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Computation
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Recent Advances in Process Modeling and Optimisation
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Recent Advances in Process Modeling and Optimisation

A special issue of Computation (ISSN 2079-3197). This special issue belongs to the section "Computational Engineering".

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 20355

Special Issue Editors

Dr. George Tsakalidis

E-Mail Website
Guest Editor
Department of Applied Informatics, University of Macedonia, PC 54636 Thessaloniki, Greece
Interests: business process redesign; business process management; cybercrime classification; cyber security
Special Issues, Collections and Topics in MDPI journals
Dr. Kostas Vergidis

E-Mail Website
Guest Editor
Department of Applied Informatics, University of Macedonia, GR 54636 Thessaloniki, Greece
Interests: business process management; enterprise architectures; fake news classification; cybercrime incident management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Process modeling and process optimization have become more relevant in an increasing spectrum of research disciplines over the last decades. Several research areas are database systems, database management, business process management, information systems, Enterprise Resource Planning (ERP), operations research, formal languages and logics, etc. The necessity of modeling processes as the basis for scientific knowledge building, along with the need to adapt to novel computational practices for process optimization is the inspiring motivation of this Special Issue of Computation. The issue is dedicated to new developments in the theoretical and computational techniques of process modelling, optimization and related applications in diverse areas. We invite researchers to contribute their original and high-quality research papers which will further drive the potential of process modeling and optimization. Potential topics include, but are not limited to:

  • Advances in Process Modeling Languages;
  • Artificial Intelligence for Process Modeling and Optimization;
  • Artificial neural networks for Process Optimization;
  • Big Data Analytics for Process Modeling and Monitoring;
  • Business Process Modeling and Optimization;
  • Business Process Management;
  • Chemical Process Modeling and Optimization;
  • Chemical-Looping Combustion (CLC) process modeling and Optimization;
  • Data-driven Design and Optimization;
  • Evolutionary Optimization Techniques;
  • Gaussian Process Modeling and Optimization;
  • Graph theory combinatorial Process Optimization;
  • IoT and Machine Learning for Process Optimization;
  • Metaheuristic optimization algorithms and applications;
  • Process Modeling in Engineering (primary and interdisciplinary branches).

Dr. George Tsakalidis
Dr. Kostas Vergidis
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Computation is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • process modeling optimization modeling languages graph theory optimization algorithms information systems database systems

Published Papers (7 papers)

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Research

21 pages, 7209 KiB  
Article
Monitoring of Temperature Measurements for Different Flow Regimes in Water and Galinstan with Long Short-Term Memory Networks and Transfer Learning of Sensors
by Stella Pantopoulou, Victoria Ankel, Matthew T. Weathered, Darius D. Lisowski, Anthonie Cilliers, Lefteri H. Tsoukalas and Alexander Heifetz
Computation 2022, 10(7), 108; https://0-doi-org.brum.beds.ac.uk/10.3390/computation10070108 - 29 Jun 2022
Cited by 13 | Viewed by 1862
Abstract
Temperature sensing is one of the most common measurements of a nuclear reactor monitoring system. The coolant fluid flow in a reactor core depends on the reactor power state. We investigated the monitoring and estimation of the thermocouple time series using machine learning [...] Read more.
Temperature sensing is one of the most common measurements of a nuclear reactor monitoring system. The coolant fluid flow in a reactor core depends on the reactor power state. We investigated the monitoring and estimation of the thermocouple time series using machine learning for a range of flow regimes. Measurement data were obtained, in two separate experiments, in a flow loop filled with water and with liquid metal Galinstan. We developed long short-term memory (LSTM) recurrent neural networks (RNNs) for sensor predictions by training on the sensor’s own prior history, and transfer learning LSTM (TL-LSTM) by training on a correlated sensor’s prior history. Sensor cross-correlations were identified by calculating the Pearson correlation coefficient of the time series. The accuracy of LSTM and TL-LSTM predictions of temperature was studied as a function of Reynolds number (Re). The root-mean-square error (RMSE) for the test segment of time series of each sensor was shown to linearly increase with Re for both water and Galinstan fluids. Using linear correlations, we estimated the range of values of Re for which RMSE is smaller than the thermocouple measurement uncertainty. For both water and Galinstan fluids, we showed that both LSTM and TL-LSTM provide reliable estimations of temperature for typical flow regimes in a nuclear reactor. The LSTM runtime was shown to be substantially smaller than the data acquisition rate, which allows for performing estimation and validation of sensor measurements in real time. Full article
(This article belongs to the Special Issue Recent Advances in Process Modeling and Optimisation)
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22 pages, 9423 KiB  
Article
Shape Optimization of a Shell in Comsol Multiphysics
by Evgenia Ermakova, Timur Elberdov and Marina Rynkovskaya
Computation 2022, 10(4), 54; https://0-doi-org.brum.beds.ac.uk/10.3390/computation10040054 - 30 Mar 2022
Cited by 3 | Viewed by 4598
Abstract
Optimization calculations are currently an actual and in-demand direction of computer-aided design. It allows not only the identification of the future characteristics of an object, but also the implementation of its exact model using a set of various optimization algorithms. The advent of [...] Read more.
Optimization calculations are currently an actual and in-demand direction of computer-aided design. It allows not only the identification of the future characteristics of an object, but also the implementation of its exact model using a set of various optimization algorithms. The advent of digital modeling has significantly facilitated the approach to optimization and its methods. Many software systems are equipped with capabilities not only for calculating the design, but also for finding its optimal variant. The calculation programs can include a special optimization module that can be based on one or more mathematical methods. The purpose of the present study is to explore a process of shape optimization through the calculation of two shells: the simple one (spherical dome) and complex one (helicoid) in Comsol Multiphysics using three optimization methods: MMA, SNOPT and IPOPT. Additionally, special attention is paid to the construction of a mesh for calculations and two types of selected element sizes: finer and fine. Then, the important task is to compare the obtained results and to find the most optimal method and most effective design solution for each shell. When calculating the sphere, the most suitable solution was obtained using the IPOPT method, with the help of which it was possible to achieve an optimal reduction in the dome along the z-axis. When calculating the helicoid, all methods showed approximately the same values and equally changed the angle of inclination of the surface relative to the horizontal plane. Full article
(This article belongs to the Special Issue Recent Advances in Process Modeling and Optimisation)
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14 pages, 350 KiB  
Article
On the Conic Convex Approximation to Locate and Size Fixed-Step Capacitor Banks in Distribution Networks
by Oscar Danilo Montoya, Walter Gil-González and Alejandro Garcés
Computation 2022, 10(2), 32; https://0-doi-org.brum.beds.ac.uk/10.3390/computation10020032 - 20 Feb 2022
Cited by 9 | Viewed by 2301
Abstract
The problem of the optimal siting and sizing of fixed-step capacitor banks is studied in this research from the standpoint of convex optimization. This problem is formulated through a mixed-integer nonlinear programming (MINLP) model, in which its binary/integer variables are related to the [...] Read more.
The problem of the optimal siting and sizing of fixed-step capacitor banks is studied in this research from the standpoint of convex optimization. This problem is formulated through a mixed-integer nonlinear programming (MINLP) model, in which its binary/integer variables are related to the nodes where the capacitors will be installed. Simultaneously, the continuous variables are mainly associated with the power flow solution. The main contribution of this research is the reformulation of the exact MINLP model through a mixed-integer second-order cone programming model (MI-SOCP). This mixed-integer conic model maintains the nonlinearities of the original MINLP model; however, it can be solved efficiently with the branch & bound method combined with the interior point method adapted for conic programming models. The main advantage of the proposed MI-SOCP model is the possibility of finding the global optimum based on the convex nature of the power flow problem for each binary/integer variable combination in the branch & bound search tree. The numerical results in the IEEE 33- and IEEE 69-bus systems demonstrate the effectiveness and robustness of the proposed MI-SOCP model compared to different metaheuristic approaches. The MI-SOCP model finds the final power losses of the IEEE 33- and IEEE 69-bus systems of 138.416kW and 145.397kW, which improves the best literature results reached with the flower pollination algorithm, i.e., 139.075 kW, and 145.860kW, respectively. The simulations are carried out in MATLAB software using its convex optimizer tool known as CVX with the Gurobi solver. Full article
(This article belongs to the Special Issue Recent Advances in Process Modeling and Optimisation)
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16 pages, 505 KiB  
Article
Fuzzy Mathematics-Based Outer-Loop Control Method for Converter-Connected Distributed Generation and Storage Devices in Micro-Grids
by Lorena Castro, Maximiliano Bueno-López and Juan Mora-Flórez
Computation 2021, 9(12), 134; https://0-doi-org.brum.beds.ac.uk/10.3390/computation9120134 - 09 Dec 2021
Cited by 3 | Viewed by 1972
Abstract
The modern changes in electric systems present new issues for control strategies. When power converters and distributed energy resources are included in the micro-grid, its model is more complex than the simplified representations used, sometimes losing essential data. This paper proposes a unified [...] Read more.
The modern changes in electric systems present new issues for control strategies. When power converters and distributed energy resources are included in the micro-grid, its model is more complex than the simplified representations used, sometimes losing essential data. This paper proposes a unified fuzzy mathematics-based control method applied to the outer loop of a voltage source converter (VSC) in both grid-connected and islanded modes to avoid using simplified models in complex micro-grids and handle the uncertain and non-stationary behaviour of nonlinear systems. The proposed control method is straightforwardly designed without simplifying the controlled system. This paper explains the design of a fuzzy mathematics-based control method applied to the outer-loop of a VSC, a crucial device for integrating renewable sources and storage devices in a micro-grid. Simulation results validated the novel control strategy, demonstrating its capabilities for real field applications. Full article
(This article belongs to the Special Issue Recent Advances in Process Modeling and Optimisation)
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32 pages, 444 KiB  
Article
Optimal Selection of Conductors in Three-Phase Distribution Networks Using a Discrete Version of the Vortex Search Algorithm
by John Fernando Martínez-Gil, Nicolas Alejandro Moyano-García, Oscar Danilo Montoya and Jorge Alexander Alarcon-Villamil
Computation 2021, 9(7), 80; https://0-doi-org.brum.beds.ac.uk/10.3390/computation9070080 - 18 Jul 2021
Cited by 10 | Viewed by 2510
Abstract
In this study, a new methodology is proposed to perform optimal selection of conductors in three-phase distribution networks through a discrete version of the metaheuristic method of vortex search. To represent the problem, a single-objective mathematical model with a mixed-integer nonlinear programming (MINLP) [...] Read more.
In this study, a new methodology is proposed to perform optimal selection of conductors in three-phase distribution networks through a discrete version of the metaheuristic method of vortex search. To represent the problem, a single-objective mathematical model with a mixed-integer nonlinear programming (MINLP) structure is used. As an objective function, minimization of the investment costs in conductors together with the technical losses of the network for a study period of one year is considered. Additionally, the model will be implemented in balanced and unbalanced test systems and with variations in the connection of their loads, i.e., Δ- and Y-connections. To evaluate the costs of the energy losses, a classical backward/forward three-phase power-flow method is implemented. Two test systems used in the specialized literature were employed, which comprise 8 and 27 nodes with radial structures in medium voltage levels. All computational implementations were developed in the MATLAB programming environment, and all results were evaluated in DigSILENT software to verify the effectiveness and the proposed three-phase unbalanced power-flow method. Comparative analyses with classical and Chu & Beasley genetic algorithms, tabu search algorithm, and exact MINLP approaches demonstrate the efficiency of the proposed optimization approach regarding the final value of the objective function. Full article
(This article belongs to the Special Issue Recent Advances in Process Modeling and Optimisation)
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22 pages, 375 KiB  
Article
Improved Genetic Algorithm for Phase-Balancing in Three-Phase Distribution Networks: A Master-Slave Optimization Approach
by Oscar Danilo Montoya, Alexander Molina-Cabrera, Luis Fernando Grisales-Noreña, Ricardo Alberto Hincapié and Mauricio Granada
Computation 2021, 9(6), 67; https://0-doi-org.brum.beds.ac.uk/10.3390/computation9060067 - 09 Jun 2021
Cited by 13 | Viewed by 2577
Abstract
This paper addresses the phase-balancing problem in three-phase power grids with the radial configuration from the perspective of master–slave optimization. The master stage corresponds to an improved version of the Chu and Beasley genetic algorithm, which is based on the multi-point mutation operator [...] Read more.
This paper addresses the phase-balancing problem in three-phase power grids with the radial configuration from the perspective of master–slave optimization. The master stage corresponds to an improved version of the Chu and Beasley genetic algorithm, which is based on the multi-point mutation operator and the generation of solutions using a Gaussian normal distribution based on the exploration and exploitation schemes of the vortex search algorithm. The master stage is entrusted with determining the configuration of the phases by using an integer codification. In the slave stage, a power flow for imbalanced distribution grids based on the three-phase version of the successive approximation method was used to determine the costs of daily energy losses. The objective of the optimization model is to minimize the annual operative costs of the network by considering the daily active and reactive power curves. Numerical results from a modified version of the IEEE 37-node test feeder demonstrate that it is possible to reduce the annual operative costs of the network by approximately 20% by using optimal load balancing. In addition, numerical results demonstrated that the improved version of the CBGA is at least three times faster than the classical CBGA, this was obtained in the peak load case for a test feeder composed of 15 nodes; also, the improved version of the CBGA was nineteen times faster than the vortex search algorithm. Other comparisons with the sine–cosine algorithm and the black hole optimizer confirmed the efficiency of the proposed optimization method regarding running time and objective function values. Full article
(This article belongs to the Special Issue Recent Advances in Process Modeling and Optimisation)
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21 pages, 302 KiB  
Article
Accurate and Efficient Derivative-Free Three-Phase Power Flow Method for Unbalanced Distribution Networks
by Oscar Danilo Montoya, Juan S. Giraldo, Luis Fernando Grisales-Noreña, Harold R. Chamorro and Lazaro Alvarado-Barrios
Computation 2021, 9(6), 61; https://0-doi-org.brum.beds.ac.uk/10.3390/computation9060061 - 27 May 2021
Cited by 22 | Viewed by 3309
Abstract
The power flow problem in three-phase unbalanced distribution networks is addressed in this research using a derivative-free numerical method based on the upper-triangular matrix. The upper-triangular matrix is obtained from the topological connection among nodes of the network (i.e., through a graph-based method). [...] Read more.
The power flow problem in three-phase unbalanced distribution networks is addressed in this research using a derivative-free numerical method based on the upper-triangular matrix. The upper-triangular matrix is obtained from the topological connection among nodes of the network (i.e., through a graph-based method). The main advantage of the proposed three-phase power flow method is the possibility of working with single-, two-, and three-phase loads, including Δ- and Y-connections. The Banach fixed-point theorem for loads with Y-connection helps ensure the convergence of the upper-triangular power flow method based an impedance-like equivalent matrix. Numerical results in three-phase systems with 8, 25, and 37 nodes demonstrate the effectiveness and computational efficiency of the proposed three-phase power flow formulation compared to the classical three-phase backward/forward method and the implementation of the power flow problem in the DigSILENT software. Comparisons with the backward/forward method demonstrate that the proposed approach is 47.01%, 47.98%, and 36.96% faster in terms of processing times by employing the same number of iterations as when evaluated in the 8-, 25-, and 37-bus systems, respectively. An application of the Chu-Beasley genetic algorithm using a leader–follower optimization approach is applied to the phase-balancing problem utilizing the proposed power flow in the follower stage. Numerical results present optimal solutions with processing times lower than 5 s, which confirms its applicability in large-scale optimization problems employing embedding master–slave optimization structures. Full article
(This article belongs to the Special Issue Recent Advances in Process Modeling and Optimisation)
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