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DESIGN AND IMPLEMENTATION OF THE SUPERVISORY MODULE AS PART OF A SYSTEM FOR CONDITION MONITORING AND CONTROL OF SOLID OXIDE ELECTROLYSIS CELL SYSTEMS
Amina Uglješa, 2023, master's thesis

Abstract: Hydrogen is playing an important role in many sectors of modern economy (green vehicles, energy conversion and storage in electrical grids, processing industry). Solid oxide electrolysis cell (SOEC) is an emerging technology for the production of hydrogen from steam and electrical energy as well as for renewable energies storage. Unfortunately, operating at high current and electrical transients cause degradation that leads to premature end of life. A remedy is to implement a hardware module capable to perform online condition monitoring and optimization of SOEC systems resulting in improved overall performance and extended lifetime. That is expected to significantly expand their deployment on the market. However, very little has been done so far. The H2020 project REACTT seems to be one of the first attempts to build an embedded system for monitoring, diagnosis, prognostics, and control (MDPC) for SOEC system. The underlying master's thesis contributes to the REACTT project in the segment related to the supervision of different modules of the MDPC system. The supervisor module is aimed to orchestrate the operation of various functional modules (agents) such as data acquisition, system optimization, diagnosis, prognostics, and mitigation. The thesis focuses on the design of the supervisor module and its implementation on a control platform based on Raspberry Pi 4. The main contributions of the thesis are twofold. First, the dynamic operation of the supervisor modelled by using the state transition diagram (STD). Second, the code for implementation of the supervisor on the target platform done in Python in a way that complies with the requirements imposed in the project.
Keywords: supervisor, module, agent, method, solid oxide electrolysis cell system, diagnosis, prognostics, real-time optimization, Python programming, state transition diagram
Published in RUNG: 20.06.2023; Views: 1222; Downloads: 17
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3.
Genetic Algorithm Based Approaches to Install Different Types of Facilities
Soumen Atta, Priya Ranjan Sinha Mahapatra, 2014, published scientific conference contribution

Abstract: Given a set P of n-points (customers) on the plane and a positive integer k (1 ≤ k ≤ n), the objective is to find a placement of k circles (facilities) such that the union of k circles contains all the points of P and the sum of the radii of the circles is minimized. We have proposed a Genetic Algorithm (GA) to solve this problem. In this context, we have also proposed two different algorithms for k=1 and 2. Finally, we have proposed a GA to solve another optimization problem to compute a placement of fixed number of facilities where the facilities are hazardous in nature and the range of each such facility is circular.
Keywords: Facility Location, Enclosing Problem, Optimization Problem, Genetic Algorithm
Published in RUNG: 05.06.2023; Views: 1090; Downloads: 0
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Multi-Objective K-Center Sum Clustering Problem
Soumen Atta, Priya Ranjan Sinha Mahapatra, 2015, published scientific conference contribution

Abstract: Given a set P of n objects in two dimensional plane and a positive integer k (≤ n), we have considered the problem of partitioning P into k clusters of circular shape so as to minimize the following two objectives: (i) the sum of radii of these k circular clusters and (ii) the number of points of P covered by more than one circular cluster. The NSGA-II based multi-objective genetic algorithm (MOGA) has been proposed to solve this problem.
Keywords: k-center sum problem, Clustering problem, Multi-objective optimization, NSGA-II, Facility location problem
Published in RUNG: 05.06.2023; Views: 1109; Downloads: 0
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A new variant of the p-hub location problem with a ring backbone network for content placement in VoD services
Soumen Atta, Goutam Sen, 2021, original scientific article

Abstract: In this article, the single allocation p-hub location problem (SApHLP) with a ring backbone network for content placement in VoD services is proposed. In VoD services, a large volume of digital data is kept as data segments in spatially distributed hubs. In SApHLP, each user is restricted to be allocated only to a single hub, and here hubs form a ring backbone network. SApHLP jointly addresses (i) the locations of hubs, (ii) the placement of segments to hubs, (iii) the allocation of users to hubs as per their demands, and (iv) the optimal paths to route the demands from users to hubs. We have introduced network flow-based 3-subscripted and path-based 4-subscripted MILP formulations of SApHLP. This article presents a novel discrete particle swarm optimization (PSO)-based approach where factoradic numbers are used to encode solution. It also incorporates three problem-specific solution refinement methods for faster convergence. In this article, SApHLP instances are generated from a real-world database of video files obtained from a movie recommender system. The benchmark solutions are generated using IBM’s CPLEX optimizer with default settings and Benders decomposition strategy. The performance of the proposed PSO is compared with the benchmark results produced by CPLEX.
Keywords: Single allocation p-hub location problem, Ring backbone network, VoD services, Particle Swarm Optimization (PSO), Factoradics, CPLEX
Published in RUNG: 17.04.2023; Views: 970; Downloads: 0
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Solving a new variant of the capacitated maximal covering location problem with fuzzy coverage area using metaheuristic approaches
Soumen Atta, Priya Ranjan Sinha Mahapatra, Anirban Mukhopadhyay, 2022, original scientific article

Abstract: The Maximal Covering Location Problem (MCLP) is concerned with the optimal placement of a fixed number of facilities to cover the maximum number of customers. This article considers a new variant of MCLP where both the coverage radii of facilities and the distance between customer and facility are fuzzy. Moreover, the finite capacity of each facility is considered. We call this problem the capacitated MCLP with fuzzy coverage area (FCMCLP), and it is formulated as a 0–1 linear programming problem. In this article, two classical metaheuristics: particle swarm optimization, differential evolution, and two new-generation metaheuristics: artificial bee colony algorithm, firefly algorithm, are proposed for solving FCMCLP. Each of the customized metaheuristics utilizes a greedy deterministic heuristic to generate their initial populations. They also incorporate a local neighborhood search to improve their convergence rates. New instances of FCMCLP are generated from the traditional MCLP instances available in the literature, and IBM’s CPLEX solver is used to generate benchmark solutions. An experimental comparative study among the four customized metaheuristics is described in this article. The performances of the proposed metaheuristics are also compared with the benchmark solutions obtained from CPLEX.
Keywords: Facility Location Problem (FLP), Fuzzy Capacitated Maximal Covering Location Problem (FCMCLP), Particle Swarm Optimization (PSO), Differential Evolution (DE), Artificial Bee Colony (ABC), Firefly Algorithm (FA)
Published in RUNG: 08.03.2023; Views: 1644; Downloads: 0
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Optimized Routing in Forestry with the Consideration of Multiple Economical and Ecological Objectives
Ahmad Hosseini, Ola Lindroos, Eddie Wadbro, published scientific conference contribution abstract

Keywords: Optimization, Forestry, Strip Road
Published in RUNG: 15.02.2023; Views: 1265; Downloads: 0
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9.
An introduction to dynamic generative networks: Minimum cost flow
Ahmad Hosseini, 2011, original scientific article

Keywords: Optimization, Network/graphs, Linear programming, Decomposition methods
Published in RUNG: 14.02.2023; Views: 1069; Downloads: 0
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10.
A Model-Based Approach and Analysis for Multi-Period Networks
Ahmad Hosseini, 2013, original scientific article

Keywords: Optimization, Process systems engineering, Linear programming, Decomposition methods, Production planning
Published in RUNG: 14.02.2023; Views: 1118; Downloads: 0
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