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Innovative approaches to wear reduction in horizontal powder screw conveyors : a design of experiments-guided numerical study
Marko Motaln, Tone Lerher, 2024, izvirni znanstveni članek

Opis: Numerical simulations play a vital role in the modern engineering industry, especially when faced with interconnected challenges such as particle interactions and the structural integrity of conveyor systems. This article focuses on the handling of materials and emphasizes the importance of using parametric numerical analysis to improve efficiency, reduce wear, and enhance the structural integrity of horizontal screw conveyors. Through the utilization of the Design of Experiments, we systematically investigated critical parameters such as screw pitch, clearance, wear, rotational velocity, and additional structural factors. This examination was carried out within a well-defined parametric framework, utilizing a combination of software tools provided by the Ansys suite and Minitab. The findings demonstrate the effectiveness of the Design of Experiments analysis in achieving improved performance and provide valuable insights for engineers and researchers involved in the design of conveyor systems. Furthermore, this comprehensive approach clarifies how conveyor systems respond to changes in parameters and highlights the complex interaction between transported particles and the conveyor system. We present a detailed analysis that clarifies the complex relationships and dependencies among different parameters, providing engineers and researchers with valuable insights. By understanding the interactions of these factors, the methodology provides not only results but also a strategic framework for advancing conveyor system design and engineering practices.
Ključne besede: discrete element method, design optimization, horizontal screw conveyors, parametric study, conveying equipment, bulk handling, bulk solids, abrasive wear, screw conveyor, FEA, performance analysis
Objavljeno v DKUM: 09.04.2024; Ogledov: 55; Prenosov: 5
.pdf Celotno besedilo (10,51 MB)
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Detection of technical errors and construction optimization for custom tissue arrays at transplanting and casting
Miha Munda, 2018, izvirni znanstveni članek

Ključne besede: tissue arrays, construction problems, optimization, histology
Objavljeno v DKUM: 05.04.2024; Ogledov: 102; Prenosov: 0
.pdf Celotno besedilo (227,21 KB)
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DynFS: dynamic genotype cutting feature selection algorithm
Dušan Fister, Iztok Fister, Sašo Karakatič, 2023, izvirni znanstveni članek

Ključne besede: feature selection, nature-inspired algorithms, swarm intelligence, optimization
Objavljeno v DKUM: 05.04.2024; Ogledov: 43; Prenosov: 4
.pdf Celotno besedilo (1,14 MB)

Optimization of chaboche material parameters with a genetic algorithm
Nejc Dvoršek, Iztok Stopeinig, Simon Klančnik, 2023, izvirni znanstveni članek

Ključne besede: Chaboche material model, parameter optimization, genetic algorithm, finite element method
Objavljeno v DKUM: 04.04.2024; Ogledov: 32; Prenosov: 1
.pdf Celotno besedilo (3,62 MB)
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A graph pointer network-based multi-objective deep reinforcement learning algorithm for solving the traveling salesman problem
Jeewaka Perera, Shih-Hsi Liu, Marjan Mernik, Matej Črepinšek, Miha Ravber, 2023, izvirni znanstveni članek

Opis: Traveling Salesman Problems (TSPs) have been a long-lasting interesting challenge to researchers in different areas. The difficulty of such problems scales up further when multiple objectives are considered concurrently. Plenty of work in evolutionary algorithms has been introduced to solve multi-objective TSPs with promising results, and the work in deep learning and reinforcement learning has been surging. This paper introduces a multi-objective deep graph pointer network-based reinforcement learning (MODGRL) algorithm for multi-objective TSPs. The MODGRL improves an earlier multi-objective deep reinforcement learning algorithm, called DRL-MOA, by utilizing a graph pointer network to learn the graphical structures of TSPs. Such improvements allow MODGRL to be trained on a small-scale TSP, but can find optimal solutions for large scale TSPs. NSGA-II, MOEA/D and SPEA2 are selected to compare with MODGRL and DRL-MOA. Hypervolume, spread and coverage over Pareto front (CPF) quality indicators were selected to assess the algorithms’ performance. In terms of the hypervolume indicator that represents the convergence and diversity of Pareto-frontiers, MODGRL outperformed all the competitors on the three well-known benchmark problems. Such findings proved that MODGRL, with the improved graph pointer network, indeed performed better, measured by the hypervolume indicator, than DRL-MOA and the three other evolutionary algorithms. MODGRL and DRL-MOA were comparable in the leading group, measured by the spread indicator. Although MODGRL performed better than DRL-MOA, both of them were just average regarding the evenness and diversity measured by the CPF indicator. Such findings remind that different performance indicators measure Pareto-frontiers from different perspectives. Choosing a well-accepted and suitable performance indicator to one’s experimental design is very critical, and may affect the conclusions. Three evolutionary algorithms were also experimented on with extra iterations, to validate whether extra iterations affected the performance. The results show that NSGA-II and SPEA2 were greatly improved measured by the Spread and CPF indicators. Such findings raise fairness concerns on algorithm comparisons using different fixed stopping criteria for different algorithms, which appeared in the DRL-MOA work and many others. Through these lessons, we concluded that MODGRL indeed performed better than DRL-MOA in terms of hypervolumne, and we also urge researchers on fair experimental designs and comparisons, in order to derive scientifically sound conclusions.
Ključne besede: multi-objective optimization, traveling salesman problems, deep reinforcement learning
Objavljeno v DKUM: 28.03.2024; Ogledov: 46; Prenosov: 5
.pdf Celotno besedilo (7,89 MB)
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Active BIM system for optimized multi-project ready-mix-concrete delivery
Hana Begić, Mario Galić, Uroš Klanšek, 2023, izvirni znanstveni članek

Opis: Purpose – Ready-mix concrete delivery problem (RMCDP), a specific version of the vehicle routing problem (VRP), is a relevant supply-chain engineering task for construction management with various formulations and solving methods. This problem can range from a simple scenario involving one source, one material and one destination to a more challenging and complex case involving multiple sources, multiple materials and multiple destinations. This paper presents an Internet of Things (IoT)-supported active building information modeling (BIM) system for optimized multi-project ready-mix concrete (RMC) delivery. Design/methodology/approach – The presented system is BIM-based, IoT supported, dynamic and automatic input/output exchange to provide an optimal delivery program for multi-project ready-mix-concrete problem. The input parameters are extracted as real-time map-supported IoT data and transferred to the system via an application programming interface (API) into a mixed-integer linear programming (MILP) optimization model developed to perform the optimization. The obtained optimization results are further integrated into BIM by conventional project management tools. To demonstrate the features of the suggested system, an RMCDP example was applied to solve that included four building sites, seven eligible concrete plants and three necessary RMC mixtures. Findings – The system provides the optimum delivery schedule for multiple RMCs to multiple construction sites, as well as the optimum RMC quantities to be delivered, the quantities from each concrete plant that must be supplied, the best delivery routes, the optimum execution times for each construction site, and the total minimal costs, while also assuring the dynamic transfer of the optimized results back into the portfolio of multiple BIM projects. The system can generate as many solutions as needed by updating the real-time input parameters in terms of change of the routes, unit prices and availability of concrete plants. Originality/value – The suggested system allows dynamic adjustments during the optimization process, andis adaptable to changes in input data also considering the real-time input data. The system is based on spreadsheets, which are widely used and common tool that most stakeholders already utilize daily, while also providing the possibility to apply a more specialized tool. Based on this, the RMCDP can be solved using both conventional and advanced optimization software, enabling the system to handle even large-scale tasks as necessary.
Ključne besede: active building information modeling, BIM, internet of things, IoT, multi-project environment, optimization, ready-mix-concrete delivery, RMC
Objavljeno v DKUM: 19.03.2024; Ogledov: 58; Prenosov: 6
.pdf Celotno besedilo (3,93 MB)
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Improvement of biogas production utilizing a complex anaerobic digestion model and gradient-based optimization
Tina Kegl, Breda Kegl, Marko Kegl, 2024, izvirni znanstveni članek

Opis: : Anaerobic digestion (AD) is a promising technology for renewable energy production from organic waste. In order to maximize the produced biogas quantity and quality, this paper deals with the optimization of the AD process in a CSTR bioreactor of a full-scale biogas plant. For this purpose, a novel approach was adopted coupling, a highly complex BioModel for AD simulation, and a gradient-based optimization method. In order to improve AD performance, the dosages of various types of biological additives, the dosages of inorganic additives, and the temperature in the bioreactor were optimized in three different scenarios. The best biogas quality was obtained using multi-objective optimization, where the objective function involves the following two conflicting objectives: the maximization of biogas production and minimization of the needed heating energy. The obtained results show that, potentially, the content of CH4 can be increased by 11%, while the contents of H2, H2S, and NH3 can be reduced by 30%, 20%, and 81% when comparing the simulation results with the experimental data. The obtained results confirm the usefulness of the proposed approach, which can easily be adapted or upgraded for other bioreactor types.
Ključne besede: additives, anaerobic digestion, approximation method, BioModel, complex substrate, gradient-based optimization, process conditions
Objavljeno v DKUM: 12.03.2024; Ogledov: 125; Prenosov: 7
.pdf Celotno besedilo (7,33 MB)
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Innovative approach for the determination of a DC motor’s and drive’s parameters using evolutionary methods and different measured current and angular speed responses
Marko Jesenik, Miha Ravber, Mislav Trbušić, 2024, izvirni znanstveni članek

Opis: The determination is presented of seven parameters of a DC motor’s drive. The determination was based on a comparison between the measured and simulated current and speed responses. For the parameters’ determination, different evolutionary methods were used and compared to each other. The mathematical model presenting the DC drives model was written using two coupled differential equations, which were solved using the Runge–Kutta first-, second-, third- and fourth-order methods. The approach allows determining the parameters of controlled drives in such a way that the controller is taken into account with the measured voltage. Between the tested evolutionary methods, which were Differential Evolution with three strategies, Teaching-Learning Based Optimization and Artificial Bee Colony, the Differential Evolution (DE/rand/1/exp) can be suggested as the most appropriate for the presented problem. Measurements with different sampling times were used, and it was found out that at least some measuring points should be at the speed-up interval. Different lengths of the measured signal were tested, and it is sufficient to use a signal consisting of the drive’s acceleration and a short part of the stationary operation. The analysis showed that the procedure has good repeatability. The biggest deviation of calculated parameters considering 10 repeated measurements was 6% in case of the La calculation. The deviations of all the other parameters’ calculations were less than 2%.
Ključne besede: differential evolution, artificial bee colony, teaching-learning based optimization, DC motors, electric drive
Objavljeno v DKUM: 26.01.2024; Ogledov: 110; Prenosov: 8
.pdf Celotno besedilo (5,41 MB)

Optimal positioning of mobile cranes on construction sites using nonlinear programming with discontinuous derivatives
Matjaž Hozjan, Uroš Klanšek, 2023, izvirni znanstveni članek

Opis: Mobile cranes represent conventional construction machinery that is indispensable for the erection of most prefabricated buildings, especially those containing heavy components. However, it is also common knowledge that the engagement of these machines has a significant influence on the environment, various social aspects of the construction process, and its economic benefits. Optimal positioning of the mobile crane on the construction site, primarily driven by the contractor’s interest to perform assembly operations with expensive machinery as effectively as possible, considerably reduces not only the costs of engaging such a machine but indirectly also its negative impacts on construction sustainability. This paper discusses an exact nonlinear model for the optimization task. The optimization model consists of a cost objective function that is subject to various duration and positioning constraints for the mobile crane, including bounds on its degrees of freedom of movement and stop positions. Because the model formulation includes discontinuous and non-smooth expressions, nonlinear programming with discontinuous derivatives (DNLP) was employed to ensure the optimal solution was reached. The model provides the mobile crane operator with exact key information that enables the complete and optimal assembly of the building structure under consideration. Additionally, the information gained on the optimal distribution of the mobile crane rental period to assembly operations allows for a detailed duration analysis of the entire process of building structure erection, which can be used for its further improvement. An application example is given in this study to demonstrate the advantages of the proposed approach.
Ključne besede: construction sustainability, mobile crane, nonlinear programming with discontinuous derivatives, optimization, positioning
Objavljeno v DKUM: 18.12.2023; Ogledov: 237; Prenosov: 7
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