-norm arithmetic is applied in the formulation of the IFLRMs to avoid wide spreads in the predicted IFN responses. The sign of the parameters is determined in the formulation process. We propose a mathematical programming problem to find the optimal IFN parameters. The goal of the optimization is to minimize the absolute distances between the observed and predicted IFNs. To enhance computational efficiency, a three-step procedure is proposed for solving a mathematical programming problem when the number of explanatory variables or the size of the observation data set is large. Comparisons with existing approaches indicate that the proposed approach has outstanding performance in terms of similarity and distance measures.
This work is about mission planning in teams of mobile autonomous agents. We consider tasks that are spatially distributed, non-atomic, and provide an utility for integral and also partial task completion. Agents are ...
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This work is about mission planning in teams of mobile autonomous agents. We consider tasks that are spatially distributed, non-atomic, and provide an utility for integral and also partial task completion. Agents are heterogeneous, therefore showing different efficiency when dealing with the tasks. The goal is to define a system-level plan that assigns tasks to agents to maximize mission performance. We define the mission planning problem through a model including multiple sub-problems that are addressed jointly: task selection and allocation, task scheduling, task routing, control of agent proximity over time. The problem is proven to be NP-hard and is formalized as a mixed integer linear program (MILP). Two solution approaches are proposed: one heuristic and one exact method. Both combine a generic MILP solver and a genetic algorithm, resulting in efficient anytime algorithms. To support performance scalability and to allow the effective use of the model when online continual replanning is required, a decentralized and fully distributed architecture is defined top-down from the MILP model. Decentralization drastically reduces computational requirements and shows good scalability at the expenses of only moderate losses in performance. Lastly, we illustrate the application of the mission planning framework in two demonstrators. These implementations show how the framework can be successfully integrated with different platforms, including mobile robots (ground and aerial), wearable computers, and smart-phone devices.
Many decisions must be made when determining design and operation of a chromatographic process. Such decisions should optimize the performance, including product purity, recovery, productivity, and desorbent consumpti...
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Many decisions must be made when determining design and operation of a chromatographic process. Such decisions should optimize the performance, including product purity, recovery, productivity, and desorbent consumption. For this problem, model-based optimization has been shown to be a powerful approach, aided by recent developments in modeling of chromatographic processes. In this article, past studies on model-based optimization for single and multi-column chromatographic processes are reviewed. Focus is placed on applications of mathematical programming techniques. More challenging problems including online optimization and flowsheet design are also discussed.
Notwithstanding its complexity in terms of numerical implementation and limitations in coping with problems involving extreme deformation, the finite element method (FEM) offers the advantage of solving complicated ma...
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Notwithstanding its complexity in terms of numerical implementation and limitations in coping with problems involving extreme deformation, the finite element method (FEM) offers the advantage of solving complicated mathematical problems with diverse boundary conditions. Recently, a version of the particle finite element method (PFEM) was proposed for analyzing large-deformation problems. In this version of the PFEM, the finite element formulation, which was recast as a standard optimization problem and resolved efficiently using advanced optimization engines, was adopted for incremental analysis whilst the idea of particle approaches was employed to tackle mesh issues resulting from the large deformations. In this paper, the numerical implementation of this version of PFEM is detailed, revealing some key numerical aspects that are distinct from the conventional FEM, such as the solution strategy, imposition of displacement boundary conditions, and treatment of contacts. Additionally, the correctness and robustness of this version of PFEM in conducting failure and post-failure analyses of landslides are demonstrated via a stability analysis of a typical slope and a case study on the 2008 Tangjiashan landslide, China. Comparative studies between the results of the PFEM simulations and available data are performed qualitatively as well as quantitatively.
This letter considers a Radio Frequency (RF) powered Internet of Things (IoT) network that exploits ambient backscatter communications to maximize the minimum number of samples of targets. We propose a Maximum Backsca...
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This letter considers a Radio Frequency (RF) powered Internet of Things (IoT) network that exploits ambient backscatter communications to maximize the minimum number of samples of targets. We propose a Maximum Backscatter Opportunity Search (MBOS) heuristic algorithm to construct set covers to ensure complete targets coverage. Our results demonstrate that the performance of MBOS is within 91% of the optimal number of samples and it is 25% higher as compared to not using backscattering.
The primary aim of process-network synthesis, or PNS in short, is to determine the best process network achieving a desired goal, e.g., producing a set of desired products or satisfy demands. PNS has a long history, a...
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The primary aim of process-network synthesis, or PNS in short, is to determine the best process network achieving a desired goal, e.g., producing a set of desired products or satisfy demands. PNS has a long history, and numerous methods for executing it are available. Its acceleratedly increasing importance can be attributed to the need to respond to the rapid emergence of new technologies and fast changes in the economic environment. It is highly desirable that any corporation be able to ascertain if a new technology is viable for its business as well as to assess if its current technology remains sustainable in the changing environment. Herein, a novel method and software for PNS are proposed for generating, optimizing, and analyzing alternative process designs at the conceptual level. The method is illustrated by synthesizing alternative process designs with different network structures for the production of butanol, ethanol, and acetone from grains. Furthermore, the sustainability of the resultant process designs is analyzed. This is executed by varying the payout period and the production rate, i.e., load.
A new approach for trajectory optimization of musculoskeletal dynamic models is introduced. The model combines rigid-body and muscle dynamics described with a Hill-type model driven by neural control inputs. The objec...
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A new approach for trajectory optimization of musculoskeletal dynamic models is introduced. The model combines rigid-body and muscle dynamics described with a Hill-type model driven by neural control inputs. The objective is to find input and state trajectories that are optimal with respect to a minimum-effort objective and meet constraints associated with musculoskeletal models. The measure of effort is given by the integral of pairwise average forces of the agonist-antagonist muscles. The concepts of flat parameterization of nonlinear systems and sum-of-squares optimization are combined to yield a method that eliminates the numerous set of dynamic constraints present in collocation methods. With terminal equilibrium, optimization reduces to a feasible linear program, and a recursive feasibility proof is given for more general polynomial optimization cases. The methods of the article can be used as a basis for fast, and efficient solvers for hierarchical, and receding-horizon control schemes. Two simulation examples are included to illustrate the proposed methods.
This paper presents a comparison of two heat exchanger network retrofit methods as they are applied to crude units: the well-known and widely used Pinch Design Method (PDM) and a recently developed Heat Integration Tr...
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This paper presents a comparison of two heat exchanger network retrofit methods as they are applied to crude units: the well-known and widely used Pinch Design Method (PDM) and a recently developed Heat Integration Transportation Model (HIT), a recently developed mathematical programming-based MILP model [Nguyen et al. Ind. Chem, Eng. Res. 2010, 49, 13]. We show that the three-step procedure (targeting, design, and evolution) used by Pinch Technology renders solutions with excessive and unrealistic splitting of streams as well as visibly less profit compared to the results of HIT.
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